Lifeboat Foundation

Using #AI to define the chemical “reactome”—the important functional sites in small molecules.

High-throughput experimentation (HTE) has great utility for chemical synthesis. However, robust interpretation of high-throughput data remains a challenge. Now, a flexible analyser has been developed on the basis of a machine learning-statistical analysis framework, which can reveal hidden chemical insights from historical HTE data of varying scopes, sizes and biases.

MIT CSAIL scientists, using new AI models, have been successful at identifying potential high-risk patients of pancreatic cancer.

Time to move philosophers into the lab. AI is making theories of consciousness testable.

We are already living in the era of the fourth industrial revolution, but in the near future we will be facing another one that could really change everything. We are talking about the revolution of humanoid robots — versatile, intelligent and dexterous machines that can not only help, but also replace humans in tight places. In this video, we’ll tell you about the top 10 newest and most advanced humanoid robots in the world, and what technologies will make them truly versatile! Onward to a brighter future)

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A college student published a study which he says disproves the long-held belief that all fingerprints are unique, leading to pushback among experts.

The HiP framework developed MIT_CSAIL creates detailed plans for robots using the expertise of three different foundation models, helping it execute tasks in households, factories, and construction that require multiple steps.

“All we want to do is take existing pre-trained models and have them successfully interface with each other,” says Anurag Ajay, a PhD student in the MIT Department of Electrical Engineering and Computer Science (EECS) and a CSAIL affiliate. “Instead of pushing for one model to do everything, we combine multiple ones that leverage different modalities of internet data. When used in tandem, they help with robotic decision-making and can potentially aid with tasks in homes, factories, and construction sites.”

These models also need some form of “eyes” to understand the environment they’re operating in and correctly execute each sub-goal. The team used a large video diffusion model to augment the initial planning completed by the LLM, which collects geometric and physical information about the world from footage on the internet. In turn, the video model generates an observation trajectory plan, refining the LLM’s outline to incorporate new physical knowledge.

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A team of computer scientists led by the University of Massachusetts Amherst recently announced a new method for automatically generating whole proofs that can be used to prevent software bugs and verify that the underlying code is correct.

This new method, called Baldur, leverages the artificial intelligence power of large language models (LLMs), and when combined with the state-of-the-art tool Thor, yields unprecedented efficacy of nearly 66%. The team was recently awarded a Distinguished Paper award at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.

“We have unfortunately come to expect that our software is buggy, despite the fact that it is everywhere and we all use it every day,” says Yuriy Brun, professor in the Manning College of Information and Computer Sciences at UMass Amherst and the paper’s senior author.

Organoid intelligence is the growing of mini-brains from human stem cells, which has potential benefits for medical research and treatments.

However, there are significant ethical concerns related to the possibility of creating conscious entities and the potential for misuse. Organoid intelligence could offer valuable insights into neurological diseases, but we must establish a framework for their creation and treatment to ensure ethical use. As we continue to develop this technology, we must approach it with caution due to the potential dire consequences of its misuse.

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Artificial intelligence can accelerate the process of finding and testing new materials, and now researchers have used that ability to develop a battery that is less dependent on the costly mineral lithium.

Lithium-ion batteries power many devices that we use every day as well as electric vehicles. They would also be a necessary part of a green electric grid, as batteries are required to store renewable energy from wind turbines and solar panels. But lithium is expensive and mining it damages the environment. Finding a replacement for this crucial metal could be costly and time-consuming, requiring researchers to develop and test millions of candidates over the course of years. Using AI, Nathan Baker at Microsoft and his colleagues accomplished the task in months. They designed and built a battery that uses up to 70 per cent less lithium than some competing designs.