Autonomous AI

new computing innovations :: Article Creator The Future Of Cloud Computing: AI-Powered And Driven By Innovation AI sign displayed on a phone screen - Photo by Jakub Porzycki/NurPhoto via Getty Images NurPhoto via Getty Images Both Microsoft and Alphabet, the parent company of Google, have recently reported their respective Q2 earnings results, showcasing their resilience and growth in the dynamic digital and macroeconomic landscape. Microsoft, a 48-year-old tech behemoth, achieved its highest-ever earning results, fueled by the transformative power of artificial intelligence (AI). Conversely, Google outperformed expectations, bolstered by robust advertising revenue from its Google search business and YouTube platform. I recently had the opportunity to spend time on the Trade Talks show with Jill Malandrino and the TD Ameritrade Network during the same week as these earning announcements. In these interviews, I double-clicked on the growth str

natural language processing with classification and vector spaces :: Article Creator Testing Language Models With The Philosophy Of Wittgenstein Nowadays everybody is talking about the new large language models (LLMs), such as GPT. I feel like it's time to talk about a point of view that is too often forgotten while testing them. More than ever, we are confronted with models in various contexts, and it is our job to ensure their reliability, robustness, and unbiasedness. Too often we rely on some technical method that some expert decided is the best fit for modern AI. What matters now is to question language itself, which forms the basis of LLMs after all. Since I have a bit of a linguistics background, in this article, I'm going to introduce you to some basic ideas demonstrating that some of the best insights might actually not be technical in nature. Entering the realm of language and technology: who would be more suitable than the analy

natural language in computing :: Article Creator Researchers Discover New Vulnerability In Large Language Models Large language models (LLMs) use deep-learning techniques to process and generate human-like text. The models train on vast amounts of data from books, articles, websites and other sources to generate responses, translate languages, summarize text, answer questions and perform a wide range of natural language processing tasks. This rapidly evolving artificial intelligence technology has led to the creation of both open- and closed-source tools, such as ChatGPT, Claude and Google Bard, enabling anyone to search and find answers to a seemingly endless range of queries. While these tools offer significant benefits, there is growing concern about their ability to generate objectionable content and the resulting consequences. Researchers at Carnegie Mellon University's School of Computer Science (SCS), the CyLab Security and Privac

nlp analysis :: Article Creator Analysis Of Court Transcripts Reveals Biased Jury Selection Cornell researchers have shown that data science and artificial intelligence tools can successfully identify when prosecutors question potential jurors differently, in an effort to prevent women and Black people from serving on juries. In a first-of-its-kind study, researchers used natural language processing (NLP) tools to analyze transcripts of the jury selection process. They found multiple quantifiable differences in how prosecutors questioned Black and white members of the jury pool. Once validated, this technology could provide evidence for appeals cases and be used in real time during jury selection to ensure more diverse juries. The study, "Quantifying Disparate Questioning of Black and White Jurors in Capital Jury Selection," was published July 14 in the Journal of Empirical Legal Studies. First author is Anna Effenberger. Strikin