GamingChairModel

joined 3 years ago
[–] GamingChairModel@lemmy.world 5 points 1 day ago (1 children)

It hasn't felt t like there's been much significant performance increases or development in RAM in the last.. decade?

In memory? There's been a ton of improvement, even if most of the coolest stuff isn't making it into DIMMs that are installed in user laptops/desktops.

Advanced packaging technology has allowed chip manufacturers to put different silicon dies together with increasingly high performance (high bandwidth, low latency) connections in the same package, including with some three dimensional stacking. That way they can mix and match different silicon dies for greater cost effectiveness, yield, performance, etc.

This also means that in-package memory is now the standard in certain chips. Apple's M-series silicon has its memory packaged right into the CPU/GPU package, as a system-in-a-package, so that the connection between the logic and memory is comparatively much higher performance, several times higher bandwidth than desktops or laptops that don't follow that kind of architecture.

Similarly, in data centers, the AI boom has caused all the memory manufacturers to switch their production lines to high bandwidth memory, where they vertically stack a bunch of DRAM chips on each other, with ultra-fast, high bandwidth connections, so that they can shove terabytes of memory into these data center servers. These recent generations have been improving speed and bandwidth in ways that make consumer level DDR5 RAM look like child's play.

So they're improving things. Just not in ways that really show up in DIMM sticks.

[–] GamingChairModel@lemmy.world 2 points 1 day ago (1 children)

Not strictly, there are usualy hurdles to overcome for home usage of datacentre tech, but it's possible.

The hurdles are basically insurmountable with the hardware released after 2024.

The NVL72 for the Blackwell generation cost about $3 million and takes up a single server rack. The power consumption is about 130 kW, and most configurations require dedicated plumbing for the liquid cooling.

To put things in perspective, a residential electrical hookup is usually 50A or 100A for a house, with recommendations that anyone who is going to be charging electric cars should have 100A service. 100A at 240V is 24 kW.

So one server rack uses as much power as the maximum electrical capacity of 5 homes. You'll never be able to pull that off in an actual residential environment.

Oh, and the newest 2026 generation, the Rubin NVL72s, use something like 230 kW of electrical power, almost twice as much as the previous 2024 generation.

There's always going to be a robust used market for phones that were purchased outright, to be resold on a different cycle than every 2 years (plenty of rich people changing phones every year, and plenty of people replacing on a 3, 4, 5, or 6 year cycle). You can expect the market to basically settle on a curve where it depreciates along a predictable rate.

Leases don't really change that, any more than leases changed the market for used cars, or even certified pre-owned by the same dealers and organized by the same manufacturers who sell new cars.

There will be times that the predefined lease terms will unexpectedly prove to be either beneficial or detrimental to the consumer. Sometimes external factors will affect the entire used market, like currency issues, or component pricing issues (imagine if RAM prices dramatically swing again for new devices in a way that affects the value of the already-sold devices out in the world), where the predefined lease prices turn into a windfall for someone. Like in 2021 or so when expiring car leases allows the lessee to buy out the car at the end of the lease for much cheaper than the car itself was worth.

It's generally going to be a less than ideal financial decision to lease, but it also won't collapse the used device market and it won't be that far off the practice of selling your old phone when you buy a new one.

Law enforcement can legally trick you into giving up your password, too, and that's full access right there. Having an unlocked phone but no password isn't enough to get into certain parts of the core system/security settings, and trying to get into those will prompt a password anyway (and that generally gatekeeps the access to the phone through a physical connector plugged into the port).

Neither pathway is perfect but I think for real world usage and real world adversaries (not just law enforcement, but also criminal thieves/scammers/hackers, and governmental adversaries that aren't bound by legal limits, like foreign intelligence agencies), it's better to have biometrics so that you are physically punching in your PIN/password much less frequently. Especially on modern systems that get spooked easily and require a password anyway when the phone has been idle too long or when the wrong face looks at it too many times.

[–] GamingChairModel@lemmy.world 5 points 2 days ago (1 children)

The other underappreciated threat model is shoulder surfing, especially in an age of ubiquitous high resolution cameras. Punching in a numerical PIN within view of a camera potentially leaks that secret, and some high resolution cameras can even pick up letters and symbols from the on screen keyboards.

