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How we built Fin
@fergal_reid
Fin
GPT / LLMs
• Internet sized tech change
• Change in capability
• Change in how we build and use AI
Level 1: GPTs are incredible!
Level 1: GPTs are incredible!
Level 1: GPTs are incredible!
Level 2: GPTs make things up and aren’t trustworthy.
Level 1: GPTs are incredible!
Level 2: GPTs make things up and aren’t trustworthy.
Level 3: GPTs can be incredible when used right
See them as engineering components
See them as engineering components
Separate out aspects accidentally bundled
What is GPT?
Training objective: token prediction
Training objective: token prediction
• A sequence model
• That uses ‘attention’
• Gradient descent
• A sequence model
• That uses ‘attention’
• Gradient descent
Not a useful model
Not a useful model
• Human = genes and evolution ?
Not a useful model
• Human = genes and evolution ?
• Distrust:
‘It can’t do X because its just trained to predict the next
word’
Model: Database + Reasoning Engine
Model: Database + Reasoning Engine
• The reasoning engine is key
Model: Database + Reasoning Engine
• The reasoning engine is key
• Often, the database is a liability
Reasoning capabilities
Model: Human intuition
Ask a human to answer a historical question
vs
Give them a history book and ask them the question
Note: Context window limited
• Thousands of words
• Can’t put a whole KB, or context, in it
• Synergizes well with Vector Search
Model:
‘Interpolative’ vs
‘Extrapolative’
tasks
• Less reliable at extrapolation
• Less reliable at extrapolation
• Favour interpolation
• Less reliable at extrapolation
• Favour interpolation
• Perform a task, given a context
• Less reliable at extrapolation
• Favour interpolation
• Perform a task, given a context
• ‘Retrieval Augmented Generation’
30 November 2022:
ChatGPT
First features we built
• Summarisation
• Edit tone of voice
• Expand from shorthand
• 5th Dec: Starting
• 20th Dec: Internal use
• ~13th Jan: Customer beta
• 31st Jan: Launch with testimonials
• March: Thousands of customers
Timeline
• GPT-powered
question answering Bot
Fin
Design goals
Design goals
• Converse Naturally
Design goals
• Converse Naturally
• Answer questions about just your
business
Design goals
• Converse Naturally
• Answer questions about just your
business
• Reduce hallucinations
Design goals
• Converse Naturally
• Answer questions about just your
business
• Reduce hallucinations
• Minimal con
fi
guration
How to build with AI
Principles
• Sometimes you have to work back from the technology
AI
• Sometimes you have to work back from the technology
• Skunkworks approach
AI
• A small senior ML team owns technical core
• Engineers/Scientists help establish design envelope
Tactics
• A small senior ML team owns technical core
• Engineers/Scientists help establish design envelope
• Team must have quantitative skills
Tactics
• A small senior ML team owns technical core
• Engineers/Scientists help establish design envelope
• Team must have quantitative skills
• Must have the ability to ship code
Tactics
• Fast customer contact
Tactics
• Fast customer contact
• Build v1 of most ML with powerful LLM
Tactics
• Test with real data ASAP
Tactics
• Test with real data ASAP
• Design in failure tolerance
Tactics
Tactics
• How we build ML software has changed
• “LLMs aren’t all of AI”
Tactics
• Good adoption
• Working on v2
• CS will be hugely changed by AI
Fin current state
Guessing what’s next
• Breakneck progress
• Breakneck progress
• Smaller models, open?
• Exciting but overhyped today
• Breakneck progress
• Smaller models, open?
• Exciting but overhyped today
• Productisation
• Breakneck progress
• Smaller models, open?
• Exciting but overhyped today
• Productisation
• Larger models
Thank you!
@fergal_reid

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