NOTE: This story has been revised from when it was originally published in October 2015 to reflect the latest information. Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened.

Professor Russ Altman, left, and Professor Emeritus Yoav Shoham are members of a study group that will take a very long-term look at artificial intelligence. The two scientists were among the participants in a event looking at artificial intelligence's social benefits at Stanford on Thursday. As artificial intelligence emerges from science fiction to everyday life, the power to shape and direct this world-changing technology remains within society's reach. That overarching theme animated a crowd of more than 300 people at a Stanford event Thursday evening. The discussion was titled, "The Future of Artificial Intelligence: Emerging Topics and Societal Benefit."

Iyad Rahwan was the first person I heard use the term society-in-the-loop machine learning. He was describing his work which was just published in Science, on polling the public through an online test to find out how they felt about various decisions people would want a self-driving car to make -- a modern version of what philosophers call "The Trolley Problem." The idea was that by understanding the priorities and values of the public, we could train machines to behave in ways that the society would consider ethical. We might also make a system to allow people to interact with the Artificial Intelligence (AI) and test the ethics by asking questions or watching it behave. Society-in-the-loop is a scaled up version of human-in-the-loop machine learning -- something that Karthik Dinakar at the Media Lab has been working on and is emerging as an important part of AI research.

In the last couple of years, magic started happening in AI. Techniques started working, or started working much better, and new techniques have appeared, especially around machine learning ('ML'), and when those were applied to some long-standing and important use cases we started getting dramatically better results. For example, the error rates for image recognition, speech recognition and natural language processing have collapsed to close to human rates, at least on some measurements. So you can say to your phone: 'show me pictures of my dog at the beach' and a speech recognition system turns the audio into text, natural language processing takes the text, works out that this is a photo query and hands it off to your photo app, and your photo app, which has used ML systems to tag your photos with'dog' and'beach', runs a database query and shows you the tagged images. There are really two things going on here - you're using voice to fill in a dialogue box for a query, and that dialogue box can run queries that might not have been possible before.

The tough part here is designing the algorithms that will control these self-driving rides, and how to teach the artificial intelligence deal with unavoidable harm. It's tricky and raises the question of which lives are more important, those outside the vehicle or its passengers? When humans make split-second decisions, it's out of instinct and self-preservation -- not programming. But if someone knowingly bought an autonomous vehicle that favored passengers over pedestrians, would they be held liable if a loss of public life occurred?

The Weka machine learning workbench is a powerful and yet easy to use platform for predictive modeling. In this post you will discover how you can install Weka on your workstation fast, and get started with machine learning. How to Download and Install the Weka Machine Learning Workbench Photo by Nicholas A. Tonelli, some rights reserved. All versions of Weka can be downloaded from the Weka download webpage. Select the version of Weka that you would like to install then visit the Weka download page to locate and download your preferred version of Weka.

In this paper, we present two approaches to analyzing pass event data to uncover sometimes-nonobvious insights into the game of soccer. We illustrate the utility of our methods by applying them to data from the 2012–2013 La Liga season. We first show that teams are characterized by where on the pitch they attempt passes, and can be identified by their passing styles. Using heatmaps of pass locations as features, we achieved a mean accuracy of 87% in a 20-team classification task. We also investigated using pass locations over the course of a possession to predict shots.

A mix of artificial intelligence programs has been used to design, direct and edit a music video. Ad agency Saatchi & Saatchi commissioned the film for a song by a French electro band. It made its debut at the Cannes Lions advertising festival to coincide with the anniversary of AI pioneer Alan Turing's birth. However, the band - which does not want to be named - is not allowing the final edit to be made public.

Two years ago, our ambitious team of data scientists, engineers and visualisation experts set out to tackle the challenging problem of interconnecting the entire universe of research. Using this incredibly powerful knowledge graph, we aimed to provide breakthrough insights about the past and present of research, and by applying predictive techniques we sought to outline the future of research at a global scale. Using big data analytics, machine learning and artificial intelligence, our team worked determinedly for two years piecing together the world's most comprehensive and continuously updating knowledge graph. Today, we are excited to introduce wizdom.ai, Our goal is to utilise this powerful research graph, representing the collective knowledge of human civilisation to answer the most fundamental questions for researchers, research institutions, publishers, funding organisations, businesses and governments – explore the extensive range of questions addressed by our team on the wizdom.ai

Since we moved away from chat a few days ago, we've had a lot of users come and ask us why we decided to bring chat to a bare minimum in our app. While several of our users loved the chat feature, for a lot of our users chat was a cumbersome way of doing things (too many taps, easier if they did it themselves etc.). After extensive debate internally, we took the call to phase out chat from our app and create the same level of experience using automation and simple user interfaces. The rest of the app remains the same – but it is simplified, fast and without any delays. Since a lot of people believe that chat is the new universal UI (like we once passionately believed), I thought it might be useful to talk about our experience and learning.