Introduction

ἐντελέχεια in Artificial Intelligence

As artificial intelligence (AI) steadily develops and transforms, it carries the capacity to reshape numerous facets of our existence. While advancing, it is vital not only to concentrate on its technical prowess but also to contemplate the ethical and societal consequences. We can take inspiration from Aristotle’s ancient Greek idea of entelecheia, which conveys the notion of achieving one’s full potential or accomplishing one’s purpose.

In the context of AI, entelecheia suggests that these technologies should effectively achieve their intended goals while incorporating human-like intelligence features such as understanding context, handling uncertainty, and adapting to change. Additionally, AI should adhere to ethical guidelines that resonate with societal values.

Natural language processing (NLP) is an area where AI can make a significant impact. NLP uses algorithms to understand, interpret, and generate human language. To unlock NLP’s full potential, it’s essential to design it in a manner that genuinely mirrors human language comprehension, capturing the nuances of context and meaning.

AI also has the potential to greatly influence finance and economics by analyzing market data, executing trades, identifying risks, and forecasting market trends. Ensuring these technologies align with societal values, such as fairness and privacy, is of utmost importance.

Additionally, there’s increasing concern about AI systems that can imitate or even surpass human intelligence, commonly known as AGI or Strong AI. The creation and implementation of such technologies must follow a well-defined ethical framework that safeguards humanity’s well-being.

Aristotle’s concept of entelecheia can provide valuable guidance for AI, underlining the importance of realizing its full potential and achieving intended goals. As AI advances, we must address its ethical and societal consequences and strive for entelecheia, ensuring AI serves the greater good of humanity.

