BRN Discussion Ongoing
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Intel to enter the Angstrom nanometer chip era with US$88 billion 'made in Europe' processor investment
Europe will be ready for the fractional nanometer Angstrom processor era with a giant US$88 billion investment by Intel. New fabs in Germany are scheduled to become operational and churn out Angstrom era chips by 2027, while Intel plans a US$33 billion investment in its Irish foundries for...
Hopefully we remain years in front still with our tech and ongoing NDA’s and contracts we can really be a household name by the time the bigger players finish building there giant factories.
Thanks Jase
D
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What a great version.
HopalongPetrovski
I'm Spartacus!
No. We have all been furiously awaiting it, but it's release to market is not within Brainchip's control.
We are not producing it, but are only envisioned to provide either the Akida chip or the IP which makes this sensor smart.
There has been much previous discussion and high hopes for the product being a company maker for us, as well as it's obvious beneficial use case, particularly in relation to Covid, but of course, the tech is capable of non-invasively detecting so many other potential conditions that it still would be a high viability product. Other advantages include its lack of reliance on an internet connection, portability, cost effectiveness per unit and per test and low power footprint. I certainly hope and expect the product, and our involvement to proceed, but testing and regulatory requirements for medical devices are notoriously stringent.
It would however be a wonderfully apt showcase for Akida's comparative offering and help differentiate us in the wider market place.
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Everybody knows they need AKIDA technology to have any chance of building robots that can work beside people cooperatively but apparently not everyone has heard it is available NOW. I suppose that is the downside of coming back from the future the World needs to catch up:
www.nature.com
My opinion only DYOR
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AKIDA BALLISTA
Embodied neuromorphic intelligence - Nature Communications
A grand challenge in robotics is realising intelligent agents capable of autonomous interaction with the environment. In this Perspective, the authors discuss the potential, challenges and future direction of research aimed at demonstrating embodied intelligent robotics via neuromorphic technology.
My opinion only DYOR
FF
AKIDA BALLISTA
Dhm
Regular
Discussions are here: https://thestockexchange.com.au/threads/brainchip-nanose.2346/
Ongoing as far as I can tell.
Meanwhile there should be an announcement tomorrow by a company I hold about diagnosing Covid by the sound of your forced cough.
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I disagree to a certain extent re: the testing and regulatory requirements in the current environment. Worldwide pandemic changes the game. If Vaccines can be created and released on the public there is no way that a non-invasive device should take so long to be developed, tested and approved - if it does what it claims to do. As shareholders we seem a great deal more excited about this project than BRN does. This is one instance where I think we should be getting an update in progress. IMO
D
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Here's an extract from an article published in Forbes on the 8 October 2021, written by tech analyst Bob O'Donnell.
Firstly, the answer to the title of this article is "yes" and secondly, can someone call Bob and let him know about AKIDA?
(Extract)
What’s needed is a type of computing that can really think and learn on its own and then adapt its learning to those unexpected scenarios. As crazy and potentially controversial as that may sound, that’s essentially what researchers in the field of neuromorphic computing are attempting to do. The basic idea is to replicate the structure and function of the most adaptable computing/thinking device we know of—the human brain—in digital form. Following the principles of basic biology, neuromorphic chips attempt to re-create a series of connected neurons using digital synapses that send electrical pulses between them, much as biological brains do.
It’s an area of academic research that’s been around for a few decades now, but only recently has it started to make real progress and gain more attention. In fact, buried in the wave of tech industry announcements that have been made over the last few weeks was news that Intel had released the second generation of its neuromorphic chip, named Loihi 2, along with a new open-source software framework for it that they’ve dubbed Lava.
To put realistic expectations around all of this, Loihi 2 is not going to be made commercially available—it’s termed a research chip—and the latest version offers 1 million neurons, a far cry from the approximately 100 billion found in a human brain. Still, it’s an extremely impressive, ambitious project that offers 10x the performance, 15x the density of its 2018-era predecessor (it’s built on the company’s new Intel 4 chip manufacturing process technology), and improved energy efficiency. In addition, it also provides better (and easier) means of interconnecting its unique architecture with other more traditional chips.
Intel clearly learned a great deal from the first Loihi, and one of the biggest realizations was that software development for this radically new architecture is extremely hard. As a result, another essential part of the company’s news was the debut of Lava, an open-source software framework and set of tools that can be used to write applications for Loihi. The company is also offering tools that can simulate its operation on traditional CPUs and GPUs so that developers can create code without having access to the chips.
