BRN Discussion Ongoing

[Frangipani]

And calling GPT2 a LLM, isn't quite accurate, in the context, that even GPT3 has more than ten times the parameters.

It is accurate, nevertheless, as all members of Open AI’s GPT family qualify as LLMs.

Size Matters: How Big Is Too Big for An LLM?

Compute-optimal large language models according to the Chinchilla paper

pub.towardsai.net

 

[BrainShit]

BrainChip's #neuromorphic tech in Lower Orbit! Akida's journey unfolds, bringing groundbreaking possibilities on earth and beyond. Stay tuned as the story continues to unfold!

 

[DingoBorat]

Now we know why Peter retired.

Hopkins engineers collaborate with ChatGPT4 to design brain-inspired chips | Hub (jhu.edu)

https://hub.jhu.edu/2024/03/04/chatgpt4-brain-inspired-chips/

HOPKINS ENGINEERS COLLABORATE WITH CHATGPT4 TO DESIGN BRAIN-INSPIRED CHIPS

Systems could power energy-efficient, real-time machine intelligence for next-generation autonomous vehicles, robots 20240303



Through step-by-step prompts to ChatGPT4, starting with mimicking a single biological neuron and then linking more to form a network, they generated a full chip design that could be fabricated.

I actually sent an email to the Company a couple or so months ago (after someone here, posted an article about a company that had developed an enhanced electric motor design, through the use of Generative A.I.) asking whether they were making use of Generative A.I. to advance developments and problem solve (fearing that pride may make them avoid, or not consider this).

The reply, was along the lines of, that they were using whatever "tools" were available.

One of the things that concerns me, is that Generative A.I. when appropriately directed at a specific task, can achieve things, that our brightest minds, may not have actually "thought" of.

AlphaGo, the A.I. developed to play "Go" against human players (considered one of the hardest and oldest board games) came up with game strategies, that humans hadn't "come up with" in the ~4000 years, that the game had been played.
(It has been said, that there are more possible combinations of moves, than atoms in the Universe).

If you look at the way Sora (OpenA.I.) is able to generate visuals from text, it is just incredible.
Imagine future versions, where whole books, by your favourite author, can be reproduced as a film, trimmed to whatever length you like.
Want to watch it again, but with maybe a "darker" theme, just prompt it.
In fact just redoing it, is likely to produce a different result (watch the same film again, but not..)
Turn the Texas Chainsaw Massacre, into a children's fairytale? Easy.. (well maybe ..)

It doesn't surprise me, that these tools can produce these kind of things, even patents can be easily circumnavigated.

Sorry, that's my rain for this early morning..

That's why sealing deals, is so important now.

The Technological Clock hands are spinning faster than ever before.
At some point, the hands will become irrelevant.

 

[DingoBorat]

It is accurate, nevertheless, as all members of Open AI’s GPT family qualify as LLMs.

Size Matters: How Big Is Too Big for An LLM?

Compute-optimal large language models according to the Chinchilla paper

pub.towardsai.net

View attachment 58614

View attachment 58615

Fair enough, but I still disagree

The definition of something, with changing relative context, can not remain constant, in my opinion.

Not even sure if that made sense, but it's past even my bedtime...

 

[Frangipani]

BrainChip's #neuromorphic tech in Lower Orbit! Akida's journey unfolds, bringing groundbreaking possibilities on earth and beyond. Stay tuned as the story continues to unfold!

Hi BrainShit,

I am just reposting the picture in the X post you shared as a thumbnail image, as people who don’t click on the attachment may not even notice that the Brainchip synapse symbol in it is actually stylised as a satellite! Love it!

Whoever thought of this and also of the catchy phrase “The sky is no longer the limit.” (that was already featured in the rocket liftoff images posted shortly after the Transporter-10 rideshare mission launch) deserves an extra round of applause! Awesome!

 

[IloveLamp]

This is hit and run too, Fact Finder just put up a post on HC at 6:02PM this evening post# 72778782 regarding Ericsson 6G zero energy and Akida.

I am having problems with getting links and images up on here at present, hence no charts lately and being time poor means some one else needs to go over to HC and get this very interesting post copied and put up here.

Hi McHale,

I had the same trouble a while ago. Have you tried pressing this icon?

 

[TECH]

BrainChip's #neuromorphic tech in Lower Orbit! Akida's journey unfolds, bringing groundbreaking possibilities on earth and beyond. Stay tuned as the story continues to unfold!

Is that a UAP ? oh no sorry, it's Peters Spiking Neuron, one of Brainchip's Trade Marks....great promo article.

