BRN Discussion Ongoing

[Getupthere]

Nvidia’s GeForce GPUs are selling well, but its AI GPU sales are ridiculous — Ars Technica

Data center revenue is up 279% from the same quarter last year.

apple.news

 

[Schnitzel lover]

BrainChip integrates Akida with Arm Cortex-M85 Processor, Unlocking AI Capabilities for Edge Devices

Explore how this integration revolutionizes the field of artificial intelligence, enabling efficient and powerful AI processing at the edge.

brainchip.com

I suppose the million dollar question is when?

And?

 

[IloveLamp]

MUST listen.......

How is AI Transforming the Automotive Industry?

Patrick Perez, director of Valeo.ai discusses his team’s research and the AI automotive applications they develop to make mobility safer.

www.valeo.com

Valeo on LinkedIn: 🤖✨ 10 years ago, there was no real AI in cars. Today, most new cars are…

🤖✨ 10 years ago, there was no real AI in cars. Today, most new cars are packed with software, a lot of which is AI related. Today, artificial intelligence is…

www.linkedin.com

 

[jtardif999]

Here's the Startup That Could Win Bill Gates' AI Race

It's not ChatGPT.

Though artificial intelligence is not necessarily new, the field entered a notably new environment when ChatGPT launched in November. Something that had long been under the hood of the internet became visible. And it wasn't long before it became exceptionally popular.

That introduction of an AI chatbot that users are free to engage with seemed to kick off the AI race: OpenAI has ChatGPT; Google has Bard and is working on connecting AI solutions across its Google Suite, something that Microsoft is mirroring. Meta is developing its own AI and Nvidia is surging amid explosive demand for the chips that power these models.

And Microsoft co-founder Bill Gates, speaking at a conference May 22, said that the company that wins the so-called AI race will be the company that develops a personal digital assistant.

Personal.ai is doing just that, and going even further.

DON'T MISS: Human Extinction From AI is Possible, Developers Warn

How It Works

Personal.ai is a personal language model system. Rather than being trained by an enormous data set to produce generic knowledge, Personal.ai is trained by each individual user.

Each user's AI profile is trained regularly on what is essentially a constantly growing memory of that user, including his/her thoughts, beliefs, opinions, etc. The goal is to create an artificial extension of each user.

And that individual data collected over time is used to train that specific user's model; data isn't aggregated. And the ownership of the data remains with the users.

Personal.ai's founder and CEO Suman Kanuganti, inspired by the idea of being able to speak, in some form, to someone who has passed away, is intent on creating more than just a digital assistant.

It can handle tasks, read and write emails and even converse with people to sound like you.

He wants to create digital imprints with the goal of keeping humans in the loop and enhancing human presence and communication.

How It's Different From The Competition

Let's start with ChatGPT.

ChatGPT is a large language model, while Peronal.ai is a small language model. The immediate difference is obvious; ChatGPT is much larger with GPT-3 at around 170 billion parameters, while Personal.ai has only 120 million parameters. (Think of parameters like settings that help a model generate content).

The greater size necessitates a much higher operational cost.

And the creators of ChatGPT, OpenAI, are working to create AGI, a hypothetical that could represent artificial intelligence greater than that of humans. Personal.ai, meanwhile, is working on enhancing human connections, rather than taking them out of the loop.

And, while big-name tech giants like Microsoft and Google are starting to roll out AI assistants across their suite of apps, Kanuganti said that his models are designed to work across platforms and that, with Personal.ai, data sharing is not an issue.

"As a startup, I think we have an advantage of trust. We have an advantage of interoperability," he said. "Meta, Google, Microsoft, they will never share the data with each other."

And because Personal.ai is trained on user data, rather than the internet or aggregate data, issues of misinformation and bias that have been par for the course of AI for a while now, are much less of a problem.

And while Kanuganti is keen on the many ways his small AI model can enhance productivity in all corners of his users' lives, his focus remains on safety and ethical responsibility through data ownership and the democratization of these models.

"Otherwise, all your data is going into Apple, Google, etc. I want to be able to say, 'okay, but now take control.' Put all the goodness that you feel in the digital space in one model," he said. "Personal computing should be personal intelligence. If personal computing you can buy from the store, you can buy personal intelligence for yourself as well."

One Stock We Believe Will Win in The AI Race (It's not Nvidia!)

