Patients with absolutely no control over their body have finally been able to communicate, say scientists. A brain-computer interface was used to read the thoughts of patients to answer basic yes-or-no questions. One man was able to repeatedly refuse permission for his daughter to get married. The study on four patients in Switzerland - published in PLOS Biology - also showed they were happy despite the effects of being "locked-in". The patients all had advanced forms of amyotrophic lateral sclerosis, in which the brain loses the ability to control muscles. It eventually traps people in their own body - they are able to think, but incapable of moving or talking.

It is often talked about as the ultimate prize of artificial intelligence: a machine that can think like a human. But human minds are only one example of the kinds of minds on earth. So what are those other minds like? How do they work and how can we understand them? Suppose we do create human level cognition in artificial intelligence (AI), does that widen the'space of possible minds' to include AI alongside humans and animals?

Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world's best professional poker players in a marathon 20-day poker competition, called "Brains Vs. Once the last of 120,000 hands of Heads-up, No-Limit Texas Hold'em were played on Jan. 30, Libratus led the pros by a collective $1,766,250 in chips. The developers of Libratus -- Tuomas Sandholm, professor of computer science, and Noam Brown, a Ph.D. student in computer science -- said the sizable victory is statistically significant and not simply a matter of luck. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," Sandholm said. This new milestone in artificial intelligence has implications for any realm in which information is incomplete and opponents sow misinformation, said Frank Pfenning, head of the Computer Science Department in CMU's School of Computer Science. Business negotiation, military strategy, cybersecurity and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. "The computer can't win at poker if it can't bluff," Pfenning said. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications.

Libratus, an AI developed by Carnegie Mellon University, has defeated four of the world's best professional poker players in a marathon 120,000 hands of Heads-up, No-Limit Texas Hold'em poker played over 20 days, CMU announced today (Jan. Libratus led the pros by a collective $1,766,250 in chips.* The tournament was held at the Rivers Casino in Pittsburgh from 11–30 January in a competition called "Brains Vs. The developers of Libratus -- Tuomas Sandholm, professor of computer science, and Noam Brown, a Ph.D. student in computer science -- said the sizable victory is statistically significant and not simply a matter of luck. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," Sandholm said. "This is the last frontier, at least in the foreseeable horizon, in game-solving in AI." This new AI milestone has implications for any realm in which information is incomplete and opponents sow misinformation, said Frank Pfenning, head of the Computer Science Department in CMU's School of Computer Science. Business negotiation, military strategy, cybersecurity, and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. "The computer can't win at poker if it can't bluff," Pfenning explained. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

We study the problem of estimating the mean of a random vector $X$ given a sample of $N$ independent, identically distributed points. We introduce a new estimator that achieves a purely sub-Gaussian performance under the only condition that the second moment of $X$ exists. The estimator is based on a novel concept of a multivariate median.

Similarly, the observation is limited in angular resolution by the resolving power of the telescope (R λ/D) and, if taken from the ground, by the distortions caused by the moving atmosphere (the "seeing"). The total blurring introduced by the combination of the telescope and the atmosphere is described by the point spread function (PSF). An image taken by a telescope can therefore be thought of as a convolution of the true light distribution with this point spread function plus the addition of various sources of noise. The Shannon-Nyquist sampling theorem (Nyquist 1928; Shannon 1949) limits the ability of deconvolution techniques in removing the effect of the PSF, particularly in the presence of noise (Magain et al. 1998; Courbin 1999; Starck et al. 2002). Deconvolution has long been known as an "ill-posed" ABSTRACT Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here we train a generative adversarial network (GAN) on a sample of 4, 550 images of nearby galaxies at 0.01 z 0.02 from the Sloan Digital Sky Survey and conduct 10 cross validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance which far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low-signal-to-noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.

In this paper, we present a novel approach for initializing deep neural networks, i.e., by turning PCA into neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.

Free variables occur frequently in mathematics and computer science with ad hoc and altering semantics. We present the most recent version of our free-variable framework for two-valued logics with properly improved functionality, but only two kinds of free variables left (instead of three): implicitly universally and implicitly existentially quantified ones, now simply called "free atoms" and "free variables", respectively. The quantificational expressiveness and the problem-solving facilities of our framework exceed standard first-order and even higher-order modal logics, and directly support Fermat's descente infinie. With the improved version of our framework, we can now model also Henkin quantification, neither using quantifiers (binders) nor raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a choice operator with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports reductive proof search optimally.

When it comes to voice recognition software, SoundHound is definitely not as big a name as Google or Amazon -- yet. SoundHound has raised $75 million in support of its efforts to create artificial intelligence systems capable of recognizing complex human voices and commands, Bloomberg reports. Samsung and Nvidia, two companies that have previously partnered with SoundHound, were among the investors. SoundHound already has Houndify, a proprietary AI technology capable of interpreting complex speech and other audio. The latest $75 million investment will go toward expanding this system into new territories and devices.