Tech.

 

[IloveLamp]

With no news about NEW IP agreement, is it possible that if a Company did take up
an IP agreement but specify a NDA, could this be happening

I believe that the answer to your question is yes and no.....

It could be happening in the early stages, but non disclosure becomes illegal at a point where the company "knows" it's material in nature..... (my interpretation, dyor) ......

.....a lot of grey in there to delay announcing things to give customers the advantage they so desperately seek.....

I also would not be surprised to learn that the u.s government had a hand in keeping us under wraps just a little longer too considering our military and government links......

Pure speculation,....... but is it impossible?

 

[Andy38]

Hi Tech.
Just wondering if you were up for a catch up as I am currently staying in Manganui (pub) for a few days on way up to the 90 mile next week.
I haven't met anyone who is into Brainchip other than friends and family who I coerced into joining me.
I do like you thoughts and input very positive.

Cheers

Great spot!

 

[supersonic001]

Brainchip (ASX:BRN): All systems go in 2024! | Stocks Down Under

Brainchip (ASX:BRN): All systems go in 2024! 🚀 Marc Kennis spoke with BrainChip (ASX:BRN) CEO Sean Hehir about the company’s technical and commercial progress as well as what’s in store for BRN investors in 2024 😃 #ASX #Brainchip #BRN #neuromorphicprocessing #stocksdownunder

www.linkedin.com

 

[mcm]

Brainchip (ASX:BRN): All systems go in 2024! | Stocks Down Under

Brainchip (ASX:BRN): All systems go in 2024! 🚀 Marc Kennis spoke with BrainChip (ASX:BRN) CEO Sean Hehir about the company’s technical and commercial progress as well as what’s in store for BRN investors in 2024 😃 #ASX #Brainchip #BRN #neuromorphicprocessing #stocksdownunder

www.linkedin.com

View attachment 58622

Anyone have an idea as to who the customer might be that Sean is meeting with in Australia?

 

[AARONASX]

Anyone have an idea as to who the customer might be that Sean is meeting with in Australia?

stab in the dark, and most likely wrong!

Real-Time Monitoring and Security Solutions | Spectur

Integrate monitoring, surveillance and warning with Spectur’s AI and IoT-driven solutions. See how our interoperable smart systems can adapt to your needs.

spectur.com.au

 

[Boab]

Anyone have an idea as to who the customer might be that Sean is meeting with in Australia?

I'm buying him lunch and he's going to tell me everything
No Doubt About it.

 

[mcm]

stab in the dark, and most likely wrong!

Real-Time Monitoring and Security Solutions | Spectur

Integrate monitoring, surveillance and warning with Spectur’s AI and IoT-driven solutions. See how our interoperable smart systems can adapt to your needs.

spectur.com.au

Great stab. If they're not talking to Sean ... they bloody well should be!

 

[JDelekto]

Exactly.
And finally answer your legitimate question whether or not Sean Hehir had indeed told select participants of that by-invitation-only shareholder meeting in Sydney in November that Brainchip had succeeded in developing and running a number of LLMs on Akida 2.0 (as claimed by a poster here on TSE), and if true, why the remaining shareholders (which constitute the vast majority) still have not been informed about this amazing breakthrough via official channels four months later.

I also wonder why none of the other attendees of that Sydney gathering have so far shared with us their recollection of what Sean Hehir actually said.

There is something that I would like to point out about LLMs. LLMs can have large memory requirements, depending upon the model quality, the number of parameters, and quantization. For example, a 70-billion parameter CodeLlama Instruct model (useful for writing computer code) with an 8-bit quantization requires about 70+ GB of RAM. You can still get decent results and save space with a lower quantization but it suffers a loss in quality if the quantization is too low.

That same CodeLlama Instruct model with 4-bit quantization is about 40 GB of RAM. I have a desktop with 64 GB and I can partially offload the model to 24 GB of GPU RAM with an Nvidia GTX 3080 card. The more I get off the CPU and onto the video card (which does all the math faster), the faster my responses to chat. Unless one plays a lot of resource-hungry video games or does work with AI, you will not find most consumers that have desktop PCs with that much memory to run those models.

Now, given a base system with a CPU and enough memory to hold a much smaller model (like 7 billion parameters instead of 70) and quantized down to about 4 bits, you're looking at slightly under 4 GB for a model. I think this is something an Edge device can handle and I think that may be useful depending on the use case.

