Why Don’t Americans Welcome AI as Much as People in Other Countries?
The post Why Don’t Americans Welcome AI as Much as People in Other Countries? appeared first on Data & Society .
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From Kubernetes Dashboard to Headlamp: Understanding the Transition
For many people, Kubernetes Dashboard was their first window into Kubernetes. It offered a simple visual way to see what was running in a cluster, inspect resources, and build confidence without relying on the command line. For years, it helped developers, students, and operators make sense of Kubernetes, and it served as an important onramp into the ecosystem. The Kubernetes Dashboard project has now been archived. We deeply respect the work the team did and the role Dashboard played in making Kubernetes more approachable for so many users. Headlamp builds on that foundation and carries it forward. It keeps the clarity of a visual interface while adding capabilities that match how Kubernetes is used today. This includes multi-cluster visibility, application-centric views, extensibility through plugins, and flexible deployment options that work both in-cluster and on the desktop. This guide is meant to help you navigate that transition with confidence. Before diving into the mechanics of migration, we start with familiar ground by looking at how common Kubernetes Dashboard workflows map to Headlamp. We also cover what stays the same and what improves after the switch. The goal is not just to replace a tool, but to honor a user-centered legacy and help you land in a UI that can grow with you as your Kubernetes usage evolves. Mapping Kubernetes Dashboard workloads to Headlamp If you have used Kubernetes Dashboard before, many workflows in Headlamp will feel familiar. Headlamp…
The Import Tax Is Gone
Bulk import is now free up to 1 TB on Standard and Enterprise plans. Starting June 1, a $250 credit is applied automatically — and the rate drops to $0.25/GB after that, down from $1/GB.
Investigador CENIA participó en conversatorio GAM para debatir sobre identidad humana, creatividad e IA
En la biblioteca del Centro Cultural Gabriela Mistral (GAM), el investigador asociado de CENIA, Abel Wajnerman Paz, participó de Ventanal Alameda, un espacio de conversación y debate sobre temas de actualidad organizado por el GAM. En esta sesión, tres panelistas discutieron una pregunta cada vez más urgente: ¿es la creatividad un rasgo exclusivo de nuestra […] The post Investigador CENIA participó en conversatorio GAM para debatir sobre identidad humana, creatividad e IA appeared first on CENIA .
What Happens After A 1,000,000x AI Compute Leap? | Jeff Dean
Thank you to Google for the invite! 🙏 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu Chapters: 00:00 Intro 02:07 Are We Running Out of AI Data? 06:22 The 90% Shift: Why Inference is Taking Over 09:34 The End of the Pre-Training and Post-Training Split 12:02 What Happens After a 1,000,000x Compute Leap? 15:03 How Distillation is Supercharging Open Models 16:17 The Quest for a "Lifetime AI" 17:25 Multi-Agent Workflows 18:40 AI Generating Operating Systems (and Running Doom) 20:15 Solving The Attention Problem 22:13 Data Center Disasters: Supernovas and Cosmic Rays 24:45 The Lightning Round: Jeff Dean Chuck Norris Jokes 25:40 The One Thing Jeff Dean Got Wrong (Healthcare AI) 26:50 The Ultimate Developer Debate: Vim vs. Emacs
New Server Hopes to Break Through AI’s “Memory Wall”
Memory is arguably the most serious constraint on modern AI large language models (LLMs). According to one influential paper , LLM token generation is an inherently memory-bound task, meaning the rate at which models output text is limited by how quickly data can be read in from memory. The severity of this bottleneck grows with model size. This creates a “memory wall” that holds back LLM inference performance. AI hardware startup Majestic Labs is taking a direct—and comprehensive—approach to solving this problem. It’s developing a new AI server, Prometheus, with up to 128 terabytes of memory. That’s over 60 times more than Nvidia’s DGX B300 server , a cutting-edge AI processing rack. Sha Rabii , co-founder and president of Majestic Labs, believes that this drastic increase in memory will provide his company an edge. While he acknowledges that “Nvidia’s done a phenomenal job creating a system that can scale out,” he argues that it becomes less economical as models grow and “ends up greatly over-provisioning on compute and starving on memory.” DRAM-Centric Architecture for LLM Memory Majestic Labs plans to surmount the “memory wall” with an architecture that fundamentally differs from competitors’. Nvidia’s current servers have fast high-bandwidth memory (HBM), which is typically used to read in an LLM’s model weights. In addition, there’s an often larger but slower pool of dynamic random access memory (DRAM), which handles LLM and server overhead. Majestic instead goes all i…
How LinkedIn Uses PyTorch to Solve Extreme-Scale Optimization Problems
TL;DR: This case study demonstrates how LinkedIn re-architected its distributed linear programming solver, DuaLip, by developing a GPU-accelerated PyTorch version to handle extreme-scale optimization challenges like web applications. This transition...
Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems
Do you feel as though you are living in a revolution?
🔥 We checked. Again. Still no bubble.
A customer-led boom with a few fraying edges.
Open and closed models are on different exponentials
Where marginally higher intelligence drives value, and where it doesn't.
Mellum2 Goes Open Source: A Fast Model for AI Workflows
Trained from scratch and designed for practical deployment, Mellum2 is built for routing, Q&A, sub-agents, and private AI use in software engineering systems. Today, we’re open-sourcing Mellum2, a 12B model engineered to solve the hardest parts of production AI: latency, throughput, and cost. Built from scratch and released under the Apache 2.0 license, Mellum2 offers […]
Chinese experts urge Beijing to push past obstacles to a unified national market
Chinese experts urge Beijing to push past obstacles to a unified national market Linda_Heyer Mon, 06/01/2026 - 14:43 picture alliance/dpa Comment Jun 02, 2026 5 min read Chinese experts urge Beijing to push past obstacles to a unified national market This series looks at how China debates the issues the country faces at home and abroad. Covering domestic policy, social change, technology, geopolitics and economics and focusing mainly on expert debates, each article draws on analysis from universities, think tanks, government-linked research institutes, business associations and investment groups. The upside of undoing the country’s patchwork of regional protectionism is seen as greater than ever, says Alexander Davey. The challenge will be to bring local cadres along. Integrating China’s domestic market for goods and services will help Xi Jinping realize his key goals of insulating the economy against external risks, boosting industrial upgrading, and reducing the country’s reliance on exports, according to Chinese scholars. A MERICS Expert Debate Analysis shows why the task – widely recognized as a huge challenge – appears more urgent than at any time since economic liberalization began in the 1980s. Although Xi raised the issue after coming to power in 2012, it was not until 2022 that Beijing began legislating to undo the patchwork of regional rules and protectionism – and progress has been slow. Exhibit 1 Experts cast the unified national market as China’s shield against…
Comment on Offshore Betting Platform Usage Increased Nearly 14% After Online Gambling Ban: CUTS Survey by Offshore Betting Ads Surge Despite Gaming Ad Ban, ASCI Finds
[…] trend becomes more significant when viewed alongside CUTS International’s survey in Delhi, which found offshore platform usage rose from 68.3% to 82% among former real-money gamers […]
The AI Climate Hoax: Behind the Curtain of How Big Tech Greenwashes Impacts
The tech companies driving AI expansion claim that AI will eventually help solve climate change. Our analysis indicates that such claims are not based on credible and verifiable data. On the contrary, the evidence for any significant positive climate impacts from AI is weak, while its substantial climate damage is clearly documented.
Why Don’t Americans Welcome AI as Much as People in Other Countries?
The post Why Don’t Americans Welcome AI as Much as People in Other Countries? appeared first on Data & Society .
Einstein vs Feynman, Who Wins? 🧠🤔 #physics #ai #science #feynman #research
Check the pinned comment for the link to the full interview. In this quick clip, we explore which legendary scientist ranks higher among the experts. It's a fun debate that leads into an even bigger discussion about AI's role in future scientific breakthroughs. You won't want to miss the full deep dive with Demis Hassabis! ⚡️
Reinforcement learning is an infrastructure problem
What we've seen helping teams run Reinforcement Learning at scale on Modal. Plus an open-source library to skip the scaffolding.
AI Weekly Issue #498: Anthropic files for an IPO. NVIDIA ships its stack.
