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The Rise of

Generative AI

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Farrukh Nauman 

RISE Research Institutes of Sweden AB

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RISE

  • ~3500 employees
  • Spread all over Sweden
  • Public Research Organization
  • Headquarters: Borås

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Center for applied AI

Transport and mobility

Built environment

Digital innovation

Computer�vision

Interaction design

Food and agriculture

Energy systems

Digital ethics

Advanced�data analysis

Deep�learning

Chemistry and pharma

Healthcare

Automated reasoning

Policy innovation

Industrial automation

Climate

change

Knowledge representa-tion

Natural�language

processing

Planning and scheduling

Education

Cyber security

AI agenda

AI �platforms and edge

Software engineering

AI at RISE�(nearly 500 AI researchers/developers)

Materials and production

ICE�data center

ToD:�Cyber Range

Telecom

Technology�areas��Synergistic�areas��Application�areas��Test & Demo��Special�projects

ToD:�5G/Edge

ToD:�AstaZero

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Hot off the press

  • LLMs: Two more GPT-4 class models �Claude-Opus�Gemini-Ultra
  • 3D Vision and Video models�OpenAI Sora�Stable Diffusion Video
  • Others �Hardware improvements: Intel, AMD, …�Agents: Devlin (Autonomous software engineer),

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Agenda

  • Overview: AI, Why now?
  • Large Language Models: Training, Evaluations, RAG
  • Multi-modal Models
  • Societal Impact

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AI Overview

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What is AI?

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Stuart J. Russell and Peter Norvig, "Artificial Intelligence A Modern Approach (Third Edition)", Pearson, 2010.

ISO/IEC 22989:2022 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology

AI

<discipline> research and development of mechanisms and applications of AI systems

AI System

engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives

Thinking Humanly:

The cognitive modelling approach

Thinking Rationally:

The “laws of thought” approach

Acting Humanly:

The Turing Test approach

Acting Rationally:

The rational agent approach

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What is AI?

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AI

ML

Symbolic

AI

Deep Learning

Hybrid

AI

Expert Systems

Statistical Learning

Neural Symbolic Systems

Logic

Illustration Inspired by:

Digitalized Transformation Guides vol. 2, Loughborough University

Jerod Santo, “Disentangling AI, machine learning, and deep learning”, Changelog news, 2021-04-29

Calegari, R., Ciatto, G., Denti, E., & Omicini, A. (2020). Logic-based technologies for intelligent systems: State of the art and perspectives. Information, 11(3), 167.

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Kaiming He, Deep Learning Bootcamp, MIT Schwarzman College of Computing

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Kaiming He, Deep Learning Bootcamp, MIT Schwarzman College of Computing

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Why now?

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Model architecture

Data

Computing power

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Large Language Models

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Best LLMs (March 13th, 2024)

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https://chat.lmsys.org/

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Best LLMs (Hallucinations)

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https://huggingface.co/spaces/vectara/leaderboard

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Large Language Models���Training��Scaling��Enhancements: RAG, Agents��Evaluations

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Phase 1

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Jay Alammar, How GPT3 Works - Visualizations and Animations

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Phase 2: Instruction finetuning

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Hyung Won Chung, Large Language Models in 2023.

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Why Phase 3: RLHF?

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1. Pretraining: Predict the next token, minimize perplexity.

2. Instruction finetuning: Given an input instruction, predict the single correct response. ("behavior cloning" in Reinforcement Learning).

Problem: How do we get answers to more complex questions?

Hyung Won Chung, Large Language Models in 2023.

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Reinforcement Learning

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Hyung Won Chung, Large Language Models in 2023.

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Annotations

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Weng, Lilian. (Feb 2024). “Thinking about High-Quality Human Data”. Lil’Log.

Aroyo, L., & Welty, C. (2015). Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation. AI Magazine36(1), 15-24.

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Human preferences

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“Training language models to follow instructions with human feedback”, Oyang et al.,  2022; arXiv:2203.02155.

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Hyung Won Chung, Large Language Models in 2023.

Using human preference annotations

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Scaling

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Sasha Rush, LLMs in 5 formulas

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Scaling

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Sasha Rush, LLMs in 5 formulas

Overparametrized

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Emergence

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  1. Unpredictability: Transition at unexpected scales.
  2. Sharpness: Sudden jump in performance.

Are Emergent Abilities of Large Language Models a Mirage?, R. Schaeffer, B. Miranda, Sanmi Koyejo, NeurIPS Outstanding Paper 2023

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Emergence

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Are Emergent Abilities of Large Language Models a Mirage?, R. Schaeffer, B. Miranda, Sanmi Koyejo, NeurIPS Outstanding Paper 2023

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Limitations

  • Inference speed (Solutions: speculative decoding, mixture of experts, …)�
  • Finetuning too expensive (Solutions: LoRA – Low ranked quantization that does not change base weights)�
  • Exposure of private data (Solutions: Use a locally hosted small LM; Azure GPT-4 custom finetunes)�
  • High compute requirements (Solutions: Use (expensive) APIs – no need for local compute; Small LMs, New hardware improvements – hard coded transformer weights, neuromorphic computing, Intel Gaudi2, …)�
  • Hallucination and data duplication risks (Solutions: Better evaluations and finetunes)

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Limitations

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Yann LeCun, Do large language models need sensory grounding for meaning and understanding?

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Retrieval Augmented Generation

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Akari Asai, https://acl2023-retrieval-lm.github.io/

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Retrieval Augmented Generation

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Akari Asai, https://acl2023-retrieval-lm.github.io/

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Retrieval Augmented Generation

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Akari Asai, https://acl2023-retrieval-lm.github.io/

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Retrieval Augmented Generation

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Akari Asai, https://acl2023-retrieval-lm.github.io/

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Akari Asai, https://acl2023-retrieval-lm.github.io/

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Agents

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Lilian Weng, Open AI

Information retrieval

Translation across languages and frameworks

Improve productivity

Planning

Code and content creation

Chain of thought and self reflection

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Evaluations

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Open-source benchmarks not reliable due to data leakage.��GPT-4/Opus as judge

Gold standard dataset (specific to domain; not shared publicly)

Arenas are generally more reliable:�Chatbot Arena�RAG Arena�VLM Arena�

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Multi-Modal Models

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Multi-modal models

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https://huggingface.co/spaces/WildVision/vision-arena

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CAD

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Generating realistic 3D objects using a text prompt – useful for 3D printing, 3D design, etc.

TheGradient Blog: text to cad

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3D and Video

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Input: �Text�Single image

Novel view synthesis

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3D and Video

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Societal Impact

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Concerns

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Concerns

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Risk mitigation

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Conclusions

  • Generative AI is exciting and enables innovation.�
  • Challenges: �1. Deployment (speed, local vs cloud hosting).�2. Risks (privacy, data duplication, …)�
  • A two edged-sword

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Farrukh

Nauman

Researcher in�Generative AI

farrukh.nauman@ri.se

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KONTAKTER

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