The Dark Matter of AI: Common Sense Is Not So Common

In the present era of Artificial Intelligence, Deep Learning, advanced quantum computing, we humans are literally surrounded by machines, everywhere, everyday.

Many critics point to Artificial Intelligence as the main threat to humankind; while on the other hand, the supporters of AI claim that humans can never be replaced by machines, and would only ever compliment our abilities.

Over the past decade, Artificial Intelligence has undoubtedly emerged as one of the technological successes and with the amount of research and investment going into this domain, it is nowhere near an end.

AI has impacted our lives greatly, with so many services and products relying on it that it is irrevocably connected with our everyday world. Whether it be our smart home devices or a simple Google search, the impact of AI is everywhere. But there are still areas where AI lacks and causes problems — I would say frustration, to the end-users, and these areas pose a great challenge for researchers trying to improve AI.

Machines are dumb boxes — they can only perform the tasks they have been trained for. It is our dynamic thinking ability and common sense that makes us much superior to the machines.

No matter how much we train our models, how much test cases we train our machines for, there is still a room of uncertainty that could be in reality very simple to solve but AI would fail because it lacks the trait of Common Sense.

Many renowned researchers have been trying, investing an admirable amount of time and resources to solve the problem of lack of common sense in artificial intelligence technologies.

One such person was the renowned philanthropist, co-founder of Microsoft, Mr. Paul Allen. Before passing away in 2018, Allen had invested heavily to help incorporate common sense in AI technologies.

Allen founded the “Allen Institute of Artificial Intelligence” in 2014 whose main focus is to research and engineer artificial intelligence. Allen launched a project named “Alexandria” — a research program to help solve the problem of common sense in AI.

We would be focusing on Project Alexandria a bit later in this blog. For now, let’s take a look at the different techniques researchers have adopted to provide common sense to AI technologies.

This technique is now commonly referred to as the “ Good Old Fashioned Artificial Intelligence”.

Symboling reasoning basically refers to mathematical logic, providing explicit embedding of human traits and knowledge into the machines. This initiative came into existence in the 19670s-1980s; however, was not very successful in providing common sense to machines, because there are millions and millions of rules that need to be programmed explicitly to the machines; which is not possible when dealing with real-world scenarios, and creates fuzziness, which in turn makes the process complicated.

Semantic networks basically refer to representing data and the relationship among data in the form of graphs, nodes, and links. This network was able to solve the fuzziness problem faced by symbolic reasoning, but the other problem faced by this technique was that the relationship among data is dependent on the creator and would vary from creator to creator and hence would mean different meanings to different people — put simply: semantic networks are not intelligent.