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Apr 11, 2024

European car manufacturer will pilot Sanctuary AI’s humanoid robot

Posted by in categories: Elon Musk, robotics/AI, transportation

Sanctuary AI announced that it will be delivering its humanoid robot to a Magna manufacturing facility. Based in Canada, with auto manufacturing facilities in Austria, Magna manufactures and assembles cars for a number of Europe’s top automakers, including Mercedes, Jaguar and BMW. As is often the nature of these deals, the parties have not disclosed how many of Sanctuary AI’s robots will be deployed.

The news follows similar deals announced by Figure and Apptronik, which are piloting their own humanoid systems with BMW and Mercedes, respectively. Agility also announced a deal with Ford at CES in January 2020, though that agreement found the American carmaker exploring the use of Digit units for last-mile deliveries. Agility has since put that functionality on the back burner, focusing on warehouse deployments through partners like Amazon.

For its part, Magna invested in Sanctuary AI back in 2021 — right around the time Elon Musk announced plans to build a humanoid robot to work in Tesla factories. The company would later dub the system “Optimus.” Vancouver-based Sanctuary unveiled its own system, Phoenix, back in May of last year. The system stands 5’7” (a pretty standard height for these machines) and weighs 155 pounds.

Apr 11, 2024

SpaceX launches Space Force weather satellite designed to take over for a program with roots to the 1960s

Posted by in categories: military, satellites

SpaceX launched a military weather satellite designed to replace aging satellites from a program dating back to the 1960s. The United States Space Force-62 (USSF-62) mission featured the launch of the first Weather System Follow-on Microwave (WSF-M) spacecraft.

Apr 11, 2024

Galactic Genesis Unveiled: JWST Witnesses the Dawn of Starlight

Posted by in category: space

Groundbreaking observations by the James Webb Space Telescope of an early galaxy merger indicate faster and more efficient star formation than previously understood, revealing complex stellar populations and challenging current cosmological theories. Galaxies and stars developed faster after t.

Apr 11, 2024

Researchers at Stanford and MIT Introduced the Stream of Search (SoS): A Machine Learning Framework that Enables Language Models to Learn to Solve Problems by Searching in Language without Any External Support

Posted by in categories: information science, policy, robotics/AI

Language models often need more exposure to fruitful mistakes during training, hindering their ability to anticipate consequences beyond the next token. LMs must improve their capacity for complex decision-making, planning, and reasoning. Transformer-based models struggle with planning due to error snowballing and difficulty in lookahead tasks. While some efforts have integrated symbolic search algorithms to address these issues, they merely supplement language models during inference. Yet, enabling language models to search for training could facilitate self-improvement, fostering more adaptable strategies to tackle challenges like error compounding and look-ahead tasks.

Researchers from Stanford University, MIT, and Harvey Mudd have devised a method to teach language models how to search and backtrack by representing the search process as a serialized string, Stream of Search (SoS). They proposed a unified language for search, demonstrated through the game of Countdown. Pretraining a transformer-based language model on streams of search increased accuracy by 25%, while further finetuning with policy improvement methods led to solving 36% of previously unsolved problems. This showcases that language models can learn to solve problems via search, self-improve, and discover new strategies autonomously.

Recent studies integrate language models into search and planning systems, employing them to generate and assess potential actions or states. These methods utilize symbolic search algorithms like BFS or DFS for exploration strategy. However, LMs primarily serve for inference, needing improved reasoning ability. Conversely, in-context demonstrations illustrate search procedures using language, enabling the LM to conduct tree searches accordingly. Yet, these methods are limited by the demonstrated procedures. Process supervision involves training an external verifier model to provide detailed feedback for LM training, outperforming outcome supervision but requiring extensive labeled data.

Apr 11, 2024

Fractal pattern identified at molecular scale in nature for first time

Posted by in category: futurism

An enzyme in a cyanobacterium can take the unusual form a triangle containing ever-smaller triangular gaps, making a fractal pattern.

By Alex Wilkins

Apr 11, 2024

Artificial ovary? First atlas of human ovary, a fertility breakthrough

Posted by in categories: biotech/medical, innovation

Researchers have created an “atlas” of the human ovary, which could lead to the development of artificial ovaries and restore fertility in patients.

Apr 11, 2024

Researchers develop paper battery that generates power from water, air

Posted by in category: energy

Researchers at Tohoku University have developed a paper-based Magnesium-air battery that is eco-friendly and powerful.

Apr 11, 2024

Robot dogs train at 6,000ft in snow-clad mountains for moon missions

Posted by in categories: robotics/AI, space

A multidisciplinary team is teaching dog-like robots to navigate the moon’s craters and other challenging planetary surfaces.

As part of the research funded by NASA, researchers from various universities and NASA Johnson Space Center tested a quadruped named Spirit at Palmer Glacier on Oregon’s Mount Hood.

Continue reading “Robot dogs train at 6,000ft in snow-clad mountains for moon missions” »

Apr 11, 2024

How AI 50 Companies Are Powering A New Tech Economy

Posted by in categories: economics, robotics/AI

Generative AI companies are dominant on this year’s list of the most promising AI startups, heralding a coming productivity revolution.

Apr 11, 2024

Intel Challenges Nvidia With Gaudi 3 AI Accelerator

Posted by in category: robotics/AI

In a move that directly challenges Nvidia in the lucrative AI training and inference markets, Intel announced its long-anticipated new Intel Gaudi 3 AI accelerator at its Intel Vision event.

The new accelerator offers significant improvements over the previous generation Gaudi 3 processor, promising to bring new competitiveness to training and inference for LLMs and multimodal models.

Gaudi 3 dramatically increases AI compute capabilities, delivering substantial improvements over Gaudi 2 and competitors, particularly in processing BF16 data types, which are crucial for AI workloads.

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