Inside the Black Box: Cracking AI and Deep Learning (Feed Disabled)

This episode challenges the old idea of a single language box in the brain, using aphasia studies and hospital control groups to show how language processing is more distributed than once thought. It also explores how different languages rely on word order or verb marking in different ways, and why those differences matter when the brain is under stress.

Can a model learn past tense the way children seem to, if you remove the tokenizer from the equation? This final installment tests raw letters and raw sound, probes whether a U-shaped pattern can be forced, and shows why the answer points to distributed, frequency-driven learning rather than a discrete rule.

Part 3 of 4. After two episodes arguing there is no discrete past-tense rule inside a language model, this one turns the strongest possible search on the question: gradient descent, hunting for the single internal direction that best forces goed over went.

It works — 100% of the time, even on held-out verbs it was never tuned on. Then one control collapses the whole result. That same direction drives ordinary regular verbs like walked to zero, and under it the model stops producing words at all, emitting only edededed.

Opening up the direction shows why: it simply screams the two-letter token ed, burying every whole-word rival. The optimizer never found grammar — it found the cheapest trick that games a narrow metric, and a caution about measuring only the thing you set out to change.

Read the original post: Forcing a Neural Network to Add -ed

This episode explores a modern twist on Jean Berko’s famous wug test, comparing how large language models handle made-up verbs versus familiar ones. It digs into tokenization, internal feature probes, and why neural networks seem to approximate grammar through probabilistic patterns rather than clean symbolic rules.

This episode revisits the famous debate over whether language is learned through symbolic rules or distributed neural patterns, using the classic irregular verb U-shaped curve as the battleground. It then compares child language development with training snapshots from modern AI models, showing that transformers learn past tense in a strikingly different way: starting with the broad rule and gradually memorizing the exceptions.

When you fine-tune an AI model, what changes inside doesn't predict what changes outside. This week on Inside the Black Box, I break down why — and what it means for anyone auditing or regulating these systems.

This episode explores why smooth, coherent language can feel more credible than it is, and how processing fluency, familiarity, and authority cues shape what we believe. It also digs into why conversational AI is especially persuasive, from polished explanations to confident-sounding confabulations.