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
- Introduction to NLP
- Deep Learning for NLP
- Advances in AI and NLP
- AI Art (Generative AI)
- Machine Learning Systems Design
- Data Science for Economics and Finance
- Introduction
- 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
- Software Engineering
- Large Language Models