Anthropic confidentially filed a draft S-1 with the SEC today for a proposed public offering. The company also shipped Claude Opus 4.8 last week with a 4x code-reliability gain. NVIDIA used GTC Taipei to open Cosmos 3, ramp Vera Rubin into production, and put a 1-petaflop AI box on developer laptops. Google retires Gemini 2.0 Flash today. California's SB 867 — banning AI companion chatbots in children's toys — cleared the Senate; Illinois's data-center regulation stalled in committee. The labs sprint. The states crawl.
Neural Network which can have an arbitrary number of hidden layers and different hidden layer sizes
I have currently followed the example from geeks for geeks at this site: https://www.geeksforgeeks.org/machine-learning/backpropagation-in-neural-network/ for writing my own Neural Network from Scratch in Python. I have tried to convert the example so that I can put any number of hidden layers in and also have any layer size. At the moment I currently have this code: #Multiple Layers import numpy as np class NeuralNetwork: def __init__(self, input_size, hiddenLayerSizes, output_size): self.input_size = input_size self.hiddenLayerSizes = hiddenLayerSizes self.output_size = output_size self.hiddenLayerWeights = [] self.hiddenLayerBiases =[] self.weights_input_hidden1 = np.random.randn(self.input_size, self.hiddenLayerSizes[0]) for i in range(0, len(hiddenLayerSizes)-1): self.hiddenLayerWeights.append(np.random.randn(self.hiddenLayerSizes[i], self.hiddenLayerSizes[i+1])) self.hiddenLayerBiases.append(np.zeros((1, self.hiddenLayerSizes[i]))) self.weights_hidden_output = np.random.randn(self.hiddenLayerSizes[len(self.hiddenLayerSizes)-1], self.output_size) self.bias_output = np.zeros((1, self.output_size)) def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def sigmoid_derivative(self, x): return x * (1 - x) def feedforward(self, x): self.hidden_activations = [] self.hidden_outputs = [] self.hidden_activations.append(np.dot(X, self.weights_input_hidden1) + self.hiddenLayerBiases[0]) self.hidden_outputs.append(self.sigmoid(self.hidden_activations[0])) for i in range(0, len(self.hidd…
Writing a Neural Network which can have an arbitrary number of hidden layers, could someone tell me if this is the best way to do it?
#Multiple Layers import numpy as np class NeuralNetwork: def __init__(self, input_size, hiddenLayerSizes, output_size): self.input_size = input_size self.hiddenLayerSizes = hiddenLayerSizes self.output_size = output_size self.hiddenLayerWeights = [] self.hiddenLayerBiases =[] self.weights_input_hidden1 = np.random.randn(self.input_size, self.hiddenLayerSizes[0]) for i in range(0, len(hiddenLayerSizes)-1): self.hiddenLayerWeights.append(np.random.randn(self.hiddenLayerSizes[i], self.hiddenLayerSizes[i+1])) self.hiddenLayerBiases.append(np.zeros((1, self.hiddenLayerSizes[i]))) self.weights_hidden_output = np.random.randn(self.hiddenLayerSizes[len(self.hiddenLayerSizes)-1], self.output_size) self.bias_output = np.zeros((1, self.output_size)) def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def sigmoid_derivative(self, x): return x * (1 - x) def feedforward(self, x): self.hidden_activations = [] self.hidden_outputs = [] self.hidden_activations.append(np.dot(X, self.weights_input_hidden1) + self.hiddenLayerBiases[0]) self.hidden_outputs.append(self.sigmoid(self.hidden_activations[0])) for i in range(0, len(self.hiddenLayerSizes)-1): self.hidden_activations.append(np.dot(self.hidden_outputs[i], self.hiddenLayerWeights[i]) + self.hiddenLayerBiases[i]) self.hidden_outputs.append(self.sigmoid(self.hidden_activations[i])) self.output_activation = np.dot(self.hidden_outputs[len(self.hidden_outputs)-1], self.weights_hidden_output) + self.bias_output self.predicted_output = self.sigmoid…
The Ex-Pentagon Chief Sounding the Alarm on AI Weapons — Brad Carson
Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy group he co-founded. Keith Duggar spends roughly eighty minutes pushing back. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Carson's whole case rests on one line: the genie is not out of the bottle. We have pulled dangerous tech back before. Asilomar halted recombinant DNA in 1975, and the West still controls the chips AI runs on. Calling it unstoppable, he says, is the most dangerous idea in the room. Then Keith drags him somewhere darker. A Palantir heat map scores you 0.73 on whether you are a combatant, and a strike follows. The model is wrong some accepted share of the time, and when it is, nobody answers for it. You cannot court-martial a model, and not even the interpretability researchers can say why it picked you. — Note: after recording, we learned that Americans for Responsible Innovation is backed by EA-aligned philanthropy (not sponsored) --- TIMESTAMPS: 00:00:00 From the Pentagon to AI governance 00:04:52 Regulatory capture vs Silicon Valley networks 00:07:56 Transparency and the Claude tier changes 00:09:40 Tort liability when AI tools cause harm 00:13:40 AI is a product, not a person 00:16:01 Children, suicide, a…
CENIA desarrolla un sistema de IA que simula encuestas de opinión pública con testimonios de personas similares
Un paper liderado por el investigador de CENIA Andrés Abeliuk, que será presentado en la conferencia IC2S2 2026, propone modelar la opinión pública a partir de testimonios de personas con características similares al encuestado, reduciendo costos y mejorando la representatividad de grupos históricamente excluidos. Investigadores del Centro Nacional de Inteligencia Artificial (CENIA) desarrollaron un método […] The post CENIA desarrolla un sistema de IA que simula encuestas de opinión pública con testimonios de personas similares appeared first on CENIA .
Lanzamiento de GerencIA 2.0: El programa que enseña a la plana ejecutiva a liderar organizaciones en la era de la IA
Diseñado por CENIA, SOFOFA y Futuro del Trabajo SOFOFA Capital Humano, esta iniciativa formativa entrega las herramientas necesarias para que líderes de empresas aprendan a gestionar la IA desde una perspectiva estratégica. Con el objetivo de fortalecer la capacidad de las organizaciones para adaptarse a los cambios que está impulsando la Inteligencia Artificial, CENIA, SOFOFA […] The post Lanzamiento de GerencIA 2.0: El programa que enseña a la plana ejecutiva a liderar organizaciones en la era de la IA appeared first on CENIA .
The Chatbots and Agents Are Going To Merge
Could the AI labs intuit your next move and accomplish it for you? That seems to be where they're heading.
Live with Azeem: AI & ROI
A recording from Azeem Azhar's live video
#497 – Biggest Mysteries in Physics: Antimatter, Dark Energy & ToE – Don Lincoln
Don Lincoln is a particle physicist at Fermilab who has spent decades working at the frontiers of high energy physics. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep497-sc See below for timestamps, and to give feedback, submit questions, contact Lex, etc. CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Don’s Facebook: https://facebook.com/Dr.Don.Lincoln/ Don’s Website: https://drdonlincoln.com/ Don’s LinkedIn: https://bit.ly/4nHeNiF Don’s YouTube Playlist: https://bit.ly/3PCIW67 Don’s X: https://x.com/DrDonLincoln Don’s Books: https://amzn.to/4uYbkOZ Don’s Great Courses: https://shop.thegreatcourses.com/don-lincoln
Startup offers free home cleaning—if it can record it all for robot training
The latest twist in paying humans to wear head cameras for robot training data.
Democracy in Retreat
Freedom House's Yana Gorokhovskaia discusses the political and ethical stakes of two decades of global freedom decline.
How We Use AlphaEvolve to Make Complex IDE Algorithms Faster
AlphaEvolve is a Google DeepMind algorithm-discovery system that uses Gemini to generate, test, and refine possible algorithm improvements. Its job is not to answer questions; it searches for faster ways to solve complex algorithmic problems. We tried it on a narrow but important part of IntelliJ-based IDEs: indexing, the background work that makes navigation, search, […]
Adventures in Vibecoding Policy
can policy microsites save America?
