ChatGPT: AI tools, applications and implications

Ali Abbas

Agenda

• Intro to ChatGPT
• State of the art
• Examples
• Tools using OpenAI models like ChatGPT (or similar) models
• Issues
• Summary

Not an expert so talking as someone with an interest in this area Brief overview of OpenAI and ChatGPT Some examples using OpenAI models - like GPT 3 Might take you on a tour of an AI tool Issues and summary at the end

ChatGPT

Overview

• ChatGPT: Optimizing Language Models for Dialogue
• Created by OpenAI
• Debut in November 2022
• Viral due to usability and accessibility
• Built on GPT-3.5 series with Reinforcement Learning from Human Feedback
• Trained on a dataset of over 40GB of text data
• With 175 billion parameters

(Some of) Applications

• Text generation
  • Coherent, creative on any topic and in specific style
• Language translation
  • Translation, idiomatic expressions and remembers context
• Dialogue systems
  • Conversational agents such as chatbots
• Summarization
  • Long to short summary and extract key information

State of the Art

Taxonomy AI models

State of the art - popular generative models Gozalo-Brizuela and Garrido-Merchan (2023)

Companies

AI companies Gozalo-Brizuela and Garrido-Merchan (2023)

Timeline

Timeline Gozalo-Brizuela and Garrido-Merchan (2023)

Examples

Note to neighbour

Poem

Poem on benefits of Walking and Cycling

Perplexity

GPT based search engine

Children’s exposure to PM2.5 affecting their health

Elicit

GPT-3 powered AI tool for Literature Review

TutorAI

OpenAI powered AI tool for creating educational content

RTutor

OpenAI’s powerful text-davinci-003 language model to translate natural language into R/Python code

GPT Tools

Comment code

Convert a Script into Functions

GPT Studio

Add-on for RStudio for incorportaing OpenAI into project workflows

Code

OpenAI R library

Example

Specify a string to create an image

library(openai)
library(magick)

img <- openai::create_image("simplicity")

print(image_read(img$data$url))

  format width height colorspace matte filesize density
1    PNG  1024   1024       sRGB FALSE  3147977   72x72

Directory of AI tools

Futurepedia

An AI tools library

Issues and Summary

Issues

• Easier access and universal applicability
• Black box model

  • Limited knowledge on how they work

  • When they fail

  • Capabilities due to emergent properties - anxiety about unanticipated consequences

  • Context is not known either

  • Downstream bias: intrinsic bias, representational bias, underrepresented and over-represented (Anglocentric population) Bommasani et al. (2021)

  • Compounds existing inequities by producing unfair outcomes, entrenching systems of power, and disproportionately distributing negative consequences of technology to those already marginalized Bommasani et al. (2021)

• (Almost) exclusively build by industry (Google, Facebook, Microsoft, or Huawei, or startups such as OpenAI) Bommasani et al. (2021)
• Nefarious purpose: fake news/propagandas, misinformation or any other harmful purpose

Summary

• Useful in assistance but with almost no transparency or reproducibility, so hard to justify in an academic setting

• Needs to be audited before wider use by academia or general public - legislation perhaps?

• Rumour has it Google is lobbying DC to shut down other AI efforts, by scaremongering about how unsafe they are

• Need a similar but more transparent and less power/computational model

  • Among others, Alan Turing Institute is already working on it

• Unless careful attention is applied, it may produce erroneous or even dangerous outputs

References / further reading

• Futurepedia: AI Tools directory
• Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., ... & Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
• Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., ... & Wu, Y. (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. arXiv preprint arXiv:2301.07597.
• Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023). An Analysis of the Automatic Bug Fixing Performance of ChatGPT. arXiv preprint arXiv:2301.08653.

Bommasani, Rishi, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, et al. 2021. “On the Opportunities and Risks of Foundation Models.” https://doi.org/10.48550/ARXIV.2108.07258.

Gozalo-Brizuela, Roberto, and Eduardo C. Garrido-Merchan. 2023. “ChatGPT Is Not All You Need. A State of the Art Review of Large Generative AI Models.” https://doi.org/10.48550/ARXIV.2301.04655.