BRN Discussion Ongoing

[Boab]

There’s a lot going on with Tachyum. They offer SNN for data centres as a licensable IP core but I’m trying to decipher if it’s software or hardware.

I see our resident verification engineer is on the job.

 

[dippY22]

This is why the US governments recent FDA approval for over the counter hearing aids will be a game changer - the current system is cost prohibitive.

Those who need them most are unable to afford them because they’re no longer earning an income.

Below is my post regarding hearing aids from a while back, which involves Sony and WS Audiology @Taproot. No idea if it holds any weight, I haven’t looked into it for months, but I have no doubt akida will power at least one hearing aid in the future. It just makes too much sense for akida not to given it is always on, processing without the cloud at ultra low power with on-chip learning.

Post in thread 'BRN Discussion Ongoing'
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-181429

Thank you for your enthusiasm and the repost, too, SERA2g. I am very amped up about the hearing aid thing myself. There has got to be something to this. My big clue has to be the constant use of hearing aid products in the employment description of what Brainchip does in job postings. I mean really, could they make it any more clear than that???

Anyway, I hope someday soon we all get to "hear" that Akida is indeed part of the smart hearing aid solution coming to a pharmacy near you and me.

Regards,.....hard of hearing, dippY

 

[Learning]

Nerds sell AKIDA too. Straight from the Brainchip website this is what Brainchip expects of its Nerds:

Developing CNNs for Neuromorphic Hardware​

(Been There! Done That!)​

By Nikunj Kotecha​

Often, we hear that neuromorphic technology is cool, classy, low power, next-gen hardware for AI and the most suitable technology for edge devices. Neuromorphic technology mimics the brain, the most efficient computation engine known, to create a computing and natural learning paradigm for devices. Neuromorphic design is complemented with Spiking Neural Networks (SNNs) which emulate how neurons fire and hence only compute when absolutely necessary. This is unlike today’s “MAC monsters" engines -ones that execute lots of MACs (Multiply Accumulate operations which are the basis of most AI computation) in parallel, many of which often get discarded.
So neuromorphic hardware is exciting! However, it is very difficult to develop and deploy current state-of-the-art solutions onto neuromorphic hardware. It’s also extremely limiting to use existing convolutional neural networks (CNNs) based on these platform models. The difficulty primarily stems from the assumption that neuromorphic hardware is analog, and they only run advanced algorithms with SNNs, which are currently in short supply. Therefore, the production models of today —typically accelerated by traditional Deep Learning Accelerators (DLAs) as a safe path to commercialization—are not supported by neuromorphic hardware. But BrainChip Akida TM is changing the game.
Akida is a fully digital, synthesizable and thus process-independent, and silicon-proven neuromorphic technology. It is designed to be scalable and portable across foundries and architected to be embedded into low-power edge devices. It fully supports acceleration for feed-forward CNNs and accelerates other neural networks like Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) and more, while providing the efficiency benefits of a neuromorphic design. It removes risks and simplifies.

Figure 1. Akida Tech Foundations. Fundamentally different and extremely efficient.​

At BrainChip, our mission is to Unlock the future of AI. We believe we can do this by enabling Edge devices with Akida, next-generation technology to advance the growth and smartness of these devices and ultimately provide end users with a sense of privacy and security, energy and cost savings while having access to new features. We realize to achieve our mission, we must make it easy for our end users (who may be experts or non-experts in the field of AI) to use our technology and support current solutions. BrainChip provides development boards of their reference chip AKD1000 for anybody in the community to use and build prototype modules. For commercial use, BrainChip provides licenses of the technology to get it integrated into a custom ASIC, board, or a module that can be used in millions of edge devices.
BrainChip promotes neuromorphic technology with proven silicon, like AKD1000, but also focuses on enabling end users to use the benefits of this technology with little to no knowledge of neuromorphic science. There are three ways to leverage Akida technology (refer to Figure 2) and deploy complex models:

Figure 2. BrainChip development ecosystem and access to deployment of solutions to Akida technology​

1. Through BrainChip MetaTF™ framework: It is a unique and free robust ML framework that has Python packages for model development and conversion of TensorFlow/Keras models into Akida. It is a framework that is very popular among AI experts and custom developers. Python packages for MetaTF framework are public, and developers can access the framework here: https://doc.brainchipinc.com
2. Through Edge Impulse studio: It is a unique platform that provides end-to-end development and deployment of Machine Learning models on targeted technology with little to no-code AI expertise. Core functions of BrainChip MetaTF framework are embedded into Edge Impulse studio to deploy models onto Akida targeted silicon, such as AKD1000 SoC. To learn more about how to develop using Edge Impulse, click here or visit https://www.edgeimpulse.com
3. Through Solutions Partners of BrainChip such as NVISO: BrainChip has partnered with solutions providers and enabled them to create complex models using MetaTF and build applications for specific and most common AI use cases such as Human Monitoring solutions. This allows for faster time to market solutions using Akida technology. To learn more about BrainChip Solutions Partners, contact us at sales@brainchip.com.
These avenues provide an opportunity to create and develop a functioning model that is suitable for running on Akida technology. The models are converted using MetaTF and are saved into a serialized byte file. These models can be evaluated offline by running simulations using the Software Runtime provided with MetaTF or can be evaluated on AKD1000 mini PCIe development board using Hardware backend as shown in Figure 3a. Once through the evaluation stage, these models can be deployed into production on any target device with Akida technology. Akida Runtime library, which is a low-level library that is OS agnostic, is used to compile the saved model and inference on any target device that has Akida technology. Customers who license Akida technology for their device are able to compile this Akida
Runtime library with any of their application software and host OS, as shown in Figure 3b.

Figure 3a. Using MetaTF Software backend for simulations and Hardware backend for model deployment on AKD1000 mini PCIe development board​

Figure 3b. Using low-level Akida Runtime Library for production deployment of models in target devices with Akida technology​

BrainChip is very excited about the ecosystem that is available for our end users to use, develop, and deploy complex AI models on Akida neuromorphic technology. Expert AI developers who are familiar with CNN architectures can use BrainChip MetaTF framework to deploy familiar models on Akida. Developers with little to no code experience can use Edge Impulse studio to deploy models on Akida technology and users who want faster time to market can work with Solutions Partners such as NVISO.
To learn more about how you can harness the power of AI, request a demo or visit BrainChip.com.
Nikunj Kotecha is a Machine Learning Solutions Architect at BrainChip. With many years of experience and a strong programming background, Kotecha brings a passion for AI-driven solutions to the BrainChip team, with a unique eye for data visualization and analysis. He develops neural networks for neuromorphic hardware and event-based processors and optimizes CNN-based networks for conversion to SNN. Nikunj has an MS in Computer Science from Rochester Institute of Technology.

A multiple choice question.

Who is Brainchip selling its AKIDA technology IP too?

1. The person in the street;
2. Homemakers;
3. Shareholders;
4. other technology Nerds building Edge Devices.

My opinion only DYOR
FF

AKIDA BALLISTA

Could the answer be;

4: other Nerds

Learning

 

[robsmark]

Well done Peter and the Brainchip technical Team, a well deserved recognition of accomplishment.

 

[Humble Genius]

 

[jk6199]

And the winner is Akida!!!

Just what the shorters wanted to read lol.

 

[buena suerte :-)]

View attachment 33139

Welcome aboard 'The Bus' @Humble Genius

 

[charles2]

Nerds sell AKIDA too. Straight from the Brainchip website this is what Brainchip expects of its Nerds:

Developing CNNs for Neuromorphic Hardware​

(Been There! Done That!)​

By Nikunj Kotecha​

Often, we hear that neuromorphic technology is cool, classy, low power, next-gen hardware for AI and the most suitable technology for edge devices. Neuromorphic technology mimics the brain, the most efficient computation engine known, to create a computing and natural learning paradigm for devices. Neuromorphic design is complemented with Spiking Neural Networks (SNNs) which emulate how neurons fire and hence only compute when absolutely necessary. This is unlike today’s “MAC monsters" engines -ones that execute lots of MACs (Multiply Accumulate operations which are the basis of most AI computation) in parallel, many of which often get discarded.
So neuromorphic hardware is exciting! However, it is very difficult to develop and deploy current state-of-the-art solutions onto neuromorphic hardware. It’s also extremely limiting to use existing convolutional neural networks (CNNs) based on these platform models. The difficulty primarily stems from the assumption that neuromorphic hardware is analog, and they only run advanced algorithms with SNNs, which are currently in short supply. Therefore, the production models of today —typically accelerated by traditional Deep Learning Accelerators (DLAs) as a safe path to commercialization—are not supported by neuromorphic hardware. But BrainChip Akida TM is changing the game.
Akida is a fully digital, synthesizable and thus process-independent, and silicon-proven neuromorphic technology. It is designed to be scalable and portable across foundries and architected to be embedded into low-power edge devices. It fully supports acceleration for feed-forward CNNs and accelerates other neural networks like Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) and more, while providing the efficiency benefits of a neuromorphic design. It removes risks and simplifies.

Figure 1. Akida Tech Foundations. Fundamentally different and extremely efficient.​

At BrainChip, our mission is to Unlock the future of AI. We believe we can do this by enabling Edge devices with Akida, next-generation technology to advance the growth and smartness of these devices and ultimately provide end users with a sense of privacy and security, energy and cost savings while having access to new features. We realize to achieve our mission, we must make it easy for our end users (who may be experts or non-experts in the field of AI) to use our technology and support current solutions. BrainChip provides development boards of their reference chip AKD1000 for anybody in the community to use and build prototype modules. For commercial use, BrainChip provides licenses of the technology to get it integrated into a custom ASIC, board, or a module that can be used in millions of edge devices.
BrainChip promotes neuromorphic technology with proven silicon, like AKD1000, but also focuses on enabling end users to use the benefits of this technology with little to no knowledge of neuromorphic science. There are three ways to leverage Akida technology (refer to Figure 2) and deploy complex models:

Figure 2. BrainChip development ecosystem and access to deployment of solutions to Akida technology​

1. Through BrainChip MetaTF™ framework: It is a unique and free robust ML framework that has Python packages for model development and conversion of TensorFlow/Keras models into Akida. It is a framework that is very popular among AI experts and custom developers. Python packages for MetaTF framework are public, and developers can access the framework here: https://doc.brainchipinc.com
2. Through Edge Impulse studio: It is a unique platform that provides end-to-end development and deployment of Machine Learning models on targeted technology with little to no-code AI expertise. Core functions of BrainChip MetaTF framework are embedded into Edge Impulse studio to deploy models onto Akida targeted silicon, such as AKD1000 SoC. To learn more about how to develop using Edge Impulse, click here or visit https://www.edgeimpulse.com
3. Through Solutions Partners of BrainChip such as NVISO: BrainChip has partnered with solutions providers and enabled them to create complex models using MetaTF and build applications for specific and most common AI use cases such as Human Monitoring solutions. This allows for faster time to market solutions using Akida technology. To learn more about BrainChip Solutions Partners, contact us at sales@brainchip.com.
These avenues provide an opportunity to create and develop a functioning model that is suitable for running on Akida technology. The models are converted using MetaTF and are saved into a serialized byte file. These models can be evaluated offline by running simulations using the Software Runtime provided with MetaTF or can be evaluated on AKD1000 mini PCIe development board using Hardware backend as shown in Figure 3a. Once through the evaluation stage, these models can be deployed into production on any target device with Akida technology. Akida Runtime library, which is a low-level library that is OS agnostic, is used to compile the saved model and inference on any target device that has Akida technology. Customers who license Akida technology for their device are able to compile this Akida
Runtime library with any of their application software and host OS, as shown in Figure 3b.

Figure 3a. Using MetaTF Software backend for simulations and Hardware backend for model deployment on AKD1000 mini PCIe development board​

Figure 3b. Using low-level Akida Runtime Library for production deployment of models in target devices with Akida technology​

BrainChip is very excited about the ecosystem that is available for our end users to use, develop, and deploy complex AI models on Akida neuromorphic technology. Expert AI developers who are familiar with CNN architectures can use BrainChip MetaTF framework to deploy familiar models on Akida. Developers with little to no code experience can use Edge Impulse studio to deploy models on Akida technology and users who want faster time to market can work with Solutions Partners such as NVISO.
To learn more about how you can harness the power of AI, request a demo or visit BrainChip.com.
Nikunj Kotecha is a Machine Learning Solutions Architect at BrainChip. With many years of experience and a strong programming background, Kotecha brings a passion for AI-driven solutions to the BrainChip team, with a unique eye for data visualization and analysis. He develops neural networks for neuromorphic hardware and event-based processors and optimizes CNN-based networks for conversion to SNN. Nikunj has an MS in Computer Science from Rochester Institute of Technology.

A multiple choice question.

Who is Brainchip selling its AKIDA technology IP too?

