Lifeboat Foundation

Medically, AI is helping us with everything from identifying abnormal heart rhythms before they happen to spotting skin cancer. But do we really need it to get involved with our genome? Protein-design company Profluent believes we do.

Founded in 2022 in Berkeley, California, Profluent has been exploring ways to use AI to study and generate new proteins that aren’t found in nature. This week, the team trumpeted a major success with the release of an AI-derived protein termed OpenCRISPR-1.

The protein is meant to work in the CRISPR gene-editing system, a process in which a protein cuts open a piece of DNA and repairs or replaces a gene. CRISPR has been actively in use for about 15 years, with its creators bagging the Nobel prize in chemistry in 2020. It has shown promise as a biomedical tool that can do everything from restoring vision to combating rare diseases; as an agricultural tool that can improve the vitamin D content of tomatoes, and slash the flowering time of trees from decades to months; and much more.

Have you ever wondered how machine learning systems can improve their predictions over time, seemingly getting smarter with each new piece of data? This is not just a trait of all machine learning models but is particularly pronounced in Bayesian Machine Learning (BML), which stands apart for its ability to incorporate prior knowledge and uncertainty into its learning process. This article takes you on a deep dive into the world of BML, unraveling its concepts and methodologies, and showcasing its unique advantages, especially in scenarios where data is scarce or noisy.

Note that Bayesian Machine Learning goes hand-in-hand with the concept of Probabilistic Models. To discover more about Probabilistic Models in Machine Learning, click here.

Bayesian Machine Learning (BML) represents a sophisticated paradigm in the field of artificial intelligence, one that marries the power of statistical inference with machine learning. Unlike traditional machine learning, which primarily focuses on predictions, BML introduces the concept of probability and inference, offering a framework where learning evolves with the accumulation of evidence.

Are you ready for the “Big Crunch?”

In just 2 hours, small metal robots can capture most nanoscopic plastic particles from a sample of water.

By Karmela Padavic-Callaghan

Researchers have developed a new technique that can “reprogram” magnetic cilia to move in new directions as needed. Magnetic cilia – an artificial hair-like structure containing embedded magnetic particles – are used frequently in soft robots, transporting objects and mixing liquids.

Synthesia’s new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.

In many high-stakes situations, large language models are not worth the risk. Knowing which outputs to throw out might fix that.

The agency opened the probe in 2021, and reviewed hundreds of crashes that involved Autopilot. It says there’s a ‘critical safety gap.’

The OpenAI Startup Fund, a venture fund related to — but technically separate from — OpenAI that invests in early-stage, typically AI-related companies across education, law and the sciences, has quietly closed a $15 million tranche.

According to a filing with the U.S. Securities and Exchange Commission, two unnamed investors contributed the $15 million in new cash on or around April 19. The paperwork was submitted on April 25, and mentions Ian Hathaway, the OpenAI Startup Fund’s manager and sole partner.

The capital was transferred to a legal entity called a special purpose vehicle, or SPV, associated with the OpenAI Startup Fund: OpenAI Startup Fund SPV II, L.P.