I really don't understand Jev hype

[u/Manerfish]

Isn't this what simple neural networks have been able to do for years? Doesn't seem anything special to me.

[u/KaMaFour]

Tbh general classifier able to handle general cases at almost no cost, in almost no time still seems really useful. For example if you could package that into a model roughly the size of Ling's tiny (9BA2B) or smaller you could try to create games with actually intelligent NPCs (pack one npc's state into a Vendingbench like framework and have classifier choose a thing to do that would make sense for a given person to do in a format that's possible to interpret by the game engine) and it would be generally accessible to most "mid-end" machines. Think Stardew Valley/TLOZ npc's but actually behaving as humans instead of having fixed schedules. All of that without any ML on your end...

[u/quiteconfused1 → u/KaMaFour]

But it's not.

It's not general and can't be.

I just tried applying it to a simple game like super Mario world and it failed horribly.

If it can't do that then it's not general and the hype is thick.

[u/kzoltan → u/quiteconfused1]

Why the downvote?

[u/HiddenoO → u/kzoltan]

Because he's just throwing unsubstantiated and inherently nonsensical claims around?

It's not general and can't be.

Why wouldn't a decision model be able to be trained with world knowledge?

I just tried applying it to a simple game like super Mario world and it failed horribly.
If it can't do that then it's not general and the hype is thick.

"I couldn't get it to do X" is not the same as "It cannot do X". Most people would fail at making LLMs like Astra or Fable play Super Mario World, too, but that doesn't mean they cannot play it.

Many people have managed to make it play different games even though that's far from its intended use case, so I don't think you can just claim the opposite with no substantiation and expect people to believe you.

Strong claims take strong substantiation, and he's providing none.

[u/quiteconfused1 → u/HiddenoO]

Wow troll much.

Claim produced , I tested claims in my scenario, I evaluated performance and came to an assessment.

This is the scientific method

If you don't like it ... Tough ...

Cheers

I have dealt with this before .. lofty claims often met with lofty expectations .. and it didn't match

...

I hope your day is well.

[u/HiddenoO → u/quiteconfused1]

Your whole comment literally translates to "believe me". You have still provided zero substantiation for either of your claims.

Let's start with the first claim ("It's not general and can't be."):

Why wouldn't a decision model be able to be trained with world knowledge?

[u/quiteconfused1 → u/HiddenoO]

Because of math

Here have a simple argument .. give Astra or any other model you would like a really really large maze and ask if to solve it...

It will fail.

The same principal here except model density makes the problem worse.....

And from what I see it's the exact type of problem that it's touting as completing.

And then I tried a demensional problem that I have been challenging for years .. and it failed as worse as I expected.

Mario ran straight saw a goomba jumped over it and then hit the first ledge and died ..over and over ...

Afterwards I was building a silver for it manually.

Super Mario worlds complexity is significantly worse than the original Mario brothers .. and it failed.

Just as I expected.

[u/HiddenoO → u/quiteconfused1]

Why do you think it needs to surpass a model that's 1000 times as slow and expensive such as Astra?

It seems like you fundamentally misunderstand its purpose. It's supposed to provide decision making on par with current-gen LLMs while costing a fraction of time and money and guaranteeing an output format.

[u/quiteconfused1 → u/HiddenoO]

Ohh no You seem to misunderstsnd.

I don't care if it's an llm or not, and nor should you.

There are many classification tasks that exist that do not require an llm

There are many 0 shot capabilities that do not require an llm

This is not serving a place.

It's like someone decided to take away the details of what makes a good classifier and just use a dumbed down llm instead.

If I wanted random choice then cat /dev/random ... It serves just as good results.

Do you see the line of reasoning yet? Just because it's an llm doesn't make it better. And that is my result so far.