BRN Discussion Ongoing

[Frangipani]

Mine is the one with a few holes in it.

I never thought I’d say this, but I can now verify the above is true, as imec (Interuniversity Microelectronics Centre) is actually headquartered in Leuven, Belgium, whereas Zürich is home to INI (Institute of Neuroinformatics).

Imec R&D, nano electronics and digital technologies

Imec is the world-leading R&D and innovation hub in nanoelectronics and digital technologies.

www.imec-int.com

 

[MadMayHam | 合氣道]

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.

This patent was published (granted) in 2021. Our excitement over this was expressed on the other forum (which name shall not be mentioned) two years ago. Unfortunately nothing seems to have become of it.

 

[Frangipani]

Hi Frederick,

IMEC are from Zurich and Zurich leans strongly to analog NNs. I haven't found any patents from IMEC which indicate they have a finger in the digital SNN pie, but there is an 18 month blackout on patent applications.

Analog has many theoretical charms, but the reality is, as you intimate, fraught. IC manufacturing, while incredibly precise on a macro-scale, has a lot of dimensional variability on the nano scale, resulting in significant component variability in nano-dimensioned devices such as capacitors and memristors, which causes inconsistency in operations such as adding electric currents. Basically, IC manufacture relies on chemical etching to create patterns on silicon, and this can be affected by such things as the crystal orientation structure of the silicon and doped layers.

And it looks like imec is hungry for the digital SNN pie after all:

(PDF) SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture

PDF | SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications... | Find, read and cite all the research you need on ResearchGate

www.researchgate.net

“SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications inside or near sensors where ultra-low power and adaptivity features are required. SENeCA is optimized to exploit unstructured spatio-temporal sparsity in computations and data transfer. It is a digital IP, that contains interconnected Neuron Cluster Cores, with a RISC-V-based instruction set, an optimized Neuromorphic Co-Processor, and an event-based communication infrastructure. SENeCA improves state of the art by Addressing the flexibility issue in neuromorphic processors by allowing a fully programmable neuron model and learning/adaptivity algorithms; Improving the area efficiency by employing a 3-level memory hierarchy which allows using novel embedded memory technologies; Efficient deployment of advanced learning mechanisms and optimization algorithms by accelerating neural operations in three data types: int4, int8 and BrainFloat16; Efficient event communication by using a new Network-on-Chip with multicasting, a compression mechanism, and source-based routing. The implemented digital IP can be tuned for different applications to have a flexible number of cores and Neural Processing Elements (NPEs) per core and optional use of off-chip memory. Next to the hardware, the SENeCA platform includes an SDK and a hardware-aware simulator for close-loop synthesis/mapping optimization 1.”

Interestingly, one of the paper’s authors, Gert-Jan van Schaik (from imec the Netherlands, Eindhoven), shares the same surname with André van Schaik, Director of WSU’s International Centre for Neuromorphic Systems. Maybe our Dutch or Flemish posters can tell us whether or not it is a common surname? Possibly the two of them are related?

 

[Diogenese]

I never thought I’d say this, but I can now verify the above is true, as imec (Interuniversity Microelectronics Centre) is actually headquartered in Leuven, Belgium, whereas Zürich is home to INI (Institute of Neuroinformatics).

Imec R&D, nano electronics and digital technologies

Imec is the world-leading R&D and innovation hub in nanoelectronics and digital technologies.

www.imec-int.com

Geography never was my strong suit.

 

[Gies]

And it looks like imec is hungry for the digital SNN pie after all:

(PDF) SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture

PDF | SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications... | Find, read and cite all the research you need on ResearchGate

www.researchgate.net

“SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications inside or near sensors where ultra-low power and adaptivity features are required. SENeCA is optimized to exploit unstructured spatio-temporal sparsity in computations and data transfer. It is a digital IP, that contains interconnected Neuron Cluster Cores, with a RISC-V-based instruction set, an optimized Neuromorphic Co-Processor, and an event-based communication infrastructure. SENeCA improves state of the art by Addressing the flexibility issue in neuromorphic processors by allowing a fully programmable neuron model and learning/adaptivity algorithms; Improving the area efficiency by employing a 3-level memory hierarchy which allows using novel embedded memory technologies; Efficient deployment of advanced learning mechanisms and optimization algorithms by accelerating neural operations in three data types: int4, int8 and BrainFloat16; Efficient event communication by using a new Network-on-Chip with multicasting, a compression mechanism, and source-based routing. The implemented digital IP can be tuned for different applications to have a flexible number of cores and Neural Processing Elements (NPEs) per core and optional use of off-chip memory. Next to the hardware, the SENeCA platform includes an SDK and a hardware-aware simulator for close-loop synthesis/mapping optimization 1.”

