Gen AI models aren’t just good for creating pictures—they can be fine-tuned to generate useful robot training data, too.
…the tools that DeepMind was building could suggest and design experiments based on a given hypothesis and give scientists a probabilistic view on a proposed experiment's potential success or failure.
…notes that the most impressive part of Meta’s Orion demo was when he looked at a set of ingredients and the glasses told him what they were and how to make a smoothie out of them.
The chatbot excels at science, beating PhDs on a hard science test. But it might ‘hallucinate’ more than its predecessors.
The rise of advanced artificial intelligence technologies motivated their application to drug discovery…Traditional molecular representation methods rely on a large amount of domain knowledge…motivated by computer vision and image-based deep learning technologies, we presented a self-supervised image representation learning framework that combines molecular image and protein representations for the accurate prediction of compound-protein interactions.
Newly discovered figures dating back to 200BCE nearly double the number of known geoglyphs at enigmatic site
Designing a chip layout is not a simple task. Computer chips consist of many interconnected blocks, with layers of circuit components, all connected by incredibly thin wires…Similar to AlphaGo and AlphaZero, which learned to master the games of Go, chess and shogi, we built AlphaChip to approach chip floorplanning as a kind of game…The method has been used to design superhuman chip layouts in the last three generations of Google’s custom AI accelerator
There are more ways to synthesize a 100-amino acid (aa) protein than there are atoms in the universe...Deep neural networks are increasingly being used to navigate high-dimensional sequence spaces1..we show that the genetic architecture of at least some proteins is remarkably simple..Our results indicate that protein genetics is actually both rather simple and intelligible.
The AI model, called TxGNN, is the first one developed specifically to identify drug candidates for rare diseases and conditions with no treatments...It identified drug candidates from existing medicines for more than 17,000 diseases, many of them without any existing treatments. This represents the largest number of diseases that any single AI model can handle to date. The researchers note that the model could be applied to even more diseases beyond the 17,000 it worked on in the initial experiments.
A new white paper co-authored by ABB and Porsche Consulting details how robotic automation in surface treatment applications can transform productivity and profitability in the electronics manufacturing industry.
