Today at SXSW in Austin, Texas, a panel of Disney executives talked about the expanding field of artificial intelligence and machine learning, along with other technologies. Jon Snoddy, of Disney's Imagineering group, mentioned the importance of new technologies serving the stories the company wanted to tell, and said that sometimes they scrap the use of technology entirely when it doesn't meet that goal. The panel also showed video clips from their R&D lab. One showed an animatronic "Pascal," inspired by the movie Tangled, which could be carried around and respond to input from people that touched it. Another video showed a completely autonomous droid that people would quickly learn to interact with.

The European Parliament is calling for EU-wide civil law rules addressing the fast-developing field of technology – robotics and artificial intelligence. Robots assisting in the field of medicine or the automotive industry is already an everyday reality, however the civil law rules need to be adapted in order to boost innovation and creativity, address issues of liability in the case of damages and set ethical standards. The European Parliament is the first parliament to debate robotics and artificial intelligence. The Parliament's Resolution initiates a timely debate on a wide range of issues related to robotics and AI including standardisation, safety and security, data protection, autonomous vehicles, care and medical robots, human repair and enhancement, drones, liability rules, ethical questions, but also considers education and employment. "European industry in the field of robotics and AI deserves a legal framework within which it can continue to grow. Innovations go beyond borders and are carried out by experts from several member states working together. This collaboration requires our support. The creation of EU-wide rules on robotics is a necessary step forward to allow the full exploitation of the economic potential of the sector, to promote growth and innovation, and to protect and create more jobs", said Therese Comodini Cachia MEP, Parliament's Rapporteur for robotics.

TensorFlow has become the most popular AI programming project on software code sharing service GitHub, leapfrogging well-regarded systems created by universities and corporate rivals, according to data gathered by Bloomberg. Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We've used it to demonstrate that concepts like "cat" can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet's Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.

Reflecting the rapidly increasing interest and investment in cloud computing, 10,000 developers, engineers, IT executives, and Google employees and partners gathered at Next '17, Google's annual cloud event for enterprise customers. Google showcased customer testimonials from Disney, Verizon, HSBC, Colgate-Palmolive, and Ebay; support from a number of new partners, including SAP; and a series of AI and cloud infrastructure-related announcements. Analysts were not impressed (see here and here, for example). Google, the argument goes, is a consumer company and does not understand or have ready access to enterprise customers. Amazon's first mover advantage has produced an insurmountable market lead (last year, it reported cloud revenues of $12.22 billion, compared to Microsoft's $2.42 billion and Google's $900 million, Deutsche Bank estimates).

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

Artificial intelligence is progressing rapidly. Advancements in deep learning have propelled the idea of an AI-powered world from a faraway fantasy to a fast-approaching reality. AI, whether it be in the form of personal assistants on our smartphones or data miners who predict our spending habits, is becoming more deeply integrated with our everyday lives. What's more, we're starting to see artificial intelligence make its way into larger applications. Perhaps the most exciting applications are seen in nascent AI healthcare technologies.

These are some of the reasons why this model is working nowadays, even though there are advocates who claim incumbents to not really be maximally open (Bostrom, 2016) and to only release technology somehow old to them. My personal view is that companies are getting the best out of spreading their technologies around without paying any costs and any counter effect: they still have unique large datasets, platform, and huge investments capacity that would allow only them to scale up. Regardless the real reasons behind this strategy, the effect of this business model on the AI development is controversial. According to Bostrom (2016), in the short term, a higher openness could increase the diffusion of AI. Software and knowledge are non-rival goods, and this would enable more people to use, build on top on previous applications and technologies at a low marginal cost, and fix bugs.

The Google-owned star British artificial intelligence company DeepMind is in talks with the National Grid about a potential partnership, with the possibility of using the technology to make the supply of energy across the UK more efficient. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," said a spokesperson for the company. "One really interesting possibility is whether we could help the National Grid maximise the use of renewables through using machine learning to predict peaks in demand and supply." DeepMind's AI technology, which became famous after beating a human player at the chess-like game Go, has already been put to work for Google, reducing the energy needed for cooling its data centres by 40 per cent last year and increasing efficiency by 15 per cent. And co-founder Mustafa Suleyman outlined last year his hopes that this same technique could be applied to the National Grid and other large scale infrastructure. Read more: Here's how Google's DeepMind is using blockchain-like technology Now that has developed into early-stage talks taking place more recently between DeepMind – named City A.M's most innovative company of the year at the City A.M. Awards – and the National Grid, although there is no guarantee of anything being agreed.

A fun new online interactive musical experience between humans and computer gives us a good example of machine learning. The experimental gadget is called A.I. Duet and it calls Tone.js which allows you to play a question-answer game with the computer on a MIDI keyboard. The machine listens to you play, then replies to you. In terms of programming, what makes this example interesting is that the algorithm knows nothing of musical theory, nothing of musical notation or rhythm, at least nothing in the form of rules as we would imagine. Its reference is some musical examples which the programmer has given it as models to build its neural networks.

Welcome to the Alchemy system! Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy Lite is a software package for inference in Tractable Markov Logic (TML), the first tractable first-order probabilistic logic. Alchemy Lite allows for fast, exact inference for models formulated in TML. Alchemy Lite can be used in batch or interactive mode.