By ignoring their goals, evolutionary algorithms have solved longstanding challenges in artificial intelligence.
Chip maker Intel has been chosen to lead a new initiative led by the U.S. military’s research wing, DARPA, aimed at improving cyber-defenses against deception attacks on machine learning models.
Machine learning is a kind of artificial intelligence that allows systems to improve over time with new data and experiences. One of its most common use cases today is object recognition, such as taking a photo and describing what’s in it. That can help those with impaired vision to know what’s in a photo if they can’t see it, for example, but it also can be used by other computers, such as autonomous vehicles, to identify what’s on the road.
But deception attacks, although rare, can meddle with machine learning algorithms. Subtle changes to real-world objects can, in the case of a self-driving vehicle, have disastrous consequences.
AI has revealed that mice have a range of facial expressions that show they feel — offering fresh clues about how emotional responses arise in human brains.
Scientists at the Max Planck Institute of Neurobiology in Germany made the discovery by recording the faces of lab mice when they were exposed to different stimuli, such as sweet flavors and electric shocks. The researchers then used machine learning algorithms to analyze how the rodents’ faces changed when they experienced different feelings.
Social roboticist, Heather Knight, sees robots and entertainment a research-rich coupling. So she programmed a charming humanoid robot named DATA with jokes, and equipped it with sensors and algorithmic capabilities to help with timing and gauging a crowd. Then Knight and DATA hit the road on an international robot stand-up comedy tour. Their act landed stage time at a TED conference and Knight was profiled in Forbes 30 Under 30. Watching Data perform is much like watching an amateur stand-up comedian cutting her/his chops at an open mic night doing light comedy with a sweet but wooden delivery.
Knight’s goal is specific:
Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.
The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.
Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of electric vehicles: it’s also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn’t have much of an effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan.
In order to better solve complex challenges at the dawn of the third decade of the 21st century, Alphabet Inc. has tapped into relics dating to the 1980s: video games.
The parent company of Google reported this week that its DeepMind Technologies Artificial Intelligence unit has successfully learned how to play 57 Atari video games. And the computer system plays better than any human.
Continue reading “Alphabet’s DeepMind masters Atari games” »
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A Google interview candidate recently asked me: “What are three big science questions that keep you up at night?” This was a great question because one’s answer reveals so much about one’s intellectual interests — here are mine:
Q1: Can we imitate “thinking” from only observing behavior?
Suppose you have a large fleet of autonomous vehicles with human operators driving them around diverse road conditions. We can observe the decisions made by the human, and attempt to use imitation learning algorithms to map robot observations to the steering decisions that the human would take.
