Started in the 1950's, Artificial Intelligence, or AI, has experienced several ups and downs until 2016, when AlphaGo (built by DeepMind, a Google company) defeated the world champion of Go and AI becomes popular in the general public again. The drives for AI's currently popularity are three important breakthroughs: supercomputer, big data, and machine learning algorithm. How is and will AI be influencing the business world? The authors have interviewed Professor J----rgen Schmidhuber, the father of contemporary AI. Professor J----rgen Schmidhuber's lab created Long short-Term Memory (LSTM) deep learning algorithm in the 1990's, which greatly advanced the development of deep learning and AI.

Artificial intelligence has made great strides in the past few years, but it's also generated much hype over its current capabilities. That's one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science. Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is "way too much hype" regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are "trained" on massive amounts of conversation data so that they learn to interact with people. Get Data Sheet, Fortune's technology newsletter.

Six Great Articles About Quantum Computing and HPC This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. Six Great Articles About Quantum Computing and HPC This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.

His impact on artificial intelligence research has been so deep that some people in the field talk about the "six degrees of Geoffrey Hinton" the way college students once referred to Kevin Bacon's uncanny connections to so many Hollywood movies. Dr. Hinton's students and associates are now leading lights of artificial intelligence research at Apple, Facebook, Google and Uber, and run artificial intelligence programs at the University of Montreal and OpenAI, a nonprofit research company. "Geoff, at a time when A.I. was in the wilderness, toiled away at building the field and because of his personality, attracted people who then dispersed," said Ilse Treurnicht, chief executive of Toronto's MaRS Discovery District, an innovation center that will soon house the Vector Institute, Toronto's new public-private artificial intelligence research institute, where Dr. Hinton will be chief scientific adviser. Dr. Hinton also recently set up a Toronto branch of Google Brain, the company's artificial intelligence research project. His tiny office there is not the grand space filled with gadgets and awards that one might expect for a man at the leading edge of the most transformative field of science today.

Last year, Intel Corp. acquired neural-network hardware maker Nervana and built Nervana's chip, integrating it with Intel's own on-processor deep-learning and artificial-intelligence (AI) capabilities. This month, Intel Capital invested in AI startups CognitiveScale, Aeye Inc., and Element AI. Intel fellow Pradeep Dubey outlined the big picture for Intel's growing AI portfolio. Intel is investing in AI startups, acquiring others, and blending the mix with its own AI expertise to ensure a leadership position in machine learning, deep learning, and brainlike neural networks based on its AI hardware and software. The company is aiming at all applicable industries, from drug screening with the Xeon Phi to software-defined visualization with its graphics hardware, Dubey said.

According to Demis Hassabis, the founder of the leading artificial intelligence company DeepMind, intelligence is computational and computers can replicate it. Hassabis sees this as an incredible tool, like having the world's best research assistant at your fingertips. Where this leads -- solving humanity's greatest challenges or the possibility of AI being used by people with ill intent -- is part of a wide-ranging conversation Hassabis had with The Nikkei on the sidelines of the Future of Go summit held on May 23-27 in Wuzhen, China. Excerpts from the interview follow. Q: You've demonstrated how powerful the combination of deep learning and reinforcement learning is, and based on that, AI can be better than humans in a narrow domain like the game of Go. But how far along are you in terms of fundamentally understanding what intelligence is, and its processes and its mechanisms?

Earlier this year, Kaggle ran a $1 million contest to build algorithms capable of identifying signs of lung cancer in CT scans, helping to fuel a larger effort to apply neural networks to health care.

Memory and context play a huge role in helping humans interpret the world. If you encounter a word like "spring" while reading, you don't usually have to ask yourself whether it's a verb meaning "to jump," or a noun referring to the season after winter, or another noun referring to a coil of metal, because the context of the sentence makes it clear. But as with many tasks, what's effortless for humans can be incredibly difficult for computers. We've spent a good deal of time looking at various types of neural networks and their applications. We started with feedforward networks and their ability to sort and label objects based on shared characteristics, and then we explored convolutional networks, which are particularly well-suited to decoding images.

Former Baidu chief scientist Andrew Ng today announced plans to launch a new business called Deeplearning.ai. The company launched with little information about what to expect beyond an exploration of the "frontiers of AI," but alludes to additional details being shared in August about Deeplearning.ai's Deep learning is a type of AI that involves training large artificial neural networks on a pool of information, then getting them to make inferences about new data. Considered one of the top minds in deep learning today, Ng left Baidu in March and was with the company since 2014 following work as cocreator of the Google Brain AI research project. Ng has also worked as director of the Stanford University's Artificial Intelligence Lab (SAIL) and is cofounder of online education company Coursera.

In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which capture the relations among inputs. In many cases, more than two maps are strongly related, so it is wise to make multiplicative unit learn relations among more input maps, in other words, to find the optimal relational-order of each unit. In order to enable our machine to learn relational order, we developed a reinforcement-learning method whose optimality is proven to train the network.