1. The person in the street;
2. Homemakers;
3. Shareholders;
4. other technology Nerds building Edge Devices.

My opinion only DYOR
FF

AKIDA BALLISTA

Simple concept and easy for all to grasp (I know, somewhat redundant)

"At BrainChip, our mission is to Unlock the future of AI"

 

[Foxdog]

Nerds sell AKIDA too. Straight from the Brainchip website this is what Brainchip expects of its Nerds:

Developing CNNs for Neuromorphic Hardware​

(Been There! Done That!)​

By Nikunj Kotecha​

Often, we hear that neuromorphic technology is cool, classy, low power, next-gen hardware for AI and the most suitable technology for edge devices. Neuromorphic technology mimics the brain, the most efficient computation engine known, to create a computing and natural learning paradigm for devices. Neuromorphic design is complemented with Spiking Neural Networks (SNNs) which emulate how neurons fire and hence only compute when absolutely necessary. This is unlike today’s “MAC monsters" engines -ones that execute lots of MACs (Multiply Accumulate operations which are the basis of most AI computation) in parallel, many of which often get discarded.
So neuromorphic hardware is exciting! However, it is very difficult to develop and deploy current state-of-the-art solutions onto neuromorphic hardware. It’s also extremely limiting to use existing convolutional neural networks (CNNs) based on these platform models. The difficulty primarily stems from the assumption that neuromorphic hardware is analog, and they only run advanced algorithms with SNNs, which are currently in short supply. Therefore, the production models of today —typically accelerated by traditional Deep Learning Accelerators (DLAs) as a safe path to commercialization—are not supported by neuromorphic hardware. But BrainChip Akida TM is changing the game.
Akida is a fully digital, synthesizable and thus process-independent, and silicon-proven neuromorphic technology. It is designed to be scalable and portable across foundries and architected to be embedded into low-power edge devices. It fully supports acceleration for feed-forward CNNs and accelerates other neural networks like Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) and more, while providing the efficiency benefits of a neuromorphic design. It removes risks and simplifies.

Figure 1. Akida Tech Foundations. Fundamentally different and extremely efficient.​

At BrainChip, our mission is to Unlock the future of AI. We believe we can do this by enabling Edge devices with Akida, next-generation technology to advance the growth and smartness of these devices and ultimately provide end users with a sense of privacy and security, energy and cost savings while having access to new features. We realize to achieve our mission, we must make it easy for our end users (who may be experts or non-experts in the field of AI) to use our technology and support current solutions. BrainChip provides development boards of their reference chip AKD1000 for anybody in the community to use and build prototype modules. For commercial use, BrainChip provides licenses of the technology to get it integrated into a custom ASIC, board, or a module that can be used in millions of edge devices.
BrainChip promotes neuromorphic technology with proven silicon, like AKD1000, but also focuses on enabling end users to use the benefits of this technology with little to no knowledge of neuromorphic science. There are three ways to leverage Akida technology (refer to Figure 2) and deploy complex models:

Figure 2. BrainChip development ecosystem and access to deployment of solutions to Akida technology​

1. Through BrainChip MetaTF™ framework: It is a unique and free robust ML framework that has Python packages for model development and conversion of TensorFlow/Keras models into Akida. It is a framework that is very popular among AI experts and custom developers. Python packages for MetaTF framework are public, and developers can access the framework here: https://doc.brainchipinc.com
2. Through Edge Impulse studio: It is a unique platform that provides end-to-end development and deployment of Machine Learning models on targeted technology with little to no-code AI expertise. Core functions of BrainChip MetaTF framework are embedded into Edge Impulse studio to deploy models onto Akida targeted silicon, such as AKD1000 SoC. To learn more about how to develop using Edge Impulse, click here or visit https://www.edgeimpulse.com
3. Through Solutions Partners of BrainChip such as NVISO: BrainChip has partnered with solutions providers and enabled them to create complex models using MetaTF and build applications for specific and most common AI use cases such as Human Monitoring solutions. This allows for faster time to market solutions using Akida technology. To learn more about BrainChip Solutions Partners, contact us at sales@brainchip.com.
These avenues provide an opportunity to create and develop a functioning model that is suitable for running on Akida technology. The models are converted using MetaTF and are saved into a serialized byte file. These models can be evaluated offline by running simulations using the Software Runtime provided with MetaTF or can be evaluated on AKD1000 mini PCIe development board using Hardware backend as shown in Figure 3a. Once through the evaluation stage, these models can be deployed into production on any target device with Akida technology. Akida Runtime library, which is a low-level library that is OS agnostic, is used to compile the saved model and inference on any target device that has Akida technology. Customers who license Akida technology for their device are able to compile this Akida
Runtime library with any of their application software and host OS, as shown in Figure 3b.

Figure 3a. Using MetaTF Software backend for simulations and Hardware backend for model deployment on AKD1000 mini PCIe development board​

Figure 3b. Using low-level Akida Runtime Library for production deployment of models in target devices with Akida technology​

BrainChip is very excited about the ecosystem that is available for our end users to use, develop, and deploy complex AI models on Akida neuromorphic technology. Expert AI developers who are familiar with CNN architectures can use BrainChip MetaTF framework to deploy familiar models on Akida. Developers with little to no code experience can use Edge Impulse studio to deploy models on Akida technology and users who want faster time to market can work with Solutions Partners such as NVISO.
To learn more about how you can harness the power of AI, request a demo or visit BrainChip.com.
Nikunj Kotecha is a Machine Learning Solutions Architect at BrainChip. With many years of experience and a strong programming background, Kotecha brings a passion for AI-driven solutions to the BrainChip team, with a unique eye for data visualization and analysis. He develops neural networks for neuromorphic hardware and event-based processors and optimizes CNN-based networks for conversion to SNN. Nikunj has an MS in Computer Science from Rochester Institute of Technology.

A multiple choice question.

Who is Brainchip selling its AKIDA technology IP too?

1. The person in the street;
2. Homemakers;
3. Shareholders;
4. other technology Nerds building Edge Devices.

My opinion only DYOR
FF

AKIDA BALLISTA

Well I'm glad you're getting with the times and using the correct vernacular FF

 

[AARONASX]

Is it possible that if we're in the M85 sales could sky rocket more than we think?

Assuming recurring customers know/have the Arm chip integration already understood...but have no idea how to use akida just yet but know they'll need it...they will throw it in and figure it out later on that way they are not left behind and all it would take is an update to bring forward their product with the implementation of akida?

 

[thelittleshort]

RT spreading the good word

Edge Impulse on LinkedIn: Some insightful sessions during yesterday's Edge ML Series event in San…

Some insightful sessions during yesterday's Edge ML Series event in San Jose by our partners Texas Instruments, BrainChip , Alif Semiconductor, and Nordic…

www.linkedin.com

 

[Falling Knife]

Not sure this has been posted.

Neural Processor Market Review, Trend, Research Report, Analysis, Regions, Forecast To 2030 | BrainChip, Inc., BrainCo


A costly research paper at that interestingly mentions Brainchip in the title.
US $ too I believe.

Congratulations BRN team for the award.
Great progress seen every week resulting from your dedication and effort to bringing AI to the world.

The tipping point is closer every day.

