CIODigital Disruption

5 Trends That Will Drive Machine Learning Adoption

An insignificant percentage of companies are investing in machine learning right now, because it is just too early on to be a worthwhile investment for them. This will not hold true for very long though. In an article for InformationWeek, Lisa Morgan shares five trends that will accelerate machine learning development, taken from a Deloitte report:

  1. Automating data science
  2. Reducing the need for training data
  3. Accelerating training
  4. Explaining results
  5. Deploying locally

Readying to Learn

Everyone knows there are not enough data scientists to go around, so if data science could simply be automated—that would be a game-changer. How much can truly be automated remains to be seen, but the proposition is enticing enough to get businesses thinking about machine learning.

Another thing that will make machine learning more attractive is a reduction in the amount of training data needed to make the machine learning model useful. “Synthetic data” has been identified as a method to enable this reduction:

Using synthetic data, Deloitte was able to reduce the actual amount of data required for training by 80%. In other words, 20% of the data was actual data and the remaining 80% was synthetic data.

“How far we can go in reducing the need for training data has two kinds of question marks: How far can you reduce the need for training data and what characteristics of data are most likely minimized and which require massive datasets?” said [David Schatsky, managing director at Deloitte].

New processor architectures will also speed up machine learning, again making it more palpable to the masses. At present, machine learning just hogs up too many resources to be practical in many cases, but people are working on how to deploy it more locally.

One more thing that will make businesses salivate for machine learning is the time when machine learning is able to provide explanations for the results it yields. Right now, machine learning just provides an answer and that is the end of the story; it is up to someone else to place faith in it. When machine learning can also provide a reasoning though, everyone will be lining up for it.

For additional thoughts, you can view the original article here:


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