SECO, a global leader in delivering end-to-end technological solutions for industrial digitalization, has released the StudioX AI platform for businesses to create AI-powered support services that enhance internal roles as well as customer experiences.

Businesses can build tools with StudioX that present a chatbot for users to interact and quickly find information or support they need. The platform leverages cutting-edge technologies including generative AI, Large Language Models (LLMs), machine learning, deep learning, and computer vision. Services can be tailored to support roles such as operations management, workflow analysis, research and development, and product marketing, to boost productivity, elevate product quality, increase business efficiency, and help create new revenue streams. Interacting directly with the StudioX chatbot front-end, users can navigate a vast data landscape to quickly obtain accurate and timely information to guide their decisions. StudioX is available via SMS, web, phone, email, and WhatsApp.

This means StudioX is an ideal platform to create powerful customer experiences that cut waiting times and increase satisfaction. Multiple-language support enables services to cater for diverse linguistic preferences and services can be customized, with analysis to identify patterns and drive continuous improvement.

This Enterprise AI tool can be trained using organisational data such as manuals, technical specifications, and troubleshooting guides, quickly becoming a tool capable of providing instant assistance. In operation, StudioX can ingest data directly from operational equipment, such as machinery on the factory floor, and provide access to AI-generated knowledge in real time. Moreover, the customised StudioX is always available and provides multi-language support.

An operations manager can quickly get information about production status, equipment downtime, defects and other performance criteria, interacting through StudioX’s natural-language interface. Staff in other roles, such as R&D, can filter information from datasheets and other sources, create graphs using StudioX generative tools, greatly simplifying and accelerating information retrieval.

StudioX analyzes time-series data, parses and compares many types of documents, and classifies data from diverse sources such as image sensors, cameras, and audio. When integrated with real business systems and workflow, StudioX can provide advanced predictive and forecasting solutions and conduct real-time manufacturing quality inspections.
SECO recently hosted an exclusive webinar focused on harnessing the power of artificial intelligence in manufacturing through StudioX.

The post SECO to demonstrate StudioX AI platform for tailored business support and customer experiences with state-of-the-art tools appeared first on IoT Business News.

IoT Analytics released a research article that highlights 6 out of 17 industry trends included in the Embedded World 2024 Event Report.

This report presents key highlights and in-depth insights assembled by the IoT Analytics analyst team from one of the world’s leading fairs for the embedded community.

Key Insights:

• The current state of embedded systems was on full display at Embedded World 2024, with a clear emphasis on edge AI.
• As part of the Embedded World 2024 Event Report, IoT Analytics’ team of four on-the-ground analysts identified 17 industry trends related to IoT chipsets and edge computing—this article highlights 6 of these trends related to edge AI.

Key Quotes:

Satyajit Sinha, Principal Analyst at IoT Analytics, remarks:

“The shift towards edge AI will necessitate that CPU vendors develop not only high-performance multi-core CPUs but also integrate specialized NPUs into their SoC designs. The recent increase in demand for NVIDIA GPUs—driven by AI workloads—and the prevailing AI chip shortages have led to upward pressure on prices within the AI chipset market and could continue to do so for the foreseeable future.”

About Embedded World 2024

Embedded World is a leading event for the embedded systems community. This year, it took place from April 9 to April 11 in Nürnberg, Germany, and once again, it showcased the latest developments and innovations in embedded systems, embedded software, chipsets, edge computing, and related topics.

Attendance was up 19% from the previous year and has returned to pre-pandemic participation levels (~32,000 visitors). The number of vendors, too, returned to and even surpassed pre-pandemic levels, with a record 1,100.

IoT Analytics had a team of four analysts on the ground. They visited more than 60 booths and conducted over 35 individual interviews to comprehensively understand the most recent developments in embedded systems, with a special focus on IoT.

Embedded World 2024 emphasized the integration of AI within embedded systems, with a clear focus on edge AI. Corporate research subscribers can refer to the 67-page Embedded World 2024 Event Report for more information about the event, including highlights from keynote speeches, important announcements and launches, and major trends identified by the team. Here, the team shares only six of these trends, each based on observations about the future of edge AI.

6 edge AI trends observed at Embedded World 2024

“Edge AI will reshape our world in a profound way.”

Edge AI was the key theme throughout the conference. Salil Raje, SVP of adaptive and embedded computing at AMD, best captured the energy around this topic during his keynote address, stating, “We stand on the brink of an era where edge AI will reshape our world in a profound way.”

On the stage, Salil Raje and Eiji Shibata, CDO at carmaker Subaru, discussed how AMD and Subaru are collaborating on an edge AI system for autonomous driving based only on cameras—with the vision to achieve zero accidents by 2030.

Below, the team highlights 6 trends it observed on the topic of edge AI.

1. NVIDIA becoming a key edge (AI) computing company

US-based chipmaker NVIDIA has played a crucial role in driving the adoption and implementation of AI technologies across various sectors. NVIDIA’s GPUs, renowned for their high-performance capabilities, specifically in data centers, are also becoming integral to deploying complex AI models at the edge. With a partner network of over 1,100 companies, NVIDIA has established a dominant position in the AI technology market, far ahead of its competitors AMD and Intel.

At Embedded World 2024, one such partner, Taiwan-based embedded systems provider Aetina, introduced its AI-driven industrial edge solutions powered by NVIDIA GPUs, such as its AIB-MX13/23, which is powered by NVIDIA’s Jetson AGX Orin GPU capable of 275 trillion or tera operations per second (TOPS). Using a portable ultrasonic testing device connected to the AIB-MX13/23, Aetina and its partner, Finland-based defect recognition solutions provider TrueFlaw, demonstrated a non-destructive evaluation method for fault detection.

Additionally, Taiwan-based fabless semiconductor company MediaTek showcased four new embedded systems-on-chips (SoCs) for automotive applications—CX-1, CY-1, CM-1, and CV-1—which support NVIDIA’s DRIVE OS 3 autonomous vehicle reference operating system. This application demonstrates how NVIDIA’s technologies are expanding into new domains beyond the gaming and data center GPUs they are generally known for.

2. Simplifying on-device AI inferencing processes for developers

The integration of on-device AI comes with various challenges. One key challenge that developers often face is the dilemma of investing in new devices before they can evaluate the performance of the AI chipset and its compatibility with an AI model. Evaluation factors for developers can include device TOPS, CPU/NPU percent utilization, and temperature. To solve this and other related problems, companies are launching new AI developer platforms that can simulate on-device AI performance, allowing developers to test AI model deployment using specific edge device/chipset resource specifications without purchasing the physical hardware.

One solution on display at Embedded World 2024 was Taiwan-based IoT and embedded solutions provider Advantech‘s EdgeAI SDK platform. This platform supports deploying AI models over widely recognized AI chipsets like Intel, NVIDIA, Qualcomm, and Hailo. Advantech showcased a pose detection model running on an AIMB-278 industrial motherboard integrated with Intel’s ARC A380E embedded systems GPU. Advantech’s EdgeAI SDK facilitated the model’s deployment.

3. AI model training shifting to the thick edge

AI model training is shifting from centralized cloud setups to thick-edge locations like servers or micro data centers. This is possible due to the integration of high-performance CPUs and GPUs that enable powerful computing at the edge, AI training, and multiple AI inferencing capabilities. Further, AI training can also happen on vendor premises, reducing reliance on cloud infrastructure, lowering costs, enhancing privacy, and improving the responsiveness of AI applications on edge devices.

Just before Embedded World 2024, US-based computer builder MAINGEAR and Taiwan-based memory controller manufacturer Phison announced the launch of MAINGEAR PRO AI workstations integrated with 4x NVIDIA’s RTX 5000 Ada or 4x RTX 6000 Ada GPUs with more than 1000 TFLOPS computing power.

At the event, Aetina launched its AIP-FR68 Edge AI Training platform, supporting various 4x NVIDIA GPUs with up to 200 teraflops—the number of float-point operations a chip can perform—of computing power, a lot for a single GPU.

4. Accelerating micro- and thin-edge AI through NPU integration

Integrating dedicated NPUs within edge devices greatly enhances AI inference capabilities. Additionally, it results in power savings, improved thermal management, and efficient multitasking, enabling the deployment of AI in power-sensitive and latency-critical applications, such as wearables and sensor nodes.

At the fair, the Netherlands-based semiconductor manufacturer NXP showcased its new MCX N Series MCUs, which provide 42 times faster ML inference than CPU cores alone. Additionally, UK-based semiconductor design company ARM demonstrated an ARM Cortex A55-only setup and an ARM Cortex A55 + ARM Ethos U65 NPU setup for AI inferencing. The latter setup offloaded 70% of AI inferencing from the CPU to the NPU, with an 11x improvement in inference performance.

5. Localizing autonomous decision-making via cellular-connected micro- and thin-edge AI

Integrating AI-enabled chipsets directly into cellular IoT devices is on the rise, marking a transformation toward intelligent, autonomous IoT systems capable of localized decision-making. This trend will likely substantially impact industries like smart cities and factories, and it brings significant advantages, including real-time data processing, reduced latency, and greater efficiency due to smaller form factors.

An example is the intelligent mowing robot solution displayed by China-based wireless communications module vendor Fibocom. It utilizes a Qualcomm-based intelligent module for powerful on-device computation, allowing it to not only map its environment and avoid obstacles but also perform cost-effective boundary recognition, all without constant reliance on the cloud. This practical application demonstrates the tangible value of AI-enabled chipsets in IoT devices.

Further, the US-based IoT solutions joint venture Thundercomm showcased its EB3G2 IoT edge gateway, which leverages a Qualcomm SoC for on-device AI model execution. This SoC enables immediate data analysis, reducing latency and cloud dependence. The gateway’s algorithms are capable of human detection and tracking, making it valuable for security and traffic management.

6. Tiny AI/ML bringing micro-edge AI capability to traditional devices

As the name suggests, tiny AI/ML are small-sized AI and ML models capable of running on resource-constrained devices, such as sensor-based micro-edge devices. The analyst team noted several cases of tiny ML being integrated into everyday objects and tools, enabling them to perform decision-making functions autonomously without the need for cloud connectivity. This approach bolsters privacy and data security by processing information directly on the device—at the very edge.

UK-based voice intelligence platform developer MY VOICE AI showcased NANOVOICE TM, a speaker verification solution powered by tiny ML and designed for ultra-low-power edge AI platforms. The solution combines passcode verification with speaker recognition for enhanced security.

Likewise, US-based AI/ML software company SensiML demonstrated a proof-of-concept for a smart drill that uses AI/ML models to classify different screw fastening states. The model is capable of both real-time edge sensing and anomaly detection. Further, Norway-based fabless semiconductor company Nordic Semiconductor showcased its Thingy53 IoT prototyping device embedded with Nordic’s nRF5340 chipset, which enables anomaly detection via embedded ML. When paired with an accelerometer, the Thingy:53 senses equipment vibrations using an embedded tiny ML model. As an example, this system could cut off power to a device or machine when it detects anomalies.

The future of the embedded world: what these edge AI trends mean for IoT embedded systems

Embedded World 2024 emphasized the growing role of edge AI within IoT systems. The developments the team witnessed focused on easier AI inferencing and a spectrum of edge AI solutions (micro, thin, and thick), pointing to greater intelligence at network edges.

Edge AI is shifting intelligent computation away from cloud-centric models and moving it closer to data sources. Driving this shift are reduced network traffic, near-instantaneous decision-making for time-critical applications (e.g., manufacturing, autonomous systems), and enhanced privacy by processing data locally. Ultimately, edge AI reduces reliance on hyperscalers and promotes broader AI usage outside centralized infrastructure. It holds transformative potential across healthcare, automotive, and robotics, with the capability to reshape operational paradigms within these industries.

Looking ahead, edge AI will have varying impacts across edge levels:

• Thick edge AI: Facilitate the execution of multiple AI inference models on edge servers or at the network periphery and support AI model training or retraining for scenarios involving sensitive data on premises
• Thin edge AI: Enhance the intelligence of existing sensors and devices by utilizing gateways, IPCs, and PLCs for AI processing at the network edge
• Micro edge: Enable direct AI integration into sensors, improve the scalability of intelligent systems, and empower everyday connected devices to make autonomous decisions

The post The top 6 edge AI trends – as showcased at Embedded World 2024 appeared first on IoT Business News.

Leveraging Conversational AI for Operational Excellence and Strategic Advancement

SECO is pleased to announce an upcoming exclusive webinar focused on harnessing the power of artificial intelligence in manufacturing with AI virtual assistants.

During the event, participants will explore how conversational AI enhances operational efficiency, improves product quality, and fosters innovation. This webinar offers a unique opportunity to delve into the future of smarter, more efficient, and more profitable manufacturing.

Airdate:
May 7, 2024, 5:00 pm CET

Speakers:
Ajay Malik, (Chief AI Service Officer of SECO)

What’s in store from this webinar

In an era where integrating artificial intelligence (AI) with manufacturing is not just an innovation but a strategic necessity, the use of AI assistants to ensure product excellence and operational efficiency is on the rise. From unlocking organizational intelligence and streamlining customer interactions to revolutionizing quality monitoring and maintenance, chat-based AI assistants play a crucial role in transforming traditional manufacturing processes.

With a suite of capabilities that spans various stages of production, chatbots for the manufacturing industry leverage your company’s intelligence to generate actionable insights, automate repetitive tasks to allow employees to focus on more strategic activities, and aid your workforce in addressing production needs in real-time, AI provides tangible benefits to employees, management, and customers, giving you a competitive edge.

Join us for an exclusive webinar on harnessing the power of artificial intelligence in manufacturing with AI virtual assistants. Learn how conversational AI enhances operational efficiency, improves product quality, and fosters innovation. Start your journey towards a smarter, more efficient, and more profitable future.

Why attend?

• AI-Powered Efficiency: Gain insights into how chatbots for the manufacturing industry enhance operational efficiency, from production to quality control, through advanced AI integration.
• Unlock Collective Intelligence: Learn about the role of chat-based tools in harnessing your team’s knowledge, making vital information accessible and actionable across your organization.
• Transform Customer Service: Discover how to enhance customer service through an AI-powered virtual agent, providing instant, accurate responses and significantly improving customer satisfaction levels.
• AI as Your Production Assistant: Explore how chatbots can optimize production paths, process product information for more informed decision-making, predict and prevent operational issues, and assist in real-time troubleshooting, filling knowledge gaps and ensuring operational continuity.
• Embracing AI with StudioX: Learn how StudioX, the virtual assistant from SECO, seamlessly integrates AI into manufacturing, aligning with your data systems and workflows for a customized AI journey that meets your business goals.

