In the Media
Facebook’s New AI Teaches Itself to See With Less Human Help
Quest Corporate director Aude Oliva comments on a new method for training image-recognition models that eliminates the need for most labels.
Nine Experts on the Single Biggest Obstacle Facing AI and Algorithms in the Next Five Years
Leading experts across research, business, and academia discuss the advancements and challenges the field of artificial intelligence will face in the near future including image classification and speech recognition challenges, ethical AI, and algorithmic advancements.
Is neuroscience the key to protecting AI from adversarial attacks?
Creating AI systems that are resilient against adversarial attacks has become an active area of research and researchers at MIT and MIT-IBM Watson AI Lab have found that mapping features of the mammalian visual cortex onto deep neural networks creates AI systems that are more predictable in their behavior and more robust to adversarial perturbations.
AI Algorithms Are Slimming Down to Fit in Your Fridge
Artificial intelligence programs typically are power guzzlers. New research from Song Han's lab at MIT shows how to generate computer vision from a simple, low-power chip.
IBM and MIT researchers find a new way to prevent deep learning hacks
In a new study, researchers from MIT and the MIT-IBM Watson AI Lab explore whether the human brain can offer clues on how to make deep neural networks even more powerful and secure. Turns out it can.
Researchers fit more AI than ever onto IoT microchips
Researchers from the MIT-IBM Watson AI Lab unveil a technique for squezing more AI than ever onto the simple chips that power connected devices, from medical wearables to coffeemakers.
Is neuroscience the key to protecting AI from adversarial attacks?
Researchers at MIT and IBM show that directly mapping features from the visual cortex onto deep neural networks creates AI systems that are more predictable in their behavior and more robust to adversarial perturbations.
Have You Heard of Neurosymbolic AI?
Artificial intelligence has been criticized for lacking common sense. Could a hybrid form of AI, merging deep neural networks and symbolic programs, change that? David Cox, IBM director of the MIT-IBM Watson AI Lab, weighs in.
How the Coronavirus Pandemic Is Breaking AI and How to Fix It
Machine learning models leverage correlations between different variables to make predictions, but the pandemic has shown how ephemeral and context-driven these correlations can be. “Without an understanding of the underlying causes and effects that drive those correlations, your predictions will be wrong,” says David Cox, IBM director of the MIT-IBM Watson AI Lab.
An AI system that infers music from silent videos of musicians
Researchers at the MIT-IBM Watson AI Lab unveil a new AI system — Foley Music — that can generate “plausible” music from silent videos of musicians playing instruments.
Have we squeezed as much out of deep learning as we can?
In a paper on the pre-print server arXiv, MIT and IBM researchers argue that it may no longer be economically or environmentally feasible to continue scaling deep learning systems.
How MIT and IBM Are Fighting Covid-19 with AI
David Cox, IBM Director of the MIT-IBM Watson AI lab, speaks with Tonya Hall about ten new projects MIT and IBM have launched to combat COVID-19.
Neurosymbolic AI to Give Us Machines With True Common Sense
Researchers want AI not to just recognize objects, but to be able to understand what it sees and apply reasoning to navigate new situations. In a project funded by DARPA, researchers at MIT, IBM, Harvard and Stanford aim to train computers to learn more like humans.
Neural-Network Can Identify a Melody Through Musicians’ Body Movements
Researchers at the MIT-IBM Watson AI Lab have developed a tool that exploits the observable hand and body movements captured on video to isolate the sounds of individual instruments.
AI Takes Research to the Next Level
Launched two years ago, the MIT Quest for Intelligence—now part of the MIT Stephen A. Schwarzman College of Computing—aims to bring the MIT community together to answer two monumental questions: How does human intelligence work, and how can human intelligence be reverse-engineered to build smarter machines that will benefit the world?
MIT researchers claim augmentation technique can train GANs with less data
A research team led by MIT's Song Han says it's developed a method that improves the efficiency of generative adversarial networks (GANs) by augmenting both real and fake data samples.
The World Has Changed. AI Is Struggling to Cope.
One way to close the gap between AI's promise and its usefulness for business is to make its decision-making process explainable, says David Cox, IBM director of the MIT-IBM Watson AI Lab. "If people don't understand or trust those tools, it's going to be a lost cause."
A Conversation with the New Computing Dean: Alumnus Daniel Huttenlocher
The MIT Technology Review sat down with Daniel Huttenlocher, the first dean of the MIT Schwarzman College of Computing, to find out what it’s been like to return to campus as an alum in order to lead one of the greatest structural changes in MIT’s history.
MIT researchers release Clevrer to advance visual reasoning and neurosymbolic AI
A new dataset released by researchers at MIT, Harvard and IBM at the International Conference on Learning Representations is aimed at training machines to understand causal relationships among objects.
MIT presents AI frameworks that compress models and encourage agents to explore
In a pair of papers accepted to the International Conference on Learning Representations, MIT researchers investigate new ways to motivate software agents to explore their environment and they unveil a new compression method for making AI models run faster.
The status of artificial adversarial intelligence
Journalist Tonya Hall speaks with MIT's Una-May O'Reilly about the current state of artificial adversarial intelligence and the strengths of adversarial dynamics over model-based malware detectors.
MIT aims for energy efficiency in AI model training
In a new study, researchers at MIT and IBM propose a system for training and running AI models that saves energy and produces fewer carbon emissions.
