AI steps up in the battle against Coronavirus
We take a look into how artificial intelligence is aiding the fight against the coronavirus pandemic, how effective will AI prove to be?
Artificial intelligence may have been hyped - but when it comes to medicine, it already has a proven track record.
So can machine learning innovation rise to this challenge of finding a cure for this terrible disease?
Oxford-based Exscientia, the first to put an AI-discovered drug into human trial, is trawling through 15,000 drugs held by the Scripps research institute, in California. And Healx, a Cambridge company set up by Viagra co-inventor Dr David Brown, has repurposed its AI system developed to find drugs for rare diseases.
The system is divided into three parts that:
Trawl through all the current literature relating to the disease
Study the DNA and structure of the virus
Consider the suitability of various drugs
Drug discovery has traditionally been slow, but AI is providing much faster results.
Healx hopes to turn that information into a list of drug candidates by May and is already in talks with labs to take those predictions into clinical trials.
For those working in the field of AI drug discovery, there are two options when it comes to coronavirus; find an entirely new drug but wait a couple of years for it to be approved as safe for use or repurpose existing drugs.
But, Dr Brown said, it was extremely unlikely one single drug would be the answer.
And for Healx, that means a detailed analysis of the eight million possible pairs and 10.5 billion triple-drug combinations stemming from the 4,000 approved drugs on the market.
Normally, just getting them all to work together would take "a year of paperwork", said Scipher's chief executive Alif Saleh.
But a series of Zoom calls with a "group of people with an unprecedented determination to get things done, not to mention a lot of time on their hands" sped things up.
"The last three weeks would normally take half a year. Everyone dropped everything," he said.
Already, their research has yielded surprising results, including:
The suggestion the virus may invade brain tissues, which may explain why some people lose their sense of taste or smell)
The prediction that it may also attack the reproductive system of both men and women
Schiefer Medicine combines AI with something it calls network medicine - a method that views a disease via the complex interactions among molecular components.
"A disease phenotype is rarely due to malfunction of one gene or protein on its own - nature is not that simple - but the result of a cascading effect in a network of interactions between several proteins," Mr Saleh said.
Using network medicine, AI and a fusion of the two has led the consortium to identify 81 potential drugs that could help.
"AI can do a little better, not only looking at higher-order correlations but little bits of independent information that traditional network medicine might miss," said Prof Albert-Laszlo Barabasi.
But AI technology alone would not have worked, they needed all three approaches.
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.