Lifeboat Foundation

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.

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?|

As it turns out, new developments are changing that. Modern scanning techniques and machine learning tools have made it possible to read fragments of the heavily-damaged Herculaneum scrolls. Hopes are now that more of the ancient writings will be salvaged, giving us a new insight into the ancient past.

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.

Continue reading “Isaac Asimov Predicts The Future In 1982. Was He Correct?” »

In recent years, the field of artificial intelligence has witnessed remarkable advancements, with researchers exploring innovative ways to utilize existing technology in groundbreaking applications. One such intriguing concept is the use of WiFi routers as virtual cameras to map a home and detect the presence and locations of individuals, akin to an MRI machine. This revolutionary technology harnesses the power of AI algorithms and WiFi signals to create a unique, non-intrusive way of monitoring human presence within indoor spaces. In this article, we will delve into the workings of this technology, its potential capabilities, and the implications it may have on the future of smart homes and security.

The Foundation of WiFi Imaging: WiFi imaging, also known as radio frequency (RF) sensing, revolves around leveraging the signals emitted by WiFi routers. These signals interact with the surrounding environment, reflecting off objects and people within their range. AI algorithms then process the alterations in these signals to form an image of the indoor space, thus providing a representation of the occupants and their movements. Unlike traditional cameras, WiFi imaging is capable of penetrating walls and obstructions, making it particularly valuable for monitoring people without compromising their privacy.

AI Algorithms in WiFi Imaging: The heart of this technology lies in the powerful AI algorithms that interpret the fluctuations in WiFi signals and translate them into meaningful data. Machine learning techniques, such as neural networks, play a pivotal role in recognizing patterns, identifying individuals, and discerning between static objects and moving entities. As the AI model continuously learns from the WiFi data, it enhances its accuracy and adaptability, making it more proficient in detecting and tracking people over time.

The Matic is a fully autonomous robot vacuum that its founders claim will clean your floors without getting stuck on cables or toys and without sending a map of your home to the cloud. And it’ll only cost you $1,800.

The Matic is a new robot vacuum with a different approach to cleaning your floors. Built by two former Google Nest engineers, it’s designed to move around your home in the same way most humans would, processing things visually instead of spatially. It uses five RGB cameras to navigate, rather than the sensors, bumpers, and lidar tech found on most of today’s robot vacs. In theory, this makes it less prone to common robot vacuum pitfalls —such as high-pile rugs, cables, and tight spaces — because it can actually see where it’s going in real time rather than relying on a preprogrammed map. It also operates locally — with no cloud component at all. Mapping is done on the device, and it doesn’t require an internet connection to run, so your data should never leave your home. $1,800 robot vacuum thinks it can beat the best of them.