Artificial Intelligence (AI) has been receiving a lot of hype as a magical solution for many difficult problems. Some have started to worry that AI would not only take over their jobs but also take over control of work and perhaps personal life. In this talk, I attempt to demystify most of these concerns by simply describing in reality what has worked and what has not. It turns out, most of AI has failed (with some very notable exceptions) and this has led to two AI winters in the early 1980’s and in the Iate 1990’s.  We are on the verge of a third AI winter. I present this in the context of my own version of a “Brief History of AI”.  This will help us to crystallize what has actually worked and why. It turns out – Machine learning (ML) is behind most of what has worked.  This has major implications as most of ML algorithms succeeded not because of great algorithm design, but primarily because a lot more data became available. This means that Data is critical to making ML work, and hence to making most of AI work.  We thus go into what the issues are in making data, especially BigData – which allows for the utilization of the majority of data in any organization (unstructured data) to be utilized properly.  Despite all the hype, with the right Data in place, ML/AI are making their way into all kinds of business operations today as more companies explore how they can put their data to work. Among the most common reasons to apply ML to business processes is the ability of the technology to perform certain functions at scale, which would otherwise require significant amounts of time and resources. It’s also common to rely on ML for processing large volumes of data analysis beyond what’s possible for human capability. Data is the fuel that drives a machine learning solution and machine learning is the key to make most AI algorithms practical.

 

We will include several case studies on how success was achieved and we highlight the right approach for creating an environment that enables successful machine learning and data science, especially leveraging unstructured data as a resource.