We have some algorithm that's given a handful of labeled examples, say 10 images of dogs with the label 1 ("Dog") and 10 images of other things with the label 0 ("Not dog")--note that we're mainly sticking to supervised, binary classification for this post. The algorithm "learns" to identify images of dogs and, when fed a new image, hopes to produce the correct label (1 if it's an image of a dog, and 0 otherwise). We have some algorithm that's given a handful of labeled examples, say 10 images of dogs with the label 1 ("Dog") and 10 images of other things with the label 0 ("Not dog")--note that we're mainly sticking to supervised, binary classification for this post. The algorithm "learns" to identify images of dogs and, when fed a new image, hopes to produce the correct label (1 if it's an image of a dog, and 0 otherwise). This setting is incredibly general: your data could be symptoms and your labels illnesses; or your data could be images of handwritten characters and your labels the actual characters they represent.

It's easy to joke that the internet was invented to give people around the world the opportunity to share pictures of cats. However, according to a new report, those kitty pictures may one day turn out to save your life. That is based on work being done by Dr. Alvin Rajkomar, an assistant professor at the University of California, San Francisco Medical Center. Rajkomar trained a deep learning neural network to be able to automatically detect life-threatening abnormalities in chest X-rays. "When I was a medical resident, I ordered a stat X-ray of a patient who I suspected had a life-threatening pneumothorax -- air outside of his lung compressing his heart -- and happened to be standing next to the digital X-ray machine as it was being taken," he told Digital Trends.

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. One reason that understanding language is so difficult for computers and AI systems is that words often have meanings based on context and even the appearance of the letters and words. In the images that accompany this story, several artists demonstrate the use of a variety of visual clues to convey meanings far beyond the actual letters.

As AI integration becomes more prominent, one can't help but to think about just how intelligent deep learning technology will be in the future. One of the first place many of our minds go is to AI becoming too intelligent and taking matters into its own virtual hands. How accurate are those portrayals, though? Will it get to a point where we're overpowered by AI, to the point where we're under their metaphorical thumb? TNW Conference is back for its 12th year.

We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.

Jeremy P. Howard, @JeremyPHoward, is a leading Machine Learning and Deep learning researcher and entrepreneur. His current startup is fast.ai Previously, he was CEO and founder of Enlitic, Kaggle President, and #1 ranked Kaggle competitor. Jeremy initiatives attracts a lot of attention in the industry, so I was very interested to learn from him about his latest project, a first Deep Learning for coders MOOC at course.fast.ai. The course is totally free and includes no advertising - Jeremy created it purely as a service to the community.

On the west coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth--part of an effort to prevent the extinction of these endangered marine mammals. The trouble is that Hodgson and her team don't have the time needed to examine all those aerial photos. There are too many of them--about 45,000--and spotting the dugongs is far too difficult for the untrained eye. Deep learning is remaking Google, Facebook, Microsoft, and Amazon.

Scientists and engineers normally create AI that can learn via deep learning, and training them usually takes up weeks. However, it seems Microsoft and scientists from the Swiss National Computing Centre have taken this one step further and trained AI via deep learning within minutes! This is an astonishing feat in the realm of robotics and artificial intelligence, one that could easily generate results within hours or minutes. This, coupled with the introduction of supercomputing technology, customers will now have the ability to solve problems such as image, video and speech recognition, as well as natural language processing. This can enable future researchers to be able to make technologies that could only previously been seen in science fiction.

Alphabet DeepMind, the company's moonshot AI factory, is announcing today (Dec. The software, available on GitHub this week, looks like a cartoonish video game--but it has been carefully built to give AI developers control over how their bots learn. It's not just fun and games--the Lab is a virtual environment that attempts to teach AI strategy, planning, time management, and motor control. Videos released by DeepMind depict a human navigating through the Lab's world as an AI would. "The only known examples of general-purpose intelligence in the natural world arose from a combination of evolution, development, and learning, grounded in physics and the sensory apparatus of animals," DeepMind researchers write in a blog post.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.