In May, as part of an otherwise unremarkable corporate strategy meeting, Sony CEO Kenichiro Yoshida made an interesting announcement. The company's artificial intelligence research division, Sony AI, would be collaborating with PlayStation developers to create intelligent computer-controlled characters. "By leveraging reinforcement learning," he wrote, "we are developing game AI agents that can be a player's in-game opponent or collaboration partner." Reinforcement learning is an area of machine learning in which an AI effectively teaches itself how to act through trial and error. In short, these characters will mimic human players.

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.

One day in March 2010, Isak McCune started clearing his throat with a forceful, violent sound. The New Hampshire toddler was 3, with a Beatles mop of blonde hair and a cuddly, loving personality. His parents had no idea where the guttural tic came from. They figured it was springtime allergies. Soon after, Isak began to scream as if in pain and grunt at his parents and peers. When he wasn't throwing hours-long tantrums, he stared vacantly into space. By the time he was 5, he was plagued by insistent, terrifying thoughts of death. "He would smash his head into windows and glass whenever the word'dead' came into his head. He was trying to drown out the thoughts," says his mother, Robin McCune, a baker in Goffstown, a small town outside Manchester, New Hampshire's largest city.

Spirit AI is using artificial intelligence to combat toxic behavior in game communities. The London company has created its Ally social intelligence tool to decipher online conversations and monitor whether cyberbullying is taking place. It is the brainchild of researchers at New York University, according to Mitu Khandaker, creative partnerships director at Spirit AI and an assistant arts professor at the NYU game center. The company uses AI, natural language understanding, and machine learning to help data science and customer service teams to understand the general tenor of an online community. It also helps predict problems before they escalate.

Critics noted that the conceit resembled the cyclical experience of playing a video game, in which dying resets a staged arrangement of obstacles. Often these are enemies, and the most common way they're surpassed is by the player violently dispatching them. Some games have kept this as cartoonish as Mario jumping on a Goomba's head, but others strive for vivid action and more-lifelike foes to pit the player against. But we know what enemies look like today -- how will we treat them in the games to come? Put another way: How will violence in gaming change in the future? The question is broad, and a little loaded. Gaming's evolution was stricken by moral panic about the effect of violent video games on kids. In those days, Mortal Kombat and Doom convinced the fearful that engaging in bloody digital combat -- as real as it looked in glorious 16-bit -- would warp players' minds.