 

[Tothemoon24]

Get Smart or Get Left Behind

Using a TI SK-TDA4VM Starter Kit with RTSP streaming and an Edge Impulse model, monitor the status on your factory equipment and take action when needed.

www.edgeimpulse.com

 

[Stable Genius]

This is recent from KNOW LABS:

Know Labs Forms New Scientific Advisory Board, Appoints Respected Researchers in Medical Innovation
o days ago
o 6 min read
Know Labs Forms New Scientific Advisory Board, Appoints Respected Researchers in Medical Innovation
SEATTLE – March 22, 2023 – Know Labs, Inc. (NYSE American: KNW), an emerging developer of non-invasive medical diagnostic technology, today announced the establishment of a Scientific Advisory Board (SAB) comprised of distinguished researchers, innovators and experts in medical technology and human health. SAB members will advise the company and its strategic partners on advancing the company’s progress in algorithm refinement, device development, clinical trial design and research publication strategy. SAB members will be working alongside Know Labs’ current Medical and Regulatory Advisory Board members who have been supporting the team since 2020 and will be pivotal in helping Know Labs accelerate the development and delivery of its Bio-RFID™ technology and the world’s first non-invasive glucose monitoring medical devices.
“We’re fortunate to be partnering with a team of respected scientific and technical advisors whose areas of expertise are directly related to the work we have underway,” said Ron Erickson, CEO and Chairman at Know Labs. “Their insights will help us build upon the knowledge and skills of our own team and our strategic partners to further best practices that meet the highest possible standards and to propel us toward clear, unequivocal scientific and public validation of our technology in 2023.”
Initial members of the Know Labs SAB are:
Benjamin Smarr, Ph.D., an assistant professor at the Halicioğlu Data Science Institute and the Department of Bioengineering at the University of California, San Diego. Dr.Smarr has a demonstrated history of academic and public-private research excellence focused on developing personalized, predictive tools for future medicine, earning him many federal awards from the National Institutes of Health, National Science Foundation and Department of Defense, as well as from industry research partners. Over the last several years, his research has focused on unlocking the potential of continuous physiological data generated from wearables and related technologies for women’s health, education outcomes, long-term care and monitoring in illness of diverse populations. His peer-reviewed work has been published broadly across sleep, circadian, endocrine and engineering journals and his research and views have been repeatedly covered by global media outlets.
Jessica Zendler, Ph.D., Special Consultant at Rimkus and Adjunct Research Assistant Professor in the School of Kinesiology at the University of Michigan. Dr. Zendler’swork is focused on understanding the role of movement in injury, physical performance, and health, and the use of wearable technology to monitor athletes. Dr. Zendler advises sports organizations, national governing bodies and manufacturers on scientific research, validation and policy development related to human performance and wearable technology. She has been published in numerous peer-reviewed journals, including the American Journal of Sports Medicine, serves as co-chair of the Sports Tech Research Network’s Quality Assessment of Sports Technologies Group and is a member of the NBA Sport Science Committee, American Society of Biomechanics and International Society of Biomechanics.
Carl Johan Sundberg, M.D., Ph.D., a licensed physician and Professor of Exercise Physiology at Karolinska Institute in Sweden and Head of the Department of Learning, Informatics, Management and Ethics (LIME). Dr. Sundberg’s research is focused on human genetics, genomics and epigenetics in relation to exercise in elite athletes, in non-athlete populations and in patients (cardiovascular, diabetes, psychiatry, oncology). Another main research area concerns AI/ML-based computerized history taking. Dr. Sundberg is an elected member of the Swedish Academy of Engineering Sciences and has served as a member or chairman of numerous academic and industry boards and for several biotechnology companies. For 10 years, he worked as investment director at a 60 million euros life science venture fund. In 2022, Dr.Sundberg received H.M. the King of Sweden’s medal for “significant contributions to research and science communication”. He has also received the European Commission’s Descartes Communication Prize for Excellence in Science Communication.

Mark Aloia, Ph.D., currently the Head of Sleep and Behavioral Science at Sleep Number (NASDAQ: SNBR), the $1.9 billion maker of Sleep Number and Comfortaire beds and accessories. At Sleep Number, Dr. Aloia oversees sleep science research, partnerships and collaborations with the world’s leading physicians, researchers and institutions, as well as the development of health-focused innovations. In his past industry role, Dr. Aloia oversaw clinical sleep research for Philips Respironics for 16 years and, later, served as their Global Lead for Behavior Change. He has also been on the faculty at the University of Rochester and at Brown University as a prominent sleep researcher. As a researcher with more than $15 million in funded National Institutes of Health research grants and over 60 peer-reviewed publications, Dr. Aloia has a proven track record of using sound scientific methods to assess efficacy. He serves as an Associate Professor in the Section of Sleep Medicine and Department of Medicine at National Jewish Health.

“Ben, Jessica, CJ and Mark bring expertise, insights and independent perspectives to our medical diagnostic device development at Know Labs,” Erickson continued. “We look forward to their contributions. We plan to bring on additional respected medical and technical experts to our advisory boards, as we continue work to deliver our non-invasive diagnostic technology.”

In 2023, Know Labs is prioritizing external validation of its Bio-RFID technology in detecting and measuring glucose and other analytes in the body non-invasively at high levels of accuracy. The company will continue working with its strategic partners and existing advisors and diabetes experts. This includes work alongside Know Labs’ Medical and Regulatory Advisory Board members, Dr. James Anderson, Know Labs Chief Medical Officer and former Senior Medical Director, Diabetes and CardioMetabolic Medicine with Eli Lilly and Company; Larry Ellingson, Vice President of the National Diabetes Volunteer Leadership Council; and Donna Ryan, RN, RD, MPH, CDE, former President of the American Association of Diabetes Educators.

For more information on Know Labs, visit www.knowlabs.co.
About Know Labs, Inc.
Know Labs, Inc. is a public company whose shares trade on the NYSE American Exchange under the stock symbol “KNW.” The Company’s technology uses spectroscopy to direct electromagnetic energy through a substance or material to capture a unique molecular signature. The Company refers to its technology as Bio-RFID™. The Bio-RFID technology can be integrated into a variety of wearable, mobile or bench-top form factors. This patented and patent-pending technology makes it possible to effectively identify and monitor analytesthat could only previously be performed by invasive and/or expensive and time-consuming lab-based tests. The first application of our Bio-RFID technology will be in a product marketed as a non-invasive glucose monitor. It will provide the user with real-time information on blood glucose levels. This product will require U.S. Food and Drug Administration clearance prior to its introduction to the market.
Safe Harbor Statement
This release contains statements that constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 and Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements appear in a number of places in this release and include all statements that are not statements of historical fact regarding the intent, belief or current expectations of Know Labs, Inc., its directors or its officers with respect to, among other things: (i) financing plans; (ii) trends affecting its financial condition or results of operations; (iii) growth strategy and operating strategy; and (iv) performance of products. You can identify these statements by the use of the words “may,” “will,” “could,” “should,” “would,” “plans,” “expects,” “anticipates,” “continue,” “estimate,” “project,” “intend,” “likely,” “forecast,” “probable,” “potential,” and similar expressions and variations thereof are intended to identify forward-looking statements. Investors are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, many of which are beyond Know Labs, Inc.’s ability to control, and actual results may differ materially from those projected in the forward-looking statements as a result of various factors. These risks and uncertainties also include such additional risk factors as are discussed in the Company’s filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended September 30, 2022, Forms 10-Q and 8-K, and in other filings we make with the Securities and Exchange Commission from time to time. These documents are available on the SEC Filings section of the Investor Relations section of our website at www.knowlabs.co. The Company cautions readers not to place undue reliance upon any such forward-looking statements, which speak only as of the date made. The Company undertakes no obligation to update any forward-looking statement to reflect events or circumstances after the date on which such statement is made.

