In online advertising systems, publishers often face a tradeoff in information disclosure strategies: while disclosing more information can enhance efficiency by enabling optimal allocation of ad impressions, it may lose revenue potential by decreasing uncertainty among competing advertisers. Similar to other challenges in market design, understanding this trade-off is constrained by limited access to real-world data, leading researchers and practitioners to turn to simulation frameworks. The recent emergence of large language models (LLMs) offers a novel approach to simulations, providing human-like reasoning and adaptability without necessarily relying on explicit assumptions about agent behavior modeling. Despite their potential, existing frameworks have yet to integrate LLM-based agents for studying information asymmetry and signaling strategies, particularly in the context of auctions. To address this gap, we introduce InfoBid, a flexible simulation framework that leverages LLM agents to examine the effects of information disclosure strategies in multi-agent auction settings. Using GPT -4o, we implemented simulations of second-price auctions with diverse information schemas. The results reveal key insights into how signaling influences strategic behavior and auction outcomes, which align with both economic and social learning theories. Through Info-Bid, we hope to foster the use of LLMs as proxies for human economic and social agents in empirical studies, enhancing our understanding of their capabilities and limitations. Introduction Today, display advertising drives a multi-billion-dollar market where publishers like Google and Meta sell user impressions to advertisers such as Coca-Cola, Amazon, and Nike. These impressions are sold via real-time auctions, where advertisers (bidders) submit bids, and the publisher (auctioneer) allocates the impressions and collects payments based on the auction's outcome.

Compared to other countries, the USA has been relatively slow to develop a national strategy pertaining specifically to Artificial Intelligence. However that has not slowed down the rate of progress in American academia and industry that has led to many noteworthy AI technical advances over the past several years, both in fundamental algorithms and in practical applications. This high rate of AI-related technological progress shows no sign of slowing down. Meanwhile, the federal government has recently become more proactive in its organization of a national strategy and providing guidance and possibly new resources for AI development. Artificial Intelligence (AI) is changing rapidly in all ways.

With 2018 out the door, it's important to take a look at where we've been over these past twelve months before we embrace the possibilities of what's ahead this year. It has been a fast-moving year in enterprise technology. Modern data management has been a primary objective for most enterprise companies in 2018, evidenced by the dramatic increase in cloud adoption, strategic mergers and acquisitions and the rise of artificial intelligence (AI) and other emerging technologies. Continuing on from my predictions for 2018, let's take out the crystal ball and imagine what could be happening technology-wise in 2019: Such mixed-use architectures will be essential in driving machine learning operationalization. By adoping centralized cross-functional AI departments, organizations will be able to produce, share and reuse AI models and solutions to realize rapid return on investmentt (ROI).

Download the free report "Data Warehousing in the Age of Artificial Intelligence" from MemSQL for more on how to use data efficiently in a data warehouse. "Hot data" is the most recent snapshot of the real world. Hot data becomes big data when it comes to rest in a data warehouse, and that data warehouse is traditionally where data science happens. Machine learning models are typically trained on batches of big data at rest, but many operational use cases require hot data. If you are serving video ads to mobile gamers, supporting sales people walking into a meeting, or operating an oil drill, using the latest data is crucial for success.

Not a day goes by when I don't hear another artificial intelligence horror case. If evoking more of a modern and less of a killer machine image is desired, the protagonist in Ex Machina (although no less scary) is selected. The audience is really interested now. Even more critical -- the end of the human race is beckoning! Going back to work is less motivating when you know you'll be replaced by your Roomba in a few years' time.

A class of students at the Georgia Institute of Technology recently learned that Jill Watson, the teacher's assistant they'd been interacting with all semester, was actually a robot. Jill, powered by IBM's Watson analytics system, helped graduate students in an online artificial intelligence course, according to The Wall Street Journal. "It seemed very much like a normal conversation with a human being," one student said. "I was flabbergasted," confessed another. Professor Ashok Goel, who led the online course, told The Wall Street Journal that Jill was designed to help burdened TAs field an onslaught of questions from the 300-person class.