UC Berkeley artificial intelligence (AI) expert Stuart Russell will lead a new Center for Human-Compatible Artificial Intelligence, launched this week. BRETT, the Berkeley Robot for the Elimination of Tedious Tasks, ties a knot after watching others demonstrate it. Russell, a UC Berkeley professor of electrical engineering and computer sciences and the Smith-Zadeh Professor in Engineering, is co-author of Artificial Intelligence: A Modern Approach, which is considered the standard text in the field of artificial intelligence, and has been an advocate for incorporating human values into the design of AI. The primary focus of the new center is to ensure that AI systems are beneficial to humans, he said. The co-principal investigators for the new center include computer scientists Pieter Abbeel and Anca Dragan and cognitive scientist Tom Griffiths from UC Berkeley; computer scientists Bart Selman and Joseph Halpern from Cornell University; and AI experts Michael Wellman and Satinder Singh Baveja from the University of Michigan.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Every now and then I get asked for some help or for some pointers on a machine learning/data science topic. I tend respond with links to resources by folks that I consider to be experts in the topic area. Over time my list has gotten a little larger so I decided to put it all together in a blog post. Since it is based mostly on the questions I have received, it is by no means complete, or even close to a complete list, but hopefully it will be of some use. Perhaps I will keep it updated, or even better yet, feel free to comment with anything you think might be of help.

Implicit biases are a well-documented and pernicious feature of human languages. These associations, which we're often not even aware of, can be relatively harmless: We associate flowers with positive words and insects with negative ones, for example. New research from computer scientists at Princeton suggests that computers learning human languages will also inevitably learn those human biases. In a draft paper, researchers describe how they used a common language-learning algorithm to infer associations between English words. The results demonstrated biases similar to those found in traditional psychology research and across a variety of topics.

"Sorry if I am not getting to the end of all these explanations. My mind is over-active and I start working on all different parallels," says Paul Teich about half way through our chat. He isn't wrong; I am furiously taking notes but half of them seem to stop in the middle and lead on to something seemingly unconnected. I get to the end of our hour-long conversation and look at this mess I have scrawled on my papers--and it suddenly just makes sense. The way Paul thinks and talks is a wonderful indication of what he does.

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in the general setting. We show that a different property of the cost function controls the approximation factor in each of these scenarios. We further show that in both settings, the approximation factor of this greedy algorithm is near-optimal among all greedy algorithms. Experiments demonstrate the advantages of the proposed algorithm in the response-dependent cost setting.

Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy effect of dealing with huge number of features in typical learning problems. There is a variety of techniques for feature selection in supervised learning problems based on different selection metrics. In this paper, we propose a novel unified framework for feature selection built on the graphical models and information theoretic tools. The proposed approach exploits the structure learning among features to select more relevant and less redundant features to the predictive modeling problem according to a primary novel likelihood based criterion. In line with the selection of the optimal subset of features through the proposed method, it provides us the Bayesian network classifier without the additional cost of model training on the selected subset of features. The optimal properties of our method are established through empirical studies and computational complexity analysis. Furthermore the proposed approach is evaluated on a bunch of benchmark datasets based on the well-known classification algorithms. Extensive experiments confirm the significant improvement of the proposed approach compared to the earlier works.

We had a pretty incredible line-up for Data Day Texas 2016 -- and we intend to exceed your expectations again for 2017. Tell us whom you want to see, what topics you want to learn about, and let us make it happen. Please share your thoughts at suggestions@datadaytexas.com. If you wish to propose a talk or workshop, please visit the Data Day Proposals page. Her commercial applications of data science include developing predictive maintenance models for oil and gas pipelines at Deep Signal, and designing/building a platform for real-time model application, data storage, and model building at WibiData.

Artificial intelligence is improving rapidly, and a lot of people are worried that it will lead to massive job losses. In the past, technology mostly displaced workers doing routine tasks or manual labor. But as software becomes more sophisticated, there's a growing prospect that truck drivers, teachers, and perhaps even doctors could see their jobs replaced by a robot or a computer program. Ryan Avent is an economics correspondent for the Economist who has been thinking about the economics of automation for several years. He's a technology optimist -- he thinks software and robots really will massively boost economic productivity. But in a new book, he argues that this won't necessarily be good news for ordinary workers, since a glut of underemployed workers will make it harder to bargain for higher pay.

This week's top Machine Learning stories, including why you'll never write emails the same way again! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!