How automated machine learning is helping to bridge the AI skills gap Posted on : Oct 22 - 2018

In 2017, over half of senior artificial intelligence (AI) professionals stated that a lack of qualified personnel in their field is the single biggest barrier to AI implementation across businesses. As more and more companies choose to explore AI, deep learning and machine learning (ML), this knowledge gap begins to cause some serious problems. The solution may be in de-humanising ML functions by using automatic (also known as augmented or assisted) ML techniques.

The AI skills gap

AI experts are costly, with a reported average annual salary of $314,000. But before you can even worry about affording an AI expert, you have to find one. A recent study by Element AI concludes that there are only 22,000 qualified computer scientists, worldwide, who are capable of building AI systems. Of these, only 3,000 are actually seeking work, and there are an estimated 10,000 available positions currently for AI experts in the US alone.

Too few scientists have the knowledge or experience needed for work in the industry, but as the AI industry evolves so fast, it is hard for academic institutions to keep pace with the changing needs of corporations and to provide graduates with the needed skills.

As an increasing number of companies, sectors, and markets branch into AI-related technologies, the gap between required and available personnel widens, and the need for another solution becomes more urgent. Small, unqualified or overwhelmed teams of developers are more likely to make mistakes, to try and apply existing models inappropriately to new data, or to miss problems with the data. All of these issues can cause significant damage to the systems being developed.

What is AutoML?

There are several different names for automated ML, including AutoML, Augmented ML and Assisted ML. All describe an evolving machine learning tool that can help developers and businesses address complex scenarios without the use of full AI systems. AutoML revolves around two fundamental concepts: data acquisition and prediction.

Data acquisition is necessary for any machine learning or AI system, and the quality and quantity of data can determine the overall reliability, efficacy, and usability of any system, regardless of its purpose.

AutoML assumes that any person or company already has a large, reliable dataset. Users are able to input their dataset, identify labels, choose sections of code or applicable methodologies, click a button, and generate a trained, optimised model that is ready to predict. Thus, all of the steps between data acquisition and prediction and handled by AutoML.

This is a significant change from the traditional ML workflow and enables analysts to stay focused on the business problem rather than getting lost in the process. Many platforms are compatible with Android and iOS so that models can be smoothly and quickly integrated into mobile applications. View More