From Silence to Syntax: How the Machine Learned Language
Tracing the evolution of artificial intelligence from logic games to language models
There’s a scene in Dostoevsky’s The Brothers Karamazov where Ivan hallucinates a conversation with the devil. But this devil isn’t fire and brimstone, he’s awkward, tired, almost mundane. He quotes books, complains about the 19th century, and insists he’s just a projection of Ivan’s overheated brain. It’s an odd scene, but it captures something useful about how ideas evolve. What once seemed terrifying or impossible becomes familiar, and even boring.
Artificial Intelligence is going through something like that now.
A decade ago, AI was still mostly theoretical for the average person. You might have used predictive text or asked Siri to play a song, but it felt more like a gimmick than a revolution. Fast forward to now, and AI is writing emails, making art, editing films, diagnosing cancer, and passing university exams. Tools like ChatGPT, Sora, and Midjourney aren’t just novelties; they’re starting to shape how people work, learn, and even think.
So what exactly is AI?
At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, for example, things like recognising patterns, understanding language, or making decisions. Most of what we call AI today is based on machine learning: systems trained on massive datasets to detect patterns and generate responses. That includes everything from spam filters to autonomous driving software to large language models.
And it’s accelerating. In 2020, AI could barely string a paragraph together. By 2023, it could write a legal brief. By 2024, it could generate a film trailer. In 2025, you can ask a bot to plan a trip, run your inbox, draft code, or manage a side hustle.
What’s next? Predictions vary. Some say we’re heading toward Artificial General Intelligence- systems that can do pretty much anything a human can. Others think we’re still far from that, and what we’re seeing is just advanced pattern-matching on a big scale. Either way, it’s clear AI is going to be a defining part of the next decade; economically, culturally, and philosophically.
So how did we get here? Let’s take a quick walk through the history of AI, from Cold War labs to TikTok filters.
1950 — “Can machines think?”
That’s the question Alan Turing asked in his 1950 paper Computing Machinery and Intelligence. Instead of debating definitions, he proposed a practical test: if a machine could hold a conversation and convince a human they were talking to another human, would that count as thinking? This idea became known as the Turing Test, and it set the tone for the next few decades of AI research. The goal wasn’t necessarily to build a brain, but to create behaviour that looked intelligent from the outside.
Turing’s question was bold for the time. Computers were still massive, room-sized machines used mostly for calculations. The idea that they could eventually carry on a convincing conversation, or do anything resembling human thinking, sounded far-fetched to most people. But Turing saw it as not only possible, but inevitable.
Philosophically, the Turing Test raises a big question: is intelligence just about what you do, or does it also matter what you are? In other words, if a machine talks like a person, does it understand anything, or is it just mimicking? These questions are still at the centre of debates about AI today.
1956 — The Dartmouth Conference and the birth of AI
In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College in New Hampshire for a workshop that would quietly launch one of the most influential research fields of the 20th century. The proposal, written by John McCarthy, Marvin Minsky, Claude Shannon, and
Nathan Rochester, stated:
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
That single sentence effectively coined the term artificial intelligence, and set the agenda for decades of research: human intelligence, the thinking went, was ultimately a computational process, and therefore replicable.
This was the era of symbolic AI (sometimes later called "GOFAI," or Good Old-Fashioned AI), where intelligence was modelled as rule-based symbol manipulation. The brain was seen as a kind of formal system, and the idea was that if you gave a machine the right logic and rules, it could solve problems just like a person.
The approach drew heavily from formal logic, computational logic, computer science, and analytic philosophy, especially from thinkers like Bertrand Russell, Alfred North Whitehead, and Hilary Putnam. Intelligence was treated as abstract, logical, and decontextualized.
There was also a lot of optimism. Researchers believed human-level AI might be just a few decades away. As McCarthy later admitted, this was “a mistake”. Still, the Dartmouth Conference marked a turning point. It gave AI a name, a research program, and a set of ambitious goals. It also embedded a particular philosophical stance: that the mind is ultimately a machine, and as such that machines, in principle, can think.
1960s–70s — Logic, rules, and the promise of symbolic AI
Following the Dartmouth Conference, the early decades of AI were dominated by the belief that intelligence could be reduced to logic and rules; if thinking was symbol manipulation, machines should be able to think.
