AI News, Artificial intelligence
How artificial intelligence outsmarted the superbugs
One of the seminal texts for anyone interested in technology and society is Melvin Kranzberg’s Six Laws of Technology, the first of which says that “technology is neither good nor bad;
By this, Kranzberg meant that technology’s interaction with society is such “that technical developments frequently have environmental, social and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances”.
The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics – a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.
The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brain’s architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known – including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources.
This produced a hundred candidates for physical testing and led to one (which they named “halicin” after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens – notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.
So while the main beneficiaries of machine learning for, say, a toxic technology like facial recognition are mostly authoritarian political regimes and a range of untrustworthy or unsavoury private companies, the beneficiaries of the technology as an aid to scientific discovery could be humanity as a species.
Our customers have achieved success across a range of hardware: massive inference deployment on CPUs across multiple data centers for content recommendation engines, VPU-based cameras that protect endangered species from poachers in real-time, and FPGAs that provide acceleration for fast search results.
We’re working closely with the AI community to accelerate discoveries and make meaningful progress in how we use artificial intelligence to add value to our work and lives, whether it’s finding water on the moon, speeding critical medical diagnoses, or seeing product defects faster.
- On 10. april 2021
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