Ever since my first blog entry in this recent series on artificial intelligence, I've been highlighting the lesser, calculational nature of machine intelligence and learning-- as well as the valuable role it nonetheless can play in driving more effective human understanding and decisions. I've been doing this by articulating mainly what machines do, as that is the primary interest of mine and most who would read a developerWorks blog. Still, our interests will be served by taking an entry to discuss human learning as a counterpoint or contrast.
The multiple linear regression example in my last post is a good example to start with because it highlights the difference between accuracy versus understanding. If there is a linear relationship among the data, then an MLR can have a very high predictive accuracy, but it has no explanatory power whatsoever. The MLR model does not have, nor does it convey, any understanding as to why the relationship exists.
Let's see how this predictive accuracy rates in terms of human intelligence and learning. In this case, we can benefit from an instance of that delightful human propensity to apply ideas to themselves. Specifically, we humans have applied our learning abilities to the phenomenon of our learning abilities, with many useful results including Bloom's Taxonomy.
According to Bloom's taxonomy, the very lowest level of cognitive learning is the knowledge level, or the ability to remember and recall what is learned. When you think about it, you realize that an MLR model, like many predictive analytics, is really a storage mechanism for something that has been machine learned from data. In MLR, we store the constants of a linear formula as the representation of what has been learned from linearly related data.
The next higher level of Bloom's taxonomy is comprehension, which is where understanding and true explanatory power begin to surface. But human learning is so much more sophisticated than the knowledge level of machine learning that there are a number of levels above comprehension. There's the application level, in which we can use our knowledge to solve new problems, including being able to explain why the new solution works. The analysis level drills deeper into our ability to make inferences and generalizations. The synthesis level begins to get at our ability to be creative with what we've learned and come up with new ideas and solutions. Finally, the evaluation level gets at our ability to be subjective and judge quality and creativeness of ideas and solutions. We are beginning to see some faint glimmers of some elements of some of these levels in cognitive computing efforts like IBM Watson, but it is early days indeed.
While we're on the subject of human learning and Bloom's Taxonomy, it makes sense to digress for a bit and mention the IBM Social Learning product. This is a SaaS educational platform intended to help enterprises achieve a Smarter Workforce. A few reasons for the digression are
- this is a product I currently work on,
- both it and the whole Smarter Workforce initiative are being featured at the IBM Connect 2014 conference being held right now, and
learning is a key ingredient of how a human workforce becomes smarter.
The IBM Social Learning product has a very nice feature that enables educational administrators to implement Bloom's Taxonomy in their learning materials. A component of the product is the Kenexa LCMS, or learning content management system, which includes various subcomponents like a course designer and a metadata dictionary. The educational administrator can add any metadata tag, such as "Learning Goal", and any tag values, such as "Basic Knowledge", "Comprehension", "Application", etc. Once this is done, the educational administrator can use the metadata tag values to classify any learning item in the LCMS accord to Learning Goal. Once these classified learning materials are published, learners can use the "Learning Goal" as a new faceted search criterion in the platform's learning library. A learner would be able to isolate and focus on "knowledge" level learning in a subject area before proceeding to comprehension and then application, for example. This will enable learners to effectively use the natural way in which their learning blooms, i.e. Bloom's Taxonomy.
Finally, there is an aspect of human learning that goes beyond Bloom's taxonomy, and it's an area that is highlighted by the IBM Social Learning product. There is a very important word in the product title: Social. This is crucial because it underscores the central role of communication and collaboration in the human learning process. We are an order of magnitude more effective at learning based on our interconnectedness to others who think and learn, rather having access to just data. This is pertinent to the advancement of artificial intelligence because "social" goes quite beyond the computing architecture underlying a lot of today's machine learning efforts.