Don’t believe anyone has posted this yet. Brainchip mentioned in the key players list ( I am sure not a surprise for all of us here 
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BRN Discussion Ongoing
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JDelekto
Regular
I used the stop light as an example because it's more concrete than driving a variated speed limit. It's either I stop when the light turns yellow (because I can get a ticket for running a red light) or go through it. If the company was approached for posting news, then maybe it was done at a frequency they didn't like. If they don't communicate how frequently they can post information, then the reaction will be to post none at all.
Another person noted that they saw the news through their trading platform. I also saw it appear through Fidelity, Yahoo Finance, and E*Trade as a press release.
My question would be, if more than two other trading platforms are showing this article, why isn't the platform you are using showing these press releases?
"We keep making progress. Step by step."
This comment says it all, based on this mornings early news, I can't say whom I'm quoting, but believe me when I say it's genuine.
The current building blocks that are being placed at our foundations are more representative of where we are currently, not where we
were 3 or more years ago, there's solid and then there's solid !!
The key personnel all know that we Aussie shareholders appreciate their individual efforts, and sometimes that's what it comes down to,
being able to relate to an individual on a one-on-one basis within any given company, in this situation I'm referring to Rob, his sideways
movement within the company's leadership group was a masterstroke, meaning, having your key personnel in the best position to advance
the company, whereby they are comfortable in the role they have been gifted.
On another note, has anyone ever raised the point that maybe Manny didn't wish to leave his role as Chair, and was actually asked to stand
aside to make way for Antonio? just thought I'd put that out there. I have decided to vote NO.
Like for like I'd accept, but not the current proposal, I know voting is a private thing for many, but I have always been a free spirit, and don't
really care if you know my personal position, I'll always stand by my convictions.
Have a nice evening or sleep or breakfast, whatever the case may be.....my regards.....Tech
This comment says it all, based on this mornings early news, I can't say whom I'm quoting, but believe me when I say it's genuine.
The current building blocks that are being placed at our foundations are more representative of where we are currently, not where we
were 3 or more years ago, there's solid and then there's solid !!
The key personnel all know that we Aussie shareholders appreciate their individual efforts, and sometimes that's what it comes down to,
being able to relate to an individual on a one-on-one basis within any given company, in this situation I'm referring to Rob, his sideways
movement within the company's leadership group was a masterstroke, meaning, having your key personnel in the best position to advance
the company, whereby they are comfortable in the role they have been gifted.
On another note, has anyone ever raised the point that maybe Manny didn't wish to leave his role as Chair, and was actually asked to stand
aside to make way for Antonio? just thought I'd put that out there. I have decided to vote NO.
Like for like I'd accept, but not the current proposal, I know voting is a private thing for many, but I have always been a free spirit, and don't
really care if you know my personal position, I'll always stand by my convictions.
Have a nice evening or sleep or breakfast, whatever the case may be.....my regards.....Tech
Tothemoon24
Top 20
robsmark
Regular
Excuse me. If I’m annoying you, that’s your problem. Learn some manners. I was simply responding to a post in a civil manner, there’s no need to insult people with differing viewpoints.
BaconLover
Founding Member
Kachoo
Regular
TTM your on top of these posts some awsome stuff. Yet another solid product!
Wow!!! This week just keeps getting better. Love it.
Tothemoon24
Top 20
Fullmoonfever
Top 20
@Tothemoon24Bio-inspired device captures images by mimicking human eye
Credit: Photo Provided . All Rights Reserved.
MAY 2, 2023
By Matthew Carroll
UNIVERSITY PARK, Pa. – Drawing inspiration from nature, Penn State scientists have developed a new device that produces images by mimicking the red, green and blue photoreceptors and the neural network found in human eyes.
“We borrowed a design from nature — our retinas contain cone cells that are sensitive to red, green and blue light and a neural network that starts processing what we are seeing even before the information is transmitted to our brain,” said Kai Wang, assistant research professor in the Department of Materials Science and Engineering at Penn State. “This natural process creates the colorful world we can see.”
To achieve this in an artificial device, the scientists created a new sensor array from narrowband perovskite photodetectors, which mimic our cone cells, and connected it to a neuromorphic algorithm, which mimics our neural network, to process the information and produce high-fidelity images.