Register now to learn how StudioX can position your operations at the forefront of the manufacturing industry. Secure your spot and take the first step towards a future where your manufacturing operations are powered by intelligence, efficiency, and innovation: https://zoom.us/webinar/register/4617061040145/WN_vMLVrNhSQFC6r_f-6Rs29w

The post Revolutionize your factory with AI: join SECO’s exclusive webinar on May 7, 2024 appeared first on IoT Business News.

IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today has published its latest research on the global cellular IoT module and chipset market.

The report reveals that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, our research projects the market to climb back to near 2022 levels in 2024. The research article further delves into the evolution of cellular IoT modules and chipsets with the latest technological innovations in smart and AI-enabled cellular IoT modules as key drivers for the project growth.

Key insights:

• The latest update to the Global Cellular IoT Module and Chipset Market Tracker and Forecast shows that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, the tracker projects the market to climb back almost fully to 2022 levels in 2024.
• The rise of smart and AI-enabled cellular IoT modules, which enable data processing and decision-making near or at the edge, is helping to drive this projected growth.
• AI is not the same across the board—the capabilities of AI-enabled cellular IoT modules vary between low, medium, and high based on the speed of AI inference, typically driven by the chipsets used.

Key quotes:

Satyajit Sinha, Principal Analyst at IoT Analytics, remarks “IoT devices are evolving beyond connecting devices and expanding to analyzing the data they produce to make swift, informed choices. As a result, there is a growing need for more computational power and intelligence, especially at the edge closer to the data generated. This trend is also apparent in the cellular IoT field, where integrating AI with cellular IoT modules and chipsets leads to more autonomous decision-making. It also minimizes data transmission over cellular networks, reducing bandwidth and costs. On-device AI models powered by NPUs enhance this capability by enabling smart decision-making at the edge.”

Cellular IoT module market update

Global shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023, according to the updated Global Cellular IoT Module and Chipset Market Tracker & Forecast (Q1/2024 Update). Two factors contributed to this decline:

1. Inventory optimization: Initial supply shortages caused by the COVID-19 pandemic and trade tensions around the same time led to manufacturers overordering modules and chipsets in 2021 and 2022, resulting in a surplus of these on the market. To address the surplus in 2023, manufacturers prioritized using existing modules and chipsets, thus delaying new orders.
2. Economic uncertainty: Inflation, rising interest rates, recession fears, and renewed US–China trade tensions have created a cautious investment climate, impacting new orders.

Fortunately, corporate executives appear to be easing their economic concerns, which could lessen the impact of the second factor as supply rebalances with demand. As this balance is achieved, cellular IoT module and chipset shipments are expected to rebound in the near term and are forecasted to grow at a 22% CAGR until 2027, with the slump of 2023 almost fully eradicated by the end of 2024.

Also likely to help rejuvenate this market is the rise of smart and AI-enabled cellular IoT modules—technologies that leverage embedded computational resources to execute advanced data analysis or even AI inference directly on IoT devices. Together, shipments of these advanced modules are forecasted to grow at a CAGR of 76% until 2027.

Smart and AI-enabled cellular IoT modules represent the latest frontiers of cellular IoT connectivity—the latest interactions of cellular IoT technology operating alongside their more basic, yet still quite capable, predecessor modules worldwide. Below, we will look at the evolution of IoT modules and chipsets and delve further into AI-enabled cellular IoT modules, including a look at their various processing capabilities and some applications for these intelligent modules.

Evolution of IoT cellular modules

The evolution of IoT cellular connectivity can be seen as 3 overlapping generations: legacy, smart, and AI-enabled.

“AI-driven productivity is inevitably evolving as an essential to extend the capabilities of IoT devices, significantly [improving] operational efficiency by enriching the IoT device with edge computing.” – Eden Chen, General Manager of MC BU at Fibocom

In the beginning: Legacy cellular IoT modules

Legacy cellular IoT models have been around for nearly two decades, simply providing connectivity for IoT devices to send and receive data from other locations. They include a cellular chipset/baseband to connect to a specified cellular technology, e.g., 2G, 3G, 5G, or NB-IoT.

In 2023, legacy cellular IoT modules comprised 96% of global cellular IoT module shipments. While shipments of these modules are forecasted to grow at a CAGR of 18% until 2027, their share of global cellular IoT module shipments will begin to give way to smart and AI-enabled cellular IoT modules, as discussed below.

Example of a legacy cellular IoT module

An example of a legacy cellular IoT module is the EM9190 5G New Radio (NR) Sub-6 GHz Module from Sierra Wireless, a Canadian wireless communications equipment manufacturer. This module enables devices to connect to 5G networks with 4G and 3G fallback when 5G is unavailable. Sierra Wireless announced the EM91 series of these legacy modules in August 2020, which is fairly recent; however, this reflects that legacy cellular IoT modules are still in demand when edge processing is unnecessary.

*Note: US-based semiconductor and IoT systems provider Semtech acquired Sierra Wireless in January 2023.

A move toward the edge: Smart cellular IoT modules

Smart cellular IoT modules have been on the market for nearly a decade. In addition to providing the connectivity capability found with their legacy counterparts, these smart modules include powerful CPUs and GPUs for on-device data processing. They can also support operating systems like Linux or Android to enable advanced functions and multimedia capabilities.

In 2023, these smart modules comprised 2% of global cellular IoT module shipments; however, the tracker forecasts this number to rise to 10% by 2027, with a CAGR of 79%.

Example of a smart cellular IoT module

An example of a smart cellular IoT module is the CQS290 Smart Cellular IoT Android Module from US-based cellular IoT module manufacturer Cavli Wireless. Cavli announced the unveiling of this module at the India Mobile Congress in October 2023. This LTE Cat 4 module, with Android 12, runs on an ARM Cortex A53 quad-core processor and has a built-in Adreno 702 graphics processing unit (GPU).

Intelligence at the edge: AI-enabled cellular IoT modules

AI-enabled cellular IoT modules are relatively newer than their legacy and smart counterparts, having been on the market for over 5 years. Along with the connectivity capabilities of the other types of cellular IoT modules, AI-enabled versions include NPUs, TPUs, PPUs, or other dedicated parallel-processing chipsets (e.g., GPUs) for AI inference.

While still in its early stages, AI and cellular IoT convergence holds immense potential to revolutionize industries. Integrating AI directly into IoT modules means AI inference can occur at the edge, allowing for rapid and intelligent decision-making at the edge. This reduces data transmission over cellular networks, saves bandwidth and costs, and facilitates immediate, autonomous decision-making for time-sensitive applications. Further, embedding AI chipsets within connectivity modules can save space and streamline the form factor of IoT devices. In all, these modules are evolving from mere data communication enablers to intelligent edge nodes capable of handling certain workloads independently.

In 2023, AI-enabled cellular IoT modules comprised 2% of global cellular IoT module shipments. The tracker forecasts that by 2027, this will grow to 9%, with a CAGR of 73%.

Example of an AI-enabled cellular IoT module

In November 2023, China-based wireless communications modules vendor Fibocom announced the release of its SC228 LTE smart module, which is powered by Qualcomm’s SM6225 (aka Snapdragon 680) SoC. With its 8 processing cores (4 x A73 at 2.4GHz and 4 x A53 at 1.9GHz), the SC228 is designed to handle AI algorithms, such as image processing algorithms. It is geared toward industrial IoT, smart retail, in-vehicle infotainment, and similar applications. The system comes with Android 14 but is upgradable as new software develops. For connectivity, it supports 4G LTE, 3G, WiFi, and Bluetooth.

Capabilities and applications of AI-enabled cellular IoT modules

AI is not the same across all applications. Within AI-enabled cellular IoT modules, there are varying processing capabilities based either on the needs of specific applications or the limitations of the hardware. IoT Analytics generalizes these modules’ capabilities into three categories: low, medium, and high.

1. Low AI capability

Cellular IoT modules with low AI capability conduct AI inference at less than 5 trillion (or tera) operations per second (TOPS), the standard measure of AI performance based on the number of computing operations an AI chip can handle in one second. Common applications of these modules include:

• Acoustic event detection
• Gesture/Activity recognition
• Voice ID/ Keyword spotting

These low AI capability modules comprised 59% of global AI-enabled cellular IoT module shipments in 2023. While the tracker projects the number of shipments of these modules to grow at a CAGR of 30% until 2027, cellular IoT modules with medium and high AI capabilities are expected to grow faster.

Example of a cellular IoT module with low AI capability

Fibocom’s SC138-EAU module features a Qualcomm QCM6125 SoC with an AI engine capable of 1 TOPS.

2. Medium AI capability

Cellular IoT modules with medium AI capability conduct AI inference at 5–10 TOPS. Common applications for these modules include:

• Human detection
• Vehicle detection
• People counting
• Face detection

In 2023, these medium AI capability modules comprised 36% of all global AI-enabled cellular IoT module shipments. The tracker projects that the shipment of these modules will grow at a CAGR of 102% until 2027.

Example of a cellular IoT module with medium AI capability

Quectel’s SG-530C-CN module hosts a UNISOC P778 SoC, which contains an NPU and is capable of 8 TOPS.

3. High AI capability

Finally, cellular IoT modules with high AI capability conduct edge AI inference at over 10 TOPS. Common advanced applications for these modules include:

• AI-driven predictive maintenance
• Enhanced decision-making with advanced analytics
• AI-enhanced driver safety solutions
• Real-time monitoring for drowsiness and distractions
• Comprehensive safety analysis
• Intelligent voice assistance

According to the tracker, these high AI capability modules comprised 5% of all global AI-enabled module shipments in 2023. The tracker forecasts the shipments of these modules to grow at a CAGR of 128% until 2027.

Example of a cellular IoT module with medium AI capability

MeiG’s SRM930 module bears a Qualcomm QCM6490 SoC, which includes Qualcomm’s 6th Gen AI Engine capable of reaching an AI performance of 12 TOPS.

Analyst takeaway

IoT is evolving beyond mere connectivity—it now encompasses connecting devices, understanding the data they generate, and making fast, informed decisions based on this data. As such, computing power and intelligence are becoming increasingly essential, particularly closer to where data is generated—at the edge. Thus, it is beneficial to have a dedicated chipset, such as a GPU or NPU, that can be used for AI inference directly on IoT devices, whether embedded in the printed circuit board (PCB) or as a component within the main processor.

Cellular IoT modules are undergoing a similar evolution. Although still in the early stages, integrating AI with cellular IoT promises to transform various industries. However, the core technology is driven by chipset companies like Qualcomm, Sony Altair, and UNISOC. Other chipset companies like MediaTek and ST may enter this market soon. So far, as seen above, vendors are predominately using Qualcomm chipsets equipped with AI engines that utilize the chipsets’ CPU, GPU, or NPU components.

With the rise of AI-enabled cellular IoT modules, two trends are emerging that are worth watching:

• AI-enabled 5G modules in automotive: The adoption of AI-enabled cellular modules focused on automotive applications, especially with 5G connectivity, is expected to accelerate. By 2027, AI-enabled 5G modules for automotive applications are projected to constitute 21% of all AI-enabled cellular module shipments.
• AI in cellular LPWA modules. So far, most of the modules are focused on standard 5G and 4G technology (with 2G and 3G as fallbacks). However, cellular LPWA modules are already entering the scene. For example, the Sony Altair ALT1350 is a low-power, LTE-M/NB-IoT SoC equipped with AI capabilities for low-power acceleration. This chipset is designed for edge processing and tiny ML model inference, opening doors for AI-enabled modules in the cellular LPWA segment.

Disclosures

Companies mentioned in this article—along with their products—are used as examples to showcase market developments. No company paid or received preferential treatment in this article, and it is at the discretion of the analyst to select which examples are used. IoT Analytics makes efforts to vary the companies and products mentioned to help shine attention to the numerous IoT and related technology market players.

It is worth noting that IoT Analytics may have commercial relationships with some companies mentioned in its articles, as some companies license IoT Analytics market research. However, for confidentiality, IoT Analytics cannot disclose individual relationships.

The post The rise of smart and AI-capable cellular IoT modules appeared first on IoT Business News.

Fibocom, in collaboration with Qualcomm Technologies Inc., is proud to announce the launch of their cutting-edge Linux-based edge AI solutions which integrates a series of Qualcomm Technologies-powered Fibocom smart modules.

This new series of solutions utilize a wide array of Fibocom’s smart modules SC171, SC171L, SC138, and SC126 series that are developed based on the Qualcomm® QCM6490/QCS6490, Qualcomm® QCM5430/QCS5430, Qualcomm® QCM6125, and Qualcomm® QCM2290 processors from Qualcomm Technologies with integrated Linux system, will unleash the peak performance of Linux-based industrial applications with robust connectivity and scalable operating system compatibility, and also accelerate the integration of advanced 5G, edge AI across emerging industries like robotics. With the expansion of digitalized industries, Fibocom will intensify the Linux-focused embedded computing intelligence, and leverage expertise in edge AI inclusion to help industry customers realize responsive decision-making, real-time communication and ultra-reliable system interoperability in industrial automation, smart manufacturing, smart retailing through the partnership.

At the “heart” of Industry 4.0, interoperability is crucial for operators to get insights into the equipment’s performance in the field. This groundbreaking series solutions is poised to advance the industrial applications that demand wireless connection, high-integration expertise with design-in Linux operating system for easy integration, and long-term product lifecycle optimization. By adopting Linux-supported Fibocom’s smart modules, Linux engineers around the world have the ability to develop industrial devices such as industrial PCs (IPCs), edge AI workstations, smart POS terminals, and industrial routers with higher efficiency as well as potent multimedia processing capabilities.

Harnessing the interoperability of the Linux operating system, industry-focused customers catered to the benefits below:

• Utilizing the stability and reliability of the Linux system, the Linux-based edge AI solutions integrated with Qualcomm Technologies-powered smart modules ease the concerns of diverse system integration to industrial control systems and enable the 24/7 data transmission and management of industrial equipment with minimum risk of malfunction and downtime.
• The Linux-based Edge AI Solutions are highly flexible and customizable compatible with industrial standards, and can be widely deployed in industrial PCs (IPCs), industrial cameras, edge AI workstations, smart POS terminals, and robotics.
• Inherently beneficial from the robust feature and open-source nature of the Linux operating system, the solutions allow equipment manufacturers to safeguard product development and management with long-term upgradable support through the entire lifecycle.

