LLMs & Generative AI - Page 13 of 63
Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
Hollywood studios suing AI companies for copyright infringement is a clean, righteous story. Until the accused asks what the studios are doing in their own back rooms.
The fantasy of running your own AI isn't about freedom or digital sovereignty. It's about wanting to type a stupid question about lunch without it becoming part of a data broker's training set. You don't need philosophy.
Forget the jargon. Modern AI has learned to see. That's the fact. For decades, a human expert had to tell a computer what to look for. To spot a tumor, you'd need to define the exact shape, density, and texture of cancerous tissue.
We keep turning simple file folders into expensive AI projects. One developer just turned theirs back. The task was familiar: take a pile of messy text notes and build a clean, interlinked wiki.
The obsession with trillion-parameter models has become a bad joke. A useful one is happening with small ones. A new architecture called Wiola has just been published, and it doesn’t look like anything you’ve seen.
A new kind of AI model is starting to predict the future, or at least next quarter's sales and next week's server load. These time-series foundation models take the same basic engine powering chatbots and apply it to streams of numbers.
Video models are stuck on a bad idea. They treat every second of footage the same, forcing the same dense grid of data tokens onto a simple scene and a complex one. This wastes computation.
Engineers are now judged by their AI appetite. Teams track token counts, and some have leaderboards. It’s like measuring productivity by lines of code again, but this time the meter is running in real dollars.
Restaurants are about to start getting orders from chatbots. The economics might actually work this time. Square has connected its point-of-sale system directly to ChatGPT and Claude.
The Trump administration lifted export controls on Anthropic’s Claude Fable 5 after the company agreed to add a new guardrail.
Anthropic rolled out Claude Sonnet 5 this week. Check the pricing page: the per-token rates haven't budged. That's the headline fact, and it's a classic misdirect. Independent testing from THE DECODER exposes the actual calculus.
Anthropic hid a simple trap in its code. Version 2.1.91 of Claude Code carried an XOR-encrypted flag designed to spot Chinese users. The release notes said nothing about it.
Most AI code assistants are glorified autocomplete. They write fast and wrong. The trick isn't finding a better writer. It's finding a better critic. Pair Claude Code, which writes the code, with Codex Exec, which reviews it.
OpenAI is making its freebie users a lot cheaper. Engineers at the company told colleagues they have more than halved the cost of running ChatGPT for people without accounts.
Science moves at the pace of its tools. A researcher’s insight, whether into a genome’s variant, a single cell’s fate, or a molecule’s shape, is only as fast as the computation that supports it. For decades, that meant waiting.
AI medical chat is mostly a fantasy of sales teams. The real problem isn't getting a right answer. It's conducting a conversation where a model looks at a scan, asks the right follow-ups, and knows when to admit it's guessing.
Most AI benchmarks are polite conversations in a quiet room. The new GPTNT benchmark is a screaming match in a burning building. It uses the cooperative bomb-defusal game *Keep Talking and Nobody Explodes*.
Vision AI models fail in boring, predictable ways. They choke on a new camera angle, a weirdly lit warehouse, a product they haven't seen before.
Getting an AI to reason is hard. Making it right is the real crisis. Chain-of-Thought and other methods just give models more time to think. They don't correct a path already veering off-course.
Your washing machine, your dishwasher, your EV charger—they all whisper usage data to your home network. This stream holds real value for optimizing energy consumption, but piping it raw to a cloud AI poses a glaring privacy threat.
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