What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
Defining artificial intelligence isn't just difficult; it's impossible, not the least because we don't really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn't than what artificial intelligence is. But whatever AI is, we've clearly made a lot of progress in the past few years, in areas ranging from computer vision to game playing. AI is making the transition from a research topic to the early stages of enterprise adoption. Companies such as Google and Facebook have placed huge bets on AI and are already using it in their products. But Google and Facebook are only the beginning: over the next decade, we'll see AI steadily creep into one product after another. We'll be communicating with bots, rather than scripted robo-dialers, and not realizing that they aren't human. We'll be relying on cars to plan routes and respond to road hazards. It's a good bet that in the next decades, some features of AI will be incorporated into every application that we touch and that we won't be able to do anything without touching an application. Given that our future will inevitably be tied up with AI, it's imperative that we ask: Where are we now? What is the state of AI? And where are we heading? Descriptions of AI span several axes: strength (how intelligent is it?), Each of these axes is a spectrum, and each point in this many-dimensional space represents a different way of understanding the goals and capabilities of an AI system.
Five'table ronde' or round table were organised mostly with academics on the different aspects of the societal moves due to Artificial Intelligence (AI or IA in French): It was pointed that some milestone progress on deep learning has been achieved. Machines have surpassed human champions in most intellectually challenging games, including Chess, Scrabble, Othello, even Jeopardy. On March 2016, the Google AlphaGo DeepMind's Artificial Intelligence program beat Lee Sedol, the strongest Go player in the world. Go--a 2,500-year-old game is far more complex than Chess. An exceptional powerful computer had to process more than 30 million moves.