Fact Finder
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And this is what he works on at AMD:
No opinion just a fact so DYOR
FF
AKIDA BALLISTA
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
No opinion just a fact so DYOR
FF
AKIDA BALLISTA
Fact Finder
Top 20
AMD is making much of CES 2023:
AMD CEO, Dr. Lisa Su, To Host Opening CES 2023 Keynote on 4th January: RDNA 3, Zen 4 X3D & More To Be Unveiled
AMD has announced that its CEO Dr. Lisa Su would be live and presenting the opening keynote for CES 2023 on the 4th of January.
My opinion only DYOR
FF
AKIDA BALLISTA
jtardif999
Regular
and I thought you were going to say ‘… there’s an ass worth blowing it up’
jtardif999
Regular
4 cm per second
Deadpool
Did someone say KFC
Warning up ramping proceeding.
As another year ends, I was left wondering what the future has installed for us. As renowned futurist Ray Kurzweil predicted some 15 years ago that the singularity—the time when the abilities of a computer overtake the abilities of the human brain, will occur in about 2045.
As I have pondered, what may come about from the very secretive early access partner program over the last couple of years in which countless numbers of engineers working diligently in secure labs with BRN SNN architecture and incorporating it into their own inhouse designs, the resulted products must be so technology advanced from any product that has come before it, and next week some of these companies will be showcasing their wares at CES23 for the first time for the world to admire. None of this would be possible without their collaboration with BRN and ultimately the vision and brilliance of one man.
The future as I see it, as Akida becomes more readily available to the masses, from the idea of one man to the many thousands of future computer science students and engineers with curious minds and imaginations, what this new architecture will reveal, the mind boggles as to what may be achieved from beneficial Ai and what is installed for mankind, I would surmise the current Akida based SNN technology and the resulted beneficial Ai component will evolve exponentially from now on, but what happens when cortical columns and maybe even quantum computing are introduced into the mix.
Maybe the singularity might be coming a lot sooner than 2045.
I’m also thinking that 7th January 2023 as per Greek Christmas day may just be the tipping point for BRN and shareholder fulfilment.
Anyway, Toodals all, I must be off to the naval architects to design my new super yacht BRAiN STORM tm
and will be setting a coarse to somewhere in the Med in the not too distant future, all are welcome aboard.
Happy new year everybody.
As another year ends, I was left wondering what the future has installed for us. As renowned futurist Ray Kurzweil predicted some 15 years ago that the singularity—the time when the abilities of a computer overtake the abilities of the human brain, will occur in about 2045.
As I have pondered, what may come about from the very secretive early access partner program over the last couple of years in which countless numbers of engineers working diligently in secure labs with BRN SNN architecture and incorporating it into their own inhouse designs, the resulted products must be so technology advanced from any product that has come before it, and next week some of these companies will be showcasing their wares at CES23 for the first time for the world to admire. None of this would be possible without their collaboration with BRN and ultimately the vision and brilliance of one man.
The future as I see it, as Akida becomes more readily available to the masses, from the idea of one man to the many thousands of future computer science students and engineers with curious minds and imaginations, what this new architecture will reveal, the mind boggles as to what may be achieved from beneficial Ai and what is installed for mankind, I would surmise the current Akida based SNN technology and the resulted beneficial Ai component will evolve exponentially from now on, but what happens when cortical columns and maybe even quantum computing are introduced into the mix.
Maybe the singularity might be coming a lot sooner than 2045.
I’m also thinking that 7th January 2023 as per Greek Christmas day may just be the tipping point for BRN and shareholder fulfilment.
Anyway, Toodals all, I must be off to the naval architects to design my new super yacht BRAiN STORM tm
and will be setting a coarse to somewhere in the Med in the not too distant future, all are welcome aboard.Happy new year everybody.
Fact Finder
Top 20
AKIDA outshines all the rest and has 1.2 million neurons but are the unsung heroes the 10 billion synapses. The following recently exposed research suggests they might just be:
Published on30 December 2022
Washington [US]Dec. 29 (ANI): From the time you read your Wi-Fi password off the coffee shop’s menu board to the time you can go back to your laptop to enter it, you have to remember it.
