Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of formal hierarchies and informal networks, and the need to ask the right questions. To address this, we propose an agent-based framework leveraging large language models (LLMs) to iteratively reconstruct dataset descriptions through interactions with employees. Modeling knowledge dissemination as a Susceptible-Infectious (SI) process with waning infectivity, we conduct 864 simulations across various synthetic company structures and different dissemination parameters. Our results show that the agent achieves 94.9% full-knowledge recall, with self-critical feedback scores strongly correlating with external literature critic scores. We analyze how each simulation parameter affects the knowledge retrieval process for the agent. In particular, we find that our approach is able to recover information without needing to access directly the only domain specialist. These findings highlight the agent's ability to navigate organizational complexity and capture fragmented knowledge that would otherwise remain inaccessible.

There Will Be Blood begins nearly in silence. Its stunning opening 20 minutes follow a solitary figure as he struggles through an American wasteland, digging, bleeding, building. In its howling quiet, its violent yet graceful choreography, the film presents an iconic image of the ravages of greed, the inextricable link between the mythology of American exceptionalism and the circuits of capital--a lone tragic hero representing the creation of the American dream as well as its inevitable apocalyptic end. Paul Thomas Anderson's 2007 epic begins with one of cinema's greatest depictions of the desire and despair at the heart of American capitalism. But then, so does WALL-E. Nearly everything about the knockout opening of Anderson's masterpiece is also true of Pixar's masterpiece, released in theaters the following year, and, as of this week, the first film from the animation studio to be inducted into the vaunted Criterion Collection.

Adrienne Williams and Milagros Miceli are researchers at the Distributed AI Research (DAIR) Institute. Timnit Gebru is the institute's founder and executive director. She was previously co-lead of the Ethical AI research team at Google. The public's understanding of artificial intelligence (AI) is largely shaped by pop culture -- by blockbuster movies like "The Terminator" and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert's millions of viewers.

The excitement around artificial intelligence is palpable. It seems that not a day goes by without one of the giants in the industry coming out with a breakthrough application of this technology, or a new nuance is added to the overall body of knowledge. Horizontal and industry-specific use cases of AI abound and there is always something exciting around the corner every single day. However, with the keen interest from global leaders of multinational corporations, the conversation is shifting towards having a strategic agenda for AI in the enterprise. Business heads are less interested in topical experiments and minuscule productivity gains made in the short term.

Amazon has become the second publicly traded company to be worth $1 trillion, hot on the heels of Apple. Shares of the e-commerce giant climbed roughly 2% in mid-morning trading on Tuesday to hit an all-time high of $2,050.50. This briefly pushed its market cap past the magic $1 trillion mark, before the stock settled lower at $2,035.54 in afternoon trading. Shares of the e-commerce giant climbed roughly 2% in mid-morning trading on Tuesday to hit an all-time high of $2,050.50. Amazon now accounts for an astonishing 49 percent of every e-commerce dollar in the United States.

In building a case for an American manufacturing renaissance, economists cite increasing productivity, cheap natural gas, and rising value-added figures to show that manufacturing is in good shape and will get better. Some of these positivists also claim that rising labor costs in Asia and the creation of U.S. manufacturing jobs since 2010 are evidence of a big turnaround in manufacturing. There are also some mysterious predictions, shared without data to back them up, that manufacturing exports will grow and imports will shrink. Manufacturing has been battered so badly by China and other Asian countries and by American multinational corporation offshoring that people are desperate for positive news. But the question is, are these stories based on truth or are they just "happy talk"? For example, the McKinsey Global Institute says growth in manufacturing is right around the corner.