This post was originally published by at Medium [AI]
Data science and AI are not only accelerating COVID-19 research and recovery today but helping organizations uncover insights and opportunities to succeed in the new normal.
[Editor’s note: This podcast was originally published on June 19, 2020.]
Data analytics and artificial intelligence are playing a key role in the race to understand and ultimately find a cure for COVID-19. But beyond enabling an unprecedented level of collaboration among researchers―and thereby speeding outcomes―data science is uncovering the insights businesses need to decrease risk, realign strategies, and thrive in a changed world.
“One of the things that’s under-appreciated is that a foundation, a data platform, makes data managed and accessible so you can contextualize and make stronger decisions based on it. That’s going to be critical,” says Arti Garg, head of advanced AI solutions and technologies at Hewlett Packard Enterprise.
In this Voice of AI Innovation podcast, Garg joins Glyn Bowden, chief technologist for AI and data at HPE Pointnext Services, and host Dana Gardner, principal analyst at Interarbor Solutions, to explore the impact of data analytics and AI not only in the context of the current crisis but on business going forward. They discuss how the pandemic has accelerated the use of data and AI, the positives and negatives of expanded access to data, and the tools that can help businesses as they look toward a data-driven future.
Here are some excerpts:
Gardner: We’re in uncharted waters in dealing with the complexities of the novel coronavirus pandemic. Arti, why should we look to data science and AI to help when there’s not much of a historical record to rely on?
Garg: Because we don’t have a historical record, I think data science and AI are proving to be particularly useful right now in understanding this new disease and how we might potentially better treat it, manage it, and find a vaccine for it. And that’s because at this moment in time, raw data that are being collected from medical offices and through research labs are the foundation of what we know about the pandemic.
This is an interesting time because, when you know a disease, medical studies and medical research are often conducted in a very controlled way. You try to control the environment in which you gather data, but unfortunately, right now, we can’t do that. We don’t have the time to wait.
And so instead, AI―particularly some of the more advanced AI techniques―can be helpful in dealing with unstructured data or data of multiple different formats. It’s therefore becoming very important in the medical research community to use AI to better understand the disease. It’s enabling some unexpected and very fruitful collaborations, from what I’ve seen.
Gardner: Glyn, do you also see AI delivering more, even though we’re in uncharted waters?
Bowden: The benefits of something like machine learning (ML), for example, which is a subset of AI, is very good at handling many, many features. So, with a human being approaching these projects, there are only so many things you can keep in your head at once in terms of the variables you need to consider when building a model to understand something.
But when you apply ML, you are able to cope with millions or billions of features simultaneously―and then simulate models using that information. So it really does add the power of a million scientists to the same problem we were trying to face alone before.
Gardner: And is this AI benefit something that we can apply in many different avenues? Are we also modeling better planning around operations, or is this more research and development? Is it both?
Garg: There are two ways to answer the question of what’s happening with the use of AI in response to the pandemic. One is actually to the practice of data science itself.
Right now data scientists are collaborating directly with medical science research and learning how to incorporate subject matter expertise into data science models. This has been one of the challenges preventing businesses from adopting AI in more complex applications. But now we’re developing some of the best practices that will help us use AI in a lot of domains.
In addition, businesses are considering the use of AI to help them manage their businesses and operations going forward. That includes things such as using computer vision (CV) to ensure that social distancing happens with their workforce or other types of compliance we might be asked to do in the future.
Right now data scientists are collaborating directly with medical science research and learning how to incorporate subject matter expertise into data science models.
Gardner: Are the pressures of the current environment allowing AI and data science benefits to impact more people? We’ve been talking about the democratization of AI for some time. Is this happening more now?
Bowden: Absolutely, and that’s both a positive and a negative. The data around the pandemic has been made available to the general public. Anyone looking at news sites or newspapers and consuming information from public channels―accessing the disease incidence reports from Johns Hopkins University, for example―we have a steady stream of it. But those data sources are all over the place and are being thrown to a public that is only just now becoming data-savvy and data-literate.
As they consume this information, add their context, and get a personal point of view, that is then pushed back into the community again―because as you get data-centric you want to share it.
So we have a wide public feed―not only from universities and scholars, but from the general public, who are now acting as public data scientists. I think that’s creating a huge movement.
Garg: I agree. Making such data available exposes pretty much anyone to these amazing data portals, like Johns Hopkins University has made available. This is great because it allows a lot of people to participate.
It can also be a challenge because, as I mentioned, when you’re dealing with complex problems, you need to be able to incorporate subject matter expertise into the models you’re building and in how you interpret the data you are analyzing.
And so, unfortunately, we’ve already seen some cases―blog posts or other types of analysis―that get a lot of attention in social media but are later found to be not taking into account things that people who had spent their careers studying―epidemiology, for example―might know and understand.
This post was originally published by at Medium [AI]