We're hearing a lot about automated machine learning lately, inspired in part by growing demand and the shortage of data scientists. But like many innovations, automated machine learning did not simply appear out of the blue; it is the product of at least twenty years of development. Before Unica Software launched its successful suite of marketing automation software, the company's primary business was predictive analytics, with a particular focus on neural networks. In 1995, Unica introduced Pattern Recognition Workbench (PRW), a software package that used an automated grid search to optimize model tuning for neural networks. Three years later, Unica partnered with Group 1 Software (now owned by Pitney Bowes) to market Model 1, a tool that automated model selection over four different types of predictive models.

The explosion of available data and computing power opens up many possibilities for business. Smart entepreneurs understood the possibility to rely on data-driven decisions in their activities, but it's difficult to find substance under buzzwords like "big data", "machine learning", "deep learning" and "data science". How can this stuff actually turn useful and how much does it cost? If you miss a clear idea on what machine learning is and why is useful, read here. The following picture expresses the main concepts: some amount of data is going to be fed to a learning algorithm, which in turn will train a model.

I'm happy to announce that Malo Bourgon, formerly a program management analyst at MIRI, has taken on a new leadership role as our chief operating officer. As MIRI's second-in-command, Malo will be taking over a lot of the hands-on work of coordinating our day-to-day activities: supervising our ops team, planning events, managing our finances, and overseeing internal systems. He'll also be assisting me in organizational strategy and outreach work. Prior to joining MIRI, Malo studied electrical, software, and systems engineering at the University of Guelph in Ontario. His professional interests included climate change mitigation, and during his master's, he worked on a project to reduce waste through online detection of inefficient electric motors.

I'd like to thank Moritz, and Nisheeth, and Sanjeev for letting me guest post over at Off The Convex Path. I really enjoyed writing up my thoughts, so I've decided to dive in and try this for real. We're in the middle of a very exciting time in machine learning: the theory community is hungry to learn the fine details of practice, and the applied folks are looking for more insights into accelerating the training of large models. I'm sure many fascinating results are soon to come from these interactions, and this has motivated me to blog about the interface between theory and practice in optimization and machine learning. I'm going to start by following up on my last post, which ended with a vexing question… If saddle points are easy to avoid, then the question remains as to what exactly makes nonconvex optimization difficult? First, let me say that it's a bit ridiculous to define a class of problems using the "non-" prefix.

The Machine Intelligence Research Institute is accepting applicants to two summer programs: a three-week AI robustness and reliability colloquium series (co-run with the Oxford Future of Humanity Institute), and a two-week fellows program focused on helping new researchers contribute to MIRI's technical agenda (co-run with the Center for Applied Rationality). The Colloquium Series on Robust and Beneficial AI (CSRBAI), running from May 27 to June 18, is a new gathering of top researchers in academia and industry to tackle the kinds of technical questions featured in the Future of Life Institute's long-term AI research priorities report and project grants, including transparency, error-tolerance, and preference specification in software systems. The goal of the event is to spark new conversations and collaborations between safety-conscious AI scientists with a variety of backgrounds and research interests. Attendees will be invited to give and attend talks at MIRI's Berkeley, California offices during Wednesday/Thursday/Friday colloquia, to participate in hands-on Saturday/Sunday workshops, and to drop by for open discussion days: Scheduled speakers include Stuart Russell (May 27), UC Berkeley Professor of Computer Science and co-author of Artificial Intelligence: A Modern Approach, Tom Dietterich (May 27), AAAI President and OSU Director of Intelligent Systems, and Bart Selman (June 3), Cornell Professor of Computer Science. The 2016 MIRI Summer Fellows program, running from June 19 to July 3, doubles as a workshop for developing new problem-solving skills and mathematical intuitions, and a crash course on MIRI's active research projects.

Löb's theorem and Gödel's theorem make predictions about the behavior of systems capable of self-reference with unbounded computational resources with which to write and evaluate proofs. However, in the real world, systems capable of self-reference will have limited memory and processing speed, so in this paper we introduce an effective version of Löb's theorem which is applicable given such bounded resources. These results have powerful implications for the game theory of bounded agents who are able to write proofs about themselves and one another, including the capacity to out-perform classical Nash equilibria and correlated equilibria, attaining mutually cooperative program equilibrium in the Prisoner's Dilemma. Previous cooperative program equilibria studied by Tennenholtz and Fortnow have depended on tests for program equality, a fragile condition, whereas "Löbian" cooperation is much more robust and agnostic of the opponent's implementation. Tennenholtz (2004) showed that cooperative equilibria exist in the Prisoner's Dilemma between agents with transparent source code.

Last week this space saw much of the largest fintech funding rounds taking place outside the U.S. That trend continues this week, with some San Francisco-grown AI thrown into the mix. This week we witnessed a major push forward in the process of collaboration between financial institutions (FIs) and startups. This week's top fundraising company was part of a startup accelerator based in Hong Kong. We also found a company creating artificial intelligence capable of accurately and reliably predicting stock market trends.

When Satya Nadella made machine learning the centerpiece of the Microsoft Build conference, I think it became official: 2016 is the year of machine learning. All the major clouds now (or will soon) have machine learning APIs. In fact, InfoWorld's Martin Heller has already reviewed the machine learning services offered by AWS, Azure, and IBM Cloud. Even more telling, a couple of years ago only a handful of machine learning startups were out of stealth. Now there are -- what -- a thousand?

Computers have long been good at carrying out assigned tasks but terrible at learning things on their own. Thus all the excitement around "neural networks," a breakthrough artificial intelligence technique that mimics the structure of the human brain and allows machines to learn things independently. Tech giants are using neural networks to do some pretty impressive things. Microsoft is using them to make instant translation real for Skype. Google's artificial intelligence learned Atari video games and then mastered the ancient game of Go, with its AlphaGo program beating the human champion Lee Sedol 4 to 1.

There is a class of businesses – let's call them "Gig Economy platforms" – that uses technology to engage large pools of new labor in carrying out its work. A "platform" is something on which others can build. Uber's platform allows amateur drivers to compete with the taxi business. Airbnb's platform helps people rent their homes in competition with commercial lodging providers. Facebook's platform enables people to publish pictures, news and other content in ways that have significantly expanded the media landscape.