What I Learned from the Elements of AI Course: A Clearer Way to Think About Intelligence
Reflections from the Elements of AI course: unpacking suitcase words, Bayes’ rule, and neural networks without the hype. Five lessons on how to think clearly about intelligence, bias, and lifelong learning.
Signal Boost: "Learning to Fly" by Tom Petty
A song about taking off into the unknown and a reminder that the best journeys in learning are the ones where we keep moving.
I’ve just completed the Elements of AI MOOC, and it was well worth the time. It’s a course that strips away the mystique and marketing of AI, and instead gives you the tools to think clearly about systems that learn from data. No hype. No jargon. Just solid foundations that I can use at work and in writing.
Five Reflections
1. Words matter
We throw around terms like “intelligence”, “learning”, and “understanding” as if they were single, measurable things. They’re not. They’re what Marvin Minsky called suitcase words, each packed with meanings that depend on context. The course reminded me that today’s AI is narrow: it excels at one task, but tells us nothing about others. Saying “an AI” isn’t quite right and it's better to say an AI method or an AI model trained for a purpose.
2. Problems first, then methods
Framing the problem properly is half the battle. Define the state space, transitions, and costs before jumping to algorithms. In games like chess or Go, AI searches a tree of possible moves. In the real world, uncertainty dominates and so we lean on probabilities, odds, and Bayes’ rule to update beliefs as evidence changes. It’s a humbling reminder that good models start with good questions.
3. Learning isn’t magic
Machine learning isn’t one thing. It’s a collection of techniques: supervised, unsupervised, and reinforcement learning, with each suited to different kinds of problems. Nearest neighbour and regression are workhorses; deep learning adds depth, not magic. The key takeaway: data quality trumps model complexity. Overfitting is easy, humility is essential, and there is no one algorithm to rule them all.
4. Neural nets and LLMs in perspective
Neural networks learn useful patterns through layers of simple units. Convolution helps with images; attention helps with language. Large language models (LLMs) like GPT are astonishingly capable, but they’re still predictive text systems and not to be confused with conscious entities. Intelligence and imitation are not the same thing. The course helped me separate the science from the spectacle.
5. People, policy, and practice
Algorithmic bias, privacy, and transparency aren’t side issues — they’re core design principles. GDPR’s "right to access", “right to explanation” and “right to be forgotten” push us towards accountability. Deepfakes and synthetic media remind us to question what we see and hear. The future of work will depend on our ability to learn, adapt, and apply human judgment alongside machine efficiency.
Call to Action
If you want a balanced introduction that cuts through the noise, take the Elements of AI course: https://www.elementsofai.com
It’s free, self-paced, and written for curious people. Do it with a colleague or friend, and compare how you each interpret the lessons.
What It All Comes Down To
Continuous learning is the only rational response to fast-moving technology. This course earns a place in my learning journal, alongside a few deeper dives into probability, model evaluation, and responsible AI. I’ll come back to these notes as I build and critique AI-powered work and I’d encourage anyone curious about AI to do the same. It’s not about keeping up. It’s about staying curious.
