Generative Technologies: About this guide

"From this perspective, I am convinced that the development of artificial intelligence and machine learning has the potential to contribute in a positive way to the future of humanity; we cannot dismiss it. At the same time, I am certain that this potential will be realized only if there is a constant and consistent commitment on the part of those developing these technologies to act ethically and responsibly."

"As an MIT professor with a prestigious career, he was, in his words, a “high priest, if not a bishop, in the cathedral to modern science”. But by the 1970s, Joseph Weizenbaum had become a heretic, publishing articles and books that condemned the worldview of his colleagues and warned of the dangers posed by their work. Artificial intelligence, he came to believe, was an “index of the insanity of our world.”"

"The history of artificial intelligence is, therefore, not just the history of mechanical attempts to replicate or replace some static notion of human intelligence, but also a changing account of how we think about intelligence itself."

"Artificial intelligence (AI) is the field devoted to building artificial animals (or at least artificial creatures that – in suitable contexts – appear to be animals) and, for many, artificial persons (or at least artificial creatures that – in suitable contexts – appear to be persons)."

"Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered data, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future."

"The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.”"

"A key innovation of LLMs is that they don’t need explicitly labeled data. Instead, they learn by trying to predict the next word in ordinary passages of text. Almost any written material—from Wikipedia pages to news articles to computer code—is suitable for training these models."

"If we have a large enough neural network as well as enough data, the LLM becomes really good at predicting the next word. Will it be perfect? No, of course not, since there are often multiple words that can follow a sequence. But it will become good at selecting one of the appropriate words that are syntactically and semantically appropriate."

"There’s a big difference between imitation and reality. In the later sections of this anthropological discussion, we talked about relationship. One of the big parts of relationship is having empathy for the other person. To truly have empathy, you need to actually feel something, to have emotion, not just mimic it."

"Metaphors and historical comparisons are always imperfect. They often entail a trade-off between fidelity and simplicity with a touch of motivation to drive an argument forward. Overusing them inadvertently damages the quality of the conversation in a way that affects our understanding and that of others."

"We noticed that many articles tend to mislead in similar ways, so we analyzed over 50 articles about AI from major publications, from which we compiled 18 recurring pitfalls. We hope that being familiar with these will help you detect hype whenever you see it. We also hope this compilation of pitfalls will help journalists avoid them."

"End-of-days hype surrounds many AI firms, but their technology already enables myriad harms, including routine discrimination in housing, criminal justice and health care, as well as the spread of hate speech and misinformation in non-English languages. Algorithmic management programs subject workers to run-of-the-mill wage theft, and these programs are becoming more prevalent."