AlphaGo's uncanny success at the game of Go was taken by many as a death knell for the dominance of the human intellect, but Google researcher David Silver doesn't see it that way. Instead, he sees a world of potential benefits. As one of the lead architects behind Google DeepMind's AlphaGo system, which defeated South Korean Go champion Lee Se-dol 4 games to 1 in March, Silver believes the technology's next role should be to help advance human health. "We'd like to use these technologies to have a positive impact in the real world," he told an audience of AI researchers Tuesday at the International Joint Conference on Artificial Intelligence in New York. With more possible board combinations than there are atoms in the universe, Go has long been considered the ultimate challenge for AI researchers.
Many mental health disorders can be traced to abnormal associative learning. The basolateral amygdala of the brain plays a central role in associative learning and the formation of emotional memories and motivated behaviors. The relevance of the amygdala's anatomical substructure for the acquisition of memories is less clear. Tipps et al. used neuron-specific chemogenetics to systematically probe the circuitry and signaling mechanisms involved in auditory fear learning in mice. Stimulating inhibitory interneurons or inhibiting pyramidal cells was enough to induce an association between a behavior and an auditory cue.
By analyzing CT scans from 48 patients, the deep learning algorithms could predict whether they'd die within five years with 69 percent accuracy -- "broadly similar" to scores from human diagnosticians, the paper says. "Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns." For this study, the system was looking for things like emphysema, an enlarged heart and vascular conditions like blood clotting.The deep learning system was trained to analyze over 16,000 image features that could indicate signs of disease in those organs. The goal was not to build a grim diagnostic system, and the AI only analyzed retrospective patient data.