AI & Machine Learning

In 1973, the British government asked physicist James Lighthill to review progress in artificial intelligence research. His report concluded that researchers had not achieved their stated objectives. Within a year, UK funding for AI research dropped sharply. Many programs closed. This pattern repeated across multiple countries between 1974 and 1980, then again from 1987 to 1993. Researchers coined the term “AI Winter” to describe these periods when funding dried up, companies failed, and the field nearly disappeared. Understanding what happened helps you make informed decisions about AI careers, business investments, and technology adoption today.

Expert systems came before neural networks. They worked by storing knowledge from human experts as simple rules. Think of them like a digital version of an instruction manual written by the best professionals in a field. Here is the big difference: Expert systems use clear rules that humans write down. Modern machine learning finds patterns in data automatically. Neither approach is always better. The right choice depends on your specific problem. Why should you care about expert systems today? Because they solve real problems that machine learning cannot handle well. Banks use rule systems to follow government regulations. Hospitals use them to check if prescribed medicines might interact badly. These systems need to explain their decisions clearly, which expert systems do very well.

The Dartmouth Summer Research Project on Artificial Intelligence wasn’t just another academic conference. It was the moment when scattered ideas about thinking machines coalesced into a unified field of study. For the first time, researchers had a name for what they were pursuing: Artificial Intelligence. For students diving into machine learning , researchers exploring neural networks, or anyone curious about how Siri understands your voice—understanding Dartmouth is understanding AI’s DNA. Every algorithm, every breakthrough, every debate about AI ethics traces back to the questions asked in that small New Hampshire college during the summer of 1956.

The journey of Artificial Intelligence is far older than the modern computer. To truly understand today’s advanced systems, from Machine Learning algorithms to LLMs, you must trace the intellectual history back to four core, authentic concepts. These pillars are the foundational DNA: Symbolism (Llull): Turning ideas into manipulable tokens. Logic (Hobbes/Leibniz): Reducing thought to pure binary calculation. Algorithm (Turing): Defining the universal recipe for computation. Activation (McCulloch-Pitts): Modeling the biological decision unit. Every concept below is explained with a unique, interactive demo, ensuring you learn by doing and make a deeper connection with the material.