Alexy Khabrov's talk at LX Scala caused the Scala community to raise their arms for a revolution. After his talk, Alexy discussed his thoughts on how to ease the transition for Python data scientists into the Scala community, what Scala can learn from Python as a programming language, and how machine learning will influence the future of the Scala community within the next five years.

A place for visual representations of data: Graphs, charts, maps, etc. DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the aim of this subreddit. Check the best user-made visualizations of 2013 and 2014 (Jan-July Aug-Dec). Directly link to the original source article of the visualization (not an image file) or tag the post as [OC] if you made the visualization. Only [OC] posts may link to image files.

This five-fingered robot hand developed by University of Washington computer science and engineering researchers can learn how to perform dexterous manipulation -- like spinning a tube full of coffee beans -- on its own, rather than having humans program its actions. A University of Washington team of computer scientists and engineers has built what they say is one of the most highly capable five-fingered robot hands in the world. It can perform dexterous manipulation and learn from its own experience without needing humans to direct it. Their work is described in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation. "Hand manipulation is one of the hardest problems that roboticists have to solve," said lead author Vikash Kumar, a UW doctoral student in computer science and engineering. "A lot of robots today have pretty capable arms but the hand is as simple as a suction cup or maybe a claw or a gripper."

Robots and intelligent systems have gained a significant amount of attention recently. Various authors and reports detail advances in robotics and artificial intelligence and speculate on the role of humans in the robotic future. In such commentaries, robots are often presented as independent and tireless machines that are rational and efficient; they are removed from their creators and contexts of use. Unfortunately, as appealing as this image might be, it resembles more of a science fiction hero than the contextual nitty-gritty that revolves around the development and deployment of robotic applications. To obtain a more holistic picture, let us have a look at some contextual factors that surround robotics innovation today.

Artificial intelligence – what's the worst that can happen? Working with hacktivist and entrepreneur Federico Pistono, he has come up with a set of worst-case scenarios for a potential malevolent AI, from "enslaving mankind" to "destroying the universe". Yampolskiy argues that anticipating as many negative outcomes as possible – just as cybersecurity researchers do when looking for vulnerabilities – will help us guard against disaster. "The standard framework in AI thinking has always been to propose new safety mechanisms," he says. But looking at the issue with a cybersecurity mindset puts a different spin on things: starting with a list of all the things that could go wrong will make it easier to test any safeguards we may eventually want to put in place.

I was chatting with an old friend yesterday and he reminded me of a conversation we had nearly 50 years ago. I tried to explain to him what I did for living and he was trying to understand why getting computers to understand was more complicated than key word analysis. I explained about concepts underlying sentences and explained that sentences used words but that people really didn't use words in their minds except to get to the underlying ideas and that computers were having a hard time with that. Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.

Google is launching a new artificial intelligence group called "Magenta" to see if computers can create their own masterpieces, according to Quartz. The group was reportedly outlined on Sunday by Douglas Eck, a researcher at Google Brain (one of Google's well-established AI divisions), at the Moogfest tech and music festival in Durham, North Carolina. Magenta, due to launch more publicly at the start of June, will reportedly aim to establish whether AIs can be trained to create original pieces of music, art, or video. In order to determine whether this is possible, the Magenta team will use TensorFlow -- a software library for machine intelligence that Google built and opened up to the public at the end of last year. The first product to be released by Magenta will be a simple program that's designed to help researchers import music data from MIDI music files into TensorFlow, according to Quartz.

"I strongly believe in a future where artificial intelligence will manage technology for us, allowing people to live their lives with the feeling of being unplugged," says Dr. Rand Hindi, founder and CEO of Snips. "This is where AI is headed, and Snips is the first installment of that future." Since our initial coverage of Snips, the platform has been under development and has finally been released in the form of an iOs app, which anticipates your needs by organizing and sifting through the data you collect day by day. The app is capable of recalling information based on contextual clues, such as filling in the name of your favorite coffee shop in the Uber destination tab with one click. If this is all starting to sound like the first waves of a robotic apocalypse, it shouldn't.

Google introduced a new group dedicated to making artificial intelligence more creative at Moogfest, a four-day music and technology festival in Durham, North Carolina, Quartz first reported. Called Magenta, the group will use its AI system TensorFlow to see if AI can be trained to create its own art, music, and video. The ultimate goal is to see if AI could give a listener "musical chills" by generating entirely new pieces of music, Quartz reported. Google made TensorFlow open source in November so that any developer can use it. TensorFlow works by using deep learning, a process where machines learn to complete tasks all on their own, to recognize images.

A CIO article, "Artificial Intelligence: Humankind's Best Chance for a Healthier Future," makes a really good point about the past and future of medicine. Many of the greatest advances in medicine depended upon observation and almost accidental discoveries. Really smart people had to be in the right place at the right time. For example, Pasteur noticed that people who contracted cowpox seemed immune to smallpox; Fleming noticed that mold killed bacteria in an unwashed dish. These kinds of brilliant minds managed to make connections that helped cure and prevent diseases because they noticed what other people didn't notice.