For all the bluster about generative artificial intelligence upending the world, the technology has yet to meaningfully transform white-collar work. Workers are dabbling with chatbots for tasks such as drafting emails, and companies are launching countless experiments, but office work hasn't undergone a major AI reboot. Perhaps that's only because we haven't given chatbots like Google's Gemini and OpenAI's ChatGPT the right tools for the job yet; they're generally restricted to taking in and spitting out text via a chat interface. Things might get more interesting in business settings as AI companies start deploying so-called "AI agents," which can take action by operating other software on a computer or via the internet. Anthropic, a competitor to OpenAI, announced a major new product today that attempts to prove the thesis that tool use is needed for AI's next leap in usefulness.

Last Thanksgiving, Brian Israel found himself being asked the same question again and again. The general counsel at the AI lab Anthropic had been watching dumbfounded along with the rest of the tech world as, just two miles south of Anthropic's headquarters in San Francisco, its main competitor OpenAI seemed to be imploding. OpenAI's board had fired CEO Sam Altman, saying he had lost their confidence, in a move that seemed likely to tank the startup's 80 billion-plus valuation. The firing was only possible thanks to OpenAI's strange corporate structure, in which its directors have no fiduciary duty to increase profits for shareholders--a structure Altman himself had helped design so that OpenAI could build powerful AI insulated from perverse market incentives. To many, it appeared that plan had badly backfired.

I'm a reporter on the WIRED Politics desk, and I'm taking over for Makena this week to talk about politicians rising from the dead in India and the rapper Eminem endorsing opposition parties in South Africa. These things haven't really happened, obviously, but deepfakes created by generative AI have made it seem like they have. Already, we're seeing how politicians, campaigns, and regular people are using generative AI in elections. And this is only the beginning. So today, WIRED is launching a project to track it, all over the world.

In India and Indonesia, dead leaders are rising to throw their support behind their political successors; rapper Eminem is endorsing opposition parties in South Africa; and in the United States, President Biden is telling voters in New Hampshire to stay home. All of these things "happened"–but none of them are real. The generative AI revolution is here, and it's coming for your elections. Welcome to the future, welcome to 2024. For the very first time, the widespread availability of generative AI is going to clash head-on with political campaigns and elections.

The instances listed must be found "in the wild." This means we're only going to include instances where generative AI is being used or found out in the world. This list is almost certainly an undercount. However many instances of generative AI journalists, researchers, experts, or anyone else manages to find, it's likely that there are more. There may be some instances where it will be hard to tell whether a piece of media has been manipulated the old-fashioned way--a cheapfake--or if it's an actual use of generative AI.

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://xiewenwen22.github.io/GAI-enhanced-DRL.

Scaling up deliberative and voting participation is a longstanding endeavor -- a cornerstone for direct democracy and legitimate collective choice. Recent breakthroughs in generative artificial intelligence (AI) and large language models (LLMs) provide unprecedented opportunities, but also alerting risks for digital democracy. AI personal assistants can overcome cognitive bandwidth limitations of humans, providing decision support capabilities or even direct AI representation of human voters at large scale. However, the quality of this representation and what underlying biases manifest when delegating collective decision making to LLMs is an alarming and timely challenge to tackle. By rigorously emulating with high realism more than >50K LLM voting personas in 81 real-world voting elections, we show that different LLMs (GPT 3, GPT 3.5, and Llama2) come with biases and significant inconsistencies in complex preferential ballot formats, compared to simpler and more consistent majoritarian elections. Strikingly, fair voting aggregation methods, such as equal shares, prove to be a win-win: fairer voting outcomes for humans with fairer AI representation. This novel underlying relationship proves paramount for democratic resilience in progressives scenarios with low voters turnout and voter fatigue supported by AI representatives: abstained voters are mitigated by recovering highly representative voting outcomes that are fairer. These insights provide remarkable foundations for science, policymakers and citizens in explaining and mitigating AI risks in democratic innovations.

Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains challenging. Unlike traditional centralized deployment, systematically guaranteeing the integrity of AI model services in fully decentralized environments, particularly on trustless blockchains, is both crucial and difficult. In this paper, we present a new inference paradigm called \emph{proof of quality} (PoQ) to enable the deployment of arbitrarily large generative models on blockchain architecture. Unlike traditional approaches based on validating inference procedures, such as ZKML or OPML, our PoQ paradigm focuses on the outcome quality of model inference. Using lightweight BERT-based cross-encoders as our underlying quality evaluation model, we design and implement PQML, the first practical protocol for real-world NLP generative model inference on blockchains, tailored for popular open-source models such as Llama 3 and Mixtral. Our analysis demonstrates that our protocol is robust against adversarial but rational participants in ecosystems, where lazy or dishonest behavior results in fewer benefits compared to well-behaving participants. The computational overhead of validating the quality evaluation is minimal, allowing quality validators to complete the quality check within a second, even using only a CPU. Preliminary simulation results show that PoQ consensus is generated in milliseconds, 1,000 times faster than any existing scheme.

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently extract entities from medical narratives and unstructured text generated throughout various phases of the patient hospital visit. By addressing the significant challenge of processing unstructured medical text, GAMedX leverages the capabilities of generative AI and LLMs for improved data extraction. Employing a unified approach, the methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98\%. This innovation enhances entity extraction, offering a scalable, cost-effective solution for automated forms filling from unstructured data. As a result, GAMedX streamlines the processing of unstructured narratives, and sets a new standard in NER applications, contributing significantly to theoretical and practical advancements beyond the medical technology sphere.

Spatio-temporal compression of videos, utilizing networks such as Variational Autoencoders (VAE), plays a crucial role in OpenAI's SORA and numerous other video generative models. For instance, many LLM-like video models learn the distribution of discrete tokens derived from 3D VAEs within the VQVAE framework, while most diffusion-based video models capture the distribution of continuous latent extracted by 2D VAEs without quantization. The temporal compression is simply realized by uniform frame sampling which results in unsmooth motion between consecutive frames. Currently, there lacks of a commonly used continuous video (3D) VAE for latent diffusion-based video models in the research community. Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization. To address this issue, we propose a method for training a video VAE of latent video models, namely CV-VAE, whose latent space is compatible with that of a given image VAE, e.g., image VAE of Stable Diffusion (SD). The compatibility is achieved by the proposed novel latent space regularization, which involves formulating a regularization loss using the image VAE. Benefiting from the latent space compatibility, video models can be trained seamlessly from pre-trained T2I or video models in a truly spatio-temporally compressed latent space, rather than simply sampling video frames at equal intervals. With our CV-VAE, existing video models can generate four times more frames with minimal finetuning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed video VAE.