If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, the subjects are able to perform actual motor planning, rather than just visualizing the motor movement, thus greatly improving the ease with which the motor movements can be imagined. This study also probes the added advantage of administering somatosensory cues in the subject, as opposed to the conventional auditory/visual cues. These changes in the protocol show promise in terms of the aptness of the spatial filters obtained on the data, on application of the well-known common spatial pattern (CSP) algorithms. The regions highlighted by the spatial filters are more localized and consistent across the subjects when the protocol is augmented with somatosensory stimuli. Hence, we suggest that this may prove to be a better EEG acquisition protocol for detecting brain activation in response to intended motor commands in (clinically) paralyzed/locked-in patients.
Recent reinforcement learning algorithms, though achieving impressive results in various fields, suffer from brittle training effects such as regression in results and high sensitivity to initialization and parameters. We claim that some of the brittleness stems from variance differences, i.e. when different environment areas - states and/or actions - have different rewards variance. This causes two problems: First, the "Boring Areas Trap" in algorithms such as Q-learning, where moving between areas depends on the current area variance, and getting out of a boring area is hard due to its low variance. Second, the "Manipulative Consultant" problem, when value-estimation functions used in DQN and Actor-Critic algorithms influence the agent to prefer boring areas, regardless of the mean rewards return, as they maximize estimation precision rather than rewards. This sheds a new light on how exploration contribute to training, as it helps with both challenges. Cognitive experiments in humans showed that noised reward signals may paradoxically improve performance. We explain this using the two mentioned problems, claiming that both humans and algorithms may share similar challenges. Inspired by this result, we propose the Adaptive Symmetric Reward Noising (ASRN), by which we mean adding Gaussian noise to rewards according to their states' estimated variance, thus avoiding the two problems while not affecting the environment's mean rewards behavior. We conduct our experiments in a Multi Armed Bandit problem with variance differences. We demonstrate that a Q-learning algorithm shows the brittleness effect in this problem, and that the ASRN scheme can dramatically improve the results. We show that ASRN helps a DQN algorithm training process reach better results in an end to end autonomous driving task using the AirSim driving simulator.
Today on In Case You Missed It: A quadriplegic man can now move his right arm thanks to the miracles of modern science. A team of doctors from the Cleveland Functional Electrical Stimulation center bridged the gap in his severed spine with a brain control interface and a "functional electrical stimulation" system allowing him to move his right arm. He still doesn't have a sense of touch but at least he can scratch his nose. We also take a look at new media artist Nobumichi Asai's latest work, a motion-tracking projector that paints its target's face and hands with digital designs. It's a more advanced version of what Asai set up for Lady Gaga for her David Bowie tribute.
William Kochevar of Cleveland can slowly move his right arm and hand. No big deal--except that the 56-year-old had been paralyzed from the shoulders down since a bicycling accident ten years ago. The setup that is allowing Kochevar to move his arm again is a "neuroprosthetic" involving two tiny recording chips implanted in his motor cortex and another 36 electrodes embedded in his right arm. Now, during visits he makes to an Ohio lab each week, signals collected in his brain are being captured and sent to his arm so he can make some simple voluntary movements. "I was completely amazed," says Kochevar.
It has long been claimed that women are better at multi-tasking than men. While some women relish the accolade, others suspect some males use it as an excuse for avoiding work. Now scientists have found strong proof that men are inferior at juggling two activities - at least compared to women under 60. Men asked to carry out complex thinking while walking on a treadmill without handrails were found to stop swinging their right arm while they walk. But women under 60 – described as'pre-menopausal' – were'surprisingly' not affected with both arms swung freely as before.
Sometimes it's the simplest studies that reveal how deeply culture shapes our thinking. Take a 2009 experiment involving only a researcher, a child, and a two-word instruction.1 The researcher announces, "Let's dance!" and demonstrates a series of movements: He holds his hands together at eye level and extends them--first to the left, then to the right, then to the left twice, counting with each movement ("One, two, three, four!"). After a few tries, eventually all the children could do the dance on their own. Now comes the test: The researcher spins the child around, to face the other way, and asks her to perform it again.