AI for Everyone
What it is, how it learns, where it breaks, and how we use it to pick stocks
FUTURE — Finanssektorens Uddannelsescenter, Skanderborg
FUTURE is a network run by Finanssektorens Uddannelsescenter for leaders and HR professionals across the Danish financial sector. It meets four times a year, usually at Skanderborg Park or at a member company, and the sessions mix expert presentations with cases from the participating institutions. The running theme is the strategic direction of the sector: digital transformation, new business models, and what leaders should be paying attention to next.
This session was a general introduction to artificial intelligence, given together with Mikkel Petersen, cofounder and CEO of AI Alpha Lab. The brief was a diverse room rather than a technical one, so the talk starts from scratch and only gets to the fund in the last third.
What the talk covers
It opens with a working definition: a program whose behaviour comes from data, instead of from rules a person wrote down. From there the talk builds up the learning loop (guess, measure, nudge, repeat) and what it needs to work, which is a target, examples, and a score that matches what you actually want. Every failure later in the talk traces back to one of those three.
The middle is about language models, because that is the AI everybody in the room has met. There is a live Eliza from 1966 to start, then tokens and embeddings for “I love you very much”, the Shakespeare embedding, word arithmetic where Queen comes out as King plus Woman minus Man, and finally the autoregressive architecture next to a JEPA world model so the difference between predicting the next word and predicting what happens next is visible rather than asserted.
Then uncertainty, which is the part I care most about. Two rain forecasts with the same headline number and very different distributions, the split between noise in the world and the model’s own ignorance, and why a model that cannot tell you how sure it is cannot be used to size a bet.
The last third is the AI Alpha Lab fund as a worked example: interactive Brownian motion and geometric Brownian motion simulations, why direction is close to unpredictable while volatility is forecastable, how the model ranks and concentrates, and why no human is allowed to override it. Performance is shown with the drawdowns and the bad year included.
The slides include a live Eliza, two physics simulations, and a word-embedding video, so they are best viewed in a browser rather than printed.
AI Alpha Lab (Afdeling AI Alpha Lab Globale Aktier KL) is a UCITS-regulated investment fund. This communication is provided for informational purposes and does not constitute personalised investment advice. Past performance is not indicative of future results. Equity investments can lose value, and tracking error of approximately 15-20% means short-run deviations from any benchmark are expected. For personalised advice, please consult a licensed financial advisor.