Interestingly, one of the paper’s authors, Gert-Jan van Schaik, shares the same surname with André van Schaik from WSU. Maybe our Flemish or Dutch posters can tell us whether or not it is a common surname? Possibly the two of them are related?

https://www.researchgate.net/profile/Gert-Jan-Van-Schaik

A Dutch from University of Eindhoven

 

[Deleted member 118]

This patent was published (granted) in 2021. Our excitement over this was expressed on the other forum (which name shall not be mentioned) two years ago. Unfortunately nothing seems to have become of it.

It looks like it was filed in 2022?

IL297411A - Device and method for rapid identification of viruses - Google Patents

 

[Frangipani]

https://www.researchgate.net/profile/Gert-Jan-Van-Schaik

A Dutch from University of Eindhoven

Exactly, I just edited that in. He is or was at “imec the Netherlands”.

Home | imec the Netherlands

Imec’s Dutch division collaborates with local partners to develop technological solutions for global challenges.

www.imec-int.com

 

[Slade]

So what gives you so much positive thinking for Monday ?
I have been away for a week out bush
Have I missed something exciting?

Hi @Food4 thought , I’m generally positive about the prospects of Brainchip. It’s hard not to be given all that has happened over the past 12 months. Both Renesas and MegaChips must be near to getting their Akida inspired chips back from the foundry and then with a little luck into the hands of eager customers . The recent podcast by Nandan certainly gave me a lift. While you were at bush you might have missed a LinkedIn comment by the CEO of another company (don’t have the name on hand) calling Akida a game changer. Only a little over a week until the AGM which I’m looking forward to. My comment that this week could be a good one is just that. And if not, no stress. As someone pointed out, I only have to be right once. That’s the beauty of being a long term holder. Have a great coming week all.

 

[Rise from the ashes]

So what gives you so much positive thinking for Monday ?
I have been away for a week out bush
Have I missed something exciting?

This is the comment Slade is referring to.

 

[alwaysgreen]

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

Unfortunately, I don't have that "privelige".

 

[Gearitup]

Think we’ve found one of the larger BRN shorters …..Plato funds management.

LinkedIn

This link will take you to a page that’s not on LinkedIn

lnkd.in

concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.

Extract
There are a lot of red flags on the ASX right now

Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).

"That's the second highest out of the 10,000 companies that we look at," Allen says.

"And this is a company that just a few months ago had a market cap of over $3.5 billion and has less revenue than some cafes."

He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".

 

[Tothemoon24]

Non-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare Using Machine Learning​

by Ernest Dela Aglanu, Benzinga Staff Writer
https://twitter.com/thecitizendela
May 12, 2023 7:58 AM | 4 min read


Read in App
Advertiser Disclosure

Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms to analyze vast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.
Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID™ radio frequency sensor, using machine learning algorithms from Edge Impulse.

The Benefits of Medical Machine Learning​

One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.
Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.
Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.

A Future Of Improved Health Care Through Machine Learning​

As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.
Already, early uses of machine learning in diagnosis and treatment have shown promise to diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.
With reports indicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals, saving approximately $20 billion annually.
Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)’s ability to reduce errors and save lives.
Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.
One significant area this could benefit is monitoring blood glucose non-invasively — without pricking the finger for blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.
Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc.

AAPL-0.68%+ Free Alerts
, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc.

GOOGL+0.81%+ Free Alerts
) that measure heart rate, blood oxygen levels, and a host of other metrics.

But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.

Using Machine Learning To Enhance Bio-RFID Technology​

The Seattle-based company has partnered with Edge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietary Bio-RFID technology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.
The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.
Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based on AutoML and TinyML to make AI more accessible, enabling quick and efficient machine learning modeling.
The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.

 

[Rise from the ashes]

Non-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare Using Machine Learning​

by Ernest Dela Aglanu, Benzinga Staff Writer
https://twitter.com/thecitizendela
May 12, 2023 7:58 AM | 4 min read


Read in App
Advertiser Disclosure

Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms to analyze vast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.
Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID™ radio frequency sensor, using machine learning algorithms from Edge Impulse.

