Thank you again - you've addressed the uncertainties that were concerning me the most.
I was maybe too focused on neuroscience - researching the interpretability of medical decision-making models, or AI used in medical devices, was completely off my radar. I will definitely look into this more and start searching for professors who work in this area.
Thank you very much for your informative answer. I'm glad to know that this transition is doable. It means a lot to me.
I already understood and decided that clinical activity is not for me, so the alternatives would be between research in a neuroscience subject or work in AI interpretability. For research in neuroscience I already have an almost paved road until the PhD, which I could quite easily do at my university. However, at least from the outside, AI interpretability seems to me clearly the best choice in many respects: impact, personal fit (I feel at ease with numbers), kind of work, and raw passion and excitement. I consider AI interpretability my number one interest now, since understanding what's going on in these black boxes is what makes me really curious, and since I'm convinced about the paramount importance of AI safety. If the trade-offs imply a temporary disadvantage because of retraining and initial struggling, it seems definitely a price worth paying. The real brake for me is the possibility of being in some way permanently under the top, permanently among those in the low percentile with respect to contributing to the field, not because I don't have the skills, but because I lost a critical head start. In principle, I would say that if it's a pure gap of knowledge, that gap can at some point be filled, also considering that during high school maths and physics were my strongest subjects by far — but what if there is a snowball effect that permanently favours those with a degree in maths/CS?
Another path that I sort of already discarded, but which may be worth mentioning, is doing a PhD in computational neuroscience as a bridge to AI. Computational neuroscience applies maths and coding to model brain functions. I think it is more accessible for a medical student, since it values medical neuroscience knowledge and is probably less technically demanding, and gives some more basis in maths and coding. The disadvantage is that it is probably less relevant to AI safety than a PhD in AI/ML, and computational neuroscience, while interesting, does not fit into the big picture of how I want to have an impact in the same way as AI does.
Thank you again - you've addressed the uncertainties that were concerning me the most.
I was maybe too focused on neuroscience - researching the interpretability of medical decision-making models, or AI used in medical devices, was completely off my radar. I will definitely look into this more and start searching for professors who work in this area.
Thank you very much for your informative answer. I'm glad to know that this transition is doable. It means a lot to me.
I already understood and decided that clinical activity is not for me, so the alternatives would be between research in a neuroscience subject or work in AI interpretability. For research in neuroscience I already have an almost paved road until the PhD, which I could quite easily do at my university. However, at least from the outside, AI interpretability seems to me clearly the best choice in many respects: impact, personal fit (I feel at ease with numbers), kind of work, and raw passion and excitement. I consider AI interpretability my number one interest now, since understanding what's going on in these black boxes is what makes me really curious, and since I'm convinced about the paramount importance of AI safety. If the trade-offs imply a temporary disadvantage because of retraining and initial struggling, it seems definitely a price worth paying. The real brake for me is the possibility of being in some way permanently under the top, permanently among those in the low percentile with respect to contributing to the field, not because I don't have the skills, but because I lost a critical head start. In principle, I would say that if it's a pure gap of knowledge, that gap can at some point be filled, also considering that during high school maths and physics were my strongest subjects by far — but what if there is a snowball effect that permanently favours those with a degree in maths/CS?
Another path that I sort of already discarded, but which may be worth mentioning, is doing a PhD in computational neuroscience as a bridge to AI. Computational neuroscience applies maths and coding to model brain functions. I think it is more accessible for a medical student, since it values medical neuroscience knowledge and is probably less technically demanding, and gives some more basis in maths and coding. The disadvantage is that it is probably less relevant to AI safety than a PhD in AI/ML, and computational neuroscience, while interesting, does not fit into the big picture of how I want to have an impact in the same way as AI does.