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49 articles found

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TechCrunch AI
Aug 29, 2026

"We're not doing 30 bets a year": Vijay Pande on betting small after running $4 billion at a16z

Former Andreessen Horowitz partner Vijay Pande has launched a smaller, AI-native venture firm named VZVC after leaving a16z's roughly $4 billion biotech practice. Pande explained that his new enterprise will focus on fewer high-conviction investments rather than making dozens of bets each year. He also discussed how biology is transitioning from a traditional discovery science into an engineering discipline.

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TechCrunch AI
Aug 28, 2026

An Anthropic researcher just gave us a peek at self-improving AI

An Anthropic researcher recently offered insights into new advancements in self-improving artificial intelligence systems. Automated processes were tested against 10 benchmarks targeted at specific misaligned behaviors and successfully improved results on every single test. Crucially, these self-directed improvements occurred without causing any drop in overall system performance.

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AWS Machine Learning
Jul 31, 2026

Announcing the Agentic Catalog Experience in Amazon Quick

Amazon Quick introduces the Agentic Catalog Experience, an AI-powered workflow for data curators to discover upstream catalog assets in natural language and auto-create Datasets and Topics with inherited semantics. Now in preview for AWS Glue Data Catalog and Databricks Unity Catalog.

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Zapier AI Blog
Jul 29, 2026

The best mind mapping software in 2026

Mind mapping is a creative way to brainstorm and find connections between different ideas. Done right, it's a great way to come up with new ideas and solutions to tricky problems, outline an article or presentation, and generally just get your thoughts in order. While it can be done as a group, it's often a solo practice. I do most of my mind mapping digitally-and even when I don't, I often recreate a paper mind map online so that I can have it safely stored and easily searched. (It's a weird hy

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IEEE Spectrum AI
Jul 21, 2026

Why AI Needs a "Genie Coefficient"

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient. There's often a gap between one person's request and another's understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they'll pour a cup from the pot or buy one from a coffee shop. They won't bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never speci

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AWS Machine Learning
Jul 21, 2026

Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova

In this post, we explore an idea for generating thinking tokens for datasets that lack reasoning traces in SFT customization. We first examine the reasoning suppression problem, then introduce Self-Distilled Reasoning (SDR), validate it across three benchmarks, and provide practical recommendations.

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IEEE Spectrum AI
Jul 16, 2026

How to Make an Invisible Drone

There are many words that I would never, ever use to describe a drone. Stealthy. Subtle. Whatever the opposite of obnoxious is. Much of this is because of the giant angry bee sound that drones tend to make, but it's also the way that they look in flight: With uncannily linear movements and an even less canny ability to hover perfectly still, they tend to draw the eye as affronts to nature. In a paper presented this week at Robotics Science and Systems 2026 in Sydney, roboticists from Northwestern University, Evanston, Ill., demonstrated a drone called Phantom Twist that is essentially invisibl

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AWS Machine Learning
Jul 13, 2026

Launching UI for generative AI inference recommendations in Amazon SageMaker AI

In this post, we introduce the UI for optimized generative AI inference recommendations in Amazon SageMaker AI Studio, a low-code no-code (LCNC) experience. The API already gives you programmatic access to recommendations, but it assumes you know which parameters to set and how to interpret raw benchmark output. The UI removes that assumption. It guides you through preset use-case profiles, visual comparisons of results, and one-click deployment, so teams without deep infrastructure expertise can get a validated configuration on their own.

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TechCrunch AI
Jul 10, 2026

Open source AI matters more than ever, according to Hugging Face's Clem Delangue

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start [...]

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TechCrunch AI
Jul 10, 2026

Hugging Face's CEO on why companies are done renting their AI

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start [...]

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NVIDIA Blog
Jul 8, 2026

NVIDIA Nemotron Achieves Benchmark-Leading Performance With LangChain Deep Agents Harness

NVIDIA Nemotron 3 Ultra is offering leading performance at lower cost than top closed models with the largest and most widely adopted AI agent orchestration platform. LangChain tuned its Deep Agents harness for NVIDIA Nemotron 3 Ultra, achieving the highest accuracy among open models, while completing more tasks at higher throughput and running at 10x [...]