If you’re wondering how your brain does this, you’re asking a question about working memory, which researchers have been trying to explain for decades. Now neuroscientists at MIT have published an important new insight into how it works.
In a study in PLOS Computational Biology, scientists at the Picower Institute for Learning and Memory compared measurements of the activity of brain cells in animals performing a working memory task with the output of various computer models representing the brain’s mechanisms for retaining information. Two theories of the underlying mechanism.
The results strongly support the new idea that networks of neurons store information by making transient changes to the pattern of their connections, or synapses, and contradict the traditional alternative that memory is created by neurons kept continuously active (as in idle engine)).
While both models allow information to be kept in mind, only the version that allows synapses to change connections instantaneously (“short-term synaptic plasticity”) produces patterns of neural activity that mimic what is actually observed in real brains at work. Senior author Earl K. Miller acknowledges that the idea that brain cells maintain memory by always being “on” may be simpler, but it doesn’t represent what nature is doing and doesn’t produce intermittent thinking Neural activity that may generate complex thinking flexibility supported by short-term synaptic plasticity.
“You need these mechanisms to give working memory activity the freedom it needs to be flexible,” said Miller, a professor of neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS). As simple as a light switch. But working memory is as complex and dynamic as our thoughts.”
Co-lead author Leo Kozachkov, who received his Ph.D. at MIT in November for theoretical modeling work that included this study, said matching computer models to real-world data was critical.
“Most people think that working memory ‘happens’ in neurons—that persistent neural activity produces persistent thoughts. However, this idea has recently come under scrutiny because it doesn’t quite line up with the data,” said Kozachkov, co-supervisor Say. Written by co-senior author Jean-Jacques Slotine, BCS and professor of mechanical engineering. “Using artificial neural networks with short-term synaptic plasticity, we show that synaptic activity (rather than neural activity) can underlie working memory. An important takeaway from our paper: These neural network models of ‘plasticity’ are better suited to the brain – like Quantitatively the same, with an additional functional advantage in terms of robustness.”
Models match nature
Together with co-lead author John Tauber, an MIT graduate student, Kozachkov’s goal was not just to determine how working memory information is retained, but to elucidate how nature actually does it. That means starting with “ground truth” measurements of the electrical “spike” activity of hundreds of neurons in the animal’s prefrontal cortex as it plays a working memory game. In each of many rounds, the animal sees an image, which then disappears. After a second, it sees two pictures including the original, and has to view the original to get a little reward. The critical moment is that second in the middle, known as the “latency period,” during which the image must be kept in mind before the test.
The team consistently observed what Miller’s lab had observed many times before: neurons spiking profusely when they saw the original image, spiking only intermittently during the delay, and then again when they had to recall the image during testing. Spikes occur (these dynamics are caused by the interplay of beta and gamma frequency brain rhythms). In other words, spikes are strong when information must be initially stored and must be recalled, but only when it must be maintained. Spikes are not continuous during latency.
In addition, the team trained a software “decoder” to read out working memory information from measures of spike activity. They are very accurate when the spikes are high, but not when the spikes are low, such as during lag periods. This suggests that the spikes do not represent information during the delay. But this raises a key question: If the spikes don’t remember information, what can?
Researchers, including Oxford’s Mark Stokes, have proposed that changes in the relative strength, or “weight,” of synapses could act as a proxy for storing information. The MIT team tested this idea by computationally modeling neural networks containing two versions of each major theory. Machine learning networks were trained to perform the same working memory tasks as real animals and output neural activity that could also be interpreted by the decoder.
The upshot is that computational networks that allow short-term synaptic plasticity to encode information spike when the actual brain does, and don’t when it doesn’t. Networks that spik continuously as a method of maintaining memory spiked all the time, including when the natural brain wasn’t spiking. The decoder results showed a drop in accuracy during delays in models of synaptic plasticity, but remained unusually high in models of sustained spiking.
