The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.

Linglong Kong (mathematical and statistical sciences) is the co-lead of a new collaboration of 18 researchers across North America working together to improve the way neuroimaging data is analyzed. In the hands of the right reader, it may prove to be a very important one--such as the likelihood of a particular patient developing a neurological disorder like dementia or responding positively to a new treatment for depression or ADHD. Recent rapid innovations in technology have enabled the unprecedented collection of complex neuroimaging data to measure different perspectives on brain structures and functions. This information-rich data offers incredible potential to investigate neurological and psychiatric diseases, trace neural network changes of various disorders and understand the inner workings of the human brain--helping lay the foundation for a future with more precise, patient-specific medical treatment options. Some of the more complicated problems involve integrating complementary sources of information--such as those that arise from studies that collect data using multiple neuroimaging modalities simultaneously, or studies that aim to combine brain imaging with genomics.

What's striking in these discussions is regardless of whether you fear or love AI bots, our future with them is inevitable. People are excited about Bots, both physical and digital, because they are the next wave of industrial revolution. Industrial revolutions are not defined by individual technological improvements, but changes in labor and distribution. During the 1st and 2nd industrial revolutions, many things were invented; from looms to steam engines to new smelting iron techniques. It was an Industrial Revolution because goods were no longer produced by human hands, but could be primarily outsourced to machines and manufacturing processes.

George Hotz, the latest Silicon Valley startup founder to get a multimillion-dollar check from venture capitalists, went for a ride in a Rolls-Royce around San Francisco on Monday. At 26, Hotz thinks he could teach the legendary vehicle a few tricks. Braking should be smoother, he says. The vehicle should run each time as if the best limo driver in the world was behind the wheel. "You don't want the champagne to spill," Hotz says.

Popular chat app Kik may not have quite the clout of Facebook Messenger or Snapchat, but what they do have, as of Tuesday, is bots. Specifically, Kik now has multi-functional bots that allow you to do things like shop, play games and check the weather, similar to features offered by Skype. The humble chat bot, a marvelous curiosity of early artificial intelligence technology, saw a small surge a few years back, with bots like SmarterChild gaining a bit of internet fame and making for an afternoon of oohs, aahs and laughs. More sophisticated bots, such as the award-winning Mitsuku chat bot, are coming dangerously close to being able to pass a Turing Test, a test where a human chats with the bot and decides if the person they're talking to is a bot or a human. Some bots, however, have been developed in a different direction.

The move from humans working with computers to computers working without humans is almost upon us, and some are already calling it Industry 4.0 – or the fourth industrial revolution. For those not keeping up with your industrial revolutions, the first was considered launched by the use of steam and water power, the second by the use of electricity, and the third by the introduction of computers in the workplace. The name Industry 4.0 was first coined by the German Government, and represents the implementation of artificial intelligence, big data, and the industrial Internet of Things (IIoT) in the factories. It might be the first revolution where humans are not required, according to some. Once computers can talk to each other and automate the assembly line, and AI can understand issues and address them ahead of time, there might be no need for humans.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer that was announced today. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care. Massachusetts General Hospital runs the largest hospital-based research program in the United States, and is the top-ranked hospital on this year's U.S. News and World Report's "Best Hospitals" list. The center will train a deep neural network using Mass General's vast stores of phenotypic, genetics, and imaging data. The hospital has a database containing some 10 billion medical images. To do this, it will use the Nvidia DGX-1 -- a supercomputer designed for AI applications. Using AI, physicians can compare a patient's symptoms, tests, and history with insight from a vast population of other patients. Initially, the MGH Clinical Data Science Center will focus on the fields of radiology and pathology -- which are particularly rich in images and data -- and then expand into genomics and electronic health records. "We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation," said Keith J. Dreyer, vice chairman of Radiology at Mass General and executive director of the center, in a statement. "Guided by precision healthcare, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.

This is a NAO robot asking a human to touch its hand for science. Robots can't feel shame, which saves them from any awkwardness when they ask a human to touch their buttocks. Humans are not so lucky, and when asked by a robot to touch part of its body, humans will get uncomfortable if that body part is generally thought of as private. In a new study, Stanford researchers found that people get weirded out touching "low-accessible" parts of the robot's body (crotch, butt, that sort of thing). The paper will be presented this week in Fukuoka, Japan, at the Annual Conference of the International Communication Association.