NEW DELHI: Researchers at the Indian Institute of Technology (IIT) Madras are working on two projects that use artificial intelligence and data analytics to improve learning outcomes for students. They are analysing data from open-access learning portal NPTEL (National Programme on Technology Enhanced Learning) in order to reduce dropouts and improve understanding. NPTEL is a platform where teachers from the IITs put up videos on subjects generally related to engineering and science. Students can view them and get certifications for the courses they complete, at a nominal fee of Rs 1,000. In fact, engineering colleges have been mandated by AICTE (All India Council for Technical Education), the apex body for technical education in India, to cover 15-20% of their undergraduate syllabus through NPTEL.

This op-ed was originally published in Newsweek on November 20, 2019. At least to some extent in last Wednesday's Democratic debate, and more so in the earlier ones, candidates have offered soundbites touting crowd-pleasing "silver bullet" proposals to raise earnings and reduce inequality among US workers. To improve workers' skills, Bernie Sanders and Elizabeth Warren call for free college for all. To raise wages, many call for a rapid increase in the federal minimum wage to $15 an hour as well as resurgent unionism. And Andrew Yang continues to call for "universal basic income" in response to potentially large-scale job displacement from automation.

WBS Training Ltd organizes workshops and conferences for the capital markets and treasury divisions of investment companies worldwide, with all our efforts centered solely on the education of our clients. WBS Training does not operate to present dozens of events every year. Instead we select only the most innovative, pertinent and dynamic subjects, thus bridging the gap between the latest theoretical developments through to proven practical trading floor requirements. Therefore, we aim to ensure that such requirements can be effectively implemented in the real financial world. Our depth of experience within the training environment provides us with a greater knowledge and understanding of what our clients require from financial business training.

An education system has an embedded responsibility to absorb, internalize, equip and sometimes orchestrate change. Change that is inevitable and necessary. The world as we experience today is on a perpetual continuum of redefinition, deconstruction and invention. Classroom management and pedagogy have been kept under close radars primarily to incorporate methodologies which brew conducive environments where the already shifted focus to student centered learning gains further profundity in synthesizing knowledge. Future advancement will invariably lead to Artificial Intelligence being an integral thread of the teaching and learning process.

Advanced, graduate-level book for data scientists who wish to move into deep learning.

As I've been focusing more and more on the Big Data and Machine Learning ecosystem, I've found Azure Databricks to be an elegant, powerful and intuitive part of the Azure Data offerings. Over my last 12 months at Slalom, I have had the incredible opportunity to travel across Canada and work hand in hand with the brilliant folks at Microsoft's Data & AI practice and Databricks experts to lead project engagements, deliver technical hands-on workshops, listen to the industry experts - the folks doing Data Science for a full time living - and absorb everything in between. There's a common theme across the industry verticals that's going to be our point of discussion today. The hot topic of 21st century tech is Machine Learning - some flavor of AI/ML is thrown into almost everything we find these days (I'm pretty sure I spotted a "genius" AI/ML toothbrush at Shoppers Drug Mart today). The reality is, the mathematical techniques that power Machine Learning models have been around for almost a century.

Machine learning technology has the capacity to autonomously identify malignant tumors, pilot Teslas and subtitle videos in real time. The term "autonomous" is tricky here, because machine learning still requires a lot of human ingenuity to get these jobs done. It works like this: An algorithm scans a massive dataset. Engineers don't tell it exactly what to look for in this initial dataset, which could consist of images, audio clips, emails and more. Instead, the algorithm conducts a freeform analysis.

AI analysis may identify certain emotional traits, without providing a more holistic perspective on the person exhibiting those traits. Should AI-driven systems measure and mimic human empathy? There are two sides to this question: AI is being employed to deliver human-like responses, and is also capturing and analyzing human responses to address their states of mind. The frontline of many of today's enterprise AI systems is customer service -- chatbots and online response systems that attempt to deliver friendly, informed services on demand. Some systems come close, but AI may not be ready to deliver the kind of empathy that a live human agent can provide – such as going the extra mile to help a customer.

Generating paraphrases that are lexically similar but sema nti-cally different is a challenging task. Paraphrases of this f orm can be used to augment data sets for various NLP tasks such as machine reading comprehension and question answering with nontrivial negative examples. In this article, we pro - pose a deep variational model to generate paraphrases conditioned on a label that specifies whether the paraphrases are semantically related or not. We also present new training recipes and KL regularization techniques that improve the performance of variational paraphrasing models. Our pr o-posed model demonstrates promising results in enhancing th e generative power of the model by employing label-dependent generation on paraphrasing datasets.

Networks are one of the most powerful structures for modeling problems in the real world. Downstream machine learning tasks defined on networks have the potential to solve a variety of problems. With link prediction, for instance, one can predict whether two persons will become friends on a social network. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding encompasses various methods for unsupervised, and sometimes supervised, learning of feature representations of nodes and links in a network. Typically, embedding methods are based on the assumption that the similarity between nodes in the network should be reflected in the learned feature representations. In this paper, we review significant contributions to network embedding in the last decade. In particular, we look at four methods: Spectral Clustering, DeepWalk, Large-scale Information Network Embedding (LINE), and node2vec. We describe each method and list its advantages and shortcomings. In addition, we give examples of real-world machine learning problems on networks in which the embedding is critical in order to maximize the predictive performance of the machine learning task. Finally, we take a look at research trends and state-of-the art methods in the research on network embedding.