4 big things to expect from artificial intelligence and machine learning in 2017
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David Paul Morris/Bloomberg / Getty Images Israel

Chrysalix Venture Capital and the RoboValley Innovation Hub want to pump over $100 million into robotics startups David Paul Morris/Bloomberg / Getty Images Israel

Here’s what to look out for in the year ahead

It’s difficult to describe in a concise list with less than 1,000 words what the definitive direction of artificial intelligence is going to be in a 12-month span. 2016 surprised a number of people in terms of the speed of certain technologies’ development and the revised ETA of new AI-driven products hitting the public market.

Clearly, I say all of this because I am attempting to write just such a list. If you think something belongs on this list or want to contribute your own ideas based on your own expertise and personal opinion, please feel free to contact us at [email protected] In the meantime, here are the four trends that will dominate artificial intelligence in 2017.

1. Language processing will continue

We could call this “natural language processing” or NLP, but let’s think more broadly about language for a moment. The key to cognition, for you mavens of Psychology 101, is sophisticated communication, even internal abstract thinking. That will continue to prove critical in driving machine learning ‘deeper.’

Google Translate’s 2016 neural machine translation upgrade shows how hard it is to launch a meaningful startup in the translation vertical. Image credit: Google

One place to keep track of progress in the space is in machine translation, which will give you an idea of how sophisticated and accurate our software currently is in translating some of the nuance and implications of our spoken and written language.

That will be the next step in getting personal assistant technology like Alexa, Siri, Google Assistant, or Cortana to interpret our commands and questions just a little bit better.

2. Efforts to square machine learning and big data with different health sectors will accelerate

“I envision a system that still has those predictive data pools. It looks at the different data you obtain that different labs are giving all the time,” eBay Director of Data Science Kira Radinsky told an audience at Geektime TechFest 2016 last month, pioneering “automated processes that can lead to those types of discoveries.”

Biotech researchers and companies are trying to get programs to automate drug discoveries, among other things. Finding correlations in data and extrapolating causation is not the same in all industries, nor in any one sector of medicine. Researchers in heart disease, neurological disorders, and various types of cancer are all organizing different metrics of data. Retrieving that information and programming the proper relationship between all those variables is an endeavor.

One of the areas where this is evident is in computer vision, exemplified by Zebra Medical Vision, which can detect anomalies in CT scans for a variety of organs including the heart and liver. But compiling patient medical records and hunting for diagnostic clues there, as well as constructing better treatment plans, are also markets machine learning is opening in 2017. Other startups like Israel’s HealthWatch are producing smart clothes that constantly feed medical data to doctors to monitor patients.

This developing ecosystem of health trackers should produce enough information about individual patients or groups of people for algorithms to extract new realizations.

3. They will probably have to come up with another buzzword to go deeper than ‘deep learning’

Machines building machines? Algorithms writing algorithms? Machine learning programs will continue adding more layers of processing units, as well as more sophistication to abstract pattern analysis. Deep neural networks will be expected to draw even more observations from unsorted data, just as was mentioned above in regards to health care.

That future buzz term might be “generative” or “adversarial,” as in generative adversarial networks (GANs). Described by MIT Technology Review as the invention of OpenAI scientist Ian Goodfellow, GANs will set up two networks like two people with different approaches to a problem. One network will try to create new data (read “ideas”) from a given set of data while the other “tries to discriminate between real and fake data” (let’s assume this is the robotic equivalent to a devil’s advocate).

(4). Skynet

Just kidding.

4. Self-driving cars will force an expensive race among automotive companies

I saved this for last because many readers probably consider this patently obvious. However, the surprise many laypeople and people who might fancy themselves tech insiders had by seeing the speed of the industry’s development might be duplicated in 2017 for the opposite reason. While a number of companies are testing the technology, it will run into some pun-intended roadblocks this year.

NVIDIA’s BB8 model on display in a video demo at CES 2017. Photo credit: courtesy

While talking about an “autonomous” vehicle is all the rage, several companies in the testing stage not only are cautious to keep someone behind the wheel if needed, but are also creating entire human-administered command centers to guide the cars.

There are some companies that will likely be able to avoid burning capital because of competition. Consider how NDIVIA is developing cars in conjunction with Audi and Mercedes-Benz, but separately. Still, BMW, Mercedes-Benz, Nissan-Renault, Ford, and General Motors are all making very big bets while trying to speed up their timeline and hit autonomous vehicle research milestones more quickly.

Even if the entire industry were to be wrong in a cataclysmic way about the unstoppable future of the self-driving car (which it won’t be, but bear with me), there will still be more automated features installed in new vehicle models relatively soon. Companies will be forced to spend big and fast in order to match features offered by their competitors.

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