Recent technology and business media have given much coverage to the expanding use case of machine learning and artificial intelligence, but how much have they talked about economics of AI? At the physical layer, more objects in our lives are embedded with sensors or are sensors. The aim and consequence are to record and quantify more of our physical world, creating vast data sets that make machine learning possible. At the application layer, growing compute power and model sophistication such as dimensionality reduction and manifold learning have helped data scientists tackle messier technical problems than could be addressed even just a few years ago. Meanwhile, the pandemic has heightened the urgency and societal stakes to direct research insights toward practical problem-solving, broadly defined.

The Indian Institute of Technology (IIT) Jodhpur will offer a new undergraduate programme in artificial intelligence and data science from the academic session 2020-21. The new BTech programme will have courses in computer science, mathematics, artificial intelligence, machine learning, data science, and their applications in various domains. Students who opt for the course can also take a specialisation in areas including visual computing, socio-digital realities, language technologies, robotics, and the Artificial Intelligence of Things. Students will also have the option to pursue MBA (tech) in the fifth year as dual-degree option in the School of Management and Entrepreneurship, the IIT said in an official release. Prof Santanu Chaudhury, Director, IIT-Jodhpur, said: "Under the broad umbrella of IIT Jodhpur's unique proposition of AI for everything, students belonging to the academic programmes in AI, Data and Computational Sciences will be part of scientific innovations for solving local and global engineering and social problems in close collaboration with industry. Students will be part of the institute's initiatives for ensuring better life and livelihood for all with AI as the enabling force. IIT-Jodhpur would like AI and Data Science students to explore transdisciplinary research agenda fostering collaborative opportunities across all the departments of IIT Jodhpur and partner organisations."

This guide is intended for anyone having zero or a small background in programming, mathematics, and/or machine learning. There is no specific order to follow, but a classic path would be from top to bottom, following the order given in this article. If you don't like reading books, skip the section, if you don't want to follow an online course, you can skip this one as well. There is not a single way to become a machine learning expert, and with motivation, you can absolutely achieve it creating your own steps. But the goal of this article is to give a path for anyone wanting to get into machine learning and not knowing where to start.

This review article aims to examine how machine learning has revolutionized the capacity of science assessment in terms of tapping into complex constructs, improving assessment functionality, and facilitating scoring automaticity.

Work by Wyss Core Faculty member Peng Yin in collaboration with Collins and others has demonstrated that different toehold switches can be combined to compute the presence of multiple "triggers," similar to a computer's logic board. Credit: Wyss Institute at Harvard University DNA and RNA have been compared to "instruction manuals" containing the information needed for living "machines" to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables -- a task that is daunting to even the most intrepid human brains. Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based "toehold" sequences and predict which ones will be most effective at sensing and responding to a desired target sequence.

A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning--a subset of artificial intelligence--in science education. Although the authors initiated their review before the COVID-19 outbreak, the pandemic highlights the need to examine cutting-edge digital technologies as we re-think the future of teaching and learning. Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment. The article aims to examine how machine learning has revolutionized the capacity of science assessment in terms of tapping into complex constructs, improving assessment functionality, and facilitating scoring automaticity. Based on their investigation, the researchers identified various ways in which machine learning has transformed traditional science assessment, as well as anticipated impacts that it will likely have in the future (such as providing personalized science learning and changing the process of educational decision-making).

Despite ample motivation from costly exploration and limited trajectory data, rapidly adapting to new environments with few-shot reinforcement learning (RL) can remain a challenging task, especially with respect to personalized settings. Here, we consider the problem of recommending optimal policies to a set of multiple entities each with potentially different characteristics, such that individual entities may parameterize distinct environments with unique transition dynamics. Inspired by existing literature in meta-learning, we extend previous work by focusing on the notion that certain environments are more similar to each other than others in personalized settings, and propose a model-free meta-learning algorithm that prioritizes past experiences by relevance during gradient-based adaptation. Our algorithm involves characterizing past policy divergence through methods in inverse reinforcement learning, and we illustrate how such metrics are able to effectively distinguish past policy parameters by the environment they were deployed in, leading to more effective fast adaptation during test time. To study personalization more effectively we introduce a navigation testbed to specifically incorporate environment diversity across training episodes, and demonstrate that our approach outperforms meta-learning alternatives with respect to few-shot reinforcement learning in personalized settings.

