How Is Artificial Intelligence Combatting COVID-19?
Chris Gannatti, head of research at ETF specialist WisdomTree, explains how artificial intelligence is being used to tackle Covid-19.
Artificial Intelligence (AI) is proliferating more widely than ever before, having the potential to influence many aspects of daily life. Crisis periods, like we have seen with the Covid-19 pandemic, are often catalysts for the deployment of new innovations and technologies more quickly. The power of AI is being harnessed to tackle the Covid-19 pandemic, whether that be to better understand the rate of infection or by tracing and quickly identifying infections. While AI has been associated with the ‘future’ and ideas such as the development of driverless cars, its legacy could be how it has impacted the world during this crisis. It is likely that AI is already playing a major part in the early stages of vaccine development - the uses of artificial intelligence are seemingly endless.AI was already growing quickly and being deployed in ever more areas of our data-driven world.
Covid-19 has accelerated some of these deployments, bringing greater comfort and familiarity to the technology. To really understand how AI is making a difference, it is worth looking at some examples which illustrate the breadth of activities being carried out by AI during the pandemic.
Rizwan Malik, the lead radiologist at Royal Bolton Hospital run by the UK’s National Health Service (NHS) designed a conservative clinical trial to help obtain initial readings of X-rays for patients faster. Waiting for specialists could sometimes take up to six hours. He identified a promising AI-based chest X-ray system and then set up a test to occur over six months. For all chest X-rays handled by his trainees, the system would offer a second opinion. He would then check if the system’s conclusion matched his own and if it did, he would phase the system in as a permanent check on his trainees. As Covid-19 hit, the system became an important way to identify certain characteristics unique to Covid-19 that were visible on chest X-rays. While not perfect, the system did represent an interesting case-study in the use of computer vision in medical imagery.A great example of the collaborative efforts that can be inspired during times of crisis involved three organisations coming together to release the Covid-19 Open Research Dataset. This includes more than 24,000 research papers from peer-reviewed journals and other sources.
The National Library of Medicine at the National Institutes of Health provided access to existing scientific publications; Microsoft used its literature curation algorithms to find relevant articles; and research non-profit the Allen Institute for Artificial Intelligence converted them from web pages and PDFs into a structured format that can be processed by algorithms. Many major cities affected by Covid-19 were faced with a very real problem - getting the right care to the people who needed it without allowing hospitals to become overrun. Helping people to “self-triage”, therefore staying away from the hospital unless absolutely necessary, was extremely important. Providence St. Joseph Health System in Seattle built an online screening and triage tool that could rapidly differentiate between those potentially really sick with Covid-19 and those with less life-threatening ailments. In its first week of operation, it served 40,000 patients. The Covid-19 pandemic has pushed the unemployment rate in the US to 14.7%. This has led to unprecedented numbers of people filing unemployment claims and asking questions to different state agencies. Texas, which has received millions of these claims since early March, is using artificial intelligence-driven chatbots to answer questions from unemployed residents in need of benefits.
Other states, like Georgia and South Carolina, have reported similar activity. To give a sense of scale, the system that has been deployed in Texas can handle 20,000 concurrent users. Think of how much staff would be required to deal with 20,000 inquiries at the same time. These are but four of many, many ways in which AI has been deployed to help in the time of the Covid-19 pandemic. While we continue to hope for cures and vaccinations, which AI will help in developing, we expect to see more innovative uses of AI that will benefit society over the long-term.
How you can slow the spread of coronavirus:
Wash your hands with soap and water often – do this for at least 20 seconds
Use hand sanitiser gel if soap and water are not available
Wash your hands as soon as you get home
Cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze
Put used tissues in the bin immediately and wash your hands afterwards
SOURCE: Funds Europe
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.