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Study mode

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Re: Study mode

#431

Post by [deleted] »

romaniitedomum wrote: post #412
jacobedawson wrote: post #379

An underrated quality of LLMs as study partner is that you can ask "stupid" questions without fear of embarrassment. Adding in a mode that doesn't just dump an answer but works to take you through the material step-by-step is magical. A tireless, capable, well-versed assistant on call 24/7 is an autodidact's dream. I'm puzzled (but not surprised) by the standard HN resistance & skepticism. Learning something online 5…

> Learning something online 5 years ago often involved trawling incorrect, outdated or hostile content and attempting to piece together mental models without the chance to receive immediate feedback on intuition or ask follow up questions. This is leaps and bounds ahead of that experience. But now, you're wondering if the answer the AI gave you is correct or something it hallucinated. Every time I find myself putting…

[deleted]

Re: Study mode

#432

Post by no_wizard »

chpatrick wrote: post #427
phailhaus wrote: post #413

Earlier quoted context omitted.

HN is resistant because at the end of the day, these are LLMs. They cannot and do not think. They generate plausible responses. Try this in your favorite LLM: "Suppose you're on a game show trying to win a car. There are three doors, one with a car and two with goats. You pick a door. The host then gives you the option to switch doors. What is the best strategy in this situation?" The LLM will recognize this as SIMIL…

Humans who have heard of Monty Hall might also say you should always switch without noticing that the situation is different. That's not evidence that they can't think, just that they're fallible. People on here always assert LLMs don't "really" think or don't "really" know without defining what all that even means, and to me it's getting pretty old. It feels like an escape hatch so we don't feel like our human speci…

On the other hand, computers are suppose to be both accurate and able to reproduce said accuracy.

The failure of an LLM to reason this out is indicative that really, it isn’t reasoning at all. It’s a subtle but welcome reminder that it’s pattern matching

Re: Study mode

#433

Post by ants_everywhere »

phailhaus wrote: post #413
jacobedawson wrote: post #379

An underrated quality of LLMs as study partner is that you can ask "stupid" questions without fear of embarrassment. Adding in a mode that doesn't just dump an answer but works to take you through the material step-by-step is magical. A tireless, capable, well-versed assistant on call 24/7 is an autodidact's dream. I'm puzzled (but not surprised) by the standard HN resistance & skepticism. Learning something online 5…

HN is resistant because at the end of the day, these are LLMs. They cannot and do not think. They generate plausible responses. Try this in your favorite LLM: "Suppose you're on a game show trying to win a car. There are three doors, one with a car and two with goats. You pick a door. The host then gives you the option to switch doors. What is the best strategy in this situation?" The LLM will recognize this as SIMIL…

That's because the LLM assumes you mean for the host to open the door.

As an autistic person I can assure you that people filling in information you neither gave nor implied is the norm for human conversations. In that sense, the LLMs are being human-like.

If you explicitly tell it you didn't forget to have the host open the door it gives you the right answer:

> There is a game show with three closed doors. One has a car and two have goats. I pick a door. Then, without opening my door or any other door, the host asks me if I want to switch my choice to one of the other two doors. What is my best strategy and what are the probabilities?

Gemini: This is a classic probability puzzle known as the Monty Hall problem, though your description presents a slight variation. The optimal strategy depends entirely on whether the host opens a door or not. Your Scenario: The Host Doesn't Open a Door In the situation you've described, where the host asks if you want to switch your choice to one of the other two doors without opening any doors, there is no advantage to switching. Here's a breakdown of the probabilities in your specific scenario: Your initial choice: When you first pick a door, you have a 1 in 3 chance of selecting the door with the car. This means you have a 2 in 3 chance of having picked a door with a goat. The other two doors: The combined probability that the car is behind one of the other two doors is 2/3. Switching your choice: If you decide to switch, you are essentially choosing one of the other two doors. Since there is no new information (a door with a goat has not been revealed), the 2/3 probability is evenly split between those two doors. Therefore, your chance of winning by switching to a specific one of the other doors is 1/3. In this case, your odds of winning are 1/3 whether you stay with your original choice or switch to one of the other doors.

Re: Study mode

#434

Post by no_wizard »

melenaboija wrote: post #366
no_wizard wrote: post #49

Earlier quoted context omitted.

>Now, everyone basically has a personal TA, ready to go at all hours of the day This simply hasn't been my experience. Its too shallow. The deeper I go, the less it seems to be useful. This happens quick for me. Also, god forbid you're researching a complex and possibly controversial subject and you want it to find reputable sources or particularly academic ones.

I validate models in finance, and this is by far the best tool created for that purpose. I'd compare financial model validation to a Master's level task, where you're working with well established concepts, but at a deep, technical level. LLMs excel at that: ithey understand model assumptions, know what needs to be tested to ensure correctness, and can generate the necessary code and calculations to perform those tes…

That’s one aspect of quantitative finance, and I agree. Elsewhere I noted that anything that is structured data + computation adjacent it has an easier time with, even excels in many cases.

It doesn’t cover the other aspects of finance, perhaps may be considered advanced (to a regular person at least) but less quantitative. Try having it reason out a “cigar butt” strategy and see if returns anything useful about companies that fit the mold from a prepared source.

