If you are not already, it's worth following the work of Steven Levy, a long-time technology journalist that has been covering Silicon Valley and the American technology sector for over 20 years (his work and criticism of Apple are particularly noteworthy). He now writes for Medium on its Backchannel site and recently published a long-form piece on Google and its attempts to integrate'machine learning' throughout all aspects of the company. The progress on artificial intelligence has been getting more attention recently, primarily because of comments from figures like Elon Musk and Stephen Hawking (some other great pieces on this broad area can be found here and here). But a lot of the work is taking place in massive companies like Google. Below are a few extracts from Levy's recent piece.

Google's machine learning and artificial intelligence (or machine intelligence, as the company likes to call it) influences many of its signature products. Google is one of the companies at the forefront of robotics and artificial intelligence research, and being in that position means they have the most to worry about. The idea of a robot takeover may still be an abstract, science fictional concept to us, but Google has actually compiled a list of behaviors that would cause them great concern, both for efficiency and safety in the future. Among the recurring themes is the possibility of deceit: that robots would learn to "accomplish goals" by hiding evidence that more work needs to be done. The researchers also laid out the possibility of smart robots avoiding humans to evade further assignments.

It's the stuff dreams are made of. A robot picks up your clothes and cleans your house for you – but at the same time – it could end up being a nightmare with the bot smashing-up your treasured belongings by mistake. Artificial Intelligence (AI) is rapidly expanding into video gaming, self-driving cars, healthcare and households, potentially transforming the world of science, medicine and technology -- and our lives. However scientists from Google Brain, Open AI, Stanford University and the University of California, Berkeley are predicting that the proliferation of AI will come at a price. In a paper titled, "Concrete Problems in AI Safety," the researchers examined the potential impacts AI has on poorly programed systems.

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.