The term Enterprise AI (EAI) typically refers to AI that is used to run a business. It does not include AI that’s been incorporated into the products and services a business sells. There are many EAI implementation options, ranging from out-of-the-box tools to full custom models.
Business leaders need to understand the differences between these options before they can determine what level of data science expertise they require, what the resulting competitive advantage may be, and how an investment in EAI will impact the bottom line.
Out-of-box Tools: The easiest way for a business to leverage EAI is to implement fully functional, stand-alone tools and applications that are already embedded with AI. Language translation, speech recognition and map applications are examples of this type of AI technology. Some of these third-party tools are customizable in the same way legacy enterprise software systems can be customized, for example chat bots and virtual assistants. Integration support is typically provided or facilitated by the vendor. There is no shortage of these products, many of which tend to focus on particular industry segments, but they no longer provide real competitive advantage because they’re available to everyone.
Universal Models: Several new developments in Deep Learning – a family of machine learning (ML) algorithms – have led to two of the most exciting accomplishments in data science: 1) machines can now recognize people, places, things and activities in the world around them (computer vision) and 2) machines can communicate in natural language. While these advancements are significant, the current universal models use brute force to accomplish their results. They require huge amounts of training data, and can take weeks and months to train. The data sets used to create these models are considered “universal,” because regardless of what an end-user application is targeted to accomplish the data sets are always the same. For example, a ball is a ball, and “adios” always translates to “goodbye”.
All of the major cloud platform service providers offer inexpensive access to their computer vision and natural language models. A software developer can use a simple interface (API) without any knowledge of machine learning to retrieve results. An image file sent to the model will return the list of objects it contains, and a block of text will return a translation, sentiment, or some other requested aspect of the content.
Some applications require recognition of additional, different or more specific sub-types of objects or phrases. A technique called Transfer Learning has emerged to fill this gap. For example, a car manufacturer might need to differentiate between specific models of cars; a biologist might care about the small differences between related species of birds. With Transfer Learning, end-users are able to append their own unique layers of detail to the existing models. This transfer gives the model the ability to recognize additional objects or details without retraining the entire baseline model.
ML as a Service (MLaaS) Models: There is a lot of disagreement in the data science community about the value of shared machine learning models. At a recent VC event, a panel of AI experts was asked if the MLaaS approach was effective. Two of the panelists, both with many years of experience and graduate degrees in ML, were on opposite sides of the question.
