My points are about what it’s like current with removable batteries in cameras with current technology. Battery usage is dependant on usage to some extent. For casual use my cameras last weeks on a battery and even shooting all day e.g. in a studio, I would only get through one battery. Modern cameras also have usb-c charging and can be used whilst you do that, so that’s an option too, though less practical in my view. Yes, camera batteries are proprietary and it would be better if they weren’t, though they are generally the same across similar cameras from the manufacturer and the same in the successor cameras. Many mirrorless cameras are water resistant (with the right lenses) so the can be used in heavy rain, though not under water without a housing. Action cams like the GoPro 13 Black are waterproof with a removable battery.
Both of those things are also important in cameras, there is even sites that compare the size such as https://camerasize.com/. Cameras have got smaller in recent years and it makes the size makes a big difference to whether you take it with you on not or fits in your pocket or not for compact cameras. Ricoh’s gr4 camera is 0.5mm thinner than the previous model (gr3). Cameras are essentially smaller than they would be otherwise because they have replaceable batteries. People who need at more power usually use several batteries rather than use a bigger camera with more capacity.
Cameras also need to withstand drops for similar reasons to phones, it’s in you hand and you could drop it, also tripods can fall over, car mounts fall off etc.
It’s been long enough that people of forgotten what’s it’s like. Cameras still have replaceable batteries, there are several benefits:
I can have two (or more) batteries, if it runs out I just change it. I don’t need walk around with a USB battery pack and cable hanging off the device preventing me from using it properly.
I can put the battery on charge somewhere and leave it, even if not completely secure, because just the battery not the device. This way my expensive device and my data is not at risk.
I can use 40+ year old cameras, because I can just put a new battery in. This is not something you can do with newer device, e.g. and iPod and you can’t even find anyone who will fit them for the older models.
Battery tech moves on. There are now some batteries with charging ports on them. Other batteries offer more capacity than the original ones. Apple even did this once for me, when MacBook Air batteries were fairly easy to replace, I had mine replaced (it wore out) at the shop and they put a slightly bigger one in, which was the standard on the newer models.
The battery in my iPhone 11 pro (6.5 years old) is still basically fine as long as I charge it every day. None of my previous smart phones were able to keep enough charge to be useful after 3-4 years.
The title of site should probably have "for gaming" at the end as it doesn't consider GPUs for compute such as the A100 or the GTX 580 3GB that AlexNet was trained on.
Waterfox is dependant on Firefox still being developed. Mozilla are adding these features to try to stay relevant and keep or gain market share. If this fails, and Firefox goes away, Waterfox is unlikely to survive.
First off I’d say you can run models locally at good speed, llama3.1:8b runs fine a MacBook Air M2 with 16GB RAM and much better on a Nvidia RTX3050 which are fairly affordable.
For OpenAI, I’d assume that a GPU is dedicated to your task from the point you press enter to the point it finishes writing. I would think most of the 700 million barely use ChatGPT and a small proportion use it a lot and likely would need to pay due to the limits. Most of the time you have the website/app open I’d think you are either reading what it has written, writing something or it’s just open in the background, so ChatGPT isn’t doing anything in that time. If we assume 20 queries a week taking 25 seconds each. That’s 8.33 minutes a week. That would mean a single GPU could serve up to 1209 users, meaning for 700 million users you’d need at least 578,703 GPUs. Sam Altman has said OpenAI is due to have over a million GPUs by the end of year.
I’ve found that the inference speed on newer GPUs is barely faster than older ones (perhaps it’s memory speed limited?). They could be using older clusters of V100, A100 or even H100 GPUs for inference if they can get the model to fit or multiple GPUs if it doesn’t fit. A100s were available in 40GB and 80GB versions.
I would think they use a queuing system to allocate your message to a GPU. Slurm is widely used in HPC compute clusters, so might use that, though likely they have rolled their own system for inference.
