Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.

In this paper, we design a real-time question-answering system specifically targeted for helping sellers get relevant material/documentation they can share live with their customers or refer to during a call. Taking the Seismic content repository as a relatively large scale example of a diverse dataset of sales material, we demonstrate how LLM embeddings of sellers' queries can be matched with the relevant content. We achieve this by engineering prompts in an elaborate fashion that makes use of the rich set of meta-features available for documents and sellers. Using a bi-encoder with cross-encoder re-ranker architecture, we show how the solution returns the most relevant content recommendations in just a few seconds even for large datasets. Our recommender system is deployed as an AML endpoint for real-time inferencing and has been integrated into a Copilot interface that is now deployed in the production version of the Dynamics CRM, known as MSX, used daily by Microsoft sellers.

Recent advancements in generative artificial intelligence (AI) have raised concerns about their potential to create convincing fake social media accounts, but empirical evidence is lacking. In this paper, we present a systematic analysis of Twitter(X) accounts using human faces generated by Generative Adversarial Networks (GANs) for their profile pictures. We present a dataset of 1,353 such accounts and show that they are used to spread scams, spam, and amplify coordinated messages, among other inauthentic activities. Leveraging a feature of GAN-generated faces -- consistent eye placement -- and supplementing it with human annotation, we devise an effective method for identifying GAN-generated profiles in the wild. Applying this method to a random sample of active Twitter users, we estimate a lower bound for the prevalence of profiles using GAN-generated faces between 0.021% and 0.044% -- around 10K daily active accounts. These findings underscore the emerging threats posed by multimodal generative AI. We release the source code of our detection method and the data we collect to facilitate further investigation. Additionally, we provide practical heuristics to assist social media users in recognizing such accounts.

While AI-generated text and 2D images continue to expand its territory, 3D generation has gradually emerged as a trend that cannot be ignored. Since the year 2023 an abundant amount of research papers has emerged in the domain of 3D generation. This growth encompasses not just the creation of 3D objects, but also the rapid development of 3D character and motion generation. Several key factors contribute to this progress. The enhanced fidelity in stable diffusion, coupled with control methods that ensure multi-view consistency, and realistic human models like SMPL-X, contribute synergistically to the production of 3D models with remarkable consistency and near-realistic appearances. The advancements in neural network-based 3D storing and rendering models, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have accelerated the efficiency and realism of neural rendered models. Furthermore, the multimodality capabilities of large language models have enabled language inputs to transcend into human motion outputs. This paper aims to provide a comprehensive overview and summary of the relevant papers published mostly during the latter half year of 2023. It will begin by discussing the AI generated object models in 3D, followed by the generated 3D human models, and finally, the generated 3D human motions, culminating in a conclusive summary and a vision for the future.

In computational healthcare [3, 72], FMs can handle a variety of clinical data with their appealing capabilities in logical reasoning and semantic understanding. Examples span fields in medical conversation [241, 316], patient health profiling [48], and treatment planning [192]. Moreover, given the strength in largescale data processing, FMs offer a shifting paradigm to assess real-world clinical data in the healthcare workflow rapidly and effectively [208, 261]. FM research places a sharp focus on the data-centric perspective [318]. First, FMs demonstrate the power of scale, where the enlarged model and data size permit FMs to capture vast amounts of information, thus increasing the pressing need of training data quantity [272]. Second, FMs encourage homogenization [21] as evidenced by their extensive adaptability to downstream tasks. High-quality data for FM training thus becomes critical since it can impact the performance of both pre-trained FM and downstream models. Therefore, addressing key data challenges is progressively recognized as a research priority.

More than a decade before an AI-powered chatbot could do your homework, help you make dinner or pass the bar exam, there was @Horse_ebooks. The primitive predecessor to today's chatbot renaissance began as a Twitter account in 2010, tweeting automated excerpts from ebooks that, decontextualized, took on unexpected and strangely poetic meanings. Purportedly a spambot, the account surfaced quotes from ebooks that went viral for their absurdist fragments – phrases like "Hello saxophone," "COULD THIS BE THE", and "Today we are lucky to be talking". It amassed more than 200,000 followers at its peak and now, despite being inactive for a decade, the account still holds 131,000 followers. Its most memorable quip – "everything happens so much" – still resonates today.

During 2023, the shape of politics to come appeared in a video. In it, Hillary Clinton – the former Democratic party presidential candidate and secretary of state – says: "You know, people might be surprised to hear me saying this, but I actually like Ron DeSantis a lot. I'd say he's just the kind of guy this country needs." It seems odd that Clinton would warmly endorse a Republican presidential hopeful. Further investigations found the video was produced using generative artificial intelligence (AI).

We present a tool that leverages generative AI to accelerate the migration of on-premises applications to the cloud. The Cloud Migration LLM accepts input from the user specifying the parameters of their migration, and outputs a migration strategy with an architecture diagram. A user study suggests that the migration LLM can assist inexperienced users in finding the right cloud migration profile, while avoiding complexities of a manual approach.

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated models like OpenAI's ChatGPT, represents a significant advancement in artificial intelligence. These models, however, bring forth substantial challenges in the high consumption of computational, memory, energy, and financial resources, especially in environments with limited resource capabilities. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs. We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design. Additionally, the survey introduces a nuanced categorization of resource efficiency techniques by their specific resource types, which uncovers the intricate relationships and mappings between various resources and corresponding optimization techniques. A standardized set of evaluation metrics and datasets is also presented to facilitate consistent and fair comparisons across different models and techniques. By offering a comprehensive overview of the current sota and identifying open research avenues, this survey serves as a foundational reference for researchers and practitioners, aiding them in developing more sustainable and efficient LLMs in a rapidly evolving landscape.

Generative AI provoked a lot of discussion last year around images, text and video, but it may soon affect the gaming industry as well. Square Enix said it plans to be "aggressively applying" AI and other cutting-edge tech in 2024 to "create new forms of content," according to president Takashi Kiryu's New Year's letter. "Artificial intelligence (AI) and its potential implications had for some time largely been subjects of academic debate," he said. "However, the introduction of ChatGPT, which allows anyone to easily produce writing or translations or to engage in text-based dialogue, sparked the rapid spread of generative AIs. I believe that generative AI has the potential not only to reshape what we create, but also to fundamentally change the processes by which we create, including programming."