Artificial intelligence (AI) is the buzzword today with AI-based applications demonstrating great performance, speed, and accuracy. Their deployment is widespread in domains like healthcare, finance, retail, automotive, manufacturing, logistics, education, agriculture, telecom, travel, insurance, etc (the list includes almost every major field of work).
A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way.
We aren’t just using these AI tools as assistant anymore; they’re fixing code bugs on their own, making full movies from a sentence, and staying focused for days without forgetting the plan. We went from having helpful assistants to creating actual digital coworkers in less than a year.
NVIDIA today announced the NVIDIA Nemotron™ 3 family of open models, data and libraries designed to power transparent, efficient and specialized agentic AI development across industries.
Here you will quickly learn all about local LLM hardware, software & models to try out first. There are many reasons why one might try to get into local large language models. One is wanting to own a local and fully private, personal AI assistant. Another is a need for a capable roleplay companion or story writing helper. Whatever your goal is, this guide will walk you through the basics of local LLMs including hardware requirements, inference software options, and lightweight models to start with. Enjoy!
Almost every technological innovation of the past several years has been laser-focused on one thing: generative AI. Many of these supposedly revolutionary systems run on big, expensive servers in a data center somewhere, but at the same time, chipmakers are crowing about the power of the neural processing units (NPU) they have brought to consumer devices. Every few months, it’s the same thing: This new NPU is 30 or 40 percent faster than the last one. That’s supposed to let you do something important, but no one really gets around to explaining what that is.
SAN FRANCISCO (AP) — OpenAI CEO Sam Altman has set off a “code red” alert to employees to improve its flagship product, ChatGPT, and delay other product developments, according to The Wall Street Journal.
Liu recommends that students use generative AI to write literature reviews, draft abstracts, generate charts, and organize thoughts. She’s created slides that lay out detailed examples of good and bad prompts, along with one core principle: AI can’t replace human judgment. “Only high-quality input and smart prompting can lead to good results,” she says.
The last 48 hours have been a wild rollercoaster ride for AI hardware. On Tuesday, Google ended its I/O keynote — a roughly two-hour event with copious references to AI — with its vision for Android XR glasses. That included flashy partnerships with Gentle Monster and Warby Parker, as well as the first hands-on opportunity with its prototype glasses for the developers and the majority of tech media alike. On the ground, it was among the buzziest things to come out of Google I/O — a glimpse of what Big Tech thinks is the winning AI hardware formula.
Buy-now-pay-later company Klarna featured the AI version of CEO and co-founder Sebastian Siemiatkowski in an 83-second video about its Q1 2025 results, as reported by TechCrunch. The video’s description says that his “AI avatar” is presenting the results, and the AI avatar kicks off the video by saying that “it’s me, or rather, my AI avatar.”
In 1980 IBM, under pressure from its customers to provide computers for personal use, not just mainframes, set out to create the IBM PC; given the project’s low internal priority but high external demand they decided to outsource two critical components: Microsoft would provide the DOS operating system, which would run on the Intel 8088 processor.
Welcome to IEEE Spectrum’s 11th annual rankings of the most popular programming languages. As always, we combine multiple metrics from different sources to create three meta rankings. The “Spectrum” ranking is weighted towards the profile of the typical IEEE member, the “Trending” ranking seeks to spot languages that are in the zeitgeist, and the “Jobs” ranking measures what employers are looking for.
In our goal of trying to estimate a programming language’s popularity, we realized that no one can look over the shoulder of every person writing code, whether that be a child writing a Java script for a personal Minecraft server, a mobile app developer hoping to hit it big, or an aerospace engineer writing mission-critical code for a voyage to Mars. Our Top Programming Languages interactive tries to tackle the problem of estimating a language’s popularity by looking for proxy signals.
Artificial intelligence still can’t beat a human when it comes to programming. But it’s only a matter of time.