 

[buena suerte :-)]

Worth registering I think ... Could be interesting!!

​

Edge Impulse is hosting a fireside chat on April 5th at 11am ET/4pm CET to celebrate the release of "AI at the Edge," a new publication by O’Reilly authored by Edge Impulse’s Daniel Situnayake (Head of ML) and Jenny Plunkett (Senior Developer Relations Engineer). Join us for a conversation with the authors to discuss the book, delve into how embedded ML can solve real-world problems, and address your questions on edge AI applications. In this 45-minute virtual session, Daniel and Jenny will examine the key technologies and trends that are driving this development and provide practical guidance on how your company can implement AI at the edge — including the use of the Edge Impulse Studio. Why edge AI? Why now? Hear tips on how to achieve value in less time while lowering costs Understand which use cases are best solved with edge AI Build the right team with the skills to solve real-world problems Avoid gotchas when exploring key design patterns for edge AI applications Bonus: THREE lucky attendees will receive a free copy of "AI at the Edge." *Drawing will be held during the fireside chat. You must register and attend to be eligible.

​ Register now​

 

[4jct]

Embedded World 2023: VDC’s “Embeddy” Award Winners​

by Dan Mandell & Chris Rommel | 3/28/2023

Last week, VDC Research attended the Embedded World 2023 conference in Nuremberg, Germany and held dozens of face-to-face meetings with exhibitors. Following a barren 2020 and relatively muted attendance last year, Embedded World 2023 was a clear indication that this industry is a small step away from back to normal conference and tradeshow attendance and activities. The halls were packed and bustling like times of old, with everyone sharing a sense of relief and rejuvenated appreciation for in-person events, meetings, and connections.
VDC created and named the Embeddy Awards to highlight companies announcing important advances in the IoT and embedded software, hardware, and services industries. The Embeddy is awarded for the most cutting-edge product or service available to embedded software developers and system engineers. Nominees were judged across specific criteria including corporate, technological, and industry significance, as well as the most cutting-edge hardware and software solutions or services.
The 2023 Embeddy Award Winners include:

• IP: BrainChip Akida 2nd Generation
• HARDWARE: Eurotech ReliaCOR Secure Edge AI
• SOFTWARE: 1NCE OS

Embeddy IP Winner: BrainChip
Akida 2nd Generation: Leading up to Embedded World 2023, BrainChip, a commercial producer of ultra-low power neuromorphic AI IP, launched the 2nd generation of its Akida platform to drive provide direct support for the embedded AI processor market, which is rapidly growing, particularly at the far edge. More complex networks like RESNET-50 can be processed by Akida without CPU intervention and the platform supports 8-bit weights to go with existing 4,2,1 bit support. Akida also adds an efficient vision transformer implementation for acceleration and can also handle 3D data for applications like video object detection and target tracking as well as 1D time series data for streaming data on devices with limited memory, battery, and compute resources. Akida features separable spatial temporal convolutions called Temporal Event Based Neural Nets that allow for simpler and efficient AI processing solutions.

Could anyone suggest a reason why this would not be considered a legitimate opportunity to showcase our company to potential customers via an ASX announcement and thereby act in the best interest of our shareholders.

 

[Boab]

This is recent from KNOW LABS:

Know Labs Forms New Scientific Advisory Board, Appoints Respected Researchers in Medical Innovation
o days ago
o 6 min read
Know Labs Forms New Scientific Advisory Board, Appoints Respected Researchers in Medical Innovation
SEATTLE – March 22, 2023 – Know Labs, Inc. (NYSE American: KNW), an emerging developer of non-invasive medical diagnostic technology, today announced the establishment of a Scientific Advisory Board (SAB) comprised of distinguished researchers, innovators and experts in medical technology and human health. SAB members will advise the company and its strategic partners on advancing the company’s progress in algorithm refinement, device development, clinical trial design and research publication strategy. SAB members will be working alongside Know Labs’ current Medical and Regulatory Advisory Board members who have been supporting the team since 2020 and will be pivotal in helping Know Labs accelerate the development and delivery of its Bio-RFID™ technology and the world’s first non-invasive glucose monitoring medical devices.
“We’re fortunate to be partnering with a team of respected scientific and technical advisors whose areas of expertise are directly related to the work we have underway,” said Ron Erickson, CEO and Chairman at Know Labs. “Their insights will help us build upon the knowledge and skills of our own team and our strategic partners to further best practices that meet the highest possible standards and to propel us toward clear, unequivocal scientific and public validation of our technology in 2023.”
Initial members of the Know Labs SAB are:
Benjamin Smarr, Ph.D., an assistant professor at the Halicioğlu Data Science Institute and the Department of Bioengineering at the University of California, San Diego. Dr.Smarr has a demonstrated history of academic and public-private research excellence focused on developing personalized, predictive tools for future medicine, earning him many federal awards from the National Institutes of Health, National Science Foundation and Department of Defense, as well as from industry research partners. Over the last several years, his research has focused on unlocking the potential of continuous physiological data generated from wearables and related technologies for women’s health, education outcomes, long-term care and monitoring in illness of diverse populations. His peer-reviewed work has been published broadly across sleep, circadian, endocrine and engineering journals and his research and views have been repeatedly covered by global media outlets.
Jessica Zendler, Ph.D., Special Consultant at Rimkus and Adjunct Research Assistant Professor in the School of Kinesiology at the University of Michigan. Dr. Zendler’swork is focused on understanding the role of movement in injury, physical performance, and health, and the use of wearable technology to monitor athletes. Dr. Zendler advises sports organizations, national governing bodies and manufacturers on scientific research, validation and policy development related to human performance and wearable technology. She has been published in numerous peer-reviewed journals, including the American Journal of Sports Medicine, serves as co-chair of the Sports Tech Research Network’s Quality Assessment of Sports Technologies Group and is a member of the NBA Sport Science Committee, American Society of Biomechanics and International Society of Biomechanics.
Carl Johan Sundberg, M.D., Ph.D., a licensed physician and Professor of Exercise Physiology at Karolinska Institute in Sweden and Head of the Department of Learning, Informatics, Management and Ethics (LIME). Dr. Sundberg’s research is focused on human genetics, genomics and epigenetics in relation to exercise in elite athletes, in non-athlete populations and in patients (cardiovascular, diabetes, psychiatry, oncology). Another main research area concerns AI/ML-based computerized history taking. Dr. Sundberg is an elected member of the Swedish Academy of Engineering Sciences and has served as a member or chairman of numerous academic and industry boards and for several biotechnology companies. For 10 years, he worked as investment director at a 60 million euros life science venture fund. In 2022, Dr.Sundberg received H.M. the King of Sweden’s medal for “significant contributions to research and science communication”. He has also received the European Commission’s Descartes Communication Prize for Excellence in Science Communication.

