Diagnostic imaging studies are an increasingly important component of the workup and management of acutely presenting patients. However, ordering appropriate imaging studies according to evidence-based medical guidelines is a challenging task with a high degree of variability between healthcare providers. To address this issue, recent work has investigated if generative AI and large language models can be leveraged to help clinicians order relevant imaging studies for patients. However, it is challenging to ensure that these tools are correctly aligned with medical guidelines, such as the American College of Radiology's Appropriateness Criteria (ACR AC). In this study, we introduce a framework to intelligently leverage language models by recommending imaging studies for patient cases that are aligned with evidence-based guidelines. We make available a novel dataset of patient "one-liner" scenarios to power our experiments, and optimize state-of-the-art language models to achieve an accuracy on par with clinicians in image ordering. Finally, we demonstrate that our language model-based pipeline can be used as intelligent assistants by clinicians to support image ordering workflows and improve the accuracy of imaging study ordering according to the ACR AC. Our work demonstrates and validates a strategy to leverage AI-based software to improve trustworthy clinical decision making in alignment with expert evidence-based guidelines.

California Governor Gavin Newsom has vetoed what would have become one of the most comprehensive policies governing the safety of artificial intelligence in the U.S. The bill would've been among the first to hold AI developers accountable for any severe harm caused by their technologies. It drew fierce criticism from some prominent Democrats and major tech firms, including ChatGPT creator OpenAI and venture capital firm Andreessen Horowitz, who warned it could stall innovation in the state. Newsom described the legislation as "well-intentioned" but said in a statement that it would've applied "stringent standards to even the most basic functions." Regulation should be based on "empirical evidence and science," he said, pointing to his own executive order on AI and other bills he's signed that regulate the technology around known risks such as deepfakes. The debate around California's SB 1047 bill highlights the challenge that lawmakers around the world are facing in controlling the risks of AI while also supporting the emerging technology.

California Gov. Gavin Newsom on Sunday vetoed a hotly contested artificial intelligence safety bill after the tech industry raised objections, saying it could drive AI companies from the state and hinder innovation. Newsom said the bill "does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data" and would apply "stringent standards to even the most basic functions -- so long as a large system deploys it." Newsom said he had asked leading experts on generative AI to help California "develop workable guardrails" that focus "on developing an empirical, science-based trajectory analysis." He also ordered state agencies to expand their assessment of the risks from potential catastrophic events tied to AI use.

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of generative AI, which has empowered criminal activities ranging from sophisticated phishing schemes to the creation of hard-to-detect deep fakes, and to advanced spoofing attacks to biometric authentication systems. The exploitation of AI by criminal purposes continues to escalate, presenting an unprecedented challenge. AI adoption causes an increasingly complex landscape of fraud typologies intertwined with cybersecurity vulnerabilities. Overall, GenAI has a transformative effect on financial crimes and fraud. According to some estimates, GenAI will quadruple the fraud losses by 2027 with a staggering annual growth rate of over 30% [27]. As crime patterns become more intricate, personalized, and elusive, deploying effective defensive AI strategies becomes indispensable. However, several challenges hinder the necessary progress of AI-based fincrime detection systems. This paper examines the latest trends in AI/ML-driven financial crimes and detection systems. It underscores the urgent need for developing agile AI defenses that can effectively counteract the rapidly emerging threats. It also aims to highlight the need for cooperation across the financial services industry to tackle the GenAI induced crime waves.

Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system design and optimization opportunities. The technology is capable of understanding and responding in multiple modalities. However, the advanced capability currently comes with significant system resource demands. To sustainably scale generative AI capabilities to billions of users in the world, inference must be fast and efficient. This paper pinpoints key system design and optimization opportunities by characterizing a family of emerging multi-modal generation models on real systems. Auto-regressive token generation is a critical latency performance bottleneck, typically dominated by GPU idle time. In addition to memory-intensive attention across the generative AI models, linear operations constitute significant inference latency due to the feed forward networks in Transformer-based models. We demonstrate that state-of-the-art optimization levers, spanning from applications to system software and hardware, set a 3.88x better baseline.

Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community.

Generative AI (GAI) holds great potential to improve software engineering productivity, but its untrustworthy outputs, particularly in code synthesis, pose significant challenges. The need for extensive verification and validation (V&V) of GAI-generated artifacts may undermine the potential productivity gains. This paper proposes a way of mitigating these risks by exploiting GAI's ability to generate multiple versions of code and tests to facilitate comparative analysis across versions. Rather than relying on the quality of a single test or code module, this "differential GAI" (D-GAI) approach promotes more reliable quality evaluation through version diversity. We introduce the Large-Scale Software Observatorium (LASSO), a platform that supports D-GAI by executing and analyzing large sets of code versions and tests. We discuss how LASSO enables rigorous evaluation of GAI-generated artifacts and propose its application in both software development and GAI research.

Deep generative models, such as generative adversarial networks (GANs), are pivotal in discovering novel drug-like candidates via de novo molecular generation. However, traditional character-wise tokenizers often struggle with identifying novel and complex sub-structures in molecular data. In contrast, alternative tokenization methods have demonstrated superior performance. This study introduces a molecular GAN that integrates a byte level byte-pair encoding tokenizer and employs reinforcement learning to enhance de novo molecular generation. Specifically, the generator functions as an actor, producing SMILES strings, while the discriminator acts as a critic, evaluating their quality. Our molecular GAN also integrates innovative reward mechanisms aimed at improving computational efficiency. Experimental results assessing validity, uniqueness, novelty, and diversity, complemented by detailed visualization analysis, robustly demonstrate the effectiveness of our GAN.

It's nearly two years since OpenAI released ChatGPT on an unsuspecting world, and the world, closely followed by the stock market, lost its mind. All over the place, people were wringing their hands wondering: What This Will Mean For [enter occupation, industry, business, institution]. Within academia, for example, humanities professors agonised about how they would henceforth be able to grade essays if students were using ChatGPT or similar technology to help write them. The answer, of course, is to come up with better ways of grading, because students will use these tools for the simple reason that it would be idiotic not to – just as it would be daft to do budgeting without spreadsheets. But universities are slow-moving beasts and even as I write, there are committees in many ivory towers solemnly trying to formulate "policies on AI use".

In the past week or so, we've had a logo upgrade, a big New York Times profile, and a Moncler outerwear collaboration from LoveFrom, Jony Ive and Marc Newson's San Francisco–headquartered design studio. The real news, though, is confirmation that LoveFrom is working with OpenAI's founder Sam Altman to build a secretive as-yet-unnamed AI device with investors including Laurene Powell Jobs' Emerson Collective, and Ive himself. The former Apple chief design officer is sometimes gently mocked for his obsession with seemingly small details, but when it comes to a potential mainstream human-AI interface, the man who has spent the past five years preoccupied with buttons--going so far as to create a five-volume history of garment fasteners--could be, in a somewhat inevitable way, the exact kind of person required to walk this particular tightrope of ethics and ambition. Details so far are scarce but revealing, at least where intentions are concerned. LoveFrom is designing "a product that uses AI to create a computing experience that is less socially disruptive than the iPhone."