 

[Rach2512]

Global chip market to surge 20% in 2024, says IDC

IDC has raised its forecasts for the global chip market in 2023 and 2024 maintaining the view that 2024 will be a strong growth year.

www.eenewseurope.com

Snippet below

Accelerating demand for AI servers and AI-enabled end point devices will drive more semiconductor content in 2024 to 2026 fueling a new upgrade cycle across enterprises. We expect that by the end of our forecast period, AI silicon will account for almost $200 billion in semiconductor revenues,” he added.

 

[IloveLamp]

All very suspiciously interesting......

https://www.extremetech.com/computing/leaked-intel-lunar-lake-slides-show-a-4-plus4-design-with-embedded-memory

 

[mrgds]

I would very much like TD to do a podcast with our new CTO, so as we all could get a better understanding of the reasons why he choose to join the BRN team and what he feels he will bring to the table.

And yes, ..................... many of you are thinking "a ironed tablecloth" .................... Great minds i guess!

 

[IloveLamp]

Everyone take a quick peek at this before i delete it, don't want @Bravo getting wind of this little nugget god help us all

Cristiano Amon: ‘Next-gen vehicles evolve with you’

Automakers are embracing this global opportunity by partnering with technology leaders to deliver new services and experiences to their customers – and truly drive this evolution forward.

www.qualcomm.com

Qualcomm on LinkedIn: Cristiano Amon: ‘Next-gen vehicles evolve with you’

Did you know that someday your vehicle will be able to make customized recommendations for stops along your travel route, based on your interests and…

www.linkedin.com

 

[Bravo]

Everyone take a quick peek at this before i delete it, don't want @Bravo getting wind of this little nugget god help us all

Cristiano Amon: ‘Next-gen vehicles evolve with you’

Automakers are embracing this global opportunity by partnering with technology leaders to deliver new services and experiences to their customers – and truly drive this evolution forward.

www.qualcomm.com

Qualcomm on LinkedIn: Cristiano Amon: ‘Next-gen vehicles evolve with you’

Did you know that someday your vehicle will be able to make customized recommendations for stops along your travel route, based on your interests and…

www.linkedin.com

View attachment 50264

 

[GDJR69]

View attachment 50259
...

View attachment 50260

Why does he have such a pathological hatred of Brainchip? You have to think he's a manipulator.

 

[7für7]

Why does he have such a pathological hatred of Brainchip? You have to think he's a manipulator.

He want to buy cheap this smart M.F

 

[Bravo]

A couple of nice "likes" from the good people at Qualcomm.

 

[Bravo]

If I had to take a guess, I would say this could be Tony Lewi's daughter, who just happens to be a software engineer at Google.

 

[DingoBorat]

Here's the Startup That Could Win Bill Gates' AI Race

It's not ChatGPT.

Though artificial intelligence is not necessarily new, the field entered a notably new environment when ChatGPT launched in November. Something that had long been under the hood of the internet became visible. And it wasn't long before it became exceptionally popular.

That introduction of an AI chatbot that users are free to engage with seemed to kick off the AI race: OpenAI has ChatGPT; Google has Bard and is working on connecting AI solutions across its Google Suite, something that Microsoft is mirroring. Meta is developing its own AI and Nvidia is surging amid explosive demand for the chips that power these models.

And Microsoft co-founder Bill Gates, speaking at a conference May 22, said that the company that wins the so-called AI race will be the company that develops a personal digital assistant.

Personal.ai is doing just that, and going even further.

DON'T MISS: Human Extinction From AI is Possible, Developers Warn

How It Works

Personal.ai is a personal language model system. Rather than being trained by an enormous data set to produce generic knowledge, Personal.ai is trained by each individual user.

Each user's AI profile is trained regularly on what is essentially a constantly growing memory of that user, including his/her thoughts, beliefs, opinions, etc. The goal is to create an artificial extension of each user.

And that individual data collected over time is used to train that specific user's model; data isn't aggregated. And the ownership of the data remains with the users.

Personal.ai's founder and CEO Suman Kanuganti, inspired by the idea of being able to speak, in some form, to someone who has passed away, is intent on creating more than just a digital assistant.

It can handle tasks, read and write emails and even converse with people to sound like you.

He wants to create digital imprints with the goal of keeping humans in the loop and enhancing human presence and communication.