The Benefits of Medical Machine Learning​

One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.
Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.
Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.

A Future Of Improved Health Care Through Machine Learning​

As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.
Already, early uses of machine learning in diagnosis and treatment have shown promise to diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.
With reports indicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals, saving approximately $20 billion annually.
Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)’s ability to reduce errors and save lives.
Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.
One significant area this could benefit is monitoring blood glucose non-invasively — without pricking the finger for blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.
Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc.
AAPL-0.68%+ Free Alerts
, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc.
GOOGL+0.81%+ Free Alerts
) that measure heart rate, blood oxygen levels, and a host of other metrics.

But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.

Using Machine Learning To Enhance Bio-RFID Technology​

The Seattle-based company has partnered with Edge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietary Bio-RFID technology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.
The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.
Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based on AutoML and TinyML to make AI more accessible, enabling quick and efficient machine learning modeling.
The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.

It's like deja vu again the non-prick test

 

[Fullmoonfever]

Think we’ve found one of the larger BRN shorters …..Plato funds management.

LinkedIn

This link will take you to a page that’s not on LinkedIn

lnkd.in

concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.

Extract
There are a lot of red flags on the ASX right now

Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).


He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".



View attachment 36433

See that on the crapper as well?

(ball) Sackman posted it yesterday morn and was going back n forth with him yesterday cause I posted this over there in response

He tried to dismiss as not related to Plato vid but my point was it was founded on shorting. Apparently couldn't follow my logic

Kinda like this guy as backs up opinion with supporting logic unlike (ball) Sackman.

Opinion: Time for ASIC to ban short-selling - Hewison Private Wealth

www.hewison.com.au

 

[Tothemoon24]

iPronics: Making Photonics Viable with Programmable Chips

Spanish photonics pioneer iPronics Programmable Photonics is making photonic computing and telecommunications applications commercially viable with the world’s first reprogrammable general purpose photonics integrated circuit (PIC).

www.azooptics.com

This will result in enhanced performance in several areas, including chip coupling losses, space requirements, and power consumption. The new layer design will be produced in a pilot batch to be functionally tested, validated, and demonstrated.

The “PROMETHEUS” project is leveraging programmable PICs for neuromorphic computing architectures. Neuromorphic computing is inspired by the structure and working principles of biological brains. Neuromorphic chips create artificial “neurons” that distribute computer processing in a way that is analogous to brains.

iPronics’s PIC can be applied in neuromorphic computing through large scale photonic spiking neural networks that exploit the gigahertz firing rate of laser-neurons integrated in the chip.

With this project, iPronics will also put quantum random generators into practice, which, because they cannot be cloned, will embed physical layer encryption and authentication to the chip.

Before long, iPronics’s programmable PICs may form the basis of high-speed computing and sensing that will power the next generations of numerous cutting edge technologies.

 

[Tothemoon24]

Number 1

TECHNOLOGY

The Future of Innovation: Four Emerging Technologies Set to Disrupt the Next Decade​

written by Surya Narayan May 7, 2023 1 comment

As we move forward into the next decade, we can expect to see significant advancements in various emerging technologies. These innovations have the potential to reshape industries, redefine the way we live our lives, and even change the global economy. In this article, we will explore four groundbreaking technologies that are predicted to disrupt the next three to eight years and the impact they may have on the world.

1. Neuromorphic Computing: Revolutionizing AI and Machine Learning​

One of the most promising emerging technologies is neuromorphic computing, which aims to mimic the structure and function of the human brain using digital or analog processing techniques. By doing so, it has the potential to revolutionize the field of artificial intelligence (AI) and machine learning.
Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Early use cases include event detection, pattern recognition, and small dataset training. We can expect to see breakthrough neuromorphic devices by the end of 2023, with widespread adoption likely within five years. This technology could disrupt current AI developments, offering power savings and performance benefits unattainable with existing AI chips.

2. Self-Supervised Learning: Accelerating Data Annotation and Labeling​

Self-supervised learning is another groundbreaking technology that has the potential to transform industries reliant on AI and machine learning. This automated approach to annotating and labeling data enables machine learning algorithms to learn how information relates to other information, which is particularly useful in computer vision and natural language processing (NLP) applications.
The potential impact of self-supervised learning is extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. It is expected to take six to eight years for this technology to reach early majority adoption, significantly impacting existing products and markets.