Fibocom Unveils Intelligent Lawn Mower Robotic Solution with Linux-based Smart Module SC171 Integrated

The lawn mower robotic solution is a highly integrated solution equipped with Fibocom smart module SC171 and edge AI algorithm, empowering lawn mowers with unparalleled capabilities in environmental perception, precise positioning, map construction, path planning, autonomous obstacle avoidance, and seamless wireless connectivity. The revolutionary lawn mower robotic solution enables the autonomous navigation of the lawn mower without using boundary cables, significantly transforming the lawn mower industry. The core of this robotic solution lies in the AI-based lawn recognition algorithm, equipped with outdoor cameras, to achieve accurate detection and efficient planning of the lawn boundaries. It is worth mentioning that the solution also includes responsive obstacle avoidance and automatic recharging, in addition to the mapping function and an “edge-cutting mode” to facilitate precise mowing even at the sidelines. By deploying the solution, it will enhance the operational efficiency of the lawnmowers and reduce the time-to-market of lawn mower’s massive deployment in the global marketplace.

“We are proud to collaborate with Fibocom to help them develop these Linux-based edge AI solutions for industrial applications,” said Dev Singh, Vice President of Business Development and Head of building, enterprise & industrial automation at Qualcomm Technologies, Inc. “By utilizing Fibocom’s smart modules and Qualcomm Technologies’ powerful processors, Fibocom are enabling the integration of advanced edge AI technologies, empowering industries with responsive decision-making and real-time communication capabilities.”

“We have a clear vision for the edge AI-enabled future, and with the collaboration with Qualcomm Technologies, we will continue building the Linux-based edge AI-driven core solution for industrial-focused markets,” said Ralph Zhao, VP of MC BU at Fibocom.

“The first landed implementation in the robotic industry has infused confidence into the utilization of both Fibocom and Qualcomm Technologies’ strength to empower an intelligent, future-promising digitalized world.­­”

The post Fibocom to Unveil a Series of Linux-based Edge AI Solutions Mastering the Peak Performance for Industrial Applications Powered by Qualcomm Technologies appeared first on IoT Business News.

Ness Digital Engineering (Ness), a global full-lifecycle digital services transformation company and a subsidiary of KKR, and Zinnov, a global management and strategy consulting firm, jointly launched a comprehensive study titled “Harnessing the Power of Generative AI (GenAI) in Transforming Software Engineering Productivity.”

While productivity is a known outcome of GenAI initiatives, this study measures the actual productivity gains resulting from deploying GenAI at an engineering level. It is uniquely framed to help CTOs, CIOs, and CPOs understand the technology and psychological drivers of engineering productivity and the long-term ramifications on both business and organizational design.

Utilizing Ness’s proprietary platform Matrix to collect data, the study engaged 100+ software engineers across use cases and development settings and in-depth analysis of engineers’ real-world experiences in live engineering environments.

The study revealed that Generative AI implementation not only increases productivity but also allows for deeply assisted context, enabling companies to globalize work frictionlessly. The implication of this is not just better business outcomes but an entirely transformed organizational design. Other key observations that emerged were:

• 70% Reduction in Task Completion Time for Existing Code Updates: Engineers witnessed maximum impact when utilizing existing codebase functions, leading to reduced development cycle time.
• 48% Reduction in Task Completion Time for Senior Engineers: Senior engineers witnessed reduced task completion time and found themselves using their time to plan better and assist junior engineers.
• ~10% Reduction in High Code Complexity Tasks: Generative AI enables engineers to navigate complex coding scenarios with increased efficiency, contributing to faster and more accurate resolutions.
• 70% Improved Engagement: By simplifying tasks and fostering a more collaborative and dynamic work environment, Generative AI plays a pivotal role in creating a positive and fulfilling professional experience.

This shift challenges traditional organizational structures by focusing more on expertise and efficiencies that will be assisted by technology but controlled and decided by people. This will mean a new kind of workforce – where domain expertise and problem-solving capabilities will preside over technology skills.

Ranjit Tinaikar, CEO, Ness Digital Engineering, said, “GenAI stands poised to transform the software development landscape by offering substantial productivity enhancements and expediting innovation cycles, ultimately accelerating time to market. However, its potential could be hindered if narrowly perceived as a mere code generation tool, a misconception prevalent in the software development realm. To fully harness the power of GenAI, we collaborated with Zinnov to understand the impact of GenAI on software development and its nuances. The study serves as a guide on the impact of GenAI on product development process, organization structures, employee engagement, learning, and development.”

Speaking about the study, Pari Natarajan, CEO, Zinnov, said, “Generative AI is now integrated into software engineering workflows across many organizations, aiding in tasks like generating test cases, refactoring code, and identifying innovation opportunities. This study validates the belief that Generative AI complements rather than dictates workflows, facilitating frictionless knowledge sharing and unlocking the true value of globalization. This has led to increased confidence among CTOs and CIOs in distributing development teams globally, with minimal impact on productivity. Additionally, the widespread use of Generative AI has boosted employee morale, surpassing productivity gains. While the potential of Generative AI is vast, limitations primarily stem from hardware costs, energy consumption, and regulatory constraints.”

The post Generative AI Improves Software Engineering Productivity By 70% – Says Ness-Zinnov Study appeared first on IoT Business News.

The combination of generative AI (GenAI) and the Internet of Things (IoT) holds the potential to reshape the future of technology and drive unprecedented innovation.

GenAI promises to revolutionize the IoT ecosystem by enhancing security, personalization, anomaly detection, on-device machine learning, and network management.

As these areas continue to evolve, businesses and individuals are ready to benefit from the innovative applications of GenAI. In this article, we’ll explore the many ways GenAI is beginning to transform the IoT landscape and examine the future possibilities of this powerful alliance.

Creating Synthetic Data for Machine Learning

One of the main challenges in developing machine learning models for IoT devices revolves around collecting and labeling massive amounts of data. However, GenAI solves this problem by creating synthetic data to train these models.

This synthetic data can be used to simulate a variety of scenarios, including machine failures. For example, a manufacturing company can use GenAI to create synthetic data that represents different machine failure scenarios. The company can then use this data to train machine learning models to anticipate potential problems in advance, enabling predictive maintenance.

Crafting Personalized Experiences

GenAI enables IoT devices to deliver personalized experiences to users by leveraging its ability to generate new and original content. Smart home systems can use GenAI algorithms to create personalized lighting and temperature settings for individual users, enhancing comfort and convenience.

And wearable devices can use GenAI to offer tailored workout recommendations based on an individual’s fitness goals and preferences. By leveraging user data from surveys, interactions, and sensor inputs, GenAI can create unique and personalized experiences tailored to the specific needs of each user.

Improving Anomaly Detection

Anomaly detection is key to ensuring the reliability and security of IoT networks. GenAI has the ability to greatly improve anomaly detection by creating synthetic data that accurately simulates normal operating conditions. By training machine learning models on this synthetic data, IoT devices can effectively identify and flag irregular events in real time.

For example, an operator of a power grid can use GenAI to generate synthetic data that mirrors typical power consumption patterns. The power grid operator can then use that data to train a machine learning model that can detect sudden spikes or irregularities in power consumption, enabling the operator to take proactive measures to prevent potential failures or security breaches.

Enabling On-Device Machine Learning

The combination of GenAI and IoT introduces exciting prospects for on-device machine learning. GenAI tackles the issue of limited computing resources in IoT devices by creating smaller and more effective machine learning models.

Anomaly detection models, for instance, can be optimized and implemented directly on IoT devices, enabling real-time analysis and decision-making without depending on cloud resources. This reduces latency as well as strengthens data privacy and security by reducing the need to send data to external servers.

Automating Network Management

Managing large-scale IoT networks requires intelligent automation. GenAI can be instrumental in automating different areas of network management, including configuring devices and optimizing network traffic. With its generative abilities, GenAI can automatically set up new devices as they join the network, simplifying the onboarding process.

Additionally, GenAI can enhance network traffic by intelligently directing data through the most efficient routes, reducing latency and maximizing the use of available bandwidth. This automation lessens the workload of network administrators and boosts the performance and efficiency of the network.

Potential Future IoT Applications

As GenAI continues to evolve, the possibilities for its integration with IoT are endless. Some potential applications include:

Creating New Types of IoT Devices: GenAI can facilitate the development of innovative IoT devices, such as smart assistants with natural language processing capabilities. These devices will be able to understand and respond to human commands and queries, revolutionizing the way we interact with technology.

Enhancing User Interactions: GenAI can enable new ways to interact with IoT devices, including gesture recognition and voice commands. This will make technology more intuitive and accessible, improving the user experience.

Improving Security and Reliability: GenAI can assist in developing advanced security measures for IoT networks, effectively mitigating cyber threats and ensuring data privacy. By generating synthetic data to train anomaly detection models, GenAI can help identify and prevent security breaches in real-time.

Democratizing IoT Access: GenAI has the potential to help bridge the digital divide by making IoT devices more affordable and accessible and allow more people to benefit from the advantages of intelligent connectivity. Democratizing IoT access will allow businesses and individuals to take advantage of IoT technologies for various applications and industries.

Conclusion

The integration of GenAI and IoT has the potential to totally transform how we interact with and benefit from intelligent connectivity. By leveraging GenAI, businesses and individuals can unlock a wide array of applications, ranging from improved anomaly detection and personalized experiences to on-device machine learning and network management automation.

As the field continues to evolve, it’s vital that we embrace the possibilities presented by GenAI and explore its potential to transform IoT. The future of GenAI in IoT is bright, promising a new era of intelligent connectivity and unprecedented opportunities for innovation.

The post Bringing the Power of GenAI to IoT appeared first on IoT Business News.

2023 was an eventful year in the tech sector, where AI went mainstream with the explosion of language learning models.

As we progress into 2024, the integration and evolution of artificial intelligence in various domains are not just changing; they are set to revolutionise the way we approach design, development, and deployment in these sectors.

With advancements in artificial intelligence accelerating at an unprecedented pace, we stand at the cusp of a new era where AI’s influence extends beyond mere automation to become a cornerstone of innovation and efficiency.

The engineering team at ByteSnap Design has been reflecting on the future of AI in the technology and electronics design industries.

Here is the team’s forecast for the pivotal AI trends likely to emerge during 2024 to redefine industry standards and drive forward a new age of technological excellence.

AI to trigger a battle of the Smart Assistants?

AI assistant tools will continue to be integrated into existing tools to make tasks easier. An example of this happening this year is Zoom’s AI companion which can summarise meetings into notes.

Expect to see development tools such as an IDE integration which can generate GIT commit notes, and release notes automatically during 2024.

We also predict companies will be trying to increase their profits with more monetisation from smart assistants such as Alexa and this will drive techy people towards open source alternatives, such as Home Assistant which run locally.

Rise of the Robotaxi

We anticipate that the first un-geofenced electric Robotaxis will become operational and start accepting paying customers over the coming months.

This is likely to scale steadily over the next few years to replace Uber as the transport medium of choice, providing legal issues can be overcome.

Ultimately, this will make car ownership a questionable decision because travelling this way could be cheaper than running a car.

Apple to enter the Generative AI race

We look forward to Apple unveiling a product to join the AI arms race with their own large language model. Other companies are embracing AI faster and already implementing it; for example, Bard into Google Assistant, and Microsoft’s push for AI in their Office 365 products. Nevertheless, Apple have a stronger than most in-house development philosophy, and it’s hard to see them allowing these products to go unchallenged.

Expect announcements in late 2024 from Apple around its generative AI offering.

Further AI disruption for the Smart Home market

Expect to see more innovation in the smart home market as consumers continue to look for ways to reduce their energy bills, with smart thermostats and TRVs becoming ever more popular. Nest are apparently trying to use AI to help understand consumers behaviour around energy consumption and we anticipate that this trend will continue.

With Apple releasing their Pro Vision headset in 2024, we also expect to see some manufacturers trying to compete with a cheaper product. Apple are excellent at design and are sometimes seen as a trend setter, but in this case are quite late to the party With Meta already well-established leaders. However, Apple have a history of knocking out incumbent leaders so this could be an interesting space to watch.

How much of a consumer appetite there is for this type of technology, however, remains open to question.

AI-enabled Integrated Circuits

We’re likely to see the greater emergence of AI on integrated circuits from companies such as Altered Carbon.

Computer chip manufacturers, Intel, for example, are incorporating AI cores into their CPUs.

AI algorithms in our view are mostly used for detection/categorisation. The classic example is using AI to detect whether an image contains a cat or a dog. However, even the way that the likes of Tesla use AI is similar – detecting images of signs for speed limits, or an image of the lines of the road – but the output is different in that it translates it into braking, accelerating or turning.

One of the projects we’ve worked on at ByteSnap sent accelerometer data into the cloud to detect people falling over. We see a scenario where a fall detector algorithm could be generated by AI and embedded within the sensor device, so that the huge amount of data does not need to be sent, allowing the product to consume less power.

Greater AI in Supply Chain Management

AI-powered forecasting is providing businesses with intelligence to prevent mishaps in the future, overcoming demand-supply mismatches to prevent overstock or understock of inventory.
This minimises costs and improves customer experience. We expect to see more of this across 2024. Additionally, AI-based algorithms are automating goods retrieval from warehouses for smooth order fulfilment, and AI-powered autonomous vehicles are reducing driver costs for delivery.

AI in software and electronics design

Software development and electronics design are both areas that AI vendors are targeting, as developers are expensive and timescales can be long. We can see initially the AI could be best at optimising PCB layouts in the hardware side and writing generic functions within software, albeit with dubious copyright infringement.

The work of translating very abstract requirements into real electronics still seems a very long way off though. This is partly due to lack of freely available models to train against in what is a fast-moving industry and little way for the circuits that are available to be assessed.

In addition, electronic engineering is actually quite a person-centred job; dealing with suppliers, customers, manufacturers, colleagues. Software AI trainers have raided github and ChatGPT was able to train linguistic models against the huge wealth of the World Wide Web.

However, for electronics, it will take another generation of AI development before engineering jobs are threatened.

The post ByteSnap Electronic Industry Predictions for 2024 appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Generative AI Market Report 2023–2030” report and highlights the landscape with its top players in the data center GPU, foundational model and platform, and generative AI services markets.

