The University of Toronto and the affiliated Vector Institute for Artificial Intelligence have announced the recruitment of two rising stars in machine learning research as part of a continued drive to assemble the best AI talent in the world. Chris Maddison and Jakob Foerster will both come to U of T having completed their doctoral research at the University of Oxford. He earned his undergraduate and master's degrees in computer science at U of T – the latter under the supervision of University Professor Emeritus Geoffrey Hinton. A senior research scientist at Google-owned AI firm DeepMind, Maddison will join U of T's departments of computer science and statistical sciences in the Faculty of Arts & Science as an assistant professor next summer. Foerster, a research scientist at Facebook AI Research, will start as an assistant professor in the department of computer and mathematical sciences at U of T Scarborough in fall of 2020.

Cognex CGNX recently announced the acquisition of Seoul-based SUALAB, a developer of deep learning-based vision software. Although the financial terms of the acquisition have been kept under wraps, per a Pulse article the transaction price is estimated to be $168.6 million. Deep learning allows Cognex to solve the most complex vision application operations in factories faster, easier and in a cost-effective manner. The addition of SUALAB's Intellectual property and highly skillful engineering team, which specializes in deep learning, is expected to strengthen the company's product portfolio. The latest acquisition will help Cognex to reap benefits from strong prospects of the global deep learning system software market.

Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data can be obtained in the relevant source domain. But most of the existing methods are based on offline data. In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially. In this paper, We are committed to applying the ensemble approach to solving the problem of online transfer learning so that it can be used in anytime setting. More specifically, we propose a novel online transfer learning framework, which applies the idea of online bagging methods to anytime transfer learning problems, and constructs strong classifiers through online iterations of the usefulness of multiple weak classifiers. Further, our algorithm also provides two extension schemes to reduce the impact of negative transfer. Experiments on three real data sets show that the effectiveness of our proposed algorithms.

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper we indicate the distinctions between automated and autonomous system as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, to train reinforcement learning agents to learn specific tasks through generalization. Once generalization is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment which focuses on how the agents can adapt to different real-world environments.

At the beginning of June, the German Weather Service counted 177,000 lightning bolts in the night sky within a few days. The natural spectacle had consequences: Several people were injured by gusts of wind, hail and rain. Together with Germany's National Meteorological Service, the Deutscher Wetterdienst, computer science professor Jens Dittrich and his doctoral student Christian Schön from Saarland University are now working on a system that is supposed to predict local thunderstorms more precisely than before. It is based on satellite images and artificial intelligence. In order to investigate this approach in more detail, the researchers will receive 270,000 euros from the Federal Ministry of Transport.

"Why not take the same machine–learning algorithms that Facebook uses to post thoughts and condolences, and use it to actually understand what …

The UAE has set up an artificial intelligence university, claimed to be the first in the world, in Abu Dhabi. The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) was inaugurated on October 17 and it offers courses for undergraduate students. It is also accepting applications for its first masters and PhD programmes this month, with classes scheduled to begin on September 20 next year. All admitted students will be given full scholarship plus benefits such as a monthly allowance, health insurance and accommodation. "AI is already changing the world, but we can achieve so much more if we allow the limitless imagination of the human mind to fully explore it. The university will bring the discipline of AI into the forefront, moulding and empowering creative pioneers who can lead us to a new AI-empowered era," said Sultan Ahmed Al Jaber, UAE Minister of State, who has been appointed Chair of the MBZUAI Board of Trustees and is spearheading the university's establishment.

If you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. Through exercises in each chapter, you†ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser.

For senior IT people, 2019 may not look to be the happiest of new years. Many experienced technologists are finding their roles outsourced, with other employers looking for only younger (read: cheaper) employees. "I had three jobs in three years," Mike, a 50-something New York-based IT specialist, told me a year ago. "They've all ended with even new hires being let go and the work outsourced. I had to go before a judge to explain my financial situation, and he said I should take a class to update my skills. As if that would fix it."

The normal life cycle of a machine learning model includes several stages, see Figure 1. There are countless online courses and articles about preparing the data and building models but there is much less material about model deployment. Yet, it is precisely at this stage where all the hard work of data preparation and model building starts to pay off. This is where models are used to score (or get predictions for) new cases and extract the benefits. My intent here is to fill this gap, so that you will be fully prepared to deploy your model using time tested resources.