The significance: These emissions will add up quickly. The generative-AI boom has led big tech companies to integrate powerful AI models into many different products, from email to word processing. They are now used millions, if not billions, of times every single day. The bigger picture: The study shows that while training massive AI models is incredibly energy intensive, it's only one part of the puzzle. Most of their carbon footprint comes from their actual use.

In the surprise ouster and restoration of Sam Altman as chief executive officer at OpenAI, only one person, Adam D'Angelo, managed to play a role on each side of the drama. D'Angelo, a former Facebook executive and founder of the question-and-answer platform Quora, was one of four members of the board who fired Altman, and the sole surviving director named to a new board of the artificial-intelligence company that took over on Wednesday.

Microsoft is significantly expanding the functions of Paint in Windows 11. The app is also getting a new version. The outdated program is to become a modern image editor that also contains AI functions. In the future, you will be able to use the OpenAI-LLM Dall-E directly in Windows 11 and in Paint. The new functions are also available after installing the Microsoft Paint app from the App Store.

At around 11:30 a.m. on the Friday before Thanksgiving, Microsoft's chief executive, Satya Nadella, was having his weekly meeting with senior leaders when a panicked colleague told him to pick up the phone. An executive from OpenAI, an artificial-intelligence startup into which Microsoft had invested a reported thirteen billion dollars, was calling to explain that within the next twenty minutes the company's board would announce that it had fired Sam Altman, OpenAI's C.E.O. and co-founder. It was the start of a five-day crisis that some people at Microsoft began calling the Turkey-Shoot Clusterfuck. Nadella has an easygoing demeanor, but he was so flabbergasted that for a moment he didn't know what to say. He'd worked closely with Altman for more than four years and had grown to admire and trust him.

Artificial intelligence seems predestined to become a bigger part of our lives. To what extent is the A.I. push being led by Sam Altman and the OpenAI team a cause for concern? If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

Their work, which is yet to be peer reviewed, shows that while training massive AI models is incredibly energy intensive, it's only one part of the puzzle. Most of their carbon footprint comes from their actual use. The study is the first time researchers have calculated the carbon emissions caused by using an AI model for different tasks, says Sasha Luccioni, an AI researcher at Hugging Face who led the work. She hopes understanding these emissions could help us make informed decisions about how to use AI in a more planet-friendly way. Luccioni and her team looked at the emissions associated with 10 popular AI tasks on the Hugging Face platform, such as question answering, text generation, image classification, captioning, and image generation.

Radiologists produce unstructured data that could be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares performance of system using domain-adapted language model (RadLing) and general-purpose large language model (GPT-4) in extracting common data elements (CDE) from thoracic radiology reports. Three radiologists annotated a retrospective dataset of 1300 thoracic reports (900 training, 400 test) and mapped to 21 pre-selected relevant CDEs. RadLing was used to generate embeddings for sentences and identify CDEs using cosine-similarity, which were mapped to values using light-weight mapper. GPT-4 system used OpenAI's general-purpose embeddings to identify relevant CDEs and used GPT-4 to map to values. The output CDE:value pairs were compared to the reference standard; an identical match was considered true positive. Precision (positive predictive value) was 96% (2700/2824) for RadLing and 99% (2034/2047) for GPT-4. Recall (sensitivity) was 94% (2700/2876) for RadLing and 70% (2034/2887) for GPT-4; the difference was statistically significant (P<.001). RadLing's domain-adapted embeddings were more sensitive in CDE identification (95% vs 71%) and its light-weight mapper had comparable precision in value assignment (95.4% vs 95.0%). RadLing system exhibited higher performance than GPT-4 system in extracting CDEs from radiology reports. RadLing system's domain-adapted embeddings outperform general-purpose embeddings from OpenAI in CDE identification and its light-weight value mapper achieves comparable precision to large GPT-4. RadLing system offers operational advantages including local deployment and reduced runtime costs. Domain-adapted RadLing system surpasses GPT-4 system in extracting common data elements from radiology reports, while providing benefits of local deployment and lower costs.

Innovative foundation models, such as GPT-4 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future wireless communications systems. In this article, we outline the applications of diffusion models in wireless communication systems, which are a new family of probabilistic generative models that have showcased state-of-the-art performance. The key idea is to decompose data generation process over "denoising" steps, gradually generating samples out of noise. Based on two case studies presented, we show how diffusion models can be employed for the development of resilient AI-native communication systems. Specifically, we propose denoising diffusion probabilistic models (DDPM) for a wireless communication scheme with non-ideal transceivers, where 30% improvement is achieved in terms of bit error rate. In the other example, DDPM is employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance.

Last week, after briefly deposed CEO Sam Altman was reinstalled at OpenAI, two reports claimed that a top-secret project at the company had rattled some researchers there with its potential to solve intractable problems in a powerful new way. "Given vast computing resources, the new model was able to solve certain mathematical problems," Reuters reported, citing a single unnamed source. "Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*'s future success." The Information said that Q* was seen as a breakthrough that would lead to "far more powerful artificial intelligence models," adding that "the pace of development alarmed some researchers focused on AI safety," citing a single unnamed source. Reuters also reported that some researchers sent a letter expressing concerns about Q*'s potential power to the nonprofit board that ejected Altman, although a WIRED source familiar with the board's thinking says that was not the case.

ChatGPT is one year old today, and it's accomplished a lot in its first trip around the sun. The chatbot has upended or outright killed high-school and college essay writing and thoroughly scrambled the brains of academics, creating an on-campus arms race that professors have already lost. It has been used to write books, article summaries, and political content, and it has flooded online marketplaces with computer-generated slop. As we've gotten to know ChatGPT, we've noticed how malleable it is. The li'l bot loves clichés.