Iterative RAG for multi-hop question answering faces challenges with lengthy contexts and the buildup of irrelevant information. This hinders a model's capacity to process and reason over retrieved content and limits performance. While recent methods focus on compressing retrieved information, they are either restricted to single-round RAG, require finetuning or lack scalability in iterative RAG. To address these challenges, we propose Notes Writing, a method that generates concise and relevant notes from retrieved documents at each step, thereby reducing noise and retaining only essential information. This indirectly increases the effective context length of Large Language Models (LLMs), enabling them to reason and plan more effectively while processing larger volumes of input text. Notes Writing is framework agnostic and can be integrated with different iterative RAG methods. We demonstrate its effectiveness with three iterative RAG methods, across two models and four evaluation datasets. Notes writing yields an average improvement of 15.6 percentage points overall, with minimal increase in output tokens.

While humans intuitively manipulate garments and other textile items swiftly and accurately, it is a significant challenge for robots. A factor crucial to human performance is the ability to imagine, a priori, the intended result of the manipulation intents and hence develop predictions on the garment pose. That ability allows us to plan from highly obstructed states, adapt our plans as we collect more information and react swiftly to unforeseen circumstances. Conversely, robots struggle to establish such intuitions and form tight links between plans and observations. We can partly attribute this to the high cost of obtaining densely labelled data for textile manipulation, both in quality and quantity. The problem of data collection is a long-standing issue in data-based approaches to garment manipulation. As of today, generating high-quality and labelled garment manipulation data is mainly attempted through advanced data capture procedures that create simplified state estimations from real-world observations. However, this work proposes a novel approach to the problem by generating real-world observations from object states. To achieve this, we present GARField (Garment Attached Radiance Field), the first differentiable rendering architecture, to our knowledge, for data generation from simulated states stored as triangle meshes. Code is available on https://ddonatien.github.io/garfield-website/

Rewards play a crucial role in reinforcement learning (RL). A good choice of reward function motivates an agent to explore and learn which actions are valuable. The feedback that an agent receives via rewards allows them to update their behavior and learn useful policies. However, designing reward functions is complicated and cumbersome, even for domain experts. Automatically inferring a reward function is more desirable for end-users interacting with a system.

Jon: You are not funny. Garfield: That wasn't a laugh, that was a snorta sound. Jon: I'm gonna get you one day, ya big lug. The dialogue was generated using GPT-3. The data I supplied was a basic summary of Garfield, Jon, and Odie's personalities, and three transcripts of sample comics.

Federal officials have largely come around to the idea that research funding is crucial for U.S. leadership in artificial intelligence, but there are ways to accelerate innovation besides pouring in more money, according to tech experts. For one, they said, the government could map a long-term strategy for advancing the technology. "The U.S. has been slow in making this a national imperative," Dean Garfield, president and CEO of the Information Technology Industry Council, said Thursday on a panel hosted by Politico. "The signal that comes from the top … is critically important here and has the opportunity to really catalyze that action in a way that wouldn't happen without it." The Office of Science and Technology Policy on Wednesday requested industry input on updating an AI research and development strategy the White House published in 2016.

Don a mixed-reality viewer and you see the sun-dappled altar under a sweeping tower of windows. Turn around, and the cathedral's pews march off into the distance, two by two. Now, for the best part, look up. And for a few moments at the Museum of History & Industry's new "Mont-Saint-Michel: Digital Perspectives on the Model" Microsoft AI-powered exhibit, you get that dizzy feeling you get when you stare straight up into lofty spaces. It's as close as you can come to looking up into the towering vaults of the stunning island-bound cathedral without visiting France.

Two years ago a novelist and as-yet-unproduced screenwriter named Nic Kelman went to work for Wizards of the Coast, the company that makes the popular collectible card game Magic: The Gathering. Kelman's job, though he might not put it this way, was to write a grimoire--a kabbalistic story bible. "Rules for magic out of the rules for Magic," as Kelman says. The company needed that grimoire because it was going to try to cast a spell in the real world--to transform a popular albeit niche game, complicated and nerdy, into a cross-media franchise. That has happened for comic books, for literature, even for toys, heaven help us.

Federal officials have largely come around to the idea that research funding is crucial for U.S. leadership in artificial intelligence, but there are ways to accelerate innovation besides pouring in more money, according to tech experts. For one, they said, the government could map a long-term strategy for advancing the technology. "The U.S. has been slow in making this a national imperative," Dean Garfield, president and CEO of the Information Technology Industry Council, said Thursday on a panel hosted by Politico. "The signal that comes from the top … is critically important here and has the opportunity to really catalyze that action in a way that wouldn't happen without it." The Office of Science and Technology Policy on Wednesday requested industry input on updating an AI research and development strategy the White House published in 2016.

The White House unveiled a hands-off regulatory approach to foster the development of artificial intelligence at a gathering of more than 40 companies in Washington Thursday. A top White House technology adviser, Michael Kratsios, told representatives of companies including Alphabet Inc.'s Google, Facebook Inc., Goldman Sachs Group Inc. and Boeing Co. that they'll have the greatest possible latitude to develop AI, according to a copy of his remarks that was provided to Bloomberg. "We didn't cut the lines before Alexander Graham Bell made the first telephone call," Kratsios said in his prepared remarks. "We didn't regulate flight before the Wright Brothers took off at Kitty Hawk." Kratsios also announced the formation of a committee on artificial intelligence consisting of senior federal R&D officials within the National Science and Technology Council of the White House.

The White House and President Donald Trump are creating an artificial intelligence task force. Deputy U.S. Chief Technology Officer Michael Kratsios announced the new committee on Thursday in Washington, D.C., during an AI summit with government officials, members of academia, and several companies like Google (goog), Microsoft (msft), and Amazon (amzn), according to news site FedScoop. The new Select Committee on Artificial Intelligence will operate under the National Science and Technology Council and consist of several federal officials from various government agencies like the National Science Foundation and the Defense Advanced Research Projects Agency, the report said. "As artificial intelligence transforms everything from agriculture to manufacturing to transportation, the potential for AI remains breathtaking," Kratsios said in prepared remarks. "But we cannot be passive. To realize the full potential of AI for the American people, it will require the combined efforts of industry, academia, and government."