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Spiking neural network promises improved AI


20th April 2021
11:39 am
20th April 2021
11:39 am
A biomimicking neural network housed in a microchip could lead to faster and more efficient applications of artificial intelligence.
Advances in artificial intelligence technology is leading to the development of neural networks that mimic the biology of the brain (© 2021 KAUST)
This is the claim of researchers at KAUST in Saudi Arabia whose ‘spiking’ neural network lays the foundation for much improved hardware-based AI computing systems.
Artificial intelligence is gaining traction in areas including advanced automation, data mining and healthcare. These systems are based on a mathematical artificial neural network (ANN) composed of layers of decision-making nodes. Labelled data is fed into the system to ‘train’ the model to respond a certain way, then the decision-making rules are locked in and the model is put into service on standard computing hardware. ....

Saudi Arabia , Wenzhe Guo , Transactions On Neural Networks , Neural Networks , Learning Systems , சவுதி அரேபியா , பரிவர்த்தனைகள் ஆன் நரம்பியல் நெட்வொர்க்குகள் , நரம்பியல் நெட்வொர்க்குகள் , கற்றல் அமைப்புகள் ,

Critique of 2018 Turing Award for Drs. Bengio & Hinton & LeCun


Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature:
Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors, ....

United States , Holland Library , New York , United Kingdom , British Columbia , Mountain View , San Diego , San Francisco , World University , Noord Holland , Andhra Pradesh , Baden Wüberg , Eteläuomen Läi , Brighton And Hove , University Of Colorado At Boulder , City Of , Comunidad Autonoma De Cataluna , Long Beach , Systemei Monatshefte , Technische Universit , Ivakhnenko Lapa , Sherrington Kirkpatrick , Terrence Sejnowski , Birkhaeuser Basel , Principia Mathematica , Konrad Zuse ,

Algorithm helps artificial intelligence systems dodge "adversarial" inputs | MIT News | Massachusetts Institute of Technology


Caption:
A deep-learning algorithm developed by MIT researchers is designed to help machines navigate in the real world. In tests with the video game Pong, the researchers introduced an “adversary” that pulled the ball slightly further down than it actually was.
Credits:
Image: Jose-Luis Olivares, MIT
Terms of Use:
Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a
Creative Commons Attribution Non-Commercial No Derivatives license.
You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided ....

Michael Everett , Ford Motor Company , Department Of Aeronautics , Transactions On Neural Networks , Certified Adversarial Robustness , Deep Reinforcement , Neural Networks , Astro Professor Jonathan , Jonathan How , Bjorn Lutjens , Mit Aeroastro , Deep Reinforcement Learning , Adversarial Attacks , Autonomous Vehicle Navigation , Collision Avoidance , மைக்கேல் எவரெட் , ஃபோர்ட் மோட்டார் நிறுவனம் , துறை ஆஃப் வான்செலவுத்துறை , பரிவர்த்தனைகள் ஆன் நரம்பியல் நெட்வொர்க்குகள் , ஆழமான வலுவூட்டல் , நரம்பியல் நெட்வொர்க்குகள் , ஆஸ்ட்ரோ ப்ரொஃபெஸர் ஜொனாதன் , ஜொனாதன் எப்படி , ஆழமான வலுவூட்டல் கற்றல் , தன்னாட்சி வாகனம் வழிசெலுத்தல் ,

Algorithm helps artificial intelligence systems dodge


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In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward.
Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action steer right, steer left, or continue straight to avoid hitting a pedestrian that its cameras see in the road.
But what if there s a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called adversarial inputs, it might take unnecessary and potentially dangerous action. ....

Michael Everett , Ford Motor Company , Department Of Aeronautics , Transactions On Neural Networks , Certified Adversarial Robustness , Deep Reinforcement , Neural Networks , Astro Professor Jonathan , Mathematics Statistics , Algorithms Models , Technology Engineering Computer Science , Computer Science , Multimedia Networking Interface Design , Software Engineering , Theory Design , Research Development , Robotry Artificial Intelligence , மைக்கேல் எவரெட் , ஃபோர்ட் மோட்டார் நிறுவனம் , துறை ஆஃப் வான்செலவுத்துறை , பரிவர்த்தனைகள் ஆன் நரம்பியல் நெட்வொர்க்குகள் , ஆழமான வலுவூட்டல் , நரம்பியல் நெட்வொர்க்குகள் , ஆஸ்ட்ரோ ப்ரொஃபெஸர் ஜொனாதன் , கணிதம் புள்ளிவிவரங்கள் , தொழில்நுட்பம் பொறியியல் கணினி அறிவியல் ,