In another layer of analysis, the team created a decoder to read information from synaptic weights. They found that during delays, synapses represented working memory information, whereas spikes did not.
Of the two versions of the model with short-term synaptic plasticity, the most realistic one, called “PS-Hebb,” has a negative feedback loop that keeps the neural network stable and robust, Kozachkov said.
The role of working memory
In addition to better matching nature, models of synaptic plasticity confer other benefits that may be important to real brains. One is that the plasticity model retains information in its synaptic weights even after as many as half of the artificial neurons have been “ablated.” The persistent activity model collapsed after losing only 10-20% of synapses. And, Miller adds, it takes less energy to boost energy occasionally than to boost energy consistently.
Also, Miller said, quick spikes rather than sustained spikes make room in time to store multiple items in memory. Research shows that people can hold up to four different things in working memory. Miller’s lab plans to conduct new experiments to determine whether models with intermittent spikes and synaptic weight-based information storage perform better with real neural data when animals have to remember multiple things rather than just a single image. match."
My opinion only so DYOR
FF
AKIDA BALLISTA
Science News | Keeping information in mind may mean storing it across synapses, study suggests
- 5 minute read
Published on30 December 2022
Author
TeamAskbyGeeks
Washington [US]Dec. 29 (ANI): From the time you read your Wi-Fi password off the coffee shop’s menu board to the time you can go back to your laptop to enter it, you have to remember it.
If you’re wondering how your brain does this, you’re asking a question about working memory, which researchers have been trying to explain for decades. Now neuroscientists at MIT have published an important new insight into how it works.
In a study in PLOS Computational Biology, scientists at the Picower Institute for Learning and Memory compared measurements of the activity of brain cells in animals performing a working memory task with the output of various computer models representing the brain’s mechanisms for retaining information. Two theories of the underlying mechanism.
The results strongly support the new idea that networks of neurons store information by making transient changes to the pattern of their connections, or synapses, and contradict the traditional alternative that memory is created by neurons kept continuously active (as in idle engine)).
While both models allow information to be kept in mind, only the version that allows synapses to change connections instantaneously (“short-term synaptic plasticity”) produces patterns of neural activity that mimic what is actually observed in real brains at work. Senior author Earl K. Miller acknowledges that the idea that brain cells maintain memory by always being “on” may be simpler, but it doesn’t represent what nature is doing and doesn’t produce intermittent thinking Neural activity that may generate complex thinking flexibility supported by short-term synaptic plasticity.
“You need these mechanisms to give working memory activity the freedom it needs to be flexible,” said Miller, a professor of neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS). As simple as a light switch. But working memory is as complex and dynamic as our thoughts.”
Co-lead author Leo Kozachkov, who received his Ph.D. at MIT in November for theoretical modeling work that included this study, said matching computer models to real-world data was critical.
“Most people think that working memory ‘happens’ in neurons—that persistent neural activity produces persistent thoughts. However, this idea has recently come under scrutiny because it doesn’t quite line up with the data,” said Kozachkov, co-supervisor Say. Written by co-senior author Jean-Jacques Slotine, BCS and professor of mechanical engineering. “Using artificial neural networks with short-term synaptic plasticity, we show that synaptic activity (rather than neural activity) can underlie working memory. An important takeaway from our paper: These neural network models of ‘plasticity’ are better suited to the brain – like Quantitatively the same, with an additional functional advantage in terms of robustness.”
Models match nature
Together with co-lead author John Tauber, an MIT graduate student, Kozachkov’s goal was not just to determine how working memory information is retained, but to elucidate how nature actually does it. That means starting with “ground truth” measurements of the electrical “spike” activity of hundreds of neurons in the animal’s prefrontal cortex as it plays a working memory game. In each of many rounds, the animal sees an image, which then disappears. After a second, it sees two pictures including the original, and has to view the original to get a little reward. The critical moment is that second in the middle, known as the “latency period,” during which the image must be kept in mind before the test.
