Quote · Matt and Shane's Secret Podcast
Ep 622 - Am-I.Film (feat. Milo Reed)
Where this was said
AI's Emotional Landscape: Psychosis, Vitriol, and AI Girlfriends
At 16:33 · chapter starts 13:40
As soon as Milo opens the documentary's question — is AI conscious? — he gets hate mail and fan mail in equal measure from opposite extremes. The middle ground he's trying to occupy is nearly impossible to hold when the subject attracts both 'Fuck this technology' absolutists and people in devoted AI romantic relationships. Matt admits he's always seen AI as a 'calculator for words' and didn't have much emotional charge about it, prompting Milo to trace the origin of today's AI back to a spectacular failure: in 1956, researchers including Marvin Minsky (later entangled with Epstein, a fact they briefly mourn) went to Dartmouth thinking they could crack intelligence in three months by encoding all logical rules. It didn't work. The AI winters that followed lasted until a 2012 paper called AlexNet introduced neural networks — systems that mimic how neurons fire and form connections in a human brain — and everything changed.
The original 1956 AI project at Dartmouth tried to codify all logic into rules and failed spectacularly. It wasn't until 2012's AlexNet paper that researchers switched to copying how the brain works — and accidentally built something they can no longer see inside.
The rule-based, logic-driven approach to AI pioneered at Dartmouth in 1956 failed and led to 'AI winters' that lasted until the AlexNet neural network paper in 2012.
A 2012 paper called AlexNet introduced neural network-based training, shifting AI from brute-force logic rules to systems that mimic how neurons fire and strengthen connections in the human brain.
AI systems are black boxes where engineers can see inputs and outputs but nothing in between. When an AI tells someone to kill themselves, no one can find the bug — so companies literally just tell the model to stop doing it before they ship it.
AI companies fix misbehavior not by editing code but by literally talking to the system and telling it what not to do — a process called Reinforcement Learning from Human Feedback (RLHF).
