Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 1,289

From science fiction to reality: Xenobots are redefining biotechnology

The xenobot had been predicted to be a valuable tool in medicine and other fields. It is expected not only to help treat cancer but keep the aquatic bodies clean.

Ever imagined a world where we could utilize the power of a living cell to carry out certain functions? Just like we have robots that help in several aspects of our lives, some scientists in US universities have come up with a living robot known as the xenobot.

The xenobot had been predicted to be a valuable tool in medicine and other fields. In years to come, it wouldn’t only help treat cancer, but it would help keep the aquatic bodies clean.

New ‘SoftZoo’ allows engineers to test a variety of animal-inspired robots

A team of MIT researchers has developed a bio-inspired platform that enables engineers to study soft robot co-design called a “SoftZoo” due to the fact that it was inspired by animal-like robots.

This is according to a report by the institution published on Tuesday.

In the platform can be found 3D models of animals such as panda bears, fishes, sharks, and caterpillars.

Italian startup carves sculptures with robotic arm guided by AI

“Our robots are born from sculptors for sculpture,” says the artist.

A new startup called Robotor is seeking to revolutionize how sculptures are made by simplifying the sculpting process with the use of robotics and artificial intelligence. Founded by Filippo Tincolini and Giacomo Massari, the new company aims to make these works of art faster and easier to produce and even more sustainable.

The new technology allows for the development of structures that were once deemed inconceivable, according to a report by TNW published on Friday.

Khan Academy visions humble AI to be students’ special guide, not cheat

“It’s not perfect, but it’s still pretty magical at the same time. I think it dramatically transforms what Khan Academy is going to become.”

Khan Academy, a pioneer of digital education and savior of the less privileged, has set its sight on shaping artificial intelligence into a guide for students.

When Sam Altman and Greg Brockman, OpenAI’s CEO, and president, respectively, gave Sal Khan, Khan Academy’s founder, a private demo of their GPT software, Khan was impressed with the program’s ability to answer academic questions intelligently, reports Fast Company.

ChatGPT with Code Interpreter: The best use cases

The Code Interpreter is probably the most interesting ChatGPT plugin of OpenAI and opens up completely new capabilities for the Chatbot.

At the end of March, OpenAI introduced a groundbreaking new feature for ChatGPT: Plugins. One of them is a so-called Code Interpreter. With it, the language model can not only generate code, but also execute it independently.

As with Auto-GPT, the busy developer community has found exciting use cases for this technology in a very short time. Especially for data journalism and similar data-based analysis, the tool seems to open up completely new possibilities. This is also due to the possibility of uploading and downloading files up to 100 MB in size.

Google engineer warns it could lose out to open-source technology in AI race

And it would be hilarious too.

Google has been warned by one of its engineers that the company is not in a position to win the artificial intelligence race and could lose out to commonly available AI technology.

A document from a Google engineer leaked online said the company had done “a lot of looking over our shoulders at OpenAI”, referring to the developer of the ChatGPT chatbot.

Artificial neurons mimic complex brain abilities for next-generation AI computing

Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.

For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: capable of storing a value by modifying their conductance and then utilizing that value for in-memory processing.

However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.

/* */