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Zapier AI Blog
Jul 7, 2026

Paragon vs. Zapier: Which is best for your business? [2026]

In the 1999 cult classic Office Space, three employees take an error-prone office printer outside and smash it to pieces with a bat. I can relate. My last printer-may it rest in pieces-was so unreliable that I occasionally drove to the print shop to avoid dealing with its endless excuses. Its go-to error was the classic "nonexistent paper jam," but occasionally, to mix things up, it sent me on a wild goose chase to find a new device driver, or refused to print black-and-white documents due to a

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AWS Machine Learning
Jul 7, 2026

Data modeling best practices for Amazon Quick Sight multi-dataset relationships

Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and perform runtime joins at query time. Instead of flattening tables ahead of time, you keep each table as its own Quick Sight dataset and declare how those datasets relate to one another inside a Quick Sight Topic.

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NVIDIA Blog
Jul 7, 2026

NVIDIA and Hugging Face Bring New Models and Frameworks to LeRobot for the Open Robotics Community

Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated by costly and fragmented resources, from large datasets and robot foundation models to simulation, compute and validation tools. NVIDIA and Hugging Face are [...]

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NVIDIA Blog
Jul 6, 2026

How Open Models Are Driving AI Research

Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. This year's accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done. NVIDIA had 74 papers accepted at ICML 2026. Approximately [...]

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IEEE Spectrum AI
Jun 30, 2026

Emily Bender Sets the Record Straight on "Stochastic Parrots"

In March 2021, a group of four researchers-a collaboration of linguists and computer scientists-published their now legendary paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" The paper received significant attention at the time (in part because Google fired two of the authors, Timnit Gebru and Margaret Mitchell, shortly before its publication). It argued that large language models (LLMs) generate text by statistically predicting likely sequences of words rather than understanding what they are saying-a process the authors captured with the metaphor of a "stochas

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IEEE Spectrum AI
Jun 27, 2026

ConlangCrafter Turns AI to Imagining Languages

There are over 7,000 natural languages today, but that doesn't stop people from occasionally making up completely new ones. These constructed languages, or conlangs, include Dothraki, Klingon, and various Elvish languages. Now, an AI model called ConlangCrafter is also capable of generating new languages-and it is particularly good at it. In a paper published 27 June in the Proceedings of the Association of Computational Linguists, researchers analyzed ConlangCrafter's language-generation abilities, reporting that it can develop a diverse array of novel languages that consistently abide by the

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AWS Machine Learning
Jun 24, 2026

AI-powered BI with Snowflake and Amazon Quick

In this post, you will learn how to build an end-to-end integration between Snowflake semantic views and Amazon Quick. The sample data is user review data for a media company. You start by loading movie review data from Amazon Simple Storage Service (Amazon S3) into Snowflake, define a semantic view in SQL to add business meaning, explore it with natural-language queries through Cortex Analyst, and then generate an Amazon Quick dataset and dashboard. The dataset can be created manually or with a provided automation script. By the end, your BI team or AI team can ask natural-language questions

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IEEE Spectrum AI
Jun 24, 2026

AI Is Designing Radio Chips That Humans Couldn't Even Imagine

Summary RFIC design is a complex "dark art" that limits progress in wireless technologies like 5G, autonomous vehicles, and satellite communications. Princeton researchers use reinforcement learning and inverse design to rapidly create RFICs from scratch. Diffusion models rapidly generate novel or human-interpretable RF layouts, achieving record performance and drastically reducing design time. Future progress needs large, shared chip design datasets and open ecosystems so AI can learn universal electromagnetic and circuit behaviors. Take a moment and try to imagine your life without the wirel

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NVIDIA Blog
Jun 12, 2026

NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark

NVIDIA reports that its Blackwell Ultra-based systems lead the first round of AgentPerf, an agentic AI infrastructure benchmark from Artificial Analysis. In the published results, the NVIDIA GB300 NVL72 platform ran up to 20x more agents per megawatt than older NVIDIA systems. The post explains why agentic AI is a fundamentally different workload than single chat completions and how AgentPerf measures real-world agentic performance using coding agent trajectories.

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Google Cloud AI
Jun 12, 2026

Introducing the Open Knowledge Format

Google Cloud introduced the Open Knowledge Format (OKF), an open specification that turns the emerging LLM-wiki pattern into a portable, vendor-neutral standard. OKF v0.1 represents knowledge as a directory of markdown files with YAML frontmatter and a small set of shared conventions. The goal is to let knowledge written by one producer be consumed by different AI agents without translation, addressing the fragmented context landscape inside most organizations.

