BRN Discussion Ongoing

[Deleted member 118]

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?

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

 

[Build-it]

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.

www.nature.com

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

Edge Compute

screenrant

 

[Home101]

New Podcast out now

 

[Dozzaman1977]

Podcast has been out for 2 weeks, it's not new home101

 

[thelittleshort]

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.

 

[Diogenese]

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.

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.

 

[Fact Finder]

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

"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."

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

 

[Fact Finder]

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?

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.

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

 

[M_C]

Some would have you believe that the eqxx is only a concept car and Mercedes isn't actually going to use AKIDA.................- I beg to differ

2min40sec "The Vision eqxx is a trailblazer which underlines where our ENTIRE COMPANY IS HEADED" -

 

[HopalongPetrovski]

Some would have you believe that the eqxx is only a concept car and Mercedes isn't actually going to use AKIDA.................- I beg to differ

2min40sec "The Vision eqxx is a trailblazer which underlines where our ENTIRE COMPANY IS HEADED" -

I want one! I want one NOW.

 

[Fact Finder]

I want one! I want one NOW.

Well at release which was January, 2022 they said as a straight forward no ifs buts or maybes that it would be road legal and proving the comfortable 1,000 kilometre range in six months. Somewhere else I read an on road price estimate of around $135,000. (Probably US $).

So maybe they will take your deposit.

I personally have absolutely no doubt Mercedes will be commercialising Mercedes brand EV’s that leverage AKIDA technology advances.

I even believe Blind Freddie’s theory that Brainchip and Mercedes have conspired to deliver the first AKD2000 with LSTM Mercedes EV that will extrapolate from events and use emergency braking to not run over children chasing balls onto roadways, drunks staggering sideways into traffic and all the while ignoring plastic bags blowing in the wind thus leaving Tesla in their dust.

But don’t tell anyone as they will think Freddie and I have lost our marbles.

My opinion and conspiracy only DYOR
FF

AKIDA BALLISTA

 

[HopalongPetrovski]

Well at release which was January, 2022 they said as a straight forward no ifs buts or maybes that it would be road legal and proving the comfortable 1,000 kilometre range in six months. Somewhere else I read an on road price estimate of around $135,000. (Probably US $).

So maybe they will take your deposit.

I personally have absolutely no doubt Mercedes will be commercialising Mercedes brand EV’s that leverage AKIDA technology advances.

I even believe Blind Freddie’s theory that Brainchip and Mercedes have conspired to deliver the first AKD2000 with LSTM Mercedes EV that will extrapolate from events and use emergency braking to not run over children chasing balls onto roadways, drunks staggering sideways into traffic and all the while ignoring plastic bags blowing in the wind thus leaving Tesla in their dust.

But don’t tell anyone as they will think Freddie and I have lost our marbles.

My opinion and conspiracy only DYOR
FF

AKIDA BALLISTA

Well, by my reckoning, $US135k converted to our shekels is 182,000.
So at $5 a share that's only 36,400 for possibly state of the art vehicle technology that I get to drive whilst I'm still young enough to enjoy it.
Bring it BrainChip.
Bring it Merc.
Would be a nice ride to our $5 party

 

[Foxdog]

Well, by my reckoning, $US135k converted to our shekels is 182,000.
So at $5 a share that's only 36,400 for possibly state of the art vehicle technology that I get to drive whilst I'm still young enough to enjoy it.
Bring it BrainChip.
Bring it Merc.
Would be a nice ride to our $5 party

We'd better talk about colour choices then - I don't want to turn up wearing the same 'dress'

 

[hamilton66]

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.

D, thanks for the unwaivering supply of info. Stella. Just poked my head into H/C for the 1st time in ages. Jesus, it's toxic. Basically no info, just downrampers vs the rest. It offers nothing, in terms of analysis, or information. Sad to see.
GLTA

 

[M_C]

Wonder what brought this on

https://www.datacenterdynamics.com/en/news/intel-amd-arm-google-and-others-set-up-universal-chiplet-interconnect-express-consortium/

Chiplets are a growing concept in the semiconductor design industry, where tiny dies are used instead of one monolithic die.

This has so far been used to split CPUs into several pieces (in the extreme, Intel Ponte Vecchio includes 47 chiplets on a single package). But with interoperability, one could build chips that mix-and-match chiplets from different companies.

Intel and AMD previously trialed this with the ‘Intel 8th Generation Core with Radeon RX Vega M Graphics' chip that included an ‘H-series’ Intel central processor and an AMD Radeon graphics processor as two chiplets on the same package, but true interoperability has been lacking.

UCIe aims to solve that - at least for the companies that have joined.

The consortium launches with founding members Advanced Semiconductor Engineering, Inc. (ASE), AMD, Arm, Google Cloud, Intel Corporation, Meta, Microsoft Corporation, Qualcomm Incorporated, Samsung, and Taiwan Semiconductor Manufacturing Company

 

[miaeffect]

D, thanks for the unwaivering supply of info. Stella. Just poked my head into H/C for the 1st time in ages. Jesus, it's toxic. Basically no info, just downrampers vs the rest. It offers nothing, in terms of analysis, or information. Sad to see.
GLTA

You've just wasted 100 watt of electricity and 10mb of your precious data.

 

[Deleted member 118]

Heron systems is about the software, what and whos hardware is the question..

Heron Systems' AI pilot just beat a human in a simulated dogfight | Engadget

A DARPA event pitted AI pilots against each other, and then the winner took on a human pilot wearing a VR helmet.

www.google.com

I can’t find anything regarding the technology they are using, but I know who’s they will be.

 

[MrNick]

Could our link with TATA have just come into play here…? Clutching at metaphorical straws perhaps.

TCS wins multi-year deal with Australian govt's Western Power

The two-year pilot aims to aggregate a network of solar-powered homes that collectively generate and store energy, in the same way as traditional thermal power plants, smoothing out the supply and demand for electricity.

m.economictimes.com

 

[BaconLover]

Press Releases Archive

A global media and thought leadership platform that elevates voices in the news cycle. Online since 1998.

www.digitaljournal.com

An oldie but a goodie.

Major Giant. Not long now.

On another note, @Fact Finder some scientists say Milky Way and Andromeda Galaxy will kiss each other goodbye and there will be a new kid in the block approximately 4.5Billion years from now. So Earth may not get the opportunity to be a hot dude roaming around and trying to tease the Sun. Will be consumed alive by the hunks from Andromeda waayyy before then.

 

[Space Cadet]

Out of China

https://ieeexplore.ieee.org/document/9661702

SC