I see two main points of interest personally. The first is adversarial examples. There have been adversarially robust generative models developed, but it seems to me that there is more to be understood here. Obviously the'adversarial examples are features, not bugs' paper lays out a convincing argument around the theoretical meaning of the problem, but... is there some overarching pattern that can help distinguish useful features from brittle features? The main area I'm personally interested in though (nowhere near knowledgable enough to be caught up with current research, but it's what I'm working towards at the moment) is unsupervised model based reinforcement learning.
Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting "smart" email replies to driving our car through rush hour traffic. In the PR realm, AI has been touted as equal parts something to celebrate (no more manual coverage reports!) and fear (er, so long, means of employment). But the truth, as usual, lies somewhere in between. Some form of intelligent technology is already embedded in the PR industry, from the tools we use to find new audiences and monitor evolving conversations to modern media placement. Bloomberg News uses AI to generate coverage on some 3,500 earnings reports every quarter.
Artificial intelligence (AI) technologies and deployments are becoming even more widespread, thanks to a combination of growing amounts of data, faster processing power, and increasingly powerful algorithms. Indeed, as AI technologies make their way into virtually every industry, enabling machines to speak, listen, move, and make decisions in unprecedented ways, a wide range of use cases are illustrating the potential business opportunities, attracting new investment, and driving changes to existing business processes. According to a new report from Tractica, AI implementations now encompass 258 discrete use cases, and the worldwide market for AI software stands at $8.1 billion as of the end of 2018, a figure the market intelligence firm forecasts to rise to $105.8 billion annually by 2025. "The AI opportunity spans a wide range of industries and geographies, from advertising and automotive, to transportation and telecommunications," says principal analyst Keith Kirkpatrick. "A significant portion of the overall revenue is concentrated in highly domain-specific markets with high-volume data needs and ontologies, as well as those with growing applications for machine perception."