How AI can help with the detection of the coronavirus vaccine

1641461727 How AI can help with the detection of the coronavirus

Murat Sonmez, managing director of the World Economic Forum, explains how big data and machine learning can help find a vaccine for COVID-19.

Dan Patterson, senior representative for CNET and CBS News, spoke to Murat Sonmez, managing director of the World Economic Forum, how big data and machine learning can help find a vaccine for COVID-19. The following is an edited transcript of their conversation.

Murat Sonmez: The World Economic Forum is a 50-year international organization, with a focus on public-private cooperation, bringing together business, international organizations, governments, academia and civil society. , to understand how we can shape a better future for the world and create action groups for that. In terms of the piece of data, we launched a campaign called the Center for the Fourth Industrial Revolution to really look at how we can accelerate emerging technologies, to deliver these solutions. acceleration. A key part of that is artificial intelligence (AI) and machine learning (ML). And we can find scenes far better and faster than human scientists can. But it thrives on data. Access to data and being able to use different datasets for individual purposes has therefore been a key area of ​​focus for us.

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The three hallmarks of a healthy life, who you are, which include genetic data, are your essentials; and where you live, environment, lifestyle; and how you live, lifestyle. Who you are, where you live, and how you live. And when you go to a doctor, they basically ask you those questions. But imagine a world where we can collect these datasets without going to a hospital or a doctor, or you may not have access to a hospital, or you may not be able to go, as we will see at the time COVID-19 emergency. What if we all had accessible devices, and our mobile phones, and through them, we disseminate all that data? How we live. Combine it with genetic information, clinical trials, hospital records, and environmental data, derived from sensors, the so-called Internet of Things (IoT) embedded in cities and the environment us, as well as sensors in the agricultural or satellite fields. data.

If you can put all that together and put it in a common place, we can use advanced analytical capabilities, which I would describe as machine learning, where the engine itself shows vision, faster and better than human beings can, because of the size of the data. They could develop human intelligence. Now, it is technically possible. The key issue is, how do we ensure that we protect the privacy of individuals, and how do we ensure that we use the data for permitted purposes? I do not want my genetic data to be used for commercial purposes, but I may want to use it to help treat COVID, for example.

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The third question is who will make money from that and the equality? What we have been doing is that privacy is obviously paramount, which is why Europe and California have taken the lead in that area, but we should not be aiming for confidentiality only at the sharing of data for common purposes. Today, if you wanted to combine all three datasets, it's impossible, because privacy laws get in the way. The idea of ​​combining different datasets for permanent use cases with data owners could bring economic benefits to data owners, create a complete set of opportunities for discovery, healthy living, and also sources of income. new entries for data owners. data.

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Dan Patterson: I like the way you frame AI as advanced analytics capabilities or capabilities. Do you have any examples of how these technologies are currently helping people in the real world?

Murat Sonmez: The most famous one, I think, is what DeepMind people did, after being acquired by Google. DeepMind is a company that started three guys out of London. And they mastered the most challenging problem in AI, which plays a game called Go. But after Google bought them, they turned the engine on their data centers and reduced energy consumption by more than 20%. They applied the same concept to a building and reduced energy consumption by more than 50%. Imagine if you send that to a city or country, we have the opportunity to reduce energy consumption, improve agricultural production, or even cure some accelerated diseases.

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The reason we have not seen much progress is because of the inability to link different datasets together. It's hard for researchers, and if we want to develop a new product, we need to have access to datasets. However, some laws established or based on case law from the 1980s may not be designed to make this possible. And again, we need to protect the privacy of data owners, but also enable these new usage issues.

Dan Patterson: How can artificial intelligence help us find a vaccine for the coronavirus faster?

Murat Sonmez: We know that if you provide a lot of data that is representative, non-partisan, machine learning algorithms come up with new insights faster than human experts can. At this point, we do not know how it is happening, but we do know what it is. So if you take it to the scientific way, which is what scientists have been using, we will start with hypotheses and experts as well, I think that this will happen. You collect datasets, and now you try to analyze and validate it all the way. We can really speed up those early stages. If we feed different datasets, as long as they are complete and from reliable sources, the artificial intelligence or machine learning algorithm can create this idea. I think we will be in a place where we can create more assumptions, much faster, and that would have to take us through the scientific method to prove that. I think he's going to be a great writer at the front end. And until we solve the black box challenge with AI and machine learning, I think that's what we can do almost right now.


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    CBS News' Dan Patterson talks to Murat Sonmez of the World Economic Forum about how big data and machine learning can help detect COVID-19 vaccines.

    Image Credit: Derek Poore

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