Google parent Alphabet on Monday reported a quarterly profit of $3.5 billion, in a sharp decline from a year ago, with a massive fine by the European Commission biting into earnings. The technology giant reported that revenue grew to $26 billion in the recently ended quarter, and that profit would have tallied nearly $6.3 billion if it weren't for a $2.74 billion antitrust fine levied on search engine Google by the European Commission. The earnings for the quarter fell 28 percent from the same period last year. Google's parent company Alphabet says its quarterly profits took a hit because of a $2.74 billion anti-trust fine imposed by the European Union Alphabet announced separately that Google chief executive Sundar Pichai would join Alphabet's board of directors. Pichai is responsible for Google s product development and technology strategy, as well as the company s day-to-day-operations.

New research suggests that certain rapid eye movements could be a tell of the developmental disorder. These rapid eye movements the research investigates are not the well-known kind that are within the human sleep cycle, but rather are the movements our eyes make as we shift focus to another location in our field of vision. The saccades, as they are officially called, are an important neurological function that helps us interact with people and objects around us. "They are crucial for navigating, and also for orienting visual attention to spatial locations containing pertinent information," according to a study in the European Journal of Neuroscience, and in that way they may also provide some insight into autism. Read: Do the Genes for Autism Also Make You Smarter? "When these neural mechanisms fail, due to damage or disease or developmental disorder, the resultant changes in eye movements can suggest specific computational errors that may be responsible, and highlight brain regions where these computations are thought to occur," the authors explain. In healthy people, saccades are fast and accurate.

A mix of machine learning and artificial intelligence-based algorithms could redefine how schizophrenia is diagnosed. Scientists at IBM Canada and the University of Alberta created a specialized program that was able to assist in predicting instances of schizophrenia with 74 percent accuracy. The algorithms sifted through de-identified brain functional Magnetic Resonance Imaging (fMRI) data from an initiative called the Function Biomedical Informatics Research Network. The neuroimaging information used in this study was of 95 patients diagnosed with schizophrenia and schizoaffective disorders as well as individuals that served as a healthy control group. Scientists can use fMRI to gage blood flow changes in specific areas of the brain, but this specific data set was reflective of research done on brain networks at different resolution levels.

There is a general consensus that artificial intelligence's impact on the daily lives of consumers today represents a tip of the iceberg of what is to come in five years or more. In fact, for 36% of those questioned in a recent global survey by ARM designs in collaboration with Northstar Research Partners, 71% predict that in five years AI will have a far more noticeable impact on their lives while 100% feel that by 2027 the impact will be far greater. With 61% of those surveyed believing that society will become better from increased automation and AI, about 22% believe it will worsen certain aspects of our society. The survey sought a global consensus of 3,938 consumers interviewed through an online survey, who had some understanding of AI and found that just over one third of people now think AI is already having a notable impact on their daily lives. "The bottom line is no matter where you look in the world, people understand and appreciate the value that AI technology is delivering and what it might deliver in the future. As a result, they want it stitched much more deeply into their digital lives. This tells me the killer-robot scenario is fast being relegated to the cutting-room floor and the outlook for AI is good," Simon Segars, CEO, ARM.

In fact, it's about a thousand of them, give or take a few. An experiment using fake ducks to stand in for the real thing has found that when it comes to counting birds, drones beat humans. Jarrod Hodgson and his colleagues at the University of Adelaide in Australia had previously used aerial images from drones to count seabirds and found that the drones had a more comprehensive view of the colonies than the people trying to count them on the ground. However, neither could provide an exact count of the number individual birds. "We couldn't test for accuracy," says Hodgson.

Rich Sutton is old school king of RL.

I fear the hype of AI. I have been exposed to too many situations of "AI for X", where founders are using this latest buzzword haphazardly in their decks, and VCs are throwing money at bad ideas based on the latest trending buzzword. What follows are 4 MUSTS for any startup dealing with AI to be tenable. These are items that must be confirmed beyond the tech, the team, and the extensive math. For founders, if these things are not honed and solved, go back to the drawing board.

There is now tremendous momentum behind initiatives to teach computer programming to a broad audience, yet many of these efforts (for example, Code.org,

There is no 16-clue Sudoku: Solving the Sudoku minimum number of clues problem via hitting set enumeration.

Alan Turing is often praised as the foremost figure in the historical process that led to the rise of the modern electronic computer. Particular attention has been devoted to the purported connection between a "Universal Turing Machine" (UTM), as introduced in Turing's article of 1936,27 and the design and implementation in the mid-1940s of the first stored-program computers, with particular emphasis on the respective proposals of John von Neumann for the EDVAC30 and of Turing himself for the ACE.26 In some recent accounts, von Neumann's and Turing's proposals (and the machines built on them) are unambiguously described as direct implementations of a UTM, as defined in 1936. "What Turing described in 1936 was not an abstract mathematical notion but a solid three-dimensional machine (containing, as he said, wheels, levers, and paper tape); and the cardinal problem in electronic computing's pioneering years, taken on by both'Proposed Electronic Calculator' and the'First Draft' was just this: How best to build a practical electronic form of the UTM?"9 "[The] essential point of the stored-program computer is that it is built to implement a logical idea, Turing's idea: the universal Turing machine of 1936."18 This statement is of particular interest because, in his authoritative biography21 of Turing (first published 1983), Hodges typically follows a much more nuanced and careful approach to this entire issue. For instance, when referring to a mocking 1936 comment by David Champernowne, a friend of Turing, to the effect that the universal machine would require the Albert Hall to house its construction, Hodges commented that this "was fair comment on Alan's design in'Computable Numbers' for if he had any thoughts of making it a practical proposition they did not show in the paper."21 "Did [Turing] think in terms of constructing a universal machine at this stage? There is not a shred of direct evidence, nor was the design as described in his paper in any way influenced by practical considerations ... My own belief is that the'interest' [in building an actual machine] may have been at the back of his mind all the time after 1936, and quite possibly motivated some of his eagerness to learn about engineering techniques. But as he never said or wrote anything to this effect, the question must be left to tantalize the imagination."21 Discussions of this issue tend to be based on retrospective accounts, sometimes even on hearsay. The most-often quoted one comes from Max Newman, who had been Turing's teacher and mentor back in the early Cambridge days and, later, became a leading figure in the rise of the modern electronic computer, sometimes collaborating with Turing. "The description that [Turing] gave of a'universal' computing machine was entirely theoretical in purpose, but Turing's strong interest in all kinds of practical experiment made him even then interested in the possibility of actually constructing a machine on these lines."6