Each year, the artificial intelligence community convenes to administer the famous -- and famously controversial -- Turing test, pitting sophisticated software programs against humans to determine if a computer can "think." The machine that most often fools the judges wins the Most Human Computer Award. But there is also a prize, strange and intriguing, for the "Most Human Human." Brian Christian, a young poet with degrees in computer science and philosophy, was chosen to participate in a recent competition. This playful, profound book is not only a testament to his efforts to be deemed more human than a computer, but also a rollicking exploration of what it means to be human in the first place.
This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future. ISBN 9781608455164, 121 pages.
Artificial intelligence has always been an interesting subject to discuss especially among fiction writers. Thousands of artificial intelligence applications have been used for years in almost every industry: scientific discovery, medical diagnosis, robot control, stock trading, remote sensing, and even toys. Stephen Hawking says that the development of full artificial intelligence could spell the end of the human race. But, a lot of scientist don't agree with him. They say artificial intelligence could damage society if and only it built or used incorrectly.
The science and application of HCI continues to evolve with more practitioners, scientists, researchers and developers seek to further what it means to human society and how it can be leveraged to address social and economic issues as well as to determine how people can think and work smarter. It's become such a relevant area of study that university programs and degrees are now available for HCI as a way of furthering the understanding and application for this segment of computer science. At the same time, new jobs are emerging to use these degrees related to furthering areas like those being studied by IBM or as new companies develop applications for artificial intelligence and connected devices that bring us further into the world of computers.
Abstract: For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than 1% of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time.