Mark Aloia, Ph.D., currently the Head of Sleep and Behavioral Science at Sleep Number (NASDAQ: SNBR), the $1.9 billion maker of Sleep Number and Comfortaire beds and accessories. At Sleep Number, Dr. Aloia oversees sleep science research, partnerships and collaborations with the world’s leading physicians, researchers and institutions, as well as the development of health-focused innovations. In his past industry role, Dr. Aloia oversaw clinical sleep research for Philips Respironics for 16 years and, later, served as their Global Lead for Behavior Change. He has also been on the faculty at the University of Rochester and at Brown University as a prominent sleep researcher. As a researcher with more than $15 million in funded National Institutes of Health research grants and over 60 peer-reviewed publications, Dr. Aloia has a proven track record of using sound scientific methods to assess efficacy. He serves as an Associate Professor in the Section of Sleep Medicine and Department of Medicine at National Jewish Health.

“Ben, Jessica, CJ and Mark bring expertise, insights and independent perspectives to our medical diagnostic device development at Know Labs,” Erickson continued. “We look forward to their contributions. We plan to bring on additional respected medical and technical experts to our advisory boards, as we continue work to deliver our non-invasive diagnostic technology.”

In 2023, Know Labs is prioritizing external validation of its Bio-RFID technology in detecting and measuring glucose and other analytes in the body non-invasively at high levels of accuracy. The company will continue working with its strategic partners and existing advisors and diabetes experts. This includes work alongside Know Labs’ Medical and Regulatory Advisory Board members, Dr. James Anderson, Know Labs Chief Medical Officer and former Senior Medical Director, Diabetes and CardioMetabolic Medicine with Eli Lilly and Company; Larry Ellingson, Vice President of the National Diabetes Volunteer Leadership Council; and Donna Ryan, RN, RD, MPH, CDE, former President of the American Association of Diabetes Educators.

For more information on Know Labs, visit www.knowlabs.co.
About Know Labs, Inc.
Know Labs, Inc. is a public company whose shares trade on the NYSE American Exchange under the stock symbol “KNW.” The Company’s technology uses spectroscopy to direct electromagnetic energy through a substance or material to capture a unique molecular signature. The Company refers to its technology as Bio-RFID™. The Bio-RFID technology can be integrated into a variety of wearable, mobile or bench-top form factors. This patented and patent-pending technology makes it possible to effectively identify and monitor analytesthat could only previously be performed by invasive and/or expensive and time-consuming lab-based tests. The first application of our Bio-RFID technology will be in a product marketed as a non-invasive glucose monitor. It will provide the user with real-time information on blood glucose levels. This product will require U.S. Food and Drug Administration clearance prior to its introduction to the market.
Safe Harbor Statement
This release contains statements that constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 and Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements appear in a number of places in this release and include all statements that are not statements of historical fact regarding the intent, belief or current expectations of Know Labs, Inc., its directors or its officers with respect to, among other things: (i) financing plans; (ii) trends affecting its financial condition or results of operations; (iii) growth strategy and operating strategy; and (iv) performance of products. You can identify these statements by the use of the words “may,” “will,” “could,” “should,” “would,” “plans,” “expects,” “anticipates,” “continue,” “estimate,” “project,” “intend,” “likely,” “forecast,” “probable,” “potential,” and similar expressions and variations thereof are intended to identify forward-looking statements. Investors are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, many of which are beyond Know Labs, Inc.’s ability to control, and actual results may differ materially from those projected in the forward-looking statements as a result of various factors. These risks and uncertainties also include such additional risk factors as are discussed in the Company’s filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended September 30, 2022, Forms 10-Q and 8-K, and in other filings we make with the Securities and Exchange Commission from time to time. These documents are available on the SEC Filings section of the Investor Relations section of our website at www.knowlabs.co. The Company cautions readers not to place undue reliance upon any such forward-looking statements, which speak only as of the date made. The Company undertakes no obligation to update any forward-looking statement to reflect events or circumstances after the date on which such statement is made.

The wearables industry is going to be massive imo.
Below is just one little story as to hw important glucose monitoring can be.
Apologies as my copy and paste skills are lacking.

Home News Road
'If a woman doesn't fuel properly, she could lose her period' - Faulkner hits back over glucose monitor disqualification.
By Stephen Farrand published 5 days ago
Kristen Faulkner argues UCI isn't taking women's health into consideration after Strade Bianche disqualification

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Comments
Kristen Faulkner (Jayco AlUla)
Kristen Faulkner (Jayco AlUla) (Image credit: Getty Images)
Kristen Faulkner has talked at length about her disqualification from Strade Bianche for wearing a continuous glucose monitor sensor, revealing she intends to push the UCI for greater clarity and use her own experience to raise awareness and start a conversation around glucose and women’s health.

“If I can have a similar mission and purpose in my life as a pro athlete, that's even more meaningful to me. This has given me a purpose behind my riding that is much greater than a third place at Strade Bianche,” Faulkner told Rouleur(opens in new tab).

The 30-year-old American revealed she began to use a Supersapiens continuous glucose monitor system last year after problems with her fueling.

 

[Fullmoonfever]

Maybe our resident verification engineer @chapman89 might want to connect (as is his skill) with this guy and let him know that Akida is available in relation to the last paragraph

Predictive networking promises faster fixes

As AI and ML are backed by more powerful neural networks, prescriptive analytics will recommend paths to better future outcomes.

www.networkworld.com

Predictive networking promises to faster fixes​

Predictive network technology promises to find and fix problems faster.​

By John Edwards
Contributing writer, Network World | 27 MARCH 2023 18:00 SGT

With the assistance of artificial intelligence (AI) and machine learning (ML), predictive network technology alerts administrators to possible network issues as early as possible and offers potential solutions.

The AI and ML algorithms used in predictive network technology have become critical, says Bob Hersch, a principal with Deloitte Consulting and US lead for platforms and infrastructure. "Predictive network technology leverages artificial neural networks and utilizes models to analyze data, learn patterns, and make predictions," he says. "AI and ML significantly enhance observability, application visibility, and the ability to respond to network and other issues."

While predictive network technology has made impressive strides over the past several years, many developers and observers are confident that the best is yet to come. "Tools and systems are available now, but like most significant evolutions in technology there are risks for the early adopters, as development and even how to assess the effectiveness of a shift are in flight," says David Lessin, a director at technology research and advisory firm ISG.

