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Mind-Boggling Neuromorphic Brain Chips (Part 1)

I can’t help myself. I keep thinking about the 1961 musical Stop the World—I Want to Get Off. After opening in Manchester, England, the show transferred to the West End, London, where it ran for 485 performances.

It’s not that the plot of this extravaganza has anything to do with what we are talking about here. It’s just that the sentiment embodied by the show’s title reflects the way I’m currently feeling about artificial intelligence (AI) and machine learning (ML).

On the one hand, the current state of play with AI and ML is tremendously exciting. On the other hand, I’m starting to think that I’ve enjoyed all the excitement I can stand.

Augmented Reality with X-Ray Vision

X-AR uses wireless signals and computer vision to enable users to perceive things that are invisible to the human eye (i.e., to deliver non-line-of-sight perception). It combines new antenna designs, wireless signal processing algorithms, and AI-based fusion of different sensors.

This design introduces three main innovations:

1) AR-conformal wide-band antenna that tightly matches the shape of the AR headset visor and provides the headset with Radio Frequency (RF) sensing capabilities. The antenna is flexible, lightweight, and fits on existing headsets without obstructing any of their cameras or the user’s field of view.

AGI Soon? 1 AI Using 2 Modalities Solves Visual IQ Test w/ 1,600,000,000 Parameters | Kosmos-1

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A new multimodal artificial intelligence model from Microsoft called Kosmos-1 is able to process both text and visual data to the point of passing a visual IQ test with 26 percent accuracy, and researchers say this is a step towards AGI. Stable Diffusion AI can now read brain waves to reconstruct images that people are thinking about. Stanford has created a world record brain computer interface device with the help of AI to allow patients to type 62 words per minute with their thoughts.

AI News Timestamps:
0:00 Microsoft Kosmos-1 AI & AGI
3:34 AI Neuroscience Tech Reads Brain Waves.
5:43 AI & BCI Breaks Record.

#technology #tech #ai

Survival Strategies in the Era of AI Taught

Dr. Li Jiang is a director of Stanford AIRE program. Many of you think ChatGPT started the era of AI. But, Dr. Jiang says it started already. AI seems much better than we do. It seems it can solve many problems. Then, what can we do? How can we survive from AI? How should we do? Dr. Jiang suggest this method for us who are facing the era of AI.

Stanford DLI Challenge is a unique program that empowers individuals to create cutting-edge digital learning solutions. With guidance from experienced educators and designers, gain hands-on experience with the latest technologies and teaching methods. Sign up now to join a community of educators and designers dedicated to transforming education for the better: https://acceleratelearning.stanford.edu/get-involved/digital…challenge/

00:00 Intro.
00:47 Know AI Thinking.
01:32 3 Things of AI Thinking.
03:45 How Do We Invent New Things?
04:29 5 Steps of Design Thinking.
07:05 I Let My Students Use ChatGPT

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Subtitles for this video were created using XL8.ai machine translation.

Deep Neural Networks for Speech and Image Processing

AERFAI Summer School on Pattern Recognition in Multimodal Human Interaction — Deep Neural Networks for Speech and Image Processing.
This is the sixth edition in a series of AERFAI Summer Schools devoted to a wide range of topics in the fields of Pattern Recognition and Machine Learning. The focus of this year’s Summer School is to provide the students the most relevant techniques to analyze and understand the information conveyed in human audiovisual communication.

Vídeo disponible en: http://tv.campusdomar.es/en/video/787.html

Scientists Believe ‘Organoid Intelligence’ Is the Future of Computing

Scientists Believe, ‘Organoid Intelligence’, Is the Future of Computing. CNN reports that as part of a new field called “organoid intelligence,” a computer powered by human brain cells could shape the future. Organoids are lab-grown tissues capable of brain-like functions, such as forming a network of connections. Brain organoids were first grown in 2012 by Dr. Thomas Hartung, a professor of environmental health and engineering, by altering human skin samples. Brain organoids were first grown in 2012 by Dr. Thomas Hartung, a professor of environmental health and engineering, by altering human skin samples. Computing and artificial intelligence have been driving the technology revolution but they are reaching a ceiling., Dr.

Open source software could deliver huge time savings for computational chemists

A new program can streamline the process of creating, launching and analysing computational chemistry experiments. This piece of software, called AQME, is distributed for free under an open source licence, and could contribute to making calculations more efficient, as well as accelerating automated analyses.

‘We estimate time savings of around 70% in routine computational chemistry protocols,’ explains lead author Juan Vicente Alegre Requena, at the Institute of Chemical Synthesis and Homogeneous Catalysis (ISQCH) in Zaragoza, Spain. ‘In modern molecular simulations, studying a single reaction usually involves more than 500 calculations,’ he explains. ‘Generating all the input files, launching the calculations and analysing the results requires an extraordinary amount of time, especially when unexpected errors appear.’

Therefore, Alegre and his colleagues decided to code a piece of software to skip several steps and streamline calculations. Among other advantages, AQME works with simple inputs, instead of the optimised 3D chemical structures usually required by other solutions. ‘It’s exceptionally easy,’ says Alegre. ‘AQME is installed in a couple of minutes, then the only indispensable input is as a simple Smiles string.’ Smiles is a system developed by chemist and coder Dave Weininger in the late 1980s, which converts complex chemical structures into a succession of letters and numbers that is machine readable. This cross-compatibility could allow integration with chemical databases and machine-learning solutions, most of which include datasets in Smiles format, explains Alegre.

What makes a neural network remember?

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals—both biochemical and electrical—and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Now, researchers at the Okinawa Institute of Science and Technology (OIST) have altered a commonly used computer model of called a Hopfield network in a way that improves performance by taking inspiration from biology. They found that not only does the new network better reflect how neurons and other cells wire up in the , it can also hold dramatically more memories.

The complexity added to the network is what makes it more realistic, says Thomas Burns, a Ph.D. student in the group of Professor Tomoki Fukai, who heads OIST’s Neural Coding and Brain Computing Unit. “Why would biology have all this complexity? Memory capacity might be a reason,” Mr. Burns says.

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