As all you have probably heard by now, an AI called AlphaStar developed by Google Deepmind has recently beaten human professionals in the real time strategy game Starcraft 2. This is an unprecedented feat in the field of AI. However, I do have some constructive criticism about the way they did it. First of all I want to clarify that I am a layman. I've been following AI development and the Starcraft 2 scene for years but I do not claim to be an expert in either topic. If you notice any misconceptions in what I'm about write please do point them out.

The possibilities go from an electric motor, to a gasoline engine, to an entire car, to perhaps somewhere far beyond our own imaginations. Using the parameters of how something is manufactured, AlphaZero should be able to optimize, to near perfection, almost any design. It could start simply: for example, AlphaZero could optimize the way the copper wire is spun around the central rotating rod of a motor. It could do this all virtually by simulating it in a computer program. It would start out by randomly moving the arm that directs the wire as the copper is spun around the central rod.

Artificial intelligence is complicated; in this field, progress is, accordingly, difficult for the layman. The make to the Alphabet Holding company belonging to the AI Company deep mind has done in the past more frequently by in-game competitions against people the Superiority of its developments in order to demonstrate research progress. Up to this proof of the dominance in the year 2015, the traditional Board game was considered to be too complicated for machines. And the South Korean Lee Sedol as a more powerful opponent who was at least 18 Times champion of the world, and because of its unconventional and creative game in his home country a national hero. Although no one would be offended that a car can cover a distance faster than a human.

A new artificial intelligence could help sort normal cells from diseased cells, researchers report in a new study. The Human Cell Atlas is a deep learning algorithm method that uses single-cell RNA sequencing to distinguish activated and deactivated cells within humans at any point, according to a study published Wednesday in Nature Communications. The ability to pinpoint healthy cells from diseased cells at a given time within a person's life cycle. "From a methodological point of view, this represents an enormous leap forward. Previously, such data could only be obtained from large groups of cells because the measurements required so much RNA," Maren Büttner, a researcher at the Institute of Computational Biology of the Helmholtz Zentrum München, said in a news release.

Web serving had the LAMP stack, and big data had its SMACK stack. But when it comes to deep learning, the technology gods have yet to give us a standard suite of tools and technologies that are universally accepted. The idea of a common "stack" upon which developers build – and administrators run -- applications has become popular in recent years. Blessed with a multitude of competing options, developers can be fearful of picking the "wrong" tools and technologies and being left on the dark side of a forked project. Administrators tasked with keeping the creations of developers running similarly are afraid of inheriting a technological albatross that weighs them down.

Deep learning researchers are hitting the books. By building AI tools to transcribe historical texts in antiquated scripts letter by letter, they're creating an invaluable resource for researchers who study centuries-old documents. Many old documents have been digitized as scans or photographs of physical pages. But while obsolete scripts like Greek miniscule or German Fraktur may be readable by experts, the text on these scanned pages is neither legible to a broad audience nor searchable by computers. Hiring transcribers to turn manuscripts into typed text is a lengthy and expensive process.

Deepmind presented an AI that could beat human champions at Starcraft II. It claimed the AI was limited to what human players can physically do, putting its achievement in the realm of strategic analysis rather than finger twitchery. But there's a problem: it was often tracked clicking with superhuman speed and efficiency. They have not succeeded in this and most likely are aware of it. I suspect Deepmind wanted to restrict it to a more human like performance but they are simply not able to.

We have been building models to follow historical trends and make predictions on what will happen based on past data. Now, we have machines that can observe the environment, learn the "unspoken rules" of the environment and adapt its actions to explore & exploit the environment like a human; just don't hold your breath for artificial general intelligence (yet). AI in 2018 is not just about games and we now see real-world applications. In healthcare, deep learning models can perform as well as a human expert in analyzing electron microscopy or in detecting eye diseases. For the environment and climate, AI is helping to build better climate models, mapping millions of solar roofs in the US, detecting hyperspectral signatures of underwater objects to monitor ocean health & many other animal conservation works.

The Deep Learning Revolution by Terry Sejnowski who directs the Computational Neurobiology Laboratory at The Salk Institute for Biological Studies. Sejnowski played an important role in the development of deep learning. His new book prepares us for a deep learning future.

Deep learning would be part of every developer's toolbox in near future. It wouldn't just be tool for experts. In this course, we will develop our own deep learning framework in Python from zero to one whereas the mathematical backgrounds of neural networks and deep learning are mentioned concretely. Hands on programming approach would make concepts more understandable. So, you would not need to consume any high level deep learning framework anymore. Even though, python is used in the course, you can easily adapt the theory into any other programming language.