Objective: Using traditional approaches, a Brain-Computer Interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g.~by transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder to achieve a reliable calibration-less decoding with a guarantee to recover the true class means. Method: We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We modified a visual ERP speller to meet the requirements of the LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with N=13 subjects performing a copy-spelling task. Results: Theoretical considerations show that LLP is guaranteed to minimize the loss function similarly to a corresponding supervised classifier. It performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance: The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the true class means. This makes it an ideal solution to avoid a tedious calibration and to tackle non-stationarities in the data. Additionally, LLP works on complementary principles compared to existing unsupervised methods, allowing for their further enhancement when combined with LLP.

A Hilbert space embedding of a distribution---in short, a kernel mean embedding---has recently emerged as a powerful tool for machine learning and inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. While initially closely associated with the latter, it has meanwhile found application in fields ranging from kernel machines and probabilistic modeling to statistical inference, causal discovery, and deep learning. The goal of this survey is to give a comprehensive review of existing work and recent advances in this research area, and to discuss the most challenging issues and open problems that could lead to new research directions. The survey begins with a brief introduction to the RKHS and positive definite kernels which forms the backbone of this survey, followed by a thorough discussion of the Hilbert space embedding of marginal distributions, theoretical guarantees, and a review of its applications. The embedding of distributions enables us to apply RKHS methods to probability measures which prompts a wide range of applications such as kernel two-sample testing, independent testing, and learning on distributional data. Next, we discuss the Hilbert space embedding for conditional distributions, give theoretical insights, and review some applications. The conditional mean embedding enables us to perform sum, product, and Bayes' rules---which are ubiquitous in graphical model, probabilistic inference, and reinforcement learning---in a non-parametric way. We then discuss relationships between this framework and other related areas. Lastly, we give some suggestions on future research directions.

The night before his newest poker competition was set to begin, Carnegie Mellon's Tuomas Sandholm and his PhD student Noam Brown sat down to play a little No Limit Texas Hold'em against the main competition: the artificial intelligence program they designed called "Libratus." "I was totally wrecked," Sandholm told The Washington Post. But he is not a serious poker player, so that's not such a big achievement. For the past 13 days, however, Libratus has been facing off against four world-champion poker players in a Pittsburgh casino. If it can beat them like it beat Sandholm, it would be an enormous breakthrough.

SAN FRANCISCO--(BUSINESS WIRE)--Kinetica, provider of the fastest, in-memory database accelerated by GPUs, today announced the availability of in-database analytics via user-defined functions (UDFs). This industry-first capability makes the parallel processing power of the GPU accessible to custom analytics functions deployed within Kinetica. This opens the opportunity for machine learning/artificial intelligence libraries such as TensorFlow, BIDMach, Caffe, and Torch to run in-database alongside, and converged with, BI workloads. Kinetica also introduced its extensible and flexible'Reveal' visualization framework for interactive, real-time data exploration. Kinetica's advanced in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one powerful platform.

DeepMind is known internationally as a leader in an area of computer science called machine learning. Now senior DeepMind staff are joining forces with UCL's Department of Computer Science to share their knowledge by delivering a state-of-the-art Master's level training module called'Advanced Topics in Machine Learning'. This new module will provide a key component of UCL's Machine Learning Master's programmes and will cover some of the most sophisticated topics in artificial intelligence. The first of these lectures will take place in January 2017. The course focuses on deep learning and reinforcement learning, and will be led by DeepMind's Thore Graepel, who also holds a UCL professorship.

The race to the moon is heating up. Soon the Google Lunar XPrize will hand out $30 million in prizes to privately-funded teams who can send a robot to the moon, move 500 meters across its surface, and send pictures back to Earth. According the contest rules, contestants had until December 31, 2016 to book a seat on a rocket ride to the moon. Five teams out of the original 34 have made it past this crucial checkpoint. These missions must launch before December 31, 2017 in order to get a shot at the $20 million grand prize and $5 million second place prize (and another $5 million total in smaller prizes).

Many claims, including the key one that "Blockchain technology has the potential to revolutionize applications and redefine the digital economy," were neither discussed nor backed up with evidence. From a scientific point of view, this is insufficient. Worse, like many blockchain proponents, Underwood failed, in my opinion, to raise the right questions. Instead of focusing on "what block-chain could do," one should address "what blockchain can do better than other technologies." In this context, blockchain is often compared to existing solutions rather than to existing technologies, as in the proverbial comparison of apples and oranges. There may be any number of reasons, including operational, economic, or social, why an existing solution (as inadequate as it may be) has not been replaced in the marketplace.

As Friday night became Saturday morning, Dong Kim sounded defeated. Kim is a high-stakes poker player who specializes in no-limit Texas Hold'Em. The 28-year-old Korean-American typically matches wits with other top players on high-stakes internet sites or at the big Las Vegas casinos. But this month, he's in Pittsburgh, playing poker against an artificially intelligent machine designed by two computer scientists at Carnegie Mellon. No computer has ever beaten the top players at no-limit Texas Hold'Em, a particularly complex game of cards that serves as the main event at the World Series of Poker. Nearly two years ago, Kim was among the players who defeated an earlier incarnation of the AI at the same casino.

The following script is from "Artificial Intelligence," which aired on Oct. 9, 2016. Charlie Rose is the correspondent. The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off. In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology.

Researchers are warning Android users off protecting their smartphones with a lock pattern, having found that the majority of them can be cracked within five attempts. The security measure lets a user unlock their handset by tracing a particular pattern across a grid of dots with their finger, and is available as an alternative to a PIN or password. Users are allowed five attempts to get the pattern right before the device detects something suspicious and locks itself. However, researchers from Lancaster University, the University of Bath and Northwest University in China claim to have cracked 95% of 120 unique lock patterns in less than five attempts, using video recordings and computer vision algorithm software. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.