It’s that time of year again when we do a look back in order to offer a look forward. What trends will speed up, what things will actually happen, and what things won’t in the coming year for data science, machine learning, and AI.
We’ve been watching and reporting on these trends all year and we scoured the web and some of our professional contacts to find out what others are thinking. There are only a handful of trends and technologies that look to disrupt or speed ahead. These are probably the most interesting in any forecast. But it also valuable to discuss trends we think are a tad overblown and won’t accelerate as fast as some others believe. So with a little of both, here’s what we concluded.
Prediction 1: Both model production and data prep will become increasingly automated. Larger data science operations will converge on a single platform (of many available).
Prediction 2: Data Science continues to develop specialties that mean the mythical ‘full stack’ data scientist will disappear.
Prediction 3: Non-Data Scientists will perform a greater volume of fairly sophisticated analytics than data scientists.
Prediction 4: Deep learning is complicated and hard. Not many data scientists are skilled in this area and that will hold back the application of AI until the deep learning platforms are significantly simplified and productized.
Prediction 5: Despite the hype, penetration of AI and deep learning into the broader market will be relatively narrow and slower than you think.
Prediction 6: The public (and the government) will start to take a hard look at social and privacy implications of AI, both intended and unintended.
Here are the 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.