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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:...

25,612 次观看 • 1 年前 •via X (Twitter)

10 条评论

Yev Marusenko, Ph.D. 的头像
Yev Marusenko, Ph.D.1 年前

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

Robert Scoble 的头像
Robert Scoble1 年前

Love! I learned a lot too!

Places Visited & Pictures Taken 的头像
Places Visited & Pictures Taken2 年前

How do you choose the best picture in less time?

SizzleAI 的头像
SizzleAI1 年前

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

Cheesecake Classics 的头像
Cheesecake Classics1 年前

@DoctorYev Terrific idea. Kudos 🍸

Avi ⚜️ 的头像
Avi ⚜️1 年前

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

Hanrii 🍌🍕 的头像
Hanrii 🍌🍕1 年前

@DoctorYev 👀

Mike Boysen (JTBD/acc) 的头像
Mike Boysen (JTBD/acc)1 年前

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

Robert Scoble 的头像
Robert Scoble1 年前

@DoctorYev What matters is your brain.

Mohan Narendran 的头像
Mohan Narendran1 年前

@DoctorYev Thanks very much @Scobleizer

相关视频

Most people think AI means we don’t need to learn anymore. But that idea crumbled last month when I sat across from Stanford Graduate School of Education Dean, Dan Schwartz. Dan read this to the audience: “The procedure is actually quite simple. First you arrange things into different groups. Of course, one pile may be sufficient depending on how much there is. If you have to go somewhere else due to lack of facilities that is the next step; otherwise you are pretty well set. It is important not to overdo things. That is, it is better to do too few things at once than too many…” No one really understood it. Then he gave them one line of context: “This is about doing laundry.” And suddenly it all made sense. Same paragraph. Same words. But once you *knew* what it was about, your brain could organize the information. Dan looked at the audience and said, “If you don’t know enough, the AI’s output is just words and sentences. You might *think* you understand it, but you don’t.” And he’s right. AI doesn’t eliminate the need to learn—it makes real knowledge more important than ever. This is a conversation we’re constantly having at Alpha. Because when a kid asks ChatGPT a question, how do they know if the answer’s even good? How do they know when to push back or ask more? It all comes back to this: you need knowledge to interpret the output. That’s why we still teach writing. That’s why we teach proportionality. That’s why we want kids to understand germ theory, gravity, and the Bill of Rights. Not because they’ll regurgitate it on a test, but because they’ll need that knowledge to make sense of the world (and the tools) around them. AI will make learning faster, more playful, and more personalized. But it won’t replace the need to learn. It’ll just expose when we haven’t. And as Dan reminded us on stage, the real purpose of education isn’t just facts or grades. It’s helping kids build meaning, curiosity, and the wisdom to use their tools well. Deep thanks to Dean Dan Schwartz for sharing his time and expertise with Alpha and our community.

MacKenzie Price

14,491 次观看 • 1 年前

🧵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. ...

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535,291 次观看 • 1 年前