Cherokee is a highly endangered Native American language spoken by the Cherokee people. The Cherokee culture is deeply embedded in its language. However, there are approximately only 2,000 fluent first language Cherokee speakers remaining in the world, and the number is declining every year. To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English. Compared to some popular machine translation language pairs, ChrEn is extremely low-resource, only containing 14k sentence pairs in total. We split our parallel data in ways that facilitate both in-domain and out-of-domain evaluation. We also collect 5k Cherokee monolingual data to enable semi-supervised learning. Besides these datasets, we propose several Cherokee-English and English-Cherokee machine translation systems. We compare SMT (phrase-based) versus NMT (RNN-based and Transformer-based) systems; supervised versus semi-supervised (via language model, back-translation, and BERT/Multilingual-BERT) methods; as well as transfer learning versus multilingual joint training with 4 other languages. Our best results are 15.8/12.7 BLEU for in-domain and 6.5/5.0 BLEU for out-of-domain Chr-En/EnChr translations, respectively, and we hope that our dataset and systems will encourage future work by the community for Cherokee language revitalization. Our data, code, and demo will be publicly available at https://github.com/ZhangShiyue/ChrEn

Emerging edge intelligence applications require the server to continuously retrain and update deep neural networks deployed on remote edge nodes in order to leverage newly collected data samples. Unfortunately, it may be impossible in practice to continuously send fully updated weights to these edge nodes due to the highly constrained communication resource. In this paper, we propose the weight-wise deep partial updating paradigm, which smartly selects only a subset of weights to update at each server-to-edge communication round, while achieving a similar performance compared to full updating. Our method is established through analytically upper-bounding the loss difference between partial updating and full updating, and only updates the weights which make the largest contributions to the upper bound. Extensive experimental results demonstrate the efficacy of our partial updating methodology which achieves a high inference accuracy while updating a rather small number of weights.

Relatedness-based evaluation - known as intrinsic evaluation in the context of embedding-based A standard approach to evaluate a relatednessbased models - requires the construction of a dataset of model is the comparison of the semantic human annotations, which may be collected via ranking it produces with the corresponding ranking two different approaches. The former relies on a determined from human annotations. However, small group of linguistic experts to create a gold the relevance of rank mismatches may depend standard dataset, which is reliable but very expensive on the involved positions; in particular, top ranks and, due to the subjectivity of relatedness and are considered more important in many contexts, to the limited number of annotations, highly susceptible two prominent examples being content-based recommenders to bias and lack of statistical significance (De Gemmis et al., 2008, 2015; Lops (Blanco et al., 2013; Faruqui et al., 2016). The latter et al., 2011; Mladenic, 1999) and semantic matching relies on a large group of non-experts, typically (Giunchiglia et al., 2004; Li and Xu, 2014; associated with a crowdsourcing service (e.g., Amazon Wan et al., 2016). The greater significance of top MTurk, ProlificAcademic, SocialSci, Crowd-ranks compared with low ranks is actually a pretty Flower, ClickWorker, CrowdSource), it is typically common phenomenon, as it can be argued from more affordable, and it has been proven to be repeatable the attempts to overweight the former in the context and reliable (Blanco et al., 2013). of ranking correlation (Blest, 2000; Pinto da In the next sections we describe and justify a Costa and Soares, 2005; Dancelli et al., 2013; Iman protocol to construct a dataset based on semantic and Conover, 1987; Maturi and Abdelfattah, 2008; relatedness between pairs of tokens