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Introduction
hofstader's thoughts on chatGPT
recap of supervised learning
first paper on sequential learning
first use of state units (RNN)
first observation of word boundary detection
first observation of word clustering
first "large" language model Hinton/Sutskever
sentiment neuron (Ilya | OpenAI)
transformer explaination
GPT-1
GPT-2
GPT-3
In-context learning
ChatGPT
tool use
Introduction to CUDA Programming
Setting Up the Environment
Starting the Exercise
Converting RGB to Grayscale
Executing the Grayscale Conversion
Understanding Cuda and Parallel Processing
The Structure of Cuda Kernels and Memory Management
Utilizing Cuda's Advanced Features for Speed
Compiling and Using Cuda Code with PyTorch
Including Necessary Components and Defining Macros
Writing the CUDA Kernel
Defining the Kernel and Calling Conventions
Creating the Output Tensor
Compiling and Linking the Code
Cuda Synchronization and Debugging
Introduction to Matrix Multiplication
Parallelizing Matrix Multiplication with Cuda
Utilizing Blocks and Threads in Cuda
Introduction to Matrix Multiplication with CUDA
Optimizing CPU Matrix Multiplication
Flexibility of Block and Thread Dimensions
Setting Up CUDA on Local Machines
Setting Up Conda
Conda's Improvements and Compatibility
Conclusion and Next Steps
Episode Preview
The inspiration for the Tiny Stories project
Sponsor: Omneky
Creating the Tiny Stories dataset
GPT-4 vs GPT-3.5
Did the TinyStories team try any other versions of GPT-4
Curriculum models and weirder curriculums
What does reasoning mean?
What does emergence mean?
The curriculum development space
The similarities between models and human development
Fewer layers vs. more layers
Attention heads
Semantic attention head
Neuron technique used in developing the TinyStories model
Interpretability work that inspires Ronen and Yuanzhi
CONNECT POWER CABLET
CONNECT USB SUPPLY TO MAINS
CONNECT SUPPLY CABLE TO MEDIA CONVERTER
REMOVE DUST COVER
CUT END OF POF WITH CUTTER
MAKE SURE THE FIBER IS FULLY INSERTED
LOCK THE FIBER CONNECTOR
CHECK FOR RED LIGHT FLASHING AT THE END OF THE FIBER
CONNECT THE MEDIA CONVERTER TO THE ETHERNET CABLE
GREEN LIGHT INDICATES LINK ESTABLISHED
Eight facts about Large Language Models
Prodigy (Sponsor)
LLMs complete sentences (like ChatGPT)
(1) LLMs get better with size
(2) LLMs surprise with emergent abilities
(3) LLMs seem to have "mental models"
(4) You cannot reliably steer LLMs
(5) Nobody knows why LLMs work
(6) They can outperform humans
(7) AI Alignment
(8) LLMs make for a great first impression
(9) They do not train for truth
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