BRN Discussion Ongoing

[BadKittyKiki]

Are you from Bravo's litter?

View attachment 32321

Yes, that's me - I'm the bad, trouble-maker, problem-child strawberry blonde on the far right that's noticing and retaining copious amounts of largely irrelevant stuff while everyone else is focused on the important big picture, but then I will come in with a slam-down and king hit the entire thesis of your argument for a TKO based on something you once flippantly said in an off-the-cuff comment in circa 1989 that I never forgot.
So, yes, that would be me.

 

[Steve10]

Four Edge AI Trends To Watch​

​

Ravi Annavajjhala
Forbes Councils Member
Forbes Technology Council
COUNCIL POST| Membership (Fee-Based)

Mar 15, 2023,06:45am EDT
Ravi Annavajjhala - CEO, Kinara Inc.

As 2023 progresses, demand for AI-powered devices continues growing, driving new opportunities and challenges for businesses and developers. Technology advancements will make it possible to run more AI models on edge devices, delivering real-time results without cloud reliance.

Based on these developments, here are some key predictions to expect:

Increased Adoption​

Edge AI technology has proven its value and we can expect to see further widespread adoption in 2023 and beyond. Companies will continue to invest in edge AI to improve their operations, enhance products (i.e., safer, additional features) and gain competitive advantages. AI’s adoption will also be driven by innovative applications such as ChatGPT, generative AI models (e.g., avatars) and other state-of-the art AI models that will be used for applications in medtech, industrial safety and security.

We are also witnessing that edge AI is transitioning from a technology problem to a deployment problem. In other words, companies are comprehending the edge AI capabilities but it’s a new challenge to get it running in a commercial product, sometimes with multiple AI models in parallel to fulfill an application’s requirements.

Nevertheless, I expect to see continued progress in this area, as companies witness the benefits of edge AI and work to overcome these challenges, as increasing awareness related to costs, energy consumption and latency of running AI in the cloud will likely drive more users to run AI at the edge.

Furthermore, as businesses grow their trust in the technology, edge AI will become increasingly integrated into a wide range of devices, from smartphones and laptops to industrial machines and surveillance systems. This will create new opportunities for businesses to harness AI’s power and improve their products and services.

Improved Performance And More Advanced AI Models​

With advancements in hardware and software, edge AI devices will become more powerful, delivering faster and more accurate results. Although edge devices will still be compute-limited compared to cloud processing and expensive and power-hungry GPUs, I expect a trend towards higher tera operations per second (TOPS) and real-world performance for edge AI processors. As a result, there will be a shift towards more compute-intensive (and accurate) models.

For AI processing, developers are most interested in using leading-edge neural networks for improved accuracy. These network models include YOLO (You Only Look Once), Transformers and MovieNet. Due to its good out-of-the-box performance, YOLO is expected to remain the dominant form of object detection in the years to come. And edge AI processors should advance alongside this technology as newer, more compute-intensive versions of YOLO become available.

Transformer models are also increasing in popularity for vision applications, as they are being actively researched to provide new approaches to solve complex vision tasks. Additionally, the ability to perform computations in parallel and capture long-range dependencies in visual features makes transformers a powerful tool for processing high-dimensional data in computer vision. With the increasing compute capability of edge AI processors, we’ll see a shift towards more transformer models, as they become more accessible for edge deployment.

Activity recognition is the next frontier for edge AI as businesses seek to gain insights into human behavior. For example, in retail, depth cameras determine when a customer's hand goes into a shelf. This shift from image-based tasks to analyzing sequences of video frames is driving the popularity of models like MovieNet.

March Towards Greater Interoperability Of AI Frameworks​

As the edge AI market matures, expect to see increased standardization and interoperability between devices. This will make it easier for businesses to integrate edge AI into existing systems, improving efficiency and reducing costs. From a software perspective, standards such as Tensor Virtual Machine (TVM) and Multi-Level Intermediate Representation (MLIR) are two emerging trends in the edge AI space.

TVM and MLIR are open-source deep-learning compiler stacks or frameworks for building a compiler that aim to standardize deployment of AI models across different hardware platforms. It provides a unified API for AI models, enabling developers to write code once and run it efficiently on a wide range of devices, including cloud instances and hardware accelerators.

While these standards are becoming more stable, they are still not expected to become mass-adopted in 2023. Neural network operator coverage remains an issue, and targeting different accelerators remains a challenge. However, the industry will see continued work in this area as these technologies evolve.

