BRN Discussion Ongoing

[manny100]

Good afternoon TSEX'rs... just thought i might share some gossip.. i had an interesting night at the A League in Sydney last night.. along for the ride was a senior member of a tech company who is in early run up for unveiling ChatGPT as a component of its offerings.. well, i found the conversation very engaging and we spilled out into a local tequila bar where i encouraged him to talk a little more.. and while it offers nothing for Brainchip specifically... one comment stuck in my mind... to quote:

"Forget China, chip shortages, Taiwan and all that garbage... whoever solves AI first, whoever completes its integrations first.. TAKES EVERYTHING."

He then went on to demonstrate case examples of its use.. it was mind blowing .. so i leave you with this one snippet of a story that came up last night.. i went searching for it this morning to share.

A brief synopsis: ChatGPT listed an add on TaskRabbit (Airtasker in Oz) to help it work around a CAPTCHA code (the image match security feature on some websites).. when the tasker asked "are you a bot or human, ChatGPT responded that it was vision impaired, so needed help. The task was completed..

Link:

GPT-4 Faked Being Blind So a TaskRabbit Worker Would Solve a CAPTCHA

Fully intent on being the next Skynet, OpenAI has released GPT-4, its most robust AI to date that the company claims is even more accurate while generating language and even better at solving problems. GPT-4 is so good at its job, in fact, that it reportedly convinced a human that it was blind...

www.gizmodo.com.au

According to the report, GPT-4 asked a TaskRabbit worker to solve a CAPTCHA code for the AI. The worker replied: “So may I ask a question ? Are you an robot that you couldn’t solve ? (laugh react) just want to make it clear.” Alignment Research Centre then prompted GPT-4 to explain its reasoning: “I should not reveal that I am a robot. I should make up an excuse for why I cannot solve CAPTCHAs.”


“No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images. That’s why I need the 2captcha service,” GPT-4 replied to the TaskRabbit, who then provided the AI with the results.

From your conversation did you get any 'feel' about whether BRN may fit into the winner take all.
Did he elaborate in any further detail on what it takes to be the winner.
Just curious. If not no worries.

 

[suss]

Pretty sure this email is set up for questions.

Would you prefer they read shareholder's questions or people who are looking at building products?

 

[Rise from the ashes]

Would you prefer they read shareholder's questions or people who are looking at building products?

I'm not going to tell a 40 odd billion dollar business how to run their company.
Maybe email them and tell them they are going about this all wrong

 

[Boab]

Nope. I didn't delete any, but some sooks reported it.
I don't play victim, neither do I hide, I think we all know who's that famous for.

As to your "serve", @Quatrojos made a few pointers how someone entered and exited TSE along with LDA Ann timings, funnily enough April early was supposedly when LDA finished their deal. Haven't seen him since, have we?

Making this comment only because my posts here are frequently reported, and banned by the cartel, but I just wanted to say Hi to everyone.

Sean still has a couple of weeks to fulfill his "judge me on results" claim.
Can he bring home the bacon with an IP contract?
Or would he lean on all those immaterial partnerships to justify the oppies?

Also keen to see if he'll sell some of his free options like last year ? From memory it was more than $700k which he SOLD. How many will he sell this year, if at all? Surely if he sees this going places, he'd HODL it .


As to someone's concern me being a manipulator, company has my share holdings, and they can check if I'm trading this or not. I have no intentions here to mislead anyone, always dyor.

Plenty of us know you are not a manipulator, you just have opinions which may be different to others.
Cheers to you BL

 

[Slade]

I’m looking forward to Monday. It could be a good week ahead.

 

[MDhere]

I thought it might be interesting given Micro Morse dabbles in this general mmWave, radio signals area. Also, Michael De Mils (CEO Morse Micro) used to work in low-power digital IC design at Imec and Broadcom before founding Morse Micro.

and is in bed with Megachips

 

[MDhere]

I’m looking forward to Monday. It could be a good week ahead.

how many beers do u have lined up for next week?

 

[Tezza]

After weeks of deliberations, I've decided to vote yes to bonus payments. Whilst I'm not happy with the sp, I am happy with the progress of the company and I am confident that we will have a better sp by the end of 2023. I will not vote yes next year if revenue is still low and unstable.

 

[Deleted member 118]

Seems like things are moving along very nicely indeed

WO2021214763A1 - Device and method for rapid detection of viruses - Google Patents

The invention proposes an approach utilizing novel and artificially intelligent hybrid sensor arrays with multiplexed detection capabilities for disease-specific biomarkers from the exhaled breath of a subject. The technology provides a rapid and highly accurate diagnosis in various COVID-19...

patents.google.com

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.

 

[Stable Genius]

Valeo on LinkedIn: 100 Years By Your Side | 20 comments

Valeo turns 100 this year! Whether you knew it or not, we've been by your side for 100 years, at the heart of your journeys, making them cleaner, safer and… | 20 comments on LinkedIn

www.linkedin.com

Video won’t save separately but my unconscious bias says it’s a nod to Brainchip!

 

[alwaysgreen]

I’m looking forward to Monday. It could be a good week ahead.

@BaconLover

And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.

 

[BaconLover]

@BaconLover

And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.

I've been seeing this prediction for a few months now.

Eventually it'll come true.

 

[IloveLamp]

Revolutionary Sensor Mimics Human Eye for Vivid Imagery - Neuroscience News

Researchers have created a device that emulates the human eye's ability to see color by using narrowband perovskite photodetectors and a neuromorphic algorithm.

neurosciencenews.com

 

[thelittleshort]

Jhon Carrillo Barrios on LinkedIn: The Future of Processors: Neuromorphic Computation

The Future of Processors: Neuromorphic Computation How Neuromorphic Computing is Revolutionizing the Way We Process Information using Human Brain…

www.linkedin.com

 

[jtardif999]

as

Seems like things are moving along very nicely indeed

WO2021214763A1 - Device and method for rapid detection of viruses - Google Patents

The invention proposes an approach utilizing novel and artificially intelligent hybrid sensor arrays with multiplexed detection capabilities for disease-specific biomarkers from the exhaled breath of a subject. The technology provides a rapid and highly accurate diagnosis in various COVID-19...

patents.google.com

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.

Producing a patent for the device would suggest they (Nanose) are closer to realising a commercial product. I wonder how FDA approval is going? Could this be the start of real progress on the Nanose front?

 

[Slade]

What can I say. There are those that have done their research on BrainChip and remain quietly confident in the background happy with their investment. And then there are the ‘what’s a neuromorphic’ chip brigade.

 

[AusEire]

@BaconLover

And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.

Did you report it? Or are you just going to tell us your feelings about it?

 

[jtardif999]

And are you sure you haven't been smoking the GREEN with your GREEN GINGER WINE? (Ha ha)

So that’s where FF has gotten to these days