Photodetectors convert light energy into electrical signals and are essential for cameras and many other optical technologies. Narrowband photodetectors can focus on individual parts of the light spectrum, like the reds, greens and blues that make up visible light, the scientists said.
“In this work, we found a novel way to design perovskite material that is sensitive to only one wavelength of light,” Wang said. “We created three different perovskite materials, and they are designed in a way that they can only be sensitive to red, green or blue colors.”
The technology may represent a way around using filters found in modern cameras that lower resolution and increase cost and manufacturing complexity, the scientists said.
Silicon photodetectors in cameras absorb light but do not distinguish colors. An external filter separates the reds, greens and blues, and the filter only allows one color to reach each section of the light sensor, wasting two-thirds of the incoming light.
“When the light is filtered, there is some loss of information and that can be avoided using our design. So we propose this work may represent a future camera sensing technique that can help people to get a higher spatial resolution.”
And because the scientists used perovskite materials, the new devices generate power as they absorb light, potentially opening the door to battery-free camera technology, the scientists said.
“The device structure is similar to solar cells that use light to generate electricity,” said Luyao Zheng, a postdoctoral researcher at Penn State. “Once you shine a light on it, it will generate a current. So like our eyes, we don’t need to apply energy to capture this information from light.”
This research could also trigger further developments in artificial retina biotechnology. Devices based on this technology could someday replace dead or damaged cells in our eyes to restore vision, according to the scientists.
The findings, reported in the journal Science Advances, represent several fundamental breakthroughs in realizing perovskite narrowband photodetection devices — from materials synthesis to device design to systems innovation, the scientists wrote in the journal.
Perovskites are semiconductors, and when light hits these materials it creates electron-hole pairs. Sending these electrons and holes in opposite directions is what generates an electrical current.
In this study, the scientists created thin-film perovskites with heavily unbalanced electron-hole transport, meaning the holes are moving through the material faster than the electrons. By manipulating the architecture of the unbalanced perovskites, or how the layers are stacked, the scientists found they could harness properties than turn the materials into narrowband photodetectors.
They created a sensor array with these materials and used a projector to shine an image through the device. Information collected in the red, green and blue layers was fed into a three-sub-layer neuromorphic algorithm for signal processing and image reconstruction. Neuromorphic algorithms are a kind of computing technology that seeks to emulate the operation of the human brain.
“We tried different ways to process the data,” Wang said. “We tried directly merging the signals from the three color layers, but the picture was not very clear. But when we do this neuromorphic processing, the image is much closer to the original.”
Because the algorithm mimics the neural network in human retinas, the findings could provide new insight into the importance of these neural networks to our vision, the scientists said.
“By joining our device and this algorithm together, we can demonstrate that the neural network functionality is really important in the vision processing in human eyes,” Wang said.
Also contributing to this research from Penn State were: Swaroop Ghosh, associate professor, and Junde Li, doctoral candidate, in the Department of Electrical Engineering and Computer Science; and Dong Yang, assistant research professor, Jungjin Yoon and Tao Ye, postdoctoral researchers, and Abbey Marie Knoepfel and Yuchen Hou, doctoral candidates, in the Department of Materials Science and Engineering. Shashank Priya, former associate vice president for research and director of strategic initiatives and professor of materials science and engineering, also contributed.
The Air Force Office of Scientific Research provided funding for this work. Researchers on the project were also supported by the U.S. Department of Energy, the National Institute of Food and Agriculture and the National Science Foundation.
Thanks for posting this one. I recall seeing it once before but had forgotten about it.
May seem tenuous to Akida at first but another small connection (read...DOT) that could be interesting now.
