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1917 articles found

· about 1 month ago· MIT Technology Review

The Download: gig workers training humanoids, and better AI benchmarks

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The gig workers who are training humanoid robots at home When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he str

#ai#mit-technology-review
· about 1 month ago· TechCrunch AI

Anthropic took down thousands of GitHub repos trying to yank its leaked source code — a move the company says was an accident

Anthropic executives said it was an accident and retracted the bulk of the takedown notices.

#ai#techcrunch-ai
· about 1 month ago· Towards Data Science

Turning 127 Million Data Points Into an Industry Report

What I learned about data wrangling, segmentation, and storytelling while building an application security report from scratch The post Turning 127 Million Data Points Into an Industry Report appeared first on Towards Data Science.

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· about 1 month ago· Towards Data Science

Building a Personal AI Agent in a couple of Hours

I’ve been so surprised by how fast individual builders can now ship real and useful prototypes. Tools like Claude Code, Google AntiGravity, and the growing ecosystem around them have crossed a threshold: you can inspect what others are building online and realize just how fast you can build today. O

#ai#towards-data-science
· about 1 month ago· Towards Data Science

How to Make Claude Code Better at One-Shotting Implementations

Make your coding agent more efficient The post How to Make Claude Code Better at One-Shotting Implementations appeared first on Towards Data Science.

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· about 1 month ago· Towards Data Science

The Map of Meaning: How Embedding Models “Understand” Human Language

Learn why embedding models are like a GPS for meaning. Instead of searching for exact words, it navigates a "Map of Ideas" to find concepts that share the same vibe. From battery types to soda flavors, learn how to fine-tune these digital fingerprints for pinpoint accuracy in your next AI project. T

#ai#towards-data-science
· about 1 month ago· TechCrunch AI

With its new app store, Ring bets on AI to go beyond home security

Ring's app store will allow the company to target broader use cases beyond security, like elder care or business needs.

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· about 1 month ago· AI News

SAP and ANYbotics drive industrial adoption of physical AI

Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that. ANYbotics’ four-legged autonomous robots will be connected straight

#ai#ai-news
· about 1 month ago· TechCrunch AI

Exclusive: Runway launches $10M fund, Builders program to support early-stage AI startups

Runway is launching a $10 million fund and startup program to back companies building with its AI video models, as it pushes toward interactive, real-time “video intelligence” applications.

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· about 1 month ago· TechCrunch AI

Nomadic raises $8.4 million to wrangle the data pouring off autonomous vehicles

The company turns footage from robots into structured, searchable datasets with a deep learning model.

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· about 1 month ago· TechCrunch AI

Alexa+ gets new food ordering experiences with Uber Eats and Grubhub

You can now order from Uber Eats and Grubhub using Alexa+, an experience Amazon says will be similar to chatting with a waiter at a restaurant or placing an order at a drive-thru.

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· about 1 month ago· TechCrunch AI

Yupp shuts down after raising $33M from a16z crypto’s Chris Dixon

Less than a year after launching, with checks from some of the biggest names in Silicon Valley, crowdsourced AI model feedback startup Yupp is closing its business, the company said Tuesday.

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· about 1 month ago· TechCrunch AI

OpenAI, not yet public, raises $3B from retail investors in monster $122B fund raise

OpenAI's latest funding round, led by Amazon, Nvidia, and SoftBank, values the AI lab at $852 billion as it nears an IPO.

#ai#techcrunch-ai
· about 1 month ago· MIT Technology Review

AI benchmarks are broken. Here’s what we need instead.

For decades, artificial intelligence has been evaluated through the question of whether machines outperform humans. From chess to advanced math, from coding to essay writing, the performance of AI models and applications is tested against that of individual humans completing tasks. This framing is s

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· about 1 month ago· TechCrunch AI

Salesforce announces an AI-heavy makeover for Slack, with 30 new features

Slack just got a whole lot more useful.

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· about 1 month ago· MIT Technology Review

The Download: AI health tools and the Pentagon’s Anthropic culture war

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. There are more AI health tools than ever—but how well do they work? In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots

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· about 1 month ago· TechCrunch AI

Anthropic is having a month

A human really borks things at Anthropic for the second time this week.

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· about 1 month ago· MIT Technology Review

Shifting to AI model customization is an architectural imperative

In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improveme

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· about 1 month ago· TechCrunch AI

Mercor says it was hit by cyberattack tied to compromise of open-source LiteLLM project

The AI recruiting startup confirmed a security incident after an extortion hacking crew took credit for stealing data from the company's systems.

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· about 2 months ago· Towards Data Science

Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection

SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a

#ai#towards-data-science
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