In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter.

Amazon Music is joining Spotify in starting to offer a generative AI-powered playlist feature. For now, Maestro is available in beta to a small number of Amazon Music users in the US on iOS and Android. Folks who are included in the beta will see Maestro on the home screen after they update to the latest version of the app. They can also access the tool by tapping the plus button to create a new playlist. The idea is to use natural language prompts to create any kind of playlist imaginable.

OpenAI named the former president of Amazon Web Services's Japan arm to spearhead its push to woo enterprise clients in the world's fourth-largest economy. The artificial intelligence darling is opening an office in Tokyo as it releases a custom GPT-4 model catering to Japanese language users. OpenAI said it has 2 million weekly active users in the country, while its enterprise clients include Daikin Industries, Rakuten Group and an affiliate of Toyota Motor. "We want to build a track record through repeated dialogue with companies in Japan," said Tadao Nagasaki, the newly named Japan president for OpenAI, during a news conference Monday. The Tokyo office -- OpenAI's third overseas outpost following offices in London and Dublin -- will grow to about 10 to 20 workers this year, he said.

The strict selectivity of the blood-brain barrier (BBB) represents one of the most formidable challenges to successful central nervous system (CNS) drug delivery. Computational methods to generate BBB permeable drugs in silico may be valuable tools in the CNS drug design pipeline. However, in real-world applications, BBB penetration alone is insufficient; rather, after transiting the BBB, molecules must bind to a specific target or receptor in the brain and must also be safe and non-toxic. To discover small molecules that concurrently satisfy these constraints, we use multi-objective generative AI to synthesize drug-like BBB-permeable small molecules. Specifically, we computationally synthesize molecules with predicted binding affinity against dopamine receptor D2, the primary target for many clinically effective antipsychotic drugs. After training several graph neural network-based property predictors, we adapt SyntheMol (Swanson et al., 2024), a recently developed Monte Carlo Tree Search-based algorithm for antibiotic design, to perform a multi-objective guided traversal over an easily synthesizable molecular space. We design a library of 26,581 novel and diverse small molecules containing hits with high predicted BBB permeability and favorable predicted safety and toxicity profiles, and that could readily be synthesized for experimental validation in the wet lab. We also validate top scoring molecules with molecular docking simulation against the D2 receptor and demonstrate predicted binding affinity on par with risperidone, a clinically prescribed D2-targeting antipsychotic. In the future, the SyntheMol-based computational approach described here may enable the discovery of novel neurotherapeutics for currently intractable disorders of the CNS.

As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these generators: the forensic traces of synthetic images from new techniques can vastly differ from those learned during training, and access to data for these new generators is often limited. To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors. E3 enables the accurate detection of images from newly emerged generators using minimal training data. Our approach does this by first employing transfer learning to develop a suite of expert embedders, each specializing in the forensic traces of a specific generator. Then, all embeddings are jointly analyzed by an Expert Knowledge Fusion Network to produce accurate and reliable detection decisions. Our experiments demonstrate that E3 outperforms existing continual learning methods, including those developed specifically for synthetic image detection.

Generative Artificial Intelligence (GAI) models such as ChatGPT have experienced a surge in popularity, attracting 100 million active users in 2 months and generating an estimated 10 million daily queries. Despite this remarkable adoption, there remains a limited understanding to which extent this innovative technology influences higher education. This research paper investigates the impact of GAI on university students and Higher Education Institutions (HEIs). The study adopts a mixed-methods approach, combining a comprehensive survey with scenario analysis to explore potential benefits, drawbacks, and transformative changes the new technology brings. Using an online survey with 130 participants we assessed students' perspectives and attitudes concerning present ChatGPT usage in academics. Results show that students use the current technology for tasks like assignment writing and exam preparation and believe it to be a effective help in achieving academic goals. The scenario analysis afterwards projected potential future scenarios, providing valuable insights into the possibilities and challenges associated with incorporating GAI into higher education. The main motivation is to gain a tangible and precise understanding of the potential consequences for HEIs and to provide guidance responding to the evolving learning environment. The findings indicate that irresponsible and excessive use of the technology could result in significant challenges. Hence, HEIs must develop stringent policies, reevaluate learning objectives, upskill their lecturers, adjust the curriculum and reconsider examination approaches.

Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore new creative ideas. These methods focus on the aesthetic quality of the 3D models, refining them to look similar to the prompts provided by the user. However, when creating 3D models intended for fabrication, designers need to trade-off the aesthetic qualities of a 3D model with their intended physical properties. To be functional post-fabrication, 3D models have to satisfy structural constraints informed by physical principles. Currently, such requirements are not enforced by generative AI tools. This leads to the development of aesthetically appealing, but potentially non-functional 3D geometry, that would be hard to fabricate and use in the real world. This workshop paper highlights the limitations of generative AI tools in translating digital creations into the physical world and proposes new augmentations to generative AI tools for creating physically viable 3D models. We advocate for the development of tools that manipulate or generate 3D models by considering not only the aesthetic appearance but also using physical properties as constraints. This exploration seeks to bridge the gap between digital creativity and real-world applicability, extending the creative potential of generative AI into the tangible domain.

Adobe is ushering in the next generation of AI art with an upcoming version of Premiere Pro. It's been about two years since Midjourney ushered in AI art, consisting of art generated entirely from scratch as well as "inpainting" and "outpainting." Outpainting attracted attention because AI art was being used to essentially extend the boundaries of photographs and paintings, creating a plausible addition to what wasn't there. Now Adobe is doing the same with Premiere Pro. On Monday, Adobe showed off a video version of what it calls Generative Fill, the same technique that it uses for Adobe Photoshop and its Adobe Firefly generative AI art.

Last year Adobe launched Firefly, its latest generative AI model building on its previous SenseiAI, and now the company is showing how it'll be used its video editing app, Premiere Pro. In an early sneak, it demonstrated a few key features arriving later this year, including Object Addition & Removal, Generative Extend and Text to Video. The new features will likely be popular, as video cleanup is one a common (and painful) task. The first feature, Generative Extend, addresses a problem editors face on nearly every edit: clips that are too short. "Seamlessly add frames to make clips longer, so it's easier to perfectly time edits and add smooth transitions," Adobe states.

The ruling Liberal Democratic Party on Monday unveiled its first poster featuring a catchphrase created using generative artificial intelligence. The slogan, written in red on a white background, pledges to the public a real feeling of economic revitalization, amid Prime Minister and LDP President Fumio Kishida's drive to raise wages to fuel economic growth. Generative AI tools, including ChatGPT, studied Kishida's remarks and party policy documents over the past three years to draw up drafts, according to people familiar with the matter. The AI-crafted slogan was chosen after LDP executives screened more than 500 candidate phrases, including ones proposed by copywriters. "This doesn't mean at all that an election will be called soon," Takuya Hirai, chair of the LDP's Public Relations Headquarters, told a news conference, referring to speculation that Kishida will call a snap general election as early as June.