{"id":22791,"date":"2021-05-07T07:35:00","date_gmt":"2021-05-07T07:35:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/learning-overrated-machine-learning-vs-knowledge-acquisition\/"},"modified":"2023-08-21T07:42:11","modified_gmt":"2023-08-21T07:42:11","slug":"learning-overrated-machine-learning-vs-knowledge-acquisition","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/learning-overrated-machine-learning-vs-knowledge-acquisition\/","title":{"rendered":"Learning Is Overrated: Machine Learning vs. Knowledge Acquisition"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Knowing-How vs. Knowing-That<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Philosophers have long recognized the difference between two types of knowledge: knowing-how<\/em> and knowing-that<\/em>, where (roughly and very informally) the former is typically associated with skills and abilities<\/strong>, and the latter is associated with propositions <\/strong>(truths\/established facts). In our everyday discourse we use the word \u2018know\u2019 for both types of knowledge, which creates some confusion. So, for example, we say things like:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Learning\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

But there are several fundamental differences between K<\/em>1 and K<\/em>2, some of which are listed in the diagram below:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"The\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Below we discuss in some details these opposing properties as they relate to K<\/em>1 and K<\/em>2 given above.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Difference\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Finally, skills are not forgettable <\/strong>\u2014 we might have some biological and\/or brain damage that makes us \u2018lose\u2019 (not forget) some skill\/ability, but recovery from that damage immediately brings back that skill and no \u2018learning\u2019 (training) from scratch will be needed. Propositional facts might however be forgotten (I might forget what is the height of Mount Everest, or what is Bayes\u2019 formula, etc. but again, I do not acquire <\/strong>it back by learning, but by being told again (i.e., reminded).<\/p>\n\n

Below are some examples of both types of knowledge:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Types\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Clearly, then, learning applies to knowing-how <\/em>and does not even apply to knowing-that<\/em>. More crucially, most of the knowledge that matters to how the universe works is knowledge that is not learned (from experience, trial and error, from observation\/data, etc.) because we are not allowed to learn it differently \u2014 it is in fact knowledge that is either discovered, or deduced, or knowledge that is acquired by instruction (i.e., by being told<\/em><\/strong> by those that have acquired that knowledge). In fact, this why it sounds awkward to say \u2018I don\u2019t know <\/strong>[some-fact]\u2019 while it sounds very natural when I state that \u2018I don\u2019t know <\/strong>[how-to]\u2019 and that is because I-do-not-know<\/strong>(established-fact<\/em>) is not sensible:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Learning\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Machine Learning or Knowledge Acquisition<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

I really don\u2019t know how do Machine Learning (ML) enthusiasts ignore the above facts about knowledge \u2014 I guess John McCarthy was right when he suggested that computer scientists should read and listen to what philosophers say. If most of the knowledge that really matters in building intelligent machines that can reason and make decisions in dynamic and uncertain environments is acquired and not learned<\/strong>, then it is clearly absurd to ignore \u2018knowledge acquisition\u2019 and suggest that ML suffices to get to AGI.<\/p>\n\n

Techniques for effective Knowledge Acquisition must be developed \u2014yes, it is a difficult problem, as the process of manually feeding handcrafted knowledge into a \u2018knowledge base\u2019 is not scalable. But problems are not solved by being ignored. It is perhaps one of the biggest challenges in AI<\/a>, and it is for this reason that I think this is where attention should be given.<\/p>\n\n

Let me end with a simple example that connects where machine learning might happen, where it stops and where knowledge acquisition might pick up and take over. Consider the image below<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Learning\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Assuming we have very robust (deep) neural networks<\/a> (DNNs) that can learn a concept like \u2018apple\u2019 \u2014 by observing similarities in their patterns. Suppose that process resulted in recognizing images that we labeled (in our semantic memory) as Apple<\/strong>, Banana<\/strong>, and Orange<\/strong>. Something like this must be happening in the early years of a child \u2014 images are recognized, by something like a DNN, and they are given a label by the parent that the child (perhaps) stores in semantic memory. But what about the next level? The level at which we group these objects into a higher category? Clearly, we categorize these concepts under the concept Fruit<\/strong> for some reason. Perhaps because we see (instances of) these objects in the same contexts: they are usually found together in a basket (in the kitchen or on the dining table); they seem to be put together on the same shelves in a supermarket, we see people eating them interchangeably, etc. In other words, it seems that they somehow belong to one category. This type of knowledge cannot be learned \u2014 the process I just described is a complex reasoning process that is not data-driven. Note: not surprisingly, we might make a mistake in recognizing an orange \u2014 since that skill is not binary, but once we know that it is an orange (or not) the fact that it is a fruit (or not) is now a binary decision. Amazing how systematic our reasoning is!<\/p>\n\n

Machine learning<\/a> is important at the data-level \u2014 where we use our sensory inputs to recognize patterns and cognize of first-level objects\u2014 but what we know is a lot more and most of what matters is knowledge that is NOT\u00a0learned\u00a0<\/strong>but is knowledge that is\u00a0acquired\u00a0<\/strong>either by discovery or deduction or by being told.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Knowledge\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Machine learning is important at the data-level \u2014 where we use our sensory inputs to recognize patterns and cognize of first-level objects\u2014 but what we know is a lot more and most of what matters is knowledge that is NOT learned but is knowledge that is acquired either by discovery or deduction or by being told.<\/p>\n","protected":false},"author":1127,"featured_media":19338,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97,574,562,92],"ppma_author":[3675],"class_list":["post-22791","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence","tag-knowledge","tag-learning","tag-machine-learning"],"authors":[{"term_id":3675,"user_id":1127,"is_guest":0,"slug":"walid-saba","display_name":"Walid Saba","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/Walid-Saba-150x150.jpeg","user_url":"https:\/\/ontologik.ai\/","last_name":"Saba","first_name":"Walid","job_title":"","description":"Walid Saba, PhD, is Co-Founder & NLU Scientist at ONTOLOGIK.AI, a developer of a natural language understanding engine that can read text and automatically construct\/feed a Knowledge Graph."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22791","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/1127"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=22791"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22791\/revisions"}],"predecessor-version":[{"id":30877,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22791\/revisions\/30877"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/19338"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=22791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=22791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=22791"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=22791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}