I am in education (level about high school/AP macro economics)
possible implications:
upskilling : faster learning through better information, more help, AI tutoring etc.
deskilling : students let the AI do the work (the learning, writing, homework etc.)
reskilling : develop new skillsets that are relevant to todays world
relevance : in a world where AI does the work what is the relevance of education
The last is the most important I think. What is the place of education in todays world. What should a kid of fifteen years old learn to be prepared for what is coming? I don’t know because I don’t know what is coming.
One thing I do know. Learning from a machine is a paradox. Yes you can learn better and faster with the help of a machine. But if the machine can teach it to you, than the machine can probably do it. And why would we want to learn things that a machine can do? To learn the things a machine can not do, we need humans. But that only works if there are things a machine cannot do.
The kid of fifteen wil be 25 in ten years. Ten years is a lot. I do not know what to tell them because I do not know. Love to hear more input on this.
That’s a good discussion topic too, but the question I was actually asking wasn’t “how can AI be used in education” or “how do AI tools affect education”. I was asking about implications for the process of human learning—how curricula and practice should be designed, given what we now know about eg what training methods are more effective for neural networks and why.
If I understand you correctly you mean this transfer between machine learning and human learning. Which is an interesting topic.
When a few years ago I learned about word2vec I was quite impressed. It felt a lot like how humans store information according to cognitive psychology. In cognitive psychology, a latent space or a word vector would be named as a semantic representation. Semantic representations are mental representations of the meaning of words or concepts. They are thought to be stored in the brain as distributed representations, meaning that they are not represented by a single unit of activation, but rather by a pattern of activation across many units.
That was sort my “o shit this is going to be a thing” moment. I realized there are similarities between human and machine understanding. This is a way to build a world model.
Now I really can try the differences in gpt4 and Palm2. To learn how they think I give them the same question as my students and when they make mistakes I guide them like I would guide a student. It is interesting to see that within the chat they can learn to improve themselves with guidance.
What I find interesting is that the understanding is sometimes quite different and there are also similarities. The answers and the responses to guidance are quite different from that of students. It is similar enough to give human like answers.
Can this help us understand human learning? I think it can. Comparing human learning to machine learning makes the properties of human learning more salient (1+1=3). As an example I studied economics and Mathematics and oftentimes it felt like I did three times the learning because I did not only learn mathematics and economics but I also learned the similarities and differences between the two.
The above is a different perspective on your question then my previews answer. I would appreciate feedback on whether I am on the right track here. I am very interested in the topic independent of the perspective taken on the topic. So we could also explore different perspectives.
I am in education (level about high school/AP macro economics)
possible implications:
upskilling : faster learning through better information, more help, AI tutoring etc.
deskilling : students let the AI do the work (the learning, writing, homework etc.)
reskilling : develop new skillsets that are relevant to todays world
relevance : in a world where AI does the work what is the relevance of education
The last is the most important I think. What is the place of education in todays world. What should a kid of fifteen years old learn to be prepared for what is coming? I don’t know because I don’t know what is coming.
One thing I do know. Learning from a machine is a paradox. Yes you can learn better and faster with the help of a machine. But if the machine can teach it to you, than the machine can probably do it. And why would we want to learn things that a machine can do? To learn the things a machine can not do, we need humans. But that only works if there are things a machine cannot do.
The kid of fifteen wil be 25 in ten years. Ten years is a lot. I do not know what to tell them because I do not know. Love to hear more input on this.
That’s a good discussion topic too, but the question I was actually asking wasn’t “how can AI be used in education” or “how do AI tools affect education”. I was asking about implications for the process of human learning—how curricula and practice should be designed, given what we now know about eg what training methods are more effective for neural networks and why.
If I understand you correctly you mean this transfer between machine learning and human learning. Which is an interesting topic.
When a few years ago I learned about word2vec I was quite impressed. It felt a lot like how humans store information according to cognitive psychology. In cognitive psychology, a latent space or a word vector would be named as a semantic representation. Semantic representations are mental representations of the meaning of words or concepts. They are thought to be stored in the brain as distributed representations, meaning that they are not represented by a single unit of activation, but rather by a pattern of activation across many units.
That was sort my “o shit this is going to be a thing” moment. I realized there are similarities between human and machine understanding. This is a way to build a world model.
Now I really can try the differences in gpt4 and Palm2. To learn how they think I give them the same question as my students and when they make mistakes I guide them like I would guide a student. It is interesting to see that within the chat they can learn to improve themselves with guidance.
What I find interesting is that the understanding is sometimes quite different and there are also similarities. The answers and the responses to guidance are quite different from that of students. It is similar enough to give human like answers.
Can this help us understand human learning? I think it can. Comparing human learning to machine learning makes the properties of human learning more salient (1+1=3). As an example I studied economics and Mathematics and oftentimes it felt like I did three times the learning because I did not only learn mathematics and economics but I also learned the similarities and differences between the two.
The above is a different perspective on your question then my previews answer. I would appreciate feedback on whether I am on the right track here. I am very interested in the topic independent of the perspective taken on the topic. So we could also explore different perspectives.