Increased Focus On Security​

As edge AI becomes more widely adopted, there will be a greater focus on securing the sensors generating the data and the AI processors consuming the data. This will include efforts to secure both the hardware and software, as well as the data transmitted and stored. State-of-the-art edge AI processors will include special hardware features to secure all data associated with the neural network’s activity.

In the context of AI models, encryption can protect sensitive data that the model is trained on—for many companies, this model information is the crown jewel. In addition, it will be important to secure the model's parameters and outputs during deployment and inference. Encrypting/decrypting the data and model helps to prevent unauthorized access of the information, ensuring the confidentiality and integrity of the data and model. Encryption and decryption can introduce latency and computational overhead, so the trick for edge AI processor companies will lie in encryption methods and carefully considering trade-offs between security and performance.

Conclusion​

In conclusion, 2023 promises to be an exciting year for the edge AI industry, with new opportunities and challenges for businesses and developers alike. As edge AI continues to mature and evolve, we’ll see increased adoption, improved performance, greater interoperability, more AI-powered devices, increased focus on security and new business models. The limitations and challenges we face today will be overcome, and I have no doubt that edge AI will bring about incredible advancements.

---

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

 

[HopalongPetrovski]

Yes, that's me - I'm the bad, trouble-maker, problem-child strawberry blonde on the far right that's noticing and retaining copious amounts of largely irrelevant stuff while everyone else is focused on the important big picture, but then I will come in with a slam-down and king hit the entire thesis of your argument for a TKO based on something you once flippantly said in an off-the-cuff comment in circa 1989 that I never forgot.
So, yes, that would be me.

Okilidokilie.

 

[M_C]

ST live sessions and conferences @ ew23 | STMicroelectronics

embedded world Exhibition&Conference 2023 starts tomorrow! Join ST live sessions and conferences where our experts present our research on artificial intelligence, wireless connectivity, antenna designs for embedded systems, and more! See you on March 14-16 in the Exhibitor Forum Hall 3 and 3A...

www.linkedin.com

View attachment 32228

More than friends ?

 

[Easytiger]

TBH I find this post kind of concerning. What’re everyone’s thoughts?

Why concerning?

 

[M_C]

Why concerning?

Because

 

[Deleted member 118]

 

[Fact Finder]

View attachment 32318

Just for interest this is the link to Spiral Blue’s Edge Compute products powered by Nvidia with detailed spec sheets:

Spiral Blue | Space Edge Computing

Spiral Blue is developing the Space Edge Computer - an onboard computing system that will give Earth observation satellites the ability to process images captured on the satellite itself. This has the potential to increase the capacity of Earth observation satellites carrying Space Edge...

www.spiralblue.space

How can AKIDA running at micro to milli watts with on chip learning and a price tag of tens of US dollars compete with their current Nvidia offerings-please note this is rhetorical humour

What did Edge Impulse say again Science Fiction AKIDA can out perform at 300 megahertz a GPU running at 900 megahertz.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Steve10]

More than friends ? View attachment 32323

I posted about this guy yesterday. ARM Cortex-M MCU again similar to Renesas. If it has ARM with Helium tech Akida can be integrated. Either M85 as announced & the M55 with Helium tech.

Remi El-Ouazzane’s Post​

Remi El-Ouazzane
10mo

Earlier today during STMicroelectronics Capital Markets Day (https://cmd.st.com), I gave a presentation on MDG contribution to our ambition of reaching $20B revenue ambition by 2025-27. During the event, I was proud to pre-announce the #STM32N6: a high performance #STM32 #MCU with our new internally developed Neural Processing Unit (#NPU) providing an order of magnitude benefit in both inference/w and inference/$ against alternative MPU solutions The #STM32N6 will deliver #MPU #AI workloads at the cost and the power consumption of #MCU. This a complete game changer that will open new ranges of applications for our customers and allow them to democratise #AI at the edge. I am excited to say we are on track to deliver first samples of the #STM32N6by the end of 2022. I I am even more excited to announce that LACROIX will leverage this technology in their next generation smart city products. Stay tuned for more news on the #STM32N6 in the coming months :=)

 

[Steve10]

Just for interest this is the link to Spiral Blue’s Edge Compute products powered by Nvidia with detailed spec sheets:

Spiral Blue | Space Edge Computing

Spiral Blue is developing the Space Edge Computer - an onboard computing system that will give Earth observation satellites the ability to process images captured on the satellite itself. This has the potential to increase the capacity of Earth observation satellites carrying Space Edge...

www.spiralblue.space

How can AKIDA running at micro to milli watts with on chip learning and a price tag of tens of US dollars compete with their current Nvidia offerings-please note this is rhetorical humour

What did Edge Impulse say again Science Fiction AKIDA can out perform at 300 megahertz a GPU running at 900 megahertz.