How It's Different From The Competition

Let's start with ChatGPT.

ChatGPT is a large language model, while Peronal.ai is a small language model. The immediate difference is obvious; ChatGPT is much larger with GPT-3 at around 170 billion parameters, while Personal.ai has only 120 million parameters. (Think of parameters like settings that help a model generate content).

The greater size necessitates a much higher operational cost.

And the creators of ChatGPT, OpenAI, are working to create AGI, a hypothetical that could represent artificial intelligence greater than that of humans. Personal.ai, meanwhile, is working on enhancing human connections, rather than taking them out of the loop.

And, while big-name tech giants like Microsoft and Google are starting to roll out AI assistants across their suite of apps, Kanuganti said that his models are designed to work across platforms and that, with Personal.ai, data sharing is not an issue.

"As a startup, I think we have an advantage of trust. We have an advantage of interoperability," he said. "Meta, Google, Microsoft, they will never share the data with each other."

And because Personal.ai is trained on user data, rather than the internet or aggregate data, issues of misinformation and bias that have been par for the course of AI for a while now, are much less of a problem.

And while Kanuganti is keen on the many ways his small AI model can enhance productivity in all corners of his users' lives, his focus remains on safety and ethical responsibility through data ownership and the democratization of these models.

"Otherwise, all your data is going into Apple, Google, etc. I want to be able to say, 'okay, but now take control.' Put all the goodness that you feel in the digital space in one model," he said. "Personal computing should be personal intelligence. If personal computing you can buy from the store, you can buy personal intelligence for yourself as well."

One Stock We Believe Will Win in The AI Race (It's not Nvidia!)

This sounds like a brilliant application of what AKIDA 3, has been hinted at being aimed at on here (SLM Small Language models, think it may have been Tech or FactFinder?)

But..

(A) AKIDA 3 wouldn't even be available as a development environment yet?..

And..

(B) There is no mention of anything here, relating to neuromorphic technology, or us..

Unless I'm missing something ..

 

[DingoBorat]

Why does he have such a pathological hatred of Brainchip? You have to think he's a manipulator.

I honestly don't know..

But on "another" note, you may find this interesting..

11 Small Breeds That Love Being Lap Dogs

There’s a pup out there for just about every personality, and these lovable lap dogs are perfect for anyone who’s looking to add a snuggle bug to their family! Whether you’d prefer a social butterfly by day and a couch potato by night, a canine who’s content to live in an apartment, or a...

iheartdogs.com

 

[jtardif999]

This sounds like a brilliant application of what AKIDA 3, has been hinted at being aimed at on here (SML Small Language models, think it may have been Tech or FactFinder?)

But..

(A) AKIDA 3 wouldn't even be available as a development environment yet?..

And..

(B) There is no mention of anything here, relating to neuromorphic technology, or us..

Unless I'm missing something ..

Nope, that’s right - but this is definitely future potential ‘for sure’ as Sean would say

 

[buena suerte :-)]

I would very much like TD to do a podcast with our new CTO, so as we all could get a better understanding of the reasons why he choose to join the BRN team and what he feels he will bring to the table.

And yes, ..................... many of you are thinking "a ironed tablecloth" .................... Great minds i guess!

I would say that Mr Lewis is a fantastic new appointment to the BoD....

He will no doubt be a great inspiration and a very supportive and influencial addition to assist Mr Hehir in every day decisions and the mapping of BRN's future moving forward.

Why did he come to us? .... He very much liked what he saw!

I have met Peter on many occasions and he really is an amazing down to earth and humble man,always a pleasure to chat to not just BRN but life in general.

I wish him all the very best on stepping aside to the 'back bench' now an observer and advisor (Semi retirement!) And spending more time with his wife, family and friends

Handing over the reigns would have been a very hard decision for him to make but his thoughts would have been that this is the best decision for him and Brainchip moving forward.

....................................."2024 The moving year"........................................

Cheers Chippers

 

[DJM263]

Why does he have such a pathological hatred of Brainchip? You have to think he's a manipulator.

"Click Bait", don't read his sh%t as all that happens he gets paid more for the more garbage he produces and the more hits he achieves, the more his encouraged to produce more crap....

Unfortunately our press has degenerated into a cesspool of sensationalising in order to get the hits up while not letting the truth interfere with a good spin...

