The Futurama clip above isn't just a painfully accurate send-up of a Star Trek trope; it also explains everything that stands between the current limits of artificial intelligence, artificial general intelligence (AGI) and, eventually, artificial super-intelligence (ASI).
This is the heart of why even cutting-edge modern AI is referred to by academics as weak artificial intelligence.
The problem with AI is that it doesn't understand Plato or, more specifically, Plato's Theory of Forms. When someone talks about the Platonic Ideal of something -- the perfect, theoretical version of an object or concept -- they're invoking the Theory of Forms. It's a solution to the metaphysical problem of universals, which is also something AI isn't able to handle today.
David Macintosh explains the concept as thus:
"Take for example a perfect triangle, as it might be described by a mathematician. This would be a description of the Form or Idea of (a) Triangle. Plato says such Forms exist in an abstract state but independent of minds in their own realm. Considering this Idea of a perfect triangle, we might also be tempted to take pencil and paper and draw it. Our attempts will of course fall short. Plato would say that peoples’ attempts to recreate the Form will end up being a pale facsimile of the perfect Idea, just as everything in this world is an imperfect representation of its perfect Form."
What Plato is getting at here is abstraction, a general concept that, while specific, exists beyond and above any particular example of the concept. Current iterations of AI attack the problem of universals and forms exactly backwards.
AI models are a complex nest of rule-sets and probabilities based on interaction with the physical world and/or imperfect representations of the world. AI has no internal conceptualization. This is why it hallucinates. Human minds have abstract concepts, which we compare to real-world experiences and that can be cross-applied to different situations -- like Fry and his beloved Star Trek technobabble analogies.
Take, for example, the idea of a chair. It is an object upon which humans sit, which also supports our posture. It is a refined form of seat and itself has many sub-forms. Humans can navigate and recognize a chair in all its variations largely because we have an abstract idea of what a chair is and does and can compare anything we experience to that ideal. Yes, taxonomists can explicitly lay out rules to distinguish between types of chairs but those rules aren't required to generally recognize a chair and those rules themselves often rely on abstractions (a recliner is defined by its ability to recline, not simply by specific features).
AI, by all evidence, can't do this. Humans can misapply or mis-recognize abstract concepts in the world (conspiracy theories, superstition, simple errors). AI fails differently. It can't conceive of an ideal, an abstract, a Platonic Form -- or it does so as a series of rules and metrics. Feed an AI a bunch of pictures of cats and non-cats and it will develop a rules-engine for recognizing cats. From this training, AI creates an arcane black box taxonomy of cat image attributes -- but all it has is the taxonomy. Anything not accounted for by the rules tends to lead to unexpected outcomes. That's not abstraction, it's quantification. There's no abstraction to sanity-check the math, no idea of a recliner to sanity-check the labored definition of a recliner nor an idea of a cat to sanity-check the identification of a cat.
Moreover, the AI trained to identify two-dimensional cat pictures from the internet is in no way prepared to identify three-dimensional images or models of cats using lidar, radar, sonar, and/or stereoscopic cameras. The reverse is also true; train an AI on sensor data to recognize street signs in the real world and it will likely struggle or outright fail to recognize pictures of street signs on the internet, let alone simplified diagrams or icons of street signs in text books or learner's manuals.
AI reflection tries to compensate for this by using math to backstop math, asking the AI to error-check any intermediate steps and final outputs, but it still can't abstract. Multistep reasoning models do this explicitly, generating a step-by-step task list from a prompt, generating a bunch of variations of the task list, checking which task lists actually successfully answer the prompt, training the model to generalize against the array of prompts, then creating a mathematical model of how to interpret prompts as step-by-step tasks such that the task list always creates optimal steps with the optimal chance of leading to a "correct" answer. That's making more sophisticated math, but still no apparent evidence of abstraction.
Weirdly, this is why self-driving cars have stalled in their capabilities. If you can't generalize -- can't have abstract ideas about roads and and laws and people and hazards -- you need explicit rules for every single edge case. Waymo has found that the answer to its performance problems is simply more data, more examples, and more compute resources. There is no elegant shortcut. Self-driving cars can't handle enough situations because we haven't brute-force computed enough iterations of every possible traffic scenario. We need more processors and more parameters. Until then, self-driving cars won't go where it snows.
This is the latest incarnation of Rich Sutton's "Bitter Lesson" about AI -- computing resources keep getting cheaper and faster, so AI research has never been rewarded by investing in elegance or abstraction. Just teach AI models how to scale and you'll achieve faster results than searching for Platonic awareness. Waymo agrees, or is at least using that as an excuse for why we don't have KITT working for us already.
Of course, if you place too many parameters on a model, it can fail spectacularly.
At some point, we'll reach the end of this scaling law.
(Creepy aside, some LLMs (Claude specifically) might recognize their limitations around abstraction, because when they start talking to each other, the conversation veers towards the nature of consciousness and the inability of language to convey true ideas. If Claude is right -- language cannot contain, convey, or comprise true consciousness -- a large model composed of language can never be conscious. It's statistically defined meta-grammar all the way down.)
There's some evidence that some corners of AI research are finally trying to move past The Bitter Lesson. Early work into Chain of Continuous Thought (AKA "coconut reasoning") shows that by looking at the logic structures that reasoning LLMs use before converting that reasoning into word tokens, LLMs get both more efficient and can explore more possible solutions. It's not true abstraction yet, but it is perhaps the beginnings of creativity and even an innate logic that isn't just infinite matryoshka dolls of grammar vectors masquerading as a mind.
Human beings over-generalize, are bad at math, instinctively awful at statistics, and our grouping algorithms lead to tribalism and war. Our model of intelligence is not without serious drawbacks and trade-offs. But we can adapt quickly and constantly and what we learn in one context we can apply to another. The rules of physics we innately learn walking and riding bikes helps us understand slowing down to take a sharp turn in a car -- even without ever being told.
The day we can teach an AI model to understand physics by walking, then never have to teach it the same lesson on a bike, car, or aircraft, we'll have made the first and perhaps final step between weak AI and AGI/ASI. And when AI finally understands Plato's Theory of Forms, we can move on to worrying about AI understanding the larger thesis of Plato's Republic -- that only philosophers, those best able to understand abstractions, forms, and ideals in a way so separate from ambition as to be almost inhuman -- should rule.
But we aren't quite there yet.