Would expect, given who the research partner in this program is, they'd probably know about Akida
Realtime Neuromorphic Cyber-Agents (Cyber-NeuroRT)
Award InformationAgency: Department of Energy
Branch: N/A
Contract: DE-SC0021562
Agency Tracking Number:0000263950
Amount: $1,650,000.00
Phase: Phase II
Program:STTR
Solicitation Topic Code:C51-03a
Solicitation Number:N/A
Timeline
Solicitation Year:2021
Award Year:2022
Award Start Date (Proposal Award Date):2022-04-04
Award End Date (Contract End Date):2024-04-03
Small Business Information
QUANTUM VENTURA INC
1 S Market Street suite 1715
San Jose, CA 95113
United States
DUNS:080159595
HUBZone Owned:Yes
Woman Owned:No
Socially and Economically Disadvantaged:Yes
Principal Investigator
Name: Srini Vasan
Phone: (424) 227-1417
Email: srini@quantumventura.com
Business Contact
Name: Srini Vasan
Phone: (424) 227-1417
Email: srini@quantumventura.com
Research Institution
Name: Pennsylvania State University Harrisburg
Address:
777 W Harrisburg Pike BLDG Olmsted
State College, PA 17057-0000
United States
Type: Nonprofit College or University
Abstract
As part of Phase 1 feasibility study, we evaluated the viability to develop a real-time HPC-scale neuromorphic cyber agent software called Cyber-NeuroRT. We evaluated several scalable neuromorphic techniques to detect and predict cybersecurity threats, compared full precision machine learning models with neuromorphic models and developed an end-to-end Proof of Concept (POC). Upon completion of Phase 2 prototype, we will produce dramatic reductions in latency and power--up to 100x--without sacrificing accuracy. This will enable quicker response times and savings in operating costs. Cyber-NeuroRT will be a real-time neuromorphic processor-based monitoring tool to predict and alert cybersecurity threats and warnings using the Neuromorphic Platforms of Intel Loihi 1 and BrainChip Akida. For our Phase 1 POC development, we used 450,000 Zeek log entries with a mixture of normal and malicious data for training the supervised ML models. As part of our study, we covered the following: Cyber Attack types covered – 8 attack types: backdoor, DDOS, DOS, injection, password, ransomware, scanning and XSS, Source files – Zeek log files and Packet Capture Format files (PCAP) containing both malicious and normal records. We used both Supervised and Unsupervised algorithms. We used algorithms including SNN and CNN-to-SNN conversion with unsupervised learning and supervised learning rules. To build a full-fledged prototype of Cyber-Neuro RT, we plan to transition the proof-of-concept work to scale to a large data set with additional threat types and other datasets from an HPC environment. HPC environments operate at larger scales than traditional IT domains and our solution should be able to monitor and predict events at more than 160,000 inferences per second. Tuning of Spike Neural Networks (SNN) parameters such as precision of weights and number of neurons used are two software parameters to explore. The chip can be tuned between high v. low power modes and performance can be studied as a function of power draw. Evaluation will be performed across a variety of datasets and parameter settings to estimate deployment performance. We will work on efficiency scaling of SNN algorithms in terms of accuracy and hardware metrics like power and energy consumption. Since cybersecurity attack classification is a temporal process, we will leverage recent advancements in the algorithm community to map temporal dynamics of SNNs to recurrent architectures. Further, to adapt to novel attack vectors, we will explore unsupervised learning techniques in a dynamic network architecture where we will grow or shrink the network as and when novel attack vectors arise. We will also perform an algorithm-hardware co-design analysis by ensuring that our algorithm proposals cater to and consider specific constraints from Akida or Loihi processors like network size, bit quantization levels, among others. 3.1 Some of the features of Cyber-NeuroRT prototype shall include: Ability to monitor, predict and provide system wide alerts of impending cybersecurity threats and warnings at scale by collecting and prioritizing data from Zeek logs and PCAP files streamed in real-time or batch. We will expand and refine different training techniques like CNN to SNN conversion, direct backpropagation training through surrogate gradient methods or local unsupervised Spike Timing Dependent Plasticity (STDP) enabled approaches. Compare performance of threat detection between neuromorphic processing vs GPU-based systems and compare between Akida and Intel Loihi processors. Ability to process the data system-wide at an unprecedented scale enabling adaptive, streaming analysis for monitoring and maintaining large-scale scientific computing integrity. Dashboards for security administrators and security analysts.
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Getupthere
Regular
Powering AI On Mobile Devices Requires New Math And Qualcomm Is Pioneering It — Forbes
We just needed a new math for all this AI heavy lifting on smartphones and other lower power edge devices, and it appears Qualcomm is leading that charge.
Stable Diffusion typically runs in the cloud via a web browser, and is driven by data center servers with big power budgets and a ton of silicon horsepower. However, the image above was generated by Stable Diffusion running on a smartphone, without a connection to that cloud data center and running in airplane mode, with no connectivity whatsoever. And the AI model rendering it was powered by a Qualcomm Snapdragon 8 Gen 2 mobile chip on a device that operates at under 7 watts or so.
Kachoo
Regular
Went with the Canadian club 12 year old!
