Growing up in Seattle, I was exposed to tech at a pretty young age. Most of my friends' parents worked for Microsoft. I spent a lot of my free time working on little coding projects, and even started my own business developing Video game mods in high school. Movies like 2001: A Space Odyssey captured my imagination, and gave me a sense that AI was going to be an important part of the future, even if it seemed distant at the time. But I really wanted to get involved. In my Senior year of High School, I took an AI summer course at Stanford.

Learn how to test & monitor production machine learning models. You've taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren't any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.

Jefferson Lab is hosting an A.I. Hack-A-Thon for those interested in learning about artificial intelligence. The purpose is to generate interest in A.I. in the field of nuclear physics by giving participants a free, hands on experience, according to the news release. The event is free and open to the public. The deadline to register is Friday. "The last 10 years have seen explosive growth in the field of A.I." according to the news release.

Algorithmic fairness has been studied mostly in a static setting where the implicit assumptions are that the frequencies of historically made decisions do not impact the problem structure in subsequent future. However, for example, the capability to pay back a loan for people in a certain group might depend on historically how frequently that group has been approved loan applications. If banks keep rejecting loan applications to people in a disadvantaged group, it could create a feedback loop and further damage the chance of getting loans for people in that group. This challenge has been noted in several recent works but is under-explored in a more generic sequential learning setting. In this paper, we formulate this delayed and long-term impact of actions within the context of multi-armed bandits (MAB). We generalize the classical bandit setting to encode the dependency of this action "bias" due to the history of the learning. Our goal is to learn to maximize the collected utilities over time while satisfying fairness constraints imposed over arms' utilities, which again depend on the decision they have received. We propose an algorithm that achieves a regret of $\tilde{\mathcal{O}}(KT^{2/3})$ and show a matching regret lower bound of $\Omega(KT^{2/3})$, where $K$ is the number of arms and $T$ denotes the learning horizon. Our results complement the bandit literature by adding techniques to deal with actions with long-term impacts and have implications in designing fair algorithms.

We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stochastic line-search procedure to gradually increase the learning rate, then automatically switches to a statistical method to decrease the learning rate. The line search procedure ``warms up'' the optimization process, reducing the need for expensive trial and error in setting an initial learning rate. The method for decreasing the learning rate is based on a new statistical test for detecting stationarity when using a constant step size. Unlike in prior work, our test applies to a broad class of stochastic gradient algorithms without modification. The combined method is highly robust and autonomous, and it matches the performance of the best hand-tuned learning rate schedules in our experiments on several deep learning tasks.

A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts' trajectories are available. We formulate representation learning as a bi-level optimization problem where the "outer" optimization tries to learn the joint representation and the "inner" optimization encodes the imitation learning setup and tries to learn task-specific parameters. We instantiate this framework for the imitation learning settings of behavior cloning and observation-alone. Theoretically, we show using our framework that representation learning can provide sample complexity benefits for imitation learning in both settings. We also provide proof-of-concept experiments to verify our theory.

To effectively enforce fairness constraints one needs to define an appropriate notion of fairness and employ representation learning in order to impose this notion without compromising downstream utility for the data consumer. A desirable notion is individual fairness as it guarantees similar treatment for similar individuals. In this work, we introduce the first method which generalizes individual fairness to rich similarity notions via logical constraints while also enabling data consumers to obtain fairness certificates for their models. The key idea is to learn a representation that provably maps similar individuals to latent representations at most $\epsilon$ apart in $\ell_{\infty}$-distance, enabling data consumers to certify individual fairness by proving $\epsilon$-robustness of their classifier. Our experimental evaluation on six real-world datasets and a wide range of fairness constraints demonstrates that our approach is expressive enough to capture similarity notions beyond existing distance metrics while scaling to realistic use cases.

The most vulnerable occupations include marketing specialists, financial advisers and computer programmers--jobs that tend to pay high wages and skew toward male, white and Asian workers, a recent study from the Brookings Institution found. Other jobs most vulnerable to being affected by AI included certain types of engineers, optometrists, graphic designers, software developers and sales managers. New technology in the workplace has generally been better for higher-skilled workers than for the lower-skilled, said Mark Muro, one of the study's authors. "Artificial intelligence could play out just the opposite." While machines have long been able to perform repetitive physical tasks or complex mathematical calculations, AI enables computers to analyze data, predict outcomes, learn from experience by recognizing patterns and make decisions.

Voice technology in education is taking over the academic sphere for both developers and learners. Marissa, from Alexa Education sheds more light on how the phenomenon is revolutionizing experiences in the education sector. Marissa has gathered massive passion for voice technology in education during her early days when she was producing CD-ROM educational content for'edutainment'. Her interest grew as she migrated to Microsoft where she worked on several projects that impacted the education industry such as Xbox and Encarta. Now, during her tenure in Amazon's Alexa Education, Marissa explains how her team is developing a solid connection between institutions and their stakeholders by providing efficient ways in which learners access educational content powered by technology.