Lifeboat Foundation

Google DeepMind has unexpectedly released the source code and model weights of AlphaFold 3 for academic use, marking a significant advance that could accelerate scientific discovery and drug development. The surprise announcement comes just weeks after the system’s creators, Demis Hassabis and John Jumper, were awarded the 2024 Nobel Prize in Chemistry for their work on protein structure prediction.

AlphaFold 3 represents a quantum leap beyond its predecessors. While AlphaFold 2 could predict protein structures, version 3 can model the complex interactions between proteins, DNA, RNA, and small molecules — the fundamental processes of life. This matters because understanding these molecular interactions drives modern drug discovery and disease treatment. Traditional methods of studying these interactions often require months of laboratory work and millions in research funding — with no guarantee of success.

Continue reading “Google DeepMind open-sources AlphaFold 3, ushering in a new era for drug discovery and molecular biology” »

A robot trained via video observation performs surgery with skill rivaling human doctors, moving robotic surgery closer to autonomous procedures.

Nvidia CEO Jensen Huang highlights the transformative impact of AI and accelerated computing on various industries, emphasizing rapid growth, enhanced productivity, and the evolution of software development through innovations like the Omniverse and advanced GPUs Questions to inspire discussion Physical AI and AGI 🤖

AlphaFold 3 inference pipeline. Contribute to google-deepmind/alphafold3 development by creating an account on GitHub.

Anthropic’s new hire is preparing for a future where advanced AI models may experience suffering.

The dream of traversing the depths of space and planting the seed of human civilization on another planet has existed for generations. For long as we’ve known that most stars in the Universe are likely to have their own system of planets, there have been those who advocated that we explore them (and even settle on them). With the dawn of the Space Age, this idea was no longer just the stuff of science fiction and became a matter of scientific study. Unfortunately, the challenges of venturing beyond Earth and reaching another star system are myriad.

When it comes down to it, there are only two ways to send crewed missions to exoplanets. The first is to develop advanced propulsion systems that can achieve relativistic speeds (a fraction of the speed of light). The second involves building spacecraft that can sustain crews for generations – aka. a Generation Ship (or Worldship). On November 1st, 2024, Project Hyperion launched a design competition for crewed interstellar travel via generation ships that would rely on current and near-future technologies. The competition is open to the public and will award a total of $10,000 (USD) for innovative concepts.

Continue reading “Project Hyperion is Seeking Ideas for Building Humanity’s First Generation Ship” »

An innovative planar spoof plasmonic neural network (SPNN) platform capable of directly detecting and processing terahertz (THz) electromagnetic signals has been unveiled by researchers at City University of Hong Kong (CityUHK) and Southeast University in Nanjing.

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making.

Much progress has been made towards diversifying automated synthesis platforms^4,5,19 and increasing their autonomous capabilities^{9,14,15,20,21,22}. So far, most platforms use bespoke engineering and physically integrated analytical equipment⁶. The associated cost, complexity and proximal monopolization of analytical equipment means that single, fixed characterization techniques are often favoured in automated workflows, rather than drawing on the wider array of analytical techniques available in most synthetic laboratories. This forces any decision-making algorithms to operate with limited analytical information, unlike more multifaceted manual approaches. Hence, closed-loop autonomous chemical synthesis often bears little resemblance to human experimentation, either in the laboratory infrastructure required or in the decision-making steps.

We showed previously¹¹ that free-roaming mobile robots could be integrated into existing laboratories to perform experiments by emulating the physical operations of human scientists. However, that first workflow was limited to one specific type of chemistry—photochemical hydrogen evolution—and the only measurement available was gas chromatography, which gives a simple scalar output. Subsequent studies involving mobile robots also focused on the optimization of catalyst performance^12,13. These benchtop catalysis workflows^11,12,13 cannot carry out more general synthetic chemistry, for example, involving organic solvents, nor can they measure and interpret more complex characterization data, such as NMR spectra. The algorithmic decision-making was limited to maximizing catalyst performance¹¹, which is analogous to autonomous synthesis platforms that maximize yield for a reaction using NMR²³ or chromatographic^10,24 peak areas.

Continue reading “Autonomous mobile robots for exploratory synthetic chemistry” »

A team of EU scientists is developing a new advanced camera that uses photonics to reveal what the eye cannot see. This innovative system is being developed to transform various industries, including vertical farming. It will allow farmers growing crops like salads, herbs, and microgreens to detect plant diseases early, monitor crop health with precision, and optimise harvest times — boosting yields by up to 20%.

A new European consortium funded under the Photonics Partnership is developing a new imaging platform that ensures everything from crops to factory products is of the highest quality by detecting things humans simply cannot.

Called ‘HyperImage’, the project aims to revolutionise quality assurance and operational efficiency across different sectors. This high-tech imaging system uses AI machine learning algorithms to identify objects for more precise decision-making.