HANOVER, GERMANY - APRIL 25: Close up of the digital display while a camera and radar system assists as artificial intelligence takes over driving the car during tests of autonomous car abilities conducted by Continental AG on the A2 highway on April 25, 2018, near Hanover, Germany. Israeli artificial intelligence (AI) startup, Hailo Technologies, has closed a $12.5 million series A from Maniv Mobility, OurCrowd, and NextGear to develop a chip for deep learning on edge devices and processing of high-resolution sensory data in real time. According to a report from Markets and Markets, edge computing will be worth $6.72 billion by 2020, and IC Insights reported that integrated circuits in cars are expected to generate global sales of $42.9 billion in 2021. In 2017, McKinsey reported in the study, Self Driving Car Technology: when will robots hit the road?, that ADAS systems grew to 140 million in 2016 from 90 million units in 2014. "Because of the low latency required for autonomous driving and advanced driving assistance, deep learning with convolutional neural networks, running on in-vehicle hardware, is necessary," offers Tom Coughlin, IEEE Fellow and President at Coughlin Associates.

For the longest time, chips were a little bit boring. But the revolution in deep learning has now opened the market for startups that build specialty chips to accelerate deep learning and model evaluation. Among those is Israel-based Hailo, which is building deep learning chips for embedded devices. The company today announced that it has raised a $12 million Series A round. Investors include Israeli crowdfunding platform OurCrowd, Maniv Mobility, Next Gear, and a number of angel investors, including Hailo's own chairman Zohar Zisapel and Delek Motors' Gil Agmon.

By now our future is clear: We are to be cared for, entertained, and monetized by artificial intelligence. Existing industries like healthcare and manufacturing will become much more efficient; new ones like augmented reality goggles and robot taxis will become possible. But as the tech industry busies itself with building out this brave new artificially intelligent, and profit boosting, world, it's hitting a speed bump: Computers aren't powerful and efficient enough at the specific kind of math needed. While most attention to the AI boom is understandably focused on the latest exploits of algorithms beating humans at poker or piloting juggernauts, there's a less obvious scramble going on to build a new breed of computer chip needed to power our AI future. One datapoint that shows how great that need is: software companies Google and Microsoft have become entangled in the messy task of creating their own chips.

While most attention to the AI boom is understandably focused on the latest exploits of algorithms beating humans at poker or piloting juggernauts, there's a less obvious scramble going on to build a new breed of computer chip needed to power our AI future. At a computer vision conference in Hawaii, Harry Shum, who leads Microsoft's research efforts, showed off a new chip created for the HoloLens augmented reality googles. The chip, which Shum demonstrated tracking hand movements, includes a module custom-designed to efficiently run the deep learning software behind recent strides in speech and image recognition. The TPU, for tensor processing unit, was created to make deep learning more efficient inside the company's cloud.

This is the first installment in a three-part review of 2016 in machine learning and deep learning. In Part Two, we cover developments in each of the leading open source machine learning and deep learning projects. Part Three will review the machine learning and deep learning moves of commercial software vendors. As organizations expand the use of machine learning for profiling and automated decisions, there is growing concern about the potential for bias. In 2016, reports in the media documented racial bias in predictive models used for criminal sentencing, discriminatory pricing in automated auto insurance quotes, an image classifier that learned "whiteness" as an attribute of beauty, and hidden stereotypes in Google's word2vec algorithm.

Deep learning has become of the most relevant trends in modern software technology. From a conceptual standpoint, deep learning is a discipline of machine learning that focuses on modeling data using connected graphs with multiple processing layers. In the last few years, deep learning has become a pivotal technology to power uses cases such as image recognition, natural language processing or even powering some of the capabilities of self-driving vehicles. The popularity of deep learning has expanded beyond just software and now the industry is starting to talk about the first generation of hardware with deep learning capabilities: a deep learning chip. A few months ago, at its I/O Conference, Google announced the design of an application-specific integrated circuit (ASIC) focused on deep learning capabilities and neural nets.

Today Silicon Valley chip maker Movidius released the Fathom Neural Compute Stick. It looks like a measly thumb drive, but inside it packs a high-end visual processing unit that can do a bunch of advanced image recognition. That chip, which is called the Myriad 2, is the same one powering the computer vision and autonomous features in DJI's latest drone. The Fathom is basically a plug-and-play version of the Myriad 2, and Movidius hopes engineers will use it to build deep learning features like like pixel-by-pixel imagine labeling and advanced video analytics into their existing products. "It lets you implement machine learning in an ad hoc manner," Cormac Brick, head of machine learning at Movidius, tells The Verge.