Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 1,081

Is deep learning a necessary ingredient for artificial intelligence?

The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward (consecutive) layers were later introduced. This is the essential component of the current implementation of deep learning algorithms. It improves the performance of analytical and physical tasks without human intervention, and lies behind everyday automation products such as the emerging technologies for self-driving cars and autonomous chat bots.

The key question driving new research published today in Scientific Reports is whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less .

“A positive answer questions the need for deep learning architectures, and might direct the development of unique hardware for the efficient and fast implementation of shallow learning,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research. “Additionally, it would demonstrate how brain-inspired shallow learning has advanced computational capability with reduced complexity and energy consumption.”

Google CEO Sundar Pichai Says A.I.’s Potential Downsides Keep Him Up At Night

Google CEO Sundar Pichai believes artificial intelligence is the most profound technology in the history of human civilization—potentially more important than the discovery of fire and electricity—and yet even he doesn’t fully understand how it works, Pichai said in an interview with CBS’s 60 Minutes that aired yesterday (April 16).

“We need to adapt as a society for it…This is going to impact every product across every company,” Pichai said of recent breakthroughs in A.I. in a conversation with CBS journalist Scott Pelley. It’s the Google CEO’s second long-form interview in a two weeks as he apparently embarks on a charm offensive with the press to establish himself and Google as a thought leader in A.I. after the company’s latest A.I. product received mixed reviews.

Google in February introduced Bard, an A.I. chatbot to compete with OpenAI’s ChatGPT and Microsoft’s new Bing, and recently made it available to the public. In an internal letter to Google employees in March, Pichai said the success of Bard will depend on public testing and cautioned things could go wrong as the chatbot improves itself through interacting with users. He told Pelley Google intends to deploy A.I. in a beneficial way, but suggested how A.I. develops might be beyond its creator’s control.

Creating Artificial Avians: A Novel Neural Network Generates Realistic Bird Pictures from Text using Common Sense

Summary: Researchers in China have developed a new neural network that generates high-quality bird images from textual descriptions using common-sense knowledge to enhance the generated image at three different levels of resolution, achieving competitive scores with other neural network methods. The network uses a generative adversarial network and was trained with a dataset of bird images and text descriptions, with the goal of promoting the development of text-to-image synthesis.

Source: Intelligent Computing.

In an effort to generate high-quality images based on text descriptions, a group of researchers in China built a generative adversarial network that incorporates data representing common-sense knowledge.

This new technology could blow away GPT-4 and everything like it

In a paper published in March, artificial intelligence (AI) scientists at Stanford University and Canada’s MILA institute for AI proposed a technology that could be far more efficient than GPT-4 — or anything like it — at gobbling vast amounts of data and transforming it into an answer.

Also: What is GPT-4? Here’s everything you need to know

Known as Hyena, the technology is able to achieve equivalent accuracy on benchmark tests, such as question answering, while using a fraction of the computing power. In some instances, the Hyena code is able to handle amounts of text that make GPT-style technology simply run out of memory and fail.

A.I. has to be regulated, not ‘thrown out the window’, says Prof. Michio Kaku

Michio Kaku, professor of theoretical physics at the City University of New York, Nilay Patel of The Verge, and Ethan Millman of the Rolling Stone discuss the future of artificial intelligence amid growing controversy. Hosted by Brian Sullivan, “Last Call” is a fast-paced, entertaining business show that explores the intersection of money, culture and policy. Tune in Monday through Friday at 7 p.m. ET on CNBC.

Human brains process social situations similarly—researchers discover a brain network for social perception

A recent study conducted at the University of Turku in Finland shows that different people have similar brain activity when perceiving social situations. Researchers discovered an extensive neural network in the human brain that effectively processes various social information.

Social interaction is central to all aspects of human life. Interaction requires the perception and interpretation of the social environment as well as flexible reacting to other people’s behavior. The is capable of such perception and decision-making automatically and rapidly. However, the processing mechanisms of the brain remain unresolved.

The study conducted at the Turku PET Centre revealed an extensive neural network in the human brain that processes various social information. The study showed that the social perceptual world of humans consists of a limited set of main dimensions, such as antisocial behavior, sexual or affiliative behavior, and communication. These social dimensions are processed in various located mainly in the back of the brain, more specifically in the occipital and temporal lobes.

Tesla Robot: News, Rumors, and Estimated Price, Release Date, and Specs

When used at home, it might take care of your yard, and even your grandparents, as Musk suggests in his piece, Believing in technology for a better future, in the Cyberspace Administration of China’s publication:

Tesla Bots are initially positioned to replace people in repetitive, boring, and dangerous tasks. But the vision is for them to serve millions of households, such as cooking, mowing lawns, and caring for the elderly.

The Tesla Bot is supposed to free up labor that you don’t want to do yourself. Since we already have machines that help us do all kinds of tasks (think: vehicles, dishwashers, forklifts), where it’d really succeed is when AI is used. That way, it can learn and recognize what needs to be done, and then do it for you by completing those last-step actions (driving to the store to get something, loading the dishwasher, etc.).