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.

COMPUTERS can already hold a massive amount of instantly retrievable data in a manner that puts most humans to shame, but getting them to actually display intelligence is an entirely different challenge. Now a team of researchers from Northwestern University just made a huge stride toward that goal with a computational model that actually outperforms the average American adult in a standard intelligence test. As PhysOrg reports, the witty computer system utilizes an AI platform called CogSketch that gives it the power to solve visual problems just by looking at them, which is something that has traditionally held back many examples of artificial intelligence, reports the New York Post. Being able to visually understand, interpret, and then use that data to come to a solution brings the computer system closer to the functioning of the human brain than many before it, and so the team pitted its creation against a popular standardised test called Raven's Progressive Matrices. The Raven's test (or RPM for short) is composed of 60 multiple-choice questions that measure the taker's ability to reason, using visual puzzles.

Oracle will launch the first of its machine-learning powered Adaptive Intelligent Apps this spring, but what's next for Oracle Data Cloud? Here's a look at promised and possible use cases. The first of five promised Oracle Adaptive Intelligent Application (AI Apps) will be generally available this spring. As for next steps for the petabyte-scale treasure trove known as the Oracle Data Cloud, we'll have to wait and see. The AI App plans emerged at the January 17 Oracle Cloud Analyst Summit in New York.

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.

Lego robot used to reverse engineer the e.dentifier2 smartcard reader (picture courtesy of Chalupar13).

Concerns have recently been widely expressed that artificial intelligence presents a threat to humanity. For instance, Stephen Hawking is quoted in Cellan-Jones1 as saying: "The development of full artificial intelligence could spell the end of the human race." Similar concerns have also been expressed by Elon Musk, Steve Wozniak, and others. Such concerns have a long history. John von Neumann is quoted by Stanislaw Ulam8 as the first to use the term the singularitya--the point at which artificial intelligence exceeds human intelligence.