Redacted @ Redacted @lemmy.world

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Joined

2 yr. ago

Probably because it's down. I for one am surprised they have no failover for when Bing goes down.

This doesn't track, Rare were banging out so many good games and as others have mentioned the Star Wars games were also awesome.

I feel you are also still missing the point about trailblazing. There was more gameplay innovation than anything since.

Yes my list was not exhaustive either and tried to focus on exclusives to make the point.

Good point.

Unlikely many of the games of the current gen will hold up in 25-30 years...

Many of the first party games on those systems broke new ground and much of modern gaming wouldn't exist without them.

Woodwork dweller here, you seem to have forgotten:

Majora's Mask

Star Fox 64

Jet Force Gemini

Donkey Kong 64

Diddy Kong Racing

Excite Bike 64

Paper Mario

Paper Mario: Thousand Year Door

Pokémon Stadium

Yoshi's Story

Pokémon Snap

Mario Party

Felt at the time that there was always a high quality "AAA" release on the horizon interspersed with some of the greatest games ever made. Many of the gameplay techniques these games pioneered during the transition from 2D to 3D are still used to this day.

Obviously a lot of them don't stand the test of time a quarter of a century on but we haven't had a system with the same consistent quality of games for a long time, if ever, IMO.

✅ Math is hard

❌ This math is hard

Nah it's just SFA with extra steps.

So if all world leaders collectively agreed to put aside their differences, ditch capitalism and mobilise their entire populations to actively work to reduce emissions tomorrow we might stand a slim chance of preventing the worst case scenario...

That's why I mentioned those other console exclusive features. Anyway the original point was about cost and I think the Series X was the best value for money at launch this gen...

• Half the price of building a similar PC at launch.
• Rewards are higher on console so recoup the cost more than PC.
• I use Game Pass on both PC and Xbox with a single account to play multiplayer so cheaper on that front.

We're half way through the generation now though. PC parts have got cheaper, Game Pass Ultimate conversion ratio has dropped and rewards are drying up so probably wouldn't advocate it anymore. PC likely to be better value next gen.

I always gamed predominantly on PC but this generation I did the maths as PC parts had become over-inflated so decided to give console a try. I still think it was a decent decision for this generation...

Game Pass can be had waaaaaay cheaper than that and you can get it all back and more in rewards points.

I spent £450ish on the console at launch including controller and a game. Equivalent GPU was £500 or more at the time.

Spent £150ish on Game Pass sub from November 2020 to July 2026 which has allowed me to play countless games I never would have bought outright.

I've made over £700 back in vouchers with over 2 years left to accumulate more. Spent half of it on games, and controllers, headset, etc. all of which I can use on my PC. Plan on saving the remaining vouchers to put towards my next PC build.

This is without mentioning other console benefits like low maintenance, Quick Resume and the fact I can use one copy of a game to play with two players online.

eXpLaIN hOw im OuT oF pOcKeT.

Yep my sentiment entirely.

I had actually written a couple more paragraphs using weather models as an analogy akin to your quartz crystal example but deleted them to shorten my wall of text...

We have built up models which can predict what might happen to particular weather patterns over the next few days to a fair degree of accuracy. However, to get a 100% conclusive model we'd have to have information about every molecule in the atmosphere, which is just not practical when we have a good enough models to have an idea what is going on.

The same is true for any system of sufficient complexity.

This article, along with others covering the topic, seem to foster an air of mystery about machine learning which I find quite offputting.

Known as generalization, this is one of the most fundamental ideas in machine learning—and its greatest puzzle. Models learn to do a task—spot faces, translate sentences, avoid pedestrians—by training with a specific set of examples. Yet they can generalize, learning to do that task with examples they have not seen before.

Sounds a lot like Category Theory to me which is all about abstracting rules as far as possible to form associations between concepts. This would explain other phenomena discussed in the article.

Like, why can they learn language? I think this is very mysterious.

Potentially because language structures can be encoded as categories. Any possible concept including the whole of mathematics can be encoded as relationships between objects in Category Theory. For more info see this excellent video.

He thinks there could be a hidden mathematical pattern in language that large language models somehow come to exploit: “Pure speculation but why not?”

Sound familiar?

models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.

Maybe there is a threshold probability of a positied association being correct and after enough iterations, the model flipped it to "true".

I'd prefer articles to discuss the underlying workings, even if speculative like the above, rather than perpetuating the "It's magic, no one knows." narrative. Too many people (especially here on Lemmy it has to be said) pick that up and run with it rather than thinking critically about the topic and formulating their own hypotheses.

And again...

You've just copied my arguments yet again.

Seek help, your projections are concerning.

You don't really have one lol. You've read too many pop-sci articles from AI proponents and haven't understood any of the underlying tech.

All your retorts boil down to copying my arguments because you seem to be incapable of original thought. Therefore it's not surprising you believe neural networks are approaching sentience and consider imitation to be the same as intelligence.

You seem to think there's something mystical about neural networks but there is not, just layers of complexity that are difficult for humans to unpick.

You argue like a religious zealot or Trump supporter because at this point it seems you don't understand basic logic or how the scientific method works.

Nonsense opinion proves unpopular.

Once again not offering any sort of valid retort, just claiming anyone that disagrees with you doesn't understand the field.

I suggest you take a cursory look at how to argue in good faith, learn some maths and maybe look into how neural networks are developed. Then study some neuroscience and how much we comprehend the brain and maybe then we can resume the discussion.

You obviously have hate issues

Says the person who starts chucking out insults the second they get downvoted.

From what I gather, anyone that disagrees with you is a tech bro with issues, which is quite pathetic to the point that it barely warrants a response but here goes...

I think I understand your viewpoint. You like playing around with AI models and have bought into the hype so much that you've completely failed to consider their limitations.

People do understand how they work; it's clever mathematics. The tech is amazing and will no doubt bring numerous positive applications for humanity, but there's no need to go around making outlandish claims like they understand or reason in the same way living beings do.

You consider intelligence to be nothing more than parroting which is, quite frankly, dangerous thinking and says a lot about your reductionist worldview.

You may redefine the word "understanding" and attribute it to an algorithm if you wish, but myself and others are allowed to disagree. No rigorous evidence currently exists that we can replicate any aspect of consciousness using a neural network alone.

You say pessimistic, I say realistic.

It's just a bit of a pointless distinction. No atheist could claim they know for sure.