First, Melissa tells me, there is a lot of disagreement about foundation models, which has taken up most of the energy and space during the latest debates. There are several definitions of the term "foundation model" floating around, which is part of what's causing the discord, but the core concept has to do with general-purpose AI that can do many different things for various applications. You've probably played around with ChatGPT; that interface is essentially powered by a foundation model, in this case a large language model from OpenAI. Making this more complex, though, is that these technologies can also be plugged into various other applications with more narrow uses, like education or advertising. Initial versions of the EU AI Act didn't explicitly consider foundation models, but Melissa notes that the proliferation of generative AI products over the past year pushed lawmakers to integrate them into the risk framework. In the version of the legislation passed by Parliament in June, all foundation models would be tightly regulated regardless of their assigned risk category or how they are used. This was deemed necessary in light of the vast amount of training data required to build them, as well as IP and privacy concerns and the overall impact they have on other technologies. But of course, tech companies that build foundation models have disputed this and advocate for a more nuanced approach that considers how the models are used. France, Germany, and Italy have flipped their positions and gone so far to say that foundation models should be largely exempt from AI Act regulations.

Unit tests play a key role in ensuring the correctness of software. However, manually creating unit tests is a laborious task, motivating the need for automation. Large Language Models (LLMs) have recently been applied to this problem, utilizing additional training or few-shot learning on examples of existing tests. This paper presents a large-scale empirical evaluation on the effectiveness of LLMs for automated unit test generation without additional training or manual effort, providing the LLM with the signature and implementation of the function under test, along with usage examples extracted from documentation. We also attempt to repair failed generated tests by re-prompting the model with the failing test and error message. We implement our approach in TestPilot, a test generation tool for JavaScript that automatically generates unit tests for all API functions in an npm package. We evaluate TestPilot using OpenAI's gpt3.5-turbo LLM on 25 npm packages with a total of 1,684 API functions. The generated tests achieve a median statement coverage of 70.2% and branch coverage of 52.8%, significantly improving on Nessie, a recent feedback-directed JavaScript test generation technique, which achieves only 51.3% statement coverage and 25.6% branch coverage. We also find that 92.8% of TestPilot's generated tests have no more than 50% similarity with existing tests (as measured by normalized edit distance), with none of them being exact copies. Finally, we run TestPilot with two additional LLMs, OpenAI's older code-cushman-002 LLM and the open LLM StarCoder. Overall, we observed similar results with the former (68.2% median statement coverage), and somewhat worse results with the latter (54.0% median statement coverage), suggesting that the effectiveness of the approach is influenced by the size and training set of the LLM, but does not fundamentally depend on the specific model.

Discriminative methods focus on modeling the conditional probability of outcome(s) given a context (such as a feature vector). In contrast, generative methods focus on modeling the joint distribution of data. Discriminative models have historically found success in classification and regression tasks in various domains (e.g., finance, healthcare, automotive, etc). On the other hand, newer generative models, such as Large Language Models (LLMs) and diffusion models, have succeeded in open-ended tasks that require versatility and creativity in addition to traditional prediction tasks. We hypothesize that the value of these new generative models is enhanced because they are user-friendly and highly adaptable, making it easier for non-experts to interact with them and produce valuable results with minimal effort. However, this is not the case with current discriminative models. In this work, we explore ways to make discriminative models more user-friendly and adaptable, which we hypothesize will increase their adoption in more applications and bring them on par with the success levels seen with generative AI tools.

The advancement of generative AI has given rise to pressing copyright challenges, particularly in music industry. This paper focuses on the economic aspects of these challenges, emphasizing that the economic impact constitutes a central issue in the copyright arena. The complexity of the black-box generative AI technologies not only suggests but necessitates algorithmic solutions. However, such solutions have been largely missing, leading to regulatory challenges in this landscape. We aim to bridge the gap in current approaches by proposing potential royalty models for revenue sharing on AI music generation platforms. Our methodology involves a detailed analysis of existing royalty models in platforms like Spotify and YouTube, and adapting these to the unique context of AI-generated music. A significant challenge we address is the attribution of AI-generated music to influential copyrighted content in the training data. To this end, we present algorithmic solutions employing data attribution techniques. Our experimental results verify the effectiveness of these solutions. This research represents a pioneering effort in integrating technical advancements with economic and legal considerations in the field of generative AI, offering a computational copyright solution for the challenges posed by the opaque nature of AI technologies.

AI-Generated Content (AIGC), as a novel manner of providing Metaverse services in the forthcoming Internet paradigm, can resolve the obstacles of immersion requirements. Concurrently, edge computing, as an evolutionary paradigm of computing in communication systems, effectively augments real-time interactive services. In pursuit of enhancing the accessibility of AIGC services, the deployment of AIGC models (e.g., diffusion models) to edge servers and local devices has become a prevailing trend. Nevertheless, this approach faces constraints imposed by battery life and computational resources when tasks are offloaded to local devices, limiting the capacity to deliver high-quality content to users while adhering to stringent latency requirements. So there will be a tradeoff between the utility of AIGC models and offloading decisions in the edge computing paradigm. This paper proposes a joint optimization algorithm for offloading decisions, computation time, and diffusion steps of the diffusion models in the reverse diffusion stage. Moreover, we take the average error into consideration as the metric for evaluating the quality of the generated results. Experimental results conclusively demonstrate that the proposed algorithm achieves superior joint optimization performance compared to the baselines.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions which can be applied to several fields and areas of work. This paper presents results and reflection of an experiment done to use the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic Analysis. Previous research on this subject has largely worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations made by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. Attempting an analysis based on human interpretation with an LLM clearly is a provocation but also a way to learn something about how these systems can or cannot be used in qualitative research. The paper presents the motivations for attempting this emulation, it reflects on how the six steps to a Thematic Analysis proposed by Braun and Clarke can at least partially be reproduced with the LLM and it also reflects on what are the outputs produced by the model. The paper used two existing datasets of open access semi-structured interviews, previously analysed with Thematic Analysis by other researchers. It used the previously produced analysis (and the related themes) to compare with the results produced by the LLM. The results show that the model can infer at least partially some of the main Themes. The objective of the paper is not to replace human analysts in qualitative analysis but to learn if some elements of LLM data manipulation can to an extent be of support for qualitative research.

About noon on Nov. 17, Sam Altman, CEO of OpenAI, logged into a video call from a luxury hotel in Las Vegas. He was in the city for its inaugural Formula One race, which had drawn 315,000 visitors including Rihanna and Kylie Minogue. Altman, who had parlayed the success of OpenAI's ChatGPT chatbot into personal stardom beyond the tech world, had a meeting lined up that day with Ilya Sutskever, chief scientist of the artificial intelligence startup. But when the call started, Altman saw that Sutskever was not alone -- he was virtually flanked by OpenAI's three independent board members. Instantly, Altman knew something was wrong.

In 2015, Elon Musk, Sam Altman, and a handful of AI researchers and technologists met at a fancy Silicon Valley hotel to discuss an exciting possibility: Could they build an AI as smart or smarter than a human? Less than a year later, OpenAI was born. OpenAI was founded as a nonprofit research lab with lofty ideals. It wouldn't just build groundbreaking AI--it would do it for the benefit of all of humanity.

We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.