In Ari Aster's movies, the price of understanding how the world really works is your sanity, if not your life. His first three movies--Hereditary, Midsommar, and Beau Is Afraid--center on characters whose feeling that there's something sinister going on beneath the surface of their existence is eventually proved to be correct, but it's as if their bodies aren't equipped to contain that knowledge. One way or another, their minds are gone. The people in Aster's polarizing fourth movie, Eddington, a Western-inflected psychodrama set during the early days of the COVID-19 pandemic, don't get off so easy. The stress test of a rapidly spreading virus with no known treatment exposes innumerable cracks in society's facade: the gap between remote workers and people forced to risk their lives in order to earn a living; between people who breathe a sigh of relief when they see a police car approaching and people who have to be sure to keep their hands in plain sight.

Hallucinations in Large Language Models (LLMs) are widely regarded as errors - outputs that deviate from factual accuracy. However, in creative or exploratory contexts, these "mistakes" may represent unexpected avenues for innovation. We introduce Purposefully Induced Psychosis (PIP), a novel approach that amplifies LLM hallucinations for imaginative tasks such as speculative fiction, interactive storytelling, and mixed-reality simulations. Drawing on Herman Melville's Moby-Dick, where Pip's "madness" reveals profound insight, we reframe hallucinations as a source of computational imagination rather than a flaw. Our method fine-tunes LLMs to encourage speculative, metaphorical, and surreal outputs - hallucinations that are useful when factual accuracy is not the chief objective. Inspired by the consensual illusions of theater and stage magic, PIP situates these creative missteps in contexts where users willingly suspend disbelief, thereby transforming "errors" into catalysts for new ways of thinking. We discuss potential applications, design principles for ensuring user consent, preliminary observations, and implications for broader AI ethics and human-AI collaboration.

When an LLM's inference is performed with a positive temperature, posing the same problem repeatedly will yield a distribution across the possible answers. If the LLM performs well, we would expect most of the probability mass to lie on the correct answer; if it performs poorly, we might expect the distribution to be more uniform across all the answers. However, this intuition does not always hold. To better understand the actual behavior of LLMs, we provide several detailed examples in Section 2. In Section 3, we perform a more comprehensive analysis at scale and use the results to suggest a taxonomy of LLMs.

The artificial intelligence (AI) world is running out of real data for training increasingly large generative models, resulting in accelerating pressure to train on synthetic data. Unfortunately, training new generative models with synthetic data from current or past generation models creates an autophagous (self-consuming) loop that degrades the quality and/or diversity of the synthetic data in what has been termed model autophagy disorder (MAD) and model collapse. Current thinking around model autophagy recommends that synthetic data is to be avoided for model training lest the system deteriorate into MADness. In this paper, we take a different tack that treats synthetic data differently from real data. Self-IMproving diffusion models with Synthetic data (SIMS) is a new training concept for diffusion models that uses self-synthesized data to provide negative guidance during the generation process to steer a model's generative process away from the non-ideal synthetic data manifold and towards the real data distribution. We demonstrate that SIMS is capable of self-improvement; it establishes new records based on the Fr\'echet inception distance (FID) metric for CIFAR-10 and ImageNet-64 generation and achieves competitive results on FFHQ-64 and ImageNet-512. Moreover, SIMS is, to the best of our knowledge, the first prophylactic generative AI algorithm that can be iteratively trained on self-generated synthetic data without going MAD. As a bonus, SIMS can adjust a diffusion model's synthetic data distribution to match any desired in-domain target distribution to help mitigate biases and ensure fairness.

Innovation,

Cue the studies and stories about lost productivity, sports betting and consumerism run amok. But for all of the "sick" days taken, office pools created and revenues generated, March Madness shows us something remarkable -- that we are, without a doubt, human. Scholars like me worry that humans are starting to behave like predictable and even programmable machines. We're surrendering our humanity to smartphones, digital assistants and fitness trackers one tap, swipe and click at a time. Surveillance capitalism, or the monetization of data acquired through surveillance, allows high-tech media to grab and keep our attention while collecting, selling and using data about our moods, preferences, habits and lifestyles to nudge us.

The chaotic, glitzy showcase for the consumer technology industry known as CES took place this first week of January. While most humans on the planet were still nursing their New Year's hangovers, the tech manufacturers, retailers, distributors, media, and regular old tourists descended on Las Vegas, Nevada, for five days. In the convention center expos and hotel ballrooms, they interacted with robotic assistants, strapped on some virtual reality headsets, and took leisurely spins around the parking lot in self-driving cars. Our photographer Roger Kisby captured some of the magic as he roamed the halls of CES 2023. Here are some highlights from tech's biggest week.

In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

While the teasers, announcements and presentations of big publishers often steal the show at every E3 convention, there are always some games made by smaller developers that can immediately grab the attention of gamers. This year's E3 hosts a number of wonderfully artistic and charming indie games that some attendees may have overlooked. E3 2021 featured the Guerrilla Collective, a digital festival of sorts that's dedicated to putting lesser-known developers and publishers in the spotlight. This year, they showed off over 70 new games across a plethora of genres, from action-adventure to horror and everything in between. Here are some of the most interesting indie games to look forward to this year.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The NCAA announced a contingency plan for the 2021 Men's Basketball Championship tournament on Monday that includes all preliminary rounds being played in one central location. The NCAA Division I Men's Basketball Committee said it has begun talks with officials in Indiana to relocate all 13 predetermined round sites to Indianapolis and the surrounding metropolitan area as a result of the pandemic. "In recent weeks, (the committee) has engaged in a thorough contingency planning process to determine the most effective way to conduct a safe and healthy March Madness for all participants for the 2021 championship," the NCAA said on its website.