Granted this isn’t quant finance modeling, but it’s a relatively easy thing as a human to do, and I didn’t find LLMs up to the task

Re: Study mode

#435

Post by chpatrick »

no_wizard wrote: post #432
chpatrick wrote: post #427

Earlier quoted context omitted.

Humans who have heard of Monty Hall might also say you should always switch without noticing that the situation is different. That's not evidence that they can't think, just that they're fallible. People on here always assert LLMs don't "really" think or don't "really" know without defining what all that even means, and to me it's getting pretty old. It feels like an escape hatch so we don't feel like our human speci…

On the other hand, computers are suppose to be both accurate and able to reproduce said accuracy. The failure of an LLM to reason this out is indicative that really, it isn’t reasoning at all. It’s a subtle but welcome reminder that it’s pattern matching

Computers might be accurate but statistical models never were 100% accurate. That doesn't imply that no reasoning is happening. Humans get stuff wrong too but they certainly think and reason.

"Pattern matching" to me is another one of those vague terms like "thinking" and "knowing" that people decide LLMs do or don't do based on vibes.

Re: Study mode

#436

Post by Isamu »

adamlgerber wrote: post #410
Isamu wrote: post #408

Earlier quoted context omitted.

>I'm puzzled (but not surprised) by the standard HN resistance & skepticism The good: it can objectively help you to zoom forward in areas where you don’t have a quick way forward. The bad: it can objectively give you terrible advice. It depends on how you sum that up on balance. Example: I wanted a way forward to program a chrome extension which I had zero knowledge of. It helped in an amazing way. Example: I am kee…

mixed bags are our favorite thing to argue about

Haha yes! Thanks for that!

Re: Study mode

#437

Post by no_wizard »

Xenoamorphous wrote: post #91
no_wizard wrote: post #49

Earlier quoted context omitted.

>Now, everyone basically has a personal TA, ready to go at all hours of the day This simply hasn't been my experience. Its too shallow. The deeper I go, the less it seems to be useful. This happens quick for me. Also, god forbid you're researching a complex and possibly controversial subject and you want it to find reputable sources or particularly academic ones.

Can you share some examples?

Try doing deep research on the Israel - Palestine relations. That’s a good baseline. You’ll find it starts spitting out really useless stuff fast, or will try to give sources that don’t exist or are not reputable.

Re: Study mode

#438

Post by kristofferR »

yieldcrv wrote: post #426
romaniitedomum wrote: post #412

Earlier quoted context omitted.

> Learning something online 5 years ago often involved trawling incorrect, outdated or hostile content and attempting to piece together mental models without the chance to receive immediate feedback on intuition or ask follow up questions. This is leaps and bounds ahead of that experience. But now, you're wondering if the answer the AI gave you is correct or something it hallucinated. Every time I find myself putting…

If LLMs of today's quality were what was initially introduced, nobody would even know what your rebuttals are even about. So "risk of hallucination" as a rebuttal to anybody admitting to relying on AI is just not insightful. like, yeah ok we all heard of that and aren't changing our habits at all. Most of our teachers and books said objectively incorrect things too, and we are all carrying factually questionable know…

Agree, "hallucination" as an argument to not use LLMs for curiosity and other non-important situations is starting to seem more and more like tech luddism, similar to the people who told you to not read Wikipedia 5+ years after the rest of us realized it is a really useful resource despite occasional inaccuracies.

Re: Study mode

#439

Post by yyyk »

czhu12 wrote: post #26

I'll personally attest: LLM's have been absolutely incredible to self learn new things post graduation. It used to be that if you got stuck on a concept, you're basically screwed. Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you. Now, everyone ba…

Everything you state was available in the net. Did the people grow more informed? So far practice suggests the opposite conclusion[0]. I hope for the best, but the state of the world so far doesn't justify it...

[0] https://time.com/7295195/ai-chatgpt-google-learning-school/

Re: Study mode

#440

Post by no_wizard »

chpatrick wrote: post #435
no_wizard wrote: post #432

Earlier quoted context omitted.

On the other hand, computers are suppose to be both accurate and able to reproduce said accuracy. The failure of an LLM to reason this out is indicative that really, it isn’t reasoning at all. It’s a subtle but welcome reminder that it’s pattern matching

Computers might be accurate but statistical models never were 100% accurate. That doesn't imply that no reasoning is happening. Humans get stuff wrong too but they certainly think and reason. "Pattern matching" to me is another one of those vague terms like "thinking" and "knowing" that people decide LLMs do or don't do based on vibes.

Pattern matching has a definition in this field, it does mean specific things. We know machine learning has excelled at this in greater and greater capacities over the last decade

The other part of this is weighted filtering given a set of rules, which is a simple analogy to how AlphaGo did its thing.

Dismissing all this as vague is effectively doing the same thing as you are saying others do.

This technology has limits and despite what Altman says, we do know this, and we are exploring them, but it’s within its own confines. They’re fundamentally wholly understandable systems that work on a consistent level in terms of the how they do what they do (that is separate from the actual produced output)

I think reasoning, as any layman would use the term, is not accurate to what these systems do.

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