Locator: 49936SEASONALFLU.
One of the things I learned in medical school was how helpful laboratory technicians could be. Classroom didactics made a lot more sense when "we" took that information and saw how it was "translated" in the laboratory. It's the same analogy that the tech sector figures out how to "monetize" their very, very high-level work.
Our professor could talk to us for two hours on antigenic properties of the various types of flu. But that information was reinforced when we went to the hospital's clinical laboratory and saw how laboratory techs identified the presence or absence of virus and what kind of influenza virus it was, if present.
It took me three attempts to get to the right question before Gemini explained what I did not understand, but this is exactly how students are now learning much more effectively and efficiently than I ever did.
My IA prompt:
Aha, so in the lab, when ruling out influenza, they place the human secretion in a broth with antigens for Hx, Nx, M, ad NP? And then go from there?
Gemini's reply:
Interestingly, I did the same "stuff" during a college research project one summer outside of Point Barrow, Alaska. I just didn't know what I was doing. LOL. I need to start life over. LOL.
Wow, I can't wait to explain this to Sophia.
1. For complete list of antigens, google: seasonal flu schematic genetic material (M), matrix, membrane, surface antigens HxNx.
2. Then link here.
AI prompt:
Once identified, how long (days, weeks, months) did it take for scientists to identify all the genes / proteins of Covid-19?
Gemini:
You all know where this is going. I will let you ask Gemini the next obvious question.
This would be one of the next obvious questions: once they had the genetic map (all genes) of Covid-19 identified, it was just a matter of time to develop a vaccine. What was the rate-limiting step in developing that vaccine and is AI likely to address that rate-limiting step?
Education: when I look at the above, I'm beginning to think at the college / university / graduate / doctorate level, we will move from "generic" professors to "facilitators." A facilitator will be defined as a professor with special skills in AI / working with chatbots and working with students to develop their own "curricula." UC-Santa Cruz has been doing that for decades and so has to Stanford, but in a more subtle way than US-Santa Cruz. Students will continue to collaborate with their professors / facilitators and fellow students on teams but a chatbot, whom they will name, will become an equal partner within the team. There is an analogy within medicine that occurred many decades ago but don't have time to talk about that now. But it's biggest fallout: physicians were now called "health care providers" and an equal member of the health care team.