At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

We are looking for a Machine Learning Researcher with a specialised focus on Reinforcement and Active Learning. The candidate will have a sound understanding of modern machine learning, deep learning, probabilistic modelling techniques and expertise in Reinforcement and Active Learning and their applications in real-world problems. You will have the opportunity to contribute to this high performing team who seek to apply their knowledge in the high impact field of improving human's capability in drug discovery. If this challenge and opportunity excites you, please email your CV and a covering letter to careers@benevolent.ai

Yesterday I had the chance to meet Andreas Liebl, partner at UnternehmerTUM, who launched a programme Applied.AI, wherein people from across the world are invited to apply. If you are an engineer, or a professional, and wish to launch an application or gain expertise in the field of AI, this is the programme for you. The institute was founded by entrepreneur Susanne Klatten in 2002 and is supported by several corporations such as Nvidia, Mercedes Benz and SAP, to name a few. What I found interesting about the institute is that not only do they train you, but can also support you, should you wish to start up. The institute has a venture capital arm that can facilitate investments towards startups with promising ideas.

Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.

What if you could create an accurate summary of a lengthy article at the touch of a button? What if you could quickly scroll through a bibliography, filtered to show only the citations relevant to your needs? What if you could get your research out into the world faster, and have that knowledge built upon sooner? Science and technology are generating more data than ever faster than ever, so it's getter harder and harder to keep up and manage this information. Therefore, it's crucial to find ways to automate the discovery and interpretation of the information we need – and only that information.

In this post, I explore strategies to switch to Data Science mid-career. This switch is not easy, but based on the experience of many who I have taught/mentored/recruited – it is possible. Most people consider PhD/MooC etc for switching their career to Data Science. But here, I will explore some non-traditional/unorthodox ways of switching to Data Science. Also, most algorithms improve previous benchmarks – but the task itself remains the same. For example, Churn prevention / Fraud detection etc are well defined industry problems.

White-collar automation has become a common buzzword in debates about the growing power of computers, as software shows potential to take over some work of accountants and lawyers. Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie--themselves. In a project called AutoML, Google's researchers have taught machine-learning software to build machine-learning software. In some instances, what it comes up with is more powerful and efficient than the best systems the researchers themselves can design. Google says the system recently scored a record 82 percent at categorizing images by their content.

"Our findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski in a paper set to be published by the Journal of Personality and Social Psychology--only he's the one now finding himself in danger. The New York Times takes a look at the quagmire Kosinski finds himself in following his decision to try--and, in some fashion, succeed--at building what many are referring to as "AI gaydar." The Stanford Graduate School of Business professor tells the Times he decided to attempt to use facial recognition analysis to determine whether someone is gay to flag how such analysis could reveal the very things we want to keep private. The Times delves into the research--first highlighted by the Economist in early September--and the many bones its many critics have to pick with it. Kosinski and co-author Yilun Wang pulled 35,000 photos of white Americans from online dating sites (those looking for same-sex partners were classified as gay) and ran them through a "widely used" facial analysis program that turns the location, size, and shape of one's facial characteristics into numbers.