Summary: The most important developments in Deep Learning and AI in the last year may not be technical at all, but rather a major change in business model. In the space of about six months all the majors have made their Deep Learning IP open source, hoping to gain on the competition from the power of the broader developer base and wide adoption. To say that the last year has been big for Deep Learning is an understatement. There have been some spectacular technical innovations like Microsoft winning the ImageNet competition with a neural net comprised of 152 layers (where 6 or 7 layers is more the norm). But the big action especially in the last six months has been in the business model for Deep Learning.

There's a reason Nvidia's stock price soared to an all-time high in December and has experienced a meteoric rise of just shy of 80 percent since November. Part of the company's success can be attributed to its strong execution in design and delivery of its core GPU products in the key markets of gaming, artificial intelligence, virtual reality and the data center, of course. However, the company's vision and its ability to execute on that vision all starts with very strong leadership from its CEO, Jen-Hsun Huang. At last week's Consumer Electronics Show in Las Vegas, Huang and Nvidia were invited to deliver the opening keynote for the show and the characteristically polished and poised CEO offered an impressive dog-and-pony of the company's key technologies and IP, supported by industry partner demonstrations. Of course gaming was one of the cornerstones of Huang's pitch, and with north of a $100 billion dollar market opportunity behind it in 2017, that stands to reason.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.

In this way, IoT plays more and more important role in daily life. The volume of data on the Internet and the Web has already been overwhelming and is still growing at stunning pace: everyday around 2.5 quintillion bytes of data is created and it is estimated that 90% of the data today was generated in the past several years [IBM12]. Sensory data, which stores the data from sensors, can be analyzed through algorithms and transformed into machine knowledge that machines have a better understanding about real human world. In this way machine can deal with human thinking somehow (someone call this kind of techniques:Artificial Intelligence). Furthermore, and most essential, we can innovate more valuable application, products and services, which changes our life automatically and dramatically.

Conventional wisdom held that the job market for machine learning and AI-related positions was hot. But according to statistics released by job search engine Indeed, "sizzling" might be a better adjective. Trend data provided by Indeed since 2014 shows job postings for artificial intelligence and machine learning positions (identified by those keywords) rising steadily from the beginning of 2014 to the start of 2016, from around 60 job postings per million to more than 100. In 2016 alone, the number of such postings jumped as much as they had over the past two -- up to 150 postings per million. Even back in 2014, artificial intelligence was solidly in the lead compared to other job postings involving emerging technologies: 3D printing, blockchain technology, IoT, virtual/augmented reality, and wearable tech.

He is a German computer scientist and artist known for his work on machine learning, Artificial Intelligence (AI), artificial neural networks, digital physics, and low-complexity art. "We need to be super careful with artificial intelligence. It is potentially more dangerous than nukes." That was Elon Musk two years ago, on Twitter. What does it mean for a technology, when it faces serious doubts from a man who is passionate about creating a better world through innovation? Since its beginnings in the 1950s, artificial intelligence has been a favourite subject of science fiction. But now AI has entered the realm of fact: several studies predict that intelligent machines will have a big impact on how we work, how we move and even how wars are fought. Innovators and scientists around the world believe that now is the time to ensure that AI is beneficial above all for humans. And even if there are plausible reasons to be anxious about machines that could one day be more intelligent than we are, many scientists are ready to take up the challenge, as we explain in our feature on the following pages. Some people fret that artificial intelligence will end civilization as we know it. Others believe it can solve every problem.

Amazon Web Services appears to be ramping up its security chops. TechCrunch has learned that the e-commerce giant's cloud services group quietly acquired cyber security firm harvest.ai. The San Diego-based startup, co-founded by a team that includes two former NSA employees, uses machine learning and artificial intelligence to analyze user behavior around a company's key IP to try to identify and stop targeted attacks before valuable customer data can be swiped. We were alerted to the acquisition by a tipster, who said the purchase price for harvest.ai The tipster also said the team's 12 employees are relocating to Amazon's Seattle headquarters.

With the year being almost at an end, let me chime in to the gang of pundits who venture into prediction land and pronounce what we get out of our glass balls. So here are my 5 plus 2 bonus ones. Alexa paved the way, the Google Assistant is on its heels, Microsoft Cortana wants to get there, too – and Apple, amazingly, is a late starter in this environment. Amazon started with a pretty smart strategy by not overselling the capabilities of its underlying AI, as Apple did with Siri, which caused some grief for Apple and some laughs for many people around. More and more helpful Alexa skills are developed and implemented that improve its usefulness.

Artificial intelligence (AI) has spread its wings: from Tesla's self-navigating cars, to Google's DeepMind defeating the world champion at Go (a board game more complicated than chess), to IBM's Watson diagnosing diseases. Software is moving beyond executing black-and-white instructions to making complex and often subjective decisions. In 2017 society will begin to trust machines in much the same way that we trust people. AI introduces a kind of unpredictability not found in traditional software. In solving complex tasks, it is too difficult to build rules to cover every eventuality. Instead, AI systems learn from experience.

As each year wraps up, experts pull their crystal balls from their drawers and start peering into them for a glimpse of what's to come. At BigML, we have been following such clairvoyant claims carefully this past holiday season to compare and contrast them with our own take on what 2017 will bring... and, our predictions may come across as quite unorthodox to some experts out there. For the TL;DR crowd, our crystal ball is showing us a cloudy (no pun intended) 2017 Machine Learning market forecast with some sunshine peeking through for good measure. To put it more directly, enterprises need to look beyond the AI hype for practical ways to incorporate Machine Learning into their operations. This starts with careful decision making when choosing an internal platform for your organization that will help build on smaller, low hanging fruit type projects that leverage their proprietary datasets.