Key Notes
Basics
- ChatGPT is the idea of n-gram models
- Given n-1 words guess nth word likely to be
- Distribution is learnt from sequence
- People tried in small values of n
- Sample from distribution of words
- More likely words more often
With large data
- Any N, Words next word
- Frequency, Conditional probability
- Generate words if the first word given
- More likely words + Patterns
Large sentences/meanings
- Abstract sequences
- Different answers every time
- Every sequence may be different generated distributions but a similar context is possible
- Chatgpt = something well written
Why it works?
- We believe in what seems realistic
- Connect to human experience
- Fact is different from possibility
- Plausible or probable or reasonable answers
Similarity to humans
- Humans are not always factual
- It can be perception based
- People can be finalized in civil society
- Machines can suggest without knowing the consequences
- Automation still may have a bias
- Being close to the truth we are impressed
Predictive modeling
Train / predict
Conditional modeling
- Can create bias in information
- Discriminate learning learns a conditional model
- Classifier then finds dogs vs generates dogs both different
Generative distribution - Joint distribution
- The prior distribution of reasonable images
- Teacher = Generative model
- Learning generative model is costlier
The human brain works by on-demand stitching
- chatgpt does something similar
- All learning is compression
- All learning is lossy compression
- jpeg lossy - approximating
- Representation of compressed details
- Significant footprint available to train systems
Good writing for all
- Picaso style pics
- Shakespeare style writing
- Racial profiling not required
- Character and form are not connected
- Generalizations help for survival
- AI as creator / editor
Badly written with original thought is human writing
- Harder to write original creative ways
- Original vs Derivative thinking
- Bad handwriting vs Good content
- Bad package vs Good product
- We have one scale good or bad
- LLM learns from human language
- Most likely completion given soceity is
- Social Enginner on Data
Is this a good representation of all ethnicity ?
How it for fine tuned ?
- RHLF
- Show results
- asks someone their likes
- Thumbs up / down to change distribution
- Re-learning it
- Collectively offensive content on web vs making a decent prompt engine
- Align to human values
- Concentration campus, Genocide - Human values
- Retrain for cultural norms
- False positive
- Different narrative, different takers
- Make LLM overwrite conditional network through prompts
- Adverserial learning prompts
- How to put knobs how it behaves well
AI systems to work with
- Basically put people to think about problem
- With enough eye balls every downside can be shallow bug
- We need more eyeballs to decide
- ChatGPT will not generate grammatically incorrect sentence
- Core problem of intelligent behavior - planning, diagnosis, reasoning
Keep Exploring!!!
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