Machine learning is fundamentally different from all the tech that came before it in one major way: As time passes, it gets smarter. It’s the opposite of a static solution because it actually provides more value with age. Unsurprisingly, companies in all industries have been eager to embrace machine learning in recent years, and that shows no signs of slowing.
We’ve already seen machine learning applications enter the mainstream via web search algorithms and product recommendations. Transformative though they may be, they’re just the inaugural applications. Moving forward, we should expect to see this technology become ubiquitous in unexpected ways.
Breaking Down Machine Learning Technologies
Current machine learning technologies are categorized based on their ability to internalize information. Unsupervised algorithms can collect and analyze data on their own (e.g., clustering customers based on demographic information). Conversely, supervised algorithms are directed by a human user feeding in specific data (like predicting financial outcomes based on accounting data).
One type of machine learning algorithm is not necessarily better or worse than another. Rather, each is appropriate for certain kinds of tasks. For instance, Google uses semi-supervised learning, gleaning insights from known data to interpret unknown data so it can quickly categorize messages in a Gmail inbox.
Another example is the cybersecurity tool AI2 (founded by machine learning company PatternEx), which utilizes unsupervised learning to analyze mass amounts of incoming data for potential threats. In this case, machine learning (that can operate on its own) ultimately takes the burden off cybersecurity monitors.
Ironically, machine learning’s sophistication is also its weakness. For machine learning to function perfectly, it must be calibrated carefully. Doing so often requires a team of mathematicians, an ocean of available data, and a lengthy process for quality assurance. Plus, leveraging the data necessary for machine learning raises questions about data privacy and cybersecurity.
For all these reasons, companies would prefer to use machine learning more than they currently are. Thankfully, implementing this upgrade doesn’t have to take a major investment — all it takes is the right plan.
6 Tips for DIY Machine Learning
At this point, machine learning has been around long enough for us to identify common setbacks and develop best practices. Learn from the experience of others to perfect a machine learning initiative:
1. Understand this technology’s inner workings. Machine learning relies on some existing technologies — but often in novel ways. Utilizing this technology to the fullest starts with understanding exactly how it works. This is especially important for getting executive buy-in. Companies should employ experts who understand the intricacies of this technology and the mathematical models it runs on. In many cases, the existing IT team is not enough.
2. Create a model and select data. The success of machine learning depends on the quality of the underlying data. Create a mathematical model for whatever problem machine learning is set to solve. Next, train the model on data by using a certain machine learning algorithm. The better the data used to train the model, the better the model will work in the future.
3. Vet various algorithms. There are many ready-made algorithms to work with, and each boasts different strengths and applications. Be sure to evaluate criteria such as reliability, efficiency, and scalability. It’s also important to consider new use cases, as my company discovered when working on a computer vision project with OpenCV. In that instance, comparing the results of two algorithms worked better than any single algorithm.
4. Rely on existing tools. One antidote to the cost and complexity of machine learning is to use existing tools in open-source libraries. Some popular examples include mlpack for C++, Caffe for Python, and NuPIC for streaming analytics. If the goal is to introduce deep learning, resources like Deeplearning4j are great for Java and Scala, whereas Theano and Neon are better for Python. Some others to consider include NLTK for human language data or Shogun, which is a great multipurpose tool.
5. Tweak the algorithms. Don’t expect algorithms to work perfectly right out of the gates. Getting to that point requires a process of evaluation and iteration. Document every step to identify where obstacles exist. When an obstacle does arise, consider whether the problem is the algorithm or the underlying data.
6. Prioritize performance. Machine learning gets smarter over time, but the opposite can occur if things are not maintained carefully. Given the dynamic nature of data, algorithms that once worked perfectly can start to underperform. Tracking the performance of machine learning algorithms and data models is the best way to identify when and where issues exist.
The potential behind machine learning is limitless. As with any transformative new technology, the barrier to entry is high — but it’s not insurmountable. With the right tools, technologies, plans, and partnerships, machine learning is accessible to anyone. And once it is in place, leaders are equipped to multiply their business intelligence exponentially.