When a person's intelligence is tested, there are exams. When artificial intelligence is tested, there are games. But what happens when computer programs beat humans at all of those games? This is the question AI experts must ask after a Google-developed program called AlphaGo defeated a world champion Go player in four out of five matches in a series that concluded Tuesday. Long a yardstick for advances in AI, the era of board game testing has come to an end, said Murray Campbell, an IBM research scientist who was part of the team that developed Deep Blue, the first computer program to beat a world chess champion.

Facebook is releasing for free the designs of a powerful new computer server it crafted to put more power behind artificial-intelligence software. Serkan Piantino, an engineering director in Facebook's AI Research group, says the new servers are twice as fast as those Facebook used before. "We will discover more things in machine learning and AI as a result," he says. The social network's giveaway is the latest in a recent flurry of announcements by tech giants that are open-sourcing artificial-intelligence technology, which is becoming vital to consumer and business-computing services. Opening up the technology is seen as a way to accelerate progress in the broader field, while also helping tech companies to boost their reputations and make key hires.

I have voraciously read endless pro and con scenarios about artificial intelligence since first writing about it years ago. At this point, there is no doubt that concerns about the dangers of runaway AI raised by Elon Musk, Stephen Hawking, Bill Gates, Bill Joy and others are genuine. There also is no doubt whatsoever that the new organizations aimed at mitigating the dangers -- OpenAI, The Future of Life Institute, Machine Intelligence Research Institute and others -- are extremely important developments. Clearly, no sane person or organization wants to see, let alone encounter, runaway AI. However, a base problem is that no one knows where the actual crossover point -- the edge or tipping point -- exists, and thus we mortals are unlikely to be able to prevent it from occurring. Said differently, there is a very high probability that we will misjudge where that crossover point is and will thus go beyond the key threshold.

With the advent of Amazon's Alexa and Siri's consistent capacity to take on more chores (and get more and more sassy), many are wondering: what's next for natural language understanding and conversational voice interfaces? There are several companies neck-and-neck in this race. There's Wit.ai, the company Facebook acquired -- you can toy around with demo. Apple has its HomeKit and, with it, is doing what Apple does best -- kicking ass. One company hot on the trail of natural language understanding is MindMeld. MindMeld provides its natural language understanding capabilities to other companies that are looking to add intelligent voice interfaces to their products, services, or devices.

The James Webb telescope will be the world biggest and most powerful telescope when it launches in 2018. Nasa describes it as a'time machine' that can peer back 200 million years after the Big Bang. This week, Nasa engineers in Maryland got a little closer to launch with the completion of testing on its science cameras and the installation of the final flight mirrors. NASA's James Webb Space Telescope completed primary mirror sits in the cleanroom at NASA Goddard Space Flight Center, and supported over it on the tripod is the secondary mirror After over a year of planning, nearly four months of final cold testing and monitoring, the testing on the science instruments module of the observatory was completed. They were removed from a giant thermal vacuum chamber at Nasa Goddard Space Flight Center in Greenbelt, Maryland called the Space Environment Simulator.

Sony has announced the closure of the UK's Evolution Studios. The Cheshire-based video games development house dates back to 1999, and had most recently worked on the troubled PlayStation title Driveclub. Sony has confirmed that there will be at least some compulsory redundancies as a consequence of its decision. The announcement closely follows Microsoft's announcement of plans to close another British developer, Lionhead Studios. Sony said that its decision had followed a review of its European operations.

ABSTRACT: This talk will review several recent papers which aim to modify popular machine learning methods for problems of causal inference, such as evaluating the impact of a treatment using experimental or observational data. We will focus on estimation of treatment effect heterogeneity (that is, which individuals are predicted, based on their features, to have higher or lower benefits of a treatment) in settings such as A/B testing platforms or medical trials where it is important to provide confidence intervals in addition to estimated effects. We analyze the tradeoffs between evaluating model fit based on observed outcomes, and evaluating model fit based on an estimate of the (unobserved) treatment effect. We build on this work to show how random forests can be modified to provide asymptotically centered estimates of treatment effect heterogeneity in experiments or observational studies.

It is made up of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, reviews, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. It is extremely accurate because we've built 55M controlled vocabularies (Ex. Being the noob I am, I am trying to understand how our real time classification capabilities can improve the efficiency of machine learned processes. I understand that supervised training models require a corpus of text from which a model can determine entities, ontological connections, and apply statistical models to understand what people, places, things, concepts are and how they may be connected, but we've already built out the taxonomy to understand connections between things, and can provide greater context to "what" something truly is.

I will start by giving some background information. Currently I am a final year (graduation year) CS student who got interested in machine learning about 6 months ago. I started with the Andrew NG course from Coursera which I recently finished (about 3 weeks ago). When I finished the Coursera course I saw a suggestion that if you'd like to continue to learn more about machine learning you could follow the online Coursera specialization from the Washington university. In this AMA he suggested that if you'd like to learn more about machine learning one of the things you could do was to follow and complete the Coursera course from Andrew NG and their specialization course.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied See the License for the specific language governing permissions and limitations under the License. This repo uses an approach based on creating many small patches of the original input images and then performing machine learning tasks on the image patches. The combination of Python and SAS files enables you to conduct different learning tasks in different orders. Use threaded_tile.py to create many small uniform patches of the input images.