He kept applying. Then he flew three shuttle missions, walked in space four times, and made two trips to the International Space Station. On the mission that didn’t go there, he was outside the orbiter rehearsing how to build it.
In 2011, I ambushed him with a camera at Singularity University and got 20 minutes.
Dan is not just an astronaut. He holds a doctorate in electrical engineering from Princeton and a doctorate in medicine from Miami, and he left NASA in 2005 to build #robotics for people with disabilities. So when our conversation turned to #ArtificialIntelligence, and specifically to what happens when we arm it, he was not speculating. He was describing machines he understood from the inside.
Armed drones were already flying in 2011. What nobody had done yet was hand the machine the decision to fire. Fifteen years on, that line is thinner than most people realize.
We also got into Asimov’s three laws, the Turing test, his 109 project to improve a billion lives in a decade, and whether we survive the #Singularity at all.
Zahi A. Fayad, PhD, is the Lucy G. Moses Professor of Medical Imaging and Bioengineering at the Icahn School of Medicine at Mount Sinai, where he also serves as Vice Chair for Research in Radiology and holds professorships in Medicine (Cardiology) and AI & Human Health. He is the founding Director of the BioMedical Engineering and Imaging Institute (BMEII), home to one of the nation’s top NIH-funded radiology programs (#2 in 2025 per Blue Ridge rankings). Dr. Fayad also co-leads Mount Sinai’s system-wide Healthspan initiative, coordinating research, clinical, and digital infrastructure to advance precision prevention across the enterprise.
Dr. Fayad is Principal Investigator on multiple major grants, including five NIH-funded projects (3 R01s, 2 P01s) supported by the National Heart, Lung, and Blood Institute, NIAID, and NIDA. A leader in biomedical engineering, his interdisciplinary work integrates advanced imaging, AI, and nanomedicine to drive precision medicine, with research interests focused on how lifestyle stressors — chronic stress, diet, exercise, and sleep — affect long-term cardiovascular and whole-person health.
A Clarivate Highly Cited Researcher since 2018 (~190,000 citations; h-index 142), Dr. Fayad’s seminal contributions include MRI vessel wall imaging (leading to Carotid Plaque-RADS), FDG PET imaging of vascular inflammation, and defining the link between amygdala activity, systemic inflammation, and cardiovascular risk. His research on HDL-based nanoparticles for immune modulation is progressing toward clinical translation for cancer, autoimmune diseases, and transplant rejection — work he is advancing commercially as co-founder of Trained Therapeutix Discovery (TTxD), an early-stage biotech company. He is also a recipient of the Jean Paul II Award for Medicine and Research.
His current projects span cardiovascular, neuroimmune, and transplant-focused research, including stress-induced immune dysregulation; mitral valve prolapse and arrhythmia risk; cocaine use–related carotid atherosclerosis and cognitive impairment; cardiac sarcoidosis therapy monitoring; and immune tracking in organ rejection using nanobiologics — together shifting care upstream toward risk prediction and intervention before clinical events.
He also leads the Mount Sinai DigiTwin Project, an AI-driven platform designed to personalize health optimization by integrating imaging, multi-omics, and real-time physiologic data — initially focused on cardiovascular health and now expanding to whole-person healthspan modeling. Dr. Fayad and colleagues at Mount Sinai are finalists in the $80m XPRIZE Healthspan competition, where they are evaluating a multimodal strategy to meaningfully extend human healthspan.
Almost 200 Silicon Valley companies, including Proton and Y Combinator, are urging the Trump administration not to cut off access to Chinese open-weight artificial intelligence models or risk crippling the next generation of U.S. startups.
Ayzenberg et al. describe how principles from child development can be used to improve the capacities and biological plausibility of AI models. These principles can be incorporated into every stage of the modeling process, from the architectures to the benchmarks.
Artificial neural networks have become powerful tools for finding patterns in complex data, from classifying images to predicting protein structures and assisting mathematical discovery. Yet their success has so far relied almost entirely on classical hardware. Recent developments in quantum-computing technologies make it timely to ask whether trainable models can also make use of quantum effects such as superposition and the intrinsic uncertainty associated with quantum measurements. What’s more, running neural networks on real quantum processors could potentially turn these networks into probes, revealing how different hardware architectures shape networks’ behaviors.
Many modern technologies, from optical communications and artificial intelligence (AI) hardware to advanced sensors and medical imaging, depend on photonic and semiconductor devices that precisely control the interaction between light and electrons. Designing these devices, however, remains a major challenge because existing simulation tools often require researchers to choose between modeling an entire device or capturing the detailed behavior of electrons. Few can do both within the same model.
Researchers from the Singapore University of Technology and Design (SUTD) and National University of Singapore (NUS) have developed a new computational approach that extends the widely used open-source particle-in-cell (PIC) method with condensed-matter physics. The result is a single platform that can simulate a much broader range of light-matter interactions in metals, semiconductors and emerging quantum materials.
Published in Computer Physics Communications, the research, “Particle-in-cell simulations of quantum plasmas,” demonstrates how an established plasma physics tool can be adapted to study condensed-matter systems, opening new possibilities for designing photonic and quantum technologies.
In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.
Methods that simulate electron-level mechanisms on supercomputers are widely used to explore novel materials and understand biological phenomena. There is strong demand for new approaches that can deliver faster predictions while maintaining high accuracy.
A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within “complex media,” which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve an existing imaging technique.
In tests with standard calibration images obscured by complex media, the new system more than doubled the signal-to-noise ratio compared with a previous generation of the technology. The system also created images in near real time—thousandths of a second. The findings are published in the journal Light: Science & Applications.
Enterprise AI will continue expanding because the business benefits are clear. The challenge is ensuring that productivity gains do not come at the expense of security.
The most effective approach is to incorporate AI into existing identity, data protection and incident response strategies rather than treating it as a separate security domain. Organizations should evaluate AI security controls based on how well they integrate with existing governance and security operations while providing visibility into AI usage, permissions and policy violations.
For managed service providers (MSPs) and enterprise security teams, there is an opportunity to extend cyber resilience strategies to include AI governance.
As one legal analyst noted, the settlement may be seen by the tech industry as simply “a price of conducting business in a fiercely competitive space”—a cost of doing business rather than a genuine deterrent. Meanwhile, the fair-use ruling on training means Anthropic retains the legal right to train on copyrighted works, as long as it doesn’t pirate them to do so.
Anthropic is accusing three Chinese AI companies of setting up more than 24,000 fake accounts with its Claude AI model to improve their own models.
The labs — DeepSeek, Moonshot AI, and MiniMax — allegedly generated more than 16 million exchanges with Claude through those accounts using a technique called “distillation.” Anthropic said the labs “targeted Claude’s most differentiated capabilities: agentic reasoning, tool use, and coding.”
The accusations come amid debates over how strictly to enforce export controls on advanced AI chips, a policy aimed at curbing China’s AI development.