Researchers built systems that could solve logic puzzles, prove mathematical theorems, and even play simplified games. One early program, Logic Theorist (1956), created by Allen Newell and Herbert Simon, was described by its creators as “the first artificial intelligence program”. It successfully proved 38 of the first 52 theorems in Principia Mathematica, sometimes more elegantly than Russell and Whitehead themselves. This was the golden age of symbolic AI- a method where intelligence is modelled as manipulating symbols based on formal rules. The program SHRDLU (1970), developed by Terry Winograd, could interpret natural language commands and manipulate virtual blocks in a simulated world. In demos, it looked like a breakthrough, meaning the computer could understand English and respond meaningfully. But there was a catch. SHRDLU’s world was tiny and hand-coded so it only worked within very narrow, predefined contexts and as soon as you stepped outside its “block universe”, the illusion of understanding collapsed.
Logic Theorist proved mathematical theorems; SHRDLU could follow English commands to move virtual blocks. These systems gave the impression of understanding, but only within tightly controlled environments.
Philosophers began to push back. Hubert Dreyfus, in What Computers Can’t Do (1972), argued that human intelligence isn’t just rule-based, but rather, it’s embodied, intuitive, and deeply embedded in a messy, unpredictable world. Drawing from Heidegger and Merleau-Ponty, he claimed that trying to formalise all of human cognition was a category mistake. AI wasn’t just missing a few rules, it was missing the whole structure of human experience.
1980s — The first AI winter
By the early 1980s, the limits of symbolic AI were becoming hard to ignore. Systems that had performed well in narrow, controlled settings struggled in the real world. Adding more rules didn’t scale, the complexity ballooned, and the dream of general-purpose AI began to stall.
This period saw the rise and fall of ‘expert systems’, which were programs designed to replicate human decision-making in specific domains like medicine or engineering. The most famous, MYCIN (developed in the 1970s), could recommend antibiotics based on lab data. But ‘expert systems’ required constant manual updating and they failed to adapt to new or unexpected inputs. As such, they weren’t learning; they were just executing pre-coded logic.
Governments noticed. In the US, funding dried up after the promises of the 1960s and ’70s didn’t materialise. The UK’s Lighthill Report (1973) had already cast doubt on the field, concluding that AI had failed to deliver on its ambitions outside of narrow academic settings. Japan's massive investment in the Fifth Generation Computer Systems Project (launched in 1982) raised hopes briefly, but by the end of the decade, it too had fizzled.
This disillusionment came to be known as the AI winter, a period marked by declining investment, pessimism, and reputational damage; it became common in both academia and industry to avoid the term “AI” altogether.
The problems highlighted deeper issues. As John Searle argued in his famous Chinese Room thought experiment (1980), syntactic manipulation doesn’t equal semantic understanding. A program could follow rules without any grasp of meaning. Just like SHRDLU before it, expert systems might appear intelligent, but they didn’t understand anything.
AI hadn’t been abandoned, but it was clear that rule-based approaches alone weren’t enough. Something new was needed.
1990s — Learning to learn
After the AI winter, the field needed a rebrand, and a rethink. Instead of trying to hand-code intelligence, researchers began shifting toward a different idea: what if machines could learn from data?
This was the rise of machine learning and it marked a quiet but revolutionary pivot.
The math had been around for decades (Bayes' theorem dates back to the 1700s), but the 1990s saw advances in computing power and data availability that finally made it practical. Instead of feeding a system explicit rules, you gave it a mountain of examples and let it figure things out statistically. The change was subtle but powerful. The field stopped trying to mimic how humans think, and started focusing on what machines could do reliably.
One early milestone came in 1997, when IBM’s Deep Blue beat world chess champion Garry Kasparov. It wasn’t thinking in any meaningful way, it was brute-forcing millions of moves using probability and pruning, but it worked. For many, it was the first time a machine had visibly outperformed a human expert at something intelligent-seeming.
And while symbolic AI was still alive in some corners, machine learning was where the action was. Researchers experimented with decision trees, support vector machines, and naive Bayes classifiers, terms that sound dry, but laid the groundwork for everything that followed. The mood was pragmatic, even humble: don’t build minds, just build systems that get results.
