Andrew Ng (Stanford University) is building robots to improve the lives of millions. From autonomous helicopters to robotic perception, Ng's research in machine learning and artificial intelligence could result one day in a robot that can clean your house. STAN: Society, Technology, Art and Nature, was Stanford University's prototype conferecne for TEDxStanford, and showcased some of the university's top faculty, students, alumni and performers in an intense four-hour event laced with surprising appearances and memorable experiences. STAN, modeled after TED, explored big questions about society, technology, art and nature in a format that invites feedback and engagement.

HANNOVER, GERMANY - MARCH 02: Robots play football in a demonstration of artificial intelligence at the stand of the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) at the CeBIT Technology Fair on March 2, 2010 in Hannover, Germany. More and more companies are turning to artificial intelligence or AI to give better service to their customers. But, is AI also efficient in making hiring less biased? There is no doubt that looking for the most competitive, talented, skilled candidate from a pool of applicants is difficult. You have to consider their personal traits, educational background, experience and more.

Cogitai, Inc., a new AI-based startup focusing on continual learning, is seeking highly skilled and motivated individuals for immediate hire. About Cogitai Cogitai, Inc. is dedicated to building artificial intelligences (AIs) that learn continually from interactive experience with the real world. Our goal is to build the brains, i.e., the continual-learning AI software, that will let everyday things that sense and act get smarter, more skilled, and more knowledgeable over time with experience. This experience will be shared across things and across domains to allow the rapid scaling-up of experience-based learning. In short, we aim to become the world leader in continual learning.

Niki.ai, an artificial intelligence (AI) based chatbot, announced on Thursday that Ratan Tata, Chairman Emeritus of Tata Sons, had invested in the startup along with Ronnie Screwvala's Unilazer, which did a follow up round to their first seed investment. Niki will utilise the funds raised to increase its merchant base and also to strengthen its technological capabilities. The startup leverages natural language processing (NLP) and machine learning to converse with customers over a simple chat interface, and places orders on their behalf in seconds with partner businesses. The founding team initially spent a couple of months in Udaipur before moving to Bengaluru in July 2015. The product had initially been in beta for seven months, and the team has focussed on building and enhancing the product, while working closely with a limited set of beta users.

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning is very useful in IoT since it can be used to learn hidden relationships in the Big Data which flows in the system and used to make real-time complex classifications for taking actions based on them. There are many machine learning packages such as Apache Spark, Mahout, and Weka, each with its advantages and disadvantages. This blog shows how to use the easy-to-use powerful Java Statistical Analysis Tool library (JSAT) for a courier parcel pick up website app integrated with RAPIFIRE. The example illustrates how a user can get the estimated waiting time of a courier parcel pick up based on the GPS position of trucks. The machine learning component is used to get the waiting time classification ( 15min, 15min-30min, 30min) based on the input of the truck's sensor data of distance and average speed.

From self-driving vehicles to social robots, artificial intelligence is evolving at a rapid pace, creating vast opportunities as well as complex challenges. Recognizing that, the White House Office of Science and Technology Policy is co-hosting four public workshops on artificial intelligence -- the first of them May 24 at the University of Washington. Subsequent events will take place in Washington, D.C.; in Pittsburgh, Pennsylvania; and in New York City. Put on by the UW School of Law and the UW Tech Policy Lab, the session will focus on legal and policy issues around artificial intelligence, or AI. Etzioni will provide an overview on the current state of artificial intelligence, followed by two panel discussions.

Google has done well out of its search box. As our collective dependence on computers and the Internet has grown, we have come to use it more and more--helping Google swell to its gigantic size. But on Wednesday Google's CEO, Sundar Pichai, said it was time to move on from the conventional search engine that his company was built on. He unveiled Google Assistant, an evolution of Google search designed to act like a virtual concierge. If you search Google for "movies tonight" on your phone today, it will display the films in local theaters.

Sirius [1] is an open end-to-end standalone speech and vision based intelligent personal assistant (IPA) similar to Apple's Siri, Google's Google Now, Microsoft's Cortana, and Amazon's Echo. Sirius [1] implements the core functionalities of an IPA including speech recognition, image matching, natural language processing and a question-and-answer system. Sirius [1] is developed by Clarity Lab at the University of Michigan. Sirius [1] is published at the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2015. If you're interested in contributing to Sirius, fork our repo or post to sirius-users.

I've written previously about the way AI has become increasingly capable in creative tasks, whether it's playing jazz or cracking jokes. There have even been a couple of projects utilizing AI to help us develop smarter and more engaging video content. A Japanese company are promising to take this to a new level with the development of a robot that will provide creative advice for commercials and other marketing projects. The AI has been developed and deployed by the marketing agency McCann Japan, and will attempt to provide input on the various projects taken on by the agency. It will mine a large database of previous creative projects to suggest possibly new creative directions for an advert. It's a very similar approach to that taken by the robot jazz player mentioned above, which also mined a huge back catalog of past jazz performances to suggest creative new avenues the'player' could go down.

Before leaving Helsinki, we arXived [from the Air France lounge!] the paper Jean-Michel presented on Monday at ABCruise in Helsinki. This paper summarises the experiments Louis conducted over the past months to assess the great performances of a random forest regression approach to ABC parameter inference. I think the major incentives in exploiting the (still mysterious) tool of random forests [against more traditional ABC approaches like Fearnhead and Prangle (2012) on summary selection] are that (i) forests do not require a preliminary selection of the summary statistics, since an arbitrary number of summaries can be used as input for the random forest, even when including a large number of useless white noise variables; (b) there is no longer a tolerance level involved in the process, since the many trees in the random forest define a natural if rudimentary distance that corresponds to being or not being in the same leaf as the observed vector of summary statistics?(y); To the point that deriving a different forest for each univariate transform of interest is truly a minor drag in the overall computing cost of the approach. An intriguing point we uncovered through Louis' experiments is that an unusual version of the variance estimator is preferable to the standard estimator: we indeed exposed better estimation performances when using a weighted version of the out-of-bag residuals (which are computed as the differences between the simulated value of the parameter transforms and their expectation obtained by removing the random trees involving this simulated value). Another intriguing feature [to me] is that the regression weights as proposed by Meinshausen (2006) are obtained as an average of the inverse of the number of terms in the leaf of interest.