Introduction to Generative AI An ethical societal and legal overview 1st Edition by Numa Dhamani, Maggie Engler ISBN 9781638354345 1638354340

1 Large language models: The power of AI

Evolution of natural language processing

The birth of LLMs: Attention is all you need

Explosion of LLMs

What are LLMs used for?

Language modeling

Question answering

Coding

Content generation

Logical reasoning

Other natural language tasks

Where do LLMs fall short?

Training data and bias

Limitations in controlling machine outputs

Sustainability of LLMs

Revolutionizing dialogue: Conversational LLMs

OpenAI’s ChatGPT

Google’s Bard/LaMDA

Microsoft’s Bing AI

Meta’s LLaMa/Stanford’s Alpaca

Summary

2 Training large language models

How are LLMs trained?

Exploring open web data collection

Demystifying autoregression and bidirectional token prediction

Fine-tuning LLMs

The unexpected: Emergent properties of LLMs

Quick study: Learning with few examples

Is emergence an illusion?

What’s in the training data?

Encoding bias

Sensitive information

Summary

3 Data privacy and safety with LLMs

Safety-focused improvements for LLM generations

Post-processing detection algorithms

Content filtering or conditional pre-training

Reinforcement learning from human feedback

Reinforcement learning from AI feedback

Navigating user privacy and commercial risks

Inadvertent data leakage

Best practices when interacting with chatbots

Understanding the rules of the road: Data policies and regulations

International standards and data protection laws

Are chatbots compliant with GDPR?

Privacy regulations in academia

Corporate policies

Summary

4 The evolution of created content

The rise of synthetic media

Popular techniques for creating synthetic media

The good and the bad of synthetic media

AI or genuine: Detecting synthetic media

Generative AI: Transforming creative workflows

Marketing applications

Artwork creation

Intellectual property in the LLM era

Copyright law and fair use

Open source and licenses

Summary

5 Misuse and adversarial attacks

Cybersecurity and social engineering

Information disorder: Adversarial narratives

Political bias and electioneering

Why do LLMs hallucinate?

Misuse of LLMs in the professional world

Summary

6 Accelerating productivity: Machine-augmented work

Using LLMs in the professional space

LLMs assisting doctors with administrative tasks

LLMs for legal research, discovery, and documentation

LLMs augmenting financial investing and bank customer service

LLMs as collaborators in creativity

LLMs as a programming sidekick

LLMs in daily life

Generative AI’s footprint on education

Detecting AI-generated text

How LLMs affect jobs and the economy

Summary

7 Making social connections with chatbots

Chatbots for social interaction

Why humans are turning to chatbots for relationship

The loneliness epidemic

Emotional attachment theory and chatbots

The good and bad of human-chatbot relationships

Charting a path for beneficial chatbot interaction

Summary

8 What’s next for AI and LLMs

Where are LLM developments headed?

Language: The universal interface

LLM agents unlock new possibilities

The personalization wave

Social and technical risks of LLMs

Data inputs and outputs

Data privacy

Adversarial attacks

Misuse

How society is affected

Using LLMs responsibly: Best practices

Curating datasets and standardizing documentation

Protecting data privacy

Explainability, transparency, and bias

Model training strategies for safety

Enhanced detection

Boundaries for user engagement and metrics

Humans in the loop

AI regulations: An ethics perspective

North America overview

EU overview

China overview

Corporate self-governance

Toward an AI governance framework

Summary

9 Broadening the horizon: Exploratory topics in AI

The quest for artificial general intelligence

AI sentience and consciousness?

How LLMs affect the environment

The game changer: Open source community

Summary

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