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Aakash Gupta
Jun 12, 2026

How to Open Files in CMD: A Quick Guide for 2026

You're probably here because of a very specific moment. An engineer shares a path to a config file, a dataset sample, or a log folder on Windows, and you realize you can't quickly inspect it without clicking through a maze of folders. Or you're on a call, someone says "just open it from cmd," and [...] The post How to Open Files in CMD: A Quick Guide for 2026 appeared first on Product Growth.

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Zapier AI Blog
Jun 9, 2026

The best Docusign alternatives in 2026

More than 20 Docusign alternatives compete in 2026, ranging from free signing apps like SignWell to enterprise tools like Adobe Acrobat Sign and open-source platforms such as DocuSeal. Because all of these methods produce legally binding signatures under US and EU law, the right pick comes down to your signing volume, budget, and the software your team already uses. Many tools offer free tiers covering three to five documents a month, while pay-as-you-go options charge per document instead of a flat subscription.

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IEEE Spectrum AI
Jun 1, 2026

New Server Hopes to Break Through AI's "Memory Wall"

AI hardware startup Majestic Labs is building a new server called Prometheus with up to 128 terabytes of memory, over 60 times more than Nvidia's DGX B300, to address what the industry calls the memory wall in large language model inference. The company uses a DRAM-centric architecture with a proprietary copper-cable memory interface and custom aggregation chips. Prometheus pairs this memory with a custom AI processor called Ignite and supports common frameworks without code changes.

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Google Cloud AI
May 28, 2026

Evolving Dataflow to process massive datasets for machine learning

Google created MapReduce more than 20 years ago to handle its early data-processing scaling problems. The company has since evolved its internal data platform, Flume, the successor to MapReduce, with work focused on scalability, efficiency, and developer experience. Many of those features are now available in Dataflow, Google's fully managed batch and streaming platform. The post explains the new capabilities and how Google Cloud customers apply them.

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IEEE Spectrum AI
May 16, 2026

AI Rings on Fingers Can Interpret Sign Language

A new study describes a set of electronic rings, wirelessly connected to an AI system, that translate multiple sign languages into text. Led by Ki Jun Yu of Yonsei University, the work uses seven rings with accelerometers and a deep-learning system to recognize signs without smart gloves or cameras. In testing with people who did not help train the system, it recognized 100 American Sign Language and 100 International Sign Language words with 88.3 and 88.5 percent accuracy.

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Berkeley BAIR
Mar 13, 2026

Identifying Interactions at Scale for LLMs

This Berkeley BAIR post introduces SPEX and ProxySPEX, algorithms designed to identify influential interactions in large machine learning systems, including large language models, at scale. It frames the work within interpretability research, which seeks to make model decision-making more transparent for safer and more trustworthy AI. The core challenge is that the number of possible interactions grows exponentially, making exhaustive analysis infeasible, so SPEX uses ideas from signal processing and coding theory to find the small set of interactions that truly drive behavior.

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Berkeley BAIR
Jan 10, 2026

Information-Driven Design of Imaging Systems

Researchers at Berkeley present a framework for designing imaging systems based on how much useful information their measurements contain rather than how images look. The approach uses mutual information, estimated directly from noisy measurements and a known noise model, to evaluate and optimize imaging hardware. In a NeurIPS 2025 paper, the team shows the metric predicts system performance across four imaging domains and produces designs matching state-of-the-art end-to-end methods with less memory and compute.

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Microsoft Copilot
Dec 26, 2025

Main Character Energy: 2025 trend recap

Microsoft's Copilot team published a lighthearted 2025 trend recap from MAI, summarizing what people talked about with Copilot over the year. The post frames Copilot's mission as making the user the main character and shares playful comparisons of trending topics, with stats curated by MAI technical staff member Sophia Chen. It points readers to a fuller MAI blog post and paper for deeper analysis of how people use Copilot over time.

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OpenAI
Dec 18, 2025

Deepening our collaboration with the U.S. Department of Energy

OpenAI and the U.S. Department of Energy have signed a memorandum of understanding to deepen collaboration on AI and advanced computing in support of scientific discovery. The agreement builds on ongoing work with national laboratories and helps establish a framework for applying AI to high-impact research across the DOE ecosystem.

