Latest from MIT : A smarter way to develop new drugs

Pharmaceutical companies are using artificial intelligence to streamline the process of discovering new medicines. Machine-learning models can propose new molecules that have specific properties which could fight certain diseases, doing in minutes what might take humans months to achieve manually. But there’s a major hurdle that holds these systems back: The models often suggest new…

Latest from MIT : Estimating the informativeness of data

Not all data are created equal. But how much information is any piece of data likely to contain? This question is central to medical testing, designing scientific experiments, and even to everyday human learning and thinking. MIT researchers have developed a new way to solve this problem, opening up new applications in medicine, scientific discovery,…

Latest from Google AI – Google at ICLR 2022

Posted by Cat Armato and Callan Hajosy, Program Managers The 10th International Conference on Learning Representations (ICLR 2022) kicks off this week, bringing together researchers, entrepreneurs, engineers and students alike to discuss and explore the rapidly advancing field of deep learning. Entirely virtual this year, ICLR 2022 offers conference and workshop tracks that present some…

UC Berkeley – Should I Use Offline RL or Imitation Learning?

Figure 1: Summary of our recommendations for when a practitioner should BC and various imitation learning style methods, and when they should use offline RL approaches. Offline reinforcement learning allows learning policies from previously collected data, which has profound implications for applying RL in domains where running trial-and-error learning is impractical or dangerous, such as…

Latest from MIT : An easier way to teach robots new skills

With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down. Reprogramming that robot involves hand-labeling thousands of images that show it…

Latest from Google AI – Pix2Seq: A New Language Interface for Object Detection

Posted by Ting Chen and David Fleet, Research Scientists, Google Research, Brain Team Object detection is a long-standing computer vision task that attempts to recognize and localize all objects of interest in an image. The complexity arises when trying to identify or localize all object instances while also avoiding duplication. Existing approaches, like Faster R-CNN…

Latest from MIT Tech Review – The Download: Language-preserving AI, and hackers showed it’s frighteningly easy to breach critical infrastructure

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A new vision of artificial intelligence for the people In the back room of an old building in New Zealand, one of the most advanced computers for artificial intelligence is helping to redefine…

Latest from MIT Tech Review – A new vision of artificial intelligence for the people

In the back room of an old and graying building in the northernmost region of New Zealand, one of the most advanced computers for artificial intelligence is helping to redefine the technology’s future. Te Hiku Media, a nonprofit Māori radio station run by life partners Peter-Lucas Jones and Keoni Mahelona, bought the machine at a…

Latest from Google AI – Hidden Interfaces for Ambient Computing

Posted by Alex Olwal, Research Scientist, Google Augmented Reality and Artem Dementyev, Hardware Engineer, Google Research As consumer electronics and internet-connected appliances are becoming more common, homes are beginning to embrace various types of connected devices that offer functionality like music control, voice assistance, and home automation. A graceful integration of devices requires adaptation to…

Latest from MIT : A new state of the art for unsupervised vision

Labeling data can be a chore. It’s the main source of sustenance for computer-vision models; without it, they’d have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the…