In 1950, a man named Alan Turing published a paper that basically predicted everything we are arguing about today on Twitter and LinkedIn. It wasn't called "How to build a chatbot" or "The End of Humanity." He titled it Computing Machinery and Intelligence.
Honestly, most people talking about AI right now haven't actually read it. They should.
Turing didn't start with a bunch of complex equations or heavy-handed philosophical jargon about the soul. He started with a game. He called it the "Imitation Game." You might know it as the Turing Test. But here’s the thing: Turing wasn't actually trying to prove that machines could "think" in the way humans do. He thought the question "Can machines think?" was too meaningless to even discuss. Instead, he wanted to know if a computer could successfully mimic a human well enough to fool an interrogator.
It was a brilliant pivot. By shifting the goalposts from internal consciousness to external behavior, he gave us the foundation for every LLM (Large Language Model) we use today. When you’re chatting with GPT-4 or Claude, you aren’t checking for a soul. You’re playing the Imitation Game.
The Mathematical Reality vs. The Sci-Fi Dream
We often get bogged down in this idea that AI is "evolving" toward biological consciousness. Turing was much more grounded. He looked at the "Discrete State Machine."
Basically, these are machines that move in sudden jumps or clicks from one quite definite state to another. Think of a digital clock. It’s either 12:01 or 12:02. There is no in-between. Humans, on the other hand, are continuous. Our brains are messy, chemical, and analog. Turing knew this. In Computing Machinery and Intelligence, he spent a significant amount of time addressing the "Mathematical Objection."
This objection usually points to Gödel’s Incompleteness Theorem. The argument goes that there are certain things a logical system (like a computer) simply cannot prove, even if they are true. Therefore, the human mind must be superior because we can "see" these truths. Turing’s response was kind of hilarious and blunt. He basically said: "Sure, machines have limitations, but so do humans." We make mistakes. We get tired. We have blind spots. Just because a machine isn't perfect doesn't mean it isn't "intelligent" in a practical sense.
Lady Lovelace and the "It Can Only Do What We Tell It" Myth
If you've ever heard someone say, "Computers only do what they’re programmed to do," you’re quoting Ada Lovelace. She said it back in the 1840s.
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Turing spent a whole section of his 1950 paper dismantling this. He called it "Lady Lovelace’s Objection." He argued that machines could, in fact, surprise us. Anyone who has ever coded a complex system or watched an AI find a "shortcut" in a physics simulation knows this feeling. It’s the "surprising" behavior that emerges when simple rules interact in complex ways.
Why We Still Get Turing Wrong
Most people think the Turing Test is a high bar. It isn't. It’s actually a very narrow, linguistic bar.
We’ve already reached a point where AI can fool people in short bursts. But Turing’s vision in Computing Machinery and Intelligence was broader than just a chat interface. He was fascinated by the idea of "learning machines." He suggested that instead of trying to program an adult mind, we should program a child’s mind and then teach it.
Think about that for a second.
In 1950, without a single modern GPU or a kilobyte of cloud storage, he predicted the exact path of Machine Learning. We don't hard-code "how to be a lawyer" into an AI. We give it the "child brain" (the architecture) and then we give it "schooling" (the training data).
But here’s where the nuance kicks in. Turing wasn't a total optimist. He acknowledged the "Argument from Consciousness" brought up by Professor Jefferson in 1949. Jefferson argued that until a machine can write a sonnet or compose a concerto because of emotions felt, it isn't really thinking.
Turing’s comeback? He called it a "solipsist" view. If we follow that logic, the only way to know if you are thinking is to be you. Since we can't be each other, we rely on communication. If a machine communicates as well as a human, why should we deny it the label of intelligence? It’s a pragmatic, slightly cynical, and deeply British way of looking at the world.
The Nine Objections: Turing's Greatest Hits
In the paper, Turing literally lists the reasons people would hate his ideas and then knocks them down one by one. It’s a masterclass in anticipating your critics.
- The Theological Objection: "Thinking is a function of man's immortal soul." Turing basically says he doesn't want to get into a religious fight, but if God can give a soul to a human, why couldn't He give one to a machine?
