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.

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.

In one back room at Skyline High School, you can learn all you need to know about St. Vrain Valley School District. It's there that bins of materials sit next to past projects, exposing the district's DNA. Boxes holding glue, Popsicle sticks, tape, pipe cleaners, compasses, zip ties and rulers lie nestled inside a 6-foot-high, student-constructed rack. Behind the storage unit sits a rectangular wooden box stuffed with bicycle tires filled with Silly Putty to replicate human intestines. For that biomedical project, students had to create a probe and learn to maneuver it sight unseen from behind a curtain on the box's opening to procure a sample from the intestine/bike tire.

Follow leaders in ML on twitter to see what research papers/blog posts/etc. This is a very effective but highly under-rated way to get good at ML. Having seen a lot of new Stanford PhD students grow to become great researchers, I can say confidently that replicating others' results (not just reading the papers) is one of the most effective ways to see and make sure you understand the details of the latest algorithms. Many people jump too quickly into trying to invent something new, which is also worth doing, but is actually a slower way to learn and build up your foundation of knowledge. When you do build something new, publish it in a paper or blog post and consider open-sourcing your code, and share it back out with the community! Hopefully this will help you get more feedback from the community, and further accelerate your learning.

We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed bandit problems, the manager needs to simultaneously "explore" and "exploit" with her inventory decisions, in order to minimize the cumulative cost. We make no probabilistic assumptions---importantly, independence or time stationarity---regarding the mechanism that creates the demand sequence. Our goal is to shed light on the hardness of the problem, and to develop policies that perform well with respect to the regret criterion, that is, the difference between the cumulative cost of a policy and that of the best fixed action/static inventory decision in hindsight, uniformly over all feasible demand sequences. We show that a simple randomized policy, termed the Exponentially Weighted Forecaster, combined with a carefully designed cost estimator, achieves optimal scaling of the expected regret (up to logarithmic factors) with respect to all three key primitives: the number of time periods, the number of inventory decisions available, and the demand support. Through this result, we derive an important insight: the benefit from "information stalking" as well as the cost of censoring are both negligible in this dynamic learning problem, at least with respect to the regret criterion. Furthermore, we modify the proposed policy in order to perform well in terms of the tracking regret, that is, using as benchmark the best sequence of inventory decisions that switches a limited number of times. Numerical experiments suggest that the proposed approach outperforms existing ones (that are tailored to, or facilitated by, time stationarity) on nonstationary demand models. Finally, we extend the proposed approach and its analysis to a "combinatorial" version of the repeated newsvendor problem.

Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable quantum transverse field to generate tunneling processes. A key challenge is to identify classes of non-convex optimization problems for which quantum annealing remains efficient while thermal annealing fails. We show that this happens for a wide class of problems which are central to machine learning. Their energy landscapes is dominated by local minima that cause exponential slow down of classical thermal annealers while simulated quantum annealing converges efficiently to rare dense regions of optimal solutions.

I started my deep learning journey a few years back. I have learnt a lot in this period. But, even after all these efforts, every Neural network I train provides me with a new experience. If you have tried to train a neural network, you must know my plight! But, through all this time, I have now made a workflow, which I will share with you today.

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too! If you thought learning data science is difficult or deep neural nets is not your cup of tea – look up to the role models who created them. Following these role models provides you a daily inspiration, a motivation to find bigger purpose in life and to achieve it. Role models set goals for you and try to make you as good as they are. In this article, I'll introduce you to a league of ultimate data scientists in the world.