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The recruitment process has begun for the world's very first university dedicated to artificial intelligence, here in the UAE. More than 3,000 people have started the process to attend The Mohamed bin Zayed University of Artificial Intelligence, which will open for students from September 2020. World's first artificial intelligence university to open in Abu Dhabi The university, based in Abu Dhabi's Masdar City, received the majority of applications from the UAE, Saudi Arabia, Algeria, Egypt, India and China. More than 230 eager students have completed applications to attend next year's course - less than two weeks after the launch of the university was announced. "It is gratifying that there has already been such a strong expression of interest so quickly after the announcement," said Prof Sir Michael Brady, interim president of MBZUAI.

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. This is the fifth and final course of the Deep Learning Specialization. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

Students are then asked to focus on a topic, performing a form of meditation. The device and its software measures a student's level of concentration. This high tech gadget measures neurological impulses of each student, assigning scores to their level of attention or focus. Higher scores are awarded to those with greater concentration or attentiveness to the lesson. Teachers can view these scores at any moment throughout their lessons, adjusting their lesson delivery to the results.

The Science Research Council has been receiving an increasing number of applications for research support in the rather broad field with mathematical, engineering and biological aspects which often goes under the general description Artificial Intelligence (AI). The research support applied for is sufficient in volume, and in variety of discipline involved, to demand that a general view of the field be taken by the Council itself. In forming such a view the Council has available to it a great deal of specialist information through its structure of Boards and Committees; particularly from the Engineering Board and its Computing Science Committee and from the Science Board and its Biological Sciences Committee. These include specialised reports on the contribution of AI to practical aims on the one hand and to basic neurobiology on the other, as well as a large volume of detailed recommendations on grant applications. To supplement the important mass of specialist and detailed information available to the Science Research Council, its Chairman decided to commission an independent report by someone outside the AI field but with substantial general experience of research work in multidisciplinary fields including fields with mathematical, engineering and biological aspects. I undertook to make such an independent report, on the understanding that it would simply describe how AI appears to a lay person after two months spent looking through the literature of the subject and discussing it orally and by letter with a variety of workers in the field and in closely related areas of research. Such a personal view of the subject might be helpful to other lay persons such as Council members in the process of preparing to study specialist reports and recommendations and working towards detailed policy formation and decision taking. The report which follows must certainly not be viewed as more than such a highly personal view of the AI field. The author is grateful for the large amount of help and advice readily given in reply to his many requests. He must emphasize, however, that none but himself is responsible for the opinions expressed in this report. They represent mere!y the broad overall view of the subject which he reached after such limited studies as he was able to make in the course of two months. Readers might possibly have expected that the report would include a summary, but the author decided against this partly because considerable material is summarised already in almost every paragraph.

While the familiar idiom "you say tomayto, I say tomahto" is meant to showcase the triviality of differences, the irony lies in its illustration of the wide variation in English pronunciation. Such vagaries in pronunciation can make English difficult for many nonnative speakers unused to pronouncing certain sounds. English is a stress-based language, meaning that it requires emphasis on particular syllables, said Sarah Daniels, CEO and co-founder of English-learning startup Blue Canoe. "If someone is not proactively thinking about stress ... we, in our system, can teach them where it is and how to do it." Bellvue-based Blue Canoe's mobile app directs its users to repeat sentence prompts and record them.

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.

As IT organizations grow, so does the size of their codebases and the complexity of their ever-changing developer toolchain. Engineering leaders have very limited visibility into the state of their codebases, software development processes, and teams. By applying modern data science and machine learning techniques to software development, large enterprises have the opportunity to significantly improve their software delivery performance and engineering effectiveness. In the last few years, a number of large companies such as Google, Microsoft, Facebook and smaller companies such as Jetbrains and source{d} have been collaborating with academic researchers to lay the foundation for Machine Learning on Code. Machine Learning on Code (MLonCode) is a new interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series.

There were 2 final examinations, one being non-proctored and the other proctored. The non-proctored exam was open book, and tested your ability to look at data and the various analytical techniques, and interpret the results of the analyses. The proctored final exam was closed book and covered general concepts. This was a great overview of some of the more important topics in machine learning. I was able to get a good theoretical background in these topics, and learned the coding necessary to perform these. This is a great foundation upon which to add more advanced and in-depth use of these techniques. This course really challenged me to rethink what analytical techniques I should be learning and applying in the future, to the point that I am going to change my specialization to Artificial Intelligence and Deep Learning.

I have come across a few great resources that I wanted to share. For students taking a machine learning class (like Northwestern University's MSDS 422 Practical Machine Learning) these are great references, and a way to learn about them before, during, or after the class. This is not a comprehensive list, just a starter. There is a free online textbook, Neural Networks and Deep Learning. There is a great math visualization site called 3Blue1Brown and they have a YouTube channel.