"From the invention of writing and the printing press, to television and social media, human history is punctuated by major innovations in communication technology, which fundamentally altered how ideas spread and reshaped our culture. Recent chatbots powered by generative artificial intelligence constitute a novel medium that encodes cultural patterns in their neural representations and disseminates them in conversations with hundreds of millions of people. Understanding whether these patterns transmit into human language, and ultimately shape human culture, is a fundamental question. While fully quantifying the causal impact of a chatbot like ChatGPT on human culture is very challenging, lexicographic shift in human spoken communication may offer an early indicator of such broad phenomenon. Here, we apply econometric causal inference techniques [10] to 740,249 hours of human discourse from 360,445 YouTube academic talks and 771,591 conversational podcast episodes across multiple disciplines. We detect a measurable and abrupt increase in the use of words preferentially generated by ChatGPT—such as delve, comprehend, boast, swift, and meticulous—after its release. These findings suggest a scenario where machines, originally trained on human data and subsequently exhibiting their own cultural traits, can, in turn, measurably reshape human culture. This marks the beginning of a closed cultural feedback loop in which cultural traits circulate bidirectionally between humans and machines. Our results motivate further research into the evolution of human-machine culture, and raise concerns over the erosion of linguistic and cultural diversity, and the risks of scalable manipulation." https://arxiv.org/pdf/2409.01754

While thinking how to recommend the book Nexus, and remembering there were some book talks before, I've had an idea: How about some kind of book club around the different subjects of this course? We could share all kinds of books about AI, Data Science from the instructional, reflexive, fun, motivational aspects. Edit: The Book Club is on and is here: https://www.skool.com/data-alchemy/data-alchemy-book-club?p=12996f1d

Hello Everyone, I'm currently working on an assignment to develop a machine learning program for calculating credit scores, and I wanted to reach out to tap into the collective expertise of this community. One of the approaches I’ve come across is basing the credit score on the payment history of the last 6 months. The payment history could be extracted from sources such as bank statements or mobile wallet transaction records. Before moving forward, I’d love to hear your thoughts on this approach. Do you think using a 6-month payment history is a reliable predictor of creditworthiness? Are there any other factors or data points you would suggest integrating for a more robust model? Any feedback or suggestions would be greatly appreciated! Looking forward to your insights.

The EU Commission has just published the guidelines to clarify how to properly assess if an AI system poses unacceptable risks to humans (and thus cannot be commercialised within the EU market). Only an egyptologist can understand them properly but I will put all my efforts to clarify them anyway. https://digital-strategy.ec.europa.eu/en/library/commission-publishes-guidelines-prohibited-artificial-intelligence-ai-practices-defined-ai-act. Any suggestion is welcome!

The EU has announced a $56M investment to develop this new open-source large language model. Apparently it will provide for fully open models, software and data (that can be fine-tuned at a later stage). The final purpose is to create an open-source LLM - ex ante in compliance with the (burdensome) EU regulations - that European firms and public authorities can build upon. Here you can find the press release: https://openeurollm.eu/launch-press-release. Views?