Accountability would be the key

 

[7für7]

I need a price sensitive Ann till end of this week. more than a coffee in the morning. Or no maybe not… Relaxing in the chair, exhaling with a content expression, I would prefer to enjoy my coffee in the morning. Soooo… if you read this Mr. Sean Hehir… thanks in advance

 

[Fact Finder]

Hi All​

Instead of being concerned about the ravings of a would be journalist who would not know sequential data analysis if it tapped him on the shoulder shareholders need to dig deeply into the consequences for the future value of their investment as a result of the invention of TENNS. There is no better explanation than the following which appears on the Wevolver website. I have highlighted in Orange text some very significant parts and suggest that you consider in parallel with this article the stated purpose of Tata Elxsi in partnering with Brainchip. While like you I do not know if Tata Elxsi was one of the companies that received early access to AKIDA 2.0 and hence TENNS but the following paragraph taken from the announcement certainly suggests this to be the case: ​

"The combination of our user-centric design expertise with leading-edge technologies is key to helping enterprises reimagine their products and services to improve operational efficiency, reduce costs and deliver new services to their customers,” said Manoj Raghavan, CEO and MD at Tata Elxsi. “This cannot be possible without our global ecosystem of partners. By partnering with Brainchip and implementing Akida technology into medical and industrial solutions, we are able to deliver innovative solutions at a faster time to market than otherwise possible."​

My opinion only so DYOR
Fact Finder

AKIDA BALLISTA

The huge potential of sequential data analysis at the edge​

BrainChip Team
fromBrainChip
09 Nov, 2023

FOLLOW

How BrainChip's Akida technology is revolutionizing time series data analysis.​

Artificial Intelligence
- Big Data
- Neural Network
This article is based on an article titled, BrainChip Sees AI Gold in Sequential Data Analysis at the Edge, from the Cambrian AI website.
Amidst the whirlwind of excitement surrounding Large Language Model (LLM) generative AI, it's easy to lose sight of other AI domains that have seemingly dissolved into the shadows cast by ChatGPT's prominence. One underappreciated domain is the analysis of sequential data streams, which includes monitoring fluctuating stock prices and interpreting video feeds.
BrainChip has identified this niche — the adept handling of sequential data — as a pivotal niche for deploying its Akida technology. Akida stands out for its prowess in Event-Based Neuromorphic computing, adeptly handling various neural network architectures like Vision Transformers (ViT), Convolutional Neural Networks (CNN), Temporal Evolution Network (TENN), and Recurrent Neural Networks (RNN).
This article explores the need for efficient edge AI for time-series data.

Sequential data analysis​

Sequential analysis refers to the process of analyzing and extracting insights from data that is collected and organized in chronological order. This data type typically involves measurements or observations taken at regular intervals over time. Time-series analysis techniques aim to understand data patterns, trends, and dependencies and make predictions or forecasts based on historical patterns.
A few use cases of sequential data analysis include:
1. Financial Analysis: Time-series analysis is extensively used in finance to study stock market trends, analyze economic indicators, and forecast future market conditions. It helps model asset prices, trends, risk assessment, and portfolio optimization.
2. Demand Forecasting: Sequential analysis is crucial in demand forecasting for retail, supply chain management, and manufacturing industries. By analyzing historical sales or demand data, businesses can predict future demand patterns and optimize their production, inventory, and supply chain accordingly.
3. Predictive Maintenance: Predictive maintenance can monitor equipment and machinery in real-time. Analyzing sensor data and historical patterns can help detect anomalies and predict potential failures, enabling proactive maintenance and minimizing downtime.
As well as applications in Energy Consumption Analysis and IoT Sensor Data Analysis.
The market size for applying AI in time-series data analysis is continually growing as organizations recognize the value of extracting insights and making accurate predictions from temporal data. While specific market size figures for this realm are not readily available, the broader AI market, including applications in time-series analysis, is expected to grow substantially.

According to a report by Grand View Research, the global AI market size was valued at USD 62.35 billion in 2020 and is projected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028.

This growth encompasses various AI applications, including time-series analysis, across multiple industries.