My opinion only DYOR
FF

AKIDA BALLISTA

The Spiral Blue CEO finding out about Akida.

 

[SERA2g]

The first of many new customer enquires as a result of this news:

Taofiq Huq
Founder and CEO of Spiral Blue
2h

Very very interesting! Would be keen to know more about the Akida and whether we can incorporate it into our Space Edge Computer in some way”

Spiral Blue | About Us

About Us

www.spiralblue.space

My opinion only DYOR
FF

AKIDA BALLISTA

In the interest of potentially answering some initial questions others might have about Spiral Blue...

From @Diogenese (with permission) earlier this month. I ran Spiral Blue's space edge computer past him for his opinion.



"Hi SeRA2,

No published patent docs, but they are not published until 18 moths after filing.

They use Nvidia, so they are probably software.

https://spiralblue.space/space-edge-computing

Space Edge Computers use the NVIDIA Jetson series, maximising processing power while keeping power draw manageable. They carry polymer shielding for single event effects, as well as additional software and hardware mitigations. We provide onboard infrastructure software to manage resources and ensure security, as well as onboard apps such as preprocessing, GPU based compression, cloud detection, and cropping. We can also provide AI based apps for object detection and segmentation, such as Vessel Detect and Canopy Mapper.

Note our friend "segmentation" is along for the ride."

 

[TopCat]

I’ve been reading some more again from the end of 2019 company progress update and I can’t work out what ADE stands for. Anyone know?

Talk about intellectual property licensing a bit more. There were a lot of questions about it, and I think in part that's because we've voiced a strong opinion, coming in advance of actual device sales. There's no manufacturing process involved. There's no inventory. There's no loan package qualification by the customer. We released that in 2019. We have received strong response from prospective customers. The ADE, in the hands of one major South Korean company, is being exercised almost as much as we exercise it. They really have dug in, validated some of the benchmark results that we've provided, and now they're moving onto some of their own proprietary networks to do validation.

 

[SiDEvans]

How I’m feeling about BRN today!

 

[Deleted member 118]

 

[Steve10]

I’ve been reading some more again from the end of 2019 company progress update and I can’t work out what ADE stands for. Anyone know?

Talk about intellectual property licensing a bit more. There were a lot of questions about it, and I think in part that's because we've voiced a strong opinion, coming in advance of actual device sales. There's no manufacturing process involved. There's no inventory. There's no loan package qualification by the customer. We released that in 2019. We have received strong response from prospective customers. The ADE, in the hands of one major South Korean company, is being exercised almost as much as we exercise it. They really have dug in, validated some of the benchmark results that we've provided, and now they're moving onto some of their own proprietary networks to do validation.

Akida™ Development Environment (ADE).

BrainChip’s Akida Development Environment Now Freely Available for Use​

Develop and Deploy on Akida Deeply Learned Neural Networks in a standard TensorFlow/Keras Environment

SAN FRANCISCO–(BUSINESS WIRE)–
BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance edge AI technology, today announced that access to its Akida™ Development Environment (ADE) no longer requires pre-approval, now allowing designers to freely develop systems for edge and enterprise products on the company’s Akida Neural Processing technology.

ADE is a complete, industry-standard machine learning framework for creating, training and testing deeply learned neural networks. The platform leverages TensorFlow and Keras for neural network development, optimization and training. Once the network model is fully trained, the ADE includes a simple-to-use compiler to map the network to the Akida fabric and run hardware accurate simulations on the Akida Execution Engine. The framework uses the Python scripting language and its associated tools and libraries, including Jupyter notebooks, NumPy and Matplotlib. With just a few lines, developers can easily run the Akida simulator on industry-standard datasets and benchmarks in the Akida model zoo such as Imagenet1000, Google Speech Commands, MobileNet among others. Users can easily create, modify, train and test their own models within a simple use development environment.

