A new era is starting for human creativity, communication and understanding.
Its impact should be comparable to the printing press, telephone or the internet
If we freeze the capabilities of GenAI to what they can do only today, we will still see transformational change.
But GenAI still has much more potential
Gemini Ultra is trained on 30+ trillion tokens.
A human would take 100,000 years to read the same amount of material.
We perform better than the most advanced GenAI models on a fraction of the reading material.
Vision is much higher bandwidth {than text}: about 20MB/s… In a mere 4 years, a child has seen 50 times more data than the biggest LLMs trained on all the text publicly available on the internet.
Yann LeCun, Chief AI Scientist at Meta
The GenAI we have today is the worse it will ever be
And today it can do this
We now have a new meta layer for interacting with information.
Language → GenAI → Python/SQL → C++ → Binary → …
Poets → Programmers → Engineers → Physicists → …
To take advantage of emergent properties of information we will need poets and philosophers rather than programmers or engineers to interact with data
How we interact with data will be transformational
But improving communication between people will have biggest impact
Biggest Impact for GenAI in Production
Enable poets to talk to databases
Enable people to communicate better with each other
Lets try it
Most presentations people only remember 20% of the content.
Can we improve this using GenAI?
multivac.sunholo.com
Log in to multivac.sunholo.com and ask a question! This presentation and background material is uploaded to it.
2. What is needed for production ready GenAI
Components for production GenAI
Table 1: Considerations for GenAI production
Feature
Notes
Data
Your unique data is the difference
Model
Capabilities of the model
Prompt
Instructions for the model
More components for production GenAI
Table 2: Seconday Considerations for GenAI production
Feature
Notes
UI
How people interact with your output
Monitoring
Checking performance
Authentication
Protecting your data
Scaling
Keeping up with demand
Speed
New data updates, time to first token, etc
Data
Key differentiator
Unstructured data like PDFs, images, docs can be extracted and embedded to be useful for GenAI techniques such as RAG
Structured data within databases can be queried by GenAI agents via generated code
Domain knowledge locked up within your collegues
Treasure your data - it is the unique offering your company can bring to GenAI
Personal data is much more valuable now
Past emails, tweets, photos, messages, SMS take on a new power to communicate who you are.
Creation of digital Philosophical Zombies are possible
We can already transfer your likeness
Will we be able to transfer your behaviour too?
Embeddings are a new data type
What do vector embeddings measuring semantic meaning - mean?
[0.4344, 1.232323, 0.232323, -2.1, ... ]
["Redness", "Fear", "Loving", "Cats", ...] ??
Convincing representations of humanity can be contained within ~1000 dimensions
Use cases for embedding
Find similar movies/pictures/people - recommendation engines
Match user question to a document chunk (RAG)
Match user question history to another user’s data (Profile matching)
Find anti-match between uploaded picture and uploaded description (Clean up dirty data)
Vector stores (Google)
Vectorstores on GCP
Vector Store
Notes
AlloyDB
PostgreSQL, pgvector and in-database embeddings
BigQuery
New! Your data probably in there already
Vertex AI Search and Conversation
Abstracts away embedding, scales up
Vertex Vector Search
Enterprise usage, top performance but costs $$$
Vector stores (Non-Google)
Table 3: non-Google Vectorstores
Vector Store
Notes
Supabase
Cheap and open-source but tricky to host and slows down
LanceDB
Cheap and quick, backed up by Cloud Storage, immature
Pinecone
Popular 3rd party hosted service
Qdrant
Rust based fast enterprise service
How to get GenAI data for a company?
Look at all your current methods of communicating ideas - workshops, emails, meetings. Can they be improved?
Unlock your data - use embedding techniques to extract information out of your unstructured (PDFs, Videos, etc)
Use Language to SQL/code to extract from structured (databases)
Components for production GenAI
Table 4: Considerations for GenAI production
Feature
Notes
Data
Your unique data is the difference
Model
Capabilities of the model
Prompt
Instructions for the model
No time for these others today, but ask within multivac.sunholo.com
Summary - what is needed for production ready GenAI data?
Your data is key to your production GenAI application
Reconsider all of your existing data streams, both structured and unstructured
Embeddings are a new data type that allows more use cases above traditional search
Store those embeddings in a vector store that suits your application
3. Case Studies of GenAI Production
Multivac
Lets look at how Multivac works with prompts using Langchain’s Langsmith
amass.tech
Helping advance knowledge within life sciences
Unique parsing of life science formats
Helping life science professionals find research related to their own technology
Use public and private data to enable new discovery
Our New Energy
Summary
Enabling GenAI offers new communication possibilities
Your data is the key to unlocking its potential within your own business
Embeddings unlock your existing data
Allow your users to experiment with these new tools
Become part of the Multivac
Free Virtual Agent Computers (VACs) open-source code within this github repository
github.com/sunholo-data/vacs-public
We will host your own VACs
Thanks
Questions?
Ask here! https://multivac.sunholo.com
multivac@sunholo.com
linkedin.com/company/sunholo/
github.com/sunholo-data
Appendix - about Sunholo
Mark Edmondson - Founder
Founder of Holosun ApS from Nov 1st, 2023
Google Developer Expert - Google Cloud since 2015
MSci Physics, Kings College London
Wrote an O’Reilly book on Google Analytics 4 and Google Cloud integrations
