WITH the recent triumph of the Google AlphaGo program over Go master Lee Se-dol in Seoul, the doomsayers are in full chorus again over the spectre of Hollywood-style artificial intelligence (AI) taking over humanity. It was the same fear in the late 1990s when IBM's Deep Blue supercomputer beat then reigning chess world champion Garry Kasparov. There are a few differences however: Go is considered a much more intricate game than chess, and AI technology has improved quite a bit since then, and we're seeing breakthroughs such as Google's self-driving cars, virtual assistants like Apple's Siri and Microsoft's Cortana, and even IBM Watson's win in popular trivia quiz Jeopardy!. Enough that even sober scientists are taking note. In a December 2014 interview with the British Broadcasting Corporation (BBC), renowned physicist Stephen Hawking expressed his concerns, saying that AI poses a threat to humanity's existence, despite its usefulness. "It would take off on its own and redesign itself at an ever-increasing rate," he said, echoing futurist Ray Kurzweil's warning of the technological'singularity,' the hypothetical point at which an AI can develop and build even smarter machines beyond human understanding.

At Strata Hadoop World "R Day" Tutorial, Tuesday, March 29 2016, San Jose, California we spent some time on classifier measures derived from the so-called "confusion matrix." We repeated our usual admonition to not use "accuracy" as a project goal (business people tend to ask for it as it is the word they are most familiar with, but it usually isn't what they really want). And we worked through the usual bestiary of other metrics (precision, recall, sensitivity, specificity, AUC, balanced accuracy, and many more). We surveyed over a dozen common measures the data scientist is expected to know. While this may seem complicated, this is much better than the traditions used when trying to estimate inter-observer or tagger agreement (where there are around 100 measures, many of which combine effect size and significance, and requires significant research to understand which measures are monotone related to each other; see: Warrens, M. (2008).

Google has announced plans to open its machine-learning software to developers. The multinational tech company is hoping this will attract more companies to its cloud-computing services, which already includes image identification, voice recognition and AI technology. The market for cloud-computing services is a competitive one, with Microsoft, Amazon and IBM offering similar products. While modern cloud systems are based on "decades-old" technology, Google said, the company's forthcoming products and services are designed for the next wave of cloud computing. And Google is already a leader in AI, using the technology to support its web-based services and apps.

When Anne Taylor walks into a room, she wants to know the same things that any person would. Where is there an empty seat? Who is walking up to me, and is that person smiling or frowning? What does that sign say? For Taylor, who is blind, there aren't always easy ways to get this information. Perhaps another person can direct her to her seat, describe her surroundings or make an introduction.

It was one of the strongest fields of development during 2015 and it will also be in 2016. It will certainly be one of the sectors generating most profits for years or decades to come. Imagine an area of hundreds of hectares of arable land, but few planted seeds. Companies like Google, Facebook or Microsoft take time to develop Artificial Intelligence products, generally linked to natural language processing and speech recognition. Some estimates state that the market for Artificial Intelligence-related applications alone will be worth 11.1 billion by 2024.

An actual-size model of a Warhammer robot stands just inside the door of the Rams' new temporary offices in Agoura Hills. The action figure was left behind by a video game company that occupied the space before the Rams officially opened for business in Southern California. On Monday, the character was adorned in a No. 3 Rams jersey. "It's exciting for the staff to finally be together for the first time in an office space, to be the Los Angeles Rams," Kevin Demoff, the team's chief operating officer, said during a brief interview in his office. "It's fantastic to have the group here and really laying the foundation for the 2016 season and beyond."

Google, of course, can't give away all of its secrets. That's why TensorFlow's release into the wild only includes part of the code that allows it to run on a single machine. Despite being shared under what's called an Apache 2 license (meaning anyone is free to use it). These days, the big Internet giants frequently share the software sitting at the heart of their online operations. Open source accelerates the progress of technology.

Facebook today announced the launch of a feature called automatic alternative text. It's a big step forward in accessibility, because it will allow people who use screen readers -- software that provides spoken descriptions of what's onscreen -- to receive summaries of what's contained in photos that people post on Facebook. Until this point, only the name of the person, the person's share text, and the word "photo" was declared by a screen reader's automated voice when a post with a photo appeared in the News Feed. Now, thanks to artificial intelligence (AI), certain people with disabilities will be able to get a better understanding of what their friends are trying to communicate. The new captions are short and at times so simple that they're poetic.

Facebook is now using artificial intelligence to automatically generate captions for photos in the News Feed of people who can't see them. The tool is called Automatic Alternative Text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks, the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow.

You can find this dataset in this archive. It is not perfect but will work in a pinch.