ADE comprises three main Python packages:

• the Akida Execution Engine including the Akida Simulator is an interface to the BrainChip Akida neural processing hardware. To allow the development, optimization and testing of Akida models, it includes a software backend that simulates the Akida NSoC. The output of the Akida Execution Engine generates all necessary files to run the Akida neural processor hardware as well.
• the CNN development tool utilizes TensorFlow/Keras to develop, optimize and train deeply learned neural networks such as CNNs
• the Akida model zoo contains pre-created neural network models built with the Akida sequential API and the CNN development tool using quantized Keras models.

“The enormous success of our early-adopters program allowed us to make ADE available to developers looking to use an Akida-based environment for their deep machine learning needs,” said Louis DiNardo, CEO of BrainChip. “This is an important milestone for BrainChip as we continue to deliver our technology to a marketplace in search of a solution to overcome the power- and training-intense needs that deep learning networks currently require. With the ADE, designers can access the tools and resources needed to develop and deploy Edge application neural networks on the Akida neural processing technology.”

Akida is available as a licensable IP technology that can be integrated into ASIC devices and will be available as an integrated SoC, both suitable for applications such as surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis, and Industrial Internet-of-Things (IoT). Akida is a complete neural processing engine for edge applications, which eliminates CPU and memory overhead while delivering unprecedented efficiency, faster results, at minimum cost. Functions like training, learning, and inferencing are orders of magnitude more efficient with Akida.

Access to ADE is currently available online at https://doc.brainchipinc.com/. Among the resources are installation information, user guide, API reference, Akida examples, support and license documentation. ADE requires TensorFlow 2.0.0. Any existing virtual environment previously used would need to be updated as per the installation step.

 

[M_C]

Innoviz works with vw and bmw

 

[TopCat]

Akida™ Development Environment (ADE).

BrainChip’s Akida Development Environment Now Freely Available for Use​

Develop and Deploy on Akida Deeply Learned Neural Networks in a standard TensorFlow/Keras Environment

SAN FRANCISCO–(BUSINESS WIRE)–
BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance edge AI technology, today announced that access to its Akida™ Development Environment (ADE) no longer requires pre-approval, now allowing designers to freely develop systems for edge and enterprise products on the company’s Akida Neural Processing technology.

ADE is a complete, industry-standard machine learning framework for creating, training and testing deeply learned neural networks. The platform leverages TensorFlow and Keras for neural network development, optimization and training. Once the network model is fully trained, the ADE includes a simple-to-use compiler to map the network to the Akida fabric and run hardware accurate simulations on the Akida Execution Engine. The framework uses the Python scripting language and its associated tools and libraries, including Jupyter notebooks, NumPy and Matplotlib. With just a few lines, developers can easily run the Akida simulator on industry-standard datasets and benchmarks in the Akida model zoo such as Imagenet1000, Google Speech Commands, MobileNet among others. Users can easily create, modify, train and test their own models within a simple use development environment.

ADE comprises three main Python packages:

• the Akida Execution Engine including the Akida Simulator is an interface to the BrainChip Akida neural processing hardware. To allow the development, optimization and testing of Akida models, it includes a software backend that simulates the Akida NSoC. The output of the Akida Execution Engine generates all necessary files to run the Akida neural processor hardware as well.
• the CNN development tool utilizes TensorFlow/Keras to develop, optimize and train deeply learned neural networks such as CNNs
• the Akida model zoo contains pre-created neural network models built with the Akida sequential API and the CNN development tool using quantized Keras models.

“The enormous success of our early-adopters program allowed us to make ADE available to developers looking to use an Akida-based environment for their deep machine learning needs,” said Louis DiNardo, CEO of BrainChip. “This is an important milestone for BrainChip as we continue to deliver our technology to a marketplace in search of a solution to overcome the power- and training-intense needs that deep learning networks currently require. With the ADE, designers can access the tools and resources needed to develop and deploy Edge application neural networks on the Akida neural processing technology.”

Akida is available as a licensable IP technology that can be integrated into ASIC devices and will be available as an integrated SoC, both suitable for applications such as surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis, and Industrial Internet-of-Things (IoT). Akida is a complete neural processing engine for edge applications, which eliminates CPU and memory overhead while delivering unprecedented efficiency, faster results, at minimum cost. Functions like training, learning, and inferencing are orders of magnitude more efficient with Akida.

