This seems like a step in the right direction. Much like language translation, doing it on-device is the only way to preserve people's data agency / privacy.
this post was submitted on 22 Dec 2023
110 points (100.0% liked)
Technology
37712 readers
251 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
Of course, otherwise would mean investing in huge data centers for running LLM models, or worse, buying hardware from NVIDIA.
Optimization is the key. Privacy is just an added bonus.
I just want Swype to return. It got pulled out of Play and iOS stores in 2018. Swipe/glide keyboard input via gboard is crap in comparison.
You can already run a llm natively on Android devices.
The hard part isn’t running ai on a device.. it’s doing so while retaining battery life, performance, and privacy.
Privacy is also easy with a local LLM. Performance and battery not so much.
Which one do you use? I tried MLCChat, but all 3 times it either showed a java error or generated giberrish, what's worked for you?
I believe that the decentralisation of computing power is both good for our privacy but also for the environment.
Wouldn't this absolutely hammer the battery though, or at least give the CPU a hard time? My understanding is that offloading the work to a cloud platform means that the processor-intensive inputting, parsing, generating, and outputting operations are done in purpose-built datacentres, and end user devices just receive the prepared answer.
Wouldn't this rinse the battery and increase the overall device temperature for "normal" end users?
Fair warning: I haven't read the two papers outlined in the article.
CPUs can have special hardware accelerators for stuff like this, and you'd be surprised how powerful our little phone CPUs are and how optimized stuff like this can become.
Awesome, thanks for the insight.
I'm showing my age here, but much like we had math coprocessors running beside the 286 and 386 gen CPUs to take on floating point operations; then graphics cards offloaded geometry-based math operations to GPU's - are we looking at AI-style die or chips to specifically work on AI functions?
Excuse my oversimplification, this isn't my field of expertise!
Well, your not too off. Like ASICs are made for mining cryptocurrency. Specialized processing designed for specific computations. This indeed make it's efficiency greater than a general purpose CPU.
Apple added (a while back) what they call a “Neural Engine,” which is hardware dedicated to efficient execution of ML workloads.
https://en.m.wikipedia.org/wiki/Apple_A11
They have been refining it ever since. I would not be surprised if they made advancements in both the hardware and software used for local GAI.
And Google did the same with the Tensor Processor Unit in the Pixels.
Yes!
not a dedicated chip per se, the trend is to build it directly into the SoC (mobile devices) or the dedicated GPU
Apple already does a lot of this stuff. For example, it'll do offline face recognition for your photos while your phone is charging overnight.
Plus, Apple is ahead of the curve when it comes to performance on this stuff. You don't want to be running Stable Diffusion on your iPhone, but smaller AI is perfectly fine. Plus, unlike on Android, there are huge amounts of devices with ML accelerator chips that can run these models efficiently, allowing for power consumption optimisations by not having to provide a CPU fallback.
We'll have to see how effective this will be in practice, but Apple generally doesn't bring these types of features to their newer devices until they're ready for daily use.
Running AI is pretty low power and efficient, especially if you have purpose built chips.
Training AI is another can of worms
It’s a technical challenge but I wouldn’t rule it out. Apple has been using a “neural engine” in their SoC for faced id, etc. for a while. So it’s something they’ve been working on. It will need to get better, but AI models are also getting more efficient.
If the scope of "Ai" isn't wide, I'd imagine the battery and cpu usage would be minimized.
While Apple has its shares of problems, it feels like they at least care about their users, unlike say...Google...
Finally Knight Rider's KITT comes as reality
🤖 I'm a bot that provides automatic summaries for articles:
Click here to see the summary
Apple’s latest research about running large language models on smartphones offers the clearest signal yet that the iPhone maker plans to catch up with its Silicon Valley rivals in generative artificial intelligence.
The paper was published on December 12 but caught wider attention after Hugging Face, a popular site for AI researchers to showcase their work, highlighted it late on Wednesday.
Device manufacturers and chipmakers are hoping that new AI features will help revive the smartphone market, which has had its worst year in a decade, with shipments falling an estimated 5 percent, according to Counterpoint Research.
Running the kind of large AI model that powers ChatGPT or Google’s Bard on a personal device brings formidable technical challenges, because smartphones lack the huge computing resources and energy available in a data center.
Apple tested its approach on models including Falcon 7B, a smaller version of an open source LLM originally developed by the Technology Innovation Institute in Abu Dhabi.
Academic papers are not a direct indicator of how Apple intends to add new features to its products, but they offer a rare glimpse into its secretive research labs and the company’s latest technical breakthroughs.
Saved 74% of original text.