When given a prompt, an app built on the GPT-3 language model can generate an entire essay. Why would we need such an essay? Maybe the more important question is: What harm can such an essay bring about? I couldn’t get that question out of my mind after I came across a tweet by Abeba… Continue reading What is the good in GPT-3?
GPT-3 has to be the most-hyped AI technology of the past year. Headlines said its predecessor, GPT-2, was “too dangerous” to be released publicly. Then it was released. The world did not end. Less than a year later, the more advanced (next generation) GPT-3 was released by OpenAI. Why are people so excited about GPT-3?… Continue reading GPT-3 and automated text generation
Yesterday I summarized the first two articles in a series about algorithms and AI by Hayden Field, a technology journalist at Morning Brew. Today I’ll finish out the series. The third article, This Powerful AI Technique Led to Clashes at Google and Fierce Debate in Tech. Here’s Why, explores the basis of the volatile situation… Continue reading The trouble with large language models
Let’s begin at the beginning, with Attention Is All You Need (Vaswani et al., 2017). This is a conference paper with eight authors, six of whom then worked at Google. They contended that neither recurrent neural networks nor convolutional neural networks are necessary for machine translation of languages, and hence the Transformer, “a new simple… Continue reading Attention, in machine learning and NLP
It was a challenge for me to figure out how to teach non–computer science students about word vectors. I wanted them to have a clear idea of how words and their meanings are represented for use in an AI system — otherwise, I worried they would assume something like a written dictionary with text and… Continue reading Figuring It Out: Transformers for NLP