Lifeboat Foundation

We’ve all heard about the potential of artificial intelligence in the life sciences field. In 2020, the launch of AlphaFold 2, pioneered by Google DeepMind, took the world by storm and marked a new age in protein structure prediction. But now, AlphaFold 3 is transforming the landscape again. In this news highlight, we explore the new tech, compare it to its predecessor and take a look to the future.

Before the AI revolution, protein structure prediction heavily relied on experimental methods, such as X-ray crystallography, NMR spectroscopy and, later, some complex computational methods like homology modelling. These methods were time consuming and costly, and were a major limiting step in drug discovery and development processes in particular. For years, scientists have been attempting to integrate the latest and greatest AI models into the field, in order to speed up the process and improve accuracy.

Enter AlphaFold, an artificial intelligence tool developed by Google’s DeepMind. The first version of the technology was released in 2018, but it was 2020’s AlphaFold 2 that made headlines – winning the prestigious Critical Assessment of Structure Prediction (CASP) 14 competition. Having gone through multiple major iterations, the most recent release, AlphaFold 3, is set to further transform the protein space. But what does it do, and how may it outperform its predecessor?

Researchers from the University of California, Irvine have discovered the neurons responsible for “item memory,” deepening our understanding of how the brain stores and retrieves the details of “what” happened and offering a new target for treating Alzheimer’s disease.

Memories include three types…

Finding significantly deepens understanding of crucial component of cognitive function.

Continue reading “UC Irvine discovery of ‘item memory’ brain cells offers new Alzheimer’s treatment target” »

face_with_colon_three year 2023.

Researchers are testing the use of mRNA to get damaged livers to repair themselves, in a move that could one day lessen the need for organ transplants.

Algorithms like OpenAI’s GPT-4 are like brains frozen in time. A new study shows how future AIs could learn continuously in response to a changing world.

I argue for a pattern theory of self as a useful way to organize an interdisciplinary approach to discussions of what constitutes a self. According to the pattern theory, a self is constituted by a number of characteristic features or aspects that may include minimal embodied, minimal experiential, affective, intersubjective, psychological/cognitive, narrative, extended, and situated aspects. A pattern theory of self helps to clarify various interpretations of self as compatible or commensurable instead of thinking them in opposition, and it helps to show how various aspects of self may be related across certain dimensions. I also suggest that a pattern theory of self can help to adjudicate (or at least map the differences) between the idea that the self correlates to self-referential processing in the cortical midline structures of the brain and other narrower or wider conceptions of self.

Keywords: self, pattern theory, cortical midline structures, first-person perspective.

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Dr. Joscha Bach introduces a surprising idea called “cyber animism” in his AGI-24 talk — the notion that nature might be full of self-organizing software age…

Logistics companies on the ground solve similar problems every day and transport goods and commodities across the globe. So, researchers can study how these companies manage their logistics to help space companies and agencies figure out how to successfully plan their mission operations.

One NASA-funded study in the early 2000s had an idea for simulating space logistics operations. These researchers viewed orbits or planets as cities and the trajectories connecting them as routes. They also viewed the payload, consumables, fuel and other items to transport as commodities.

This approach helped them reframe the space mission problem as a commodity flow problem – a type of question that ground logistics companies work on all the time.

Several pulsar timing array collaborations recently reported evidence of a stochastic gravitational wave background (SGWB) at nHz frequencies. While the SGWB could originate from the merger of supermassive black holes, it could be a signature of new physics near the 100 MeV scale. Supercooled first-order phase transitions (FOPTs) that end at the 100 MeV scale are intriguing explanations, because they could connect the nHz signal to new physics at the electroweak scale or beyond. Here, however, we provide a clear demonstration that it is not simple to create a nHz signal from a supercooled phase transition, due to two crucial issues that could rule out many proposed supercooled explanations and should be checked. As an example, we use a model based on nonlinearly realized electroweak symmetry that has been cited as evidence for a supercooled explanation.