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I've been working on www.revision.ai since 2019 It's a study tool that turns documents/videos into fun flashcards, automatically. Originally it used more trad

I've been working on www.revision.ai since 2019 It's a study tool that turns documents/videos into fun flashcards, automatically. Originally it used more traditional techniques, but from 2019-2021 was highly built into the HuggingFace eco-system, with things like finetuned T5 models. That allowed us to create powerful flashcards for certain subjects, like psychology, more accurately than competitors, especially with specific document analysis tech (think OCR on steroids). ChatGPT really replaced that part though and smaller models for things like identifying acronyms just don't make sense these days. I have also worked with embeddings to automatically plan lessons. I think there's potential there. The problem is overcoming the human-crafted quality bonus and now, with big players in edtech able to easily integrate ChatGPT to existing content, there is a difficult competitive landscape vs the 6 month to 3 year lead ML gave you in the past. Art, creativity and innovation pales in comparison to distribution.

tmaly 3y ago
Where do you see tutoring going with AI or even the ability to expand on topics a student would have trouble with in a regular set of lessons?
- james-revisoai 3y ago
  Basically I see three main directions: A) The natural conversational interface with callbacks to earlier in conversation sets up the precursor for widespread customised oral exams. At least for serious learners, this is a genuine way to test skills despite AI cheating options, and harbours potential for skill upkeep too[1]. I have been predicting this for a long time now(2015+) and at recent tech conferences, it's became clear this is beginning to happen. B) Personalised addressing of knowledge gaps and interest can both improve motivation and success for students. An AI tutor can now robustly head towards Blooms 2-sigma effect for learning (i.e. that private/small group learning is much more effective than class-wide learning, largely due to adaptivity). The larger potential is to do class-wide work which you identify wide misunderstandings (e.g. like Vertiasium' channels PhD dissertation on using videos with misconceptions to teach better), and enable students to tackle them together[2]. C) Use of AI to use a students understanding of one topic/area (such ideas from a game they love like Red Alert 2) to teach another one(like how cells interact with threats). Things like using comparisons, metaphors. This can be truly powerful for certain student groups. An alternative here is using image generation AI to show certain concepts on a rolling, scroll-to-see kind of basis to try and loop into that human psychology. I was too early with this as I spent a lot of time with GANs/early Diffusion when the tech was a bit naff. I did win 2nd place on a Cohere Hackathon with "Learn Visually" which explored spatially-aligned AI generated images, but like said, that was before the real revolution, so the quality wasn't quite there. [1] One of my controversial opinions is that most people are capable of 2-sigma+ performance on many skills, but fail to upkeep their knowledge or tackle problems regularly enough to ever obtain it, instead tackling problems repetitively or with cognitively-easy techniques, giving us a distorted view on human potential. Summer is particularly damaging. [2] Because of social pressures to work and compete

tmaly 3y ago

Where do you see tutoring going with AI or even the ability to expand on topics a student would have trouble with in a regular set of lessons?

james-revisoai 3y ago
Basically I see three main directions: A) The natural conversational interface with callbacks to earlier in conversation sets up the precursor for widespread customised oral exams. At least for serious learners, this is a genuine way to test skills despite AI cheating options, and harbours potential for skill upkeep too[1]. I have been predicting this for a long time now(2015+) and at recent tech conferences, it's became clear this is beginning to happen. B) Personalised addressing of knowledge gaps and interest can both improve motivation and success for students. An AI tutor can now robustly head towards Blooms 2-sigma effect for learning (i.e. that private/small group learning is much more effective than class-wide learning, largely due to adaptivity). The larger potential is to do class-wide work which you identify wide misunderstandings (e.g. like Vertiasium' channels PhD dissertation on using videos with misconceptions to teach better), and enable students to tackle them together[2]. C) Use of AI to use a students understanding of one topic/area (such ideas from a game they love like Red Alert 2) to teach another one(like how cells interact with threats). Things like using comparisons, metaphors. This can be truly powerful for certain student groups. An alternative here is using image generation AI to show certain concepts on a rolling, scroll-to-see kind of basis to try and loop into that human psychology. I was too early with this as I spent a lot of time with GANs/early Diffusion when the tech was a bit naff. I did win 2nd place on a Cohere Hackathon with "Learn Visually" which explored spatially-aligned AI generated images, but like said, that was before the real revolution, so the quality wasn't quite there. [1] One of my controversial opinions is that most people are capable of 2-sigma+ performance on many skills, but fail to upkeep their knowledge or tackle problems regularly enough to ever obtain it, instead tackling problems repetitively or with cognitively-easy techniques, giving us a distorted view on human potential. Summer is particularly damaging. [2] Because of social pressures to work and compete