Access to ADE is currently available online at https://doc.brainchipinc.com/. Among the resources are installation information, user guide, API reference, Akida examples, support and license documentation. ADE requires TensorFlow 2.0.0. Any existing virtual environment previously used would need to be updated as per the installation step.

Thank you @Steve10 and appreciate all the research you’ve provided lately

 

[gex]

I can say one thing. In the shitty last few days brn has been my best performer.
Is that one thing?

 

[Steve10]

Ford autonomous driving systems re-launched as Latitude AI after Argo AI with VW ceased operations last year.

Ford establishes Latitude AI to develop autonomous driving technology​

6 March 2023

Automotive giant Ford has established a new subsidiary dedicated to autonomous driving systems for passenger vehicles.

The new firm, Latitude AI, comprises around 550 employees with expertise across machine learning, robotics, cloud platforms, mapping, sensors, compute systems, test operations, systems and safety engineering.

The majority of employees are formerly of Argo AI, a previous joint venture between Ford and Volkswagen that was announced to begin ceasing operations in October.

The decision to conclude Argo AI was made due to growing losses – amounting to billions – hitting both Ford and Volkswagen, as well as ongoing uncertainty surrounding when Level 4 autonomous driving technology would become commercially available.

Through its new wholly-owned subsidiary, Ford seeks to completely automate driving – hands-free, eyes-off-the-road – during particularly tedious and unpleasant situations, such as sitting in bumper-to-bumper traffic, or driving on long stretches of highway.

Ford’s current hands-free driving technology, BlueCruise, enables drivers to take their hands off the wheel on over 130,000 miles of prequalified North American roads. The technology – currently available in Ford’s Mustang Mach-E SUV, F-150 Truck, F-150 Lightning Truck and Expedition SUV models – has already accumulated more than 50 million miles of hands-free driving. It does however require the driver to keep their eyes on the road, which is ensured via a driver-facing camera.

“We see automated driving technology as an opportunity to redefine the relationship between people and their vehicles,” said Doug Field, chief advanced product development and technology officer at Ford. “Customers using BlueCruise are already experiencing the benefits of hands-off driving. The deep experience and talent in our Latitude team will help us accelerate the development of all-new automated driving technology – with the goal of not only making travel safer, less stressful and more enjoyable, but ultimately over time giving our customers some of their day back."

Sammy Omari, executive director of ADAS Technologies at Ford, will also serve as the CEO of Latitude. “We believe automated driving technology will help improve safety while unlocking all-new customer experiences that reduce stress and in the future will help free up a driver’s time to focus on what they choose," he said. “The expertise of the Latitude team will further complement and enhance Ford’s in-house global ADAS team in developing future driver assist technologies, ultimately delivering on the many benefits of automation.”

Latitude is headquartered in Pittsburgh, Pennsylvania, with additional engineering hubs in Dearborn, Michigan and Palo Alto, California. The company will also operate a highway-speed test track facility in Greenville, South Carolina.

 

[Steve10]

How embedded vision and AI will revolutionise industries in the coming future​

15 March 2023

As we stand on the cusp of the Fourth Industrial Revolution, the integration of embedded vision systems and artificial intelligence (AI) is poised to unleash a wave of disruption that will revolutionise industries as diverse as healthcare, manufacturing, transportation, and retail.

With the ability to process massive amounts of data in real time and make complex decisions with astonishing speed and accuracy, these technologies have the potential to transform the way businesses operate, optimise supply chains, enhance product quality, and deliver unparalleled customer experiences. As we look to the future, it is clear that those companies that are able to harness the power of embedded vision systems and AI will be the ones that thrive in an increasingly competitive and dynamic marketplace.

The application of embedded vision systems and AI has extended beyond their traditional use cases, spurring new and innovative solutions across industries. For instance, the use of AI-powered chatbots has significantly improved customer service, providing 24/7 support and reducing response times. Additionally, AI algorithms have been used to predict and prevent equipment failure in manufacturing, reducing downtime and improving overall efficiency. In the healthcare industry, embedded vision systems and AI have enabled the development of precision medicine, allowing for accurate diagnoses and targeted treatment plans.

