Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. However, the use of such systems raises significant ethical and safety concerns, including privacy violations, misinformation and intellectual property theft. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. To mitigate these risks, there is an urgent need of policies and regulations responsible and ethical development in the field of generative AI. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity, lack of interoperability across lines of jurisdictions, restricting innovation, and hindering free market competition. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative. However, they have clear deficiencies in terms of lack of adequacy of oversight and difficulty of enforcement of ethical and safety standards, and are thus not enough by themselves as a regulation mechanism. We propose a marriage of these two strategies via a framework we call Dual Governance. This framework proposes a cooperative synergy between centralized government regulations in a U.S. specific context and safety mechanisms developed by the community to protect stakeholders from the harms of generative AI. By implementing the Dual Governance framework, we posit that innovation and creativity can be promoted while ensuring safe and ethical deployment of generative AI.

Nowadays, big data, deep learning models, optimization methods, and computational power are essential in promoting the development of artificial intelligence. Recent advances are focused on generative artificial intelligence (GenAI), which paves unprecedented paths to exploring the mechanisms behind the creation of new things (texts, images, videos, or other contents) rather than simply performing discriminative learning tasks. GenAI's emergence, e.g., the large model, has changed the landscape of deep learning research and inevitably influenced individuals in both work and life. Furthermore, GenAI holds tremendous potential to reshape robotics research, national governance, and life sciences. Consequently, a pressing question arises: "Will GenAI inspire a new round of technological revolution?"

The ongoing race to expand generative AI technology is reaching digital assistants -- one of many people's first introductions to an AI companion. Such is the case with Google, which is working on a revamp for its Assistant that will include generative AI-powered technology, according in an internal email obtained by Axios. Google Assistant's vice president Peeyush Ranjan and product director, Duke Dukellis, explained their rationale to staffers, stating: "As a team, we need to focus on delivering high-quality, critical product experiences for our users. We've also seen the profound potential of generative AI to transform people's lives and see a huge opportunity to explore what a supercharged Assistant, powered by the latest LLM technology, would look like." Notably, the email revealed that Google is already working on doing this for mobile devices.

It's now well understood that generative AI will increase the spread of disinformation on the internet. From deepfakes to fake news articles to bots, AI will generate not only more disinformation, but more convincing disinformation. But what people are only starting to understand is how disinformation will become more targeted and better able to engage with people and sway their opinions. When Russia tried to influence the 2016 US presidential election via the now disbanded Internet Research Agency, the operation was run by humans who often had little cultural fluency or even fluency in the English language and so were not always able to relate to the groups they were targeting. With generative AI tools, those waging disinformation campaigns will be able to finely tune their approach by profiling individuals and groups.

While nonconsensual deepfake porn has been used to torment women for years, the latest generation of AI makes it an even bigger problem. These systems are much easier to use than previous deepfake tech, and they can generate images that look completely convincing. Image-to-image AI systems, which allow people to edit existing images using generative AI, "can be very high quality … because it's basically based off of an existing single high-res image," Ben Zhao, a computer science professor at the University of Chicago, tells me. "The result that comes out of it is the same quality, has the same resolution, has the same level of details, because oftentimes [the AI system] is just moving things around." You can imagine my relief when I learned about a new tool that could help people protect their images from AI manipulation.

Graphics rendering has always revolved around a basic premise: faster performance equals a better experience. Of course, graphics processing units (GPUs) that render the complex three-dimensional (3D) images used in video games, augmented reality, and virtual reality can push visual performance only so far before reaching a hardware ceiling. All this has led researchers down the path of artificial intelligence--including the use of neural nets--to unlock speed and quality improvements in 3D graphics. In 2022, for example, Nvidia introduced DLSS 3 (Deep Learning Super Sampling), a neural graphics engine that boosts rendering speed by as much as 530%.a The technology uses machine learning to predict which pixels can be created on the fly using the GPU.

For more than a decade, we have speculated about the impact of artificial intelligence (AI)/machine learning (ML) on the environmental sustainability of computing (see ACM2). It has become clear that Al's carbon emissions (scope 2), lifecycle carbon (scope 3), and other negative environmental impacts are growing explosively. Generative AI capabilities and applications exemplified and popularized in ChatGPT, DALL-E 2, Stable Diffusion, and Copilot, are the drivers. Giga$$$s of increased spending on AI computing equipment and infrastructure is driving a dramatic increase in infrastructure: AI computing silicon and datacenters. From May 2022 to April 2023 (12 months), Nvidia's datacenter group sold $15.5B of GPUs.

Meta's consumer-facing generative AI tools based on its new Llama 2 model may not be far off. The company appears to be working on several new generative AI features for Instagram, including labels that allow creators to identify images "generated by Meta AI." Paluzzi recently posted a screenshot that shows an in-app message detailing how posts created with generative AI tools may soon be labeled within Instagram. "The creator or Meta said that this content was created or edited with AI," the message explains. Additional labels indicate it was "generated by Meta AI" and that "content created with AI is typically labeled so that it can be easily detected." A spokesperson for Meta declined to comment.

This article is from Big Technology, a newsletter by Alex Kantrowitz. Amazon's absence from this year's generative–A.I. bonanza has been a bit puzzling. The company invented Alexa, intuiting people's interest in speaking with computers, yet when OpenAI released ChatGPT it seemed to cede the territory. But rather than sitting out the game, Amazon is waiting to play on its terms. Instead of building one A.I. product, it wants a piece of all of them.

The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.