Key insights:

• The generative AI market went from nearly nothing to a hot market within a year, as shown by IoT Analytics’ latest research report.
• IoT Analytics analyzed 3 interconnected markets for generative AI: 1) data center GPUs, 2) foundational models and platforms, and 3) generative AI services. Each has distinct aspects and market players.
• NVIDIA leads the data center GPU segment with a 92% market share, while OpenAI and Microsoft have a combined share of 69% in the foundational models and platforms market. The services market is more fragmented, with Accenture currently seen as the leader with a 6% market share.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “The speed of generative AI innovation with new offerings coming on the market on a weekly basis is fascinating monitor. Nvidia with 92% market share for data center GPUs as well as Microsoft and OpenAI with a combined 69% market share in the models and platforms segment are firmly in the lead in their respective market segments. With hyperscalers developing their own data center chips, with the availability of powerful open-source models and with giants like AWS and Google looking to differentiate with their new offerings, it will be interesting to watch how much the early lead is worth for the current market leaders. I personally do expect both Microsoft and Nvidia to maintain their strong positions in the coming years but the gap to the competition will likely close a bit.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“The Generative AI market is rapidly evolving, with established leaders and a growing number of startups. In 2024, it’s a make-or-break year for Gen AI vendors, as they navigate a crowded field of competitors.”

The leading generative AI companies

The rise of generative AI

Following its release of ChatGPT in 2022, OpenAI experienced an impressive one-year, zero-to-$1 billion revenue bump—surpassed only by US-based chipmaker NVIDIA, which managed to increase its data center GPU sales from $3.6 billion in Q4 2022 to an expected $16 billion in Q4 2023. When it comes to generative AI companies, these two stand out.

The generative AI foundational models and platforms market is expected to reach nearly 5% of global software spending by 2030

According to IoT Analytics’ Generative AI Market Report 2023–2030 (published December 2023), the generative AI software and services market reached $6.2 billion in 2023. Although it is still very early to forecast where things are going from here, the IoT Analytics research team expects the generative AI foundational models and platforms market to make up nearly 5% of global software spending by 2030 due to its disruptive nature and tremendous value potential.

However, this does not include the market for individual generative AI solutions. The team believes generative AI will become standard within most software in the near future. This also does not include the hardware market, such as for data center GPUs, since this market is looked at separately from software but is discussed below.

In this article, we dive into the data center GPU, generative AI foundational model and platform, and generative AI services markets, discussing what aspects of the generative AI field make up each market and highlighting the leading generative AI companies within them.

Market segment 1: Data center GPU market

a.) Market overview

The data center GPUs market refers to specialized GPUs designed to handle the extensive computation demands of modern data centers, which are the backbone of generative AI. Originally designed for rendering graphics, GPUs excel at parallel processing, which is fundamental for deep learning computations used in generative AI.

The report shows the data center GPUs market reached $49 billion in 2023—a booming increase from 2022 (+182%), mostly driven by one company alone: NVIDIA. Although the market for data center GPUs has seen steep price increases and is undergoing severe supply constraints, there is currently no reason to believe demand will decline in the next two years.

b.) Leading data center GPU companies

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

1. NVIDIA

NVIDIA leads the data center GPU market by a long shot, owning 92% of the market share. In 2023, the company’s quarterly revenue jumped 272%, from $4.3 billion in Q1 to a forecasted $16 billion in Q4.

The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon.

2. AMD

The Data Center segment of US-based semiconductor AMD player, NVIDIA’s first real GPU challenger, grew by 21% from Q2 2023 to Q3 2023 and shared 3% of the market. However, AMD has big ambitions in 2024 to eat into NVIDIA’s market share. In early December 2023, it announced the release of its Instinct MI300 Series accelerators, which are cheaper than NVIDIA’s comparable accelerators and, as AMD claims, faster. AMD’s CEO, Dr. Lisa Su, forecasted at least $1 billion in revenue in 2024 through this chip alone, and Microsoft, Meta, and OpenAI stated they would use the Instinct MI300X in their data centers. AMD also recently launched ROCm 6.0 to provide developers with an ecosystem that is equally attractive to CUDA.

3. Intel and others

US-based chipmaker Intel, the traditional competitor to NVIDIA and AMD, has lagged behind on the data center GPU front. In May 2022, Intel’s Habana Labs released its second generation of AI processors, Gaudi 2, for training and inferencing. Though not as fast as NVIDIA’s popular H100 GPU, it is considered a viable alternative when considering price to performance.

Meanwhile, in July 2023, startup chipmaker Cerebras announced it had built its first of nine AI supercomputers in an effort to provide alternatives to systems using NVIDIA technology. Cerebras built the system, Condor Galaxy 1, in partnership with the UAE, which has invested in AI research in recent years.

Market segment 2: Generative AI foundational models and platforms market

a) Market overview

The foundational models and platforms market comprises two related areas. Foundational models are large-scale, pre-trained models that can be adapted to various tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.

Generative AI platforms, in turn, refer to software that enables the management of generative AI-related activities outside of foundational models. Notably, IoT Analytics identified six platform types: 1) development, 2) data management/databases, 3) AI IaaS/GPU as-a-service, 4) middleware & integration, 5) MLOps, and 6) user interface and experience (UI/UX).

The foundational models and platforms market exploded with the public release of ChatGPT in late 2022, reaching $3.0 billion in 2023. This is substantial growth over 2022, which saw next to nil in terms of revenue. IoT Analytics’ analysis projects strong market growth in the coming years as enterprises invest billions in—and report real value from—generative AI implementations and continuous improvements.

b.) Leading generative AI foundational model and platform companies

Unsurprisingly, the foundational model and platform market are currently led by OpenAI, with several well-known technology companies trying to catch up.

1. OpenAI

With the November 2022 launch and subsequent success of ChatGPT, OpenAI leads in the share of the foundational model and platform vendors market with 39%. Since the release of ChatGPT, OpenAI’s generative pre-trained transformer (GPT) models went from GPT-3.5 to GPT-4 to GPT-4 Turbo, showcasing the continued development of the model. OpenAI’s models continue to impress in independent model assessments and rankings—often coming out in the top three of all tested models. Although many experts expect the foundational model space to become a commodity over time, at this point, OpenAI’s flagship models remain the top foundational model on the most common benchmarks.

According to IoT Analytics’ What CEOs Talked About series, in 2023, ChatGPT skyrocketed in boardroom discussions in Q1, but as other foundational models and generative AI applications became available, mentions of ChatGPT steadily declined as “generative AI” separated and continued to rise. (The What CEOs Talked About in Q4 2023 report and blog is expected to be released mid-December 2023.)

2. Microsoft

On OpenAI’s heels at 30% market share is Microsoft, its largest shareholder. Microsoft’s platform, Azure AI, offers Azure OpenAI, which uses OpenAI’s LLMs but goes beyond the public ChatGPT offering by promising greater data security and custom AI apps. This is suited for enterprises who want to secure their proprietary data when leveraging the benefits of generative AI since ChatGPT’s terms of use state that they can store and use content (both input and output) to improve their services. In November 2023, Microsoft reported over 20,000 active paying customers for its Azure AI platform, adding that 85% of Fortune 100 companies used it in the past year.

Despite Microsoft’s strong partnership with OpenAI, Microsoft also heavily promotes the usage of other models, such as Llama 2, via its platform, thereby enabling customers to freely choose and test different models and providers. Another key priority for Microsoft is integrating AI capabilities into its existing product portfolio, such as Azure, Microsoft/Office 365, and Bing.

3. AWS

AWS has an 8% share of this market. Its Bedrock service, publicly released in September 2023, provides access to models from several AI companies, such as Anthropic, AI21 labs, and Cohere (each with a 2% share of this market), and combines them with developer toolsets to help customers build and scale generative AI applications.

AWS has quickly claimed the third spot in this market because the company is the market leader in public cloud services and quickly got its existing customer base excited about its differentiated approach to Generative AI. In contrast to Google and Microsoft, AWS Bedrock focuses on providing a platform service that gives users access to a number of both general and domain-specific foundational models from a variety of vendors—providing choice, flexibility, and independence.

4. Google

In 2022, most experts credited Google as being the one tech company at the forefront of AI. Many experts interviewed by the IoT Analytics team continuously praised Google for its AI and its data products and innovations. In 2023, the picture is different, and Google is fighting to defend its position as an AI leader.

Vertex AI is Google Cloud’splatform focused on machine learning (ML) ops. It is integrated with other Google Cloud services, such as BigQuery and Dataproc, and offers a Jupyter-based environment for ML tasks. In early December 2023, Google released a preview version of its new multi-modal flagship model, Gemini. The related technical report states that the largest of the Gemini family outperformed other existing models in 30 out of 32 common ML benchmarks. Initially, the announcement of Gemini was widely received as positive, but a popular demo video released by Google later turned out to be staged.

Market segment 3: Generative AI services market

a) Market overview

The generative AI services market represents a specialized segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate generative AI capabilities. With generative AI having risen as one of the top discussion points in boardrooms, services companies are sensing a large opportunity in helping companies formulate their generative AI strategies (e.g., what use cases to implement), advising them on technical architecture choices (e.g., which models to use) and helping them implement and build individual solutions.

IoT Analytics assesses that the generative AI services sector’s opportunity is now. Due to the novelty of generative AI, organizations often lack skills and experience, and the only option is to look for professional services firms that have or are in the process of building up the required expertise.

b) Leading generative AI services companies

The generative AI services market is more dispersed than the other two markets highlighted here.

1. Accenture

Accenture is estimated to have the largest generative AI services market share at 6%. The company announced in June 2023 that it is investing $3 billion in data and AI practice over three years to double its AI talent and develop new capabilities. Additionally, Accenture disclosed in its Q4 2023 earnings press release that its revenue for generative AI projects grew to $300 million for 2023.

In November 2023, Accenture announced plans to launch a network of generative AI studios in North America where companies can explore ways to integrate generative AI applications. These studies are expected to be at Accenture Innovation Hubs in Chicago, Houston, New York, San Francisco, Toronto, and Washington, DC.

2. IBM

US-based technology corporation IBM makes up 2% of this market. To position itself for the opportunities that generative AI brings, the company announced it had established a “Center of Excellence (CoE) for generative AI,” which as of May 2023, already had over 1,000 consultants specialized in generative AI. The CoE operates alongside IBM’s AI and Automation practice, which includes over 21,000 data and AI consultants.

3. Capgemini

France-based IT services company Capgemini also has a 2% share in this market, offering consulting services intended to help clients adopt key technologies such as the cloud and AI. In July 2023, Capgemini announced the launch of a portfolio of generative AI services, including in the following areas:

• Strategy
• Customer experience
• Software engineering
• Custom solutions for enterprise

One of Capgemini’s current customers is London Heathrow Airport which aims to improve traveler experiences through its “Generative AI for Customer Experience” offer. Heathrow’s Director of Marketing and Digital, Pete Burns, stated that the project is intended to “assist, empower and delight passengers” with tailored customer service solutions.

4. The many others

Past this point, the remaining 86% of the market becomes a cornucopia of specialized generative AI services providers and larger general consulting and system integration companies, each taking a bite of the rapidly growing segment.

As an example, in April 2023, UK-based professional services company PwC announced plans to invest $1 billion over three years to not only grow its AI offerings but also transform how it works by using generative AI. Additionally, in July 2023, global consultancy firm McKinsey & Company announced it partnered with AI startup Cohere to provide customized AI solutions to its enterprise clients.

Generative AI company landscape outlook

The enterprise generative AI market is roughly a year old, and already, the generative AI companies landscape appears vast.

IoT Analytics released its first generative AI report, the Generative AI Trend Report, in March 2023. Since then, more foundational models and platforms have emerged, e.g., OpenAI’s GPT4 Turbo, Google’s Gemini, or Microsoft’s Phi-2. At the same time, the demand for data center GPUs exploded, which is also mirrored in NVIDIA’s stock performance (+231% year-to-date as of 14 December 2023). Finally, consulting giants have made investments to position themselves in the generative AI services market, such as Accenture’s $3B investment in AI and its pledge to double “AI talent.”

As part of this research, we talked to 30+ experts in the field and gathered information on 270+ generative AI projects and analyzed which industries and departments are fastest to adopt and which vendors are most selected today.
The coming months will reveal how many of those projects will deliver value besides just being a marketing coup or how many of those currently in the proof-of-concept stage will move forward. Most companies are only now forging their generative AI strategies and considering whether to build foundational models from scratch based on industry-specific data, use an out-of-the-box propriety model, or fine-tune open-source models. All of this comes as generative AI companies release new products at unprecedented speed.

There is still a lot of movement in the generative AI company landscape, and there will be more in the foreseeable future. IoT Analytics will stay on top of this space, with a follow-up report expected in 2024.

The post The leading generative AI companies appeared first on IoT Business News.

The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

The post AI and IoT: Post-AI Summit reflections on safe integration and data integrity appeared first on IoT Business News.

AI will not take all jobs nor end civilization. But it will help businesses make better decisions.

Artificial intelligence (AI) is everywhere. And stories are rampant about its promise and its threat. Will AI’s potential be realized in the year ahead?

SAS, the leader in AI and analytics, asked executives and experts across the company to predict trends and key business and technology developments in AI for 2024.

Below are some of the predictions they shared.

Generative AI will augment (not replace) a comprehensive AI strategy

“Generative AI technology does a lot of things, but it can’t do everything. In 2024, organizations will pivot from viewing generative AI as a stand-alone technology to integrating it as a complement to industry-specific AI strategies. In banking, simulated data for stress testing and scenario analysis will help predict risks and prevent losses. In health care, that means the generation of individualized treatment plans. In manufacturing, generative AI can simulate production to identify improvements in quality, reliability, maintenance, energy efficiency and yield.” – Bryan Harris, Chief Technology Officer, SAS
[Note: Earlier this year, SAS committed $1 billion to AI-powered industry solutions.]