Introducing the Dimension of Time to Neural Networks​

Traditional CNNs have been around for 30+ years and combine multiple hidden layers trained in a supervised manner. These are sequential and hence referred to as feed-forward neural networks. Bi-directional networks, also called RNNs, invented at the turn of the century, added capability for more complex learning, such as language modelling. But for applications to time series, a machine learning engineer needed a combination of CNNs and a temporal network for spatial-temporal analysis. While academia developed networks that did temporal convolution, they have yet to be power efficient or easy to train to make it to the far Edge.

Temporal Event-based Neural Networks​

Temporal Artificial Neural Networks (TENNs). This approach simplifies training and reduces model size while maintaining accuracy, resulting in improved performance and efficiency for Edge AI devices.

Using TENNs for data analysis offers several advantages, such as the ability to learn the temporal structure of the data, which is crucial for tasks such as forecasting and anomaly detection.

TENNs can make predictions for future time steps, and they are capable of learning from large datasets of time series data.

Overall, these features make TENNs a valuable tool for data analysis in various domains.

Traditional time series analysis applies to all kinds of signals. Image credit: Cambrian AI

Beyond traditional Edge time series applications, BrainChip suggests that TENNs could minimize or eliminate the need for DSP filtering of raw audio signals and vital signs in health monitoring. Thus, offering compact solutions for wearable, hearing, and implantable devices with minimal power consumption is a significant advance for preventative healthcare.
Additionally, TENNs also excel at treating streaming inputs as a time series of frames, performing 3D convolutions composed of a temporal convolution on the time axis and a spatial convolution on the XY axis. This is efficiently achieved to improve the detection of higher-resolution video objects in low-power scenarios.

Treating streaming inputs as a 3D time series of frames with a highly efficient 3D convolution. Image credit: Cambrian AI.

TENNs offer developers the flexibility to configure them in either buffered temporal convolution or recurrent modes, enabling them to adapt the network to their specific application requirements.

Furthermore, TENNs can be efficiently trained on parallel hardware, such as GPUs and TPUs in the cloud, similar to convolutional networks, while retaining the compactness of RNNs for efficient inference at the edge.

This approach helps to minimize the exponentially growing cost of training, which is a constant concern.

Akida 2nd Generation Processor and TENNs​

The BrainChip Akida processor is a digital portable processor IP inspired by the energy-efficient functionality of the human brain. Unlike traditional neuromorphic approaches, which are analog, it utilizes fully digital technology and offers a range of capabilities, including image classification, semantic segmentation, odor recognition, and time-series analysis. It supports various neural network architectures, including TENNs.
The Akida processor utilizes highly parallel event-based neural processing cores, merging neuromorphic processing with native support for traditional convolutional capabilities and functions, along with hardware support for TENN networks. Its neuromorphic processing cores communicate using sparse, asynchronous events, making it ideal for efficient time-series data analysis and managing high-speed, asynchronous, and continuous data streams.

The TENN operation may combine transform from convolutions in training to recurrence operation for inference. Image credit: Cambrian AI.
BrainChip’s Akida allows the analysis of vision, video, and three-dimensional data as a time series, improving video object detection. Akida's support for spatial-temporal convolutions enhances speed and reduces energy consumption. This ability is possible because the processor features a high-speed, low-power digital design optimized for edge computing applications, offering real-time processing and low-latency analysis. It can handle structured and unstructured data, learning and recognizing patterns from streaming data, making it suitable for time-series data analysis.

The foundations of the Akida architecture. Image credit: Cambrian AI.
Additionally, Akida includes a traditional CNN accelerator, as well as TENN and ViT logic, providing a comprehensive solution for sequential processing. The Akida processor is particularly effective for real-time data classification, anomaly detection, and predictive analytics. Consequently, the second-generation Akida processor, with TENN support, extends its efficiency and accelerated hardware solutions to multi-dimensional applications like video object detection and vision using event-based processing paradigms.

Conclusion​

The rapid expansion of the AI field has primarily focused on image processing and LLMs, leaving a significant gap in the development of sequential data analysis. BrainChip's Akida technology, featuring TENNs, simplifies training, reduce model size, and enhance performance and efficiency in time series data analysis.

This innovative approach can be applied in different industries, including finance, demand forecasting, predictive maintenance, energy consumption analysis, and IoT data analysis, meeting the growing demand for effective AI solutions in these domains.

The Akida processor extends its efficiency to multi-dimensional applications improving real-time data analysis, predictive analytics, and video object detection for event-based processing.