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Nandan likes this........why.........
View attachment 46881
Google Tensor G3: The new chip that gives your Pixel an AI upgrade
Tensor G3 on Pixel 8 and Pixel 8 Pro is more helpful, more efficient and more powerful.blog.google
Indeed ... why?! I bought more because of his like ... and because I can't help but feel something big is about to be announced.Nandan likes this...............why.........
View attachment 46881Google Tensor G3: The new chip that gives your Pixel an AI upgrade
Tensor G3 on Pixel 8 and Pixel 8 Pro is more helpful, more efficient and more powerful.
buena suerte :-)
BOB Bank of Brainchip
today I got you a Green one 
Apropos of nothing in particular, some interesting IP statistics:
Google's patents which utilize NNs - about 200;
Google's patents which cover solid state NN architecture - about zilch.
buena suerte :-)
BOB Bank of Brainchip
I think we have a 'New'! Rocket.!. on board..!!??
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Boab
I wish I could paint like Vincent
My thoughts exactly.I think we have a 'New'! Rocket.. on board..!!??
buena suerte :-)
BOB Bank of Brainchip
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Neuromorphia
fact collector
buena suerte :-)
BOB Bank of Brainchip
rgupta
Regular
Very nice and presentable. Just one question why there is 2024 below merc?
Anyone have a guess here.
Baron Von Ricta
Regular
I think we have a 'New'! Rocket.!. on board..!!??
Especially if you put some petrol on those goolies
Justchilln
Regular
LOI with oculi for 250k units?
Hi Dio,
There was some discussion earlier this month (post #66,770 and #66,790) where it was said that PVDM owns the foundation patent (circa 2008), and this ownership is good until 2028. It was said in the post this foundation patent is owned by Peter, not the company. I don't know if this is true myself but remember thinking this is sensational, as it's another reason why our IP and lead from our competitors is maintained.
Frangipani
Regular
Meet global futurist Dr Bruce McCabe, who is extremely passionate about neuromorphic computing! 
I listened to his podcast with Dr Alexandre Marcireau from WSU’s ICNS (International Centre for Neuromorphic Systems) a couple of weeks ago, but can’t seem to find it posted here, yet, via the search function. No mention of Brainchip, but worthwhile listening to nevertheless.
A link to the podcast transcript is also provided below.
One thing that confuses me, though, in the article & podcast below as well as in other publications, is how the term “analog” is being used here (eg “The Future of AI is analog“).
Is it correct to say in this context it doesn’t refer to the analog vs digital logic circuitry design specifically (such as Akida being fully digital vs. eg Mythic’s analog compute architecture) but rather to the general concept of the extremely power-efficient way our brain processes information (neurons working asynchronously and in parallel etc) which differs fundamentally from the way a digital computer operates on data expressed in binary code?
So is analog here essentially just being used as a synonym for neuromorphic?
www.brucemccabe.com
I’m calling neuromorphic computing the most important computer engineering research in the world, now and through the next 20 years. That’s right, more important than quantum computing (you heard it hear first!). Why? Because everything we want to do in the future of AI, everywhere we want to go long-term, is predicated on transitioning to more fit-for-purpose computer architectures, and the most fit-for-purpose architectures are most certainly those inspired by nature.
CHECK OUT THE PODCAST TRANSCRIPT →
And when it comes to the eye-watering energy demands of AI, the comparative advantages with respect to classical chips for AI-related functions are immense. If things keep going as they are, the ruinous energy demands of crypto-mining will be as nothing compared to the future energy demands from exponential AI. Conclusion: long-term we will HAVE TO transition AI workloads off traditional architectures.
The software side is a particularly innovative space. How to best to receive all that parallel data? How best to process it? How best to navigate the analog/digital interfaces? How can we take full advantage of super-fast and super-local AI decision-making ‘at the edge.’ With so many different possibilities and no baked standards (because, hey, it’s still way too early for those) this field is a boon for creative and talented programmers.
