(This is part 1 in a series on reasoning in artificial intelligence)

A while ago, I wrote an article on the six easy and not-so-easy pieces in AI. Reasoning was the first item on my list of not-so-easy pieces. In this series of articles, I take a closer look at reasoning — what it is, why it is important, and how it differs from the machine learning that is currently in vogue. Along the way, I trace the history of computational reasoning and highlight a few important ideas and systems that are still relevant today.

Starting in the 1960s, AI research has diverged into two camps — classical approaches like rule-based systems, and machine learning. The early advantage went to the classical techniques. But, especially, in the last couple of decades, the trend has seen a near complete reversal, with machine learning being the primary source of major advances in AI. ML has been so successful that it is in the mainstream with applications in vision, audio, and language. But, more complex tasks like Reasoning are still very much outside the comfort zone of what is possible today. Classical approaches, on the other hand, have focused nearly exclusively on higher-order problems like reasoning. I believe we are at an inflection point now — one where classical and machine learning approaches have developed enough that bringing them together can push AI to the next level.

Artificial Intelligence can do some amazing things right now. It can answer our questions, drive our cars, make world leaders sing, create movies from a single picture, and even create art. All of these abilities are examples of prediction — taking an image, audio, or text as input and returning a category, text, or control commands as output. These types of predictions are not trivial and can be the result of arbitrarily complicated pipelines and use combinations of methods from control systems, traditional machine learning, and deep learning. But in all cases, the steps in these pipelines are fixed (though they may be skipped) and the resulting model (or pipeline) is targeted at a very narrow problem (question answering, image classification, controlling a vehicle). You can’t ask a question answering model to control a car, or a game playing model to detect tumors in MRIs. Every AI today is purpose-built and heavily trained to achieve that one (narrow) function.

Now compare that to how the human brain works — we can drive cars, make coffee, answer questions, play video games, and most importantly, learn to do very different and new things. We can even learn to do old tasks differently. And we learn to do these tasks from very few examples while continuing to get better as we keep doing them.

What about AlphaGo (Alphastar)?

AlphaGo is a tremendous achievement in our understanding of how to construct machine learning models that can learn complex tasks. AlphaGo uses one of the most successful discoveries in the past two decades — deep neural networks — and it uses these networks effectively to learn how to solve Go. But, someone sat down and crafted the networks and reinforcement learning strategy that trained AlphaGo to learn how to play Go. And after it became the best at Go, AlphaGo had to be completely redone to create AlphaStar, the AI that plays Starcraft II. (Link to a simple explanation of how AlphaGo works)

So how is reasoning different?

At its simplest, reasoning can be thought of as at least one level above prediction in complexity — if prediction is a matter of training a model given the right architecture and pipeline, reasoning is about figuring out the architecture and pipeline itself. Put another way, if the solution to a problem is a sequence of prediction (and other engineering) steps, then reasoning is the ability to construct these sequences of steps. Reasoning can be applied to many different types of tasks. When you reason about how to do something, it’s called Problem Solving. When you reason about why something happened, it’s called Causal Reasoning.

Why is reasoning a more difficult problem than prediction?

Intuitively, it makes sense that constructing a sequence of steps on top of training this sequence is more difficult than just the training itself. But to understand how much more difficult reasoning is, we have to use an analogy… Consider the problem of making coffee. Given a coffee maker, milk, coffee beans, a grinder, a frother and a set of instructions on what to do, making a latte is just a question of finding the right amounts of each ingredient to use (the amount of coffee, the burr on the grinder, how much milk to use, how long to froth, the ratio of milk to froth and so on). This step requires some trial and error, but after a few tries you get it right. Prediction is similar. You are given a bunch of resources and instructions on how to use them and in what order, and prediction is the process of finding the right amounts of each ingredient to use.

#reasoning #artificial-intelligence #machine-learning #deep learning