Being compelled to give biometrics doesn't do enough for an adversary (including government adversaries) to do everything with a phone, the way having the password or PIN does, and I would argue that governments would be better at tricking people into inadvertently giving up their PINs and passwords than they'd be at compelling biometrics within the time window that they still work (before the phones lockout biometrics as a valid unlocking method), or being able to do stuff to exploit extraction tools past the lock screen.

So the threat model needs to be understood for what it is.

There are degrees, and this particular artist is pretty far down the spectrum for the extent of AI use, not relying on AI for any of the creative parts.

It's written/composed by a human and performed by a human, then fed through a voice changer that relies on AI as a filter.

Here is the creator's TED talk on AI in music.

[–] GamingChairModel@lemmy.world 5 points 4 days ago (1 children)

It's fun when your laptop decides to do this kind of nonsense from inside your laptop bag, smothering itself in its own heat.

Sam Altman is in the singularity, in that he is now convinced LLMs are smarter than him

I'm reminded of a quote that used to always come up when I was studying cryptography. Bruce Schneier would always remind people that it's easy to design an encryption scheme that you can't break. But it's hard to design an encryption scheme that nobody else can break, either.

It is a good reminder that there needs to be a degree of intellectual humility by those who want to design big systems that affect the whole world.

[–] GamingChairModel@lemmy.world 3 points 2 weeks ago (1 children)

They can't close on the Warner deal yet (court ordered the deal be paused for 28 days to consider blocking it), and if the courts hold up the merger for a few months, and Oracle stock plummets based on their bad investments, the Ellisons' personal guarantee on the purchase price might not be enough to actually complete the merger.

I don't think it's very likely, but I can dream, at least for now.

[–] GamingChairModel@lemmy.world 26 points 2 weeks ago

Yeah, last I saw this, it was revealed that it was actually properly cantilevered and the homeowner thought it would be funny to shove some thin boards underneath as a joke.

[–] GamingChairModel@lemmy.world 1 points 2 weeks ago

It'll only make a difference during the times in which it is being hit by a beam that wouldn't otherwise hit the earth, since whatever it reflects will be offset by some shadow behind it. I wonder if, as a matter of orbital mechanics, it would be possible to make the satellite orbit in such a way so that it never casts a shadow and is always in the sunlight.

[–] GamingChairModel@lemmy.world 0 points 2 weeks ago (1 children)

"Technical debt" is a phrase that was invented to explain the phenomenon in a way that bean-counting business bosses could understand: we can do it this way in a way that costs us less today but will incur a debt that will slow us down in the future. It was always an attempt to quantify the concept in a way that we have to think about tradeoffs between our current resources and our future resources.

 

(Note: McSweeney's is a satirical publication)

 

I've read some of Ed Zitron's long posts on why the AI industry is a bubble that will never be profitable (and will bring down a lot of companies and investors), and one of the recurring themes is that the AI companies are trying to capture growing market share in an industry where their marginal profits are still negative, and that any increase in revenue necessarily increases their costs of providing their services.

But some of the comments in various HackerNews threads are dismissive, saying that each new generation of models makes the cost of inference lower, so that with sufficient customer volume, the companies running the models can make enough profit on inference to make up for the staggering up-front capital expenditures it took to build out the data centers, train their models, etc.

It's all pretty confusing to me. So for those of you who are familiar with the industry, I have several questions:

  1. Is the cost of running any given pretrained model going down, for specific models? Are there hardware and software improvements that make it cheaper to run those models, despite the model itself not changing?
  2. Is the cost of performing a particular task at a particular quality level going down, through releases of newer models of similar performance (i.e., a smaller model of the current generation performing similarly to a bigger model of the previous generation, such that the cost is now cheaper)?
  3. Is the cost of running the largest flagship frontier models going down for any given task? Or does running the cutting edge show-off tasks keep increasing in cost, but where the companies argue that the improvement in performance is worth the cost increase?

I suspect that the reason why the discussion around this is so muddled online is because the answers are different depending on which of the 3 questions is meant by "is running an AI model getting cheaper over time?" And the data isn't easy to synthesize because each model has different token prices and different number of tokens per query.

But I wanted to hear from people who are knowledgeable about these topics.

 

Curious what everyone else is doing with all the files that are generated by photography as a hobby/interest/profession. What's your working setup, how do you share with others, and how are you backing things up?

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