Finland is offering a wildly popular Elements of Artificial Intelligence online course to all EU citizens for free. The initiative was launched to mark the approaching end of Finland's presidency of the Council of the EU. The Finnish Ministry of Economic Affairs and Employment will finance the project, which was valued at 1.7 million euros. The Finnish government announced that the goal of the project is to attract one per cent of EU citizens or five million people to complete the course in 2020 and 2021. The offer of the online programme represents an innovative way to say goodbye to more traditional gifts such as books or scarves.

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for. Most companies are open about the expectations for the role being interviewed for, the interview process, and preparation tips.

Shounak Banerjee and Pradeep Chandra from IIT Kharagpur opted for winter internship 2019 at Leadingindia.ai, a hotspot for Artificial Intelligence (AI) mentoring at Bennett University, Greater Noida. Similarly, Lovepreet Singh from IIT Gandhinagar selected Bennett University to learn Artificial Intelligence and Data Science after receiving outstanding feedback from the previous interns. As per'MARKETSANDMARKETS' report, the artificial intelligence (AI) market is expected to reach USD 190 billion by 2025, at a CAGR of 36.62%. Linkedin, in a recent report, mentions Artificial intelligence as one of the most sought after technical skills by businesses in the coming year. The trend is no different at Bennett University with a growing number of internship applications compared to last year.

World is on the cusp of a revolution about new possibilities in AI and Machine Learning. Deep Learning is being used to solve many critical healthcare related issues apart from other important areas that impact society. India has aspiring young students in thousands of educational institutions in the country. Due to lack of quality faculty and curriculum design issues many of these students are not able to get access to latest skill sets required by the industry. Industry all over the world is facing huge scarcity of trained manpower in machine intelligence.

NEW DELHI: From diagnosing diseases to categorising huskies, Artificial Intelligence has countless uses but mistrust in the technology and its solutions will persist until people, the "end users", can fully understand all its processes, says a US-based Indian scientist. Overcoming the "lack of transparency" in the way AI processes information - popularly called the "black box problem" - is crucial for people to develop trust in the technology, said Sambit Bhattacharya who teaches Computer Science at the Fayetteville State University "Trust is a major issue with Artificial Intelligence because people are the end-users, and they can never have full trust in it if they do not know how AI processes information," Bhattacharya told . The computer scientist, whose work includes using machine learning (ML) and AI to process images, was a keynote speaker at the recent 4th International and 19th National Conference on Machines and Mechanisms (iNaCoMM 2019) at the Indian Institute of Technology in Mandi. To buttress his point that users don't always trust solutions provided by AI, Bhattacharya cited the instance of researchers at Mount Sinai Hospital in the US who applied ML to a large database of patient records containing information such as test results and doctor visits. The'Deep Patient' software they used had exceptional accuracy in predicting disease, discovering patterns hidden in the hospital data indicating when patients were on the way to different ailments, including cancer, according to a 2016 study published in the journal Nature.

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing the question: does a non-technical audience understand a basic definition of ML fairness? We develop a metric to measure comprehension of one such definition--demographic parity. We validate this metric using online surveys, and study the relationship between comprehension and sentiment, demographics, and the application at hand.

This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach that is, in theory, applicable to supervised, unsupervised, and reinforcement learning, although our experiments only focus on the supervised case. GTNs are deep neural networks that generate data and/or training environments that a learner (e.g. a freshly initialized neural network) trains on for a few SGD steps before being tested on a target task. We then differentiate through the entire learning process via meta-gradients to update the GTN parameters to improve performance on the target task. GTNs have the beneficial property that they can theoretically generate any type of data or training environment, making their potential impact large. This paper introduces GTNs, discusses their potential, and showcases that they can substantially accelerate learning. We also demonstrate a practical and exciting application of GTNs: accelerating the evaluation of candidate architectures for neural architecture search (NAS), which is rate-limited by such evaluations, enabling massive speed-ups in NAS. GTN-NAS improves the NAS state of the art, finding higher performing architectures when controlling for the search proposal mechanism. GTN-NAS also is competitive with the overall state of the art approaches, which achieve top performance while using orders of magnitude less computation than typical NAS methods. Speculating forward, GTNs may represent a first step toward the ambitious goal of algorithms that generate their own training data and, in doing so, open a variety of interesting new research questions and directions.

Abstract-- VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation. Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems and facilitate the VLSI design cycle. In this paper, we focus on a heterogeneous OPC framework that assists mask layout optimization. Preliminary results show the efficiency and effectiveness of proposed frameworks that have the potential to be alternatives to existing EDA solutions. I Introduction VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation. Recent studies have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring the potential of dedicated machine learning solution for DFM problems and facilitate the VLSI design cycle [1, 2].

Abstract--In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and dev elop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized patient sy stem to allow medical students to simulate a diagnosis strategy o f an abdominal surgical emergency. We exploited the semantic properties captured by distributed word representations t o search for similar questions in the virtual patient dialogue syste m. We created two dialogue systems that were evaluated on dataset s collected during tests with students. The first system based on handcrafted rules obtains 92.29% as F 1-score on the studied clinical case while the second system that combines rules an d semantic similarity achieves 94.88%. It represents an error reduction of 9.70% as compared to the rules-only-based system. The medical diagnosis practice is traditionally bedside taught. Theoretical courses are supplemented by internshi ps in hospital services. The medical student observes the practi ce of doctors and interns and practices himself under their contr ol. This type of learning has the disadvantage to confront immediately the medical student with complex situations withou t practical training (technical and human) beforehand.

The hype machine for AI and machine learning has been going full throttle, and one can be forgiven for thinking that every organization from the mega-techs to the corner store is turning over processes or decisions to AI. If you're still stuck trying to figure out how AI and machine learning can fit into your operations, don't worry -- so is everyone else, actually. Companies may be increasing their investments in machine learning and machine learning development, but, for the most part, are still in the early learning stages. That's the major takeaway from a survey of 750 technology managers and professionals released by Algorithmia, which specializes in such things. Survey respondents represent companies that are actively engaged in building machine learning lifecycles.