So I think that technically, Akida could run an LLM if it were properly "massaged" (I believe I ran across a GitHub repo where an individual was attempting to run an LLM with a Spiking Neural Network), but running an LLM the size and quality of GPT-4 or Claude-3 would not be practical.

 

[Stable Genius]

ASX fined $1.05m by ASIC, blames 'system configuration error'

ASX Limited has paid a penalty of $1.05 million following an investigation by the Australian Securities and Investments Commission (ASIC) into its compliance with the market integrity rules. It is the first time ASIC has issued an infringement notice to a market operator.

www.capitalbrief.com

 

[overpup]

Fair enough, but I still disagree

The definition of something, with changing relative context, can not remain constant, in my opinion.

Not even sure if that made sense, but it's past even my bedtime...

That old saying attributed to Einstein: "Insanity is doing the same thing over and over and expecting a different result"...
You can tell Albert never worked with computers!

 

[IloveLamp]

 

[Diogenese]

There is something that I would like to point out about LLMs. LLMs can have large memory requirements, depending upon the model quality, the number of parameters, and quantization. For example, a 70-billion parameter CodeLlama Instruct model (useful for writing computer code) with an 8-bit quantization requires about 70+ GB of RAM. You can still get decent results and save space with a lower quantization but it suffers a loss in quality if the quantization is too low.

That same CodeLlama Instruct model with 4-bit quantization is about 40 GB of RAM. I have a desktop with 64 GB and I can partially offload the model to 24 GB of GPU RAM with an Nvidia GTX 3080 card. The more I get off the CPU and onto the video card (which does all the math faster), the faster my responses to chat. Unless one plays a lot of resource-hungry video games or does work with AI, you will not find most consumers that have desktop PCs with that much memory to run those models.

Now, given a base system with a CPU and enough memory to hold a much smaller model (like 7 billion parameters instead of 70) and quantized down to about 4 bits, you're looking at slightly under 4 GB for a model. I think this is something an Edge device can handle and I think that may be useful depending on the use case.

So I think that technically, Akida could run an LLM if it were properly "massaged" (I believe I ran across a GitHub repo where an individual was attempting to run an LLM with a Spiking Neural Network), but running an LLM the size and quality of GPT-4 or Claude-3 would not be practical.

Hi JD,

That's some impressive technowizardry.

As you know, PvdM's "4 Bits are enough" white paper discusses the advantages of 4-bit quantization.

https://brainchip.com/4-bits-are-enough/
...
4-bit network resolution is not unique. Brainchip pioneered this Machine Learning technology as early as 2015 and, through multiple silicon implementations, tested and delivered a commercial offering to the market. Others have recently published papers on its advantages, such as IBM, Stanford University and MIT.

Akida is based on a neuromorphic, event-based, fully digital design with additional convolutional features. The combination of spiking, event-based neurons, and convolutional functions is unique. It offers many advantages, including on-chip learning, small size, sparsity, and power consumption in the microwatt/milliwatt ranges. The underlying technology is not the usual matrix multiplier, but up to a million digital neurons with either 1, 2, or 4-bit synapses. Akida’s extremely efficient event-based neural processor IP is commercially available as a device (AKD1000) and as an IP offering that can be integrated into partner System on Chips (SoC). The hardware can be configured through the MetaTF software, integrated into TensorFlow layers equating up to 5 million filters, thereby simplifying model development, tuning and optimization through popular development platforms like TensorFlow/Keras and Edge Impulse. There are a fast-growing number of models available through the Akida model zoo and the Brainchip ecosystem.

To dive a little bit deeper into the value of 4-bit, in its 2020 NeurIPS paper IBM described the various pieces that are already present and how they come together. They prove the readiness and the benefit through several experiments simulating 4-bit training for a variety of deep-learning models in computer vision, speech, and natural language processing. The results show a minimal loss of accuracy in the models’ overall performance compared with 16-bit deep learning. The results are also more than seven times faster and seven times more energy efficient. And Boris Murmann, a professor at Stanford who was not involved in the research, calls the results exciting. “This advancement opens the door for training in resource-constrained environments,” he says. It would not necessarily make new applications possible, but it would make existing ones faster and less battery-draining“ by a good margin.”

While I have some understanding of the visual aspect, I find the NLP, covering both speech and text, more perplexing mainly because of the need for context or "attention", but, as usual, Prof Wiki has some useful background:

https://en.wikipedia.org/wiki/Natural_language_processing

 

[McHale]

Hi McHale,

I had the same trouble a while ago. Have you tried pressing this icon?

View attachment 58620

Thanks IloveLamp, I will check it out, hmmm