According to MIT Technology Review, scientists in a Beijing lab have successfully mimicked human embryo implantation—the official start of pregnancy—using a microfluidic chip. They merged human embryos from IVF centers with endometrial cell organoids, as detailed in three recent Cell Press papers. In AI news, a large language model’s parameters are described as the internal “dials and levers” controlling its behavior. While OpenAI’s 2020 GPT-3 had 175 billion parameters, Google DeepMind’s new Gemini 3 is estimated to have at least a trillion, with some guesses as high as 7 trillion, but companies are now keeping these details secret due to fierce competition.
A Window Into The Womb
This is wild, right? We’re talking about watching the very first, most fragile steps of human life unfold not in a body, but in a lab on a chip. The implications are huge. For decades, this “black box” period of early development has been nearly impossible to study ethically. Now, researchers have a model. It could revolutionize our understanding of early pregnancy loss and infertility. Think about it: if we can see exactly how an embryo attaches and what can go wrong, we might finally develop real interventions. But, of course, it also immediately raises massive ethical questions. How long do you let these lab pregnancies develop? Where do you draw the line? The science is sprinting ahead, and our ethical frameworks need to catch up, fast.
The Parameter Arms Race Goes Dark
So about those AI parameters. The analogy to a planet-sized pinball machine is a good one. Every little bumper and paddle setting influences the final path of the ball—or in this case, the model’s output. But here’s the thing: the obsession with raw parameter count is becoming a bit of a red herring. It’s not just how many dials you have, but how well you tune them. GPT-3’s 175 billion was a landmark. Now, with Gemini 3 potentially hitting 7 trillion, the scale is almost incomprehensible. And the fact that Google won’t confirm it tells you everything. The open-science phase of AI is over. We’re in a proprietary arms race now. Companies are hiding their architectures, their training data, and their parameter counts as core competitive secrets. The question is, what are we missing when the foundational tech of our era is developed entirely behind closed doors?