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OpenAI
Dec 11, 2025

Advancing science and math with GPT-5.2

GPT-5.2 is OpenAI's strongest model yet for math and science, setting new state-of-the-art results on benchmarks like GPQA Diamond and FrontierMath. This post shows how those gains translate into real research progress, including solving an open theoretical problem and generating reliable mathematical proofs.

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Berkeley BAIR
Sep 1, 2025

What exactly does word2vec learn?

Researchers from Berkeley's BAIR lab present a quantitative theory of how word2vec learns word representations. They prove that in realistic regimes the learning problem reduces to unweighted least-squares matrix factorization, and they solve the gradient flow dynamics in closed form so that the final representations are given by PCA. When trained from small initialization, word2vec learns one concept at a time in discrete steps, each incrementing the rank of the embedding matrix. The learned features turn out to be the top eigenvectors of a matrix defined by corpus statistics and hyperparameters.

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OpenAI
Mar 14, 2023

GPT-4

We've created GPT-4, the latest milestone in OpenAI's effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

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OpenAI
Jan 11, 2023

Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk

OpenAI researchers collaborated with Georgetown University's Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and int

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OpenAI
Jul 14, 2022

DALL·E 2: Extending creativity

As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as key voices as we've made decisions about DALL·E's features.

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OpenAI
Jun 23, 2022

Learning to play Minecraft with Video PreTraining

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents.

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OpenAI
Dec 5, 2019

Deep double descent

We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don't yet fully understand why it happens, and view further study of this phenomenon as an important research direction.

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OpenAI
Aug 20, 2019

GPT-2: 6-month follow-up

We're releasing the 774 million parameter GPT-2 language model after the release of our small 124M model in February, staged release of our medium 355M model in May, and subsequent research with partners and the AI community into the model's potential for misuse and societal benefit. We're also releasing an open-source legal agreement to make it easier for organizations to initiate model-sharing partnerships with each other, and are publishing a technical report about our experience in coordinating with the wider AI research community on publication norms.

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OpenAI
Jul 10, 2019

Why responsible AI development needs cooperation on safety

We've written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety. We hope these strategies will encourage greater cooperatio

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OpenAI
Mar 21, 2019

Implicit generation and generalization methods for energy-based models

We've made progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models. Generation in EBMs spends more compute to continually refine its answers and doing so can generate samples competitive with GANs at low temperatures, while also having mode coverage guarantees of likelihood-based models. We hope these findings stimulate further research into this promising class of models.

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OpenAI
Feb 19, 2019

AI safety needs social scientists

We've written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI systems with human values requires resolving many uncertainties related to the psychology of human rationality, emotion, and biases. The aim of this paper is to spark further collaboration between machine learning and social science researchers, and we plan to hire social scientists to work on this full time at OpenAI.

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OpenAI
Feb 14, 2019

Better language models and their implications

We've trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization-all without task-specific training.

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OpenAI
Oct 22, 2018

Learning complex goals with iterated amplification

We're proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale, by demonstrating how to decompose a task into simpler sub-tasks, rather than by providing labeled data or a reward function. Although this idea is in its very early stages and we have only completed experiments on simple toy algorithmic domains, we've decided to present it in its preliminary state because we think it could prove to be a scalable approach to AI safety.

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OpenAI
Jun 11, 2018

Improving language understanding with unsupervised learning

We've obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we're also releasing. Our approach is a combination of two existing ideas: transformers and unsupervised pre-training. These results provide a convincing example that pairing supervised learning methods with unsupervised pre-training works very well; this is an idea that many have explored in the past, and we hope our result motivates further research into applying this idea on larger and more diverse datasets.

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OpenAI
Apr 18, 2018

Evolved Policy Gradients

We're releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training.

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OpenAI
Feb 26, 2018

Ingredients for robotics research

We're releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We've used these environments to train models which work on physical robots. We're also releasing a set of requests for robotics research.

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OpenAI
Feb 20, 2018

Preparing for malicious uses of AI

We've co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.

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OpenAI
Feb 15, 2018

Interpretable machine learning through teaching

We've designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept-for instance, the best images to describe the concept of dogs-and experimentally we found our approach to be effective at teaching both AIs

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OpenAI
Aug 16, 2017

More on Dota 2

Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, given sufficient compute. In the span of a month, our system went from barely matching a high-ranked player to beating the top pros and has continued to improve since then. Supervised deep learning systems can only be as good as their training datasets, but in self-play systems, the available data improves automatically as the agent gets better.

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