The direct conversion of methane gas to liquid methanol at the site where it is extracted from the Earth holds enormous potential for addressing a number of significant environmental problems. Developing a catalyst for that conversion has been a critical focus for Associate Professor Heather Kulik and the lab she directs at MIT. As important as that research is, however, it is just one example of the innumerable possibilities of Kulik's work. Ultimately, her focus is far broader, the scope of her exploration infinitely more vast. "All of our research is dedicated toward the same practical goal," she says.

We revisit the classical online portfolio selection problem. It is widely assumed that a trade-off between computational complexity and regret is unavoidable, with Cover's Universal Portfolios algorithm, SOFT-BAYES and ADA-BARRONS currently constituting its state-of-the-art Pareto frontier. In this paper, we present the first efficient algorithm, BISONS, that obtains polylogarithmic regret with memory and per-step running time requirements that are polynomial in the dimension, displacing ADA-BARRONS from the Pareto frontier. Additionally, we resolve a COLT 2020 open problem by showing that a certain Follow-The-Regularized-Leader algorithm with log-barrier regularization suffers an exponentially larger dependence on the dimension than previously conjectured. Thus, we rule out this algorithm as a candidate for the Pareto frontier. We also extend our algorithm and analysis to a more general problem than online portfolio selection, viz. online learning of quantum states with log loss. This algorithm, called SCHRODINGER'S BISONS, is the first efficient algorithm with polylogarithmic regret for this more general problem.

The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have therefore witnessed great effort in developing causal learning algorithms aiming to help AI achieve human-level intelligence. Due to the lack-of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference, and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with big data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal-inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We then examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of big data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning.

We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by learning about its state dynamics and rewards, an RN is a partial proxy that learns to augment or replace rewards. We use bi-level optimization to evolve SEs and RNs: the inner loop trains the RL agent, and the outer loop trains the parameters of the SE / RN via an evolution strategy. We evaluate our proposed new concept on a broad range of RL algorithms and classic control environments. In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment. However, once such an SE has been learned, we do not need any interactions with the real environment to train new agents. Moreover, the learned SE proxies allow us to train agents with fewer interactions while maintaining the original task performance. Our empirical results suggest that SEs achieve this result by learning informed representations that bias the agents towards relevant states. Moreover, we find that these proxies are robust against hyperparameter variation and can also transfer to unseen agents.

Now that AI is heading into the mainstream of IT architecture, the race is on to ensure that it remains secure when exposed to sources of data that are beyond the enterprise's control. From the data center to the cloud to the edge, AI will have to contend with a wide variety of vulnerabilities and an increasingly complex array of threats, nearly all of which will be driven by AI itself. Meanwhile, the stakes will be increasingly high, given that AI is likely to provide the backbone of our healthcare, transportation, finance, and other sectors that are crucial to support our modern way of life. So before organizations start to push AI into these distributed architectures too deeply, it might help to pause for a moment to ensure that it can be adequately protected. In a recent interview with VentureBeat, IBM chief AI officer Seth Dobrin noted that building trust and transparency into the entire AI data chain is crucial if the enterprise hopes to derive maximum value from its investment.

You have finished solving a machine learning problem. The accuracy of your model is awesome. Till now your work is probably a Jupyter notebook, which is full of code, a few visuals, and very little documentation. If you see your work after a month or so, you might struggle to understand your own creation. To make matter worse, the Jupyter notebook does not have all decisions and assumptions you have taken in the solution.

The technology market share is just increasing like an oil spill in the ocean and becoming more and more complex and overwhelming to cope with. To help you learn and understand the ever-changing landscape of technology, we are extending a list of 30 Top LinkedIn Voice in Technology. Allie is the Global Head of Machine Learning Business Development, Startups, and Venture Capital at Amazon Web Service (AWS). Her area of expertise includes AI, Machine Learning, Crypto, Web3 & NFTs. Emmanuel is an undergraduate student of Chemical Engineering, named as the Young Influencer of the year by TIBA.

Coursera is an ed-tech learning platform that partners with more than 200 leading universities and companies to bring flexible, affordable, job-relevant online learning to individuals and organizations worldwide. They offer a range of learning opportunities--from hands-on projects and courses to job-ready certificates and degree programs.

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors--one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project. "The systems we performed our demonstrations with look nothing like each other, and they seem to [be] having nothing to do with handwritten-digit recognition or vowel classification, and yet you can train them to do it."

Welcome to Deep Learning Fundamentals, Artificial Neural Network. This course covers the basic theory and Python practice of artificial neural networks. This course is designed for beginners who are interested in deep learning. Having knowledge of undergraduate level mathematics is preferable, but not a must. Artificial intelligence is a technology that makes machines imitate intelligent human behavior and human cognitive functions.