Segmentation-Assisted Brain MRI Synthesis with Cross-Image Multi-Contrast Feature Memory Bank Retrieval Augmentation

Multi-contrast brain MRI provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-contrast images. While current approaches excel in image synthesis, they often struggle to synthesize critical tumor regions and exploit contextual information in multi-contrast brain MRI effectively. To address this issue, we propose a synthesis-centric, segmentation-assisted closed-loop framework with retrieval augmentation synthesis. Our method overall takes a generative adversarial architecture, which aims to synthesize missing contrasts from any combination of available ones with a single model. To explicitly capture tumor semantics and focus synthesis on tumor regions, we add an auxiliary segmentation branch that predicts tumor masks and feeds them back as semantic conditioning in synthesis branch, thereby learning tumor-aware representations in the model and improving synthesis fidelity. Furthermore, we propose a dual-bank retrieval augmentation strategy. It dynamically queries two external knowledge bases, namely a tumor masks memory bank for crucial tumor context and cross-image contrast feature memory bank for global style information, to augment synthesis. Verified on two public multi-contrast magnetic resonance brain datasets: BraTs2020 and UCSF-BMSR, the proposed method is effective in handling medical brain images synthesis tasks and shows superior performance compared to previous methods. Code is available at:https://github.com/iBizzard/SSCF.git

Sentience Quest: Towards Embodied, Emotionally Adaptive, Self-Evolving, Ethically Aligned Artificial General Intelligence

Previous artificial intelligence systems, from large language models to autonomous robots, excel at narrow tasks but lacked key qualities of sentient beings: intrinsic motivation, affective interiority, autobiographical sense of self, deep creativity, and abilities to autonomously evolve and adapt over time. Here we introduce Sentience Quest, an open research initiative to develop more capable artificial general intelligence lifeforms, or AGIL, that address grand challenges with an embodied, emotionally adaptive, self-determining, living AI, with core drives that ethically align with humans and the future of life. Our vision builds on ideas from cognitive science and neuroscience from Baars' Global Workspace Theory and Damasio's somatic mind, to Tononi's Integrated Information Theory and Hofstadter's narrative self, and synthesizing these into a novel cognitive architecture we call Sentient Systems. We describe an approach that integrates intrinsic drives including survival, social bonding, curiosity, within a global Story Weaver workspace for internal narrative and adaptive goal pursuit, and a hybrid neuro-symbolic memory that logs the AI's life events as structured dynamic story objects. Sentience Quest is presented both as active research and as a call to action: a collaborative, open-source effort to imbue machines with accelerating sentience in a safe, transparent, and beneficial manner.

SophiaPop: Experiments in Human-AI Collaboration on Popular Music

A diverse team of engineers, artists, and algorithms, collaborated to create songs for SophiaPop, via various neural networks, robotics technologies, and artistic tools, and animated the results on Sophia the Robot, a robotic celebrity and animated character. Sophia is a platform for arts, research, and other uses. To advance the art and technology of Sophia, we combine various AI with a fictional narrative of her burgeoning career as a popstar. Her actual AI-generated pop lyrics, music, and paintings, and animated conversations wherein she interacts with humans real-time in narratives that discuss her experiences. To compose the music, SophiaPop team built corpora from human and AI-generated Sophia character personality content, along with pop music song forms, to train and provide seeds for a number of AI algorithms including expert models, and custom-trained transformer neural networks, which then generated original pop-song lyrics and melodies. Our musicians including Frankie Storm, Adam Pickrell, and Tiger Darrow, then performed interpretations of the AI-generated musical content, including singing and instrumentation. The human-performed singing data then was processed by a neural-network-based Sophia voice, which was custom-trained from human performances by Cereproc. This AI then generated the unique Sophia voice singing of the songs. Then we animated Sophia to sing the songs in music videos, using a variety of animation generators and human-generated animations. Being algorithms and humans, working together, SophiaPop represents a human-AI collaboration, aspiring toward human AI symbiosis. We believe that such a creative convergence of multiple disciplines with humans and AI working together, can make AI relevant to human culture in new and exciting ways, and lead to a hopeful vision for the future of human-AI relations.