[Report]MEC2026 RoboSax workshop(Kagurazaka, May 26, 2026)
On May 26, 2026, Masatoshi Hamanaka (Music Information Intelligence Team) and Tsuyoshi Uetaki (Kumamoto University) held a RoboSax workshop at the Morito Memorial Hall in Kagurazaka as part of <a href="https://music-encoding.org/conference/2026/"
AAIN.com reviews SA AI Policy; Algeria reshuffles its AI strategy
Weekly News Digest...
Make your SQL Workflows Multimodal With LanceDB × DuckDB
A hands-on walkthrough showing how LanceDB and DuckDB work together to query multimodal data in SQL, join against multiple tables, and materialize results back into LanceDB.
Unifying the AV ML Stack: From Raw Data to Trained Model with LanceDB
A complete walkthrough of building an autonomous vehicle perception model training pipeline on top of LanceDB and the Multimodal Lakehouse.
Make Room for Women in the Rooms Where AI Policy Is Decided
The post Make Room for Women in the Rooms Where AI Policy Is Decided appeared first on Data & Society .
cascsim on R, when trying claimFitting() it doesn't find suffisient data
I hope I am asking at the right place. it's the first time I am using cascsim. I am looking to claim fit specifically the claim property and H,I am trying to fit the report lag. when I put my code it just says that the data is insufficient. I checked and there is 1175 claim with those two caracteristic so there should be the data available. I am doing module 2 of the CIA and one of the exercice is asking me to do this, I have been stuck on this for a long time. data1 $Lag reportDate) - as.Date(data1$occurrenceDate)) simobj don't hesitate to ask me further question if needed
AI Weekly Issue #497: AI's labor war just went global
This was the week the AI-and-work conflict broke into the open simultaneously across four jurisdictions. Wikipedia editors are organizing a strike over Wikimedia layoffs. Amazon employees gamed its internal AI ranking into uselessness. Chinese courts began enforcing a framework that bars AI-justified layoffs. A UK thinktank, with TUC backing, called for employees to get a real say over how AI is rolled out in their workplaces. None of it is coordinated. All of it is this week.
When AI Infrastructure Meets Enterprise Data: ClearML on the Dell AI Data Platform
By Adam Wolf Dell Technologies has published a validated integration of ClearML with the Dell AI Data Platform (AIDP), pairing ClearML’s AI infrastructure capabilities with Dell’s enterprise-managed storage and search engines. The result is a reference architecture that lets AI teams keep moving fast while platform teams keep the data foundation enterprise-grade. Here is what […]
National Standard of the People’s Republic of China: Cybersecurity Technology – Basic Safety Requirements for Generative Artificial Intelligence Services
Read our translation of a Chinese national standard designed to improve the safety and security of generative AI services. The post National Standard of the People’s Republic of China: Cybersecurity Technology – Basic Safety Requirements for Generative Artificial Intelligence Services appeared first on Center for Security and Emerging Technology .
Deep Dive: the South African Draft AI Policy
(so you don't need to read it and still sound smart)
Apple working to cram massive Gemini model into iPhone to power new Siri
As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.
Trump loses more control over AI regulation as Illinois passes landmark law
Here’s why Anthropic and OpenAI are on board with Illinois safety testing.
Data Formulator 0.7: AI-powered data analytics for enterprise data
Data Formulator introduces AI-powered analytics for enterprise data workflows. Data teams can easily bring enterprise data into an AI-ready workspace where users can explore, analyze, and visualize data with AI agents to turn raw data into actionable insights. The post Data Formulator 0.7: AI-powered data analytics for enterprise data appeared first on Microsoft Research .
CENIA participa en una de las mayores plataformas de colaboración internacional para el desarrollo de IA
Entre los días 19 y 20 de mayo de 2026, el Centro Brasileño de Investigaciones Físicas (CBPF), ubicado en Urca, Río de Janeiro, se convirtió en el punto de encuentro del GPAI Associated Innovation Workshop Rio de Janeiro. El evento, impulsado en el marco del Laboratorio Nacional de Computación Científica (LNCC) y el Ministerio de […] The post CENIA participa en una de las mayores plataformas de colaboración internacional para el desarrollo de IA appeared first on CENIA .
Arizona's Abundance Playbook
a view from the governor's office