Danielle Wood and Neil Gaikwad (MIT) Farming has always been a stressful, unpredictable occupation, but misguided government policies and increasing drought have devastated many family farms and caused suicide rates to skyrocket. In response, MIT researchers are developing an AI framework to make small-scale farming and agriculture markets more efficient. Initiated and led by graduate student Neil Gaikwad, the project will help farmers in the U.S. and India predict how much corn and cotton to plant, and when, and give them market information to make better decisions. The framework is also meant to help policymakers fairly allocate shared resources like water and electricity.
Understanding Real-World Actions as They Unfold
Aude Oliva (MIT) and Daniel Gutfreund (IBM) The brain has a remarkable ability to size up a scene and quickly understand what’s going on. MIT-IBM researchers are training machines to do something similar with a dataset of 1 million short video clips called Moments in Time. The models learn to recognize what’s happening in any particular frame, whether that’s pandas playing or robots dancing or a poodle jumping for joy. As AI systems learn to understand the gist of dynamic scenes, the hope is that this knowledge can be transferred to other domains.
Designing a Robot with Common Sense
Leslie Kaelbling, Tomas Lozano-Perez and Joshua Tenenbaum (MIT) A robot that can break down high-level tasks and run for weeks without getting stuck is still a long way from being built. But MIT researchers hope to crack the problem by applying what they know about computers and the human brain. They are currently building an experimental infrastructure that will allow computer simulators and eventually, real robots, to perceive and interact with the world around them, and ultimately achieve a semblance of common sense.
Identifying Patients at High Risk for Cardiovascular Death
Collin Stultz(MIT) and Kenney Ng (IBM) More cardiac patients could be saved each year if doctors could catch high-risk patients earlier and give them more aggressive treatment. Using machine learning tools to analyze patient medical records, MIT-IBM researchers have discovered 11 new features that indicate patients face a higher risk of cardiovascular death — from treatments received at the hospital to whether they're taking the blood-thinner Warfarin. When the 11 features are considered with the patient’s age, systolic blood pressure, and other standard metrics, the ability to predict high-risk patients goes up significantly, researchers say.
Certifying that an AI Model Can Be Trusted
Luca Daniel (MIT), Pin-Yu Chen (IBM) As more tasks become automated by AI, concerns about malicious attacks are growing. Earlier this year, MIT-IBM researchers developed the first comprehensive measure of a neural network’s resilience to attack and are now building an efficient, general framework for certifying its robustness. The method is scalable and allows developers to report how much a model’s inputs can be disturbed before the model is tricked. The ability to certify a model’s trustworthiness is especially important in safety-critical applications like self-driving cars, and is a critical step to building overall confidence in AI.
Debugging Neural Networks
Antonio Torralba and Stefanie Jegelka (MIT); Hendrik Strobelt (IBM) Deep learning systems are responsible for much of the recent breakthroughs in artificial intelligence, but for progress to continue they will need to do a better job of explaining themselves. MIT-IBM researchers are developing visualization tools to do just that, allowing software developers to find and fix mistakes and ward off malicious attacks. The tools will allow developers to root out bugs in neural network nodes much as they do now in lines of code.
For example, if the network confuses a construction scene with a street bazaar, the tools pinpoint the set of nodes that produced the mistake. In this case, the network incorrectly interpreted the street as a sidewalk, and the construction site as a sales booth. The mistakes would be fixed by retraining these particular network nodes.
Preventing Food Spoilage to Feed More People
Markus Buehler and Benedetto Marelli (MIT); Pin-Yu Chen and Lingfei Wu (IBM) Spoiled fruits and vegetables make up a large share of the food that goes to waste globally. What if some of it could be saved? MIT-IBM researchers are experimenting with AI to extend the life of perishable food by designing new structural biopolymers to serve as edible fruit and vegetable coatings. They are using machine learning tools to analyze the amino acid sequences that make a biopolymer edible, nontoxic and stable. They will then model the shape of their predicted biopolymers to see how their properties change. The researchers will synthesize the best biopolymer candidates in a lab to validate their predictions.
Fighting the Opioid Epidemic
David Sontag (MIT), Dennis Wei and Kush Varshney (IBM) More than 115 people in the United States die each day after overdosing on opioids. The type of opioid, how much was prescribed, and for how long, are all factors in who succumbs to addiction. That has led to a focus among public health officials to develop tools that can improve how pain-killers are prescribed. MIT-IBM Watson AI Lab researchers are applying machine learning tools to medical insurance-claim records to understand what kinds of medical histories and prescription practices raise red flags. Their goal is to develop a model that can help doctors tailor prescriptions to individual patients to minimize addiction risk.
A Human-in-the Loop System for Automated Moral Reasoning
Iyad Rahwan (MIT), Francesca Rossi (IBM) To test how ordinary people think about the ethical dilemmas raised by AI and self-driving cars, MIT researchers developed a Moral Machine platform that allowed volunteers to pick a preferred outcome in various life-threatening scenarios. The researchers found that regional variations played a major role in how people responded. In collaboration with IBM, the MIT researchers are now building models with their experimental data to understand how people and machines can communicate and reach consensus in morally-charged situations. The research is an attempt to bring a computational approach to the ethical questions raised by AI.
An App to Track Declining Brain Function
Thomas Heldt and Vivienne Sze (MIT) MIT researchers are developing low-cost tools to identify and track Alzheimer’s and other neurodegenerative diseases using a simple mobile-phone app. As patients play an eye-tracking game on their phone, the camera records how quickly and accurately their eyes respond to prompts on the screen. The resulting data can tell researchers how well the patient’s brain is functioning. The app, and the software being developed to crunch the data, could provide a way to track disease progression in patients with Alzheimer's. It could also be used as an adjunct to clinical drug trials by making it easier to track improvements over time.