Georgia Institute of Technology researchers have developed a new artificially intelligent system that crowdsources plots for interactive stories, which are popular in video games and let players choose different branching story options. With potentially limitless crowdsourced plot points, the system could allow for more creative stories and an easier method for interactive narrative generation. Current AI models for games have a limited number of scenarios, no matter what a player chooses. They depend on a dataset already programmed into a model by experts. Using the Georgia Tech approach, one might imagine a Star Wars game using online fan fiction to let the AI system generate countless paths for a player to take.

With automation becoming increasingly commonplace, tech boom wunderkinds, and everyone else, have been the debating the future of artificial intelligence. On one side Facebook founder Mark Zuckerberg, argues that more intelligent services aid humanity. On the other side is the founder of Tesla Motors and SpaceX, Elon Musk, who has frequently warned of humanity's doom at the hands of our own creations. "You know all those stories where there's the guy with the pentagram and the holy water, and he's like, sure he can control the demon?" Musk said during a talk on artificial intelligence at MIT in 2014.

New'deep learning' technique enables robot mastery of skills via trial and error. UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks -- putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more -- without pre-programmed details about its surroundings. "What we're reporting on here is a new approach to empowering a robot to learn," said Professor Pieter Abbeel of UC Berkeley's Department of Electrical Engineering and Computer Sciences. "The key is that when a robot is faced with something new, we won't have to reprogram it. The exact same software, which encodes how the robot can learn, was used to allow the robot to learn all the different tasks we gave it."

We develop a framework to solve machine learning problems in cases where latent geometric structure in the feature space may be exploited. We consider cases where the number of training examples is either very large, or signals are sequentially observed by a platform operating in real-time such as an autonomous robot. In the former case, since the sample size is large-scale, processing a few training examples at a time is necessary due to computational cost. However, doing so at a centralized location may be impractical, which motivates the use of learning techniques that may be done collaboratively by a network of interconnected computing servers. In the later case, an autonomous robot with no priors on its operating environment only has access to information based on the path it has traversed, which may omit regions of the feature space crucial for tasks such as learning-based control. By communicating with other robots in a network, individuals may learn over a broader domain associated with that which has been explored by the whole network, and thus more effectively solve autonomous learning tasks.

Google's TensorFlow has been publicly available since November, 2015, and there is no disputing that, in a few short months, it has made an impact on machine learning in general, and on deep learning specifically. There is evidence of widespread acceptance via blog posts, academic papers, and tutorials all over the web. It is, of course, difficult to estimate true adoption rates, but TensorFlow's Github repository has nearly twice the number of stars of both the next most-starred machine learning project, Scikit-learn, and closest deep learning project, Berkeley Vision and Learning Center's Caffe. While not concretely indicative of TensorFlow having become the leader in the space, it is fairly easy to surmise that, given its fairly recent release, there has been considerable interest in, and use of, Google's deep learning library. For the most part, TensorFlow is relatively straightforward to use, and neural network afficianados without experience using the library could look at a given network's code and get an intuititive sense of what is going on.

In "The Three Rs and an S," a recent op-ed in The Baltimore Sun, you and Norman Augustine write that the Next Generation Science Standards will teach students how to take on a scientific manner of thinking. Why is there a greater emphasis now on this approach to learning than in years past? The future jobs for this millennial generation will not look like the jobs of the last 40 or 50 years. They're not going to be large segments of people working in factories. Really, what's happened is that the business community itself has sort of shifted where it sees efficiency and productivity occurring and, because of this shift, the jobs are going to shift.

Qualcomm Incorporated (NASDAQ: QCOM) today announced at the Embedded Vision Summit in Santa Clara, Calif. The SDK, called the Qualcomm Snapdragon Neural Processing Engine, is powered by the Qualcomm Zeroth Machine Intelligence Platform and is optimized to utilize Snapdragon's heterogeneous compute capabilities to provide OEMs a powerful, energy efficient platform for delivering intuitive and engaging deep learning-driven experiences on device. This SDK is the latest software addition to Snapdragon 820 and demonstrates Qualcomm Technologies' continued leadership by adding value for our customers to the Snapdragon portfolio. Qualcomm Technologies, with the introduction of the Snapdragon Neural Processing Engine, is the first mobile SOC provider to offer a deep learning toolkit optimized for mobile. This SDK will allow OEMs to run their own neural network models on Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, all without a connection to the cloud.

Qualcomm Technologies, with the introduction of the Snapdragon Neural Processing Engine, is the first mobile SOC provider to offer a deep learning toolkit optimized for mobile. This SDK will allow OEMs to run their own neural network models on Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, all without a connection to the cloud. Common deep learning user experiences that can be realized with the SDK are scene detection, text recognition, object tracking and avoidance, gesturing, face recognition and natural language processing. The Zeroth Machine Intelligence Platform is a Snapdragon-optimized software platform designed for mobile machine learning. Zeroth technology currently drives visual intelligence software such as Snapdragon Scene Detect and advanced malware detection software found in Snapdragon Smart Protect.

Most Machine Learning courses are given from the perspective of a Data Scientist and focus on the techniques and algorithms that allow to learn from data. This workshop takes the perspective of an application developer and instead provides an end-to-end view of ML integration into your applications. We'll go all the way from data preparation to the integration of predictive models in your domain and their deployment in production. The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles.

Today OpenAI, a non-profit artificial intelligence research company, launched OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go. OpenAI researcher John Schulman shared some details about his organization, and how OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications. John studied physics at Caltech, and went to UC Berkeley for graduate school. There, after a brief stint in neuroscience, he studied machine learning and robotics under Pieter Abbeel, eventually honing in on reinforcement learning as his primary topic of interest.