"It figures out the best way to grab each object, right from the middle of the clutter," said Jeff Mahler, one of the researchers developing the robot inside a lab at the University of California, Berkeley. Inside Amazon's massive distribution centers -- where sorting through stuff is the primary task -- armies of humans still do most of the work. The Berkeley robot was all the more remarkable because it could grab stuff it had never seen before. Mr. Mahler and the rest of the Berkeley team trained the machine by showing it hundreds of purely digital objects, and after that training, it could pick up items that weren't represented in its digital data set.

The idea is to gather a rich data set around the first total solar eclipse to cross a large portion of the United States in almost 100 years. Technology has changed exponentially in the last century; this rare cosmic event is the first time many will experience a total eclipse, and it's also an opportunity to experience it with new technology. And in Google's case, that means using their machine learning to study this eclipse and develop new ways to study cosmic events in the future. The initiative is in collaboration with a group of scientists led by University of California, Berkeley's Space Sciences Laboratory, who came up with the idea of crowdsourcing an image archive of next week's total solar eclipse back in 2011.

It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. Machine Learning for Language Toolkit (MALLET) is a Java toolkit fro statistical natural language processing, document classification, clustering, topic modeling and information extraction.

A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn't immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an "intrinsic curiosity model" to make their learning algorithm work even when there isn't a strong feedback signal. The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, has pioneered, over the past several years, the development of computer programs and robots that exhibit simple forms of inquisitiveness.

A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn't immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an "intrinsic curiosity model" to make their learning algorithm work even when there isn't a strong feedback signal. The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, has pioneered, over the past several years, the development of computer programs and robots that exhibit simple forms of inquisitiveness.

The readers will love our list because it is Data-Driven & Objective. Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[2]

Matthew Ramirez was teaching writing classes to students at the University of California at Berkeley when he started to get frustrated. Mixing his experience as a teacher with some advanced learning technology, he and his business partner started WriteLab – a Berkeley, California-based software company that helps students strengthen their writing skills by providing quick, customized feedback. WriteLab can even adapt its feedback over time to students' individual writing styles. "Focus on problems that eliminate waste – wasted time, wasted energy, or wasted space," Ramirez advises.

The UC Berkeley-led center, directed by artificial intelligence researcher Stuart Russell, will seek to understand how human values can be built into AI's design, and create a mathematical framework that will help people build AI systems that are beneficial to humanity. Scientists might get around this communication problem by designing artificial intelligence that can watch humans and learn what their values are through their actions (though even that comes with some uncertainty, as humans don't always act in ways aligned with their values, Russell added). The USC center, co-directed by artificial intelligence researcher Milind Tambe and social work scientist Eric Rice, seems to operate in a mindset perpendicular to the one at UC Berkeley: It seeks to harness AI's existing capabilities to solve problems in messy, complicated human contexts. AI also includes a wide range of tools, including machine learning, computer vision, natural language processing and game theory (though some may consider game theory part of another discipline, Tambe said).