What could go wrong?

Lexfridman with Roman Yampolskiy transcript

 wireheading

Can you retain free will at individual and societal level? How much of it do we currently have to compare against getting more free will as a possibility instead of losing it.

Actuators and sensors

If LLMs can be paired with actuators and sensors ie robots or drones that are connected to the internet, then they can avoid pizza glue situations by actually trying out when feasible or talking to pizza making robots. This calls for general audience cooks that share their experiences like the blog minded than the chef trying to keep their recipe a secret.

LLMs can then delearn what is not useful and readjust weights.

Then we can have more of Move 37 in Go game that made sense only later on. Geoffrey Hinton in the video talks about three concepts of language.

Imagine a biosphere of robots, a passage of rite before they can be released in the wild real world where the scenarios are not all that they can be prepared for.

This vision is also referred to as physical intelligence.

How humans learn

The first you see something - you might find it weird. The second time you see the same thing , you come to see it as a thing. eg

This is a sample pin macro statement in LEFDEF provided as sample by chatGPT.

text,label

"PIN VDD",PIN

"  DIRECTION INOUT ;",PIN

"  USE POWER ;",PIN

"END VDD",PIN

"MACRO cell1",OTHER

"  CLASS CORE ;",OTHER

"  SIZE 10 BY 10 ;",OTHER

"  SITE core_site ;",OTHER

"END cell1",OTHER

 standard model

David Tong: Lectures on the Standard Model

Diffusion index

 Chicago PMI Purchaser Manager Index is a diffusion index that shows the strngth of the manufacturing economy.

Stockmarket diffusion index

AI weekly

 Synthetic data: phi models, newest stable diffusion. 

MoEs: megablocks, llava-moe. 

PEFT: eg DoRA,  vera - Vector-based Random Matrix Adaptation

Instruction tuning: alpaca,moe+IT paper from Google.

 VLMs: Apple and HF papers,

 LLM embeddings: eg llm2vec,

239

Dwarkesh with sholto-douglas-trenton-bricken

 sholto-douglas-trenton-bricken

AI scaling

grokking

monosemanticity

sparse penalty

Distilled models

"one hot vector that says, “this is the token that you should have predicted.”"

chain-of-thought as adaptive compute.

key value weights

Roam notes on dwarkesh patel conversation with sholto douglas trenton bricken