AI will create jobs

“In 2023, there was a lot of worry about the jobs that AI might eliminate. The conversation in 2024 will focus instead on the jobs AI will create. An obvious example is prompt engineering, which links a model’s potential with its real-world application. AI helps workers at all skill levels and roles to be more effective and efficient. And while new AI technologies in 2024 and beyond may cause some short-term disruptions in the job market, they will spark many new jobs and new roles that will help drive economic growth.” – Udo Sglavo, Vice President of Advanced Analytics, SAS

AI will enhance responsible marketing

“As marketers we must consciously practice responsible marketing. Facets of this are awareness of the fallibility of AI and alertness to possible bias creeping in. While AI offers the promise of enhanced marketing and advertising programs, we know that biased data and models beget biased results. In SAS Marketing, we are implementing model cards that are like an ingredient list, but for AI. Whether you create or apply AI, you are responsible for its impact. That’s why all marketers, regardless of technical know-how, can review the model cards, validate that their algorithms are effective and fair, and adjust as needed.” – Jennifer Chase, Chief Marketing Officer, SAS

Financial firms will embrace AI amid a Dark Age of Fraud

“Even as consumers signal increased fraud vigilance, generative AI and deepfake technology are helping fraudsters hone their multitrillion-dollar craft. Phishing messages are more polished. Imitation websites look stunningly legitimate. A crook can clone a voice with a few seconds of audio using simple online tools. We are entering the Dark Age of Fraud, where banks and credit unions will scramble to make up for lost time in AI adoption – incentivized, no doubt, by regulatory shifts forcing financial firms to assume greater liability for soaring APP [authorized push payment] scams and other frauds.” – Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions, SAS

Shadow AI will challenge CIOs

“CIOs have struggled with ‘shadow IT’ in the past and will now confront ‘shadow AI’ – solutions used by or developed within an organization without official sanction or monitoring by IT. Well-intentioned employees will continue to use generative AI tools to increase productivity. And CIOs will wrestle daily with how much to embrace these generative AI tools and what guardrails should be put in place to safeguard their organizations from associated risks.” – Jay Upchurch, Chief Information Officer, SAS

Multimodal AI and AI simulation will reach new frontiers

“The integration of text, images and audio into a single model is the next frontier of generative AI. Known as multimodal AI, it can process a diverse range of inputs simultaneously, enabling more context-aware applications for effective decision making. An example of this will be the generation of 3D objects, environments and spatial data. This will have applications in augmented reality [AR], virtual reality [VR], and the simulation of complex physical systems such as digital twins.” – Marinela Profi, AI/Generative AI Strategy Advisor, SAS

Digital-twin adoption will accelerate

“Technologies like AI and IoT [Internet of Things] analytics drive important sectors of the economy, including manufacturing, energy and government. Workers on the factory floor and in the executive suite use these technologies to transform huge volumes of data into better, faster decisions. In 2024, the adoption of AI and IoT analytics will accelerate through broader use of digital-twin technologies, which analyze real-time sensor and operational data and create duplicates of complex systems like factories, smart cities and energy grids. With digital twins, organizations can optimize operations, improve product quality, enhance safety, increase reliability and reduce emissions.” – Jason Mann, Vice President of IoT, SAS

Insurers will confront climate risk, aided by AI

“After decades of anticipation, climate change has transformed from speculative menace to genuine threat. Global insured losses from natural disasters surpassed $130 billion in 2022, and insurers worldwide are feeling the squeeze. US insurers, for example, are under scrutiny for raising premiums and withdrawing from hard-hit states like California and Florida, leaving tens of millions of consumers in the lurch. To survive this crisis, insurers will increasingly adopt AI to tap the potential of their immense data stores to shore up liquidity and be competitive. Beyond the gains they realize in dynamic premium pricing and risk assessment, AI will help them automate and enhance claims processing, fraud detection, customer service and more.” – Troy Haines, Senior Vice President of Risk Research and Quantitative Solutions, SAS

AI importance will grow in government

“The workforce implications of AI will start being felt in government. Governments have a hard time attracting and retaining AI talent since experts command such high salaries, however, they will aggressively recruit for expertise to support regulatory actions. And like enterprises, governments will also increasingly turn to AI and analytics to boost productivity, automate menial tasks and mitigate that talent shortage.” – Reggie Townsend, Vice President of the SAS Data Ethics Practice

Generative AI will bolster patient care

“To advance health and improve patient and member experiences, organizations will further develop generative AI-powered tools in 2024 for personalized medicine, such as the creation of patient-specific avatars for use in clinical trials and the generation of individualized treatment plans. Additionally, we will see the emergence of generative AI-based systems for clinical decision support, delivering real-time guidance to payers, providers and pharmaceutical organizations.” – Steve Kearney, Global Medical Director, SAS

Deliberate AI deployment will make or break insurers

“In 2024, one of the top 100 global insurers will go out of business as a consequence of deploying generative AI too quickly. Right now, insurers are rolling out autonomous systems at breakneck speed with no tailoring to their business models. They’re hoping that using AI to crunch through claims quickly will offset the last few years of poor business results. But after 2023’s layoffs, remaining staff will be spread too thin to enact the necessary oversight to deploy AI ethically and at scale. The myth of AI as a cure-all will trigger tens of thousands of faulty business decisions that will lead to a corporate collapse, which may irreparably damage consumer and regulator trust.” – Franklin Manchester, Global Insurance Strategic Advisor, SAS

Public health will get an AI boost from academia

“Public health is achieving technologic modernization at an unprecedented rate. Whether overdoses or flu surveillance, using data to anticipate public health interventions is essential. Forecasting and modeling are rapidly becoming the cornerstone of public health work, but government needs help. Enter academia. We will see an increase in academic researchers carrying out AI-driven modeling and forecasting on behalf of government. It is clear after COVID-19 that the protection of our population will require exceptional technology and collaboration.” – Dr. Meghan Schaeffer, National Public Health Advisor and Epidemiologist, SAS

The post AI reality vs. myth: Twelve predictions from SAS for 2024 appeared first on IoT Business News.

News highlights:

• New Arm Cortex-M52 is the smallest, most area- and cost-efficient processor enabled with Arm Helium technology, delivering enhanced AI capabilities for lower cost IoT devices
• Provides the flexibility to scale across a range of performance points and configurations, delivering DSP capabilities without a separate unit to save on area and cost
• Simplified development flows bring AI within reach on a single toolchain and single proven architecture

Generative Artificial Intelligence (AI) and Large Language Models (LLMs) are grabbing headlines, but many don’t realize how much AI is already deployed in embedded devices and impacting applications across our homes, cities and in industry – this is referred to as the Artificial Intelligence of Things (AIoT), and it’s being built on Arm. AI is critical to understanding data and enabling more seamless interactions between the physical and digital world. As this technology advances, on-device intelligence is being deployed in smaller, more cost-sensitive and often battery powered devices at the lowest cost points, with greater privacy and reliability due to less reliance on the cloud.

As AI-enabled IoT shipments continue to increase, our partners need access to more ML (Machine Learning) capabilities and simpler development flows, giving them the agility to innovate and scale quickly. To address these requirements, today we are announcing the Arm Cortex-M52, designed for AIoT applications that require a boost in digital signal processing (DSP) and ML performance without the cost overhead of dedicated DSP and ML accelerators. Cortex-M52 will unlock the potential for delivering ML on embedded computing solutions at lower price points than is possible today.

Bringing AI capabilities to a broader range of IoT devices

The Cortex-M52 includes Arm Helium technology, providing a significant performance uplift in DSP and ML applications for small, low power embedded devices, making it possible to deploy more compute intensive ML inference algorithms in endpoints without a dedicated NPU. Helium technology has already been implemented successfully in products at the far edge of the network, but the Cortex-M52 now enables Arm partners to take this capability into lower cost more power constrained devices.

By extending Helium technology into a new class of Cortex-M, Arm is delivering a step change in matrix and DSP compute on microcontrollers for smaller embedded devices. The Cortex-M52 provides a simplified migration path from the Cortex-M33 and Cortex-M4, addressing a wide range of AIoT applications to enable richer UI, voice and vision experiences, such as automotive and industrial control, predictive maintenance, and wearable sensor fusion. Cortex-M52 delivers the flexibility needed to scale across a range of performance points and configurations, providing DSP capabilities without a separate processing unit, saving on silicon area and cost.

Providing optimal performance and cost choice with robust safety and security

Cortex-M52 extends the Armv8.1-M Cortex-M line-up (which includes the Cortex-M55 and Cortex-M85) to a new efficiency point, a critical milestone in bringing ML capabilities to microcontrollers. It provides the lowest area and power implementation of any Helium-enabled Cortex-M, offering choice to silicon partners looking to trade-off performance and cost. Developers can benefit from an uplift in bothML and DSP performance, with up to 5.6x performance uplift for ML and up to 2.7x performance uplift for digital signal processing compared to previous Cortex-M generations.

Security remains critical in devices, especially when shipping at large scale, and Cortex-M52 implements the latest security extensions for Armv8.1-M, including PACBTI and Arm TrustZone technology, which offers enhanced software threat mitigation. In addition, Cortex-M52 will accelerate the route to PSA Certified Level 2 silicon, enabling the next generation of PSA Certified devices. The latest Armv8.1-M cores (including Cortex-M55 and Cortex-M85) also offer enhanced functional safety features that are crucial in many automotive and industrial control applications. The Cortex-M52 delivers these extended safety packages and features to help partners reach FuSa certification faster and more easily, compared to previous generation Cortex-Ms being deployed in these applications.

Simplifying AI development for the smallest endpoint devices

Traditional embedded developers grapple with the mathematical, data analysis, toolchain expertise and programming skills required for AI. Developer enablement is critical if we are to see an increase in AIoT shipments, and with the Cortex-M52 we’re delivering the critical features and capabilities required in a modern development flow today.

Historically, to achieve the ML and DSP performance Cortex-M52 delivers would have required the combination of a CPU, a DSP and an NPU, meaning that once the hardware is built, developers would need to write, debug and tune code for chips with three separate tool chains, three compilers, three debuggers, and so on. Now, we are bringing AI within reach on a single toolchain and single proven architecture with the industry-standard, user-friendly Arm Cortex-M portfolio. This ensures a unified development flow for traditional, DSP and ML workloads – while specific integration and optimizations for leading machine learning frameworks will ensure that developers have a seamless experience and get the best performance from any Cortex-M.

Cortex-M52 is fully software compatible with Cortex-M55 and Cortex-M85, enabling developers to benefit from and leverage the growing software and tools ecosystem around Helium, as well as free software libraries and an extensive knowledge base from our partner ecosystem. To help streamline and accelerate the IoT and embedded development process, Cortex-M52 will also be available on Arm Virtual Hardware, our cloud-based offering that enables software development in advance of silicon.

Deploying AI across the full spectrum of AIoT use cases, built on Arm

The AIoT runs on Arm and together with our partners, we identified a need to bring DSP and ML compute performance to low power embedded applications at a better cost and accessibility point for the market. The Cortex-M52 CPU delivers higher levels of AI inference performance on the smallest devices, enabling the industry to scale IoT device deployments even further.

The post Arm extends Cortex-M portfolio to bring AI to the smallest endpoint devices appeared first on IoT Business News.

The fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) is propelling us into a new era of interconnected intelligence. This synergy, often referred to as AIoT, holds the potential to revolutionize industries, enhance daily life, and enable unprecedented levels of automation and efficiency. In this article, we will delve into the world of AIoT, exploring its definition, key applications, benefits, challenges, and the promising future it holds for our interconnected world.

Defining AIoT

AIoT, or Artificial Intelligence of Things, is a convergence of AI and IoT technologies. It involves embedding AI algorithms and machine learning models into IoT devices, enabling them to process and analyze data locally and make intelligent decisions. AIoT leverages the power of data analytics and machine learning to extract valuable insights and respond to changing conditions in real time.

Key Applications of AIoT

1. Smart Home

In the realm of smart homes, AIoT is making homes more intelligent and efficient. Smart devices, such as thermostats, cameras, and voice assistants, use AI to learn user preferences and adapt to individual needs. These devices can optimize energy use, enhance security, and automate various household tasks.

2. Healthcare

AIoT plays a vital role in remote health monitoring and telemedicine. Devices equipped with AI can collect and analyze patient data, such as vital signs and symptoms, and provide real-time health recommendations or alerts. This technology improves the quality of healthcare and helps manage chronic conditions more effectively.

3. Industrial Automation

In manufacturing and industrial settings, AIoT is transforming operations. IoT sensors collect data from equipment and processes, while AI algorithms analyze this data to predict maintenance needs and optimize production. This reduces downtime, increases efficiency, and improves overall productivity.

4. Smart Cities

AIoT contributes to the development of smart cities by enhancing urban planning and management. Traffic lights, surveillance cameras, and environmental sensors use AI to analyze data and improve traffic flow, monitor security, and reduce pollution.

5. Agriculture

In smart agriculture, AIoT is revolutionizing farming practices. IoT sensors collect data on soil quality, weather conditions, and crop health. AI models analyze this data to make decisions about irrigation, fertilization, and pest control, optimizing crop yields and resource use.

6. Retail

Retail businesses use AIoT to enhance the shopping experience. Smart shelves can monitor inventory levels, and AI algorithms can analyze customer behavior and preferences to personalize marketing and promotions.

7. Autonomous Vehicles

The automotive industry is integrating AIoT in the development of autonomous vehicles. AI algorithms process data from sensors, cameras, and Lidar systems to make real-time decisions for safe and efficient self-driving.

8. Energy Management

AIoT is improving energy efficiency by allowing smart grids to optimize power distribution based on real-time demand and supply data. This reduces energy waste and supports sustainable energy practices.

Benefits of AIoT

The integration of AI and IoT offers numerous advantages:

1. Real-Time Decision Making

AIoT enables devices to make real-time decisions based on data analysis. This leads to faster responses to changing conditions and improved efficiency.

2. Enhanced Automation

By embedding AI in IoT devices, automation becomes smarter and more adaptable. Devices can learn from data and user behavior, making automation more effective.

3. Improved Predictive Analytics

AIoT systems can predict maintenance needs, security threats, and other critical events, reducing downtime and preventing issues before they occur.

4. Personalization

AIoT can provide a more personalized user experience by adapting to individual preferences and needs.

5. Energy Efficiency

AIoT optimizes resource use and energy consumption, making processes more environmentally friendly and cost-effective.

6. Cost Savings

By automating processes and reducing downtime, AIoT can lead to cost savings in various industries.

Challenges and Considerations

While AIoT offers numerous benefits, it also poses challenges and considerations:

1. Data Privacy and Security

Collecting and analyzing sensitive data using AIoT technology raise concerns about data privacy and security. Strong data protection measures are essential.

2. Complexity

Integrating AI into IoT devices and systems can be complex and require specialized expertise. Deploying and maintaining AIoT solutions can be a challenge for organizations.

