Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular substructure. Simulation and optimization of these materials are computationally challenging, as rich substructures necessitate high-fidelity finite element meshes to solve the governing PDEs. To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions. This yields a structured prediction task: macroscopic behavior is determined by the minimizer of the system's total potential energy, which can be approximated by composing these surrogate models. Composable energy surrogates thus permit simulation in the reduced basis of component boundaries. Costly ground-truth simulation of the full structure is avoided, as training data are generated by performing finite element analysis with individual components. Using dataset aggregation to choose training boundary conditions allows us to learn energy surrogates which produce accurate macroscopic behavior when composed, accelerating simulation of parametric meta-materials.

Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.

Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics.

Open Philanthropy recommended a total of approximately $2,300,000 over five years in PhD fellowship support to 10 promising machine learning researchers that together represent the 2020 class of the Open Phil AI Fellowship.1 These fellows were selected from more than 380 applicants for their academic excellence, technical knowledge, careful reasoning, and interest in making the long-term, large-scale impacts of AI a central focus of their research. This falls within our focus area of potential risks from advanced artificial intelligence. We believe that progress in artificial intelligence may eventually lead to changes in human civilization that are as large as the agricultural or industrial revolutions; while we think it's most likely that this would lead to significant improvements in human well-being, we also see significant risks. Open Phil AI Fellows have a broad mandate to think through which kinds of research are likely to be most valuable, to share ideas and form a community with like-minded students and professors, and ultimately to act in the way that they think is most likely to improve outcomes from progress in AI. The intent of the Open Phil AI Fellowship is both to support a small group of promising researchers and to foster a community with a culture of trust, debate, excitement, and intellectual excellence.

In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning.

Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.

In this work, we aim at making agents communicate On the other hand, multi-agent communication with humans in natural language. Our starting research (Foerster et al., 2016; Lazaridou et al., point is a language model that has been trained on 2017; Havrylov and Titov, 2017; Evtimova et al., generic, not task-specific language data. We then 2017; Lee et al., 2019) puts communication at the place this model in a multi-agent communication heart of agents' (language) learning. Implemented environment that generates task-specific rewards, within a multi-agent reinforcement learning setup, which are used to adapt or modulate the model, agents start tabula rasa and form communication making it task-conditional. We thus propose to decompose protocols that maximize task rewards. While this the problem of learning language use into purely utilitarian framework results in agents that two components: learning "what" to say based on successfully learn to solve the task by creating a a given situation, and learning "how" to say it. The communication protocol, these emergent communication "what" is the essence of communication that underlies protocols do not bear core properties of our intentions and is chosen by maximizing any natural language. Chaabouni et al. (2019) show that given utility, making it a functional, utility-driven protocols found through emergent communication, process. On the other hand, the "how" is a surface unlike natural language, do not conform to Zipf's realization of our intentions, i.e., the words we use Law of Abbreviation; Kottur et al. (2017) find that

Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.

Artificial intelligence caught Shashank Ojha's imagination while he was a student at Thomas Jefferson High School for Science and Technology in Alexandria, Virginia. He took the few AI courses the school offered and soon set his sights on attending Carnegie Mellon University. "I knew that CMU was the place to be for AI," he explained. His plan when he entered CMU in 2016 was to pursue a bachelor's degree in computer science, with minors in machine learning and robotics. What he hadn't counted on was the School of Computer Science's 2018 decision to launch the nation's first undergraduate AI degree program.

For this edition of My Journey In Data Science column, Analytics India Magazine got in touch with a data scientist, influencer and blogger. Rahul Agrawal, Data Scientist at Walmart Labs, shared his exciting journey in data science, and also offered advice on best practices for aspirants to thrive in the ever-changing data science landscape. Rahul is a mechanical engineer from IIT Delhi, who started his job in a steel company in 2010, but quit the job since it was not interesting enough. Then he joined Fractal Analytics in 2011 as a business analyst. "Initially, I wrote a lot of SQL and made dashboards – most of the work revolved around reporting. And it was not a love-at-first-sight for me," says Rahul.