Generative AI Concepts explained along GANs, LLMs and Diffusers

1. Generative AI
5th May 2023
Dr Detlef Nauck
Distinguished Engineer and
Head of AI & Data Science Research

2. The race to release LLMs – AI has never changed so fast
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Bing &
GPT4
Google
BARD
OpenAI:
ChatGPT
Google:
Bard
MS event:
New Bing
Google event
in Paris
Google shares
fall by 8%
OpenAI:
GPT4
Dec Jan Feb Mar Apr
FLI Open
Letter
May
Geoffrey
Hinton
leaves Google
June
1st vote on
EU AI Act
Sam Altman
Senate Hearing

3. Where it all comes from: GANs, LLMs & Diffusers
Diffusers (since 2020) – Example from DALLE2 (Open AI, 2022)
"Teddy bears mixing sparkling chemicals as mad scientists in a
steampunk style“ – create images from text and by de-noising.
This Person Does Not Exist
Generative Adversarial
Networks (2014)
The Generator creates
deep fakes to train the
Discriminator.
Large language models perform sequence-to-sequence
prediction and generate the next word in a sentence.
(Transformer networks, since 2018)
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4. How ChatGPT from Open AI was trained
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https://openai.com/blog/chatgpt

5. Videos are next
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Google Imagen Video with prompt: “Teddy bear washing the dishes”
https://imagen.research.google/

6. Taking Stock – LLMs today
Capabilities in a nutshell
• Statistical text prediction.
• Impressive text generation capabilities.
• Interesting applications scenarios if carefully controlled.
Caveats in a nutshell
• Foundation models are expensive to build and run.
• Built from largely uncurated training data.
• No control over output quality (hallucinations, bias).
• Outputs must be validated.
https://www.theatlantic.com/technology/archive/2023/01/chatgpt-ai-language-human-
computer-grammar-logic/672902/
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7. GenAI Use Cases
Code
Task sequences
Short text (copywriting)
Document search
Summarisation
Templated text
Contribute to complex text
Images, Video – creation and editing
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Guiding principle:
difficult to create, but easy to check
Easier
to
check

8. Ethical Checks
Curation of training data – e.g. has bias been
addressed, good quality, relevant?
Performance benchmarks – how good/useful is the
GenAI?
Ethical sourcing – was cheap labour involved, has
copyright been breached?
Carbon footprint – how much compute was used to
build it and how much will be required to run it?
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How was the GenAI built?

9. AI-mediated Communications
Many opportunities, but how do we embed trust?
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Actual Camera Image
NVIDIA Maxine: Reinventing Real-Time Video Communications with AI - YouTube
Reconstructed Image
Transmit
embedding
Select any
language synthesiser
Select any
image generator

10. Outlook: The Dark Horse of GenAI
Deep Mind’s Alpha Tensor
discovers more efficient matrix multiplication
Deep Mind’s Alpha Fold and Meta’s Evolutionary Scale Modelling
predict 3D shape of proteins
Yoshua Bengio’s Generative Flow Networks (Yoshua Bengio)
GT4SD: Generative Toolkit for Scientific Discovery (IBM)
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Scientific Discovery, Optimisation and Configuration

11. Myth: Making LLMs bigger and bigger will lead to AGI
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No, it won’t.
“A system trained on language alone will never
approximate human intelligence, even if trained
from now until the heat death of the universe.”
Yann LeCun, 2022
https://www.noemamag.com/ai-and-the-limits-of-language/

12. The AI Control Problem
If controlling an LLM means making it
provide accurate, truthful and appropriate responses to questions
then they are all out of control.
Developers don’t understand the deep networks they build:
too much alchemy, not enough science & engineering.
The tech industry needs to be better:
implement guardrails and be open about the data used for training.
We need more well-funded dedicated AI Safety research.
Future innovation will come from open source.
There are massive efforts underway in making
models smaller and more efficient.
The future is in combining models (e.g. LangChain)
which is even harder to control.
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We need effective guardrails

13. The Impact of GenAI
If we want it or not, we need to get our head around
GenAI because we all will be exposed to it.
GenAI will change the way we create and consume
information.
We need to encourage responsible use of GenAI.
We need a debate to support the creation of
helpful regulatory frameworks.
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We are witnessing an unprecedented social experiment on a global scale

14. Some Recommended Reading
Matteo Wong: The Difference Between Speaking and Thinking. The Atlantic, 31 Jan 2023.
Elizabeth Weil: You Are Not a Parrot. New York Magazine, 1 Mar 2023.
Kate Crawford: Atlas of AI. Yale University Press, 2021.
Melanie Mitchell: Artificial Intelligence. A Guide for Thinking Humans. Pelican Book, 2019.
Samuel R Bowman: Eight Things to Know about Large Language Models. arXiv:2304.00612, 2 Apr 2023.
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