Hi Katie Winkle, describe your role as a Postdoc Fellow at Digital Futures and why you applied for this fellowship mobility program? I applied for the Digital Futures fellowship because of the fantastic freedom it gives you to pursue your own research project, and the idea of working with researchers from a range of universities and departments really appealed to me. The title of your postdoc project is "On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People". It's a long title – tell us a bit about it? Have you already some results from your work?

It's hard to find reliable sources of information when starting out in deep learning. These will save you time, effort, and headaches. There are tons of free machine learning resources out there, but the sheer volume makes it difficult to sift through them. Not all of them are reliable or well-written. When you're starting out in deep learning, a poorly written tutorial can do more harm than good.

Are you an aspiring data scientist who is fascinated by how things workaround in the world of data science and machine learning? Well, congrats on choosing the right career path that is best suited for you at this point in time. However, did you know that you need to ace mathematics for machine learning and data science? Yes, you heard it right. No matter what kind of love-hate kind of relationship you had with maths back in school.

The role of artificial intelligence in education: AI is a booming technological domain capable of changing various aspects of the education system. The global Artificial intelligence market in education is forecasted to reach $3.68 billion. The role of artificial intelligence in education can be seen boosting efficiency, productivity, and convenience, providing the sector with a range of different benefits. I compiled a list of the 5 ways AI can revamp the education industry and bring some amazing transitions. A significant portion of working time is spent on administrative and grading tasks.

This article presents you the Data Science Starter Kit that will serve as a self-help guide to help you get started in your data science journey. Nor is it going to be a magical formula that will effortlessly instill you with data science knowledge and skills. This Data Science Starter Kit is going to cost you ZERO dollars (although the learning service providers mentioned herein does). What this starter kit can do for you is provide a framework that will help pinpoint you in the right direction and help you take your first steps. It's going to be tough journey.

Brnabic said that as early as the first grade, children are introduced to algorithmic thinking and she recalled that in 2017 programming became an obligatory subject since the fifth grade of elementary school. Since 2017 we have started with the introduction of digital textbooks and digital classrooms and the plan is to fully digitise all our schools and the educational system by the end of 2022, the Prime Minister stated. She noted that retraining of employees for the field of IT is an excellent example of how the state takes care of the digital literacy of the people of all ages and added that until now over 2,000 candidates underwent the retraining and that this process is ongoing. Speaking about the digital connectedness, the Prime Minister explained that the goal is to provide broadband network for 99 percent of households in Serbia by 2025. According to her, over the past five years Serbia achieved an incredible success in the field of digital transformation of the government.

Machine Learning (ML) increasingly informs the allocation of opportunities to individuals and communities in areas such as lending, education, employment, and beyond. Such decisions often impact their subjects' future characteristics and capabilities in an a priori unknown fashion. The decision-maker, therefore, faces exploration-exploitation dilemmas akin to those in multi-armed bandits. Following prior work, we model communities as arms. To capture the long-term effects of ML-based allocation decisions, we study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm. We focus on reward functions that are initially increasing in the number of pulls but may become (and remain) decreasing after a certain point. We argue that an acceptable sequential allocation of opportunities must take an arm's potential for growth into account. We capture these considerations through the notion of policy regret, a much stronger notion than the often-studied external regret, and present an algorithm with provably sub-linear policy regret for sufficiently long time horizons. We empirically compare our algorithm with several baselines and find that it consistently outperforms them, in particular for long time horizons.

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets. However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear. To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs. We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy. Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics). The dataset is available at https://github.com/PlusLabNLP/Com2Sense.

We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to maintain the frequency distribution of the data steam using limited storage, the proposed approach exploits an observed stream prefix to near-optimally hash elements and compress the target frequency distribution. We develop an exact mixed-integer linear optimization formulation, which enables us to compute optimal or near-optimal hashing schemes for elements seen in the observed stream prefix; then, we use machine learning to hash unseen elements. Further, we develop an efficient block coordinate descent algorithm, which, as we empirically show, produces high quality solutions, and, in a special case, we are able to solve the proposed formulation exactly in linear time using dynamic programming. We empirically evaluate the proposed approach both on synthetic datasets and on real-world search query data. We show that the proposed approach outperforms existing approaches by one to two orders of magnitude in terms of its average (per element) estimation error and by 45-90% in terms of its expected magnitude of estimation error.

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.