Combo Shots, AGI & The Diminishing Returns Of Intelligence
Intelligence is predictive power. Therefore, shouldn’t a radical increase in intelligence through AI technology also radically increase our ability to make predictions? Intuitively, it may seem obvious that the answer is yes, but the reality is more nuanced. I am fully onboard that AI will have a huge influence on our collective future, but the hype surrounding the development of Artificial General Intelligence (AGI), which is predicted by many to occur over the next few months or years*, often overstates the impact that we can expect from it. In this article, I explain why by using the game of pool in general, and combination (combo) shots in particular, as an allegory for how even super-intelligent AGI may not fare that much better than humans at making complex predictions.
Anyone who has played a little pool is familiar with the fact that combo shots are very difficult and their difficulty increases exponentially as the number of intermediary balls, and the distance between them, increases. In case you are not familiar with combo shots in pool/billiards, please see the short description at the end of the article.
The necessary angle with which the cue ball hits the first object (colored or striped) ball, in order to make a successful shot, can be estimated by sight or calculated in detail. In the platonically ideal world of mathematics, if the cue ball is struck at the correct angle (i.e., precisely 0° error), the shot will be successful no matter how complex it may be, and regardless of how many combinations are attempted. This ideal circumstance does not carry over into the real world because it assumes that the player has perfect aim, the cue is perfectly straight, the billiard table surface perfectly even and clean, and the balls perfectly spherical - or equivalently that the correct angle takes any variations perfectly into account, including factors such as angular spin, air resistance, ball and surface friction, and possibly even quantum level variations. Alas, in the real world, even the tiniest error, even if immeasurably small, is highly likely to cause the shot to fail if there are more than a minimal number of intermediate balls.
Indeed, as the number of intermediate balls in a combination shot increases, the final error in angle increases exponentially relative to the initial error.
The distance between the balls also multiply the increase in final error.
Complex problems with multiple interdependent variables and a large range of potential outcomes, like combo shots, can be exceedingly difficult to solve, regardless of how much predictive power (i.e. Intelligence) we have at our disposal. Undetectable variations in measurements and causal relationships can have a very material effect on outcomes. The difference between a pool hall amateur and a world champion will be very small when it comes to elaborate combo shots. The pro player might manage to combine with one or two additional intermediate object balls compared to the amateur, but can not realistically hope to achieve much more than that. Entropy is a powerful equalizing force with respect to predictive power.
So what does this have to do with AGI?
AGI, or artificial general intelligence, is generally defined as AI having the ability to understand, learn, and apply its intelligence to a wide variety of problems, much like human beings.
As large-language models exploded into the public consciousness over the past couple of years, their impressive new capabilities have fuelled speculation that humanity is on the verge of achieving AGI. This is considered to be very important because whenever AGI does emerge, it is likely to be followed by so-called super-intelligence shortly thereafter, which could represent an existential threat to humanity should it pursue goals misaligned with humanity’s interests. Some even go as far as suggesting that super-intelligent AI agents may regard humans in a similar way to how humans regard animals such as dogs or even ants - the idea being that they would be so vastly more intelligent that we could not begin to understand their behaviours and motivations, just as animals cannot hope to understand ours. This is a fallacy.
It makes sense to be cautious about AI development for many reasons, and AI/AGI will no doubt have an extreme impact, for good or bad, on the human condition. However, the doomer perspective on AI frequently falls prey to magical thinking by failing to recognize that increases in the quality of intelligence, will eventually have rapidly diminishing returns. The idea that superintelligent AI will have God-like powers is predicated on the idea that it will do vastly better than humans at making and capitalizing on hard-to-make predictions. Yet what we know about certain classes of complex problems, like making combo shots in pool, is that intelligence/competency only make a difference up to a certain point. Many of our current limitations with regards to predictive power are not related to our limited intelligence, but to the nature of chaos, that is, the ultra-high sensitivity to initial conditions. Thus, even for the most advanced super-intelligent agent, we may find that the effective advantage for making predictions in the context of real-world dynamical systems is surprisingly limited.
The real power of AI, and its impact on human life, is likely to manifest not through greatly better predictive power from super-intelligent AGI, but rather, through the massive scaling and acceleration of more moderate, that is human comparable, levels of intelligence / predictive power. Returning to our pool analogy, AI will allow us to pocket many more balls by taking many more simple shots.
Description of combination shots in pool/billiards: In the game of pool, a shot is taken by striking the cue ball with a cue stick. The proximate objective is to have the cue ball strike and pocket a targeted object (colored) ball. The key to pocketing an object ball (i.e. getting it to fall into a hole) is to have the cue ball strike it at the correct angle requiring predictive power (understanding of angles, spin, etc.) and dexterity. In some situations, due to the placement of the balls, it may be a good strategy to make a combination (a.k.a. combo) shot. A combo shot consists of having the cue ball first hit an intermediary object ball, which then strikes a target object ball, ideally pocketing it. In theory, a combo shot may have multiple intermediate object balls before striking and pocketing the target ball.