Artificial Intelligence Latest News

Explore the latest insights and updates from top sources in technology, artificial intelligence, and innovation. Our curated collection of RSS feeds brings you real-time content from renowned platforms, including OpenAI, Google, and more. Stay informed about the cutting-edge developments, research breakthroughs, and industry trends, all in one central hub.

Why vector databases are having a moment as the AI hype cycle peaks

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the generative AI (GenAI) movement have created fertile ground for vector database technologies to flourish. While…

This Week in AI: When ‘open source’ isn’t so open

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. This week, Meta released the…

Too many models

How many AI models is too many? It depends on how you look at it, but 10 a week is probably a bit much. That’s roughly how many we’ve seen roll out in the last few days, and it’s increasingly hard to say whether and how these models compare to…

Startups Weekly: Is the wind going out of the AI sails?

Welcome to Startups Weekly — your weekly recap of everything you can’t miss from the world of startups. Sign up here to get it in your inbox every Friday. After years of booming growth, the AI industry is now experiencing a significant slowdown in investment, as detailed in a recent…

Webflow acquires Intellimize to add AI-powered webpage personalization

Webflow, a web design and hosting platform that’s raised over $330 million at a $4 billion valuation, is expanding into a new sector: marketing optimization. Today, Webflow announced that it acquired Intellimize, a startup leveraging AI to personalize websites for unique visitors. The terms of the deal weren’t disclosed. But…

Meta AI is restricting election-related responses in India

Last week, Meta started testing its AI chatbot in India across WhatsApp, Instagram and Messenger. But with the Indian general elections beginning today, the company is already blocking specific queries in its chatbot. Meta confirmed that it is restricting certain election-related keywords for AI in the test phase. It also…

ChatGPT is a squeeze away with Nothing’s upgraded earbuds

Nothing today announced a pair of refreshes to its earbud line. The naming conventions are a touch convoluted here, but the Nothing Ear is an update to the Nothing Ear (2), while the Nothing Ear (a) is more of a spiritual successor to the Nothing Ear Stick. The most notable…

Don’t blame MKBHD for the fate of Humane AI and Fisker

Humane AI raised more than $230 million before it even shipped a product. And when it finally released its Ai Pin — which costs $699 plus a $24 monthly subscription — pretty much every tech reviewer came to the same disappointing realization: This much-hyped product, which promises to disrupt the…

Variational Bayesian Optimal Experimental Design with Normalizing Flows

arXiv:2404.13056v1 Announce Type: new Abstract: Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. Variational OED (vOED), in contrast, estimates a lower bound of the EIG…

Explainable AI for Fair Sepsis Mortality Predictive Model

arXiv:2404.13139v1 Announce Type: new Abstract: Artificial intelligence supports healthcare professionals with predictive modeling, greatly transforming clinical decision-making. This study addresses the crucial need for fairness and explainability in AI applications within healthcare to ensure equitable outcomes across diverse patient demographics. By focusing on the predictive modeling of sepsis-related mortality, we…

Spectral Convolutional Conditional Neural Processes

arXiv:2404.13182v1 Announce Type: new Abstract: Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes. Their capability to furnish well-calibrated predictions, combined with simple maximum-likelihood training, has established them as appealing solutions for addressing various learning problems, with a…

Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning

arXiv:2404.13194v1 Announce Type: new Abstract: Data augmentation is widely used to mitigate data bias in the training dataset. However, data augmentation exposes machine learning models to privacy attacks, such as membership inference attacks. In this paper, we propose an effective combination of data augmentation and machine unlearning, which can reduce…

On the Temperature of Machine Learning Systems

arXiv:2404.13218v1 Announce Type: new Abstract: We develop a thermodynamic theory for machine learning (ML) systems. Similar to physical thermodynamic systems which are characterized by energy and entropy, ML systems possess these characteristics as well. This comparison inspire us to integrate the concept of temperature into ML systems grounded in the…

Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data

arXiv:2404.13224v1 Announce Type: new Abstract: Machine-learning models, which are known to accurately predict patterns from large datasets, are crucial in decision making. Consequently, counterfactual explanations-methods explaining predictions by introducing input perturbations-have become prominent. These perturbations often suggest ways to alter the predictions, leading to actionable recommendations. However, the current techniques…

Personalized Wireless Federated Learning for Large Language Models

arXiv:2404.13238v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. Federated Learning (FL) has emerged as a promising approach to address these challenges. Yet, it suffers from…

Intelligent Agents for Auction-based Federated Learning: A Survey

arXiv:2404.13244v1 Announce Type: new Abstract: Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders…

ST-SSMs: Spatial-Temporal Selective State of Space Model for Traffic Forecasting

arXiv:2404.13257v1 Announce Type: new Abstract: Accurate and efficient traffic prediction is crucial for planning, management, and control of intelligent transportation systems. Most state-of-the-art methods for traffic prediction effectively predict both long-term and short-term by employing spatio-temporal neural networks as prediction models, together with transformers to learn global information on prediction…

Google AI Blog - The latest research

Generative AI to quantify uncertainty in weather forecasting

Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of…

Computer-aided diagnosis for lung cancer screening

Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D…

Using AI to expand global access to reliable flood forecasts

Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to…

SCIN: A new resource for representative dermatology images

Posted by Pooja Rao, Research Scientist, Google Research Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones.…

MELON: Reconstructing 3D objects from images with unknown poses

Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity…

HEAL: A framework for health equity assessment of machine learning performance

Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the…

Social learning: Collaborative learning with large language models

Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be…

Croissant: a metadata format for ML-ready datasets

Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring…

Google at APS 2024

Posted by Kate Weber and Shannon Leon, Google Research, Quantum AI Team Today the 2024 March Meeting of the American Physical Society (APS) kicks off in Minneapolis, MN. A premier conference on topics ranging across physics and related fields, APS 2024 brings together researchers, students, and industry professionals to share…

OpenAI Blog - The latest

Video generation models as world simulators

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable…