Sundar Pichai, the chief executive of Google, has said that AI "is more profound than … electricity or fire." Andrew Ng, who founded Google Brain and now invests in AI startups, wrote that "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future." There have been remarkable advances in AI, after decades of frustration. Today we can tell a voice-activated personal assistant like Alexa to "Play the band Television," or count on Facebook to tag our photographs; Google Translate is often almost as accurate as a human translator. Over the last half decade, billions of dollars in research funding and venture capital have flowed towards AI; it is the hottest course in computer science programs at MIT and Stanford.

The Applied Data Science: Machine Learning program will give you hands-on experience in one of the hottest areas of data science. You will learn tools for predictive modeling and analytics, harnessing the power of neural networks and deep learning techniques across a variety of types of data sets. Each of the four courses in this program will let you demonstrate your newly-acquired skills through a course project. ECTS credits will be awarded to learners who successfully complete all four courses and course projects as well as a final capstone project. These course details are subject to change; please refer to the program outline at the time of registration.

The November 2017 intense discussions around Bitcoin grabbed my attention and I decided to dive deep into understanding what exactly is this. I read a bunch of papers, several books and many opinions on the topic in order to get a decent understanding of its value in the current market. You have probably heard of Bitcoin, but if you want to fully acknowledge its existence, I recommend reading Andreas' book -- The Internet of Money. Of course, the thing that is most attractive to the vast majority of people is the price volatility of this asset. The increase/decrease in Bitcoin's price with large percentages over short periods of time is an interesting phenomenon which cannot be predicted at all.

Artificial intelligence is assuming a greater role in many walks of life, with research suggesting it may even help doctors diagnose disease. One new study suggests artificial intelligence (AI) might some day detect breast cancer that has spread to the lymph nodes. Researchers found that several computer algorithms outperformed a group of pathologists in analyzing lymph tissue from breast cancer patients. The technology was specifically better at catching small clusters of tumor cells known as micrometastases. "Micrometastases can easily be missed during the routine examination by pathologists," said lead researcher Babak Ehteshami Bejnordi of Radboud University Medical Center in the Netherlands.

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Since the launch of the ML-Agents platform a few months ago, I have been surprised and delighted to find that thanks to it and other tools like OpenAI Gym, a new, wider audience of individuals are building Reinforcement Learning (RL) environments, and using them to train state-of-the-art models. The ability to work with these algorithms, previously something reserved for ML PhDs, is opening up to a wider world. As a result, I have had the unique opportunity to not just write about applying RL to existing problems, but also to help developers and researchers debug their models in a more active way. In doing so, I often get questions which come down to a matter of understanding the unique hyperparameters and learning process around the RL paradigm. In this article, I want to attempt to highlight one of these conceptual pieces: bias and variance in RL, and attempt to demystify it to some extent.

But the combination of big data and fast computers is making these approaches a much more powerful tool for research.

AI-related deep learning and machine learning techniques have become a common area of discussion in big data circles. The trend is something for data managers to keep an eye on for a number of reasons, not the least of which is the new technologies' potential effect on modern data infrastructure. Increasingly at the center of the discussion is the graphics processor unit (GPU). It has become an established figure on the AI landscape. GPU deep learning has been bubbling under the surface for some time, but the pace of development is quickening.

For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do--using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too--information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way? The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton's notion, called neural networks--which later became the groundwork for "deep learning" or "machine learning"--had already been disproven. In the late '50s, a Cornell scientist named Frank Rosenblatt had proposed the world's first neural network machine. It was called the Perceptron, and it had a simple objective--to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out "apple." The Perceptron ran on an IBM mainframe, and it was ugly.

Your three-pound brain runs on just 20 watts of power--barely enough to light a dim bulb. Yet the machine behind our eyes has built civilizations from scratch, explored the stars, and pondered our existence. In contrast, IBM's Watson, a supercomputer that runs on 20,000 watts, can outperform humans at calculation and Jeopardy! James J. DiCarlo, MD/PhD, is a professor of neuroscience, an investigator in the McGovern Institute for Brain Research and the Center for Brains, Minds and Machines, and the head of the department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. Neither Watson, nor any other artificially "intelligent" system, can navigate new situations, infer what others believe, use language to communicate, write poetry and music to express how it feels, and create math to build bridges, devices, and life-saving medicines.