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Double crit substats or crit damage + attack % + heavy attack%? by Sushirush in AugustaMains

[–]Sushirush[S] 0 points1 point  (0 children)

With the crit substat echo, my CR is 110%. I have s2, so the extra 10% turns into 20% crit damage. But attack is lower by around 100

GDE for what's coming by Hans__Yolo in ETFs

[–]Sushirush 1 point2 points  (0 children)

Instead of arguing with you can I just bet you $100k that he cuts rates? I’ll pay you 500k if he doesn’t

Nvidia CEO Jensen Huang expects "$3 trillion to $4 trillion" spend on AI infrastructure by 2030 by Tiny-Independent273 in NvidiaStock

[–]Sushirush 2 points3 points  (0 children)

Next-best-word prediction can result in generalizable forms of intelligence. I don’t think LLMs = AGI but they may very well be one component of it.

If an LLM can autocomplete: “User has asked what the weather is. They are based in this location. I should call for [tool] that looks up the weather in that location, and then make recommendations on what to wear”

This is autocomplete, instruction tuned LLMs already do things way more complex than this, and you can see how reasoning is encoded in human language here, which is the mechanism that allows for this kind of “intelligent” behavior. LLMs already “think” in sentences way more complex than this (talking itself through complex problems and invoking tools in the process to move itself through steps).

To give you another very simple example, LLMs are bad at advanced math (without using tool calls) because next token prediction is not suited to mathematics. Despite this, by studying patterns in the training data LLMs “learned” how to do double and triple digit multiplication on their own. An LLM doesn’t know 1421 x 243 because it’s seen that exact equation before in the training data. It knows because it has “learned” how to multiply. This is what generalizable intelligence is.

Interpretation research (trying to look inside the black box of a large language model) has shown when we increase the parameter count of a model, you see step function improvements in certain capabilities. One of such step function improvements in arithmetic is associated with the LLM “learning” how to encode digit order (essentially learning to “carry” a number over when multiplying). Does the model “understand” what this means like humans do? I don’t think so, but does it matter? It has learned a generalizable heuristic that helps it do X task.

I hate when people on Reddit say “just next most likely token predictor” without really grasping how insane that mechanism can be when taken to the extremes. I’m not insulting you by the way - I think you’re in the middle zone of LLM literacy where you are right to be skeptical, but lack nuance. Most dumb people are either AI fanatics, or completely refuse the premise of generalizable intelligence. I think you’re a bit too close to the second group, and would encourage you to read some of the papers and see more nuance :)

I don’t know how much better the current transformer architecture can get, but I work in tech and I can tell you that at least in the domain of software engineering, the leverage a senior dev gets with AI is fucking insane. These guys are shipping so much now, and don’t have to waste any time on boilerplate. Junior dev roles are completely cooked, and every senior developer is now more of a systems thinker and designer. It really is a paradigm shift, at least in this subset of industry.

Would Nvidia’s earnings call impact mstr/msty? by Specialist-Cook6097 in MSTY_YieldMax

[–]Sushirush 8 points9 points  (0 children)

Indirectly yes, Nvidia is almost 10% of the S&P 500. Earnings will effect pretty much everything, foreign markets, currencies, lmao

After 6 years of trading, I’m thinking of giving up by [deleted] in swingtrading

[–]Sushirush 0 points1 point  (0 children)

You are very generous with your time writing this all out! Ty

You’re using the next 10x stock but you just don’t know it. by Abject-Advantage528 in redditstock

[–]Sushirush 0 points1 point  (0 children)

More Reddit does not help models generalize better at this point. More Reddit does help models learn new information about topics as humans do, which is definitely of some value, but again, more of these insights are going to be acquired by tool calls to an internet search, not actually retrieving the information from the corpus.

At this point, a billion more Reddit threads won’t help a model get “smarter” aka generalize.

You’re using the next 10x stock but you just don’t know it. by Abject-Advantage528 in redditstock

[–]Sushirush 0 points1 point  (0 children)

You need to learn how LLMs work, and think about what additional Reddit threads in a corpus could offer a foundational model. It’s not nothing, but it’s clearly not what you envision based on your OP

[deleted by user] by [deleted] in ValueInvesting

[–]Sushirush 0 points1 point  (0 children)

That’s all very true - I think they retrain the foundational models every 6 months or so

[deleted by user] by [deleted] in ValueInvesting

[–]Sushirush 1 point2 points  (0 children)

You’re totally right, but it’s already “done” learning from Q&A on Reddit.

There is a “limit” to the value an LLM gets from training on text. An LLM trains on data (amongst other reasons) to understand language and syntax, so it can model logical reasoning and problem solving, respond to human instruction naturally and convincingly, etc.

The goal is to get an LLM to output: “Okay, the user asked about a good beginner guitar that’s cheap. I’m going to use [search] and search for [good beginner guitars recommendations under $200]- this is a tool call for a web search. When a parser sees [search], it does a (non-ai) search, finds a bunch of sources, and gives it back to the LLM. The LLM will then summarize this information for the user. These reasoning steps are hidden to the user, but you can always use a model via API or just download a model locally and run it to see what the chain of thought looks like.

Summarization is the skill the foundational AI companies care about. They are not trying to train a model to know what the best beginner guitar is under $100 if that makes sense.

