Pierre Kauffmann, Senior Enterprise Architect, IBM speaking at Innovation Leaders 2018 event on Machine Learning and Artificial Intelligence.

When it comes to Machine Learning (ML), our brain produces two opposite reactions: one is the fear of an Artificial Intelligence (AI) overlord that will enslave us, and another is the excitement around the excellent capabilities that AI can bring to humankind. In other words: horror and fantasy, numerous bestsellers themes for Hollywood movies or the press. The basic concept of AI and inherently ML is to have one or several Machines crunching Data. Nothing changed since the beginning of the IT. Therefore, Data is the key.

In this talk, we take a look at the Data, the business data for the enterprise, not the social one. The business data is very different by nature and available in a very different form; consequently, companies data remains siloed. Recently a new tendency emerges: to break these silos and create so-called Data Lakes or more ambitious Data Ocean. Unfortunately, because of the limited governance, these efforts create instead Data Swamp resulting in hardly usable data. This situation is due to several factors: Data is created at speed in huge volumes, it’s rarely verified, and it comes with uncertainty meaning Data is doubtful. Moreover, most of the Data (over 80%) is unstructured, therefore meaningless to non-AI systems.

I explain then why even traditional AI systems do not bring much value if not properly fed. Mainly they can categorize. As an example, an AI system can well distinguish a human hand from a watch or can also easily classify maps. However, the challenge is now to go further and extract meaning out of this data. For example, with a 12th-century world map, the point is no longer to classify it as a map but to understand where Africa or Asia are on this map or even deeper where Italy or UK is. I make the point that it is not because you have data that you know what data means, and this is crucial for any AI system.

Before starting to train your Machine Learning algorithm, you need to spend almost 80% of your project time on preparing the Data. During this phase, you need to identify the Data, connect to its sources, collect it and understand it before you curate and enrich it. Without these costly time-consuming tasks, you will have to deal with considerable noise in your Data, making Machine Learning less helpful for your organization, see the giant Panda example.

The best analogy is to consider any AI or ML system to be like a new employee, you need to teach him or her your organization jargon, your rules, the action to take, the solution to infer and so on. Then, this new employee will make mistakes, and a supervisor needs to correct the outcome and tell the employee what is wrong and what is right in your organizational context. You will never let a new employee trained on a specific task take a business-critical decision, thus don’t let the machine decide! Instead, let the machine advise you and “Augment” you rather than replace you. Therefore, at the other end of the process, assuming that a well-trained AI system is in place, you still need to retrain your AI / ML system over and over like any new employee and justify to its peer humans why it brings value to the organization.

My final word is about being sure that when creating an Augmented Intelligent system rather than an artificial one, you seek expertise among your ecosystem following a simple motto: “No one is immune to a good idea.”

Do you want to learn more about the subject of Machine Learning? Check out also these articles:

• Machine Learning – Introduction for decision makers
• Initiating a machine learning project: the skills your company needs
• The steps to a successful machine learning project
• Chris Tucci’s talk: Introduction to Machine Learning and Artificial Intelligence
• Jérôme Kehrli’s talk: Machine learning – from skepticism to application in fraud prevention
• AI is on the roadmap, where do I start?