Scientists from the Los Alamos National Laboratory have devised a way of using machine learning in order to discover new materials with specific properties using an informatics-based adaptive strategy in combination with experiments. The new approach will help scientists find new materials in a manner that is more cost-effective and less time consuming than current procedures. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist at Los Alamos National Laboratory and senior author of the study. "Finding new materials has traditionally been guided by intuition and trial and error," he continued. "But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical."

Robots today can perform space missions, solve a Rubik's cube, sort hospital medication and even make pancakes. But most can't manage the simple act of grasping a pencil and spinning it around to get a solid grip. Intricate tasks that require dexterous in-hand manipulation--rolling, pivoting, bending, sensing friction and other things humans do effortlessly with our hands--have proved notoriously difficult for robots. Now, a University of Washington team of computer science and engineering researchers has built a robot hand that can not only perform dexterous manipulation but also learn from its own experience without needing humans to direct it. Their latest results are detailed in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation.

Aptly named TacoBot, the software will make use of AI advancements like natural language processing to let users talk with the bot, order food, and even pay for items entirely through Slack. TacoBot can also provide recommendations, answer questions, and organize group office orders. It apparently comes equipped with a "witty personality you'd expect from Taco Bell." "The TacoBot Slack integration is the latest step on our journey to make the brand more accessible wherever and whenever our fans want it," said Lawrence Kim, Taco Bell's director of digital innovation and on demand, in a statement. "Taco Bell is about food tailor-made for social consumption with friends, and that's why integrating with a social communications platform like Slack makes perfect sense. TacoBot is the next best thing to having your own Taco Bell butler… and who wouldn't want that??" Kim asks a good question, and the answer is nobody.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

Jarno M. Koponen is a designer, humanist and previously a co-founder of media discovery startup Random. Artificial Intelligence is starting to turn invisible from the outside in -- and vice versa. The exact effects and workings of AI technologies are becoming more challenging to perceive and comprehend for humans. Even the experts themselves don't always fully understand how an AI system operates. Effectively, as the impact of AI technologies increases, the more limited becomes our ability to understand their impact.

Artificial intelligence is the frontier of computer science. The science has advanced enough that AI is beating us at our own game -- or should we say, games. Some people may fear the rise of Skynet with each AI evolution, but we're a bit more optimistic. AlphaGo is the latest AI to beat a human in a board game, but it comes from a long pedigree. Though these five machines started as purpose-built programs, some have found second lives that go beyond their original callings.

In October 2011, Apple added the emoji keyboard to iOS as an international keyboard. Since then, digital language has evolved such that nearly half of comments and captions on Instagram contain emoji characters. And earlier this week, Instagram also added support for emoji characters in hashtags, which allows people to tag and search content with their favorite emoji # . In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we'll discover the hidden semantics of emoji.

Essentially, machine learning is a form of artificial intelligence that refers to the ability of a computer to detect and "remember" previously encountered patterns and to learn from new data about those patterns and any new patterns that are detected.

In a conference on Monday, U.S. Deputy Defense Secretary Bob Work outlined a key component of modern warfare strategy called Third Offset. The military intends to take advantage of cutting-edge R&D to incorporate AI-human teams to overcome an enemy's network. At the 2016 Global Strategy forum on Monday, Mr. Work noted that products with potential military applications are fast-tracked to enter the global market. "R&D is going down in the public sector, but up in the private sector. Most things that have to do with AI [artificial intelligence] and autonomy are happening in the private sector. And so all competitors are going to have access to it, it's going to be a world of fast-followers. You're going to have an instance where you're not going to have a lasting advantage."

We are looking for distributed systems platform engineers to work both on the distributed compute platform and on implementing and improving machine learning algorithms for it. You will work with the ML algo developer team on extending and improving the capabilities of the underlying system. This will include the distributed in-memory data storage layer, the compute layer which allows algorithms to be run across all the cores in parallel and non-ML capabilities such as data transformations and distributed data connectors for multi-terabyte data import. We support clusters of at least 3200 cores and tens of terabytes of in-memory applications. To apply, please email resume to careers@h2o.ai