The question becomes public
Alan Turing frames the question of machine intelligence in a way people can debate, test, and build around.
The tools feel new because they are finally easy to use, but the ideas behind AI have been developing for generations. What changed is access: useful systems are now close enough to everyday work that teams need judgment, rules, and a practical first step.
History of AI
Artificial intelligence gets its name
The Dartmouth workshop gives the field a shared label and a research agenda that will keep evolving for decades.
AI becomes visible to the public
IBM Deep Blue defeats world chess champion Garry Kasparov, making machine reasoning feel real outside the lab.
Deep learning breaks through
Neural networks make a major leap in image recognition, helping push modern AI from research promise toward practical systems.
Transformers change language AI
The transformer architecture gives AI systems a stronger way to work with language, context, and long sequences of information.
Generative AI reaches everyday users
ChatGPT turns AI from a specialist tool into something many people can try directly for writing, research, and work questions.
AI becomes multimodal and regulated
Models such as GPT-4o make voice, image, and text feel more connected, while the EU AI Act enters into force and pushes companies to think about risk and governance.
AI moves deeper into work
Frontier systems such as GPT-5 and Gemini 2.5 put more emphasis on coding, reasoning, company context, and longer work tasks.
Agentic work becomes the headline
Newer models are less like chatbots and more like systems that can plan, use tools, check work, and move through multi-step tasks. You have adopted AI and started implementing it in your business.
Your team adopts it
AI is no longer just history. If you want to understand where it fits your work, what to avoid, and what first step is worth taking, let us talk it through.
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