The team consistently observed what Miller’s lab had observed many times before: neurons spiking profusely when they saw the original image, spiking only intermittently during the delay, and then again when they had to recall the image during testing. Spikes occur (these dynamics are caused by the interplay of beta and gamma frequency brain rhythms). In other words, spikes are strong when information must be initially stored and must be recalled, but only when it must be maintained. Spikes are not continuous during latency.
In addition, the team trained a software “decoder” to read out working memory information from measures of spike activity. They are very accurate when the spikes are high, but not when the spikes are low, such as during lag periods. This suggests that the spikes do not represent information during the delay. But this raises a key question: If the spikes don’t remember information, what can?
Researchers, including Oxford’s Mark Stokes, have proposed that changes in the relative strength, or “weight,” of synapses could act as a proxy for storing information. The MIT team tested this idea by computationally modeling neural networks containing two versions of each major theory. Machine learning networks were trained to perform the same working memory tasks as real animals and output neural activity that could also be interpreted by the decoder.
The upshot is that computational networks that allow short-term synaptic plasticity to encode information spike when the actual brain does, and don’t when it doesn’t. Networks that spik continuously as a method of maintaining memory spiked all the time, including when the natural brain wasn’t spiking. The decoder results showed a drop in accuracy during delays in models of synaptic plasticity, but remained unusually high in models of sustained spiking.
In another layer of analysis, the team created a decoder to read information from synaptic weights. They found that during delays, synapses represented working memory information, whereas spikes did not.
Of the two versions of the model with short-term synaptic plasticity, the most realistic one, called “PS-Hebb,” has a negative feedback loop that keeps the neural network stable and robust, Kozachkov said.
The role of working memory
In addition to better matching nature, models of synaptic plasticity confer other benefits that may be important to real brains. One is that the plasticity model retains information in its synaptic weights even after as many as half of the artificial neurons have been “ablated.” The persistent activity model collapsed after losing only 10-20% of synapses. And, Miller adds, it takes less energy to boost energy occasionally than to boost energy consistently.
Also, Miller said, quick spikes rather than sustained spikes make room in time to store multiple items in memory. Research shows that people can hold up to four different things in working memory. Miller’s lab plans to conduct new experiments to determine whether models with intermittent spikes and synaptic weight-based information storage perform better with real neural data when animals have to remember multiple things rather than just a single image. match."
My opinion only so DYOR
FF
AKIDA BALLISTA
stuart888
Regular
Ending the year up nicely today in the USA, when the market was down.
I really don't focus on the stock price, since Brainchip should do fantastically as the implementation kicks in. Dollar cost averaging seems like a great approach to accumulate. I like to focus on 36 months out.
Too many dots here with fantastic, much needed, low power AI smarts technology! Nothing certain, but very confident from here in Florida USA! Thanks to all the dot collectors!
Boab
I wish I could paint like Vincent
Hi FF,AKIDA outshines all the rest and has 1.2 million neurons but are the unsung heroes the 10 billion synapses. The following recently exposed research suggests they might just be:
Science News | Keeping information in mind may mean storing it across synapses, study suggests
- 5 minute read
Published on30 December 2022
Author
TeamAskbyGeeks
Washington [US]Dec. 29 (ANI): From the time you read your Wi-Fi password off the coffee shop’s menu board to the time you can go back to your laptop to enter it, you have to remember it.
If you’re wondering how your brain does this, you’re asking a question about working memory, which researchers have been trying to explain for decades. Now neuroscientists at MIT have published an important new insight into how it works.
In a study in PLOS Computational Biology, scientists at the Picower Institute for Learning and Memory compared measurements of the activity of brain cells in animals performing a working memory task with the output of various computer models representing the brain’s mechanisms for retaining information. Two theories of the underlying mechanism.
The results strongly support the new idea that networks of neurons store information by making transient changes to the pattern of their connections, or synapses, and contradict the traditional alternative that memory is created by neurons kept continuously active (as in idle engine)).
