Remember Project Ara? Google's super secret modular smartphone that was supposed to set the world on fire? They killed and buried the project, but we did some digging… look what we've got our hands on:

In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another. A split point is the decision tree's version of a boundary. Picking a split point has tradeoffs. Our initial split ( 240 ft) incorrectly classifies some San Francisco homes as New York ones.

If you follow machine learning, you have definitely heard of neural networks. If you are a physicist, you may have heard of tensor networks too. Both are schemes for assembling simple units (neurons or tensors) into complicated functions: decision functions in the case of machine learning or wavefunctions in the case of quantum mechanics. But tensor networks have only linear elements. Neural networks crucially require non-linear elements for their success (specifically, non-linear neuron activation functions).

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. Google's Digital Justice League: how its Jigsaw projects are hunting down online trolls AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

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Rather than leading to the violent downfall of humankind, artificial intelligence is helping people around the world do their jobs, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild, experts said Thursday (Oct. Advancements in the field of artificial intelligence (AI) haven't always been met with enthusiasm. Famed astrophysicist Stephen Hawking warned on several occasions that a fully developed AI could destroy the human race, and Hollywood sci-fi movies are rife with fierce robots battling humans for control. But at yesterday's conference -- attended by the country's leading researchers, innovators, entrepreneurs and students -- scientists explained how newly developed AI is accelerating research and improving lives. Here is a look at five AI inventions that are already redefining technology.

Artificial intelligence has been around since 1956 and has made some giant leaps in that time: beating the best human at chess, the best human at US gameshow Jeopardy and recently beating the best human at complex strategy game Go. Brands have only recently started adopting artificial intelligence for core consumer services. Google's voice recognition technology now claims 98% accuracy and Facebook's DeepFace is said to recognise faces with a 97% success rate. IBM's Watson, which uses artificial intelligence to perform its question-answering function, is 2,400% "smarter" today than when it achieved the Jeopardy victory five years ago. There is no doubt that the relationship between men and machines is changing, and brands are on the cusp of making artificial intelligence an everyday element of their customer offerings.

Westworld's AI world might not be science fiction. If you needed any clarification that Artificial Intelligence is on everyone's mind, you need only tune into HBO's new buzzed-about series Westworld. But AI is quickly making its move from the futuristic and fictional to the realm of reality with many practical AI tools already available today. From space, robotics, transportation, healthcare, marketing, music, photography and more, many industries are affected by AI, and we've only just began to see the changes that are yet to come. Since AI is developing at such an astonishing rate, many industries are reaping the rewards of its integration.

Facebook has started rolling out its "Caffe2Go" AI platform that does advanced style transfer video effects in real time using only your iOS or Android smartphone's horsepower. While the painterly effects are cool (see the video, below), the tech behind it is much more interesting. Deep learning normally requires content "be sent off to data centers for processing on big-compute servers," Facebook wrote, but with Caffe2Go, the processing can be done "in the palm of your hand." The new platform is part of a larger AI effort that includes the machine-vision Lumos app used to suss out images that violate its community standards. It has also open-sourced similar tech on Github to non-Facebook developers.

This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer. My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the math for the beginner. This approach is unconventional because it's the top-down and results-first approach designed for software engineers. Please, feel free to make any contributions you feel will make it better. I'm following this plan to prepare for my near future job: Machine learning engineer.