Incorporating artificial intelligence, machine learning and deep learning into your business processes and workflows can sound intimidating, even if you know it will give your organization a distinct competitive advantage. In this blog, I’m going to explore how the barriers to entry are getting lower year by year, month by month, and day by day.
I see these changes on a daily basis as a Senior Curriculum Manager at Udacity, where I work with various subject matter experts to build out our courses, most often focusing on machine learning and AI. I have been an instructor on a few courses as well, such as AI on the Edge and Natural Language Processing.
As a previous accounting major, I had no prior coding experience when I decided to learn programming in 2016. Around the time I was finishing up the Intro to Programming course at Udacity to learn Python, the company also announced the Self-Driving Car Engineer Nanodegree program.
Autonomous vehicles were a particular interest area of mine, but I assumed the entry point was pretty far away from my skill level. Udacity suggested that I take the Machine Learning Nanodegree program first to prepare myself for the road ahead (pun intended). I was pleasantly surprised to find that once I got into various algorithms with the scikit-learn library, many machine learning models were very straightforward from a coding standpoint — often just containing a few lines of code.
Reducing the Barriers to Implementing Artificial Intelligence
In the five years since I made the leap, learning artificial intelligence and machine learning — and choosing to implement them in your organization — has only gotten easier. Here’s why:
There are now more high-level machine learning and deep learning libraries, and each is getting even easier to work with. When the deep learning library called TensorFlow first came out, it almost felt like learning another programming language. However, it has slowly become as easy to use as scikit-learn, where only a few lines of code are needed. Many of the algorithms are complex behind the scenes, but making use of them is quite straightforward
More data is available than ever before. Your organization can leverage open-source data along with your own data to quickly get to a high-performing model in all kinds of applications. For example, there were only a few self-driving car datasets available for public use in 2016; now, open-source datasets, like the Waymo Open Dataset, help provide hundreds of millions of frames of driving data for anyone to use in their own applications.
Creating your own custom deep learning models can still require some expertise and resources. However, you can also use pre-trained networks, where someone else has already trained an existing, high-performance model on quality data. It is also much easier to fine-tune the model onto new data as well, with substantially fewer resources needed.
Using AI and Machine Learning for Your Business
Because of these transformative shifts, it’s easier than ever to start integrating AI and machine learning into your applications. By equipping your employees with the skills to adopt AI into your workflows, you can achieve better-than-human performance with small amounts of code, combining use of public and your own private data, as well as using available high-performance pre-trained models.
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