Predictive analytics is no longer just for predicting network outages and proactively handling problems of bandwidth and application performance, says Yaakov Shapiro, CTO at telecommunications software and services provider Tangoe. "Predictive analytics are now being applied to problems surrounding the network and helping to address the downsides of SD-WAN, most notably the issue of provider sprawl and the need for wider carrier-service management and telecom-cost optimization," he says. "These have become larger issues in the age of trading MPLS—one- and two-carrier services—for broadband services comprising potentially hundreds of internet service providers."

AI is moving predictive networking forward.​

The most recent evolution of AI is the most important development in predictive network technology. "Cloud-based AI technologies can improve the quality and speed of information delivered to network technicians while giving them a valuable tool to investigate outages and other issues," says Patrick MeLampy, a Juniper Networks fellow. "AI can detect anomalies quicker than humans and can even analyze the root cause of an anomaly, helping to guide a technician to understand and repair the issue faster than before."

The integration of AI tools into predictive network technology also has the potential to be an economic game-changer. "With mature AI and ML tools at their disposal, service providers and organizations alike can reduce the costs of problem discovery and resolution," MeLampy says. In addition to bottom-line economic benefits, AI helps to simplify management, either within an enterprise or across a service provider's portfolio. "Mean-time-to repair is decreased, improving end user satisfaction as well," he says.

Bryan Woodworth, principal solutions strategist at multicloud network technology firm Aviatrix, says that predictive network technology will advance rapidly over the next few years. It already helps resolve network issues quickly and efficiently. "AI can correlate alerts and error conditions across many disparate systems, discovering related patterns in minutes or even seconds, something that would take humans hours or days,” he says.

Predictive network technology can also drastically decrease the number of false positives tucked into log and error analyses, leading to more intelligent and useful alerts, Woodworth says. "You can't heal from something you don't detect," he says. "For example, before you change the network to route around a problem, you must know where that problem is." Self-healing networks based on AI and ML provide better recommendations on how to recover from errors and avoid outages.

Predictive modeling works best in data centers.​

Network behavior analytics examines network data, such as ports, protocols, performance, and geo-IP data, to alert whenever there's been a significant change in network behavior that might indicate a threat. "In the future, this data can be fed into an AI model that can help confirm if the threat is real, and then make suggestions on how to remediate the issue by changing the network," Woodworth says. "This kind of predictive modeling works best within private networks, like the data center, because [that's where] humans have complete control over all the networking components and the data they generate."

For public networks, including those connected to the internet, the task becomes more challenging. Learning models must be designed to compensate for systems that aren't under direct control or provide incomplete data sets. This means that learning models will make less accurate predictions and may need to be tuned by humans to compensate for the missing data, Woodworth says.

To be fully effective, advanced AI and ML models should run at production level and scale for error remediation, Smith says. "Decision-makers need to trust modeling results, and technology sponsors need to execute operations efficiently," he says.

Meanwhile, ongoing advances in cloud technology and graphics processing units (GPUs) are taking modeling to new levels. "Open source and commercial frameworks are helping organizations deploy ML operations rapidly and at-scale with less risk associated with the time and complexity required to configure cloud and open source systems for AI," says Maggie Smith, managing director, applied intelligence, at consulting firm Accenture Federal Services.

Smith says that several major cloud providers have already implemented AI model optimization and management features. The technology can be found in in tools such as Amazon SageMaker, Google AI Platform, and Azure Machine Learning Studio. "Open-source frameworks like TensorRT, and Hugging Face retrain additional opportunities for model monitoring and efficiencies," Smith says.

Predictive networking analyzes cloud and edge workloads.​

Big picture, predictive AI-based networking is not as much about the network as it is about cloud workloads, edge delivery, and user endpoint devices, such as laptop computers and mobile devices. "By understanding workloads—the network traffic they generate, latency requirements, and who is consuming data how and where—the high-fidelity data needed for predictive networking can be identified to support the automatic adaptation of virtual private clouds (VPCs)," says Curt Aubley, risk and financial advisory managing director, and US cyber detect-and-respond leader at business advisory firm Deloitte.

Micro segmentation, load balancers, and traffic shapers are all helping to optimize delivery. "The same high-fidelity data used for network-focused AI can also be used to complement cyber-security teams' consolidated extended detection and response data lakes for security analytics,” Aubley says. AI models are used to detect anomalies, unknown unknowns, and lateral movement. "Using the same high-fidelity data from cloud workloads, networks, and endpoints for different use cases can help ensure confidentiality, integrity, and the availability of applications needed for business or government cyber risk management."

Routers, wireless applications, switches, and various other general networking gear don't typically collect user-specific data. While application-performance monitoring tools do measure user data, they can't correlate results into proactive network actions. "Networks must become user and application aware in order to collect the types of data necessary to build actionable models for the use of AI and predictive technologies," MeLampy says. "If a solution doesn't measure experience per user, it isn't going to be successful.”

Prescriptive analytics is the future.​

The emerging field of neuromorphic computing, based on a chip architecture that's engineered to mimic human brain structure, promises to provide highly effective ML on edge devices. "Predictive network technology is so powerful because of its ability to intake signals and make accurate predictions about equipment failures to optimize maintenance," says Gil Dror, CTO at monitoring technology provider SmartSense. He says that neuromorphic computing will become even more powerful when it moves from predictive to prescriptive analytics, which recommends what should be done to ensure future outcomes.

Neuromorphic computing's chip architecture is geared toward making intelligent decisions on edge devices themselves, Dror says. "The combination of these two technologies will make the field of predictive network technology much more powerful," he says.
Organizations including IBM, Intel, and Qualcomm are developing neuromorphic computing technologies. "Some companies have released neuromorphic computing chips for research-and-development purposes, such as IBM's TrueNorth chip and Intel's Loihi chip," Dror says. These chips aren't yet generally available for commercial use, and it's likely that there will be at least several more years of intense research and development before neuromorphic computing becomes a mainstream technology. "Once it becomes viable, the impact will be massive," he predicts.

 

[supersonic001]

Edge Impulse on LinkedIn: Some insightful sessions during yesterday's Edge ML Series event in San…

Some insightful sessions during yesterday's Edge ML Series event in San Jose by our partners Texas Instruments, BrainChip , Alif Semiconductor, and Nordic…

www.linkedin.com

 

[Fact Finder]

Maybe our resident verification engineer @chapman89 might want to connect (as is his skill) with this guy and let him know that Akida is available in relation to the last paragraph

Predictive networking promises faster fixes

As AI and ML are backed by more powerful neural networks, prescriptive analytics will recommend paths to better future outcomes.

www.networkworld.com

Predictive networking promises to faster fixes​

Predictive network technology promises to find and fix problems faster.​

By John Edwards
Contributing writer, Network World | 27 MARCH 2023 18:00 SGT

With the assistance of artificial intelligence (AI) and machine learning (ML), predictive network technology alerts administrators to possible network issues as early as possible and offers potential solutions.

