Two weeks ago, the United Arab Emirates hosted Ai Everything, its first major AI conference and one of the largest AI applications conferences in the world. The event was an impressive testament to the breadth of industries in which companies are now using machine learning. It also served as an important reminder of how the business world can obfuscate and oversell the technology’s abilities.
In response, I’d like to briefly outline the five questions I typically use to assess the quality and validity of a company’s technology:
1. What is the problem it’s trying to solve?
I always start with the problem statement. What does the company say it’s trying to do, and is it worthy of machine learning? Perhaps we’re talking to Affectiva, which is building emotion recognition technology to accurately track and analyze people’s moods. Conceptually, this is a pattern recognition problem and thus would be one that machine learning could tackle (see: What is machine learning?). It would also be very challenging to approach through another means because it is too complex to program into a set of rules.
2. How is the company approaching that problem with machine learning?
Now that we have a conceptual understanding of the problem, we want to know how the company is going to tackle it. An emotion recognition company could take many approaches to building its product. It could train a computer vision system to pattern-match on people’s facial expressions or train an audio system to pattern-match on people’s tone of voice. Here, we want to figure out how the company has reframed its problem statement into a machine-learning problem, and determine what data it would need to input into its algorithms.
