Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and classifying the data in terms of the themes (to kick-start phase 4). We employed the framework for an analysis of a dataset (n = 785) of facts descriptions from criminal court opinions regarding thefts. The goal of the analysis was to discover classes of typical thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated reasonable initial codes, and it was capable of improving the quality of the codes based on expert feedback. They also suggest that the model performed well in zero-shot classification of facts descriptions in terms of the themes. Finally, the themes autonomously discovered by the LLM appear to map fairly well to the themes arrived at by legal experts. These findings can be leveraged by legal researchers to guide their decisions in integrating LLMs into their thematic analyses, as well as other inductive coding projects.

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.

The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models, a common issue encountered by users is the need for repetitive editing of input prompts in order to receive a satisfactory image, which is time-consuming and labor-intensive. Given the demonstrated text generation power of large-scale language models, such as GPT-k, we investigate the potential of utilizing such models to improve the prompt editing process for T2I generation. We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process. We found that GPT-k models focus more on inserting modifiers while humans tend to replace words and phrases, which includes changes to the subject matter. Experimental results show that GPT-k are more effective in adjusting modifiers rather than predicting spontaneous changes in the primary subject matters. Adopting the edit suggested by GPT-k models may reduce the percentage of remaining edits by 20-30%.

Google agreed to invest up to $2 billion in Anthropic, building on its earlier investment in the artificial-intelligence company and adding fuel to the race between startups trying to achieve the next big breakthrough in the emerging technology. Google invested $500 million upfront into the OpenAI rival and agreed to add $1.5 billion more over time, people familiar with the matter said. The investment follows a separate commitment Amazon made last month to invest $4 billion in the company, which was founded by former OpenAI engineers in 2021 with the goal of developing rival generative AI models.

Ilya Sutskever, OpenAI's cofounder and chief scientist, is no longer focusing on building the next generation of his company's flagship generative AI models. Instead his new priority is to figure out how to stop an artificial superintelligence (a hypothetical future technology he sees coming with the foresight of a true believer) from going rogue. A lot of what Sutskever says is wild. But not nearly as wild as it would have sounded just one or two years ago. He thinks ChatGPT just might be conscious (if you squint).

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects and an additional dataset generated individually by teams using large language models. Overall 56 experiments have been submitted by 17 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The labels generated from large language models increase the bias in the prediction model but lead to less over-fitted results.

Do you support sustainability, social responsibility, tech ethics, or trust and safety? In his new self-published Techno-Optimist Manifesto, Andreessen presents his case for the advancement of technology under capitalism as "virtuous" and capable of creating "abundance that lifts all humans". Along the way he champions trickle-down economics (famously effective at increasing inequality), claims technology can solve any problem and suggests that slowing AI development is akin to murder. If you think such proposals sound divorced from reality, you're right. The harms of the state of technology are many: rampant surveillance, consolidation of power, bias and discrimination in automated decision-making systems, worsening power dynamics and labour conditions as a result of automation, and threats to creative workers from generative AI.

The launch of ChatGPT and other generative AI tools has ushered in rapid advances in artificial intelligence and has increased global angst around the impact the technology will have on society. Policymakers are also increasingly concerned about the impact on democracy around the world, especially as the globe enters a critical year for elections.

Instead of building the next GPT or image maker DALL-E, Sutskever tells me his new priority is to figure out how to stop an artificial superintelligence (a hypothetical future technology he sees coming with the foresight of a true believer) from going rogue. Sutskever tells me a lot of other things too. He thinks ChatGPT just might be conscious (if you squint). He thinks the world needs to wake up to the true power of the technology his company and others are racing to create. And he thinks some humans will one day choose to merge with machines.

Google has been accused of censoring Israel-Palestine responses after its AI refused to call Hamas a terrorist organization. But the tech giant's rival, OpenAI's ChatGPT, had no issue condemning the ruling part of Gaza, saying ''Hamas is designated as a terrorist organization by several countries.' It comes as Israel has launched more than 700 airstrikes on Gaza this week in retaliation for Palestine carrying out an unprecedented attack on festival goers on October 7. The same queries were fed to OpenAI's ChatGPT, which returned with detailed information and answered that'Hamas is designated as a terrorist organization by several countries.' A Google spokesperson told DailyMail.com: