This is an excerpt from a long interview between an anonymous data scientist and Logic Magazine about AI, deep learning, FinTech, and the future, conducted in November 2016. LOGIC: Alright, let's get started with the basics. What is a data scientist? Do you self-identify as one? DATA SCIENTIST: I would say the people who are the most confident about self-identifying as data scientists are almost unilaterally frauds.

If nothing else, you can this much for artificial intelligence: They're rarely afraid to look stupid. If a learning A.I. encounters something outside its preprogrammed knowledge, it will not typically be shy in asking the person with whom it is speaking to clarify. This can, however, make for rather monotonous conversation for the human involved in talking to the chatbot, voice assistant, or generally conversant robot: "What's an apple?" "What's tiramisu?" "What's cured meat?" "What's do you know literally anything about food you stupid recipe chatbot?" You get the idea, and as researchers from Japan's Osaka University point out in a recent spotlight on their work, that last line is indicative of the real problem facing A.I.: Asking questions might be the best way for them to learn, but that doesn't count for much if the barrage of questions is so irritating or tedious that the human wanders off.

While IT companies unionize and protect themselves, opinions about the impact of AI are rather sharply divided. There are plenty who fear the coming algorithmic wave. For example, Infosys ex-CEO Vishal Sikka, when talking about AI and automation in March this year, said, "If we sit still, there is absolutely no doubt that our jobs are going to be wiped out by AI. Sixty to 70 percent over the next 10 years--or maybe less than 10 years--of the jobs that we do today are going to be replaced by AI...unless we continue to evolve ourselves."

Inventor and author Ray Kurzweil, who currently runs a group at Google writing automatic responses to your emails in cooperation with the Gmail team, recently talked with WIRED Editor-in-Chief Nicholas Thompson at the Council on Foreign Relations. Here's an edited transcript of that conversation. Nicholas Thompson: Let's begin with you explaining the law of accelerating returns, which is one of the fundamental ideas underpinning your writing and your work. Ray Kurzweil: Halfway through the Human Genome Project, 1 percent of the genome had been collected after seven years. So mainstream critics said, "I told you this wasn't gonna work.

What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

Primarily, Aboleth is much reduced in scope compared to frameworks like Edward and ZhuSuan; we really only concentrate on neural net-like structures (though you can construct a lot of non-neural net models with these too), and we have only implemented SGVB and "MAP" inference. We have slightly more of a "production" focus than a research focus too (though we would love people to use Aboleth for their research) - for instance we have efficient categorical feature embedding and imputation input layers, where all of the parameters of these layers are learned with the rest of the model. Because we have a limited scope it allows for a nice clean interface and minimal abstraction over pure TensorFlow. Really, all we have done in Aboleth is implement a writer monad, i.e. function composition (the neural net) with an accumulate (the complexity penalties of the layers). This structure is easy to use, extend, and test!