Загрузка видео...

Не удалось загрузить видео

На главную

Just sat down with Yev Marusenko, Yev Marusenko, from Sizzle AI—an edtech app with millions of users that’s rethinking how our kids (and we) learn in the AI era. 📱 Sizzle isn’t just “ChatGPT for homework.” It builds entire courses from your prompts—bite-sized, mobile, interactive. 💡 Why it matters:...

25,685 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля Yev Marusenko, Ph.D.
Yev Marusenko, Ph.D.1 год назад

My pleasure! Thank you Robert. Great questions and your insights made me think! Lots of room for AI to continue disrupting education.

Фото профиля Robert Scoble
Robert Scoble1 год назад

Love! I learned a lot too!

Фото профиля Places Visited & Pictures Taken
Places Visited & Pictures Taken2 лет назад

How do you choose the best picture in less time?

Фото профиля SizzleAI
SizzleAI1 год назад

@DoctorYev Less gamification dopamine, more brain growth. 💯🎯🧠

Фото профиля Cheesecake Classics
Cheesecake Classics1 год назад

@DoctorYev Terrific idea. Kudos 🍸

Фото профиля Avi ⚜️
Avi ⚜️1 год назад

@DoctorYev @Infinilearn_ is doing a similar thing. Education is going to transform with AI.

Фото профиля Hanrii 🍌🍕
Hanrii 🍌🍕1 год назад

@DoctorYev 👀

Фото профиля Mike Boysen (JTBD/acc)
Mike Boysen (JTBD/acc)1 год назад

@DoctorYev What will annexpensive piece of paper actually be worth next year once these approaches get normalized? Not much

Фото профиля Robert Scoble
Robert Scoble1 год назад

@DoctorYev What matters is your brain.

Фото профиля Mohan Narendran
Mohan Narendran1 год назад

@DoctorYev Thanks very much @Scobleizer

Похожие видео

🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...

Lethal Intelligence

535,291 просмотров • 1 год назад