Oh, the Places You’ll Go: Top AI Predictions for 2020

AI-based technologies reached a new level of adoption in 2019 as businesses learned more about what exactly AI could do for them. 2020 promises to be even more exciting, with AI systems continuing to mature and companies extending usage and applications to address highly specialized needs. In the year ahead, organizations will be empowered to allocate resources even more wisely while achieving greater efficiency.

Here are my top predictions for AI in 2020:

AI Adopted More as an Assistant Than a Replacement

There has been some cross-industry concern that as AI continues to improve, its resulting applications will take over human jobs and displace workers. Certainly, AI is being leveraged in new and interesting ways, but rather than replace the human workforce with machines, AI-based technologies instead will become humans’ assistant.

AI and machine learning can analyze thousands of data points in seconds to yield insights that humans never could achieve alone. These insights will be used to make human decision-making easier and alleviate workers’ most mundane, time-consuming tasks so that they can concentrate on higher-order problems that don’t fit neatly into algorithms. Look for AI-based technologies to be applied strategically in the coming year to help employees become more efficient and valuable in their respective roles.

Transfer Learning Becomes More Prevalent

Transfer learning, in which machine learning algorithms improve based on exposure to other algorithms, will become a more widely used technique in 2020. To date, it has been leveraged primarily with image processing, but we will see transfer learning applied to areas like text mining continue to improve.

The benefit of transfer learning is that a wider range of industries will be able to utilize AI to create highly specific applications based on small data. As less large data is required, organizations can create state-of-the-art solutions that are faster, more accurate, and better tailored to their specific needs.

The Cloud of the Black Box Continues to Lift

For a long time, AI has suffered from a lack of transparency. With machines developing more self-learning capabilities, developers might not know exactly why a machine learning system arrived at certain conclusions. When processes are hidden, behaviors can give pause to users who wonder if they should trust data generated by such a system. To combat this problem, more interpretable models are coming to the forefront.

In 2020, the differences between data explainability, traceability and determinism will become realized in AI. What is needed at which circumstances will also be clarified. As computing elements make complex predictions more understandable, solutions can be created that help explain those predictions. By removing the mystery of the black box, organizations can refine or expand queries to deliver more valuable information.

Demand Will Rise for AI as a Service

Traditionally, machine learning models have not been straightforward to deploy for data scientists and engineers. This will change in the coming year as AI is delivered more like a service. AI models will be executed in cheaper, easier ways in the cloud.

This is a significant development on multiple fronts. By shifting to serverless deployment in the cloud, a machine learning model does not consume the same amount of computing resources as on a server. This results in a much different level of efficiency. This in and of itself will make AI as a service more popular. Moving AI to the cloud also positively influences the delivery model. Instead of coming in the form of a very heavy solution, an API can be created and shared.

These are just some ideas of where AI could go in the near future. AI and machine learning are advancing at a rapid pace, and companies are both eager and nervous to pull the trigger on new solutions. But the current momentum behind AI will continue to drive innovation, and organizations will evolve as they reap the benefits of machine learning systems.

As first published in Data Science Central.