this post was submitted on 05 Jun 2024
222 points (100.0% liked)

Technology

37719 readers
314 users here now

A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.

Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.

Subcommunities on Beehaw:

This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.

founded 2 years ago
MODERATORS
alyaza@beehaw.org
TheRtRevKaiser@beehaw.org
gyrfalcon@beehaw.org
rs5th@beehaw.org
Los@beehaw.org
coldredlight@beehaw.org
SemioticStandard@beehaw.org
TheRtRevKaiser@kbin.social
remington@beehaw.org
222
Mozilla Firefox new alt-text generator powered by "fully private on-device AI model" (hacks.mozilla.org)
submitted 5 months ago* (last edited 5 months ago) by frogman@beehaw.org to c/technology@beehaw.org
70 comments fedilink hide all child comments
 

New accessibility feature coming to Firefox, an "AI powered" alt-text generator.

"Starting in Firefox 130, we will automatically generate an alt text and let the user validate it. So every time an image is added, we get an array of pixels we pass to the ML engine and a few seconds after, we get a string corresponding to a description of this image (see the code).

...

Our alt text generator is far from perfect, but we want to take an iterative approach and improve it in the open.

...

We are currently working on improving the image-to-text datasets and model with what we’ve described in this blog post..."

you are viewing a single comment's thread
view the rest of the comments
[–] Kissaki@beehaw.org 4 points 5 months ago* (last edited 5 months ago) (3 children)

So, planned experimentation and availabiltiy

  1. PDF editor when adding an image in Firefox 130
  2. PDF reading
  3. [hopefully] general web browsing

Sounds like a good plan.

Once quantized, these models can be under 200MB on disk, and run in a couple of seconds on a laptop – a big reduction compared to the gigabytes and resources an LLM requires.

While a reasonable size for Laptop and desktop, the couple of seconds time could still be a bit of a hindrance. Nevertheless, a significant unblock for blind/text users.

I wonder what it would mean for mobile. If it's an optional accessibility feature, and with today's smartphones storage space I think it can work well though.

Running inference locally with small models offers many advantages:

They list 5 positives about using local models. On a blog targeting developers, I would wish if not expect them to list the downsides and weighing of the two sides too. As it is, it's promotional material, not honest, open, fully informing descriptions.

While they go into technical details about the architecture and technical implementation, I think the negatives are noteworthy, and the weighing could be insightful for readers.

So every time an image is added, we get an array of pixels we pass to the ML engine

~~An array of pixels doesn't make sense to me. Images can have different widths, so linear data with varying sectioning content would be awful for training.~~

~~I have to assume this was a technical simplification or unintended wording mistake for the article.~~

[–] pheet@sopuli.xyz 1 points 5 months ago

Might be a significant issue if more applications adopt these kind of festures and can’t share the resources in a meaningful way.

load more comments (2 replies)