'Retro Rewind' can make it a Blockbuster night. The two-person development team says "nostalgia is a central element" of their 90s themed simulator. Breakthroughs, discoveries, and DIY tips sent six days a week. Growing up in the early 2000s, few weekly rituals stuck with me quite like New Release Tuesday. Every week, without fail, I remember wandering the slightly-moldy-smelling, blue-carpeted aisles of our local Blockbuster while my mom scrutinized the newest covers.

Breakthroughs, discoveries, and DIY tips sent six days a week. It's difficult to explain the Blockbuster experience to those who did not get to experience it. If you never visited one of the video rental chain's over 9,000 locations during the VHS tape heyday, there's even a good chance it may be impossible to understand the ritual of scouring shelves for the perfect Friday night movie: the smell of hundreds of plastic clamshell cases, the distinctly garish interior designs of the 1990s, and early 2000. It is all but a memory, unless you make a pilgrimage to the last brick-and-mortar locale in Bend, Oregon. That's not to say you can't at least some of those nostalgic aesthetics.

Despite the great potential of large language models(LLMs) in machine comprehension, it is still disturbing to fully count on them in real-world scenarios. This is probably because there is no rational explanation for whether the comprehension process of LLMs is aligned with that of experts. In this paper, we propose SCOP to carefully examine how LLMs perform during the comprehension process from a cognitive view. Specifically, it is equipped with a systematical definition of five requisite skills during the comprehension process, a strict framework to construct testing data for these skills, and a detailed analysis of advanced open-sourced and closed-sourced LLMs using the testing data. With SCOP, we find that it is still challenging for LLMs to perform an expert-level comprehension process. Even so, we notice that LLMs share some similarities with experts, e.g., performing better at comprehending local information than global information. Further analysis reveals that LLMs can be somewhat unreliable -- they might reach correct answers through flawed comprehension processes. Based on SCOP, we suggest that one direction for improving LLMs is to focus more on the comprehension process, ensuring all comprehension skills are thoroughly developed during training.

Document visual question answering (DocVQA) pipelines that answer questions from documents have broad applications. Existing methods focus on handling single-page documents with multi-modal language models (MLMs), or rely on text-based retrieval-augmented generation (RAG) that uses text extraction tools such as optical character recognition (OCR). However, there are difficulties in applying these methods in real-world scenarios: (a) questions often require information across different pages or documents, where MLMs cannot handle many long documents; (b) documents often have important information in visual elements such as figures, but text extraction tools ignore them. We introduce M3DocRAG, a novel multi-modal RAG framework that flexibly accommodates various document contexts (closed-domain and open-domain), question hops (single-hop and multi-hop), and evidence modalities (text, chart, figure, etc.). M3DocRAG finds relevant documents and answers questions using a multi-modal retriever and an MLM, so that it can efficiently handle single or many documents while preserving visual information. Since previous DocVQA datasets ask questions in the context of a specific document, we also present M3DocVQA, a new benchmark for evaluating open-domain DocVQA over 3,000+ PDF documents with 40,000+ pages. In three benchmarks (M3DocVQA/MMLongBench-Doc/MP-DocVQA), empirical results show that M3DocRAG with ColPali and Qwen2-VL 7B achieves superior performance than many strong baselines, including state-of-the-art performance in MP-DocVQA. We provide comprehensive analyses of different indexing, MLMs, and retrieval models. Lastly, we qualitatively show that M3DocRAG can successfully handle various scenarios, such as when relevant information exists across multiple pages and when answer evidence only exists in images.

CBP's Air and Marine Operations launched a rescue operation upon request by the Cochise County Sheriff's Office. A college student was located Thursday after he fell several hundred feet while climbing an Oregon mountain. Joel Tranby was climbing North Sister in the Cascade Mountains with his girlfriend early Monday afternoon when he fell about 300 to 500 feet and was severely injured. While Tranby's girlfriend was able to use her phone to call for help, she could not see where Tranby had landed, authorities said. "Unfortunately, he stopped responding verbally before searchers arrived," Lane County Sheriff's Office Sgt.

Senior AI Programmer at PlayStation United States › Oregon › Bend (Posted May 26 2022) Do they allow remote work? Remote work is possible, see the description below for more information. PlayStation isn't just the Best Place to Play -- it's also the Best Place to Work. Today, we're recognized as a global leader in entertainment producing The PlayStation family of products and services including PlayStation 5, PlayStation 4, PlayStation VR, PlayStation Plus, PlayStation Now, acclaimed PlayStation software titles from PlayStation Studios, and more. PlayStation also strives to create an inclusive environment that empowers employees and embraces diversity.

Considered by some observers as more of an art than a science, avalanche forecasting involves a unique combination of human observation, analysis, and interpretation. Even the best forecasters only reach about 75% accuracy in their predictions. After all, they are human too and susceptible to bias and imperfection just like the rest of us. One of the biggest challenges in creating an accurate forecast lies in the fact that avalanche danger cannot be precisely measured and is therefore a matter of expert assessment (opinion). That was until last season when the Swiss Institute for Snow and Avalanche Research Group (SLF), a part of the National Swiss Federal Institute for Forest, Snow, and Landscape Research successfully tested a first-of-its-kind, artificial intelligence computer program to assist in its avalanche forecasts.

Ask any brewer and they'll admit that while beer has likely been around since the dawn of civilization, we're all still learning new ways to brew it more efficiently, creatively, and quickly. But balancing the brewer's art with modern approaches to automation, measurement, and decision making requires brewers to toe a fine line. Take the personality out of the process, and you sacrifice the "craft" in craft beer. Ignore the best tools available, and you waste precious resources that could be better spent on the creative side of the brewing equation. From their outpost on the eastern edge of the Cascades in Bend, Oregon, Deschutes Brewery has tackled this problem in a forward-thinking way, embracing their brew team's passion for tech and programming. Through their operational technology team, they're using a cutting-edge approach to brewing technology aimed at saving time and money, making higher-quality beer, and in turn freeing up company resources for an aggressive innovation program.

If you swim in the waters of insurance and financial services, it's a good time to paddle out and stand up. While the potential blockchain tsunami remains trapped behind legal and regulatory reefs, several good vibrations are creating strong waves to propel you forward. The question for insurers is, how can we get in the best position to ride these new industry dynamics? First, let's look at the line-up of waves to help you launch your insurance growth venture. At PX Venture Studio, we see four exciting trends that are driving change in the insurance market. Let's face it, this is data's world, and we're just living in it.

Amplion, a leading precision medicine intelligence company, has released Dx:Revenue, a groundbreaking software solution that enables test providers to identify ideal pharmaceutical partnership opportunities at the right time to advance precision medicine collaboration. Dx: Revenue is an extension of Amplion's core business intelligence platform that leverages proprietary machine learning to deliver tailored insights into pharma and test developer activities. The platform draws from more than 34 million evidence sources such as clinical trials, scientific publications, conference abstracts, FDA cleared and approved tests, lab developed tests, diagnostic and drug pipelines and more in real time, producing prioritized and timely partnering opportunities that are a precise match between a test provider's capabilities and pharma's specific needs. "Precision medicine has a problem," says Chris Capdevila, CEO, Amplion. "There is an insurmountable volume of information with the potential to drive the realization of precision medicine for patients, but accessing that information strategically, effectively and quickly to make the best pharma partnering decisions is beyond human scale. Our company was founded to address this issue by providing critical evidence-based intelligence that supports the strategic decisions pharmaceutical and test developers need to make to be successful."