The tech giant made thousands of engineers train their AI replacements--then fired them. When Meta engineer David Frenk posted an anti-AI farewell parody video in an internal message board, staff thought it perfectly captured shifts in company culture. Get your news from a source that's not owned and controlled by oligarchs. This week, Meta laid off 8,000 employees--10 percent of the company's staff--and reassigned another 7,000 to train AI models. Fear of the layoffs had been building around the company for weeks, compounded by the way that Meta has taken a sharp turn from a company built by coders to a company that has staked its future on AI.

In this paper, we study the problem of networked multi-agent reinforcement learning (MARL), where a number of agents are deployed as a partially connected network and each interacts only with nearby agents. Networked MARL requires all agents to make decisions in a decentralized manner to optimize a global objective with restricted communication between neighbors over the network. Inspired by the fact that sharing plays a key role in human's learning of cooperation, we propose LToS, a hierarchically decentralized MARL framework that enables agents to learn to dynamically share reward with neighbors so as to encourage agents to cooperate on the global objective through collectives. For each agent, the high-level policy learns how to share reward with neighbors to decompose the global objective, while the low-level policy learns to optimize the local objective induced by the high-level policies in the neighborhood. The two policies form a bi-level optimization and learn alternately. We empirically demonstrate that LToS outperforms existing methods in both social dilemma and networked MARL scenarios across scales.

Things to Do in L.A. A data center stands in downtown Santa Clara. This is read by an automated voice. Please report any issues or inconsistencies here . Gov. Gavin Newsom vetoed a bill that would have tracked data centers' growing water footprint in California. He says California is "well positioned" to support the data center boom, and he is reluctant to "impose rigid reporting requirements."

Age and gender biases are baked into what we see online, a large new study confirms. Get your news from a source that's not owned and controlled by oligarchs. In the 1970s, when researchers asked children to draw a scientist, 99 percent of them drew a man . As this experiment was repeated over 50 years, the number of women drawn increased, and within the past decade, more than half of girls will draw a woman when asked what a scientist looks like. Today, Google search results tend to agree with these children's drawings.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Starting Oct. 1, over 2,700 Kroger locations will be available on DoorDash. Customers will be able to shop the store's entire selection. Above, bagged purchases from a Kroger grocery store sit in a shopping cart in Flowood, Miss. This is read by an automated voice.

Things to Do in L.A. Tap to enable a layout that focuses on the article. AI is infiltrating the classroom. Here's how teachers and students say they use it A ChapGPT logo is seen in December 2023. This is read by an automated voice. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. What counts as cheating with AI? Teachers are grappling with how to draw the line This is read by an automated voice. Please report any issues or inconsistencies here . Teachers say AI cheating is "off the charts," but research shows cheating rates remain unchanged since before ChatGPT. Schools favor "AI literacy" and redesigning assignments to encourage ethical technology use.

In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting for inter-individual heterogeneity in panel choice data. We identify three specific advantages of the Functional Effects Model over traditional fixed, and random/mixed effects models: (i) by mapping individual-specific effects as a function of socio-demographic variables, we can account for these effects when forecasting choices of previously unobserved individuals (ii) the (approximate) maximum-likelihood estimation of functional effects avoids the incidental parameters problem of the fixed effects model, even when the number of observed choices per individual is small; and (iii) we do not rely on the strong distributional assumptions of the random effects model, which may not match reality. We learn functional intercept and functional slopes with powerful non-linear machine learning regressors for tabular data, namely gradient boosting decision trees and deep neural networks. We validate our proposed methodology on a synthetic experiment and three real-world panel case studies, demonstrating that the Functional Effects Model: (i) can identify the true values of individual-specific effects when the data generation process is known; (ii) outperforms both state-of-the-art ML choice modelling techniques that omit individual heterogeneity in terms of predictive performance, as well as traditional static panel choice models in terms of learning inter-individual heterogeneity. The results indicate that the FI-RUMBoost model, which combines the individual-specific constants of the Functional Effects Model with the complex, non-linear utilities of RUMBoost, performs marginally best on large-scale revealed preference panel data.

Artificial intelligence stocks suffered in 2022 as tech stocks plummeted and general stock market sell-offs led to many of the biggest companies dropping in value. We look at what’s next for artificial intelligence stocks, and why it might be a good time to buy AI.

Summary The AI in healthcare market size was valued at US$7,679.39 million in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 39.05% during 2022-2027. The key to the growth has been increasing investment and development in AI and increasing strategic moves by market players are stimulating. Additionally, key strategic partnerships and mergers and acquisitions are expected to accelerate market growth. Healthcare, including pharma, medical devices, healthcare providers, and payers, is a highly regulated industry, and therefore can be slow to adopt new technologies and modernize.However, the healthcare industry is realizing the benefits artificial intelligence (AI) can bring, and it is now being used in different areas across the entire value chain. Additionally, its use in the healthcare space is expected to continue to increase in the next five years. The integration of software with artificial intelligence is creating growth avenues for the global artificial intelligence in healthcare market.Integration of software with artificial intelligence offers immediate decision support and best results to diagnose diseases.