At the same time, neural networks, which had been dismissed for decades, began quietly improving. Inspired (very loosely) by how brains work, they’d been largely written off since the 1969 publication of Perceptrons by Minsky and Papert, which proved that early networks had severe limitations, but by the 1990s, thanks to techniques like backpropagation, they started to look promising again.
It wasn’t flashy, but AI was learning how to learn.
2000s — Big data, backprop, and the neural network comeback
By the early 2000s, symbolic AI was largely out of fashion. The new frontier was machine learning, not by mimicking human reasoning, but by mining patterns from massive datasets.
Two trends made this shift possible: data and computing power. The rise of the internet, smartphones, and digital record-keeping meant data was being generated at an unprecedented scale. At the same time, improvements in GPUs (graphics processing units) originally designed for video games made it possible to train large models far more efficiently than CPUs ever could.
This created fertile ground for the return of neural networks- a class of models inspired by the structure of the brain, but in a highly simplified form.
What is a neural network, really?
At its core, a neural network is a function approximator. You give it an input (say, an image or a sentence), and it outputs a prediction (like “dog” or “positive sentiment”). The model is made of layers of nodes (“neurons”) connected by weights, and each node performs a simple mathematical operation.
The learning happens through a process called backpropagation, reintroduced and popularised in the 1980s but only truly effective in the 2000s thanks to better hardware and larger datasets. Backpropagation is a clever application of the chain rule from calculus, allowing the model to measure how wrong its outputs are (using a loss function), and then adjust the internal weights accordingly—nudging them, step by step, to reduce error. These networks used to be shallow (just one or two layers) and struggled with complex tasks like image recognition or language modelling, but that changed with the development of deep learning.
The deep learning breakthrough
The breakthrough came from increasing the number of layers in neural networks as more layers meant more capacity to model complex, nonlinear relationships.
One key moment came in 2006, when Geoffrey Hinton, a pioneer in neural networks, co-authored a paper introducing a new way to train deep models, called ‘unsupervised pre-training’. Normally, deep networks were hard to train because of the vanishing gradient problem which was that as the training signal moved backward through the layers, it would get weaker and weaker, so the early layers barely learned anything. Hinton’s idea was to train one layer at a time, starting with the bottom and moving upward, before fine-tuning the whole network. This step-by-step approach gave the model a better starting point, allowing it to learn much more effectively.
Shortly after, in 2012, deep learning made headlines when a team led by Alex Krizhevsky, under Hinton, entered the ImageNet competition where their model, AlexNet, used convolutional neural networks (CNNs), a kind of neural network well-suited to processing images, and reduced the error rate dramatically. It was trained on GPUs and used a technique called ReLU activation, which improved training speed and stability. This wasn’t just an incremental improvement. It was a step change.
What made all this work wasn’t just math, it was data. Deep learning needs labelled examples and by the 2000s, thanks to web scraping, online databases, and user-generated content, that was finally available. So:
Natural language processing improved with models like ‘word2vec’ (2013), which mapped words to vectors based on their context, revealing semantic relationships like “king - man + woman = queen.”
Speech recognition, once a holy grail of AI, started to improve significantly. Google’s voice search, Siri, and Amazon’s Alexa all began to emerge during this period.
Recommendation systems—like those used by Netflix and Amazon—used collaborative filtering and matrix factorization, but slowly began to incorporate more neural-based models.
The 2000s marked a technical realignment of AI to data-driven learning. Though AI wasn’t yet writing poems or holding conversations, it was starting to see, hear, and translate, and that, in itself, was revolutionary.
2016 — AI hits the mainstream
If the 2000s was a turning point for researchers, 2016 was the year AI captured the public imagination. The headline moment came when AlphaGo, developed by DeepMind, defeated world champion Lee Sedol at the game of Go, a game so complex it had long been considered out of reach for AI. Unlike chess, Go has a vast search space and relies heavily on intuition. AlphaGo used a combination of deep neural networks and reinforcement learning, trained on millions of human games and then improved through self-play.
