Object oriented programming with classes Understanding the power of object oriented programming using abstract data types Defining abstract data types using classes Writing class member and static functions Understanding the class and object structure Exploiting Python's dynamic class and object behaviour

Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies. But, for universities, the most conspicuous aspect of the announcement may well have been their omission from it.

The Federal Trade Commission is hosting a public workshop Wednesday on video game "loot boxes" amid backlash from players claiming it can create a "pay to win" structure. The workshop will bring together industry experts, consumer advocates and others "to discuss concerns regarding the marketing and use of loot boxes and other in-game purchases, and the potential behavioral impact of these virtual rewards on young consumers," according to an event description on the FTC's website. The concept has generated angst among video game players concerned it encourages a "pay to play" atmosphere where gamers must spend money to gain a competitive edge. Loot boxes are rewards players receive within video games containing random prizes. For example, in the Activision Blizzard game Overwatch, loot boxes generate stickers, quotes from characters, and special costumes.

Discover the hidden side of everything with Stephen J. Dubner, co-author of the Freakonomics books. Each week, Freakonomics Radio tells you things you always thought you knew (but didn't) and things you never thought you wanted to know (but do) -- from the economics of sleep to how to become great at just about anything. Dubner speaks with Nobel laureates and provocateurs, intellectuals and entrepreneurs, and various other underachievers. Special features include series like "The Secret Life of a C.E.O." as well as a live game show, "Tell Me Something I Don't Know."

I am so thrilled to welcome you to the absolutely awesome world of data science. It is an interesting subject, sometimes difficult, sometimes a struggle but always hugely rewarding at the end of your work. While data science is not as tough as, say, quantum mechanics, it is not high-school algebra either. It requires knowledge of Statistics, some Mathematics (Linear Algebra, Multivariable Calculus, Vector Algebra, and of course Discrete Mathematics), Operations Research (Linear and Non-Linear Optimization and some more topics including Markov Processes), Python, R, Tableau, and basic analytical and logical programming skills. If you are studying the Data Science course at Dimensionless Technologies, you are in the right place.

What does the use of artificial intelligence look like in everyday health life? What is already possible today, where are the challenges and opportunities? This Meetup is supported by Aicura Medical, IBM PowerAI Vision and WWK Versicherungen and is a teaser event for this year's www.healthhackathon.com at the Xpomet from 10-12 October in Berlin. The focus of the hackathon will be on medical technology, hospital management, care and health insurance and will bring together about[masked] working medical-affine programmers and digital-affine physicians from hospitals, companies and insurance companies. We want to integrate Deep and Machine Learning as well as Blockchain and other technologies into today's health challenges.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

With the rapid development of quantum computers, a number of quantum algorithms have been developed and tested on both superconducting qubits based machines and ion trap hardware. Quantum machine learning is expected to be a potential application of quantum computer in the near future. Many quantum machine learning algorithms have been proposed to speed up classical machine learning by quantum computers. At the same time, deep learning has shown great power in solving real world problems. The aim of the workshop is to bring together world leading experts in this new field of quantum machine learning to discuss the recent development of quantum algorithms to perform machine learning tasks on large-scale scientific datasets for various industrial and technological applications and in solving challenging problems in science and engineering.

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Want to get started in Artificial Intelligence? We'll here is the first video in my 10 part course for beginners that covers what you need to know to get started in AI. Ensure to subscribe to our channel for new videos in our machine learning series. Disclaimer: The above is a matter of opinion provided for general information purposes.