3. Connectivity

Reliable internet connectivity is crucial for the seamless operation of AIoT devices. Expanding network coverage and ensuring robust connections are vital.

4. Energy Consumption

AIoT devices, especially those that rely on continuous data processing, can consume significant power. Energy-efficient designs are important to mitigate this.

5. Ethical Considerations

AIoT technologies may raise ethical concerns, particularly regarding data collection, sharing, and decision-making processes. Ethical guidelines and regulations are necessary.

The Future of AIoT

As technology continues to advance, the future of AIoT looks promising. Here are some trends and developments to watch for:

1. Edge AI

Edge computing combined with AIoT allows for data processing at or near the source, reducing latency and enabling real-time decision-making.

2. 5G Connectivity

The rollout of 5G networks will provide faster and more reliable connectivity for AIoT devices, enabling them to operate more efficiently and securely.

3. Quantum Computing

Quantum computing may revolutionize AIoT by providing unparalleled processing power, enabling more complex analyses and decision-making.

4. Ethical AI

Developments in ethical AI aim to address concerns about data privacy, fairness, and transparency in AIoT systems.

5. AIoT Ecosystems

AIoT ecosystems will become more interconnected, with devices and systems working together to provide a seamless and intelligent experience.

Conclusion

The synergy of AI and IoT, known as AIoT, is transforming industries and daily life by enabling real-time decision-making, enhanced automation, and predictive analytics. Challenges related to data security, complexity, and ethical considerations must be addressed to ensure that the benefits of AIoT are fully realized.

As AIoT technology continues to evolve, it will remain a driving force in our interconnected world, offering a glimpse into a future where devices and systems are smarter, more efficient, and adaptable, ultimately enhancing our quality of life and the efficiency of various industries.

The post The Synergy of AI and IoT: Unleashing the Power of AIoT appeared first on IoT Business News.

The semiconductor market saw its first market growth since 2021 amid continued trade tensions, slow economic growth, and chip shortages.

IoT Analytics, a premier source of market insights and strategic business intelligence for the Internet of Things (IoT), has unveiled new research on the IoT semiconductor market.

This comprehensive report delves into the current status and market trends, along with the drivers, challenges, and opportunities for IoT semiconductor firms and IoT device/equipment manufacturers.

A key trend underscored is the increasing incorporation of AI chipsets in edge devices. This integration facilitates quicker and more intelligent data processing and decision-making at the source. Further investigation reveals how AI is capitalizing on NVIDIA chipsets, which are predominantly employed for high-performance computing and deep learning applications. Instances of industrial AI platforms based on various NVIDIA GPUs, including Jetson AGX Orin and Jetson Orin Nano, are presented. Additionally, a comprehensive analysis of other significant trends and technological advancements in the IoT semiconductor market is provided.

The findings are derived from the IoT Chipset and IoT Module Trends Report 2023, which is grounded in thorough research and interviews with industry experts and stakeholders. It serves as a crucial resource for anyone keen on comprehending the current landscape and future trajectory of the IoT semiconductor market. The report can be procured from the IoT Analytics website.

KEY QUOTES:

Knud Lasse Lueth, CEO at IoT Analytics, comments that “In an age underscored by chip shortages, geopolitical tensions, and the ascendancy of AI, the semiconductor landscape pulsates with innovation. The unfolding narratives around IoT semiconductors not only offer solutions to pressing global challenges like sustainability and energy efficiency, but also seize fresh horizons, such as harnessing AI at the edge.”

Satyajit Sinha, Principal Analyst at IoT Analytics, added that:

“The ongoing trade tensions between the US, China, and Taiwan have significantly impacted global chip supply chains, leading to disruptions, increased costs, and delays in production. The semiconductor industry is experiencing a significant transformation due to the rising demand for AI chipsets and IoT connectivity. However, the industry also faces challenges, such as a severe shortage of AI GPUs and automotive chips. Also, it affects IoT chips based on mature nodes.”

The post Latest study highlights increased incorporation of AI chipsets in edge devices appeared first on IoT Business News.

• New software solution with artificial intelligence (AI) enables real-time quality control throughout the factory floor to maximize productivity and customer satisfaction while minimizing waste and rework.
• Deployable in the cloud or on premises, deviceWISE AI Visual Inspection seamlessly integrates with industrial cameras, robots and PLCs, as well as major platforms such as AWS, Azure IoT, IBM Maximo, SAP and Siemens.

Telit Cinterion, a global enabler of the intelligent edge, today announced deviceWISE® AI Visual Inspection, an AI-powered solution that enables automakers, pharmaceuticals, semiconductors, health care, energy and other manufacturers to optimize quality control, productivity, customer satisfaction and profitability.

Designed for a wide variety of factory floor use cases, deviceWISE AI Visual Inspection provides the granular, actionable insights that are critical for success in demanding production environments such as just-in-time manufacturing (JITM) and Industry 4.0.

deviceWISE AI Visual Inspection provides manufacturers with a turnkey solution for collecting visual inspection data from factory floor devices and then feeding it into analytics platforms. These insights enable manufacturers to quickly identify and address emerging quality control problems involving employee workstations, industrial robots, CNC machines and more. In the process, these insights help minimize rework and downtime and help meet production schedules.

Deployable in the cloud or on premises, deviceWISE AI Visual Inspection:

• Leverages the latest visual AI technologies to identify problems such as missing bolts, improperly installed product badges, bad welds, misaligned seams and more.
• Uses a broad array of technologies from the NVIDIA Metropolis stack — the NVIDIA Jetson edge AI platform, DeepStream SDK and TensorRT SDK.
• Provides customizable algorithms for on-the-fly model training deployment.
• Integrates seamlessly with a wide range of factory floor equipment such as cameras, PLCs, robots and CNC machines, as well as enterprise software systems including AWS, IBM Maximo, Microsoft Azure IoT, SAP and Siemens.

deviceWISE is a scalable, integrated IIoT platform that provides visibility and control over connected machines and data. deviceWISE allows customers to use NVIDIA AI technologies to boost throughput and improve quality in each step of their manufacturing process. The platform collects and transforms data, integrates machines and systems, runs edge logic and provides data visualization. deviceWISE AI Visual Inspection leverages Telit Cinterion’s decades of experience in integrating industrial machines and data orchestration, while making it easy to identify and resolve problems in real time, minimizing downtime and reducing the impact on production.

“deviceWISE AI Visual Inspection significantly enhances the ability of manufacturers to quickly find and fix problems that directly affect their competitiveness and bottom line,” said Ricardo Buranello, SVP of IoT Platforms Business Unit, Telit Cinterion.

“deviceWISE is an IIoT platform that enables manufacturers to collect, transform and integrate data from any machine to any IT system, creating full applications for Industry 4.0 and digital transformation. Now with the new module deviceWISE AI Visual Inspection, we add cameras as sensors in a fully integrated suite. This will provide all the tools manufacturers need to disruptively innovate on their manufacturing processes.”

deviceWISE AI Visual Inspection is currently in pre-release with release expected in Q3 2023.

The post Telit Cinterion deviceWISE® AI Streamlines and Refines Visual Inspection for Manufacturing with AI and Deep Learning appeared first on IoT Business News.

Three new services expand access to AI-driven insights for IoT.

Soracom, Inc., a global provider of advanced Internet of Things (IoT) connectivity, today announced three new services designed to help IoT deployments take advantage of the power and promise of generative AI (GenAI).

These new services, named Soracom Query, Soracom Relay, and Soracom Harvest Data Intelligence, can work together or separately to analyze IoT device data on the fly or connect devices to the powerful AI/ML capabilities now available through leading hyperscale platforms.

Soracom Relay lets customers use any existing RTSP/RTP-compatible camera to acquire and securely transmit audio and video data to Soracom’s Harvest Files for storage or to a cloud destination, such as AWS S3 or Amazon Kinesis Video Streams, for computer vision and video analytics. Soracom Query lets customers use SQL queries from BI tools or CLI to mine IoT device data with no need to set up their own servers or storage. This managed data warehouse capability with automatic data loading makes it easy to run complex analytical queries on large IoT datasets and feed the results to machine learning (ML) projects. Both services are now available in technical preview to interested Soracom customers.

Soracom Harvest Data Intelligence, now available in public beta, enhances Soracom’s existing serverless data storage and visualization capability with the ability to apply GenAI to analyze time series data and identify trends, patterns, outliers, and abnormalities. Soracom Harvest Data Intelligence can also use the data provided to perform further analysis. For example, a municipality can use Soracom Relay to monitor road traffic while Harvest Data Intelligence analyzes the data stored and Soracom Query can guide decisions on the best times to schedule road repairs, or to predict how changes in traffic patterns will impact existing infrastructure.

Kenta Yasukawa, CTO and Co-Founder of Soracom, said:

“Applying GenAI to analyze IoT data has the potential to discover insights that are beyond our imagination,”

“As a technology partner to the companies building tomorrow’s connected experiences, we’re committed to delivering leading-edge capabilities that accelerate their innovation and help them to succeed at scale and stay one step ahead in a changing world.”

In keeping with that commitment, Soracom has also established an IoT x GenAI Lab with Matsuo Institute, Inc., which conducts research and development projects in AI sharing the vision of Matsuo Lab, University of Tokyo. The IoT x GenAI Lab will explore the potential to gain new insights from diverse IoT data using Gen AI, develop new products, and provide professional services specializing in the area of Generative AI, including IoT and Large Language Models (LLMs).

The post Soracom Adds Generative AI Capability to IoT Connectivity appeared first on IoT Business News.

The year 2023 witnesses the ongoing redefinition of the technological landscape. Artificial intelligence and the Internet of Things join forces to reshape the fundamental structure of the business world. Together, they show the vast potential of intelligent connectivity, assisting businesses in streamlining operations, improving CX, and unlocking their full capabilities.

Collaborative harmony

The Internet of Things is rapidly changing modern businesses and entire sectors of the economy. This revolutionary technology allows for the gathering of huge data flows, resulting in an abundance of information. However, managing and interpreting it is an arduous activity.

Maximizing the power of the Internet of Things requires investment in advanced software solutions. Engineers can build machines that imitate complex behaviors and operate independently of humans.

AI and IoT examples are numerous. Let’s plunge into the most striking use cases.

Predictive maintenance

The Internet of Things implies using sensors that gather actual data from connected devices. Artificial intelligence then processes this information with exceptional accuracy.

The Internet of Things and AI can work together to shift maintenance approaches from reactive to proactive. This means potential issues can be identified before they become bigger problems, which can prevent costly breakdowns and reduce unplanned downtime. By anticipating maintenance needs, organizations can optimize operational efficiency and save money. This approach not only minimizes disruptions but also leads to significant cost savings.

As data flows are getting bigger, artificial intelligence becomes increasingly proficient at detecting subtle patterns that humans may overlook. This iterative process ensures continuous improvement, enhancing the accuracy and reliability of predictive maintenance models.

Proactive maintenance offers numerous advantages, such as heightened efficiency, prolonged equipment longevity, and increased customer contentment. Through harnessing up-to-the-minute observations furnished by the Internet of Things and intelligent algorithms, enterprises can guarantee the optimal functioning of their resources.

High-tech manufacturing

The entire sector is experiencing a considerable upswing in the implementation of artificial intelligence and the Internet of Things. According to Business Insider, the IoT market will reach an annual valuation of $2.4 trillion by 2027.

The interaction of the Internet of Things and intelligent software is ushering in an entirely new epoch. Significant manufacturing procedures can reap rewards from automated surveillance, resulting in enhanced production effectiveness, diminished mistakes, and anticipatory quality management.

The abundance of information collected from the Internet of Things acts as the cornerstone for artificial intelligence to conduct thorough examinations, revealing patterns and irregularities. Manufacturers acquire valuable perspectives on their procedures and make informed choices to amplify the effectiveness and minimize idle time.

Through ongoing monitoring and analysis of data, algorithms can detect initial indications of quality deviations, empowering business owners to implement measures that uphold product excellence and reduce the occurrence of defects.

The Internet of Things and intelligent algorithms help specialists implement automated monitoring of key processes and workflows. Real-time control by intelligent algorithms allows for the continuous observation of multiple parameters, including temperature, pressure, and performance metrics. Should any deviations or anomalies arise, automated alerts are generated, facilitating prompt intervention to preempt potential problems or equipment failures.

Enhanced management of logistics networks

Artificial intelligence and the Internet of Things produce significant outcomes for the logistics sector as well. In the face of regulatory modifications, escalating labor expenditures, amplified traffic, and unpredictable fuel prices, these technologies aid businesses in executing operations with ease and effectiveness.

The implementation of an intelligent framework empowers logistics professionals with enhanced monitoring of resources, remote administration of vehicle fleets, and heightened adherence to regulations. It facilitates the recognition and surveillance of essential assets, enables efficient logistics in smart cities, reduces concerns regarding quality, optimizes stock levels, and streamlines various procedures.

By implementing resilient systems for tracking resources, workflows can be automated and elements of artificial intelligence can be integrated, providing anticipatory maintenance, instantaneous notifications, and comprehensive oversight. Through the utilization of advanced sensors, businesses can monitor asset data without human involvement, obviating the necessity for conventional identification methods like QR codes or barcodes.

Through the transmission of real-time data from sensors, authorities can leverage advanced analytics to forecast the state of assets. By reducing periods of inactivity and optimizing the functioning of machinery, organizations can attain substantial enhancements in operational efficiency.

Real-time monitoring and management of fleets are facilitated by the Internet of Things. Effective and precise systems for tracking vehicles have demonstrated a capacity to diminish expenses associated with last-mile deliveries, with potential reductions in fuel consumption of up to 25% as indicated by Frost & Sullivan.

Installed sensors can identify warehouse capacity and send notifications to employees about specific requirements in detail.

Through the incorporation of GPS functionalities within smartphones and intelligent resources, the optimization of routes emerges as a fundamental aspect of transit logistics. Drivers can readily discern the most effective pathways, thereby diminishing fuel consumption and guaranteeing punctual delivery of products.

Personalized CX

The IoT and AI work together to collect vast amounts of data from different sources like smart devices, wearables, and connected appliances. It includes customer preferences, behavior, purchase history, and location details in real time. Businesses can gain valuable insights by integrating these devices into the customer journey, helping them understand individual preferences and requirements.