- The 'Heads in the Sand' Objection: "The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so." This is basically every AI doomer today. Turing just points out that this isn't a logical argument; it's just fear.
- The Argument from Informality of Behavior: This is the idea that you can't have a set of rules for every possible life situation. If a man had to follow a rulebook for everything he did, he’d be a robot. Turing argues that just because we haven't found the rules for human behavior doesn't mean they don't exist.
He even addresses "Extra-Sensory Perception" (ESP). Interestingly, at the time, Turing thought the evidence for telepathy was actually quite strong. It’s one of the few parts of the paper that hasn't aged well, proving that even the smartest man in the room can be a product of his era's weirdness.
Computing Machinery and Intelligence in the Age of LLMs
If you look at how OpenAI or Google builds models today, they are essentially fulfilling the "Learning Machine" prophecy from Computing Machinery and Intelligence.
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We’ve moved past the "Expert Systems" of the 80s and 90s. Those were "Adult Minds" programmed with rigid rules. They failed. Today’s AI is built on the "Child Brain" model. We give the model a massive amount of data and let it figure out the patterns of language, logic, and even coding.
However, we are hitting a wall that Turing didn't fully explore: the energy problem. Turing’s paper focuses on the logic of intelligence. He didn't have to worry about the fact that training a high-end model today requires enough electricity to power a small city. We are finding that "intelligence" as a digital process is vastly more resource-intensive than the biological version. Our brains run on about 20 watts. A GPU cluster? Millions.
What Most People Get Wrong About the "Test"
The Turing Test is often criticized today as being "too easy" or "just about trickery."
That misses the point. Turing wasn't saying "If it passes this test, it is a person." He was saying "If it passes this test, the question of whether it is 'thinking' is no longer useful for us to argue about." It was a call for intellectual honesty.
We see this play out every time a new model drops. People spend weeks trying to "jailbreak" it or find the one logical riddle it can't solve. When they find a flaw, they shout, "See! It's not real intelligence!" Turing would have laughed at this. He’d point out that humans fail logic puzzles all the time. If your standard for intelligence is "perfection," then no human is intelligent.
The Future Turing Predicted
The most underrated part of Computing Machinery and Intelligence is Turing’s prediction about the year 2000. He thought that by then, a machine would be able to play the imitation game so well that an average interrogator would have less than a 70% chance of making the right identification after five minutes of questioning.
He was off by a few decades, but not by much.
We are now at the point where "Deepfakes" and high-fidelity text generation make the 1950s version of the Turing Test look like child's play. We are now entering the "Post-Turing" era. The question isn't "Can they fool us?" We know they can. The question now is "What do we do now that they can?"
Actionable Insights for the AI-Curious
If you want to actually understand where we are going, stop reading hype cycles and go back to the source. Here is how you can apply Turing’s 1950 logic to today’s world:
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- Focus on Output, Not "Essence": When evaluating an AI tool for your business or personal life, don't worry about if it "understands." Ask if the output is indistinguishable from a high-quality human result. That's the only metric that matters for productivity.
- Embrace the "Learning Machine" Philosophy: If you're trying to implement AI, don't try to give it a thousand rules. Give it high-quality data (the "schooling" Turing talked about) and let the patterns emerge.
- Watch for "Lady Lovelace" Moments: Pay attention to when an AI does something you didn't explicitly tell it to do. These emergent behaviors are the true signs of a "Discrete State Machine" reaching a level of complexity that mimics intelligence.
- Read the Paper: Seriously. Search for "Computing Machinery and Intelligence" by A.M. Turing (1950). It’s surprisingly readable. It’s funny. It’s a reminder that the smartest people in history were often just as curious and uncertain as we are.
Turing’s work reminds us that intelligence isn't a mystical spark. It’s a process. Whether that process happens in a "wet" biological brain or a "dry" silicon chip is, to Turing, a secondary detail. He taught us that if it walks like a duck and talks like a duck, we should probably stop arguing about the definition of "duck-ness" and start figure out what to feed it.
The real takeaway from Computing Machinery and Intelligence isn't about the limits of machines. It’s about the limits of our own definitions. We are still catching up to a man who saw the future through a 1950s lens and somehow got the focus almost perfect.