Long-Context Question Answering (LCQA), a challenging task, aims to reason over long-context documents to yield accurate answers to questions. Existing long-context Large Language Models (LLMs) for LCQA often struggle with the "lost in the middle" issue. Retrieval-Augmented Generation (RAG) mitigates this issue by providing external factual evidence. However, its chunking strategy disrupts the global long-context information, and its low-quality retrieval in long contexts hinders LLMs from identifying effective factual details due to substantial noise. To this end, we propose LongRAG, a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG's understanding of complex long-context knowledge (i.e., global information and factual details). We design LongRAG as a plug-and-play paradigm, facilitating adaptation to various domains and LLMs. Extensive experiments on three multi-hop datasets demonstrate that LongRAG significantly outperforms long-context LLMs (up by 6.94%), advanced RAG (up by 6.16%), and Vanilla RAG (up by 17.25%). Furthermore, we conduct quantitative ablation studies and multi-dimensional analyses, highlighting the effectiveness of the system's components and fine-tuning strategies. Data and code are available at https://github.com/QingFei1/LongRAG.

The application of Artificial Intelligence in the medical market brings up increasing concerns but aids in more timely diagnosis of silent progressing diseases like Diabetic Retinopathy. In order to diagnose Diabetic Retinopathy (DR), ophthalmologists use color fundus images, or pictures of the back of the retina, to identify small distinct features through a difficult and time-consuming process. Our work creates a novel CNN model and identifies the severity of DR through fundus image input. We classified 4 known DR features, including micro-aneurysms, cotton wools, exudates, and hemorrhages, through convolutional layers and were able to provide an accurate diagnostic without additional user input. The proposed model is more interpretable and robust to overfitting. We present initial results with a sensitivity of 97% and an accuracy of 71%. Our contribution is an interpretable model with similar accuracy to more complex models. With that, our model advances the field of DR detection and proves to be a key step towards AI-focused medical diagnosis.

For the last decade, Amazon has been building an army of robot employees to sort packages and move products safely around its warehouses. Now the company has unveiled its latest robot called Proteus, which it describes as its'first fully autonomous mobile robot'. Proteus is designed to work alongside humans, moving large trolleys full of packages around the warehouse floor. The robot uses Amazon's own safety, perception, and navigation technology to move around autonomously and avoid bumping into human workers. 'Historically, it's been difficult to safely incorporate robotics in the same physical space as people,' the company said in a blog post.

A new report reveals that robots working in Amazon fulfillment centers are leading to more injuries among human employees - although the e-commerce giant claims the technology reduces incidents. Based on internal records from 150 warehouses, serious injuries were 50 percent higher at facilities with robots than those without, according to the Center for Investigative Reporting's news site, Reveal. There were 14,000 serious injuries in 2019 - a spike of nearly 33 percent from 2015, and nearly double the industry average. The overall injury rate for the 150 facilities was also almost double the industry standard, according to Reveal. Amazon insisted its numbers are inflated because it encourages workers to report even minor incidents.

Guess who's getting used to working with robots in their everyday lives? The very same warehouse workers once predicted to be losing their jobs to mechanical replacements. According to their makers, the machines should take on the most mundane and physically strenuous tasks. "They weigh a lot," Amazon worker Amanda Taillon said during the pre-Christmas rush at a company warehouse in Connecticut. Taillon's job is to enter a cage and tame Amazon's wheeled warehouse robots for long enough to pick up a fallen toy or relieve a traffic jam. She straps on a light-up utility belt that works like a superhero's force field, commanding the nearest robots to abruptly halt and the others to slow down or adjust their routes.

Guess who's getting used to working with robots in their everyday lives? The very same warehouse workers once predicted to be losing their jobs to mechanical replacements. According to their makers, the machines should take on the most mundane and physically strenuous tasks. "They weigh a lot," Amazon worker Amanda Taillon said during the pre-Christmas rush at a company warehouse in Connecticut. Taillon's job is to enter a cage and tame Amazon's wheeled warehouse robots for long enough to pick up a fallen toy or relieve a traffic jam. She straps on a light-up utility belt that works like a superhero's force field, commanding the nearest robots to abruptly halt and the others to slow down or adjust their routes.

A California firm has revealed a radical new AI'pilot assistance' system it hopes could change they way we travel. SkyRyse says its first craft will begin operations in Tracy, California, in January where it will provide support to the city's emergency response units, including law enforcement, search and rescue missions, and firefighters. The system will eventually allow helicopters and other vehicles to fly themselves, and its founder hopes to take on Uber and others developing'flying taxi' services. SkyRyse says its first craft will begin operations in Tracy, California, in January providing support to emergency response units. It will eventually allow helicopters and other vehicles to fly themselves.

As Amazon gears up for Cyber Monday and the busy holiday shopping season, it's getting help from thousands of robots that search through millions of items to ensure the right item gets shipped to the right customer. Kara Tsuboi takes us inside an Amazon fulfillment center in Tracy, Calif., to watch the robots in action.

The first voice you hear on the recording is mine. "Here we are," I say. My tone is cheerful, but a catch in my throat betrays how nervous I am. "Esquire," a second voice on the recording chimes in, and this one word--delivered as a winking parody of lawyerly pomposity--immediately puts me more at ease. The speaker is my dad. We are sitting across from each other in my parents' bedroom, him in a rose-colored armchair and me in a desk chair. It's the same room where, decades ago, he calmly forgave me after I confessed that I'd driven the family station wagon through a garage door. Now it's May 2016, he is 80 years old, and I am holding a digital audio recorder. Sensing that I don't quite know how to proceed, my dad hands me a piece of notepaper marked with a skeletal outline in his handwriting. It consists of just a few broad headings: "Family History." "So … do you want to take one of these cat egories and dive into it?" "I want to dive in," he says confidently. "Well, in the first place, my mother was born in the village of Kehries--K-e-h-r-i-e-s--on the Greek island of Evia …" With that, the session is under way. We are sitting here, doing this, because my father has recently been diagnosed with stage IV lung cancer. The disease has metastasized widely throughout his body, including his bones, liver, and brain. It is going to kill him, probably in a matter of months. So now my father is telling the story of his life. This will be the first of more than a dozen sessions, each lasting an hour or more. As my audio recorder runs, he describes how he used to explore caves when he was growing up; how he took a job during college loading ice blocks into railroad boxcars.

There's a good chance something you've bought online has been in the hands of a "picker" first. These are the workers in warehouses who pick, pack and ship all those things we're ordering. Experts say while the robots are replacing some human workers, the machines aren't quite ready to take over completely. To keep pace with a growing hunger for fast delivery, more pickers are being hired in the distribution industry. And on the outskirts of the Bay Area, a school is using technology to train students in these new jobs.