AI Could Save the World, If It Doesn’t Ruin the Environment First
The growing use of energy-intensive AI applications has led to a push for smaller, more efficient AI models. The MIT Quest for Intelligence and MIT-IBM Watson AI Lab recently sponsored a "Green AI" hackathon to spur innovation in this area.
Computers Already Learn From Us. But Can They Teach Themselves?
The future of AI depends on systems that learn on their own, with little to no human supervision. David Cox, IBM director of the MIT-IBM Watson AI Lab, offers ideas for how to get there.
AI Is Changing Work — and Leaders Need to Adapt
Working with AI doesn't necessarily mean people will lose their jobs — but they will need to work differently. IBM chief economist Martin Fleming discusses the results of an MIT-IBM Watson AI Lab study on AI and the future of work.
If AI’s So Smart, Why Can’t It Grasp Cause and Effect?
Causal reasoning would be useful for almost any AI system. Here's how researchers at the MIT-IBM Watson AI Lab and their colleagues are trying to embed an intuitive sense of physics in machines.
A Hybrid AI Model Lets It Reason about the World’s Physics like a Child
A new dataset co-released by the MIT-IBM Watson AI Lab reveals just how bad AI is at reasoning — and suggests that a new hybrid approach might be the best way forward.
New AI Curriculum Designed for Middle School Students
An open-source AI curriculum developed in professor Cynthia Breazeal's lab at MIT focuses on how AI systems are designed, how they can influence the public, and how AI may change the workplace.
AI could help design better drugs that don’t clash with other medication
A new system that can predict a proposed drug’s chemical structure could help prevent adverse drug interactions, a leading cause of patient death.
Top minds in machine learning predict where AI is going
AI experts look at recent trends and explain where the field is headed in 2020.
MIT and IBM develop AI that recommends documents based on topic
Researchers combine three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to compare thousands of documents per second.
The mind-bending confusion of ‘hammer on a bed’ shows computer vision is far from solved
The ObjectNet dataset compiled by scientists from MIT and IBM is testing the limits of AI vision.
An Obstacle Course to Make AI Better
MIT and IBM researchers have developed a new dataset aimed at improving how AI systems identify objects. "We don’t want them to only recognize what is very common," says MIT's Boris Katz. "We want [a robot] to recognize a chair that is upside down on the floor and not say it is a backpack."
AI May Not Kill Your Job—Just Change It
Don't fear the robots, according to a report by the MIT-IBM Watson AI Lab. Worry about algorithms replacing any task that can be automated.
Robust Robotics and MIT’s Quest for Intelligence
Nick Roy, director of the Bridge at MIT Quest, explains how cognitive science pushes artificial intelligence, enabling new engineering tools and services, and speaks about the importance of explainable and ethical AI.
This Technique Can Make It Easier for AI to Understand Videos
A research team led by MIT's Song Han is teaching artificial intelligence to process more videos while using less power, potentially making it easier to apply AI to large amounts of video.
MIT Upgrades AI Research with Satori Supercomputer
IBM's donation of an $11.6 million machine designed for AI computing coincides with the opening of the new MIT Schwarzman College of Computing.
DeepMind’s Losses Display the Challenges of the AI Industry
Industry-academia collaborations like the MIT-IBM Watson AI Lab are one way of attempting to make AI tools and technologies accessible to all.
GAN Paint Studio uses AI to add, delete, and modify objects in photos
A new GAN-painting tool, developed at the MIT-IBM Watson AI Lab, takes a common-sense approach to adding objects and scenery to photos.
What Happens When You Combine Neural Networks and Rule-Based AI?
The key to the next AI breakthrough could hinge on combining symbolic AI with neural networks. David Cox, IBM director of the MIT-IBM Watson AI Lab explains.
Solving Tech’s Ethics Problem Could Start in the Classroom
MIT philosophy professor Abby Everett Jaques created a new class, Ethics of Technology, to help future engineers understand AI's promise and pitfalls.
Teaching language models grammar really does make them smarter
Researchers with the MIT-IBM Watson AI Lab submit deep learning models to a set of psychology tests to see which ones grasp key linguistic rules.
Two Rival AI Approaches Combine to Let Machines Learn about the World like a Child
Researchers at MIT and IBM show how deep learning and symbolic reasoning together create a program that learns in a humanlike way.
Viewing the World through the Eyes of Neural Networks
A new tool developed by the MIT-IBM Watson AI Lab lets researchers peer inside generative models to understand how they see the world.
A.I. Policy Is Tricky. From Around the World, They Came to Hash It Out.
Global policymakers gathered at MIT to discuss the standards that should be set to govern the expanding use of artificial intelligence technologies worldwide.
A neural network can learn to organize the world it sees into concepts—just like we do
A new tool developed by the MIT-IBM Watson AI Lab paints what a neural net is "thinking," hinting at the concepts it has learned.
MIT Cognitive Scientist Named R&D Magazine’s 2018 Innovator of the Year
AI and cognitive science researcher Josh Tenenbaum is recognized for his pioneering research in human and machine intelligence.
To Advance Artificial Intelligence, Reverse Engineer the Brain
Progress in intelligence research will come from the convergence of engineering and neuroscience, writes James DiCarlo, a professor of neuroscience, an investigator in the McGovern Institute for Brain Research and the head of MIT's Brain and Cognitive Sciences Department.
MIT announces $1bn artificial intelligence and computing initiative
The initiative would be the largest investment in computing and AI ever made by an American university, and MIT's biggest structural change since the 1950s when academics began their pioneering research into AI.