The AI and ML algorithms used in predictive network technology have become critical, says Bob Hersch, a principal with Deloitte Consulting and US lead for platforms and infrastructure. "Predictive network technology leverages artificial neural networks and utilizes models to analyze data, learn patterns, and make predictions," he says. "AI and ML significantly enhance observability, application visibility, and the ability to respond to network and other issues."

While predictive network technology has made impressive strides over the past several years, many developers and observers are confident that the best is yet to come. "Tools and systems are available now, but like most significant evolutions in technology there are risks for the early adopters, as development and even how to assess the effectiveness of a shift are in flight," says David Lessin, a director at technology research and advisory firm ISG.

Predictive analytics is no longer just for predicting network outages and proactively handling problems of bandwidth and application performance, says Yaakov Shapiro, CTO at telecommunications software and services provider Tangoe. "Predictive analytics are now being applied to problems surrounding the network and helping to address the downsides of SD-WAN, most notably the issue of provider sprawl and the need for wider carrier-service management and telecom-cost optimization," he says. "These have become larger issues in the age of trading MPLS—one- and two-carrier services—for broadband services comprising potentially hundreds of internet service providers."

AI is moving predictive networking forward.​

The most recent evolution of AI is the most important development in predictive network technology. "Cloud-based AI technologies can improve the quality and speed of information delivered to network technicians while giving them a valuable tool to investigate outages and other issues," says Patrick MeLampy, a Juniper Networks fellow. "AI can detect anomalies quicker than humans and can even analyze the root cause of an anomaly, helping to guide a technician to understand and repair the issue faster than before."

The integration of AI tools into predictive network technology also has the potential to be an economic game-changer. "With mature AI and ML tools at their disposal, service providers and organizations alike can reduce the costs of problem discovery and resolution," MeLampy says. In addition to bottom-line economic benefits, AI helps to simplify management, either within an enterprise or across a service provider's portfolio. "Mean-time-to repair is decreased, improving end user satisfaction as well," he says.

Bryan Woodworth, principal solutions strategist at multicloud network technology firm Aviatrix, says that predictive network technology will advance rapidly over the next few years. It already helps resolve network issues quickly and efficiently. "AI can correlate alerts and error conditions across many disparate systems, discovering related patterns in minutes or even seconds, something that would take humans hours or days,” he says.

Predictive network technology can also drastically decrease the number of false positives tucked into log and error analyses, leading to more intelligent and useful alerts, Woodworth says. "You can't heal from something you don't detect," he says. "For example, before you change the network to route around a problem, you must know where that problem is." Self-healing networks based on AI and ML provide better recommendations on how to recover from errors and avoid outages.

Predictive modeling works best in data centers.​

Network behavior analytics examines network data, such as ports, protocols, performance, and geo-IP data, to alert whenever there's been a significant change in network behavior that might indicate a threat. "In the future, this data can be fed into an AI model that can help confirm if the threat is real, and then make suggestions on how to remediate the issue by changing the network," Woodworth says. "This kind of predictive modeling works best within private networks, like the data center, because [that's where] humans have complete control over all the networking components and the data they generate."

For public networks, including those connected to the internet, the task becomes more challenging. Learning models must be designed to compensate for systems that aren't under direct control or provide incomplete data sets. This means that learning models will make less accurate predictions and may need to be tuned by humans to compensate for the missing data, Woodworth says.

To be fully effective, advanced AI and ML models should run at production level and scale for error remediation, Smith says. "Decision-makers need to trust modeling results, and technology sponsors need to execute operations efficiently," he says.

Meanwhile, ongoing advances in cloud technology and graphics processing units (GPUs) are taking modeling to new levels. "Open source and commercial frameworks are helping organizations deploy ML operations rapidly and at-scale with less risk associated with the time and complexity required to configure cloud and open source systems for AI," says Maggie Smith, managing director, applied intelligence, at consulting firm Accenture Federal Services.

Smith says that several major cloud providers have already implemented AI model optimization and management features. The technology can be found in in tools such as Amazon SageMaker, Google AI Platform, and Azure Machine Learning Studio. "Open-source frameworks like TensorRT, and Hugging Face retrain additional opportunities for model monitoring and efficiencies," Smith says.

Predictive networking analyzes cloud and edge workloads.​

Big picture, predictive AI-based networking is not as much about the network as it is about cloud workloads, edge delivery, and user endpoint devices, such as laptop computers and mobile devices. "By understanding workloads—the network traffic they generate, latency requirements, and who is consuming data how and where—the high-fidelity data needed for predictive networking can be identified to support the automatic adaptation of virtual private clouds (VPCs)," says Curt Aubley, risk and financial advisory managing director, and US cyber detect-and-respond leader at business advisory firm Deloitte.

Micro segmentation, load balancers, and traffic shapers are all helping to optimize delivery. "The same high-fidelity data used for network-focused AI can also be used to complement cyber-security teams' consolidated extended detection and response data lakes for security analytics,” Aubley says. AI models are used to detect anomalies, unknown unknowns, and lateral movement. "Using the same high-fidelity data from cloud workloads, networks, and endpoints for different use cases can help ensure confidentiality, integrity, and the availability of applications needed for business or government cyber risk management."

Routers, wireless applications, switches, and various other general networking gear don't typically collect user-specific data. While application-performance monitoring tools do measure user data, they can't correlate results into proactive network actions. "Networks must become user and application aware in order to collect the types of data necessary to build actionable models for the use of AI and predictive technologies," MeLampy says. "If a solution doesn't measure experience per user, it isn't going to be successful.”

Prescriptive analytics is the future.​

The emerging field of neuromorphic computing, based on a chip architecture that's engineered to mimic human brain structure, promises to provide highly effective ML on edge devices. "Predictive network technology is so powerful because of its ability to intake signals and make accurate predictions about equipment failures to optimize maintenance," says Gil Dror, CTO at monitoring technology provider SmartSense. He says that neuromorphic computing will become even more powerful when it moves from predictive to prescriptive analytics, which recommends what should be done to ensure future outcomes.

Neuromorphic computing's chip architecture is geared toward making intelligent decisions on edge devices themselves, Dror says. "The combination of these two technologies will make the field of predictive network technology much more powerful," he says.
Organizations including IBM, Intel, and Qualcomm are developing neuromorphic computing technologies. "Some companies have released neuromorphic computing chips for research-and-development purposes, such as IBM's TrueNorth chip and Intel's Loihi chip," Dror says. These chips aren't yet generally available for commercial use, and it's likely that there will be at least several more years of intense research and development before neuromorphic computing becomes a mainstream technology. "Once it becomes viable, the impact will be massive," he predicts.

He got this bit right:

“the impact will be massive”

My opinion only DYOR
FF

AKIDA BALLISTA