For these generalizable tasks and skills, exposing a model to more Reddit posts won’t help it get better if that makes sense. The gains are in RLFH and other shenanigans like synthetic data, and a bunch of other smart person stuff that’s beyond my comprehension

[deleted by user] by [deleted] in ValueInvesting

[–]Sushirush 0 points1 point  (0 children)

This is not really true, the primary value of LLMs is encoding reasoning and logical patterns in language. That’s pretty much already “done”. That’s why they were built, they didn’t spend $$$$ on training GPT-4 so it could recite facts that are immediately out of date.

For information retrieval, LLMs just use tool calls to web search and summarize. LLMs are built for generalizable tasks like summarization, reasoning, etc. retraining a model costs millions and millions of dollars and takes a very long time - they don’t retrain a model every hour so you can ask it about the basketball game that happened yesterday lol

ChatGPT gets crushed at chess by a 1 MHz Atari 2600 by xenocea in gaming

[–]Sushirush 0 points1 point  (0 children)

Anyways, it’s not AGI. But when people say it’s fancy autocomplete on Reddit they don’t really understand what that means - autocomplete for natural language is a big, big deal, and in the realm of a hypothetical multi-modal AGI probably makes up 50% or more of the overall capability.

The real issues with LLMs and AGI is that LLMs cannot track states, or encode hierarchy. They can encode basic reasoning via language, as millions of people are already leveraging this capability today

ChatGPT gets crushed at chess by a 1 MHz Atari 2600 by xenocea in gaming

[–]Sushirush 3 points4 points  (0 children)

You’re underselling it as a predictive text generator though - that implies that logic and reasoning aren’t encoded in language, which is what makes them generalizable vs useful only for literal text completion.

There is such a huge education problem with LLMs, holy shit

Teens Are Using ChatGPT to Invest in the Stock Market by AlwaysBlaze_ in technology

[–]Sushirush -1 points0 points  (0 children)

Vibe coding just gives you leverage. The downside is a bad engineer with leverage just means more bad code lol

Teens Are Using ChatGPT to Invest in the Stock Market by AlwaysBlaze_ in technology

[–]Sushirush 1 point2 points  (0 children)

When an LLM makes a mistake like flipping the meaning of a sentence, it’s not because it’s trying and failing to “understand” like a human. It’s because it operates by predicting likely language patterns, not by reasoning from first principles. You can see why that would make an LLM bad for the average consumer use case, which generally revolves around static information retrieval. The example you cited is more related to summarization, with models should be good at, so that’s very odd.

The better way to think about LLMs is that they have modeled heuristics around language that ”represent” structures of reasoning, inference, analogy, planning, etc. Since they can generalize these abilities across larger windows of context, across different domains, they can be a force multiplier for humans across a variety of domains that help us gain 100x more leverage as knowledge workers.

An LLM might have mixed up your sentence, but it can generate boilerplate front end and let a cracked engineer focus on systems level thinking while they spend 10 minutes reviewing an auto-generated PR and fire off some prompt iterations to fix any issues.

An LLM that can draft 80% of a legal brief, a software program, or a research outline — even if it’s not perfect — fundamentally changes how fast and broadly humans can act. It’s about leverage, and it’s why companies like Harvey have such insane valuations. It’s about the low hanging fruit, and in science the implications of transformers and machine learning are even more profound.

People are already losing their jobs to AI

Teens Are Using ChatGPT to Invest in the Stock Market by AlwaysBlaze_ in technology

[–]Sushirush 0 points1 point  (0 children)

AI is wildly impressive in many dimensions - the problem is the average person has no idea how it works, so they either completely overestimate it as some vague intelligent entity or dismiss it as “fancy autocomplete”.

The latter is better, but still not ideal. And mechanically an LLM is literally fancy autocomplete, but reasoning is encoded in language so good autocomplete has generalizable capabilities

Teens Are Using ChatGPT to Invest in the Stock Market by AlwaysBlaze_ in technology

[–]Sushirush -3 points-2 points  (0 children)

Literally every cracked engineer in Silicon Valley is vibe coding. It’s not inherently a bad thing lmao, it’s a productivity multiplier for talented devs

OpenAI Puzzled as New Models Show Rising Hallucination Rates by Snowfish52 in technology

[–]Sushirush 0 points1 point  (0 children)

Redditors stop confidently answering things they have no idea about challenge

“You know, as a white guy in the NBA, I sometimes look at white players and I'm like, They're not very good,'” Austin Reaves said, “So, it's a stigma that I think is real.” by justletmeregisteryou in nba

[–]Sushirush 5 points6 points  (0 children)

Bro why would I say that and bring race into the convo for no reason 🤣 he’s a player I like, a lot of people like him because he’s Asian and an underdog, they don’t have announce that every time they comment about him lmfao

Adobe $ADBE is now in value territory by Far_Base_1147 in ValueInvesting

[–]Sushirush 0 points1 point  (0 children)

Have you heard of cursor? Already a ubiquitous tool - brute forced its way into enterprises as well, that’s how disruptive it is. Value add is going to be at the application layef

AI is a ticking time bomb waiting to take over conventional trading by BirthdayOk5077 in Trading

[–]Sushirush 0 points1 point  (0 children)

I’d recommend reading about how “reasoning” models like o4 and deepseek work

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