This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry. On the first post, we collected all the articles published in TechCrunch, VentureBeat and Recode since 2007. We also filtered out all the ones that aren't about startups. On the second post, we trained machine learning models that can tell what event is described in a piece of news (product launch, acquisition, fundraising, etc) and what industries the startup the article is about belongs to (Fintech, Machine Learning, and so on). Now, we are finally ready to conduct our analysis on more than 270,000 articles, let's go over the results!

Dr. Yoshua Bengio, one of the key researchers credited with establishing the field of deep learning, announced today that he will serve as a strategy adviser for legal tech company Botler AI, a Montreal-based startup that runs a free chatbot to assist users as they navigate the legal procedures required for immigration. "I knew this was an idea that we needed to take to the next level," Bengio said in today's press release. "I want AI to be developed towards positive social impact and this is one place where beneficial AI must happen." The techniques pioneered by Bengio and others have led to breakthroughs in natural language comprehension and translation, abilities that are critical to this kind of chatbot. "Deep Learning could help provide affordable or even free legal services to people who might otherwise not be able to have access," he said.

For investors trying to understand the opportunity and the size of the market, a report from Statista says that, "In 2017, the global AI market is expected to be worth approximately 1.25 billion U.S. dollars. Some current major uses of artificial intelligence include image recognition, object identification, detection and classification, as well as automated geophysical feature detection. The largest proportion of revenues come from the AI for enterprise applications market." Featured Company: Gopher Protocol, Inc. (OTCQB: GOPH) is a development-stage company developing Internet of Things (IoT) and Artificial Intelligence enabled mobile technology. The Company has a portfolio of Intellectual Property that when commercialized will include smart microchips, mobile application software and supporting cloud software.

Artificial Intelligence (AI) pioneer Nvidia has announced it will train 100,000 developers in "deep learning" to bolster health care research and improve treatment in diseases like cancer. Deep learning is Nvidia's term for machine learning, the idea of pushing computers to learn the way a human would in order to progress what many are calling the next revolution in technology – machines that "think" like humans. Over the past decade, it's given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In the past, cancer research institutes have looked into using Nvidia's latest advances in AI and deep learning to help pathologists with their overwhelming tasks. One project, Led by Andrew Beck, associate professor of pathology and director of bioinformatics at BIDMC, used an Nvidia Tesla K80 GPU supercomputer chip to speed up the process of training their computational models in breast cancer diagnosis.

According to a study conducted through heartbeat measurement app Cardiogram and the University of California San Francisco, the Apple Watch is 97 percent accurate in detecting the most common abnormal heart rhythm, when paired with an AI-based algorithm. The study involved 6,158 participants recruited through the Cardiogram app on Apple Watch. Most of the participants in the UCSF Health eHeart study had normal EKG readings. However, 200 of them had been diagnosed with paroxysmal atrial fibrillation (an abnormal heartbeat). Engineers then trained a deep neural network to identify these abnormal heart rhythms from Apple Watch heart rate data.

Japan's 2011 Fukushima nuclear disaster had an impact on every single person on Earth, according to scientists. The meltdown hit everyone on the planet with a dose of radiation, but fortunately, not enough to have a real impact. "We don't need to worry," said Nikolas Evangeliou, according to New Scientist. He is part of the team at the Norwegian Institute for Air Research that conducted the survey. Read: Fukushima's Nuclear Radiation So Destructive, Not Even Robots Can Survive The average person was dosed with 0.5 millisieverts of radiation from the accident. For those in the immediate vicinity of Fukushima, the amount was much higher, at about one to five millisieverts.

While feats of Deep Learning has been gathering much attention, there were also breakthroughs in a related technology of Recurrent Neural Networks (RNN). RNNs hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation. RNN is learning to paint house numbers (Andrej Karpathy) See a fantastic post by Andrej Karpathy, "The Unreasonable Effectiveness of Recurrent Neural Networks" where he uses RNNs to do amazing stuff like paint house numbers in this image, or generate text in the style of Paul Graham, Shakespeare, and even Latex. See below an excellent tutorial "General Sequence Learning using Recurrent Neural Networks" by Alec Radford, Indico Head of Research, who led a workshop on general sequence learning using recurrent neural networks at Next.ML in San Francisco, Feb 2015. Alec introduces RNNs and sketches how to implement them and cover the tricks necessary to make them work well.

Artificial Intelligence is reshaping your relationship with the world and it's just getting started. Tesla's autopilot, job automation, the products you'stumble upon' online - it's entering our daily lives, careers, businesses, even our homes with such blistering pace you probably haven't even realized it. There's a reason Andrew Ng, the founder of $100m company Coursera said "Artificial Intelligence is the new electricity" - soon it'll be as much a part of your daily life as your smartphone, except without the off button. But here's where things get really crazy. This time round, the revolution will see machines taking on tasks no human intellect could ever perform.

Under NVIDIA founder Jensen Huang's iconic leather jacket is one of the tech industry's sharpest CEOs -- a man who can not only talk eloquently about GPU architecture, machine learning and the limits of Moore's Law, but do so for hours without a strict script. It's an impressive feat, but if you're not well versed in the technology of server GPUs, his talks can be a little hard to digest. That's why we cut Huang's two hour GTC keynote into an easily digestible clipshow. NVIDIA's GPU Technology Conference may be mostly aimed at developers and businesses, but the company still had a few exciting things to show off, including Project Holodeck (no, not that project holodeck), NVIDIA's take on high-end virtual reality teleconferencing. Still, most of the presentation was focused on how the company's latest supercomputer GPU facilitates deep learning artificial intelligence -- the kind of algorithmic wizardry that lets computers teach themselves how to identify and touch up photos, play golf and autonomously drive cars. You get the bulk of them in just under 13 minutes by watching the embedded video above.