What’s particularly fascinating about how neuromorphic chips operate is that, despite the fact they function in a dramatically different fashion from both traditional CPU computing and parallel GPU-like computing models, they can be used to achieve some of the same goals. In other words, neuromorphic chips like Loihi 2 can provide the desired outcomes that traditional AI is shooting for, but in a significantly faster, more energy efficient, and less data intensive way. Through a series of event-based spikes that occur asynchronously and trigger digital neurons to respond in various ways—much as a human brain operates (vs. the synchronous, structured processing in CPUs and GPUs)—a neuromorphic chip can essentially “learn” things on the fly. As a result, it’s ideally suited for devices that must react to new stimuli in real-time.
These capabilities are why these chips are so appealing to those designing and building robots and robotic-like systems, which autonomous driving cars essentially are. Bottom line is that it could take commercially available neuromorphic chips to power the kind of autonomous cars and personal robots of our science fiction-inspired dreams.
Of course, neuromorphic computing isn’t the only new approach to advancing the world of technology. There’s also a great deal of work being done in the more widely discussed world of quantum computing. Like quantum computing, the inner workings of neuromorphic computing are extraordinarily complex and, for now, primarily seen as research projects for corporate R&D labs and academic research. Unlike quantum, however, neuromorphic computing doesn’t require the extreme physical challenges (temperatures near absolute zero) and power requirements that quantum currently does. In fact, one of the many appealing aspects of neuromorphic architectures is that they’re designed to be extremely low power, making them suitable for a variety of mobile or other battery-powered applications (like autonomous cars and robots).
Despite recent advancements, it’s important to remember that commercial application of neuromorphic chips is still several years away. However, it’s hard not to get excited and intrigued by a technology that has the potential to make AI-powered devices truly intelligent, instead of simply very well-trained. The distinction may seem subtle, but ultimately, it’s that kind of new smarts that we’ll likely need in order to make some of the “next big things” really happen in a way that we can all appreciate and imagine.
www.forbes.com
Firstly, the answer to the title of this article is "yes" and secondly, can someone call Bob and let him know about AKIDA?
Is Neuromorphic Computing The Answer For Autonomous Driving And Personal Robotics?
(Extract)
What’s needed is a type of computing that can really think and learn on its own and then adapt its learning to those unexpected scenarios. As crazy and potentially controversial as that may sound, that’s essentially what researchers in the field of neuromorphic computing are attempting to do. The basic idea is to replicate the structure and function of the most adaptable computing/thinking device we know of—the human brain—in digital form. Following the principles of basic biology, neuromorphic chips attempt to re-create a series of connected neurons using digital synapses that send electrical pulses between them, much as biological brains do.
It’s an area of academic research that’s been around for a few decades now, but only recently has it started to make real progress and gain more attention. In fact, buried in the wave of tech industry announcements that have been made over the last few weeks was news that Intel had released the second generation of its neuromorphic chip, named Loihi 2, along with a new open-source software framework for it that they’ve dubbed Lava.
To put realistic expectations around all of this, Loihi 2 is not going to be made commercially available—it’s termed a research chip—and the latest version offers 1 million neurons, a far cry from the approximately 100 billion found in a human brain. Still, it’s an extremely impressive, ambitious project that offers 10x the performance, 15x the density of its 2018-era predecessor (it’s built on the company’s new Intel 4 chip manufacturing process technology), and improved energy efficiency. In addition, it also provides better (and easier) means of interconnecting its unique architecture with other more traditional chips.
Intel clearly learned a great deal from the first Loihi, and one of the biggest realizations was that software development for this radically new architecture is extremely hard. As a result, another essential part of the company’s news was the debut of Lava, an open-source software framework and set of tools that can be used to write applications for Loihi. The company is also offering tools that can simulate its operation on traditional CPUs and GPUs so that developers can create code without having access to the chips.
What’s particularly fascinating about how neuromorphic chips operate is that, despite the fact they function in a dramatically different fashion from both traditional CPU computing and parallel GPU-like computing models, they can be used to achieve some of the same goals. In other words, neuromorphic chips like Loihi 2 can provide the desired outcomes that traditional AI is shooting for, but in a significantly faster, more energy efficient, and less data intensive way. Through a series of event-based spikes that occur asynchronously and trigger digital neurons to respond in various ways—much as a human brain operates (vs. the synchronous, structured processing in CPUs and GPUs)—a neuromorphic chip can essentially “learn” things on the fly. As a result, it’s ideally suited for devices that must react to new stimuli in real-time.