Table of Contents

Courses

• Introduction to NLP
  • Introduction
  • NLP Applications
    • Research Part I
    • Research Part II
  • Language Models
    • N-gram Language Models
    • Usage of Language Models
  • Datasets
    • Text Data Collection
    • Lab: Crawling DART Data
  • Topic Modeling
    • Topic Modeling Methodologies
    • Lab: Topic Modeling
    • Topic Coherence Measures
    • Lab: Topic Coherence
    • Lab: Tomotopy
  • Sentiment Analysis
    • Lexicon-Based Methods
    • Machine Learning-Based Methods
    • Lab: Lexicon-based Sentiment Analysis
    • Lab: ML-based Sentiment Classification
  • Tokenization
    • Understanding the Basics
    • Part-of-Speech Tagging and Parsing
    • N-grams for Tokenization
    • Tokenization in Korean
    • Word Segmentation and Association
    • Lab: Tokenization and Pre-processing
    • Lab: Korean Text Processing
  • Vector Representation
    • Vector Semantics
    • Bags of Words Model
    • TF-IDF Model
    • Word Similarity
    • Lab: Word Similarity
  • Word Embeddings
    • Neural Language Models
    • Word2Vec
    • GloVe
    • FastText
• Deep Learning for NLP
  • Large Language Models
    • Zero Shot and Prompt Engineering
    • Decoding and Search Strategies
    • Pretrained Language Models
  • Transformers
    • BERT: Bidirectional Encoder Representations from Transformers
    • BERT: Visualizing Attention
    • T5: Text-To-Text Transfer Transformer
    • ByT5: Towards a token-free future with pre-trained byte-to-byte models
  • Datasets
    • mC4 Dataset
    • Lab: Exploratory Data Analysis (EDA)
  • Tokenization
    • Subword Tokenization
    • Tokenization Pipeline
    • BPE Step-by-Step Implementation
    • WordPiece Step-by-Step Implementation
    • Unigram Step-by-Step Implementation
    • SentencePiece Tokenizer
    • Lab: Training Tokenizers
  • Training Language Models
    • Lab: Pretraining LMs - MLM
    • Lab: Pretraining LMs - CLM
    • Lab: Finetuining a MLM
  • Conversational AI and Chatbots
    • Reinforcement Learning with Human Feedback (RLHF)
    • How to Spot Machine-Written Texts
• Advances in AI and NLP
  • Writing a Thesis
  • Generative Language Models
    • DetectGPT
    • GPT-4
    • Meet the Camelids: A Family of LLMs
  • Segment Anything
• AI Art (Generative AI)
  • Introduction
  • A Brave New World
  • Art and Music in Light of AI
  • Text-to-Image Models
    • DALL·E 1
    • DALL·E 2
    • Imagen
  • Motion Capture and Motion Synthesis
  • Robot Drawing System
• Machine Learning Systems Design
  • Introduction to MLOps
  • MLOps Project
  • DevOps
    • GitOps
    • DevSecOps
  • Dotfiles
    • Dotfiles Project
    • Dotdrop Setup
    • GitHub Setup
    • SSH, GPG, and Age Setup
    • SOPS Usage
    • Pass and Passage Usage
    • Doppler Usage
    • Dockerfiles Usage
  • Security Management
    • Authentication, Encryption, and Signing
    • SSH, GPG, and AGE
    • Unix Password Managers
  • GitHub Workflow
    • Github’s Fork & Pull Workflow
    • Project Templating Tools
    • Hyperfast Python Template
    • Hyperfast Template
  • Containerization
    • Docker
    • containerd
  • Simple MLOps Pipeline
    • Server Setup & Usage
    • VPN Connectivity
  • LLMOps
    • Introduction to BentoML
    • Deploy a Voice-Based Chatbot with BentoML, LangChain, and Gradio
• Data Science for Economics and Finance
  • Introduction
    • Data Science in Economics
    • Technical Challenges
    • Data Analytics Methods
  • Central Banks
    • Alternative Data Sources for Central Banks
  • Textual Analysis of FOMC contents
    • Preparing Numerical Data
    • Preparing Textual Data
    • EDA on Numerical Data
    • EDA on Numerical Data
    • Create Training Datasets
    • Visualizing Features
    • Checking Baseline with AutoML
    • Predicting Sentiments of FOMC Corpus
    • EDA on Sentiments: Correlation
    • EDA on Sentiment Data
    • Visualize Features
    • Monetary Policy Shocks
    • Predicting the next decisions with tones
  • ESG Ratings
    • Preparing training datasets
    • Improving classification datasets
    • Training Classifiers for ESG Ratings
    • Building econ_news_kr corpus
    • Predicting ESG Categories and Polarities
    • Cross validating datasets
    • Preparing active learning data
    • Putting them together in a pipeline
• Software Engineering
  • Introduction
    • Software Engineering?
    • Software Processes
    • Software Development Life Cycle (SDLC)
    • Requirements Engineering (RE)
  • Project Proposal
    • Steps in Software Engineering Projects
    • Software Engineering Proposal Guideline
    • Project Proposal Template
  • Version Control Systems
    • 0. Introduction to version control
    • 1. Solo work with git
    • 2. Fixing mistakes
    • 3. Publishing
    • 4. Collaboration
    • 5. Fork and Pull
    • 6. Git Theory
    • 7. Branches
    • 8. Advanced git concepts
    • 9. Publishing from GitHub
    • 10. Rebasing
    • 11. Debugging With git bisect
    • 12. Working with multiple remotes
  • Software Process Models
    • Agile Software Development
  • DevOps
    • GitOps
• Large Language Models
  • Introduction
    • Large Language Models?
  • LLM Stacks
    • Generative AI Infrastructure Stack
    • LLM Application Architectures
    • LLM App Ecosystem
  • AI Agents
    • AutoGen
    • AutoGen AutoScraper Agent
  • LLM Fine-tuning
    • Fine-Tuning LLMs with Hugging Face AutoTrain
  • Retrieval Augmented Generation (RAG)
  • Parameter-Efficient Fine-Tuning (PEFT)
    • PEFT for LLMs
    • PEFT in HuggingFace Libraries
  • Q-Learning
    • Q-Star (Q*)

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