Sleep is a naturally recurring state of mind and body characterized by altered consciousness, relatively inhibited sensory activity, inhibition of nearly all voluntary muscles, and reduced interactions with surroundings.[1] It is distinguished from wakefulness by a decreased ability to react to stimuli, but is more easily reversed than the state of hibernation or of being comatose. Mammalian sleep occurs in repeating periods, in which the body alternates between two highly distinct modes known as non-REM and REM sleep. REM stands for "rapid eye movement" but involves many other aspects including virtual paralysis of the body. During sleep, most systems in an animal are in an anabolic state, building up the immune, nervous, skeletal, and muscular systems. Sleep in non-human animals is observed in mammals, birds, reptiles, amphibians, and some fish, and, in some form, in insects and even in simpler animals such as nematodes. The internal circadian clock promotes sleep daily at night in diurnal organisms (such as humans) and in the day in nocturnal organisms (such as rodents). However, sleep patterns vary among individual humans and even more widely among other species. In the last century, artificial light has in many areas of the world substantially altered sleep timing among both humans and many other species.[2] The diverse purposes and mechanisms of sleep are the subject of substantial ongoing research.[3] Sleep seems to assist animals with improvements in the body and mind. A well-known feature of sleep in humans is the dream, an experience typically recounted in narrative form, which resembles waking life while in progress, but which usually can later be distinguished as fantasy. Sleep is sometimes confused with unconsciousness, but is quite different in terms of thought process. Humans may suffer from a number of sleep disorders. These include dyssomnias (such as insomnia, hypersomnia, and sleep apnea), parasomnias (such as sleepwalking and REM behavior disorder), bruxism, and the circadian rhythm sleep disorders. In mammals and birds, sleep is divided into two broad types: rapid eye movement (REM sleep) and non-rapid eye movement (NREM or non-REM sleep). Each type has a distinct set of physiological and neurological features associated with it. REM sleep is associated with dreaming, desynchronized and faster brain waves, loss of muscle tone,[4] and suspension of homeostasis[citation needed]. REM and non-REM sleep are so different that physiologists classify them as distinct behavioral states. In this view, REM, non-REM, and waking represent the three major modes of consciousness, neural activity, and physiological regulation.[5] According to the Hobson & McCarley activation-synthesis hypothesis, proposed in 1975–1977, the alternation between REM and non-REM can be explained in terms of cycling, reciprocally influential neurotransmitter systems.[6]

"The Silicon Review 50 Smartest Companies of the Year 2016 program identifies the companies transforming the way we work via cutting-edge technology. These companies are leading the seismic shifts that today's company leaders need to be thinking about before it's too late. We selected WorkCompass because of its unique application of artificial intelligence to improve performance appraisal, its revenue growth, customer reviews and domain influence," said Manish Pandey, Editor-in-Chief of The Silicon Review Magazine. "We are honored to be recognized by The Silicon Review Magazine as the one of the 50 Smartest Companies of the Year 2016," said Denis Coleman, Founder and CEO at WorkCompass. "In 2012, I left my job to found WorkCompass. I wanted to transform performance appraisal into an ongoing process about coaching and mentoring staff to achieve their full potential. I'm incredibly proud of what we have achieved so far at WorkCompass".

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it's as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition. Deep learning is the next step to machine learning with a more advanced implementation. Currently, it's not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data.

USA TODAY tech reporter Mike Snider looks into Microsoft's 26.2 billion acquisition of LinkedIn and how it might help the company. LibreOffice is free and has many of the same features as Microsoft Office. SAN FRANCISCO - Microsoft reported first-quarter 2017 adjusted earnings of 76 cents a share on adjusted revenues of 22.3 billion Thursday. The company was expected to report adjusted earnings of 68 cents, up from 67 in the year ago quarter, on revenue of 21.7 billion (excluding deferred revenue), or about the same as a year ago, according to analysts polled by S&P Global Intelligence. Microsoft shares (MSFT) closed down .75% at 57.10, but the impressive beat caused the stock to jump 5% in after hours trading.

