Last week, the UAE announced the launch of the Mohammed bin Zayed University for Artificial Intelligence (MBZUAI), a graduate-level institution in Abu Dhabi. The world's first university of its kind, which is accepting applications for September 2020, aims to develop a workforce ready to navigate a rapidly changing, technologically advancing world. This announcement is indicative of how our economies and workforce are changing, and the UAE's continuous effort to stay ahead of the curve. A 2016 study by Stanford University exploring what our lives will be like in 2030 with the influence of artificial intelligence (AI), found that almost all areas will be impacted by this technology. Our career paths are continuously evolving, and if our skills don't evolve, we will fall behind. For instance, when I graduated from university ten years ago with a degree in mass communication, I didn't know that most of what I would be working on in my company nowadays -- from creating 10-second social media videos and exploring the digital culture to working with social influencers -- would be things we didn't even explore in the classroom.

The global artificial intelligence and education Market is significantly driven by the integration of intelligent algorithms as well as Advanced Technologies in to e-learning platforms. Education software, machine learning, and artificial intelligence are some of the Innovative learning models and Technologies change the rules and creating tremendous shift from the teaching methods. These technologies have completely transformed with a classroom. The sophistication level has increased tremendously with the increasing adoption of artificial intelligence and machine learning algorithms. These Technologies are becoming extremely useful for developing user-friendly decision support systems and used in knowledge acquisition applications, language translation, and information retrieval.

The first graduate-level AI university in the world has been announced in Abu Dhabi. The university will also engage policymakers and businesses around the world so that AI can be harnessed responsibly for positive transformation. Supervision of PhD students will happen in partnership with the Abu Dhabi-based Institute of Artificial Intelligence, and all admitted students will also be offered a full scholarship as well as monthly benefits including allowance, health insurance and accommodation. Internships will be provided in collaboration with local and global companies, and students will also be assisted in finding employment opportunities. His Excellency Dr Sultan Ahmed Al Jaber, UAE minister of state and chair of the MBZUAI board of trustees, said: "MBZUAI aligns with the vision of the UAE leadership that is based on sustainable development, progress and the overall wellbeing of humanity, and underpinned by capacity-building and active participation in finding practical solutions based on innovation and state-of-the-art technology. The MBZUAI is an open invitation from Abu Dhabi to the world to unleash AI's full potential."

With abundance of easy-to-use Machine Learning Libraries, it is often appealing to apply them and achieve greater than 80% prediction accuracy in most cases. But, 'WHAT TO TRY NEXT?' is a question that buzz me and may be other aspiring Data Scientists too. During my course'Machine Learning -- Stanford Online' at Coursera, Prof. Andrew Ng helped me sail through it. I hope this article, which briefs his explanation during one of his lectures, will help many of us to understand the importance of'debugging or diagnosing a learning algorithm'. To start with, let's call out all the possibilities or'WHAT TO TRY NEXT?' when a hypothesis makes unacceptably large errors in its predictions or when there is a need to improve our hypothesis: We will revisit this table to make smart choices and create our TOOL BOX.

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 …