These capabilities are why these chips are so appealing to those designing and building robots and robotic-like systems, which autonomous driving cars essentially are. Bottom line is that it could take commercially available neuromorphic chips to power the kind of autonomous cars and personal robots of our science fiction-inspired dreams.
Of course, neuromorphic computing isn’t the only new approach to advancing the world of technology. There’s also a great deal of work being done in the more widely discussed world of quantum computing. Like quantum computing, the inner workings of neuromorphic computing are extraordinarily complex and, for now, primarily seen as research projects for corporate R&D labs and academic research. Unlike quantum, however, neuromorphic computing doesn’t require the extreme physical challenges (temperatures near absolute zero) and power requirements that quantum currently does. In fact, one of the many appealing aspects of neuromorphic architectures is that they’re designed to be extremely low power, making them suitable for a variety of mobile or other battery-powered applications (like autonomous cars and robots).
Despite recent advancements, it’s important to remember that commercial application of neuromorphic chips is still several years away. However, it’s hard not to get excited and intrigued by a technology that has the potential to make AI-powered devices truly intelligent, instead of simply very well-trained. The distinction may seem subtle, but ultimately, it’s that kind of new smarts that we’ll likely need in order to make some of the “next big things” really happen in a way that we can all appreciate and imagine.
Is Neuromorphic Computing The Answer For Autonomous Driving And Personal Robotics?
Applications like autonomous driving and personal robotics are proving to be challenging for traditional computing architectures, but a relatively unknown, biology-inspired technology called neuromorphic computing could hold the key to new killer applications.
www.forbes.com
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SharesForBrekky
Regular
Even though it wasn't an official announcement/update about Nanose, I recall Sean stating a couple of months ago that they're working with a couple of medical devices. I guess Nanose could be one and that things are still going ok there.
D
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This company seems to be heavily into AI and it seems the US Airforce are interested in there Drone technology, but I can’t find any patents for them, so will we be seeing them becoming another EAP customer?
analyticsdrift.com
www.sbir.gov
I wonder if Shield Ai have now realized that they spent X amount of money buying Heron when in the end all they needed was Akida lol
www.army-technology.com
Shield AI to work on Swarming Drones for Air Force
Defense-related artificial intelligence-powered products developing company Shield AI announces that it will develop swarming drones and autonomous
analyticsdrift.com
Shield AI Inc. | SBIR.gov
www.sbir.gov
I wonder if Shield Ai have now realized that they spent X amount of money buying Heron when in the end all they needed was Akida lol
UAS AI technology company Shield AI acquires Heron Systems
US defence technology contractor Shield AI has acquired software company Heron Systems for an undisclosed purchase price.
www.army-technology.com
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Build-it
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Everybody knows they need AKIDA technology to have any chance of building robots that can work beside people cooperatively but apparently not everyone has heard it is available NOW. I suppose that is the downside of coming back from the future the World needs to catch up:
Embodied neuromorphic intelligence - Nature Communications
A grand challenge in robotics is realising intelligent agents capable of autonomous interaction with the environment. In this Perspective, the authors discuss the potential, challenges and future direction of research aimed at demonstrating embodied intelligent robotics via neuromorphic technology.
My opinion only DYOR
FF
AKIDA BALLISTA
Hi FF,
NASA certainly have, I look forward to the day we find out the extent that AKIDA has benefited space travel and space building.
This space is starting to heat up with the race to moon
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Dozzaman1977
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Podcast has been out for 2 weeks, it's not new home101
thelittleshort
Top Bloke
Anyone else read this article:
Machine Learning Showing Up As Silicon IP on Semiconductor Engineering?
Sounds like BrainChip are already addressing several of the issues described
Maybe Brainchip need to also sponsor the site to get more of a mention
But the appearance of IP as an option also is an indication that ML is being integrated into more functions than ever before, and that trend shows no sign of slowing down.
Machine Learning Showing Up As Silicon IP on Semiconductor Engineering?
Sounds like BrainChip are already addressing several of the issues described
Maybe Brainchip need to also sponsor the site to get more of a mention
But the appearance of IP as an option also is an indication that ML is being integrated into more functions than ever before, and that trend shows no sign of slowing down.
Hi SFB,
Biotome and Noisy Gut belt are a couple. Let's hope Nanose is a third.
Here is some information on the NaNose tests from the Technion patent application filed in April 2020.
