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.
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.
The first product from Elon Musk-led xAI was announced on Friday, and the CEO has suggested that Tesla’s vehicles may natively run a smaller version of the AI assistant.
On Saturday, X user and Tesla enthusiast Chuck Cook spotted that Musk liked a post saying that a smaller, quantized version of the AI model Grōk would run natively on Tesla with local computing power. Following Cook’s mention, Musk noted that Teslas will likely come with the largest amount of usable inference compute in the world — as long as the vehicles’ AI computer can run the Grōk model.
Provided our vehicle AI computer is able to run the model, Tesla will probably have the most amount of true usable inference compute on Earth.
Imagine trying to read a 2000-year old scroll from an ancient civilization. Now imagine that scroll is rolled up, and in a delicate, charred, carbonized form, having been engulfed by the fiery eruption of a volcano. The task would seem virtually impossible, and the information in the scroll lost forever. Right?|
Dr. Isaac Asimov was a prolific science fiction author, biochemist, and professor. He was best known for his works of science fiction and for his popular science essays. Born in Russia in 1920 and brought to the United States by his family as a young child, he went on to become one of the most influential figures in the world of speculative fiction. He wrote hundreds of books on a variety of topics, but he’s especially remembered for series like the “Foundation” series and the “Robot” series. Asimov’s science fiction often dealt with themes and ideas that pertained to the future of humanity.
The “Foundation” series for example, introduced the idea of “psychohistory” – a mathematical way of predicting the future based on large population behaviors. While we don’t have psychohistory as described by Asimov, his works did reflect the belief that societies operate on understandable and potentially predictable principles.
Asimov’s “Robot” series introduced the world to the Three Laws of Robotics, which are: A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. These laws have been influential in discussions about robot ethics and the future of AI, even though they are fictional constructs.
Like many futurists and speculative authors, Asimov’s predictions were a mix of hits and misses. Hits: He anticipated the rise of computer networks and something resembling the internet. He also foresaw the idea of robotic assistants and many issues that would arise with automation and the changing nature of work.
Misses: Some of Asimov’s predictions, like many other futurists’, were either too optimistic in terms of timeframes or overestimated certain societal shifts. For example, while he predicted a rise in automation, some of the specifics (like how society would handle the transition) have been more complex than he foresaw.
