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Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules. Images: Connor Coley, Felice Frankel.

The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT.

The technology “has the promise to help people cut out all the tedious parts of molecule building,” including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen.

NASA and the Space Center Houston are seeking designs for autonomous robots that can explore the surface of the moon—and the leading one will win up to $1 million to continue research and discovery.

On Monday, the organizations announced Phase 2 of the NASA Space Robotics Challenge, focused on virtually designing autonomous robotic operations that allow the US to expand its ability to explore space and maintain its technological leadership.

SEE: Artificial intelligence: A business leader’s guide (free PDF) (TechRepublic)

Before an A.I. system can learn, someone has to label the data supplied to it. Humans, for example, must pinpoint the polyps. The work is vital to the creation of artificial intelligence like self-driving cars, surveillance systems and automated health care.


Artificial intelligence is being taught by thousands of office workers around the world. It is not exactly futuristic work.

At iMerit offices in Kolkata, India, employees label images that are used to teach artificial intelligence systems. Credit Credit Rebecca Conway for The New York Times.

Last month, Elon Musk’s Neuralink, a neurotechnology company, revealed its plans to develop brain-reading technology over the next few years. One of the goals for Musk’s firm is to eventually implant microchip-devices into the brains of paralyzed people, allowing them to control smartphones and computers.

Although this Black Mirror-esque technology could hold potentially life-changing powers for those living with disabilities, according to Cognitive Psychologist Susan Schneider, it’s not such a great idea, and I can’t help but feel relieved, I’m with Schneider on this.

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In the past few videos in this series, we have delved quite deep into the field of machine learning, discussing both supervised and unsupervised learning.

The focus of this video then is to consolidate many of the topics we’ve discussed in the past videos and answer the question posed at the start of this machine learning series, the difference between artificial intelligence and machine learning!

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