WO2021214763A1 DEVICE AND METHOD FOR RAPID DETECTION OF VIRUSES
The aim of the study was defined as: Collecting and evaluating data of potential volatile biomarkers in the exhaled air of subjects with and without Covid-19 by the novel sensors of the invention. COVID-19 positive and negative subjects were enrolled. Classification to the 2 study arms was based on a PCR test result. Three medical centers participate in the study: Shamir Health Corporation (“Assaf Harofeh”) in Israel; Northwell Health, Inc. in the United States (“Northwell”); Zayed Military Hospital Abu Dhabi (“Zayed Hospital”).
The study was performed with the sensors installed in 2 devices: 1. The first- generation device with single use units that include the sensors. 2. a device with multi use sensors. The collected data from the devices were analyzed independently by two distinct methods.
The first dataset was collected with the first-generation device with singe use units that include the sensors of the invention. The dataset included subjects tested with the device at two sites: 35 samples from Northwell NY, and 31 samples from Shamir medical center IL. Each test file consisted of responses from duplicated sensor array, and therefore each test file was split into two sample files, based on the sensor sets. Some of the sensors failed to respond, and therefore datasets that included failed sensors were discarded. The total number of sample files that were analyzed after the error- prone samples were discarded is: Northwell - 35 sample files (representing 24 tested subjects - 17 positives, 7 negatives) and Shamir medical center - 31 sample files (representing 21 tested subjects - 14 positives, 7 negatives). The data was analyzed by Brainchip with a Spiking Neural Network, the adjacent confusion matrix shows the results on the test set. The test set included 31 samples- 21 positives and 10 negatives from 21 tested subjects. Zero out of 21 positive samples were identified correctly which represents 100% sensitivity and 4 out of 10 negative samples were identified correctly which represents 40% specificity. The overall accuracy was 80.65% The second study was performed with the multiuse NaNose sensors installed in Sniffphone device. The dataset included 165 samples taken from 141 subjects tested with Sniffphone device at Zayed Military Hospital - 65 samples from 65 COVID-19 positive subjects and 100 samples from 76 COVID-19 negative subjects (Several negative subjects were sampled two or three times). A Linear discriminative analysis was performed. The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 37 samples - 8 positive and 29 negative samples from 27 tested subjects. Seven out of eight positive samples were identified correctly which represents 87.5% sensitivity, and 25 out of the 29 negative samples were identified correctly which represents 86.2% specificity. The overall accuracy was therefore 86.5%.
The same data set was analyzed also by the SNN methodology. To make the SNN most efficient, 34 samples were discarded due to noise or improper vector dimensionality. Thus, the dataset included 131 samples taken from 126 subjects tested with Sniffphone device at Zayed Military Hospital- 62 samples from 62 COVID-19 positive subjects and 69 samples from 64 COVID-19 negative subjects (Several negative subjects were sampled two or three times). The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 53 samples - 20 positive and 33 negative samples from 53 tested subjects. Nineteen out of 20 positive samples were identified correctly which represents 95% sensitivity and 29 out of 33 negative samples were identified correctly which represents 87.87 % specificity. The overall accuracy was therefore 90.5%.
Two different analysis methods were applied on the dataset and both showed excellent results for the differentiation between COVID positive and COVID negative. While the multiuse sensors achieved a much better specificity (-87%) compared to the single use sensors (40%), this is more likely a result of the vast difference between the datasets: the dataset of the multiuse sensors included 165 samples from 141 subjects while the dataset of the single-use sensors included 66 samples from 45 subjects. During the Clinical study with COVID19 patients the company further improved the 4 components of the device: the mechanical design including the breath collection mechanism, the electronics, the sensors and the classifying algorithm.
"To make the SNN most efficient, 34 samples were discarded due to noise or improper vector dimensionality." This suggests to me that the accuracy and reliability of the data collected by sensor was not 100%, so there may have been technical issues with the NaNose sensor 2 years ago.
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"HE DID EXPOSE that deep learning fell over when they went from the laboratory to the clinic as it could not deal with the contaminants in the real world when sampling breath and their accuracy dropped to 50%. Clearly AKD1000 did not have this drop in performance and this is why AKIDA RULES."I hate videos. So painfully slow. I need to fast forward and jump around at my pace which I can do when reading . The good Professor spoke so slowly when he was younger I wanted to scream.
Had to give up even during his more recent ones about healing skin. Not sure if there is anything I missed as a result so it will be up to others to decide.
HE DID EXPOSE that deep learning fell over when they went from the laboratory to the clinic as it could not deal with the contaminants in the real world when sampling breath and their accuracy dropped to 50%. Clearly AKD1000 did not have this drop in performance and this is why AKIDA RULES.
