LLMs & Generative AI - Page 19 of 55
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.
Key-value cache memory is a quiet, expensive crisis. It grows with every conversational turn, pinching the entire system. You can't scale what you can't afford to run. Apple researchers now propose a fix: stop storing the same thing over and over.
Fine-tuning an AI model has been grueling, specialist work. Now it's over. Amazon declared it so Wednesday with a new agent for SageMaker, its cloud platform for machine learning. Developers simply state what they need in plain English.
The landscape of large language model fine-tuning has long been a battleground between power and accessibility.
When a RAG system sounds utterly certain but its answer is a mirage, you’ve hit the most dangerous failure mode: high confidence paired with low faithfulness.
Jailbreak attacks on large language models are not all the same. Some bypass safety through clever prompt engineering; others exploit subtle vulnerabilities in the model’s own reasoning.
Drilling data is a nightmare of incompatible formats, scattered sources, and buried narratives.
Topic modeling has always been a bit of a magic trick. You're handed categories like "sports" or "tech," but the magician never reveals how the rabbit got into the hat.
Polling is a silent drain. Every repeated “are you done yet?” eats compute, burns through API quota, and injects artificial lag between job completion and application awareness. Google’s Gemini API just killed that pattern.
The price of an AI token is crashing. The price of running AI is not. This is the new math, and it's breaking data centers. Every quarter, the raw cost of asking a model something falls. Yet total bills are ballooning.
A couple of months ago, a cancer diagnosis turned one person’s world into a blur of appointments, scans, and medical jargon. They built a dashboard inside Claude.
BeyondTrust researchers found a new way to steal credentials from an AI coding assistant on March 30. They got Codex's OAuth token in plain text by using a crafted GitHub branch name. OpenAI rated the finding as Critical P1.
The emergency room triage process is a high-stakes game of pattern recognition under pressure. Every second counts. Every symptom must be weighed, every piece of history parsed.
Free users of ChatGPT just became a product, whether they knew it or not. On Thursday, OpenAI quietly flipped a switch, activating marketing cookies by default for anyone on the free tier. Your conversations remain private, the company insists.
Jerry Liu watches the control systems crack. With each new AI model release, the LlamaIndex CEO sees our intricate scaffolds for managing artificial intelligence start to buckle. The machines aren't just smarter.
ChatGPT briefly, bizarrely, fell for goblins. The whole fling started with a single broken piece of code. This tiny component, built to judge answer quality, went rogue.
Multimodal AI is slow for a boring reason. Python is in the way. Before any image reaches the brains of a model like Llama 4 Vision, it gets chewed up by a Python script doing resizes and normalizations.
Your television is getting a new party trick. This week, Google is adding two features from its Gemini AI suite to Google TV: Nano Banana and Veo. Their sole purpose is goofing off.
Google just flipped a switch for millions in Europe. Starting now, opening the Gemini app means your AI assistant begins remembering you—your name, your job, that museum trip you mentioned.
Everyone else is racing to build trillion-parameter giants. Nvidia just built something useful. Meet the Nemotron 3 Nano Omni: a 30-billion-parameter model that eats text, images, video, and audio for breakfast. Its size isn't the headline.
Some AI developers are bored with the present. They made a language model that thinks the world ended nearly a century ago. This model wasn't trained on the internet. For the final stage of tuning, its creators fed it a diet of pre-1930s literature.
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