I’ve been mentoring a few people. Two situations I frequently encounter, which AI mentors can solve:
1. A learner completes a task, then doesn’t know what to do next. The solution to this problem is already a feature of formal schooling, and it can be automated by AI. Too many self directed learners end up getting lost in a confusing domain because they just don’t know where to look to do the next thing. Randomly pinballing around is risky, and mentorship can save time and frustration which ends up making all the difference for many students.
2. A learner has a specific question that’s blocking their learning. “My code isn’t compiling and the error makes no sense”. Well, you could just google it and get the answer. Easy, right? That works in the immediate term. But a mentor interprets those questions and recognizes that the learner actually has a systemic misunderstanding. Then they can create a practice regiment to cure the disease and not the symptom. Google and textbooks really struggle on this one, but AI can solve it at scale!
A final thought. There’s a bell graph of learners. Far right are gifted self-starters and self-teachers, of which there are few. Center are most students, who frequently need mentorship. Far left are lost causes. AI mentorship will lift every type of student, but I suspect it will have the biggest influence on center type students. So huge possibilities for AI mentorship!
This is a thought provoking essay. I agree with some points (not nearly enough mentoring happening) and I think I disagree with others (AI might be good at it.) I need to think on it some more, however; I might change my mind before I get a response essay written.
Once the "idea guy" has been automated, the rest is just iterative experimentation and high-speed learning. There are some human elements in Observe-Orient-Decide-Act (OODA). "Observe" = data analysis, "Decide" = ChatGPT recommendations.
Prove used to mean test, so the exception that proves the rule does so by testing it, like proving your case by testing it in court, or proving your strength by testing it in a trial. You still see this usage with proving bread which is a test that it will rise in the oven.
I’m going to quibble over definitions here. To me mentorship involves someone helping you make decisions that help you advance along an unknown path. Usually a mentor is someone who has done it before and can both answer questions and, more importantly, give you questions. AI can’t navigate the unknown.
What I’m reading is this post is more about how AI can answer questions, and find and relay good advice, it can also take in every part of your digital life and map it to another example of a successful life you might want to emulate. But it can only navigate what is known
Well, there are two benefits to mentorship: advice and encouragement.
I agree that future LLM's will be helpful for giving advice. I have already incorporated ChatGPT into my workflow whenever I am doing any kind of exploratory research/brainstorming--and it's been useful.
I can imagine that in the future, instead of going to your university's career counselor, you go to your friendly neighbourhood large language model. Just submit your transcript and comphrehensive questionaire, and the A.I will tell you what jobs/internships to apply to and what skills you will need to both get and ace the interview.
Good advice is useful and hard to find. But the other benefit of mentorshop--and perhaps the most important element--is encouragement. Tyler Cowen has talked about this in the context of Emergent Ventures. He said that the most important thing emergent venture winners get isn't money or even prestige, but "permission". Permission to do what? Permission to be ambitious, to break the mold, to take risks. And this works because there is a necessary costly signalling component: if you are an Emergent Ventures winner, Tyler has chosen *you* out of all the other bright-eyed and ambitious applicants.
I am skeptical how scalable "the permission to be ambitious" is. It's clearly *somewhat* scalable as we see geographic/culture variations in ambition. Silicon Valley (and America more broadly) encourages--demands, really--naked ambition. Other places are sleepier and more steady. But still: ambition is hard to cultivate deliberately.
I’ve been mentoring a few people. Two situations I frequently encounter, which AI mentors can solve:
1. A learner completes a task, then doesn’t know what to do next. The solution to this problem is already a feature of formal schooling, and it can be automated by AI. Too many self directed learners end up getting lost in a confusing domain because they just don’t know where to look to do the next thing. Randomly pinballing around is risky, and mentorship can save time and frustration which ends up making all the difference for many students.
2. A learner has a specific question that’s blocking their learning. “My code isn’t compiling and the error makes no sense”. Well, you could just google it and get the answer. Easy, right? That works in the immediate term. But a mentor interprets those questions and recognizes that the learner actually has a systemic misunderstanding. Then they can create a practice regiment to cure the disease and not the symptom. Google and textbooks really struggle on this one, but AI can solve it at scale!
A final thought. There’s a bell graph of learners. Far right are gifted self-starters and self-teachers, of which there are few. Center are most students, who frequently need mentorship. Far left are lost causes. AI mentorship will lift every type of student, but I suspect it will have the biggest influence on center type students. So huge possibilities for AI mentorship!
This is a thought provoking essay. I agree with some points (not nearly enough mentoring happening) and I think I disagree with others (AI might be good at it.) I need to think on it some more, however; I might change my mind before I get a response essay written.
Others have done this as well, but more exploration rather than "advice giving". https://etiennefd.substack.com/p/why-do-i-feel-inadequate-at-coming
In other news, ChatGPT is good at confidently talking but less so on thinking. https://threadreaderapp.com/thread/1598430479878856737.html https://davidrozado.substack.com/p/what-is-the-iq-of-chatgpt
Once the "idea guy" has been automated, the rest is just iterative experimentation and high-speed learning. There are some human elements in Observe-Orient-Decide-Act (OODA). "Observe" = data analysis, "Decide" = ChatGPT recommendations.
BUT Orient and Act are strictly human endeavors since robots cannot make good judgements OR execute on its own behalf. https://graymirror.substack.com/p/there-is-no-ai-risk
Prove used to mean test, so the exception that proves the rule does so by testing it, like proving your case by testing it in court, or proving your strength by testing it in a trial. You still see this usage with proving bread which is a test that it will rise in the oven.
I’m going to quibble over definitions here. To me mentorship involves someone helping you make decisions that help you advance along an unknown path. Usually a mentor is someone who has done it before and can both answer questions and, more importantly, give you questions. AI can’t navigate the unknown.
What I’m reading is this post is more about how AI can answer questions, and find and relay good advice, it can also take in every part of your digital life and map it to another example of a successful life you might want to emulate. But it can only navigate what is known
Well, there are two benefits to mentorship: advice and encouragement.
I agree that future LLM's will be helpful for giving advice. I have already incorporated ChatGPT into my workflow whenever I am doing any kind of exploratory research/brainstorming--and it's been useful.
I can imagine that in the future, instead of going to your university's career counselor, you go to your friendly neighbourhood large language model. Just submit your transcript and comphrehensive questionaire, and the A.I will tell you what jobs/internships to apply to and what skills you will need to both get and ace the interview.
Good advice is useful and hard to find. But the other benefit of mentorshop--and perhaps the most important element--is encouragement. Tyler Cowen has talked about this in the context of Emergent Ventures. He said that the most important thing emergent venture winners get isn't money or even prestige, but "permission". Permission to do what? Permission to be ambitious, to break the mold, to take risks. And this works because there is a necessary costly signalling component: if you are an Emergent Ventures winner, Tyler has chosen *you* out of all the other bright-eyed and ambitious applicants.
I am skeptical how scalable "the permission to be ambitious" is. It's clearly *somewhat* scalable as we see geographic/culture variations in ambition. Silicon Valley (and America more broadly) encourages--demands, really--naked ambition. Other places are sleepier and more steady. But still: ambition is hard to cultivate deliberately.