My opinion only DYOR
FF
AKIDA BALLISTA
The last information we had regarding Nanose was that the clinical trials being run via the FDA had a completion end date in May, 2022. If I remember correctly to hit this date Nanose had to get through a further 3,000 patient tests having a total target of 10,000 with 7.000 completed. If they have not completed the further 3,000 by the current end date they will seek and be given and extension by the FDA. Once they have finalised these trials then the data will be collated and reported on by the independent researchers and supplied to Nanose and then to the FDA.
The FDA and Nanose will then consult on the where too at that point.
The thing is if you have regard to what Professor Haick said was the short coming of deep learning which saw the over 90% accuracy achieved in the laboratory and reducing it to 50% when exposed to real world gathered data then it is very hard to imagine in what universe Nanose would elect to go back to deep learning and abandon Brainchip and AKIDA technology. The data analysed by AKIDA in Perth was gathered in the real world in Wuhan, China.
The device that Nanose is developing is far more complex and advanced than Covid-19 it is also about being able to identify 17 different forms of cancer (including gastric cancer which is notoriously a late diagnosed disease and usually fatal and a very painful death) and also they are aiming to have it capable of being updatable for new disease or variants. The Covid virus is still mutating and the next virus likely to cause a pandemic has already been identified by the WHO.
Brainchip has stated that income will ramp up this year from Renesas and others.
Nanose will happen when it happens along with Biotome, Mercedes, Nasa, MegaChips, Valeo, Ford, Socionext, DARPA (ISL & many others) and at least 11 other EAP's which probably include Tata, DELL and Samsung.
Anyone here remember Noisey Gut Belt that is going along in the background as well as the monitoring of heavy rail for preventative maintenance of bearings. Not to mention the possibility of being the brain in the fluffy round thing from Panasonic that might be a comfort to those who sold out of Brainchip and are now feeling desolate.
So many opportunities hidden from view and a world class sales and marketing team and CEO driving the bus. Multiple products available for purchase. Insatiable demand. Explosive sales expected. Next generation technology advanced to engineering and the generation after it on or beyond the bench. Three year lead increasing with each new technology generation. Industries that do not exist being disrupted. Significant cash runway and further finance locked in if required end of 2023.
Does the investment potential and returns ever become greater than presently evidenced by what we know as FACT about Brainchip?
My opinion only DYOR
FF
AKIDA BALLISTA
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Scientists have revised the life span of Earth down to 5 billion years. All the recycling under the Sun is not going to change that.And you think that's a good thing...mine is the moon ... geez FF... I think best how to reduce mining and improve recycling might be more worthy. Mercedes have invented a compostable battery, but I'm sure we won't see that on market for a long time as all companies involved with lithium etc won't be want their lunch cut by a more sustainable inovation.
Btw what's the moon got that earth doesn't?
The purpose of mining on the moon from NASA's perspective has been to provide the raw materials on the Moon from which to create the fuel and other things they will need to go beyond the Moon to Mars and further as the current action of breaking free from Earth's gravity takes so much of the needed fuel payload that it limits further exploration. It is planned to use the Earths gravitation force to sling shot space vessels that will take off from the moon saving further fuel.
Humanity has less than 5 billion years to find a way to continue somewhere else. The Earth will in its final years become hotter and hotter until it is consumed by flame and end up as an uninhabitable dead rock circling the Sun. This change in the Earth's climate will be experienced across all the planets in this solar system and the climate on Mars will be improved making it more likely to sustain life than it is today. Again once NASA can start to travel too and from Mars it will use the raw materials on Mars to do the same thing it proposes on the Moon.
The fortunate thing is that what they can find on the Moon and on Mars is what they use here on Earth which is a good thing otherwise they would need to develop a whole new form of technology to use these different raw materials.
You are invested in the most advanced artificial intelligence technology in the world and it will be used for much more than selecting plastic bottles on a production line for recycling.
These tiny robot miners will have a plethora of use cases beyond mining even down to clambering over your plastic bottle recycling dumps selecting and sorting the different types of plastic and other waste.
Their aquatic cousins can be deployed in our Oceans collecting the waste plastic and other contaminants killing our sea creatures.
The idea that mining will not be ongoing is fanciful even with recycling and so these tiny robot miners will be able to take over the dangerous mining tasks currently undertaken by human miners.
There are so many use cases but an army of tiny storm water pipe cleaners that constantly ensure that our drains are clean so that flash flooding of our roads does not occur comes to mind.
Anyway you either understand the potential of our technology and the spiderweb of world wide and space use that it will be put to or not.
My opinion only DYOR
FF
AKIDA BALLISTA
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