The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning MSc / PhD. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they're framed within thought-provoking questions and engaging stories. That is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.

Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One reason for this behavior is the encoded societal biases in the training data (e.g., females are underrepresented), which is aggravated in the presence of unbalanced class distributions (e.g., "granted" is the minority class). State-of-the-art fairness-aware machine learning approaches focus on preserving the \emph{overall} classification accuracy while improving fairness. In the presence of class-imbalance, such methods may further aggravate the problem of discrimination by denying an already underrepresented group (e.g., \textit{females}) the fundamental rights of equal social privileges (e.g., equal credit opportunity). To this end, we propose AdaFair, a fairness-aware boosting ensemble that changes the data distribution at each round, taking into account not only the class errors but also the fairness-related performance of the model defined cumulatively based on the partial ensemble. Except for the in-training boosting of the group discriminated over each round, AdaFair directly tackles imbalance during the post-training phase by optimizing the number of ensemble learners for balanced error performance (BER). AdaFair can facilitate different parity-based fairness notions and mitigate effectively discriminatory outcomes. Our experiments show that our approach can achieve parity in terms of statistical parity, equal opportunity, and disparate mistreatment while maintaining good predictive performance for all classes.

We examine the ability of machine learning (ML) and deep learning (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations and fluxes are produced from a high-resolution numerical model representing conditions in the Columbia River near the Department of Energy Hanford site located in southeastern Washington State. Random measurement error, of varying magnitude, is added to the synthetic temperature observations. The results indicate that both ML and DL methods can be used to infer the surface/ground exchange flux. DL methods, especially convolutional neural networks, outperform the ML methods when used to interpret noisy temperature data with a smoothing filter applied. However, the ML methods also performed well and they are can better identify a reduced number of important observations, which could be useful for measurement network optimization. Surprisingly, the ML and DL methods better inferred upward flux than downward flux. This is in direct contrast to previous findings using numerical models to infer flux from temperature observations and it may suggest that combined use of ML or DL inference with numerical inference could improve flux estimation beneath river systems.

Real economies can be seen as a sequential imperfect-information game with many heterogeneous, interacting strategic agents of various agent types, such as consumers, firms, and governments. Dynamic general equilibrium models are common economic tools to model the economic activity, interactions, and outcomes in such systems. However, existing analytical and computational methods struggle to find explicit equilibria when all agents are strategic and interact, while joint learning is unstable and challenging. Amongst others, a key reason is that the actions of one economic agent may change the reward function of another agent, e.g., a consumer's expendable income changes when firms change prices or governments change taxes. We show that multi-agent deep reinforcement learning (RL) can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types, in economic simulations with many agents, through the use of structured learning curricula and efficient GPU-only simulation and training. Conceptually, our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing, that are commonly used for analytical tractability. Our GPU implementation enables training and analyzing economies with a large number of agents within reasonable time frames, e.g., training completes within a day. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes. We validate the learned meta-game epsilon-Nash equilibria through approximate best-response analyses, show that RL policies align with economic intuitions, and that our approach is constructive, e.g., by explicitly learning a spectrum of meta-game epsilon-Nash equilibria in open RBC models.

As the capabilities of Artificial Intelligence systems increase everyday, government officials are under more pressure than ever to develop a comprehensive and robust set of policies and laws that holds these algorithms accountable for their decisions. The question on whether these algorithms should be held accountable has gained attention over the past few years through scandals such as Google's mislabeling of images and Microsoft Tay's racist tweets. In determining whether an algorithm should be held accountable or not, it is important to break the topic down into key questions. The first is what task is the algorithm completing? What are the implications to individuals/society resulting from the algorithm's decision.

Predicting future events is hard, but it's among the most important tasks a journalist can perform. Especially if you work at a section called Future Perfect. Our mission is to explain the world around us to our readers, and it's impossible to do that without anticipating what comes next. Will inflation continue to rise in the US and Europe, or level off? Will the Supreme Court allow states to ban abortion, eliminating legal access in red states? Will Brazil's 212 million people be led by a left-wing populist, or a far-right anti-vaxxer? All of these questions matter, and preparing ourselves for potential outcomes -- and having a good sense of how likely specific outcomes are -- is a major part of explaining the world accurately. And if policymakers could rely on accurate predictions about the outcome of a foreign war or the advisability of a budget proposal, they could make much better policy decisions. Being good at predictions is a skill like any other -- you have to practice it.

The enormous advantages of AI have a prominent impact on video conferencing, webcasting, and live streaming applications. Deemed growth of digital technology and the rapid evolution of digital platforms unfolded never-ending demands to meet end-user perceptions. Satisfying these needs will help the webcasting and live streaming platforms into a fully automated solution with the utmost competitive advantage. But the question for many of the service providers is how to integrate AI to this solution that can scale and automate according to user needs. The live streaming & Webcasting market is developing like never before.

AI regulations are coming and will be a significant focus for lawmakers in the U.S. and globally in 2022. That's according to Beena Ammanath, executive director of the Global Deloitte AI Institute, who sees a fast-moving worldwide push for AI regulation. As artificial intelligence technology use increases across enterprises, Ammanath said it will be important for governments, the private sector and consumer groups to develop regulations for AI and other emerging technologies. Broadly, advocates for AI regulation seek transparency for black box algorithms and the means to protect consumers from bias and discrimination. The U.S. has been slow to regulate AI compared to the U.K., Germany, China and Canada.

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In the week that drew 2021 to a close, the tech news cycle died down, as it typically does. Even an industry as fast-paced as AI needs a reprieve, sometimes -- especially as a new COVID-19 variant upends plans and major conferences. But that isn't to say late December wasn't eventful. One of the most talked-about stories came from the South China Morning Post (SCMP), which described an "AI prosecutor" developed by Chinese researchers that can reportedly identify crimes and press charges "with 97% accuracy." The system -- which was trained on 1,000 "traits" sourced from 17,000 real-life cases of crimes from 2015 to 2020, like gambling, reckless driving, theft, and fraud -- recommends sentences given a brief text description.