Apple plans to introduce a much smarter version of Siri this year, leveraging a bunch of machine learning technologies it recently acquired with Turi, Tuplejump, Perceptio, VocalIQ and other AI-focused acquisitions, Digitimes claims. The report seems to suggest Apple plans to introduce these features alongside introduction of a future iPhone, though it is not clear if this will be this year, or the next. However, this makes little sense given Siri is now available across all Apple's platforms – while the capabilities may differ (Siri behaves a little differently on a Mac, for example), the basic tech is similar. That's why I can't imagine new Siri features being introduced as an iPhone exclusive. The report also states other smartphone vendors, "are likely to introduce models featuring AI (artificial intelligence) applications as a means to ramp up market shares in 2017, according to industry sources."

Artificial Intelligence" Texas Hold'em Poker tournament, and a machine named Libratus is trouncing a quartet of professional human players. Should the machine maintain its substantial lead--currently at $701,242--it will be considered a major milestone in the history of AI. Given the early results, it appears that we'll soon be able to add Heads-Up, No-Limit Texas Hold'em poker (HUNL) to the list of games where AI has surpassed the best humans--a growing list that includes Othello, chess, checkers, Jeopardy!, and as we witnessed last year, Go. Unlike chess and Go, however, this popular version of poker involves bluffing, hidden cards, and imperfect information, which machines find notoriously difficult to handle. Computer scientists say HUNL represents the "last frontier" of game solving, signifying a milestone in the development of AI--and an achievement that would represent a major step towards more human-like intelligence. Google's AlphaGo has stomped to victory for a fourth time against Go world champion Lee Sedol. Artificial Intelligence" tournament began on January 11th at Rivers Casino in Pittsburgh.

Let's face it, viewing artificial intelligence (AI) simply as a labor-replacement or cost-saving mechanism is boring and uninspired. Let's start talking about a more expansive view that looks at AI as a catalyst for new ways to build markets and drive new forms of innovation. AI potentially will enable organizations to expand in ways that were unthinkable with sheer human brainpower, as powerful as that brainpower may be. The advantage AI brings to the table is that many mundane or grunt-work decisions that occupies human decision-makers' time – such as locating and fixing a data-transfer bottleneck, tracking machine-part performance, or even medical monitoring – is done by machines. In theory, at least, humans' roles are freed up and elevated to more strategic vistas. There are a million points in which machines could be making low-level decisions.

A purposely vague term, referring to an ever-growing set of tools and techniques, that are said to do stuff that people usually do, only better. AI programs have advanced from early victories in playing checkers to wins against chess masters. They have finally achieved the pinnacle of human intelligence, winning the game show Jeopardy. After decades of marching from success to success, today's leaders of Artificial Intelligence anticipate that practical applications of the technology are certain to emerge. If not, they threaten to further inflate the definition of Artificial Intelligence to encompass normal computer programs written by ordinary human beings, at which point success will be theirs -- since a computer program is, without doubt, artificial.

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.

What a wonderful treasure trove this paper is! Schmidhuber provides all the background you need to gain an overview of deep learning (as of 2014) and how we got there through the preceding decades. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond. The main part of the paper runs to 35 pages, and then there are 53 pages of references. Now, I know that many of you think I read a lot of papers – just over 200 a year on this blog – but if I did nothing but review these key works in the development of deep learning it would take me about 4.5 years to get through them at that rate! And when I'd finished I'd still be about 6 years behind the then current state of the art!

At the Robert Wood Johnson Foundation, we have a vision that one day, everyone -- no matter who they are, how much they earn or where they live -- will have the opportunity to live the healthiest life possible. As we work with others toward this future, I'm part of a team tasked with exploring the frontiers of science, medicine, culture and technology. We're examining emerging trends that could shape the trajectory of health for generations to come. Where it's possible, we'd like to harness these trends to improve health and/or mitigate the trends that are likely to harm health. Here are five of the trends we'll be watching in 2017: The secure systems that enable Bitcoin transactions have been considered as a way to ensure the privacy of health data as it is passed from provider to provider.

He worked in isolation, hiding his progress from his teacher and his fellow scientists. Thus, when his creature went on a murderous rampage, killing all of those close to him, there was no one to help Frankenstein destroy the creature or, at the very least, modify the creature's behavior. When crisis struck, there was no one to whom Frankenstein could turn for guidance. And when Frankenstein died, his creature continued to roam the earth, enraged and embittered, poised to wreak more damage. If Frankenstein had been a member of a research group, his fellow scientists could have stepped in to help control the creature and to support Frankenstein in the challenges that came to light the moment the creature attained autonomy.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8]

Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution – this distinguishes unsupervised learning from supervised learning and reinforcement learning. Unsupervised learning is closely related to the problem of density estimation in statistics.[1] However, unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data.