The practice of editing the genes of embryos remains highly controversial and risky, but Origin Genomics founder Cathy Tie argues its a "moral imperative" to address hereditary diseases. When the Chinese scientist He Jiankui announced in 2018 that he had created the first gene-edited babies, the experiment was widely condemned as reckless and premature. It ended with He in prison. But that hasn't stopped the push for gene-edited human embryos, with biotech entrepreneur Cathy Tie arguing that doing so isn't just urgent--it's a "moral imperative." Tie is the 30-year-old founder of Origin Genomics, a company that launched in March with plans to bring gene-edited embryos to IVF clinics.

She's 28, Loves God and Her Family, and Might Be the Reason You Can't Have Kids Emma Waters is leading a national fight against fertility tech--with her hot husband's permission, of course. By 5 am every morning, Emma Waters is on the sofa with a blanket, plotting America's baby-making future. For two hours, she stays off her phone, inspired by, the self-help bestseller. At 7:30, she does emails. Waters, 28 years old, is a monk of productivity who makes motherhood look like all-natural Adderall. At 8 am, it's time for breakfast and the Bible. Waters tries to live her life according to the mantra "Love God, get married, have babies." She asks that I not use the real names of her two daughters for their safety, even though they've been printed elsewhere. I'll call them Gertie and Ophelia, which are close enough. Ophelia, the baby, runs around in white tights, shirtless, clutching a pink purse; Gertie's doll won't stop crying. There isn't much to say about Jack, other than that he's hot, is studying to be a preacher, and has a big desk at home, much bigger than Waters', featuring a bust of Nietzsche. In the same room, Waters has a pull-out secretary where she takes meetings, not far from a portrait of a glowering President Trump . The only thing Waters doesn't really do is exercise; she hasn't found the time. Not many people outside of DC have heard of Waters, who mostly works from home in a dreary Pennsylvania suburb named after a long-offshored manufacturer. Yet Waters is a factory for policy papers and op-eds extolling the harms of unregulated reproductive technology, probing the moral issues that liberals don't want to touch with a 10-foot turkey baster.

Tech advances not only made IVF safer and more effective; they fundamentally changed the way we think about our reproduction. Technology is changing the way we make babies. The pioneering work of the scientists who invented IVF led to the birth of the first "test tube baby" in 1978. We've come a long, long way since then. This week, I've been working on a piece about the cutting edge of IVF technologies and what's coming next. Think AI and robots and, potentially, gene-edited embryos.

Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].

Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and In-Context Learning (ICL) through a dual-layer framework combining automatic benchmarks with blinded doctor-in-the-loop assessments. GRPO achieves the highest algorithmic accuracy across multiple decision layers, confirming the value of reinforcement-based optimization for structured prediction tasks. However, clinicians consistently prefer the SFT model, citing clearer reasoning processes (p = 0.035) and higher therapeutic feasibility (p = 0.019). In blinded pairwise comparisons, SFT attains the highest winning rate (51.2%), outperforming both GRPO (26.2%) and even physicians' original decisions (22.7%). These results reveal an alignment paradox: algorithmic improvements do not necessarily translate into higher clinical trust, and may diverge from human-centered preferences. Our findings highlight the need for alignment strategies that prioritize clinically interpretable and practically feasible reasoning, rather than solely optimizing decision-level accuracy.

In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, demographic, and procedural factors. This study develops a robust artificial intelligence (AI) pipeline aimed at predicting live birth outcomes in IVF treatments. The pipeline uses anonymized data from 2010 to 2018, obtained from the Human Fertilization and Embryology Authority (HFEA). We evaluated the prediction performance of live birth success as a binary outcome (success/failure) by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different traditional machine learning-based classifiers including random forest (RF) and decision tree, as well as deep learning-based classifiers including custom transformer-based model and a tab transformer model with an attention mechanism. Our research demonstrated that the best performance was achieved by combining PSO for feature selection with the TabTransformer-based deep learning model, yielding an accuracy of 99.50% and an AUC of 99.96%, highlighting its significant performance to predict live births. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.

Vector databases manage large collections of embedding vectors. As AI applications are growing rapidly, so are the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper first describes the tradeoff space of vector search, then the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected applications to highlight its broad applicability.

When Dina Radenkovic, CEO of Gameto, a startup engineering stem cells to craft a lightweight version of IVF, injected herself with a needle loaded with hormones last December, she wasn't trying to get pregnant. Instead, she'd signed up for her own company's study of how to "mature" human eggs in a lab dish instead of inside their bodies. Gameto is among a group of startups trying to simplify the IVF process, as well as getting it to fit into women's busy schedules more easily. But experts say its technology still has some way to go before it can be embraced more widely. AI consciousness isn't just a devilishly tricky intellectual puzzle; it's a morally weighty problem with potentially dire consequences that philosophers, cognitive scientists, and engineers alike are currently grappling with.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' AI's latest product – Remini – allows users to upload photos of themselves and their partner to generate images of what their future child could look like. There are two sides to this. First, the app lets people envision themselves as parents – potentially encouraging people to pursue, rather than delay, parenthood. As one woman said, "I can actually see myself being [pregnant] at some point."