Meta, led by Mark Zuckerberg, has announced its ambition to develop Artificial General Intelligence (AGI) and plans to make it open-source upon completion. This marks a significant shift for Meta, previously focused on product-specific AI. It aims to combine its AI research groups and invest heavily in infrastructure to achieve this goal. The move raises…

Raesetje Sefala, a South African activist, is using computer vision and satellite imagery to address the effects of spatial apartheid. She aims to map out and analyze racial segregation in housing, hoping to prompt systemic change and equitable resource allocation. Her work is providing valuable data to policymakers and organizations advocating for social justice and…

The Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) approach by researchers at Zhejiang University presents a method for attributed graph embedding. It addresses the crowding problem and enhances stability and quality of representations by preserving node-to-node geodesic similarity under a predefined distribution, demonstrating effectiveness in extensive experiments. The research aims to facilitate further application through code…

GenCast, a new generative model from Google DeepMind, revolutionizes probabilistic weather forecasting. By utilizing machine learning, GenCast efficiently generates 15-day forecasts with superior accuracy and reliability compared to leading operational forecasts. This advancement marks a significant step in embracing machine learning to enhance weather prediction, with broad implications across various industries and decision-making processes.

Machine learning’s push for personalization is transforming fields such as recommender systems, healthcare, and finance. Yet, regulatory processes limit its application in critical sectors. Technion researchers propose a framework, r-MDPs, and algorithms to streamline approval processes while preserving personalization, showing promise in simulated environments. This work marks a notable advancement in deploying personalized solutions within…

Language models are crucial for text understanding and generation across various fields. Training these models on complex data poses challenges, leading to a new approach called ‘easy-to-hard’ generalization. By initially training on easier data and then testing on hard data, models demonstrate remarkable proficiency, offering an efficient solution to the oversight problem. This approach opens…

In this week’s AI news roundup: – AI creates a comedic show mimicking George Carlin, raising ethical concerns. – CES 2024 highlights AI innovation in products like Samsung Galaxy S24 series and AI For Revenue Summit. – OpenAI’s GPT Store hosts AI “girlfriends” and reciting ChatGPT for poetry. – The rise of deep fake content…

“Puncc, a Python library, integrates conformal prediction algorithms to address the crucial need for uncertainty quantification in machine learning. It transforms point predictions into interval predictions, ensuring rigorous uncertainty estimations and coverage probabilities. With comprehensive documentation and easy installation, Puncc offers a practical solution for enhancing predictive model reliability amid uncertainty.”

The study discusses the challenges in AI systems’ adaptation to diverse environments and the proposed In-Context Risk Minimization (ICRM) algorithm for better domain generalization. ICRM focuses on context-unlabeled examples to improve out-of-distribution performance and emphasizes the importance of context in domain generalization research. It also highlights the trade-offs of in-context learning and advocates for more…

Advancements in Large Language Models (LLMs) enabled by Natural Language Processing and Generation have broad applications. However, their biased representations of human viewpoints stemming from pretraining data composition have prompted researchers to focus on data curation. A recent study introduces the AboutMe dataset to address these biases and the need for sociolinguistic analysis in NLP.