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Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly performant NF models.
Use these frameworks to optimize memory and compute resources, scale your machine learning workflow, speed up your processes, and reduce the overall cost.
Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acting as writing agents to infer a description of user preferences. Agent alignment then comes from conditioning on the inferred preference description. However, existing methods often produce generic preference descriptions that fail to capture the unique and individualized nature of human preferences.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
AI advancements will fundamentally change how enterprises use and manage data, making it essential to embrace and understand this transformation. For organizations looking to adopt AI at scale, the state of their databases is a critical success factor. Poor data quality, weak governance.
Meta is announcing its next pair of smart glasses with Oakley. The limited-edition Oakley Meta HSTN (pronounced “how-stuhn”) model costs $499 and is available for preorder starting July 11th. Other Oakley models with Meta’s tech will be available starting at $399 later this summer. Like the existing Meta Ray-Ban glasses , the Oakley model features a front-facing camera, along with open-ear speakers and microphones that are built into the frame.
Skip to main content Events Video Special Issues Jobs VentureBeat Homepage Subscribe Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation Vie
Skip to main content Events Video Special Issues Jobs VentureBeat Homepage Subscribe Artificial Intelligence View All AI, ML and Deep Learning Auto ML Data Labelling Synthetic Data Conversational AI NLP Text-to-Speech Security View All Data Security and Privacy Network Security and Privacy Software Security Computer Hardware Security Cloud and Data Storage Security Data Infrastructure View All Data Science Data Management Data Storage and Cloud Big Data and Analytics Data Networks Automation Vie
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distribution to the target distribution by solving an ordinary differential equation via integration along a velocity vector-field.
Ever felt like trying to find a needle in a haystack? That’s part of the process of building and optimizing machine learning models, particularly complex ones like ensembles and neural networks, where several hyperparameters need to be manually set by us before training them.
Uncertainty Quantification (UQ) in Language Models (LMs) is key to improving their safety and reliability. Evaluations often use metrics like AUROC to assess how well UQ methods (e.g., negative sequence probabilities) correlate with task correctness functions (e.g., ROUGE-L). We show that mutual biases--when both UQ methods and correctness functions are biased by the same factors--systematically distort evaluation.
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Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Recent research demonstrated that training large language models involves memorization of a significant fraction of training data. Such memorization can lead to privacy violations when training on sensitive user data and thus motivates the study of data memorization's role in learning. In this work, we develop a general approach for proving lower bounds on excess data memorization, that relies on a new connection between strong data processing inequalities and data memorization.
AI tools like ChatGPT have changed our personal and professional worlds, with around 52% of American adults regularly using a large language model (LLM). Now, a new study details the immense environmental costs of our prompts, and it might make you think twice about what chatbot you use and how you use it.
Have you ever found it frustrating to build AI agents that perform multiple tasks? LangGraph Studio is here to solve this problem by offering a visual and interactive way to design, manage, and debug agents. Built on the LangGraph framework, this desktop tool lets you create agent workflows using a simple drag-and-drop interface. You can […] The post LangGraph Studio appeared first on Analytics Vidhya.
Andrej Karpathy is back, this time explaining how LLMs are rewriting software.At YC AI Startup School, the former head of AI at Tesla gave a talk titled “Software Is Changing (Again),” during which he discussed with students and developers how the concepts of code, computation, and programming are being rethought at a fundamental level.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Large language models (LLMs) are increasingly capable of processing long inputs and locating specific information within them, as evidenced by their performance on the Needle in a Haystack (NIAH) test. However, while models excel at recalling surprising information, they still struggle to identify clearly omitted information. We introduce AbsenceBench to assesses LLMs' capacity to detect missing information across three domains: numerical sequences, poetry, and GitHub pull requests.
Researchers have developed an AI tool called EmoSync that boosts empathy by tailoring emotional analogies to each user’s personality and life experiences.
Apple reportedly held internal discussions to acquire AI startup Perplexity AI. The latter is an AI-powered search engine. It uses a large language model (LLM) to process the answers and presents them in an easier-to-understand format.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
Microsoft is preparing to enact thousands of layoffs in July 2025 according to Bloomberg , primarily affecting sales and customer service divisions, while concurrently investing an estimated $80 billion into artificial intelligence infrastructure over the next fiscal year. This substantial investment signals a strategic redirection of company resources.
The 'Your Brain on ChatGPT' study will make you consider the consequences. Relying on ChatGPT significantly affects critical thinking abilities, according to a new study.
OpenAI CEO Sam Altman discussed the forthcoming GPT-5 model, confirming its preparation for public release with a target timeline of summer, contingent upon the model fulfilling OpenAI’s established internal benchmarks and standards. GPT-5, designated as the next foundational model for ChatGPT, is currently undergoing preparations for its public debut.
An MIT study finds that heavy reliance on AI tools like ChatGPT can dull memory, weaken critical thinking, and lead to lazier writing. If you value critical thinking, you may want to rethink your use of ChatGPT.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Amid all the debates about how AI affects jobs, science, the environment, and everything else, there's a question of how large language models impact …
Security researchers recently identified 30 online databases collectively containing 16 billion records, likely amassed through infostealing malware, according to a new report from Cybernews. These databases briefly became accessible to the public internet before being secured, though their ownership remains undetermined. The discovered databases varied significantly in scale, with some containing millions of entries and others holding billions.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires human annotators. Voice search applications, such as digital media players, leverage ASR to allow users to search by voice as opposed to an on-screen keyboard. However, recent or infrequent movie titles may not be sufficiently represented in the E2E ASR system's training data, and hence, may suffer poor recognition.
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or even protein structures. These models are commonly trained in two stages: first, a data compressor is trained, and in a subsequent training stage a flow matching generative model is trained in the latent space of the data compressor.
The Massachusetts Institute of Technology (MIT) in its latest paper entitled “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task.” Authored by Nataliya Kos’myna et al., the paper explores the impact of OpenAI’s AI tool on the human brain.
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
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