Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rules to implement iterative retrieval. This introduces additional inference overhead and overlooks the remarkable reasoning capabilities of Large Language Models (LLMs). In this paper, we introduce Auto-RAG, an autonomous iterative retrieval model centered on the LLM's powerful decision-making capabilities. Auto-RAG engages in multi-turn dialogues with the retriever, systematically planning retrievals and refining queries to acquire valuable knowledge. This process continues until sufficient external information is gathered, at which point the results are presented to the user. To this end, we develop a method for autonomously synthesizing reasoning-based decision-making instructions in iterative retrieval and fine-tuned the latest open-source LLMs. The experimental results indicate that Auto-RAG is capable of autonomous iterative interaction with the retriever, effectively leveraging the remarkable reasoning and decision-making abilities of LLMs, which lead to outstanding performance across six benchmarks. Further analysis reveals that Auto-RAG can autonomously adjust the number of iterations based on the difficulty of the questions and the utility of the retrieved knowledge, without requiring any human intervention. Moreover, Auto-RAG expresses the iterative retrieval process in natural language, enhancing interpretability while providing users with a more intuitive experience\footnote{Code is available at \url{https://github.com/ictnlp/Auto-RAG}.

Imagine knowing, in real time, whether a patient will suffer a surgical infection as a surgeon closes up a wound. That's the kind of clinical situation that machine learning is enabling at the University of Iowa Hospitals & Clinics. To date, the health system's innovation with AI analytics has led to a 74 percent reduction in surgical site infection over a three-year period, which at scale is a $1.2 million cost savings – not including savings from value-based purchasing because of the reduced surgical site infection rate. Iowa's work with comes as more and more hospitals and tech vendors are undertaking innovative initiatives with machine learning and artificial intelligence. Johns Hopkins for instance, is using deep learning to improve how it handles pancreatic cancer and Amazon Web Services is harnessing machine learning to enable customers to better treat depression.

Imagine talking to a tiger, chatting to a cheetah, as Dr Doolittle once sang – what a neat achievement that would be. Well, Amazon has revealed that the animal-loving doctor's ambition might not be entirely fantasy. Pet translators that can turn woofs into words and make sense of miaows, might really be on the horizon, according to a report backed by the internet retailer. Futurologist William Higham of Next Big Thing, who co-authored the report for Amazon, says he believes devices that can talk dog could be less than 10 years away. "Innovative products that succeed are based around a genuine and major consumer needs. The amount of money now spent on pets – they are becoming fur babies to so many people – means there is huge consumer demand for this. Somebody is going to put this together," he says.

A prototype Moby Mart is being tested in Shanghai, China. Per Cromwell, the project's lead designer, says four to six additional mobile supermarkets are planned in the coming year. A prototype Moby Mart is being tested in Shanghai, China. Per Cromwell, the project's lead designer, says four to six additional mobile supermarkets are planned in the coming year. Browse the science fiction aisles and you can find all sorts of dystopian future visions -- environmental catastrophes, robot overlords, zombies swarms, triffids.