Palm was the market leader, it would have been the obvious choice. Palm had been around since 1996 and by 1998 had sold 30 million devices [1]. PocketPC didn’t come out until 2000, in 2001 they had only sold 1.25 million devices, equating to less than 10% market share [2]. From what I remember Palm Pilots were the go to choice for PDAs, they were simple and worked. Other devices had come and gone. It would have been odd if they chosen something else. I doubt anyone was thinking it would be used for 20 years, though I don’t think people would have thought it would go away at the time.
The latest Nvidia driver no longer supports the K40, so you’ll have to use version 470 (or lower, officially Nvidia says 460, but 470 seems to work). That supports CUDA 11.4 natively. Newer versions of CUDA 11.x are supported: https://docs.nvidia.com/deploy/cuda-compatibility/index.html though CUDA 12 is not.
In my testing, a system with a single RTX3060 was faster in tensorflow than with 3 K40s and probably close to the performance of 4 k40s.
If you are considering other GPUs, there are some good benchmarks here (The RTX3060 is not there, though the GTX1080Ti was almost the same performance in the tensorflow test they run): https://lambdalabs.com/gpu-benchmarks
As others have said Google CoLab is free option you can use.
The industry was already moving away from the big 64 bit SMP machines made Sun, SGI & IBM. In many cases a cluster of 32bit x86 machines made more sense than one expensive big machine with high priced support contracts and parts. 32 bit x86 machines already supported more than 4GB total memory with PAE, it was just that one process couldn’t use more than 4GB. Other 64bit chips were already well established (SPARC, POWER, MIPS), probably for most of the users they couldn’t easily move to a new CPU architecture. For other users by the time they needed the bigger machines x86 64bit was already available, including from Intel themselves. AMD was limited 8 sockets from what I remember, so their was still a small market for big Itanium systems (like SGI’s Altix).
The Sony RX100 series would be in most top lists. Though personally I'm not sure why they went with a slower lens from the mark 6 onwards. The ZV1 continues with the a similar lens from earlier models. I have the RX100 mark iii, that's still quite good.
With podman there is no daemon, everything is running as you. The standard setup for docker has a daemon running as root, which means when you start a container it has root privileges.
Interchangeable lens cameras now all have video features and increasingly most of the improvements are in that area. When SLRs are used for video the mirror needs to be flipped up and auto-focus system that is used for photos can’t be used, so the camera need another one on the sensor. In this case the mirror is redundant and the viewfinder can’t be used.
Tracking of fast moving subject is difficult with a SLR, the SLR cannot see the image in viewfinder mode only a focus module can, which likely only has a few hundred focus points (or less) and those points often don’t reach the edge of the frame. Additionally mirrorless cameras are able track a subject eye using AI and keep that in focus. A SLR cannot do this in the viewfinder mode as the focus sensor does not have nearly enough resolution to recognise small item like an eye or to know that it is an eye.
Burst shooting is also difficult on a SLR, for each shot the mirror needs to flip up and down and the focus module use a brief period to change focus. Canon is/was the leader in sports photography cameras. The highest end Canon SLR camera can do 16fps with autofocus, but 20fps with the mirror up. The Sony a1 (mirrorless) can do 30fps. These fast shooting rates are only possible with mirrorless cameras.
Additionally, in the 2000s we had IE-only webapps, meaning that when companies only cared about Windows clients, they still thought making webapp was a better way to reach their customers than a Win32 app.
I think the main reason is distribution. Desktop apps were/are difficult to distribute. To get an desktop app into organisation, you either needed to tell people to install something, and they need disk space, RAM, different versions of Windows, might not work on Mac, almost never on Linux. Then they need to update the app regularly somehow, still not solved for all apps. Bigger orgs have managed distribution, but then smaller groups can’t install things without going through IT. Then you’ve got servers, which require someone to setup, maintain and have their own lead time. Webapps, especially free ones, have none of these issues an individual can just start using it. Webapps mean everyone using the latest version straight away.
Phone apps seem to be modelled on webapps, typically the server is run by the app maker, installs are easy and updates are automatic. Additionally, phone apps automatically go on the home screen and have notifications, which means you’ll open them more often.