I listened to his podcast with Dr Alexandre Marcireau from WSU’s ICNS (International Centre for Neuromorphic Systems) a couple of weeks ago, but can’t seem to find it posted here, yet, via the search function. No mention of Brainchip, but worthwhile listening to nevertheless.
A link to the podcast transcript is also provided below.
One thing that confuses me, though, in the article & podcast below as well as in other publications, is how the term “analog” is being used here (eg “The Future of AI is analog“).
Is it correct to say in this context it doesn’t refer to the analog vs digital logic circuitry design specifically (such as Akida being fully digital vs. eg Mythic’s analog compute architecture) but rather to the general concept of the extremely power-efficient way our brain processes information (neurons working asynchronously and in parallel etc) which differs fundamentally from the way a digital computer operates on data expressed in binary code?
So is analog here essentially just being used as a synonym for neuromorphic?
Neuromorphic Computing AND THE FUTURE OF A.I. — Bruce McCabe
I’m calling neuromorphic computing the most important computer engineering research in the world, now and through the next 20 years. That’s right, more important than quantum computing (you heard it hear first!). Why? Because everything we want to do in the future of AI, everywhere we want to go lon
www.brucemccabe.com
NEUROMORPHIC COMPUTING AND THE FUTURE OF A.I.
BIO-INSPIRED COMPUTER CHIPS WITH DR ALEX MARCIREAU
I’m calling neuromorphic computing the most important computer engineering research in the world, now and through the next 20 years. That’s right, more important than quantum computing (you heard it hear first!). Why? Because everything we want to do in the future of AI, everywhere we want to go long-term, is predicated on transitioning to more fit-for-purpose computer architectures, and the most fit-for-purpose architectures are most certainly those inspired by nature.
MEETING DR ALEXANDER MARCIREAU
I interviewed the irrepressible Dr Alexandre Marcireau at the International Centre for Neuromorphic Systems (ICNS) at University of Western Sydney. Alex is softly-spoken, laughs easily, and as you would expect is extremely passionate about his field. He generously took me on a tour of his lab to check out his prototypes, including the neuromorphic cameras (see right-hand image above, and the cover image for this article) that are currently circulating in the International Space Station. Afterwards he sat down to answer questions and share his views on the future. He’s very much tuned-in to both the immediate applications and the long-term planetary-scale benefits his technology has to offer. I know you’ll enjoy listening to him!Podcast Episode
www.buzzsprout.com
CHECK OUT THE PODCAST TRANSCRIPT →
THE FUTURE OF AI IS ANALOG (YES, REALLY!)
Alex’s long-term dream is analog AI computing from end to end. Biology is messy, every organism is different, but it WORKS SO WELL! If we want to truly emulate the efficiencies of nature in computers that sense and learn then we MUST go analog not digital. We are slicing off and solving one sensory processing function at a time — a bio-inspired camera here, a bio-inspired microphone there — but the long-term dream is end to end. Of course we’ll still be using fast number-crunching and general purpose computing chips for everything else, so the future of computing more broadly will always be a mix of digital and analog, classical and neuromorphic.And when it comes to the eye-watering energy demands of AI, the comparative advantages with respect to classical chips for AI-related functions are immense. If things keep going as they are, the ruinous energy demands of crypto-mining will be as nothing compared to the future energy demands from exponential AI. Conclusion: long-term we will HAVE TO transition AI workloads off traditional architectures.
ENDLESS REAL-WORLD APPLICATIONS
I loved Alex’s discussion of the different opportunities at the ‘edge’ and the ‘centre’ of AI, and the lab’s real-world applications in deploying high-efficiency low-energy event-based cameras to detect lightning strikes and satellites from the International Space Station, and to track koalas and insects in forests down here on earth. We also had fun talking about using AI to decipher animal languages, such as they are doing at projectceti.org (decoding the communications of sperm whales), and neuromorphic applications to make drones vastly more capable and to enable the bi-directional brain interfaces and neuro-prosthetics I’ve been looking at lately in the future of medicine. So many good things to be transformed.BIOLOGY + COMPUTING = MAGIC
The field crosses many boundaries. Biologists, mathematicians, hardware engineers, physicists, programmers are all working together to create a new industry – what could be more exciting than that? And what about the big gaps in our understanding of various neural systems in nature, and how every improvement in our biological knowledge (such as those auditory, visual, olfactory and learning connectomes that we keep extending for fruit flies and mice) yields new opportunities? I loved Alex’s honesty when he said that the computer scientists were benefiting immensely from the biologists, but perhaps not yet giving back nearly as much. When I see all the ways AI is being used to unravel the biology of animals and plants, I don’t think it will be one-way traffic for long!HARDWARE PLUS WETWARE?
