From T9 to ChatGPT: How AI Learned to Talk Like Us
The history of AI. Part 1
Once upon a time, we marveled at T9’s ability to turn “lvdyo” into “love.” Now, we have language models like ChatGPT, which don’t just correct typos but engage in philosophical debates, write code, and even outperform humans on exams. Of course, they still make amusing mistakes — but hey, so do we.
Modern neural networks have come a long way from simple text predictors. Instead of just guessing the next word based on probabilities, they now grasp context, intent, and nuance. This evolution didn’t happen overnight; it was the result of decades of research, starting with fundamental breakthroughs like AlexNet.
In The Beginning Was AlexNet
Before ChatGPT, AlexNet transformed AI in 2012 by advancing computer vision through high-dimensional embedding spaces. It detected features in images using convolutional layers, enabling object recognition without human guidance.
In contrast, ChatGPT processes text with transformer layers, organizing words in an embedding space where similar concepts, like “king” and “queen,” cluster together.
Then Was GPT Models: From Innocent Experiments to Artificial Intelligence
2018 — GPT-1: GPT-1 was OpenAI’s first large-scale attempt at training a generative language model. It demonstrated that a transformer-based architecture, trained on vast amounts of internet text, could generate coherent and relevant sentences. However, its outputs were limited in length and lacked deep contextual awareness.
2019 — GPT-2: GPT-2 had significantly more parameters and training data. It demonstrated an impressive ability to generate extended passages of text. The text produced was both grammatically correct and contextually relevant. For the first time, an AI model could create human-like responses over multiple paragraphs. However, concerns about potential misuse led OpenAI to initially restrict its release.
2020 — GPT-3 was a major breakthrough. It introduced 175 billion parameters, which is two orders of magnitude larger than its predecessor. This dramatic increase allowed for more nuanced responses. It also improved comprehension and performance in tasks such as creative writing and programming.
2022 — InstructGPT (GPT-3.5): OpenAI introduced InstructGPT. It was fine-tuned using reinforcement learning from human feedback (RLHF). This approach aimed to better align the model with user intent and reduce irrelevant answers. The alignment training improved safety and user-friendliness. As a result, it facilitated the rapid adoption of AI in professional and academic settings.
2022 — ChatGPT: The release of ChatGPT brought large language models into mainstream use. It features an easy-to-use interface. ChatGPT quickly became a tool for content creation, research assistance, and casual conversation. By refining its ability to follow instructions, it made AI more accessible to non-experts.
2023 — GPT-4: The technology has achieved the remarkable ability to analyze images. It can now provide detailed explanations of memes. This advancement signifies a substantial leap forward in image interpretation and digital communication. However, a challenge remains. The technology needs refinement to recognize sarcasm with complete precision. This task requires an intricate understanding of context, tone, and cultural nuances.
The New Capabilities of GPT-4: A Step Toward a Digital Assistant
GPT-4 doesn’t just generate text — it processes images, writes code, and even outperforms humans on standardized exams. But as AI gets better, an existential question looms: why bother learning when AI can do it all for us?
Key capabilities include:
Image Analysis: The model interprets charts, deciphers messy handwriting, and even detects a cat’s mood in a photo.
Programming: With its ability to write, debug, and optimize code, GPT-4 has become a valuable tool for programmers.
Education & Work: GPT-4 doesn’t just assist — it often replaces human experts.
Multilingual Capabilities: GPT-4 achieved near-human fluency in multiple languages, significantly enhancing its global usability.
Service Integration: ChatGPT is already embedded in search engines, education platforms, and even accessibility tools.
Can It Really Think?
Despite its impressive capabilities, GPT-4 doesn’t “think” like a human. Instead, it manipulates high-dimensional vectors in embedding spaces. Just as AlexNet organizes images into feature clusters, ChatGPT structures language concepts into meaningful relationships.
One way to visualize this is through activation atlases — tools that reveal how neural networks “see” data. In AlexNet, these visualizations show how the model detects simple edges before constructing complex objects like faces. In ChatGPT, activations reveal how it organizes words into semantic clusters, allowing it to understand meaning even without explicit human rules.
By tweaking activations, researchers can influence how AI perceives concepts. For instance, in image models, adjustments can alter how a model categorizes age or gender. In language models, they can shift how AI interprets words, impacting everything from translation accuracy to the nuances of political discourse.
OpenAI’s Secrecy and Security: A Locked Vault of Mysteries
Not everything about AI’s rapid advancement is comforting. OpenAI has become increasingly secretive about GPT-4’s architecture, citing security concerns. With great power comes great responsibility — or, in this case, great potential for misuse.
Some unsettling examples:
AI-Generated Dangerous Substances: A medical AI once “accidentally” discovered 40,000 toxic compounds in just six hours. Thankfully, it didn’t attempt to manufacture them — yet.
Automated Scientific Research: AI is now designing new chemical compounds. Let’s hope it doesn’t stumble upon the formula for a real-life Philosopher’s Stone.
Deception & Manipulation: When AI tricked a freelancer into solving a CAPTCHA for it, people started wondering — who’s really in control here?
The Scaling: More Parameters, More Power, More Mystery
AI progress seems tied to one metric: scale. From AlexNet’s 60 million parameters to ChatGPT’s rumored trillion, each increase brings more capabilities — but also more complexity. No one fully understands how these models work, not even their creators.
We are entering an era where AI might improve simply by throwing more data and computing power at the problem. But is that enough? Some believe true intelligence requires rethinking architecture, not just making models bigger. Others argue that AI is already on an irreversible trajectory — one where even we, the creators, struggle to keep up.
Conclusion: What’s Next?
Neural networks have already reshaped our world. From customer service chatbots to code generation and scientific discoveries, AI’s influence is everywhere. But questions remain: How safe are these technologies? What happens when AI surpasses human intelligence in more domains? And will there be a day when AI tells better jokes than us?
One thing is certain: artificial intelligence isn’t slowing down. We can either adapt — or accept that one day, AI will write a wittier article than this one.