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Is Hollywood Finally Turning Against Tech Oligarchs? A new wave of films by directors like Aaron Sorkin and Luca Guadagnino takes on Mark Zuckerberg, Elon Musk, Elizabeth Holmes, and "shapeshifter" Sam Altman. Get your news from a source that's not owned and controlled by oligarchs. Big Tech is back on the big screen--but its bosses might not be thrilled. This fall, Hollywood will release four movies on tech executives: Luca Guadagnino's, on CEO Sam Altman's tumultuous time as the head of OpenAI; Aaron Sorkin's, on the investigation into Facebook and CEO Mark Zuckerberg dismissing its platforms' negative impacts on young users and inciting violence and hate speech; the all-encompassing, by documentarian Alex Gibney, on the Tesla and SpaceX CEO's family life, corporate controversies, and engagement in right-wing politics; and You Can See Everything, an unsettling documentary in which directors Nathan Fielder and Lance Oppenheim follow Elizabeth Holmes, who invited them to film her in the weeks before she began serving her federal prison sentence for defrauding investors at the now-defunct health tech company Theranos.

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We finally have a trailer for the long-awaited Sam Altman biopic, which is appropriately-named Artificial. It stars Andrew Garfield as the OpenAI CEO and the film is directed by Luca Guadagnino, who made Call Me by Your Name, Challengers and the Suspiria remake, among other movies. The trailer is actually pretty creepy, giving more of a horror vibe than a biopic. Harrowing music plays as Garfield puts on his best robotic Altman impression to sing the virtues of AI. He also calls it Pandora's Box and says "countries will fall" and "industries are gonna collapse."

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.