We've all heard of Kaggle, but that also means there's more competition -- recently, Kaggle reached 5 million users. Further, not all competitions are open to everyone in the world. "Members of the Kaggle community who are not United States Citizens or legal permanent residents at the time of entry are allowed to participate in the Competition but are not eligible to win prizes. If a team has one or more members who are not prize eligible, then the entire team is not prize eligible." By trying out other competition platforms, you can be a "big fish in a small pond," as there are a lot fewer competitors.

The Artificial Intelligence (AI) Residency Program is a one-year research training position designed to give you hands-on experience with artificial intelligence research while working in Facebook AI. This program is designed for applicants who seek advanced experience in artificial intelligence research, under the mentorship of an established researcher, with the long-term goal of obtaining an AI research scientist/engineer role. The AI Residency application is open to all fields of study, including areas ostensibly unrelated to machine learning such as (but not limited to) math, physics, finance, economics, linguistics, computational social science, neuroscience and bioinformatics. We seek people from a diverse range of technical backgrounds who have prior coding experience and are passionate about AI research. Candidates for the residency should have a foundational knowledge in ML and AI research, and have plans to pursue a career in research after completing the program.

TechRepublic's Karen Roby spoke with David Crandall, assistant professor of computer science at Indiana University about artificial intelligence (AI), computer vision, and the effects of the pandemic on higher education. The following is an edited transcript of their conversation. David Crandall: I'm a computer scientist, and I work on the algorithms, the technologies underneath AI. And I work, specifically, in machine learning and computer vision. Computer vision is the area that tries to get cameras that are able to see the world in the way that people do, and that, then, could power a lot of different AI technologies, from robotics to autonomous vehicles, to many other things.

There is a vast and growing number of Data Science resources. It can be hard to find the best ones for you. It may even be hard to find the right "Roadmap for Data Science" or "Top Skills to Learn for Data Science". I don't claim to have the best resources or the correct path to a career in Data Science. What I have is a list of useful resources and if even one of them furthers your learning my goal is accomplished.

Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making. Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016.

We are really excited to introduce the preview of new machine learning experiences in Azure Synapse Analytics, to make it easier for data professionals to enrich data and build predictive analytics solutions. AI and machine learning is an important aspect of any analytics solution. By integrating Azure Synapse Analytics with Azure Machine Learning and Azure Cognitive Services, we are bringing together the best of two worlds, to empower data professionals with the power of predictive analytics and AI. Data engineers working in Azure Synapse can access models in Azure Machine Learning's central model registry, created by data scientists. Data engineers can also build models with ease in Azure Synapse, using the code-free automated ML powered by Azure Machine Learning and use these models to enrich data.

Reference textbooks for different parts of the course are "Pattern Recognition and Machine Learning" by Chris Bishop (Springer 2006) and "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman (MIT Press 2009) and "Deep Learning" by Goodfellow, Bengio and Courville (MIT Press 2016).

When it comes to innovations in AI, Tier-1 institutes such as IIT have been trying to leave no stone unturned. IITs have been performing a lot of research work in the field of emerging technologies like AI, machine learning, blockchain, among others. The institutes are also joining hands with the government and various other prominent organisations to launch the Centre of Excellence (CoE), Research & Development Centres (R&Ds), among others. In this list, we have curated the top AI initiatives, in no particular order, by IIT in the year 2020. In January, Indian Institute of Technology, Kharagpur has evolved an AI-aided method to read legal judgements.

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Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies. Visualization has proven to be of great help during this learning process, while most current educational visualizations are targeted towards one specific architecture or use case. Unfortunately, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet, despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of deep learning research. Therefore, we propose exploRNN, the first interactively explorable, educational visualization for RNNs. exploRNN allows for interactive experimentation with RNNs, and provides in-depth information on their functionality and behavior during training. By defining educational objectives targeted towards understanding RNNs, and using these as guidelines throughout the visual design process, we have designed exploRNN to communicate the most important concepts of RNNs directly within a web browser. By means of exploRNN, we provide an overview of the training process of RNNs at a coarse level, while also allowing detailed inspection of the data-flow within LSTM cells. Within this paper, we motivate our design of exploRNN, detail its realization, and discuss the results of a user study investigating the benefits of exploRNN.