Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.

Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.

The energy consumption and carbon footprint of Artificial Intelligence (AI) have become critical concerns due to rising costs and environmental impacts. In response, a new trend in green AI is emerging, shifting from the "bigger is better" paradigm, which prioritizes large models, to "small is sufficient", emphasizing energy sobriety through smaller, more efficient models. We explore how the AI community can adopt energy sobriety today by focusing on model selection during inference. Model selection consists of choosing the most appropriate model for a given task, a simple and readily applicable method, unlike approaches requiring new hardware or architectures. Our hypothesis is that, as in many industrial activities, marginal utility gains decrease with increasing model size. Thus, applying model selection can significantly reduce energy consumption while maintaining good utility for AI inference. We conduct a systematic study of AI tasks, analyzing their popularity, model size, and efficiency. We examine how the maturity of different tasks and model adoption patterns impact the achievable energy savings, ranging from 1% to 98% for different tasks. Our estimates indicate that applying model selection could reduce AI energy consumption by 27.8%, saving 31.9 TWh worldwide in 2025 - equivalent to the annual output of five nuclear power reactors.

Composable AI offers a scalable and effective paradigm for tackling complex AI tasks by decomposing them into sub-tasks and solving each sub-task using ready-to-use well-trained models. However, systematically evaluating methods under this setting remains largely unexplored. In this paper, we introduce CABENCH, the first public benchmark comprising 70 realistic composable AI tasks, along with a curated pool of 700 models across multiple modalities and domains. We also propose an evaluation framework to enable end-to-end assessment of composable AI solutions. To establish initial baselines, we provide human-designed reference solutions and compare their performance with two LLM-based approaches. Our results illustrate the promise of composable AI in addressing complex real-world problems while highlighting the need for methods that can fully unlock its potential by automatically generating effective execution pipelines.

As artificial intelligence becomes more and more prevalent modern day computing, Microsoft's new Copilot PCs bring powerful AI capabilities right to your fingertips. Equipped with specialized hardware and advanced software, these devices promise a faster, smarter experience. In this article, we'll explore what makes Copilot PCs stand out from the rest. To ensure that the advanced AI functions can be executed quickly and smoothly, Copilot PCs must meet certain hardware requirements. The most important criterion is the presence of a special neural processor (aka the NPU).

CPUs and GPUs are old news. These days, the cutting edge is all about NPUs, and hardware manufacturers are talking up NPU performance. The NPU is a computer component designed to accelerate AI tasks in a power-efficient manner, paving the way for new Windows desktop applications with powerful AI features. All PCs will eventually have NPUs, but at the moment only some laptops have them. Here's everything you need to know about NPUs and why they're such a hot topic in the computer industry right now.

AI PCs are in vogue, especially laptops. But most consumers aren't totally clear on what an AI PC even is, much less which models qualify as one. It doesn't help that AI PCs can be physically indistinguishable from the versions without AI. Here is a simple checklist for determining whether your laptop is capable of performing AI tasks using those newfangled NPUs. In the "Settings" app (key combination Win I), go to "System" and then scroll down to "About."

Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results.

LG's Gram laptops are back for another round of updates ahead of CES 2025. A decade into the thin and light lineup's existence in LG's portfolio, the latest models load up on AI (surprise!) and boost performance while maintaining their trademark portability. Four new models are launching at CES: two variants of the Gram Pro, a new Gram Pro 2-in-1 and the entry-level Gram Book. The first version of the Gram Pro has an Intel Core H-series (Arrow Lake) processor under the hood for more traditional laptop tasks (including some gaming). A second model uses an Intel Core Ultra V-series (Lunar Lake) chip for AI tasks.