True magic happens when smart algorithms enter the picture. The amassed customer data is analyzed on a large scale to uncover patterns, correlations, and trends that might be overlooked by humans. By doing this, businesses obtain a comprehensive understanding of each customer’s preferences, habits, and aspirations. They can deliver highly personalized recommendations, offers, and experiences to their customers.

Algorithms enable dynamic pricing strategies, permitting businesses to offer tailored discounts and promotions. They also prove invaluable in generating custom content, such as personalized emails, newsletters, and targeted advertising campaigns.

Intelligent energy management

Artificial intelligence and the IoT revolutionize energy management and conservation in various sectors.

Within building management, devices like smart thermostats, lighting systems, and appliances collect data on energy consumption, which is subsequently analyzed by AI. This process identifies inefficiencies and provides suggestions for improvement.

The combination of AI and the Internet of Things has the capacity to optimize energy usage on a broader scale, encompassing cities or regions. Through the aggregation of data from intelligent meters and meteorological stations, algorithms scrutinize patterns of energy consumption, pinpointing opportunities for conservation. As a result, utilities and energy providers can forecast demand with enhanced accuracy, allocate resources in a more efficient manner, and reduce the necessity for costly investments in infrastructure.

Renewable energy sources also benefit from innovations. Smart algorithms optimize the performance of wind turbines, solar panels, and other renewable sources to achieve maximum power generation. Prompt identification and resolution of performance issues are possible through real-time monitoring.

By predicting fluctuations, artificial intelligence further contributes to renewable energy generation, aiding grid operators in effectively balancing supply and demand. This reduces dependence on fossil fuels and lessens the environmental impact.

Energy storage systems offer another application for innovative solutions. Intelligent algorithms optimize the charging and discharging of batteries, thereby extending their lifespan and minimizing overall storage costs.

Advanced agriculture

This sector is undergoing significant changes with the incorporation of artificial intelligence in the Internet of Things. Through the use of devices like soil moisture sensors and weather monitors, farmers are improving their irrigation, crop management, and pest control practices.

The integration of artificial intelligence, machine learning, robotics, unmanned aerial vehicles, and the Internet of Things in agricultural practices constitutes smart farming. This approach facilitates the monitoring of operations, decreases reliance on manual labor, and elevates the caliber and quantity of agricultural yields. It effectively streamlines and optimizes the cultivation of crops and livestock.

Precisely forecasting agricultural output is vital for efficient farm administration. Intelligent systems evaluate historical and contemporary data to furnish accurate prognostications over a duration.

Furthermore, innovative systems have the capability to assess the level of sunlight crops receive, enabling farmers to optimize their arrangements to enhance light penetration. Additionally, they utilize information from infrared sensors, satellite pictures, and thermal cameras to monitor the pace of plant growth and detect insufficiencies in nutrients.

Farmers assess relevant data on soil health, water levels, temperature, and pH. They employ drones to capture images of crops, detect problems, and forecast yield.

Connected healthcare

Modern medicine and patient care are no longer possible without artificial intelligence and the Internet of Things.

By utilizing wearable sensors and interconnected medical devices, physicians can remotely monitor vital indicators, medication compliance, and overall well-being. This facilitates the early identification of potential health complications and prompt intervention. Moreover, it diminishes the necessity for frequent face-to-face consultations, particularly for individuals with chronic ailments or residing in remote locations.

The application of artificial intelligence in predictive diagnostics is revolutionizing the process of disease detection and diagnosis. Through the analysis of extensive patient data, such as medical records, imaging examinations, and genetic details, AI algorithms can discern patterns and markers that have the potential to forecast the onset or advancement of diseases.

Connected healthcare also brings forth substantial advantages in the form of customized treatment strategies. Algorithms scrutinize unique patient information, encompassing medical records, genetic data, and treatment reactions, to devise personalized treatment plans. This tailored approach enables patients to receive more accurate and focused healthcare, resulting in enhanced treatment results and heightened patient contentment.

The growth of connected healthcare presents vast potential for transforming medicine. The projected increase in healthcare-related IoT revenues to over 135 billion by 2025 signifies the widespread recognition and acceptance of the value brought by pioneering technologies.

Smart retail

This is one of the key AI and IoT examples. Sensors and algorithms have brought about the idea of smart retail. By 2025, the retail sector empowered by the Internet of Things is projected to reach a valuation of $94 billion.

Retailers can deploy sensors throughout their stores to collect data on customer movements, interactions with products, and purchase patterns. These sensors capture information on foot traffic, duration of stay, and popular product areas, helping specialists gain a profound understanding of customer behavior.

By employing live monitoring of inventory levels, retailers can optimize their supply chain operations, guaranteeing the availability of popular products while minimizing surplus inventory.

Through the incorporation of artificial intelligence within the Internet of Things, entrepreneurs collect information pertaining to individual clientele, encompassing previous purchasing records, preferences, and browsing patterns. Consequently, they can personalize product suggestions, promotions, and discounts based on the specific requirements and interests of each customer.

Experts scrutinize up-to-the-minute data concerning demand, pricing strategies of competitors, and prevailing market conditions. They flexibly adapt pricing to optimize revenue and profit margins.

Intelligent technologies enhance store conditions and streamline operational efficiencies. As an illustration, sensors for temperature and humidity can oversee store environments, guaranteeing optimal conditions for perishable items or delicate merchandise. Artificial intelligence can analyze this information, prompting notifications or automating modifications to uphold ideal storage conditions.

Self-driving cars

Autonomous vehicles equipped with cutting-edge technologies are completely reshaping the way we commute and travel. At the heart of this revolution lies artificial intelligence of things.

They are equipped with an array of sensors that gather valuable data. So, such cars navigate and respond to dynamic traffic conditions with unparalleled precision. They spot potential hazards, react swiftly to unexpected events, and reduce the risk of accidents, ensuring safer transportation for both passengers and pedestrians.

Furthermore, this remarkable advancement harbors the capacity to enhance traffic control. Conventional systems frequently grapple with challenges such as congestion, suboptimal routing, and underutilization of road infrastructure. However, autonomous vehicles can accumulate extensive quantities of data pertaining to traffic patterns, road conditions, and vehicular motions. Through the dynamic adaptation of routes, the optimization of traffic flow, and the synchronization of signals, they can mitigate congestion, diminish travel durations, and amplify the overall efficiency of traffic.

As the prevalence of self-driving cars increases, traditional car ownership models are being challenged, giving rise to innovative mobility services. Consumers can seamlessly access shared autonomous fleets through mobile applications. This shift towards shared mobility not only alleviates traffic congestion but also provides cost-effective and environmentally friendly transportation options.

Smart cities

This concept once seemed a futuristic dream. But now it is becoming a reality. By introducing artificial intelligence in the Internet of Things, cities are transforming their infrastructures into smart systems that optimize resources, transportation, and public safety.

At the vanguard of innovation, the IoT and AI are propelling smart cities towards becoming vibrant and adaptive entities that prioritize the well-being of residents and enhance their everyday experiences.

A prominent illustration lies in the utilization of intelligent meters harnessing sensors to scrutinize and trace energy utilization. This empowers city authorities to optimize energy consumption. For instance, smart meters were effectively adopted in Barcelona, leading to a noteworthy 25% surge in water conservation and significant cost reductions.

Smart poles present a myriad of capabilities, encompassing illumination, wireless connectivity, and environmental surveillance. These astute structures amass and transmit data in real time, empowering city authorities to make well-informed choices founded on precise information. With the capacity to furnish high-speed internet connectivity and accommodate diverse functions, they possess the potential to transform urban landscapes on a global scale.

The paradigm of urban transportation is being reimagined thanks to the influence of artificial intelligence. As self-driving vehicles become commonplace, intelligent infrastructure will unlock their complete capabilities, be it in the form of autonomous cars facilitating food delivery or seamlessly catering to airport pick-up requirements.

Conclusion

The harmonious fusion of artificial intelligence within the Internet of Things has established the foundation for a revolutionary business transformation. With industries embracing these technologies, we are witnessing the emergence of pioneering solutions that streamline operations, bolster efficiency, and elevate decision-making procedures. To fully harness their potential, contemporary enterprises collaborate with top Internet of Things software development companies. Experienced IT providers deliver the expertise and customized software essential for navigating the intricacies of this rapidly evolving terrain.

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IoT has revolutionised how we interact with technology and the world. It has created a network of interconnected devices that share data and insights, making our lives more efficient and convenient. As a result, IoT has become an integral part of our daily routines, ingrained in logistics networks, supply chains, smart cities, and much more.

While IoT has already significantly impacted our lives, integrating AI into IoT systems is the likely next step in its evolution, with its potential to help IoT systems become more efficient and effective. But is the autonomy and instant decision making, in what is essential a black box a cause for concern?

Let’s explore both sides of the coin, starting with the positive aspects of AI in IoT.

Benefits of AI

There are many ways in which AI has the potential to revolutionise IoT. Firstly, AI can process and analyse vast amounts of data generated by IoT devices more efficiently and effectively than traditional methods. Using machine learning algorithms, AI can identify patterns, derive insights, and make predictions based on the data collected from IoT-connected devices. This enables organisations to extract valuable information and act proactively.

Taking this a step further, AI could take over that decision-making process and implement new strategies or approaches based on changing data, conditions, and reactions without human intervention. This can increase efficiency, reduce human error, and improve productivity across various applications such as smart homes, industrial automation, transportation, and healthcare.

These better decisions will positively impact energy usage in IoT systems. Furthermore, by analysing data from sensors and devices, AI can identify energy consumption patterns and optimise efficiency. For example, in a smart building, AI can automatically analyse occupancy data to adjust heating, cooling, and lighting systems, resulting in energy savings. Meanwhile, by improving predictive maintenance of IoT-connected devices, AI can reduce downtime, optimise performance, improving overall equipment reliability.

A final promising opportunity for AI and IoT is in Edge Computing, which has been a topic of interest in IoT for some time now. Since AI can be deployed at the edge, it is possible that real-time decision-making could be enabled, and the need for constant data transmission to the cloud could be reduced. This would improve latency, bandwidth usage, and privacy while enhancing the overall efficiency of IoT deployments.

Challenges of AI

While AI brings numerous potential benefits to B2B IoT applications, some concerns and challenges must be addressed. First and foremost, data privacy and security, due to the amount of sensitive data being collected and processed by a technology which has full autonomy and yet is completely hidden from sight of humans.

Companies must ensure adequate measures to protect data from unauthorised access, breaches, and misuse. In addition, there is a clear need to improve the transparency of the decision-making processes of AI, including the ability to take back control and/or reverse decisions, so not to lose control over the system.

The reliability and accuracy of AI algorithms in B2B IoT applications are of utmost importance. Incorrect or unreliable AI predictions can have significant consequences, especially in critical healthcare, transportation, and manufacturing applications. Therefore, ensuring the accuracy and robustness of AI models, along with rigorous testing and validation, is essential to maintain trust and confidence in B2B IoT systems.

Of course, integrating AI with existing IoT systems can be complex and challenging. For example, B2B organisations may already have established IoT infrastructure, and integrating AI capabilities into these systems requires careful planning and implementation. In addition, compatibility, scalability, and interoperability issues may arise when integrating AI algorithms into various IoT devices and platforms.

There are also a couple of elephants in the room regarding AI implementation. Firstly, there is a concern about the shortage of AI experts and data scientists who can develop, deploy, and maintain AI systems effectively. As a new technology, organisations must invest in training programs and provide resources for upskilling employees to bridge this skill gap.

The second is regulation. There are numerous calls from governments, enterprises, and even the godfathers of AI to get regulation in place rapidly. As a result, any AI implemented into IoT applications today may well be subject to legal challenges tomorrow. Compliance will be essential, but right now, it will take a great deal of future-gazing to anticipate the likely regulations that come to pass.

Conclusion

Over-reliance on AI could lead to situations where humans lose control or understanding of underlying processes. This can result in unintended consequences, such as systems behaving unexpectedly or failing entirely.

As such, any AI implementation in IoT systems must be designed with human oversight and intervention to ensure that humans retain control over the technology. Additionally, it is essential to have fail-safe mechanisms in place to prevent such unintended consequences.

Overall, there are clear opportunities in integrating AI into IoT systems, I believe we must approach with caution, and take steps to bring AI into the world of IoT in a considered manner.

While in the past moving fast with new technologies has been a strong move to make, moving fast with a self-learning, autonomous technology carries greater risk, and is worthy of a little more caution.

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Delivers AIoT (Artificial Intelligence of Things) edge technology for autonomous driving, robotics, and mobility AI vision surveillance management.

Blaize® today announced a multi-year Memorandum of Understanding with Korea Telecom (KT) — Korea’s leading AI, big data, and cloud service provider.

The strategic collaboration brings AIoT (Artificial Intelligence of Things) edge device technical development cooperation for autonomous driving, robotics, and mobility AI vision surveillance management. Blaize and KT intend to coordinate, promote, and support the advancement of manufacturing and services requirements of AI development and education for on-device technology for AI market creation in Korea. The MOU will bolster their united relationship and foster a more robust channel for exchanging views on AIoT edge device manufacturing requirements.

KT plans to expand the development of on-device AI products at the terminal end that can minimize data usage and implement various functions to strengthen the AIoT (Artificial Intelligence of Things) service business that combines AI and IoT technology. For this, Blaize’s tightly coupled software and small form factor, low power, and high-speed data processing hardware deliver an end-to-end efficient, usable AI edge workflow that will help KT expand its AIoT efforts.

Through collaboration with development hardware partners like Blaize, KT plans to commercialize specialized terminals and services, such as intelligent IoT routing equipment, micro-mobility services incorporating AIoT technology, and AIoT autonomous driving logistics transport devices for use in distribution, logistics, and smart factories.

Dinakar Munagala, CEO and Co-founder of Blaize, said:

“Our expanded collaboration with KT in AIoT edge business advances our shared understanding of technical development in some of the key Blaize markets — automotive, mobility, and smart vision. We look forward to further extending Blaize hardware and software solutions, serving the 5G+, MEC, and cloud-based AIoT markets.”

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Quectel Wireless Solutions has announced its advanced 5G modules — which securely and reliably connect devices and people to networks and services — are available now with the NVIDIA® Jetson AGX Orin 32GB system-on-module (SOM).

With its Jetson edge AI platform, NVIDIA is accelerating the next wave of robotics, automation and artificial intelligence IoT. The platform’s power-efficient, compact and scalable capabilities include pre-trained AI models, software development kits and support for cloud-native technologies across the Jetson line-up.

