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Open AI’s Codex tool claims to help developers write code faster, better

It can’t fix the code when it does not work though.

The conversational chatbot from OpenAI, ChatGPT, has attracted the attention of users worldwide. However, the lesser-known tool called Codex from OpenAI has quickly become a top favorite among developers. Codex currently powers the Copilot feature on GitHub.

How does Open AI’s Codex work?


Guillaume/ iStock.

For those, who are relatively new to the world of programming, GitHub is an open-source community where developers share the code for the software they have written for others to use. Microsoft acquired GitHub over four years ago. Working closely with OpenAI, Microsoft has gained access to ChatGPT and Codex, OpenAI’s ChatGPT-like solution for code.

Biocomputing With Mini-Brains as Processors Could Be More Powerful Than Silicon-Based AI

So why not sidestep this conundrum and use neural tissue directly as a biocomputer?

This month, a team from Johns Hopkins University laid out a daring blueprint for a new field of computing: organoid intelligence (OI). Don’t worry—they’re not talking about using living human brain tissue hooked up to wires in jars. Rather, as in the name, the focus is on a surrogate: brain organoids, better known as “mini-brains.” These pea-sized nuggets roughly resemble the early fetal human brain in their gene expression, wide variety of brain cells, and organization. Their neural circuits spark with spontaneous activity, ripple with brain waves, and can even detect light and control muscle movement.

In essence, brain organoids are highly-developed processors that duplicate the brain to a limited degree. Theoretically, different types of mini-brains could be hooked up to digital sensors and output devices—not unlike brain-machine interfaces, but as a circuit outside the body. In the long term, they may connect to each other in a super biocomputer trained using biofeedback and machine learning methods to enable “intelligence in a dish.”

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