#large language models

#large language models

This challenge is sparking innovations in the inference stack. That's where Dynamo comes in. Dynamo is an open-source framework for distributed inference. It manages execution across GPUs and nodes. It breaks inference into phases, like prefill and decode. It also separates memory-bound and compute-bound tasks. Plus, it dynamically manages GPU resources to boost usage and keep latency low. Dynamo allows infrastructure teams to scale inference capacity responsively, handling demand spikes without permanently overprovisioning expensive GPU resources.

I wasn't expecting a conversation about single cells and cognition to explain why a large language model (LLM) feels like a person. But that's exactly what happened when I listened to Michael Levin on the Lex Fridman Podcast. Levin wasn't debating consciousness or speculating about artificial intelligence (AI). He was describing how living systems, from clusters of cells to complex organisms, cooperate and solve problems. The explanation was authoritative and grounded, but the implications push beyond biology.

Welcome to Vibe Coding Video Games with Python. In this book, you will learn how to use artificial intelligence to create mini-games. You will attempt to recreate the look and feel of various classic video games. The intention is not to violate copyright or anything of the sort, but instead to learn the limitations and the power of AI. Instead, you will simply be learning about whether or not you can use AI to help you know how to create video games.

Tim Metz is worried about the "Google Maps-ification" of his mind. Just as many people have come to rely on GPS apps to get around, the 44-year-old content marketer fears that he is becoming dependent on AI. He told me that he uses AI for up to eight hours each day, and he's become particularly fond of Anthropic's Claude. Sometimes, he has as many as six sessions running simultaneously. He consults AI for marriage and parenting advice, and when he goes grocery shopping, he takes photos of the fruits to ask if they are ripe. Recently, he was worried that a large tree near his house might come down, so he uploaded photographs of it and asked the bot for advice. Claude suggested that Metz sleep elsewhere in case the tree fell, so he and his family spent that night at a friend's. Without Claude's input, he said, "I would have never left the house." (The tree never came down, though some branches did.)

When Quentin Farmer was getting his startup Portola off the ground, one of the first hires he made was a sci-fi novelist. The co-founders began building the AI companion company in late 2023 with only a seed of an idea: Their companions would be decidedly non-human. Aliens, in fact, from outer space. But when they asked a large language model to generate a backstory, they got nothing but slop. The model simply couldn't tell a good story.

"You have to pay them a lot because there's not a lot of these people for the world," Gomez said. "And so there's tons of demand for these people, but there's not enough of those people to do the work the world needs. And it turns out that these models are best at the types of things those people do."

We have the suspicion that the next step for AI models is to start reaching out into the world and affecting the world more broadly,

LeCun founded Meta's Fundamental AI Research lab, known as FAIR, in 2013 and has served as the company's chief AI scientist ever since. He is one of three researchers who won the 2018 Turing Award for pioneering work on deep learning and convolutional neural networks. After leaving Meta, LeCun will remain a professor at New York University, where he has taught since 2003.

That's a crowded market where even her previous firm, 6Sense, offers agents. "I'm not playing in outbound," Kahlow tells TechCrunch. Mindy is intended to handle inbound sales, going all the way to "closing the deal," Kahlow says. This agent is used to augment self-service websites and, Kahlow says, to replace the sales engineer on calls for larger enterprise deals. It can also be the onboarding specialist, setting up new customers.

We collapse uncertainty into a line of meaning. A physician reads symptoms and decides. A parent interprets a child's silence. A writer deletes a hundred sentences to find one that feels true. The key point: Collapse is the work of judgment. It's costly and often can hurt. It means letting go of what could be and accepting the risk of being wrong.

The startup starts with the premise that large language models can't remember past interactions the way humans do. If two people are chatting and the connection drops, they can resume the conversation. AI models, by contrast, forget everything and start from scratch. Mem0 fixes that. Singh calls it a "memory passport," where your AI memory travels with you across apps and agents, just like email or logins do today.

Previous research using DNA from soldiers' remains found evidence of infection with Rickettsia prowazekii, which causes typhus, and Bartonella quintana, which causes trench fever - two common illnesses of the time. In a fresh analysis, researchers found no trace of these pathogens. Instead, DNA from soldiers' teeth showed evidence of infection with Salmonella enterica and Borrelia recurrentis, pathogens that cause paratyphoid and relapsing fever, respectively.

From virtual assistants capable of detecting sadness in voices to bots designed to simulate the warmth of a bond, artificial intelligence (AI) is crossing a more intimate frontier. The fervor surrounding AI is advancing on an increasingly dense bed of questions that no one has yet answered. And while it has the potential to reduce bureaucracy or predict diseases, large language models (LLMs) trained on data in multiple formats text, image, and speech

Organizations have long adopted cloud and on-premises infrastructure to build the primary data centers-notorious for their massive energy consumption and large physical footprints-that fuel AI's large language models (LLMs). Today these data centers are making edge data processing an increasingly attractive resource for fueling LLMs, moving compute and AI inference closer to the raw data their customers, partners, and devices generate.

AI labs are racing to build data centers as large as Manhattan, each costing billions of dollars and consuming as much energy as a small city. The effort is driven by a deep belief in "scaling" - the idea that adding more computing power to existing AI training methods will eventually yield superintelligent systems capable of performing all kinds of tasks.