Video Friday: Child Robot Affetto Learning New Facial Expressions

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!): HRI 2020 – March 23-26, 2020 – Cambridge, U.K. ICARSC 2020 – April 15-17, 2020 – Ponta Delgada, Azores ICRA 2020 – May 31-4, 2020 – Paris, France ICUAS 2020 – June 9-12, 2020 – Athens, Greece CLAWAR 2020 – August 24-26, 2020 – Moscow, Russia Let us know if you have suggestions for next week, and enjoy today’s videos.

Demystifying the world of deep networks

Introductory statistics courses teach us that, when fitting a model to some data, we should have more data than free parameters to avoid the danger of overfitting — fitting noisy data too closely, and thereby failing to fit new data. It is surprising, then, that in modern deep learning the practice is to have orders of magnitude more parameters than data. Despite this, deep networks show good predictive performance, and in fact do better the more parameters they have. Why would that be? It has been known for some time that good performance in machine learning comes from controlling the complexity of networks, which is not just a simple function of the number of free parameters. The complexity of a classifier, such as a neural network, depends on measuring the “size” of the space of functions that this network represents, with multiple technical measures previously suggested: Vapnik–Chervonenkis dimension, covering numbers, Continue reading Demystifying the world of deep networks

Meet the Roomba’s Ancestor: The Cybernetic Tortoise

Photo: Science and Society Picture Library/Getty Images Neurophysiologist W. Grey Walter built his cybernetic tortoises to elucidate the functions of the brain. In the robotics family tree, Roomba’s ancestors were probably Elmer and Elsie, a pair of cybernetic tortoises invented in the 1940s by neurophysiologist W. Grey Walter. The robots could “see” by means of a rotating photocell that steered them toward a light source. If the light was too bright, they would retreat and continue their exploration in a new direction. Likewise, when they ran into obstacles, a touch sensor would compel the tortoises to reverse and change course. In this way, Elmer and Elsie slowly explored their surroundings. Walter was an early researcher into electroencephalography (EEG), a technique for detecting the electrical activity of the brain using electrodes attached to the scalp. Among his notable clinical breakthroughs was the first diagnosis of a brain tumor by EEG. In Continue reading Meet the Roomba’s Ancestor: The Cybernetic Tortoise

Machine learning picks out hidden vibrations from earthquake data

Over the last century, scientists have developed methods to map the structures within the Earth’s crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves — those that occur at low frequencies of around 1 hertz — that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth’s noisy seismic hum, and are therefore difficult to pick up with current detectors. Specifically generating low-frequency waves would require pumping in enormous amounts Continue reading Machine learning picks out hidden vibrations from earthquake data

To self-drive in the snow, look under the road

Car companies have been feverishly working to improve the technologies behind self-driving cars. But so far even the most high-tech vehicles still fail when it comes to safely navigating in rain and snow.  This is because these weather conditions wreak havoc on the most common approaches for sensing, which usually involve either lidar sensors or cameras. In the snow, for example, cameras can no longer recognize lane markings and traffic signs, while the lasers of lidar sensors malfunction when there’s, say, stuff flying down from the sky. MIT researchers have recently been wondering whether an entirely different approach might work. Specifically, what if we instead looked under the road?  A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a new system that uses an existing technology called ground-penetrating radar (GPR) to send electromagnetic pulses underground that measure the area’s specific combination of soil, rocks, and roots. Continue reading To self-drive in the snow, look under the road

How Adversarial Attacks Could Destabilize Military AI Systems

This piece was written as part of the Artificial Intelligence and International Stability Project at the Center for a New American Security, an independent, nonprofit organization based in Washington, D.C. Funded by Carnegie Corporation of New York, the project promotes thinking and analysis on AI and international stability. Given the likely importance that advances in artificial intelligence could play in shaping our future, it is critical to begin a discussion about ways to take advantage of the benefits of AI and autonomous systems, while mitigating the risks. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. Artificial intelligence and robotic technologies with semi-autonomous learning, reasoning, and decision-making capabilities are increasingly being incorporated into defense, military, and security systems. Unsurprisingly, there is increasing concern about the stability and safety of these systems. In a different sector, runaway interactions between autonomous trading systems in financial markets have produced a Continue reading How Adversarial Attacks Could Destabilize Military AI Systems