As artificial intelligence beats humans at more and more games, some futurists predict AI will soon master poker too. However, poker involves subtleties that highlight the fundamental constraints of even the most advanced AI systems. There are aspects of reasoning and interpretation vital for poker which AI cannot replicate.
As artificial intelligence beats humans at more and more games, some futurists predict AI will soon master poker too. However, poker involves subtleties that highlight the fundamental constraints of even the most advanced AI systems. There are aspects of reasoning and interpretation vital for poker which AI cannot replicate. To understand why, we need to first understand three important theories that show some of the perpetual gaps between AI capabilities and human understanding:
Over a century ago, American logician Charles Peirce developed a model explaining how people interpret signs and symbols. To simply put, as an economist George Gilder said "There is no necessary connection between symbols and objects. You need to interpret them. Reality is triadic." What it means is that the world is formed by 3 elements: object, sign, and interpretant.
Unlike a game like poker, Go only involves objects and symbols - the game pieces and positions on the board. There is no interpretation required to play Go optimally. An AI system can calculate strong moves simply by analyzing all possible board states. This shows that in closed domains defined completely by symbols and rules, AI can exceed human capability through computational analysis alone.
However, most real world tasks require some interpretation of how symbols relate to concrete objects. For example, Google Maps represents locations and routes with symbols overlaid on a digital map. For this to be useful, the map must accurately correspond to the actual geographic terrain. An interpreter is needed to validate that the symbolic map matches the real world.
Humans serve as this interpreter - we confirm that Google Maps provides a faithful interpretation of our surroundings. We do this through years of navigating the world, allowing us to judge if the digital map represents reality well. No such interpreter exists within the AI system itself. Without human oversight tying the map to real explored environmental knowledge, Google Maps would not be a trustworthy interpreter between symbols and reality.
AI can dominate in restricted formal systems like game rules that require no embodied understanding between symbols and objects. But ensuring symbols meaningfully represent open-ended real world phenomena requires an interpreter with grounded world experience - something AI lacks. This makes robust interpretation and reasoning impossible for AI in unconstrained domains like the creative ambiguities of poker.
In 1931, mathematician Kurt Gödel proved inherent limitations of all mathematical and logical systems. He showed it is impossible to establish all truths within a single axiomatic system. There will always be some propositions that are evidently true but unprovable based on the system’s rules.
So even in rigorous domains like math and logic, truth exceeds any finite set of formal proofs. Gödel shocked academics by showing that unambiguous foundational facts lie beyond mathematical reasoning alone. Analogously for AI, no amount of programming rules can lead to judging all aspects of a messy, ambiguous phenomenon like human behavior and language.
So AI will always have an incomplete grasp of systems including broad factors like concepts in our shared world. Gödel proved definitive gaps persist between formal symbolic analysis and complete conceptual understanding.
Alan Turing hypothesized an “oracle” computer which can bridge some of these gaps. It works by querying an imaginary all-knowing external entity to receive truths it cannot derive alone from its calculations.
Turing suggested even computing machines need intuitive guidance from an outside source at times, much like human wisdom, to make leap-of-faith judgments it cannot logically justify itself. Basically Turing believed that Oracle cannot be a machine.
Together, these three theories indicate intrinsic constraints of artificial intelligence. Peirce showed interpretations require a context AI misses; Gödel proved there are inevitable analysis blindspots even in pure logic; and Turing suggested computers would even need an external guidance to handle open-ended inference fully.
Poker is the perfect storm proving these enduring gaps between AI and general human cognition. Unlike chess with clear rules governing every possibility, poker is full of unknowns. What cards could they be holding? What style are they playing? Is this a bluff or value bet? There are no definitive rules dictating opponents’ behavior or reasoning to reveal certainty.
So poker mastery requires deeply reading people amidst incomplete information. It means interpreting the limited signs available to guess at unseen truths while avoiding misjudgment. This ability integrates mathematical odds calculation with behavioral observations, psychological instincts, and grasped meanings that feel truer than any probability figure alone suggests.
Yet as Peirce demonstrated, capturing interpretive meaning beyond naked syntax is exactly the skill AI fails at. And Gödel proved truth can surpass even a perfect statistical model. Turing then showed machines might need to defer to a type of wisdom beyond calculable knowledge. That begins to approach what humans tap into via game theory, tells analysis, table image study, and more over years immersed in the felt.
The key signs we interpret in poker - a staredown, bet sizing, reaction, banter - have no absolute significance. Their implications rely on situational context, memory of past hands, and delicate conception of fellow players' thinking. We integrate tacit senses to just feel when odds don't tell the whole story or see across possibilities through experience-crafted intuition.
An AI system may crunch ranges and probabilities faster than we can consciously process all that data. But its decision engine does not have a lived sense of the symbols it deals with. So it misses the irreducibly rich inference that makes poker cognition creative and unpredictable rather than computational. The representations form only a facet of interpretation according to Peirce.
Unlike the defined possibility space of chess, poker has unknown unknowns that Gödel's theorems suggest formal systems cannot fully cover either. And an AI system has no oracle judgment to better read those gaps in anticipation or do so over wider situations the longer it exists. Lacking access to embeddings of semantic meaning about fellow minds amidst uncertainty means poker AI remains crippled in key moments requiring a feel for the game that exceeds formulas.
In summary, the very conditions that make poker fascinating and complex for humans are exactly what stymie AI mastery - imperfect information, psychological deception, gaps of meaning. Integrating clues into an insight across possibilities needs something more than lightning fast math ability. Mastering such an interpretationally-rich game requires connecting symbols to a grounded concept of human behavior, having an oracle to judge truth where certainty calculably falters, and semiotic sense of meanings beyond the numbers. These perpetual constraints keep the highest levels of poker mastery forever an enticingly profound game of wit, intuition and spirit rather than mere computational proficiency.