While both models allow information to be kept in mind, only the version that allows synapses to change connections instantaneously (“short-term synaptic plasticity”) produces patterns of neural activity that mimic what is actually observed in real brains at work. Senior author Earl K. Miller acknowledges that the idea that brain cells maintain memory by always being “on” may be simpler, but it doesn’t represent what nature is doing and doesn’t produce intermittent thinking Neural activity that may generate complex thinking flexibility supported by short-term synaptic plasticity.
“You need these mechanisms to give working memory activity the freedom it needs to be flexible,” said Miller, a professor of neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS). As simple as a light switch. But working memory is as complex and dynamic as our thoughts.”
Co-lead author Leo Kozachkov, who received his Ph.D. at MIT in November for theoretical modeling work that included this study, said matching computer models to real-world data was critical.
“Most people think that working memory ‘happens’ in neurons—that persistent neural activity produces persistent thoughts. However, this idea has recently come under scrutiny because it doesn’t quite line up with the data,” said Kozachkov, co-supervisor Say. Written by co-senior author Jean-Jacques Slotine, BCS and professor of mechanical engineering. “Using artificial neural networks with short-term synaptic plasticity, we show that synaptic activity (rather than neural activity) can underlie working memory. An important takeaway from our paper: These neural network models of ‘plasticity’ are better suited to the brain – like Quantitatively the same, with an additional functional advantage in terms of robustness.”
Models match nature
Together with co-lead author John Tauber, an MIT graduate student, Kozachkov’s goal was not just to determine how working memory information is retained, but to elucidate how nature actually does it. That means starting with “ground truth” measurements of the electrical “spike” activity of hundreds of neurons in the animal’s prefrontal cortex as it plays a working memory game. In each of many rounds, the animal sees an image, which then disappears. After a second, it sees two pictures including the original, and has to view the original to get a little reward. The critical moment is that second in the middle, known as the “latency period,” during which the image must be kept in mind before the test.
The team consistently observed what Miller’s lab had observed many times before: neurons spiking profusely when they saw the original image, spiking only intermittently during the delay, and then again when they had to recall the image during testing. Spikes occur (these dynamics are caused by the interplay of beta and gamma frequency brain rhythms). In other words, spikes are strong when information must be initially stored and must be recalled, but only when it must be maintained. Spikes are not continuous during latency.
In addition, the team trained a software “decoder” to read out working memory information from measures of spike activity. They are very accurate when the spikes are high, but not when the spikes are low, such as during lag periods. This suggests that the spikes do not represent information during the delay. But this raises a key question: If the spikes don’t remember information, what can?
Researchers, including Oxford’s Mark Stokes, have proposed that changes in the relative strength, or “weight,” of synapses could act as a proxy for storing information. The MIT team tested this idea by computationally modeling neural networks containing two versions of each major theory. Machine learning networks were trained to perform the same working memory tasks as real animals and output neural activity that could also be interpreted by the decoder.
The upshot is that computational networks that allow short-term synaptic plasticity to encode information spike when the actual brain does, and don’t when it doesn’t. Networks that spik continuously as a method of maintaining memory spiked all the time, including when the natural brain wasn’t spiking. The decoder results showed a drop in accuracy during delays in models of synaptic plasticity, but remained unusually high in models of sustained spiking.
In another layer of analysis, the team created a decoder to read information from synaptic weights. They found that during delays, synapses represented working memory information, whereas spikes did not.
Of the two versions of the model with short-term synaptic plasticity, the most realistic one, called “PS-Hebb,” has a negative feedback loop that keeps the neural network stable and robust, Kozachkov said.
The role of working memory
In addition to better matching nature, models of synaptic plasticity confer other benefits that may be important to real brains. One is that the plasticity model retains information in its synaptic weights even after as many as half of the artificial neurons have been “ablated.” The persistent activity model collapsed after losing only 10-20% of synapses. And, Miller adds, it takes less energy to boost energy occasionally than to boost energy consistently.