This wasn’t just a victory in a game. It was a signal that AI could now tackle problems that required strategy, flexibility, and creativity.
Meanwhile, deep learning was quietly reshaping everyday life:
Image recognition became superhuman on benchmarks like ImageNet.
Voice assistants like Alexa and Siri got dramatically better at understanding speech.
Machine translation made a leap with neural models, especially Google's Neural Machine
Translation System (GNMT), which could translate entire sentences more fluently.
The core idea across these systems: take lots of data, feed it into deep neural networks, and optimize for performance; no hand-coded rules, just learning from examples. AI was no longer a lab curiosity. It was in your pocket.
2020s — The age of transformers and foundation models
By the 2020s, AI had stopped being something you used indirectly and it became something you talked to.
The key driver was the transformer, a neural network architecture. Unlike older models, transformers could process entire sequences, like sentences or images, in parallel, using a mechanism called self-attention to weigh relationships between elements. This made them both faster and better at understanding context. By 2020, transformers were powering a new kind of AI- foundation models. These are massive, general-purpose models trained on huge swaths of internet data and then fine-tuned for specific tasks. Think OpenAI’s GPT-3, Google’s BERT, or Meta’s LLaMA. They weren’t just good at one thing; they could write essays, summarise legal documents, generate code, translate languages, and even hold halfway-decent conversations.
These models had billions (or now, trillions) of parameters (the knobs they tweak to learn) and were trained using self-supervised learning, meaning they learned patterns without needing human-labelled data, and the sheer scale allowed them to generalise in ways no one expected.
In 2022, ChatGPT made this power public. Suddenly, anyone could interact with a large language model. In 2023, tools like Midjourney, DALL·E, and Runway brought the same kind of generative power to images and video. AI wasn’t just classifying or predicting, but it seemed like it was creating.
But scale comes with questions. These models are powerful, but opaque. They hallucinate, reinforce biases, and require massive energy and compute. Critics like Emily Bender and Timnit Gebru have warned of treating language models as though they understand language, rather than just predicting plausible sequences of words, a concern captured in the phrase “stochastic parrots.”
Still, we’ve entered a new phase. AI isn’t just a tool it’s a collaborator, a copilot, a creative partner. And it’s evolving fast
2023– the future — Multimodal models and the edge of generality
By 2023, AI models weren’t just reading or writing; they were seeing, hearing, and reasoning across media. The buzzword was multimodal: models like GPT-4, Gemini, and Claude could now take in text, images, and even video or audio as input. This made AI not just linguistic, but perceptual.
Technically, this shift has been driven by scaling laws which show predictable improvements as you increase data, model size, and compute. It’s also enabled by ‘mixture-of-experts architectures’, models that dynamically activate only a subset of their weights, making large systems faster and more efficient (e.g. GPT-4’s sparse activation, though details remain under wraps).
AI is also becoming more agentic. Tools like AutoGPT and Open Interpreter string together steps, take actions, use tools, and reason iteratively. Think less chatbot, more intern. Meanwhile, open-weight models (like LLaMA 3, Mistral, and xAI’s Grok) are decentralising access and accelerating experimentation.
Looking ahead, the next leaps seem likely to come from:
Improved memory and planning capabilities, possibly via neural memory systems or external tools.
More efficient training methods, like QLoRA or ‘low-rank adaptation’, to make fine-tuning accessible on consumer hardware.
Further advances in multimodality, as models learn to understand and generate interactive experiences (not just static media).
But the deeper question is what kind of intelligence this is. These models don’t “think” like humans. And so while they don't understand in the philosophical sense, they can already do a great many tasks we once thought required understanding. As LeCun puts it, “they’re smart, but not autonomous”.
So where are we now?
In 70 years, AI has gone from logic puzzles and toy problems to writing novels, analysing code, and generating photorealistic video; all within systems that don’t really “know” anything. The speed is dizzying. The philosophical questions, about intelligence, consciousness, labour, creativity, aren’t going away. If anything, they’re arriving just as the tech becomes impossible to ignore.
So before the next breakthrough hits your timeline, it’s worth stepping back. This didn’t happen all at once. And it didn’t happen by magic. It happened bit by bit—data by dataset, layer by layer.