As a global IoT solutions provider, Quectel’s products make life more convenient and efficient. Adding 5G modules to the Jetson ecosystem is set to make it easier for users to manage and build out deployments in smart cities, manufacturing, IoT, industrial applications and many other connected businesses and processes.

The latest NVIDIA Jetson offering, the Jetson AGX Orin 32GB SOM, has been integrated with the Quectel RM500Q series 5G sub-6GHz module. The most advanced module of its kind, it supports multiple concurrent AI application pipelines using an NVIDIA Ampere architecture GPU. With deep learning, vision accelerators, high-speed IO and fast memory bandwidth, the Orin enables complex AI models to solve problems such as natural language understanding, 3D perception and multi-sensor fusion.

The Orin SOM delivers up to 275 TOPS of AI performance with power configurable between 15W and 60W. This provides more than eight times the performance of the Jetson AGX Xavier in the same compact form factor, which makes the Orin ideal for robotics and other autonomous machine use cases.

“We’re delighted to announce that our RM500Q series 5G module supports the NVIDIA Jetson AGX Orin platform,” said Norbert Muhrer, President & CSO, Quectel Wireless Solutions.

“The combination of our ready-to-use Quectel 5G module and the Jetson AGX Orin platform helps simplify device development, improve product stability and accelerate time to market for customers’ terminals.”

Key features of the RM500Q series include LTE, in addition to 5G, as well as multi-constellation GNSS connectivity if desired. In addition, the module’s M.2 form factor and choice of USB 3.1 or PCIe 3.0 high-speed interfaces make it attractive to a wide range of device types and use cases. The module also offers optional voice over LTE (VoLTE) and Quectel’s Enhanced AT Commands.

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Fibocom, a global leading provider of IoT (Internet of Things) wireless solutions and wireless communication modules, announced the launch of AI smart module SCA825-W.

Powered by Qualcomm Technologies’ premium-tier SoC (System-on-Chip) QCS8250, the module is designed to meet the requirements of high-end AIoT scenarios such as HD video conferences, HD livestreaming, cloud gaming, edge computing, robotics, drones, AR/VR and so on.

The combination of AI and IoT has opened up new possibilities for industries requiring automated real-time decision-making and data analysis. The demand for 5G AIoT solutions is expected to skyrocket, according to Counterpoint Research, with shipments of 5G AIoT modules reaching a CAGR of 84% between 2022 and 2030. Fibocom’s AI smart module is bound to play a crucial part in the industry, with the potential to empower a massive range of compute-intensive use cases.

Equipped with the Qualcomm QCS8250 IoT solution, Fibocom’s cutting-edge AI smart module SCA825-W integrates an octa-core Kryo 585 CPU, Adreno 650 GPU, dedicated NPU 230 (Neural Processing Unit), as well as Hexagon DSP for machine learning. The module can deliver a computering power of up to 15 TOPS (Tera Operations per Second), enabling complex AI computing performance with exceptional features.

Featuring a powerful Spectra 480 ISP (image signal processor) Adreno 995 DPU and Adreno 665 VPU, Fibocom SCA825-W supports up to seven concurrent cameras, triple 4K display and video encode at up to 4K resolution at 120 fps, 8K at 30 fps (frames per second), offering superior image capturing, processing and displaying capabilities.

In addition, the module supports 5G, Wi-Fi 6.0, Bluetooth 5.1 as well as 2×2 Wi-Fi MIMO multi-antenna technology, which allows various wireless connectivity options for industrial and commercial use cases. It also supports Android 10 operating system and a wide range of interfaces (MIPI-DSI, I2S, PCIe, UART, USB, I2C, SPI), enabling much flexibility and ease of integration to meet the application demands of the AIoT industry.

“With AIoT continuously transforming every industry, high-performance AI modules will become an important pillar,” said Eden Chen, General Manager of MC Product Management Dept., Fibocom.

“Our newly-launched AI smart module SCA825-W is an exceptional part of the Fibocom smart module family, which will take a big step forward in the AIoT field with technological advancements.”

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• Partnership to transform the manufacturing industry with immersive experiences across the lifecycle from design through operation
• Companies will connect NVIDIA Omniverse and Siemens Xcelerator platforms to enable full-fidelity digital twins and connect software-defined AI systems from edge to cloud

Siemens, a leader in industrial automation and software, infrastructure, building technology and transportation and NVIDIA, a pioneer in accelerated graphics and artificial intelligence (AI), today announced an expansion of their partnership to enable the industrial metaverse and increase use of AI-driven digital twin technology that will help bring industrial automation to a new level.

As a first step in this collaboration, the companies plan to connect Siemens Xcelerator, the open digital business platform, and NVIDIA Omniverse, a platform for 3D-design and collaboration. This will enable an industrial metaverse with physics-based digital models from Siemens and real-time AI from NVIDIA in which companies make decisions faster and with increased confidence.

The addition of Omniverse to the open Siemens Xcelerator partner ecosystem will accelerate the use of digital twins that can deliver productivity and process improvements across the production and product lifecycles. Companies of all sizes will be able to employ digital twins with real-time performance data; create innovative industrial IoT-solutions; leverage actionable insights from analytics at the edge or in the cloud; and tackle the engineering challenges of tomorrow by making visually rich, immersive simulations more accessible.

“Photorealistic, physics-based digital twins embedded in the industrial metaverse offer enormous potential to transform our economies and industries by providing a virtual world where people can interact and collaborate to solve real-world problems. Through this partnership, we will make the industrial metaverse a reality for companies of all sizes,” said Roland Busch, President and Chief Executive Officer, Siemens AG. “For over a decade, our digital twin technology has been helping customers across all industries to boost their productivity and today offer the industry’s most comprehensive digital twin.”

“When Siemens Xcelerator is connected to Omniverse, we will enable a real-time, immersive metaverse that connects hardware and software, from the edge to the cloud with rich data from Siemens’ software and solutions.”

“Siemens and NVIDIA share a common vision that the industrial metaverse will drive digital transformation. This is just the first step in our joint effort to make this vision real for our customers and all parts of the global manufacturing industry,” said Jensen Huang, founder and CEO, NVIDIA. “The connection to Siemens Xcelerator will open NVIDIA’s Omniverse and AI ecosystem to a whole new world of industrial automation that is built using Siemens’ mechanical, electrical, software, IoT and edge solutions.”

This partnership brings together complementary technologies and ecosystems to realize the industrial metaverse. Siemens is uniquely positioned at the intersections of the real and digital world, information technology and operational technology. The Siemens Xcelerator platform connects mechanical, electrical and software domains across the product and production processes and enables the convergence of IT and OT.

NVIDIA Omniverse is an AI-enabled, physically simulated and industrial-scale virtual-world engine that enables for the first time full-fidelity live digital twins. NVIDIA AI, used by more than 25,000 companies worldwide, is the intelligence engine of Omniverse in the cloud and autonomous systems at the edge. NVIDIA Omniverse and AI are ideal computation engines to represent the comprehensive digital twin from Siemens Xcelerator.

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AI Dynamics brings high-velocity, low-code machine learning solutions to Soracom Partner Space.

Soracom, Inc., a global provider of advanced IoT connectivity, today announced that AI Dynamics, which offers low-code solutions designed to make artificial intelligence (AI) capabilities accessible to organizations of all sizes, has joined the Soracom Partner Space IoT ecosystem.

AI Dynamics’ easy-to-use and highly accurate end-to-end AI platform, called NeoPulse, is simple to implement for every industry, business and device. The company specializes in solving a wide range of business problems using AI, with a focus on healthcare, life sciences and Industry 4.0.

AI Dynamics’ entry into the Soracom Partner Space builds on previous collaboration between the two companies in Japan, where AI Dynamics has provided pre-built, fully trained AI/ML algorithm libraries for use with Soracom’s reference edge computing camera (“S+ camera basic”). These libraries let users rapidly apply AI edge capabilities to a wide range of use cases, from inventory management and industrial process optimization to parking lot capacity tracking, license plate recognition and more.

NeoPulse enables engineers to build deep learning models faster than using off-the-shelf libraries while handling dataset management, model tracking, deployment and monitoring automatically. This proven joint solution effectively gives end users a plug-and-play AI edge camera complete with an AI/ML algorithm that suits the target use case from day one with no custom code required.

Soracom announced the global expansion of the Soracom Partner Space in May 2022. The program now reaches more than 800 best-in-class members throughout the world, including more than 100 certified partners representing hardware, software, solutions and integration services. Soracom Partner Space members help IoT innovators accelerate time to market with as-needed access to complementary, best-in-class solutions and services that are ready to integrate at every level of the IoT stack.

“Running artificial intelligence and machine learning algorithms at the edge is a crucial capability for IoT, but very few customers have the tools today to develop ML systems, train models and deploy, manage, or maintain AI edge devices,” said Kenta Yasukawa, co-founder and CTO at Soracom.

“Combining AI Dynamics’ strong algorithm libraries and ML expertise with Soracom’s connectivity and edge device management platform lets IoT innovators offload undifferentiated heavy lifting and focus on bringing new products and services to market quickly and operating efficiently at scale.”

“Our founding belief remains that everyone should have access to the undisputed power of AI,” Rajeev Dutt, Founder and CEO of AI Dynamics, said. “We are extremely pleased with the ongoing partnered work happening in Japan, and we’re looking forward to offering the fruits of that labor to all Soracom customers and Partner Space members.”

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SAS and Attentis help improve flood and fire response as the climate changes.

As Earth Day approaches, we continue to see the impact of climate change everywhere. In eastern Australia, this impact can feel biblical.

Severe rain in February and March of this year has caused some of the worst flooding in history. At least 20 people have died, and tens of thousands of homes and businesses have been inundated.

And the 2019-2020 bushfire season scorched millions of acres, killing 33 people and destroying thousands of homes. The fires also decimated wildlife, with an estimated 3 billion animals in the path of the flames.

Australia’s iconic koala has seen a steep population drop and is now endangered. Among the causes? Climate-related weather events like fires and floods, as well as habitat destruction from development.

Technology drives rapid response and resilience

Attentis, an Australian technology firm, has designed and manufactured a range of intelligent sensors that provide local officials and emergency response teams with real-time information and monitoring. These sensors are powered by artificial intelligence (AI) and machine learning from SAS, the leader in analytics.

“Our sensor networks help monitor, measure and mitigate many of the effects of climate change, from fire ignition to flooding to air quality, soil and environmental health, and much more,” said Attentis Managing Director and founder Cameron McKenna.

“Attentis’ multi-sensors are now equipped with AI-embedded SAS® IoT analytics so that local officials, for the first time, can identify conditions and environmental factors – such as fire ignitions and rapid water-level rise – and respond immediately, while continuing to measure and monitor live environmental conditions to aid situational awareness.”

Powering the world’s largest environmental-monitoring network

Attentis has created the world’s first integrated, high-speed sensor network throughout Australia’s Latrobe Valley. Today, this network is the world’s largest real-time environmental-monitoring network.

Covering 913 square miles, the Latrobe Valley Information Network and its array of AI-powered sensors collects and delivers vital data that has improved local agriculture, utilities and forest industries, as well as emergency services.

Thousands of local and neighboring residents now access this data on a regular basis to monitor rainfall, air quality, fire starts, weather and more.

Collecting more real-time situational data via Attentis sensor networks and quickly uncovering key insights from that data using SAS Analytics for IoT means that local officials can make better, faster and more informed decisions that protect citizens, property and natural resources.

“SAS and Attentis boost the resiliency of the people of Latrobe Valley in the face of fires, floods and other challenges brought about by climate change,” said McKenna.

Protecting koalas and endangered species with AI

Historical data can also be used by government and academic researchers looking to protect endangered species like the koala. Understanding and monitoring threats to koalas – such as bushfires and floods – can help scientists assess the health of the population and develop strategies to sustain koala numbers.

SAS AI technologies are already used to protect other endangered species. See how WildTrack uses SAS Analytics to protect cheetahs, rhinos and more.

Artificial Intelligence of Things

Advanced analytics like AI help harness value from the Internet of Things (IoT). Data management, cloud and high-performance computing techniques help manage and analyze the influx of IoT data from sensors like those built by Attentis. Insights from streaming analytics and AI underpin digital transformation efforts in a host of industries – retail, manufacturing, energy, transportation, government and more – that improve efficiency, convenience and security.

“With fires and floods, every second matters. By combining Attentis’ intelligent sensors with our cloud-native SAS Analytics for IoT solution, we’re accelerating the speed and accuracy at which officials can respond to these environmental threats,” said Jason Mann, Vice President of IoT at SAS. “For example, with intelligent sensor networks and predictive analytics, emergency responders can now continuously and accurately assess river heights, rainfall and soil moisture in real-time. By closely monitoring and analyzing this data, these officials can quickly act on new insights and issue early flood warnings to people in high-risk areas who may be affected – or inundated – by severe weather.”

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Ayyeka unleashes the power of Artificial Intelligence (AI) to dramatically improve the data quality and reliability of Internet of Things (IoT) for critical infrastructure.

Ayyeka Inc. has a pioneering technology to transform smart cities from a futuristic promise to an everyday reality.

The promise of smart cities — which leverages IoT technologies for digitalization of critical infrastructure — has been slow to materialize. Data quality and reliability are among the top impediments to smart cities and IoT deployments at large. Ayyeka’s AI Data Curator solves this.

There are many moving parts to IoT projects, ranging from sensors through edge devices to cloud platforms, making it easy for things to go wrong. In large-scale IoT projects, it is not uncommon to have data quality and reliability issues that render the data useless. Whether it’s gaps in the data, unstable readings, or the sensor is just stuck at a certain value–the user now has only two options: either clean the data or give up on it. Detecting, fixing, and overcoming those issues is a labor-intensive process. On large-scale IoT deployments, this is just not feasible and often delays or even fails the entire project.

Ayyeka has automated this process by harnessing the power of AI, delivering more reliable data, and saving enormous amounts of time and manual labor. At the click of a button, Ayyeka’s AI Data Curator will detect, report, and automatically fix IoT sensor data for better decision outcomes and regulatory compliance.

In industries where decisions are anchored with data, that data must be solid. Yair Poleg, co-founder and Chief Technology Officer of Ayyeka, explained that

“Data quality is often overlooked in the proof-of-concept (PoC) phase of IoT deployments, but it becomes a huge problem when scaling up from the PoC phase to full-size deployments. The AI Data Curator alleviates this pain point.”

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