https://archive.md/5R7hL
"In 2019, scientists at the Massachusetts Institute of Technology (MIT) did
something unusual in modern medicine—they found a new antibiotic, halicin. In
May this year another team found a second antibiotic, abaucin. What marked
these two compounds out was not only their potential for use against two of the
most dangerous known antibiotic-resistant bacteria, but also how they were
identified.
In both cases, the researchers had used an artificial-intelligence (AI) model
to search through millions of candidate compounds to identify those that would
work best against each “superbug”. The model had been trained on the chemical
structures of a few thousand known antibiotics and how well (or not) they had
worked against the bugs in the lab. During this training the model had worked
out links between chemical structures and success at damaging bacteria. Once
the AI spat out its shortlist, the scientists tested them in the lab and
identified their antibiotics. If discovering new drugs is like searching for a
needle in a haystack, says Regina Barzilay, a computer scientist at MIT who
helped to find abaucin and halicin, AI acts like a metal detector. To get the
candidate drugs from lab to clinic will take many years of medical trials. But
there is no doubt that AI accelerated the initial trial-and-error part of the
process. It changes what is possible, says Dr Barzilay. With AI, “the type of
questions that we will be asking will be very different from what we’re asking
today.”
Drug discovery is not alone in being jolted by the potential of AI. Researchers
tackling many of the world’s most complicated and important problems—from
forecasting weather to searching for new materials for batteries and solar
panels and controlling nuclear-fusion reactions—are all turning to AI in order
to augment or accelerate their progress.
The potential is enormous. “AI could usher in a new renaissance of discovery,”
argues Demis Hassabis, co-founder of Google DeepMind, an AI lab based in
London, “acting as a multiplier for human ingenuity.” He has compared AI to the
telescope, an essential technology that will let scientists see farther and
understand more than with the naked eye alone.
Though it has been part of the scientific toolkit since the 1960s, for most of
its life AI has been stuck within disciplines where scientists were already
well-versed in computer code—particle physics, for example, or mathematics. By
2023, however, with the rise of deep learning, more than 99% of research fields
were producing AI-related results, according to CSIRO, Australia’s science
agency (see chart). “Democratisation is the thing that is causing this
explosion,” says Mark Girolami, chief scientist at the Alan Turing Institute in
London. What used to require a computer-science degree and lines of arcane
programming languages can now be done with user-friendly AI tools, often made
to work after a query to ChatGPT, OpenAI’s chatbot. Thus scientists have easy
access to what is essentially a dogged, superhuman research assistant that will
solve equations and tirelessly sift through enormous piles of data to look for
any patterns or correlations within."
Via
Future Crunch:
<
https://futurecrunch.com/good-news-child-poverty-leprosy-conservation-california/>
Cheers,
*** Xanni ***
--
mailto:xanni@xanadu.net Andrew Pam
http://xanadu.com.au/ Chief Scientist, Xanadu
https://glasswings.com.au/ Partner, Glass Wings
https://sericyb.com.au/ Manager, Serious Cybernetics
