Modern control theories such as systems engineering approaches try to solve nonlinear system problems by revelation of causal relationship or co - relationship among the components; most of those approaches focus on control of sophisticatedly modeled white - boxed systems. We suggest an application of actor - critic reinforceme nt learning approach to control a nonlinear, complex and black - boxed system. We demonstrated this approach on artificial green - house environment simulator all of whose control inputs have several side effects so human cannot figure out how to control this system easily. Our approach succeeded to maintain the circumstance at least 20 times longer than PID and Deep Q Learning.

This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multi-turn question-answering conversations for passages in SQuAD.

Today's technology landscape is looking great. Artificial intelligence has begun to move from the margins to the mainstream of the global economy and has reached a great level of interest for businesses and the general public. Among the various disciplines of AI, computer vision is acquiring considerable momentum. Let's see what it is all about. Progress in artificial intelligence and robotic technologies tends to reduce the gap between humans and machines capabilities, although there is still a substantial way to go to meet the ultimate goal of a human-like machine.

We've seen a big push in recent months to solve AI's "big data problem." And some interesting breakthroughs have begun to emerge that could make AI accessible to many more businesses and organizations. What is the big data problem? It's the challenge of getting enough data to enable deep learning, a very popular and promising AI technique that allows machines to find relationships and patterns in data by themselves. If you change'cat' to'customer,' you can see why many companies are eager to test-drive this technology.)

For decades, Hava Siegelmann has explored the outer reaches of computing with great curiosity and great conviction. The conviction shows up in a belief that there are forms of computing that go beyond the one that has dominated for seventy years, the so-called von Neumann machine, based on the principles laid down by Alan Turing in the 1930s. She has long championed the notion of "Super-Turing" computers with novel capabilities. And curiosity shows up in various forms, including her most recent work, on "neuromorphic computing," a form of computing that may more closely approximate the way that the brain functions. Siegelmann, who holds two appointments, one with the University of Massachusetts at Amherst as professor of computer science, and one as a program manager at the Defense Advanced Research Projects Agency, DARPA, sat down with ZDNet to discuss where neuromorphic computing goes next, and the insights it can bring about artificial intelligence, especially why AI succeeds and fails.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around for at least 50 years.

If you've dug into any articles on artificial intelligence, you've almost certainly run into the term "neural network." Modeled loosely on the human brain, artificial neural networks enable computers to learn from being fed data. The efficacy of this powerful branch of machine learning, more than anything else, has been responsible for ushering in a new era of artificial intelligence, ending a long-lived "AI Winter." Simply put, the neural network may well be one of the most fundamentally disruptive technologies in existence today. This guide to neural networks aims to give you a conversational level of understanding of deep learning.

Washington, DC - These days, the latest developments in artificial intelligence (AI) research always get plenty of attention, but an AI researcher at the U.S. Naval Research Laboratory believes one AI technique might be getting a little too much. Ranjeev Mittu heads NRL's Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. "I think people have focused on an area of machine learning--deep learning (aka deep networks) -- and less so on the variety of other artificial intelligence techniques," Mittu said. "The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality." Deep learning is a machine learning technique that can be used to recognize patterns, such as identifying a collection of pixels as an image of a dog.

OpenAI had announced the end of its non-profit operations a few months ago, which got a confirmation following Microsoft's investment. Since its inception, OpenAI has been working towards the development and promotion of AI which is beneficial to humanity. While it has not achieved a fully efficient AGI system yet, it will now work alongside Microsoft to achieve it. With OpenAI becoming a for-profit company, it faces competition from many companies across the globe that are working on AGI. In this article, we list nine such companies that work on similar intentions as Open AI.

A lot of startups in the San Francisco Bay Area claim that they're planning to transform the world. San-Francisco-based, Elon Musk-founded OpenAI has a stronger claim than most: It wants to build artificial general intelligence (AGI), an AI system that has, like humans, the capacity to reason across different domains and apply its skills to unfamiliar problems. Today, it announced a billion dollar partnership with Microsoft to fund its work -- the latest sign that AGI research is leaving the domain of science fiction and entering the realm of serious research. "We believe that the creation of beneficial AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," Greg Brockman, chief technology officer of OpenAI, said in a press release today. Existing AI systems beat humans at lots of narrow tasks -- chess, Go, Starcraft, image generation -- and they're catching up to humans at others, like translation and news reporting.