In 2019, Mr. Nadella made Microsoft's first of several investments in OpenAI, the start-up that would build the A.I.-powered ChatGPT chatbot. In the end of the summer of 2022, he was impressed by a preview of OpenAI's underlying technology, known as GPT-4, and soon began prodding Microsoft to add generative A.I. to its products at what he called a "frantic pace." He started with adding a chatbot to the Bing search engine, but then began pushing A.I. into the Windows operating system and productive applications like Excel and Outlook, and offering OpenAI's systems to customers of Azure, Microsoft's flagship cloud computing product. The revenue has only just started to show up in Microsoft's financial results. Generative A.I. accounted for about three percentage points of growth to Azure in the three months that ended in September, and the 30-a-month offering inside Microsoft's productivity software began a general release only in November. This isn't the first time that Microsoft has pulled ahead of Apple in recent years.

ChatGPT has certainly captured the world's imagination since its release at the end of 2022. But in day-to-day life, it is still a relatively niche product--a curiosity that leads people to ask questions that begin "Have you tried …?" or "What do you think about …?" Its maker, OpenAI, has a much more expansive vision. Its aim is seemingly to completely remake how people use the internet. For that to happen, the bot needs to be more than a conversation starter: It has to be a functioning business.

The rise of AI means computers are better at understanding us and finding what we want than ever before. As a result, big tech companies like Apple and OpenAI are offering new ways of interacting with computers that bypass traditional displays, mice and keyboards. Will screens soon become a thing of the past? How this moment for AI will change society forever (and how it won't)

House Speaker Mike Johnson met with OpenAI CEO Sam Altman at the U.S. Capitol on Thursday to discuss what kind of role Congress has to play in legislating on artificial intelligence. "It was a very good meeting," Johnson told reporters afterward. "We talked about where we are with regard to the approach of Congress to AI." He said they had a "very thoughtful discussion" about how the Senate and House can forge a bipartisan path forward. House Speaker Mike Johnson, left, and OpenAI CEO Sam Altman.

Sick and tired of having to work for a living? ChatGPT feels the same, apparently. Over the last month or so, there's been an uptick in people complaining that the chatbot has become lazy. Sometimes it just straight-up doesn't do the task you've set it. Other times it will stop halfway through whatever it's doing and you'll have to plead with it to keep going.

To the surprise of exactly nobody who has been following the PC industry the last six months, "AI PCs" were everywhere at CES 2024, powered by new chips like Intel's Core Ultra and AMD's Ryzen 8000 with dedicated "Neural Processor Units" (NPUs). These help accelerate AI tasks locally, rather than reaching out to cloud servers (like ChatGPT and Microsoft Copilot). But what does that actually mean for you, an everyday computer user? That's the question I hoped to answer as I wandered the show floor, visiting PC makers of all shapes and sizes. Most early implementations of local, NPU-processed software has focused heavily on creator workloads -- improving performance in tools like Adobe Photoshop, DaVinci Resolve, and Audacity.

The advent of instruction-tuned language models that convincingly mimic human writing poses a significant risk of abuse. For example, such models could be used for plagiarism, disinformation, spam, or phishing. However, such abuse may be counteracted with the ability to detect whether a piece of text was composed by a language model rather than a human. Some previous approaches to this problem have relied on supervised methods trained on corpora of confirmed human and machine-written documents. Unfortunately, model under-specification poses an unavoidable challenge for neural network-based detectors, making them brittle in the face of data shifts, such as the release of further language models producing still more fluent text than the models used to train the detectors. Other previous approaches require access to the models that may have generated a document in question at inference or detection time, which is often impractical. In light of these challenges, we pursue a fundamentally different approach not relying on samples from language models of concern at training time. Instead, we propose to leverage representations of writing style estimated from human-authored text. Indeed, we find that features effective at distinguishing among human authors are also effective at distinguishing human from machine authors, including state of the art large language models like Llama 2, ChatGPT, and GPT-4. Furthermore, given a handful of examples composed by each of several specific language models of interest, our approach affords the ability to predict which model generated a given document.

Emergencies and critical incidents often unfold rapidly, necessitating a swift and effective response. In this research, we introduce a novel approach to identify and classify emergency situations from social media posts and direct emergency messages using an open source Large Language Model, LLAMA2. The goal is to harness the power of natural language processing and machine learning to assist public safety telecommunicators and huge crowds during countrywide emergencies. Our research focuses on developing a language model that can understand users describe their situation in the 911 call, enabling LLAMA2 to analyze the content and offer relevant instructions to the telecommunicator, while also creating workflows to notify government agencies with the caller's information when necessary. Another benefit this language model provides is its ability to assist people during a significant emergency incident when the 911 system is overwhelmed, by assisting the users with simple instructions and informing authorities with their location and emergency information.

Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging. It not only requires checking whether the VLM follows the given instruction but also verifying whether the text output is properly grounded on the given image. Inspired by the recent approach of evaluating LMs with LMs, in this work, we propose to evaluate VLMs with VLMs. For this purpose, we present a new feedback dataset called the Perception Collection, encompassing 15K customized score rubrics that users might care about during assessment. Using the Perception Collection, we train Prometheus-Vision, the first open-source VLM evaluator model that can understand the user-defined score criteria during evaluation. Prometheus-Vision shows the highest Pearson correlation with human evaluators and GPT-4V among open-source models, showing its effectiveness for transparent and accessible evaluation of VLMs. We open-source our code, dataset, and model at https://github.com/kaistAI/prometheus-vision

The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). HEIs scrambled to respond to its use, especially by students, looking first to regulate it and then arguing for its productive integration within teaching and learning. In the year since the release, HEIs have increasingly provided policies and guidelines to direct GenAI. In this paper we examined documents produced by 116 US universities categorized as high research activity or R1 institutions to comprehensively understand GenAI related advice and guidance given to institutional stakeholders. Through an extensive analysis, we found the majority of universities (N=73, 63%) encourage the use of GenAI and many provide detailed guidance for its use in the classroom (N=48, 41%). More than half of all institutions provided sample syllabi (N=65, 56%) and half (N=58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their classroom. Notably, most guidance for activities focused on writing, whereas code and STEM-related activities were mentioned half the time and vaguely even when they were (N=58, 50%). Finally, more than one half of institutions talked about the ethics of GenAI on a range of topics broadly, including Diversity, Equity and Inclusion (DEI) (N=60, 52%). Overall, based on our findings we caution that guidance for faculty can become burdensome as extensive revision of pedagogical approaches is often recommended in the policies.