As a side note, I enjoyed hearing Alex’s comments about the use of living cells, aka ‘wetware.’ I can’t remember whether this made it onto the recorded interview or not, but when I asked about growing live neurons as a way to resolve some of the challenges of constructing massively parallel data connectors between all those pixels and all those ‘transistors,’ Alex confirmed that this is something scientists have indeed tried, although their experiments have been hampered because the computer quickly dies … literally! When I link these experiments to the work I’m seeing with brain organoids in regenerative medicine, it really opens up my thinking about the long-term (>30 years?) possibilities. Fascinating!HUGE OPPORTUNITY SPACE
A big takeaway from this interview is how much headroom there is. It’s like looking at the nascent computer chip industry circa 1950. We’ve only started knocking off the little opportunities. As fast as we explore, we identify new ones. The opportunity space is HUGE. If you are investing in computer engineering, or studying it, or building AI systems (who isn’t?) or you happen to manufacture computer chips, take note!The software side is a particularly innovative space. How to best to receive all that parallel data? How best to process it? How best to navigate the analog/digital interfaces? How can we take full advantage of super-fast and super-local AI decision-making ‘at the edge.’ With so many different possibilities and no baked standards (because, hey, it’s still way too early for those) this field is a boon for creative and talented programmers.
FUNDAMENTAL TO THE FUTURE OF AI
When I say more important than quantum, I mean it. Don’t get me wrong, quantum is big. It matters. It tackles big problem spaces, especially in molecular, atomic and sub-atomic simulations that will give us access to new drugs, enzymes and materials, and in complex system optimisation problems. But neuromorphic engineering has the potential to boost the capabilities of EVERY aspect of the biggest technological force of change in our time, artificial intelligence, and further, transitioning to bio-inspired neuromorphic hardware is fundamental to the long-term future of AI if we want it to stay on its current exponential adoption curve without smashing into an energy ceiling.
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Hi Odatt,
That was true until 2015 when it was assigned to the company:
https://worldwide.espacenet.com/patent/search/family/042038652/publication/US8250011B2?q=us8250011
US8250011B2 Autonomous learning dynamic artificial neural computing device and brain inspired system
Perhaps
Regular
Not to forget, US patents only work for the US. There still remains a lot to do with all those pending patents to have a real IP protection, especially for UK, Korea, Taiwan and Europe. Chinese patents will not work anyway.
The whole patent situation leads to defensive tactics Brainchip is forced to use.
The existing patent portfolio needs further updates, on a low level yet. Not the fault of Brainchip, just bureaucracy.
Protecting intellectual property rights (IPR) overseas
Since the rights granted by a U.S.
equanimous
Norse clairvoyant shapeshifter goddess
We are severely underrating BRN's partnership with TCS.
Perhaps
Regular
Tata Elxsi and Tata Consultancy Services are different independent companies, there should be some differentiation:
Tata Elxsi - We thrive on technology disruptions: Tata Elxsi's Manoj Raghavan
Tata Elxsi
www.tataelxsi.com
equanimous
Norse clairvoyant shapeshifter goddess
Tata Elxsi and Tata Consultancy Services are different independent companies, there should be some differentiation:
Tata Elxsi - We thrive on technology disruptions: Tata Elxsi's Manoj Raghavan
Tata Elxsi
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Perhaps
Regular
You've been talking about partnership. This is a presentation from 2019. The actual known partnership is with Tata Elxsi.
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