Also, Miller said, quick spikes rather than sustained spikes make room in time to store multiple items in memory. Research shows that people can hold up to four different things in working memory. Miller’s lab plans to conduct new experiments to determine whether models with intermittent spikes and synaptic weight-based information storage perform better with real neural data when animals have to remember multiple things rather than just a single image. match."
My opinion only so DYOR
FF
AKIDA BALLISTA
not sure if the acquisition of the JAST learning rule patent earlier this year would help in this regard?
Evermont
Stealth Mode
Great pick up @jtardif999 Do we have more than one?
Maybe this relates to the different levels of the enablement program.Also note the reference to Akida 1.0 in the technology section of the website. Is this new?
Akida Foundations
Immerse yourself in the powerful new world of the akida™ neuromorphic processor IP! The future of AI is NOW!
brainchip.com
Laguna Hills, Calif. – December 29, 2022 –BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that it will be joining partners Prophesee, Socionext, and VVDN January 5-8 at CES to showcase compelling solutions on constrained edge devices, featuring its Akida™ processors. Akida processors simplify development by supporting today’s mainstream network models and workflows while being future-proofed for next-generation edge AI solutions.
Mccabe84
Regular
Im not tech savvy and still learning but it has ultra low power and NN. https://www.hackster.io/usavswapnil/location-identification-using-sound-d35ad4
buena suerte :-)
BOB Bank of Brainchip
It definitely states 'Plural' (Processors) .... Hhhmmmmnn interesting!? nice pick up guys. Will we possibly get an announcement prior to theGreat pick up @jtardif999 Do we have more than one?
Also note the reference to Akida 1.0 in the technology section of the website. Is this new?
Akida Foundations
Immerse yourself in the powerful new world of the akida™ neuromorphic processor IP! The future of AI is NOW!
Laguna Hills, Calif. – December 29, 2022 –BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that it will be joining partners Prophesee, Socionext, and VVDN January 5-8 at CES to showcase compelling solutions on constrained edge devices, featuring its Akida™ processors. Akida processors simplify development by supporting today’s mainstream network models and workflows while being future-proofed for next-generation edge AI solutions.
to state that Akida 2.0 (or maybe it has a new name!!?? still Akida but what name/figure at the end??? ) is now available!
Last edited:
toasty
Regular
Great pick up @jtardif999 Do we have more than one?
Also note the reference to Akida 1.0 in the technology section of the website. Is this new?
Akida Foundations
Immerse yourself in the powerful new world of the akida™ neuromorphic processor IP! The future of AI is NOW!
Laguna Hills, Calif. – December 29, 2022 –BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that it will be joining partners Prophesee, Socionext, and VVDN January 5-8 at CES to showcase compelling solutions on constrained edge devices, featuring its Akida™ processors. Akida processors simplify development by supporting today’s mainstream network models and workflows while being future-proofed for next-generation edge AI solutions.
The language here has changed from previously, seemingly to point to the fact that AKIDA can be integrated into "mainstream network models". Is this an indication that we can expect quick-to-market developments that are effectively hybrids until the development of new, "pure" edge AI solutions featuring AKIDA IP are finalised? Could this be a pointer to, say, Mercedes incorporating AKIDA into the existing systems while they complete the new MBOS?
Remember these are questions, not facts or advice...........
stuart888
Regular
It rhymes in my brain with Qualcomm!For all you conspiracy theorists, I just noticed that VVDN rhymes with PVDM ..........
https://www.vvdntech.com/partners/qualcomm
stockduck
Regular
VVDN-QCS610/410 Development Kit Based on QCS 410/610
VVDN-QCS610/QCS410 development kit is a combination of the powerful Qualcomm® QCS610 / QCS410 SoC and carrier board developed by VVDN.
www.vvdntech.com
"...The development kit is suitable for all the visual intelligence applications and perform the analytics on the edge without the power of the cloud. Some of the applications include intelligent surveillance, smart retail, 180° dual-camera stitching, advanced tracking algorithms, video conferencing system, AI connected cameras, driver monitoring system (DMS), automotive, etc and many more.
