Lifeboat Foundation

With “Grok”, Elon Musk introduces a chatbot built with “X” data for “X” premium users. In contrast to OpenAI with ChatGPT, Musk gives the chatbot more creative leeway in its responses.

Musk and his company describe Grok as a humorous, witty, and rebellious chatbot that can answer almost any question. Grok uses its model knowledge based on Internet and X data, as well as real-time information from X, to provide answers. According to xAI, the chatbot also answers “spicy questions” that would be rejected by most other AI systems.

Researchers present RoboGen, a generative robotic agent that automatically learns new skills in a generative simulation.

The work by researchers from CMU, Tsinghua IIIS, MIT CSAIL, UMass Amherst, and the MIT-IBM AI Lab aims to leverage recent advances in generative AI to generate infinite training data for automated robot learning.

According to the team, RoboGen is a generative robotic agent that learns various robotic tasks automatically and en masse through generative simulation. The team is using existing foundation models, such as OpenAI’s GPT-4, to “automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision.”

They re already trainin this AI to drink, smoke cigars, and play golf. Agi almost achieved!

An AI bot proved it was capable of insider trading and lying about its actions, researchers found. The findings were presented at this week’s UK AI Safety Summit. An.

Imagine you’re visiting a friend abroad, and you look inside their fridge to see what would make for a great breakfast. Many of the items initially appear foreign to you, with each one encased in unfamiliar packaging and containers. Despite these visual distinctions, you begin to understand what each one is used for and pick them up as needed.

Inspired by humans’ ability to handle unfamiliar objects, a group from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) designed Feature Fields for Robotic Manipulation (F3RM), a system that blends 2D images with foundation model features into 3D scenes to help robots identify and grasp nearby items. F3RM can interpret open-ended language prompts from humans, making the method helpful in real-world environments that contain thousands of objects, like warehouses and households.

F3RM offers robots the ability to interpret open-ended text prompts using natural language, helping the machines manipulate objects. As a result, the machines can understand less-specific requests from humans and still complete the desired task. For example, if a user asks the robot to “pick up a tall mug,” the robot can locate and grab the item that best fits that description.

New computational techniques, “HighLight” and “Tailors and Swiftiles,” could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative AI. The work, from MIT and NIVIDIA, aims to accelerate sparse tensors for AI models by introducing more efficient and flexible ways to take advantage of sparsity.

A tangle of silver nanowires may pave the way to low-energy real-time machine learning.

They look like storm clouds that could fit on the head of a pin: Organoids are three-dimensional cell cultures that play a key role in medical and clinical research. This is thanks to their ability to replicate tissue structures and organ functions in the petri dish. Scientists can use organoids to understand how diseases occur, how organs develop, and how drugs work.

Single-cell technologies allow researchers to drill down to the molecular level of the cells. With spatial transcriptomics, they can observe which genes in the organoids are active and where over time.

The miniature organs are usually derived from stem cells. These are cells that haven’t differentiated at all, or only minimally. They can become any kind of cell, such as heart or kidney cells, muscle cells, or neurons. To make stem cells differentiate, scientists “feed” them with growth factors and embed them in a nutrient solution.

NASA’s Kepler spacecraft has discovered most of the confirmed exoplanets that we know of. But its successor, TESS (Transiting Exoplanet Survey Satellite), is catching up. New research announces the validation of eight more TESS candidates, and they’re all Super-Earths.

TESS’s planet-hunting mission has a more refined goal than its predecessor, Kepler. TESS was specifically built to detect exoplanets transiting in front of bright stars in Earth’s neighbourhood. It’s found about 400 confirmed exoplanets, but there’s a list of exoplanets awaiting confirmation that contains almost 6,000 candidates. There are only two ways to confirm all these exoplanets-in-waiting: further observations and statistical methods.

What all those unconfirmed candidates amount to is data. They’re hiding in TESS’s data, waiting for clever scientists to validate them. Further observations can help uncover them, but not alone.

An AI chatbot from Elon Musk’s xAI will be released to X Premium+ subscribers once it’s out of beta, the CEO tweeted. He also shared screenshots of conversations with the AI, and said it is designed to have humorous responses and has access to real-time information from X.