In an era where artificial intelligence (AI) is increasingly permeating various aspects of our lives, the academic world is also faced with the challenge of dealing with this rapid technological development. This is particularly true regarding final theses and term papers, raising the question of how we, as educational institutions, should handle the use of foundation models like ChatGPT, Google Gemini, and other language-based models (LLMs).
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Category: NLP
Posts on topics about Natural Language Processing, incl. Large Language Models (LLMs) like ChatGPT
Building and Backtesting a Volatility-based Trading Strategy with ChatGPT
This post presents a real highlight: We will build and backtest a quantitative trading strategy in R with the help of OpenAI’s ChatGPT-4! If you want to get a glimpse into the future of trading system development, read on!
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Attention! What lies at the Core of ChatGPT? (Also as a Video!)
Word embedding, self-attention, and next-word prediction lie at the core of LLMs like ChatGPT. If you are curious about how these techniques work and want to see a simple example in R, read on!
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Create Texts with a Markov Chain Text Generator… and what this has to do with ChatGPT!
Everybody and their dog are talking about ChatGPT from OpenAI. If you want to get an intuition about what lies at the core of such Language Models, read on!
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ELIZA Chatbot in R: Build Yourself a Shrink
More and more companies use chatbots for engaging with their customers. Often the underlying technology is not too sophisticated, yet many people are stunned at how human-like those bots can appear. The earliest example of this was an early natural language processing (NLP) computer program called Eliza created 1966 at the MIT Artificial Intelligence Laboratory by Professor Joseph Weizenbaum.
Eliza was supposed to simulate a psychotherapist and was mainly created as a method to show the superficiality of communication between man and machine. Weizenbaum was surprised by the number of individuals who attributed human-like feelings to the computer program, including his own secretary!
If you want to build a simple Eliza-like chatbot yourself with R read on!
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Learning Data Science: Sentiment Analysis with Naive Bayes
As we have already seen in former posts simple methods can be surprisingly successful in yielding good results (see e.g Learning Data Science: Predicting Income Brackets or Teach R to read handwritten Digits with just 4 Lines of Code).
If you want to learn how some simple mathematics, known as Naive Bayes, can help you find out the sentiment of texts (in this case movie reviews) read on!
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Clustering the Bible
During this time of year, there is obviously a lot of talk about the Bible. As most people know the New Testament comprises four different Gospels written by anonymous authors 40 to 70 years after Jesus’ supposed crucifixion.
Unfortunately, we have lost all of the originals but only retained copies of copies of copies (and so on) which date back hundreds of years after they were written in all kinds of different versions (renowned Biblical scholar Professor Bart Ehrmann states that there are more versions of the New Testament than there are words in the New Testament). Just as a fun fact: there are many more Gospels but only those four were included in the “official” Bible.
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