......"
Don`t know, what this could mean? Any thoughts?
Happy new year to everyone here and may all be healthy in 2023.
(me praying for more peace on earth)
Realinfo
Regular
That’s a wrap !
On 31 December ‘21 our share price closed at 0.68…after a late flurry yesterday, it closed at 0.745. Given everything that’s occurred over the past twelve months, I think this is absurd...but as hey hey it’s Saturday says…the share price is what it is. That said, I stick by my prediction of a $5.00 share price by the end of next year, and if it isn’t, my offer to shout everyone a big day out at The Briars remains firmly on the table.
Wise souls say that licenses done today take around three years to end up in products and thus generate royalties…so how far away are we from this?
Akida Development Environments (ADE’s) and their release to early access customers go back to 2019. Feedback from this was taken on board causing a twelve month delay in the completion of the Akida IP. On 8 April 2020, TSMC commenced production of the Akida 1000 chip, which proved to work better than expected. By this time over 100 NDA’s were in place, and from these Brainchip selected around 24 household name Fortune 500 companies most likely to bring commercial success, to work closely with. The names of some of these, and indeed others a little later to the party have gradually emerged.
Me, myself personally believes three years is about up, so I’m expecting hey hey’s advice to watch the financials should now begin to bear fruit…
”There was movement at the station, for the word had passed around
That the colt (Akida) from Old Regret (PVDM) had got away”. A.B. ‘Banjo’ Paterson.
On 31 December ‘21 our share price closed at 0.68…after a late flurry yesterday, it closed at 0.745. Given everything that’s occurred over the past twelve months, I think this is absurd...but as hey hey it’s Saturday says…the share price is what it is. That said, I stick by my prediction of a $5.00 share price by the end of next year, and if it isn’t, my offer to shout everyone a big day out at The Briars remains firmly on the table.
Wise souls say that licenses done today take around three years to end up in products and thus generate royalties…so how far away are we from this?
Akida Development Environments (ADE’s) and their release to early access customers go back to 2019. Feedback from this was taken on board causing a twelve month delay in the completion of the Akida IP. On 8 April 2020, TSMC commenced production of the Akida 1000 chip, which proved to work better than expected. By this time over 100 NDA’s were in place, and from these Brainchip selected around 24 household name Fortune 500 companies most likely to bring commercial success, to work closely with. The names of some of these, and indeed others a little later to the party have gradually emerged.
Me, myself personally believes three years is about up, so I’m expecting hey hey’s advice to watch the financials should now begin to bear fruit…
”There was movement at the station, for the word had passed around
That the colt (Akida) from Old Regret (PVDM) had got away”. A.B. ‘Banjo’ Paterson.
Fact Finder
Top 20
Yes. @Diogenese has covered their failures a number of times.
Prophesee’s CEO Luca Verre by saying that until they found AKIDA they felt they may have been building a house of straw made clear the truth of @Diogenese ’s revelations.
My opinion only DYOR
FF
AKIDA BALLISTA
Dhm
Regular
I'm not suggesting that we have anything to do with ChatGPT but the wording on their website describes the response as more 'conversational'.
The EQXX MB also described the in cabin 'assistant' as being more conversational. See this link:
https://openai.com/blog/chatgpt/
How does this tech compare to the google search engine? This from the following website:
https://www.cnbc.com/2022/12/15/goo...ppened-when-i-swapped-services-for-a-day.html
The EQXX MB also described the in cabin 'assistant' as being more conversational. See this link:
https://openai.com/blog/chatgpt/
How does this tech compare to the google search engine? This from the following website:
https://www.cnbc.com/2022/12/15/goo...ppened-when-i-swapped-services-for-a-day.html
Mccabe84
Regular
Thanks heaps for all your replies it’s definitely helping to educate me.
equanimous
Norse clairvoyant shapeshifter goddess
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