Lifeboat Foundation

MIT researchers use AI to empower robots for versatile tool use in simulations and real-world settings.

MIT researchers have utilized artificial intelligence (AI) models to combine data from multiple sources to help robots learn better.

The technique employs diffusion models, a type of generative AI, to integrate multiple data sources across various domains, modalities, and tasks.

Continue reading “MIT: New AI power teaches robots how to hammer, screw nuts, cook toast” »

When Storm Ciarán battered northwestern Europe in November 2023, it left a trail of destruction. The low-pressure system associated with Storm Ciarán set new records for England, marking it as an exceptionally rare meteorological event. The storm’s intensity caught many off guard, exposing the limitations of current weather-prediction models and highlighting the need for more accurate forecasting in the face of climate change. As communities grappled with the aftermath, the urgent question arose: How can we better anticipate and prepare for such extreme weather events?

A recent study by Charlton-Perez et al. (2024) underscored the challenges faced by even the most advanced AI weather-prediction models in capturing the rapid intensification and peak wind speeds of Storm Ciarán. To help address those challenges, a team of Microsoft researchers developed Aurora, a cutting-edge AI foundation model that can extract valuable insights from vast amounts of atmospheric data. Aurora presents a new approach to weather forecasting that could transform our ability to predict and mitigate the impacts of extreme events—including being able to anticipate the dramatic escalation of an event like Storm Ciarán.

Aurora’s effectiveness lies in its training on more than a million hours of diverse weather and climate simulations, which enables it to develop a comprehensive understanding of atmospheric dynamics. This allows the model to excel at a wide range of prediction tasks, even in data-sparse regions or extreme weather scenarios. By operating at a high spatial resolution of 0.1° (roughly 11 km at the equator), Aurora captures intricate details of atmospheric processes, providing more accurate operational forecasts than ever before—and at a fraction of the computational cost of traditional numerical weather-prediction systems. We estimate that the computational speed-up that Aurora can bring over the state-of-the-art numerical forecasting system Integrated Forecasting System (IFS) is ~5,000x.

The rapid advancement of technology, specifically in the development of sex robots with AI capabilities, could potentially lead to the replacement of real-life partners and have a detrimental effect on meaningful romantic relationships.

Questions to inspire discussion.

Continue reading “The Future of Relationships: Could Women Be Replaced by AI Sex Robots?” »

Through the looking glass: Researchers now have a way to conduct experiments on biological neurons in vitro. These human brain organoids – there are 16 in total – are aimed at developing the world’s first living processor and can be accessed remotely through an online platform for $500 a month if you are a university or educational institution.

The platform was developed by FinalSpark, a Swiss biocomputing startup, which reports that three dozen universities have expressed interest in using their platform. FinalSpark highlights the significant energy savings it could offer in the training and operation of large artificial neural networks, such as those used in large language models. However, this achievement is still a ways off: the project is just in its beginning phases, and co-founder Fred Jordan states that such an ambitious goal can only be achieved through international collaboration.

The biological component uses forebrain organoids derived from human induced pluripotent stem cells. These organoids can survive for years and contain neurons, oligodendrocytes, and astrocytes characteristic of the forebrain region, according to a paper published in the scientific journal Frontiers.

Researchers at NCI and Memorial Sloan Kettering Cancer Center have developed an artificial intelligence (AI) tool that uses routine clinical data to predict whether someone’s cancer will respond to immune checkpoint inhibitors, a type of immunotherapy drug that helps immune cells kill cancer cells.

AI tool uses routine clinical data to predict whether someone’s cancer will respond to immune checkpoint inhibitors.

In the brain, timekeeping is done with neurons that relax at different rates after receiving a signal; now memristors—hardware analogs of neurons—can do that too.

Artificial neural networks may soon be able to process time-dependent information, such as audio and video data, more efficiently. The first memristor with a ‘relaxation time’ that can be tuned is reported today in Nature Electronics, in a study led by the University of Michigan.

Energy Efficiency and AI.

What if the singularity heralds not AI dominance, but a profound unity consciousness, connecting human and machine minds into a symbiotic, transcendent intelligence?

Scientists used machine learning to discover what they say could be a new way to speed up the process of breaking down plastic significantly, Vice reports.

As detailed in a new paper published in the journal Nature, a research team from the University of Texas at Austin modified an enzyme to break down the individual components of polyethylene terephthalate (PET), a commonly used plastic that makes up a staggering 12 percent of global waste.

Impressively, the modified enzyme also reduced the amount of time it takes for the plastic to degrade from months to a just single week.

Battery technologies that can reliably operate at very low temperatures could be highly valuable for a wide range of applications. These batteries could, for instance, power devices, vehicles, and robotic systems in outer space, deep under the sea, and in other extreme environments.