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Today's Deep Learning Frameworks Won't Change The Machine Learning Adoption Curve Posted on : Feb 21 - 2018

Frameworks are only an intermediary step to the wider adoption of machine learning in applications. What’s needed are more visual products and those are still a couple of years away.

The current machine learning (ML) focus on frameworks is a middle step in the needed evolution of the productization of ML and its inclusion through the application environment. In order to truly succeed, the ML vendors need to think more like a business user and less like a programmer. One way to start is to learn the lesson the business intelligence (BI) sector provides.

There is an aphorism that history doesn’t repeat itself but it rhymes (often attributed to Mark Twain, but there’s no proof he said it). When it comes to the adoption curve for machine learning in business, it has a ring of truth. Deep learning (DL) frameworks, such as TensorFlow and Caffe, are getting a lot of technical press coverage, because that’s exactly what they are – technical. To understand their limitations, let’s review the standard definitions of computer language generations:

  • 1st: Machine code, binary 0s and 1s.
  • 2nd: Assembly language.
  • 3rd: Logic coded in text, what most of us consider to be a computer “language,” such as Python, C, Fortran, and Cobol.
  • 4th: Environments with modern user experiences (UX), such as Business Intelligence (BI), Visual C, PowerBuilder, Oracle ORCL -1.62% and SAP and other development tools.

Each generation allows more programming to be done (as measured by debugged lines of code) with less work. Most importantly, each level allows a wider group of people to accomplish tasks, because each level is a layer of abstraction that hides the gory details of the layer(s) below. Much of 4th generation work is focused on allowing people to “program” without coding.

Another term often used is that of the magical “data scientist.” The main problem with the myth is that such a person should exist in the long term. I believe the phrase should refer to teams of people in the early days of a new technology solution, when in-depth knowledge is required to solve problems.

However, take the first term in the 4th generation list: BI. Originally, adding analytics to existing business applications required in-depth knowledge of statistical modeling in order to code that information in a 3rd generation language. This is when people first began referring to data scientists. View More