Furthermore, the convergence of these technologies has also facilitated the development of new forms of human-machine interaction, such as gesture recognition and voice-controlled interfaces. These innovations have led to the creation of more intuitive and seamless user experiences in areas such as gaming, virtual reality, and augmented reality. The widespread adoption of embedded vision systems and AI has also resulted in a growing demand for skilled professionals capable of designing, developing, and implementing these solutions. As such, academic institutions and training programmes are increasingly offering courses and degrees focused on these technologies, providing the necessary skills for individuals seeking careers in these areas.

Together, embedded vision systems and AI have the potential to revolutionise industries in the coming future. Some of the industries that are set to benefit from this technological convergence are discussed here.

Autonomous vehicles​

The automotive industry is a hotbed of innovation, and the integration of embedded vision systems and AI represents a significant leap forward in this industry's technological capabilities. One of the most compelling examples of this integration is in the development of autonomous vehicles, which are poised to revolutionise transportation as we know it. Embedded vision systems, comprising cameras, lidar, and other sensors, capture visual data in real-time, which is then transmitted to AI algorithms that analyse the information and make decisions based on predefined parameters.

AI-powered embedded vision systems comprising cameras, lidar, and other sensors have enabled the development of autonomous vehicles (Image: Shutterstock/Scharfsinn)

In addition to revolutionising consumer transportation, autonomous vehicles will undoubtedly also be a game-changer for the logistics industry, where they are already being deployed to pick and transport packages in warehouses.

Healthcare​

The healthcare industry also stands to benefit greatly from the integration of embedded vision systems and AI, as it promises to transform the field of medical imaging and diagnosis. The ability of embedded vision systems to capture high-resolution images of internal organs and tissues has already shown promise in detecting and diagnosing health issues. However, the true potential of this technology lies in AI's ability to analyse these images and identify patterns that may not be visible to the human eye. By leveraging machine learning algorithms, medical professionals can achieve faster and more accurate diagnoses, leading to better patient outcomes. This technology could also lead to reduced costs by minimising the need for additional tests and consultations.

With the integration of embedded vision systems and AI, the healthcare industry has the potential to revolutionise the way medical diagnoses are made, paving the way for more efficient and effective healthcare solutions.

Sport broadcasting​

Sports broadcasting is another industry poised for significant transformation with the integration of embedded vision systems and AI. With the help of AI, sports broadcasters can enhance the viewing experience of their audiences by providing them with real-time statistics, player tracking, and other vital information during live matches. Embedded vision systems can capture and process high-definition images of sports events, and AI algorithms can analyse this data to provide viewers with unique insights into the game, such as player heat maps, ball speed and trajectory, and other performance metrics.

This data can be used by coaches and analysts to make critical decisions, while sports broadcasters can use it to create more engaging and informative content for their viewers. With the integration of embedded vision systems and AI, the sports broadcasting industry is set to undergo a significant transformation, providing viewers with a more immersive and insightful experience.

Retail​

The retail industry holds immense potential for significant advancement through the integration of embedded vision systems and AI, which could revolutionise the shopping experience for customers and improve business outcomes for retailers.

One of the most revolutionary and innovative applications of these technologies is the concept of autonomous shopping, which entails a comprehensive network of high-tech cameras, state-of-the-art sensors, and AI-powered algorithms automating the entire shopping experience. This cutting-edge approach provides customers with the ability to simply walk into a store, select the items they desire, and exit without the need for interaction with a cashier or checkout system. Essential to this process, embedded vision systems enable instantaneous object detection and recognition, facilitating the AI algorithms' identification of products while simultaneously tracking their placement into the customer's cart. Furthermore, data gathered through these systems is instrumental in supporting retailers to optimise their inventory management, streamline store layout, and enhance overall customer experience.

In addition, AI-powered recommendation engines, which analyse customer data to provide personalised product recommendations, represent a major advancement in the field of customer engagement. The amalgamation of embedded vision systems and AI therefore has great potential to revolutionise the retail industry, dramatically transforming the way we approach the shopping experience as a whole.

Conclusion​

The convergence of embedded vision systems and AI has tremendous implications for a wide range of industries, with there being great promise for the transformative potential of these technologies. The progress shared here that has been made thus far in the automotive, healthcare, sports broadcasting and retail industries underscores the potential of this technology to improve efficiency, reduce costs, and enhance productivity. Nevertheless, it is important to address challenges such as privacy concerns and the ethical development of AI to ensure that these technologies are used responsibly and sustainably.

As industry experts continue to explore the possibilities of embedded vision systems and AI, it is clear that this technology will continue to shape and transform industries in the years to come.