Wherever there's data there’s a use case to be made for artificial intelligence. But that doesn’t mean all applications of the technology are equally relevant. For its part, the Institute for Experiential AI has zeroed in on five focus areas of grave scientific and economic importance: health, life sciences, climate and sustainability, responsible AI, and hybrid languages.
At this year’s business conference, leaders from three of those disciplines sat down with Institute Director of Research Ricardo Baeza-Yates to explain how each stands at a critical junction in human development, and why AI can help tackle some of the world’s biggest challenges.
AI + Health
For Gene Tunik, director of AI + Health, human health has to be considered in a broad sense in order to address obstacles that transcend borders, individuals, or even industries. Health may redound to the individual but its problem set (and opportunities) is truly global. In the roundtable discussion, Gene identified four priorities for the Institute’s health focus area:
Curation of global federated data sets supporting complex learning tasks
AI at the point of care
AI to accelerate the time to market
AI experiential education for health providers
As Gene explained, curating large data sets has to involve dynamic, rapidly changing information about biology, cognition, mobility, and physiology in order to be useful. “We want to be able to fuse that data together with static information about patient health to make informed decisions where AI can be a member of the clinical support team.”
AI is opening new territory for how to deliver more accessible health care at home, lessening the need for and strain on hospitals, rehabilitation centers, and other settings. Simulations and predictive analytics can also be leveraged to shorten the time-to-market for new drugs and devices. And uniting all of these fronts is a drive to improve AI literacy for clinicians, health professionals, policy makers, and other stakeholders.
AI + Life Sciences
The life sciences have really come into its own in the era of big data, and Sam Scarpino, director of AI + Life Sciences, views the institute’s mission on this front as intimately tied to both the availability and responsible use of all that data. In a short talk he identified the three core priorities for his focus area.
In the first, he highlighted the need to develop legal, operational, and ethical frameworks to ensure that data are used in accordance with the original reasons for their collection. Universities, to that extent, serve as trusted intermediaries between organizations to help ensure that data are used properly.
The second priority has to do with reducing the risk of opportunistic infections in an age when antibiotic resistance is on the rise alongside new, border-hopping viruses and pathogens. Modern analytics, data science, and AI, Sam explained, can be used to address real world problems that keep people in hospitals longer than they need to be, running up medical bills and increasing the risk of opportunistic infections.
“We could be leveraging high-resolution mobility data to improve contact tracing efforts to keep schools and businesses open,” Sam said. “We could be leveraging wet lab ‘in the loop’ methods to rapidly accelerate our ability to bring biologicals like antibodies to market.”
That means integrating wet lab experiments with AI in a kind of feedback loop, where outcomes inform the AI system, which in turn informs the next set of experiments. This is in contrast to the traditional model, which is a one-way trade-off between data and experiment.
AI for Climate and Sustainability (AI4CaS)
Climate is nothing if not complex. For AI to be useful as a tool for improving the models scientists use to predict everything from the weather to hydrological shifts, it needs to be able to account for the highly chaotic aspects of nature.
Ardeshir Contractor, director of research for the AI for Climate and Sustainability (AI4CaS) focus area, identified three priority areas:
Combine specialities: advanced computational science, engineering, and data solutions with existing physics, bio-geoscience, and techno-special principles.
Bridge gaps: Combine AI with domain knowledge to bridge the gap between climate science and stakeholder insights.
Focus on resilience: communities, cities, transportation, networks, energy
Echoing sentiments from Gene and Sam, Ardeshir stressed the importance of bringing in diverse data sets to drive new AI models. That means including engineering data, as well as data from agriculture and biology to bridge the gaps that inevitably form whenever you attempt to create models that span complex global networks.
“I think the most important thing,” Ardeshir said, “is that we want to create a focus on resilience — the ability for communities, cities, transportation networks, energy outlays to actually be able to weather the extremes that we are going to see because of climate change.”
For Gene, Sam, and Ardeshir, progress on AI as well each of their research areas hinges on a collaborative ethos—an idea found in the very DNA of the Institute for Experiential AI. How else to understand the revolutionary potential of a technology built on data in all its forms?
Watch the complete panel discussion here.