LLMs & Generative AI - Page 58 of 59
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.
OpenAI has run out of ideas. About its own technology. So it’s hiring a committee to do the thinking instead. Formally, it’s the eight-person Expert Council on Well-Being and AI.
Forget scrolling. Soon you'll just talk your cart into existence. Walmart is hooking its entire product catalog directly into OpenAI's ChatGPT.
OpenAI wants its chatbot to be Switzerland. A new research paper states the goal plainly: "ChatGPT shouldn't have political bias in any direction." The company is trying to engineer a state of perfect, bloodless neutrality.
Imagine a screensaver that turns your living room into a glossy catalog, where you, your dog, and your aunt are the models, and every pixel is for sale. DirecTV is about to make that real.
The wireless earbud market in 2025 is a graveyard of broken promises. Miss on battery life. Fumble the noise canceling. Deliver a fit that fails the first real jog.
You don't need to spin up a GPU cluster or wrestle with model weights. The most powerful language models are already available as cloud APIs, ready to answer, generate, and reason with a single HTTP call.
Google added new visual styles to the narrated summaries NotebookLM creates from uploaded documents.
Most tutorials on transformers are useless. They explain the math and then just stop. This one is different. Consider a tensor with the shape [1, 10, 4, 128]. What do these four numbers actually mean? That’s the only question that matters.
Bigger isn’t better anymore. For years, the AI world chased scale, more parameters, more GPUs, more raw compute, as if size alone guaranteed intelligence. That era is over.
Everyone in AI is building a bigger model. A team in Montreal just built a smarter one, and it’s microscopic. The Tiny Recursive Model from Samsung’s SAIL lab has seven million parameters. That’s a rounding error. GPT-4o has hundreds of billions.
The stock market flinched. OpenAI said something, and now half of Silicon Valley is rubbing its temples, wondering if the entire AI boom is just a collective delusion.
AI agents don’t crash. They don’t throw stack traces or blue screens. They just get a little worse, one update, one prompt tweak, one model swap at a time. The regression is silent. It compounds.
Spotify just handed your music history to a chatbot. You can now link your account to ChatGPT, giving the AI a direct feed of every song you've ever loved and skipped.
OpenAI’s latest model is 30% less politically biased than its predecessors, but the asymmetry hasn’t vanished.
Imagine a lock that doesn’t care how big the door is, only that the right key, tiny and unassuming, slides into its slot.
OpenAI is executing a tech land grab at machine speed. It isn't picking a lane. It's taking every lane. The company is forcing ChatGPT into the shape of an operating system. Apps now live inside it.
Zepto's promise of 10-minute grocery delivery relies on a finite warehouse stocked in real time.
For years, large language models have been trained to do one thing above all else: guess the next word. They chew through oceans of text, learning statistical patterns, but never truly *thinking* about what comes next.
Inference speed is the lifeblood of production AI. But there’s a silent killer draining it: workload drift. Most speculative decoding models are frozen in time, trained once on a fixed set of assumptions, then deployed and forgotten.
Every AI coding assistant sells you a custom fit. Most hand you a static toolbox. That’s why the beta launch of Claude Code’s plugin support stands out.
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