Yeah we used to joke that if you wanted to sell a car with high-resolution LiDAR, the LiDAR sensor would cost as much as the car. I think others in this thread are conflating the price of other forms of LiDAR (usually sparse and low resolution, like that on 3D printers) with that of dense, high resolution LiDAR. However, the cost has definitely still come down.

I agree that perception models aren’t great at this task yet. IMO monodepth never produces reliable 3D point clouds, even though the depth maps and metrics look reasonable. MVS does better but is still prone to errors. I do wonder if any companies are considering depth completion with sparse LiDAR instead. The papers I’ve seen on this topic usually produce much more convincing pointclouds.

I think it’s been about a year? IIRC Intel only started using TSMC for their processors with Meteor Lake, which was released in late 2023.

I believe their discrete GPUs have been manufactured at TSMC for longer than that, though.

I use a lot of AI/DL-based tools in my personal life and hobbies. As a photographer, DL-based denoising means I can get better photos, especially in low light. DL-based deconvolution tools help to sharpen my astrophotos as well. The deep learning based subject tracking on my camera also helps me get more in focus shots of wildlife. As a birder, tools like Merlin BirdID’s audio recognition and image classification methods are helpful when I encounter a bird I don’t yet know how to identify.

I don’t typically use GenAI (LLMs, diffusion models) in my personal life, but Microsoft Copilot does help me write visualization scripts for my research. I can never remember the right methods for visualization libraries in Python, and Copilot/ChatGPT do a pretty good job at that.

Yeah there’s a good chance you’re right. Maybe something to do with memory management as well.

Long term I’ll probably end up switching back to Darktable. I used it before and honestly it is quite good, but I currently have a free license for CC from my university and the AI denoise features in LR are pretty nice compared to the classical profiled denoise from Darktable. It does also help that the drivers for my SD card reader are less finicky on Windows so it’s easier for me to quickly copy over images from my camera on there instead of Linux. Hopefully that also gets better over time!

I don’t know exactly, but it’s apparently a thing. Some game anti-cheat software such as Easy Anti-Cheat will give you an error message saying something along the lines of “Virtual machines are not supported.” Some are easy to bypass by just tweaking your VM config, others not so much.

Fair enough! I think it’s more common for games to do that, but sometimes I had trouble with software on Windows that used virtualization elements themself. I probably just didn’t properly configure HyperV settings, but I know nested virtualization can be tricky.

For me it’s also because I’m on a laptop, and my Windows VM relies on me passing through an external GPU over TB3 but my laptops’ dedicated GPU has no connection to a display, so it would be tricky to try and do GPU passthrough on the VM if I were on the go. I like being able to boot Windows on the go to edit photos in Lightroom, for example, but otherwise I’d prefer to run the Linux host and use the Windows VM only as needed.

I’m a fan of dual booting AND using a passthrough VM. It’s easiest to set up if your machine has two NVMe slots and you put each OS on its own drive. This way you can pass the Windows NVMe through to the VM directly.

The advantage of this configuration is that you get the convenience of not needing to reboot to run some Windows specific software, but if you need to run software that doesn’t play nice with virtualization (maybe a program has too large a performance hit with virtualization, or software you want to run doesn’t support virtualized systems, like some anticheat-enabled games), you can always reboot to your same Windows installation directly.

A lot of the cheap tablet SoC vendors like Rockchip (whose SoCs end up in low cost SBCs) really only do the bare minimum when it comes to proper linux support. There’s usually next to no effort to upstreaming their patches so oftentimes you’re stuck on their vendor kernel. Luckily for the RK3588(S), Collabora has done a considerable amount of work on supporting the SoC and its peripherals upstream. I run my Orange Pi 5 Plus (RK3588) on a mainline kernel and it works for my needs.

This practice is a lot easier to defend for a low cost SoC compared to something as expensive as a Snapdragon Elite though…

I work in an ML-adjacent field (CV) and I thought I’d add that AI and ML aren’t quite the same thing. You can have non-learning based methods that fall under the field of AI - for instance, tree search methods can be pretty effective algorithms to define an agent for relatively simple games like checkers, and they don’t require any learning whatsoever.

Normally, we say Deep Learning (the subfield of ML that relates to deep neural networks, including LLMs) is a subset of Machine Learning, which in turn is a subset of AI.

Like others have mentioned, AI is just a poorly defined term unfortunately, largely because intelligence isn’t a well defined term either. In my undergrad we defined an AI system as a programmed system that has the capacity to do tasks that are considered to require intelligence. Obviously, this definition gets flaky since not everyone agrees on what tasks would be considered to require intelligence. This also has the problem where when the field solves a problem, people (including those in the field) tend to think “well, if we could solve it, surely it couldn’t have really required intelligence” and then move the goal posts. We’ve seen that already with games like Chess and Go, as well as CV tasks like image recognition and object detection at super-human accuracy.