It has become very popular to associate a product with AI. Every day, you can stumble upon a new startup using “.ai” in their URL or in their marketing communication. This hype wave helps marketers a lot! Why? Because it sells and it helps to attract potential investors. However, are they really using AI… ? or just some advanced analytics?
Based on my experience, many AI companies are actually using basic data analysis, logic, Robotic Process Automation based bots or “pseudo-AI”. Through a specific communication, all these technologies can appear as AI if you only know a little bit about them.
In this article, I assume that AI means weak AI and that Machine Learning (ML) is an advanced solution! I know too well that ML algorithms are generally just memorizing and running statistical models. Despite this, these ML solutions are often considered as the best of artificial intelligence.
Back to our topic, organizations want to become more data-driven. As a consequence, many SaaS companies or AI development teams are proposing “augmented analytics” or other solutions… which most of the time can’t be considered as AI! For me, AI systems can get smarter with the more data they analyze and become increasingly capable with experience. It is not just a compilation of algorithms or just a bot created to automate a task by replicating a human action.
In my last project, I had to make sure that an image recognition model was actually able to learn and find new ways to help the end-user.
For instance, I see many decision-making tools using the term “augmented analytics”, even though all they really do is use data analytics and visualization to highlight data in a more clever way. The tool doesn’t get more intelligent over time, it is not learning and adapting to data.
As Vance Reavie said, “Machine learning is a continuation of the concepts around predictive analytics, with one huge difference: The AI system is able to make assumptions, test and learn autonomously. “ (source)
Machine learning is also about predictions and has this capacity to recalibrate models in real-time automatically. This feature is very important for most organizations. Meanwhile, predictive analytics must be refreshed with “change” data.