Tothemoon24
Top 20
Bravo
If ARM was an arm, BRN would be its biceps💪!
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Deadpool
Did someone say KFC
Hi @Jchandel good find.Don’t believe anyone has posted this yet. Brainchip mentioned in the key players list ( I am sure not a surprise for all of us here
This article lists the key players, and BRN is well and truly rubbing shoulders with the big boys now for all to see, there's no down playing this anymore.
According to Next Move Strategy Consulting the market for artificial intelligence (AI) is expected to show strong growth in the coming decade. Its value of nearly 100 billion U.S. dollars is expected to grow twentyfold by 2030, up to nearly two trillion U.S. dollars ($2,000,000,000,000) The AI market covers a vast amount of industries.
The Global Neuromorphic Computing Market Size is expected to reach USD 8,275,900,000 U.S. dollars by 2030, at a CAGR of 85.73% during the forecast period 2021 to 2030.
As the old master used to say, if BRN captures just 1% of the market.
1% of AI market 2030 =$200 billion
1% of Neuromorphic market 2030 =$82,759,000
Happy days are just around the bend, my opinion only.
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Fullmoonfever
Top 20
Just found this LinkedIn post by QV from several months ago on their paper re Cyber-Neuro RT.
Was the comment I found interesting.
I "hope" he gets the chance too
in.linkedin.com
Quantum Ventura Inc.
196 followers
7mo
We're proud to share a recent paper from our group on machine learning for cybersecurity, done under a DOE contract. Detecting malicious network traffic was shown with full precision and neuromorphic deep neural networks with good accuracy. Much more to come as we scale this system for larger datasets, updated models, etc.! Please contact Aaron Goldberg for more details! Thank you Wyler Zahm, Tyler Stern, Malyaban Bal, Abhronil Sengupta, Aswin Jose, Suhas Chelian, and srini vasan for the awesome work! #machinelearning #deeplearning #cybersecurity #neuromorphic
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Ganesan Narayanasamy
Senior Technical Computing Solution and Client Care Manager at IBM
7mo
Congratulations team hope to run this solution on POWER systems
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Was the comment I found interesting.
I "hope" he gets the chance too
We're proud to share a recent paper from our group on machine learning for cybersecurity, done under a DOE contract. | Quantum Ventura Inc.
We're proud to share a recent paper from our group on machine learning for cybersecurity, done under a DOE contract. Detecting malicious network traffic was shown with full precision and neuromorphic deep neural networks with good accuracy. Much more to come as we scale this system for larger...
Quantum Ventura Inc.
196 followers
7mo
We're proud to share a recent paper from our group on machine learning for cybersecurity, done under a DOE contract. Detecting malicious network traffic was shown with full precision and neuromorphic deep neural networks with good accuracy. Much more to come as we scale this system for larger datasets, updated models, etc.! Please contact Aaron Goldberg for more details! Thank you Wyler Zahm, Tyler Stern, Malyaban Bal, Abhronil Sengupta, Aswin Jose, Suhas Chelian, and srini vasan for the awesome work! #machinelearning #deeplearning #cybersecurity #neuromorphic
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Ganesan Narayanasamy
Senior Technical Computing Solution and Client Care Manager at IBM
7mo
Congratulations team hope to run this solution on POWER systems
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Yoda
Regular
I agree, we are in THE growth market of the next decade which is soon going to be gushing money, with a unique solution that no one else has but many will need, we have a product that has almost limitless applications, we have a wall of patents, we have partners signing up now on a regular basis to develop products with us, we have 2 of the biggest chip manufacturers in the world who have already signed IP Agreements and are busily making products and we have the likes of Mercedes publicly endorsing us. That sounds like a pretty good launch pad for a business for the next decade.! Anyone that really believes we will never sell Akida or who can only see red flags - feel free to reach out to me and sell me your shares - because I can see a chequered flag in the distance at the finish line (a flag hanging with dividend cheques that is). In my opinion, it's just the waiting game now. I've got a lot on the line with this company, I could have sold out at $2 and made millions but I didn't because I am GOING FOR GOLD with this and you don't get a chance to go for gold like this and make really big returns in the market every day, every week or even every decade. This is still a rare opportunity. Hang in there, it's a hell of a ride, but it's all about where we end up not where we came from AKIDA THEY-NEED-YA and when they discover you they won't want to live without you.
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