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Introducing the #AIIndex2026: Our most comprehensive, independently sourced data analysis of AI’s trajectory, with a clear-eyed assessment of the critical gaps that remain. As AI advances rapidly, can the systems built around it keep up? Explore the data:

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Stanford HAI

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120,036 görüntüleme • 3 ay önce •via X (Twitter)

EğitimHaberler & PolitikaBilim & Teknoloji#AIIndex2026
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New from the Anthropic Economic Index: the first comprehensive analysis of how AI is used in every US state and country we serve. We've produced a detailed report, and you can explore our data yourself on our new interactive website.
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Check this!! Microsoft open-sourced a no-code data analysis tool. It's called Data Formulator and it provides AI-powered data analysis and an drag-and-drop UI for viz tasks. It also works beyond the initial dataset by creating relevant fields and the corresponding viz.
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Consumers Are Bearing The Majority of Tariff Costs The PCE goods price data shows a clear shift upward in trajectory starting in January and suggests that most of the tariff cost so far is falling on the shoulders of households.
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Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

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AI will absolutely replace millions of jobs. Pretending otherwise is delusional. But the solution isn’t stopping AI. It’s AI dividends and shared ownership. Our data trained these systems. Our society built the infrastructure. If AI creates trillions in wealth, the public should own a piece of it and receive dividends from that productivity.

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Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

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ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

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FRIEDBERG: “With the number of satellites going up with Starlink, and to actually deploy data centers that can output data on those nodes on that network, SpaceX has largely built a backup Internet.”
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FRIEDBERG: “With the number of satellites going up with Starlink, and to actually deploy data centers that can output data on those nodes on that network, SpaceX has largely built a backup Internet.”

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The US government has warned that China is harvesting neural data to train AI for military and state surveillance purposes. At least a portion of that data? Sourced from the unwitting brains of elite athletes. Hunterbrook
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The US government has warned that China is harvesting neural data to train AI for military and state surveillance purposes. At least a portion of that data? Sourced from the unwitting brains of elite athletes. Hunterbrook

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Data centers play a critical role in our lives. But AI has turbocharged the need for bigger data centers, putting a growing demand on the existing power grid. So what happens when the digital backbone of our lives needs more power than the world can offer?
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Data centers play a critical role in our lives. But AI has turbocharged the need for bigger data centers, putting a growing demand on the existing power grid. So what happens when the digital backbone of our lives needs more power than the world can offer?

Bloomberg

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A senior data scientist on call, 24/7. That's the dream. Well, these three built it. This is Data Scout, the agent that connects to Snowflake, Notion, Slack, Github, digs into your data, and sends recurring reports automatically. Honestly, one of our most-used agents at Notion. You can build one too.

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