3. The Metaverse: Creating an Immersive Digital Environment​

Another highly anticipated emerging technology is the metaverse, an immersive digital environment that intersects with the physical world’s real-time, spatially organized, and indexed content. The metaverse will bring together various independently evolving trends and technologies, such as spatial computing, digital persistence, decentralization tech, high-speed networking, and AI applications.
While the benefits and opportunities of the metaverse are not immediately viable, we can expect the transition toward the metaverse to be as significant as the one from analog to digital. It may take more than eight years for the metaverse to reach early majority adoption, but its impact on existing products and markets will be substantial.

4. Human-Centered AI: Enhancing Cognitive Performance and Ethics​

Human-centered AI (HCAI) is an AI design principle that emphasizes the benefit of AI to people and society. By focusing on improving transparency and privacy, HCAI aims to create a partnership between humans and AI, enhancing cognitive performance, learning, decision-making, and new experiences.
HCAI enables vendors to manage AI risks, be ethical, responsible, and more efficient with automation, while complementing AI with a human touch and common sense. It is expected to take three to six years for HCAI to reach early majority adoption, with a substantial impact on existing products and markets.
The next decade will be an exciting time for technology and innovation. Neuromorphic computing, self-supervised learning, the metaverse, and human-centered AI are just four of the many emerging technologies that will disrupt industries and reshape the world. As we continue to explore and develop these groundbreaking innovations, we can expect to see significant advancements that will redefine the way we live, work, and interact with the world around us.

 

[Diogenese]

Think we’ve found one of the larger BRN shorters …..Plato funds management.

LinkedIn

This link will take you to a page that’s not on LinkedIn

lnkd.in

concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.

Extract
There are a lot of red flags on the ASX right now

Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).


He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".



View attachment 36433

Hmmmm ....

This bloke admits he does not know what's happening with AI, but he doesn't seem to have done anything to remedy that deficit. Is his reference to picks and shovels an indication of the field in which he is most comfortable?

I don't know what his 126 flags are, nor the 22 that BRN earned other than it is down 80% from its peak, and it has no income. He also pays a lot for his coffee.


He refers to BRN's peak SP, but he does not mention what triggered that spike.

I wonder how he developed a flag system for a first-in-the-universe digital spiking neural network IP licensing company start-up. There must have been lots of examples to develop 126 flags.

Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).

"That's the second highest out of the 10,000 companies that we look at," Allen says.

"And this is a company that just a few months ago had a market cap of over $3.5 billion and has less revenue than some cafes."

"ASML (NASDAQ: ASML) has 88% market share, so a virtual monopoly in the deep ultraviolet lithography machines that are required to build the chips that are used in all of the technology around us, but specifically all of AI," he says.

"If you ask who is going to be the eventual winner of the AI race, it's very hard to say. But what you can say is that this picks and shovels approach of investing in the companies that are producing the machines that are producing the chips are going to win."

Yes, the US taking chip manufacture back in-house will cause a spike in UV lithography machine sales, and AI will increase the demand for new chips, but that does not make ASML an AI company.

Even though he makes most of his money from shorts, surely the time to short BRN was when it was $2.34?

Why would he choose to publish this negative post now that the BRN share price decline seems to have bottomed?

 

[Deleted member 118]

I think it started already

AI creator on the risks, opportunities and how it may make humans 'boring'

AI boss on the future of artificial intelligence and what will happen if computers become more clever than humans.

www.bbc.com

 

[Deena]

I have a question that I hope someone can help me with. I am attending the upcoming AGM in Sydney (for the first time) and was wondering for those attending the meeting how is your vote recorded at the meeting and linked to your shareholding?
Obviously everyone's shareholding varies, so how do they link this? Do you get a number, or have to record your result on paper?
Anyone?

Also, do we know how many TSX contributors are attending?
Deena

 

[Deleted member 118]

I have a question that I hope someone can help me with. I am attending the upcoming AGM in Sydney (for the first time) and was wondering for those attending the meeting how is your vote recorded at the meeting and linked to your shareholding?
Obviously everyone's shareholding varies, so how do they link this? Do you get a number, or have to record your result on paper?
Anyone?

Also, we know how many TSX contributors are attending?
Deena

I think you need to vote prior to the meeting as you can’t vote on the day and going on what I’ve read maybe a dozen members could be attending.