Kavli Foundation President Cynthia Friend (front row center) and Director of Public Engagement Brooke Smith (second row right) visited UC Berkeley in November to discuss the new Kavli Center with campus researchers. Every day, algorithms select which news stories appear in our social media feeds. Airplanes allow global travel at nearly the speed of sound while emitting greenhouse gases that accelerate the impacts of climate change. And recent advances in DNA sequencing and editing enable us to understand our fundamental genetic programming -- and potentially change it. While it may be challenging to anticipate where science might lead us next, researchers at the University of California, Berkeley, are taking steps to ensure that the public has a greater say in future scientific advances, and that questions of ethics and social equity take a prominent role in scientific decision-making. UC Berkeley announced today that the campus will be home to a new Kavli Center for Ethics, Science, and the Public, which, alongside a second center at the University of Cambridge in the United Kingdom, will connect scientists, ethicists, social scientists, science communicators and the public in necessary and intentional discussions about the potential impacts of scientific discoveries.

In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow. Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach. We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to $5$ -- $1,000\times$ less data than model-based RL baselines and $10^3$ -- $10^5\times$ less data than model-free RL baselines. We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data.

Forecasting volatility has a fundamental scope for financial economics with applications in asset pricing, risk management as well as systemic risk monitoring due to the fact that forecasts of asset return volatilities are essential inputs for pricing models (Bollerslev et al. (2020)). A vast body of literature has been devoted to model design capable of accurately capturing volatility dynamics and producing reliable volatility forecasts. Furthermore, the increasing availability of high frequency data pushed the development of methods such as latent variable models such as the GARCH specifications as well as models for Stochastic Volatility (as in Bollerslev (1986) and Hansen and Lunde (2005)). Moreover, the inclusion of high frequency filters via the use of estimators for the true latent integrated volatilities has been examined in various studies such as in Andersen and Bollerslev (1998), Barndorff-Nielsen and Shephard (2002), Andersen et al. (2001), Andersen et al. (2003), Andersen et al. (2007) and Aït-Sahalia and Jacod (2014). In practise, time series observations for realized volatility measures at a given frequency (such as daily) are typically obtained by summing higher frequency squared returns (e.g.

While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments. In contrast, the classical robotics community have developed a range of controllers that can safely operate across most states in the real world given their explicit derivation. These controllers however lack the dexterity required for complex tasks given limitations in analytical modelling and approximations. In this paper, we propose Bayesian Controller Fusion (BCF), a novel uncertainty-aware deployment strategy that combines the strengths of deep RL policies and traditional handcrafted controllers. In this framework, we can perform zero-shot sim-to-real transfer, where our uncertainty based formulation allows the robot to reliably act within out-of-distribution states by leveraging the handcrafted controller while gaining the dexterity of the learned system otherwise. We show promising results on two real-world continuous control tasks, where BCF outperforms both the standalone policy and controller, surpassing what either can achieve independently. A supplementary video demonstrating our system is provided at https://bit.ly/bcf_deploy.

Image-to-image (I2I) translation is an established way of translating data from one domain to another but the usability of the translated images in the target domain when working with such dissimilar domains as the SAR/optical satellite imagery ones and how much of the origin domain is translated to the target domain is still not clear enough. This article address this by performing translations of labelled datasets from the optical domain to the SAR domain with different I2I algorithms from the state-of-the-art, learning from transferred features in the destination domain and evaluating later how much from the original dataset was transferred. Added to this, stacking is proposed as a way of combining the knowledge learned from the different I2I translations and evaluated against single models.

Its use dates back to early civilizations, and today it is the most popular composite choice in the world. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too. Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti's research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation.

Once upon a time, data science was valuable only for a handful of Big Tech companies. Data science is now revolutionizing many "traditional" sectors: from automotive to finance, from real estate to energy. Research by PwC estimates that AI will contribute over 15.7 trillion US dollars to the global GDP by 2030 -- for reference, the GDP of the Eurozone in 2018 was worth 16 trillion dollars [1]. All businesses now perceive their data as assets and the insights they can gain as a competitive advantage. Yet, more than 80% of all data science project fails [2]. Each failed project fails for its own peculiar reasons, but, in three years of experience, we noticed some patterns.

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared which focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.

Natural language communication is core to intelligence, as it allows ideas to be efficiently shared between humans or artificially intelligent systems. The generality of language allows us to express many intelligence tasks as taking in natural language input and producing natural language output. Autoregressive language modelling -- predicting the future of a text sequence from its past -- provides a simple yet powerful objective that admits formulation of numerous cognitive tasks. At the same time, it opens the door to plentiful training data: the internet, books, articles, code, and other writing. However this training objective is only an approximation to any specific goal or application, since we predict everything in the sequence rather than only the aspects we care about. Yet if we treat the resulting models with appropriate caution, we believe they will be a powerful tool to capture some of the richness of human intelligence. Using language models as an ingredient towards intelligence contrasts with their original application: transferring text over a limited-bandwidth communication channel. Shannon's Mathematical Theory of Communication (Shannon, 1948) linked the statistical modelling of natural language with compression, showing that measuring the cross entropy of a language model is equivalent to measuring its compression rate.