Investigators eager to uncover the genetic basis of autism could now have hundreds of promising new leads thanks to a study by Princeton University and Simons Foundation researchers. In the first effort of its kind, the research team developed a machine-learning program that scoured the whole human genome to predict which genes may contribute to autism spectrum disorder (ASD). The results of the program's analyses -- a rogue's gallery of 2,500 candidate genes -- vastly expand on the 65 autism-risk genes currently known. Researchers have recently estimated that 400 to 1,000 genes underpin the complex neurodevelopmental disorder. This newest research provides a manageable, "highly enriched" pool from which to pin down the full suite of ASD-related genes, the researchers said.

Memory fills the gaps in information that even todays best AI algorithms can't Google's DeepMind artificial intelligence lab does more than just develop computer programs capable of beating the world's best human players in the ancient game of Go. The DeepMind unit has also been working on the next generation of deep learning software that combines the ability to recognize data patterns with the memory required to decipher more complex relationships within the data. Now, it's at this point where when we say "memory" you might think that we're referring to the type of RAM or some other form of hardware computer memory you stick into servers, but no. After all, say for example we asked you to predict an outcome then it's likely that not only will you draw on the information you have on hand at the time but you'll also – consciously or sub-consciously – draw on past memories that, when accessed and interpreted correctly, will help you create a better, more informed answer – or opinion. It's this "human" type memory – this "external" memory as researchers call it – that we're increasingly seeing being used to augment today's most advanced deep learning AI's – such as DeepMind. Deep learning is the latest buzz word for artificial intelligence algorithms called neural networks that can learn over time by filtering huge amounts of relevant data through many "deep" layers.

After 23 years in office, the notorious Maricopa County Sheriff Joe Arpaio (R) might not win re-election. The latest Arizona Republic/Morrison/Cronkite News poll of the Arizona county sheriff's race shows Arpaio nearly 15 points behind Democrat Paul Penzone, 45.9 percent to 31.1 percent. Arpaio, who calls himself "America's toughest sheriff," made national headlines last year when he spoke out in favor of Donald Trump and joined him in the mission to "seek the truth" about President Obama's birth certificate. Arpaio's massive slip is likely due -- at least in part -- to voters' widespread opposition to building a border wall and deporting all undocumented immigrants, both proposals Arpaio supports. The poll found that 30.8 percent of the county's voters strongly disagree with mass deportation, while 41.7 percent disagree.

There are a few moments in the history of artificial intelligence (AI) that are considered major breakthroughs – events that showed the power of machine intelligence in matching or surpassing human performance. Two examples are Deep Blue versus Kasparov in 1997 and Watson versus Jennings in 2008. Most recently, AlphaGo versus Lee Sedol became another major victory, this time driven by a fast developing field known as "deep learning." Deep learning–a machine learning technique based on artificial neural networks–is growing in popularity due to a series of developments in the science and business of data mining. Prior to AlphaGo's victory over the currently best Go player Lee Sedol, computer programs that played Go had only been able to beat average players.

Data is pouring into the hands of cancer researchers, thanks to improvements in imaging, models and understanding of genetics. Today the data from a single patient's tumor in a clinical trial can add up to one terabyte--the equivalent of 130,000 books. But we don't yet have the tools to efficiently process the mountain of genetic data to make more precise predictions for therapy. And it's needed: treating cancer remains a complex moving target. We can't yet say precisely how a specific tumor will react to any given drug, and as a patient is treated, cancer cells can continue to evolve, making the initial therapy less effective.

Indeed, with so many cyborgs living in our midst, it may not be too early to consider an impending shift in our conception of an "able bodied" human. Perhaps "unenhanced" humans – what nowadays passes for "normal" – could come to be seen as "disabled" because they fail to incorporate some sort of AI in their own being. In thinking about how we might govern AI while getting away from Asimov's "us vs them"mentality, the following three points are worth keeping in mind: If a computer chip is planted in you, then potentially others can operate it, if they know the code. Today's fears of hackers shutting down the internet could morph into tomorrow's fears of terrorists shutting down entire populations – and that might be the way future wars are won. But who should be entrusted to ensure that none of this happens?