It's a huge difference. If you had AI sufficiently good running locally on a phone, you could devise workflows for things like basic digital hygiene, technical assistance, and tedious tasks like inbox management, image sorting, device updates, and so on. Privacy and security gets a big boost past some local competence threshold, and we're nearly there.
Make the local AI competent enough to do good image generation and editing, realtime voice and music generation, handle agentic tasks with a framework like Hermes, and you can take your AI places to do tasks in contexts that are inaccessible to or inappropriate for cloud.
Frontier big platform models will be the best, but there's a level of "good enough" for local uses that we're already seeing flourish, and "good enough" for the average joe is almost here.
Phones and laptops are terrible devices for local AI, way too constrained by bad thermals and small batteries. MiniPC's (many of them using mobile hardware) don't have that particular issue, and can easily run on a 24/7 basis.
That level of local AI is also more or less what you need for competent autonomous robots, too. If your household robots are orchestrated from your phone, the local security and cloud convenience converge on a single device. No extra servers, etc, reduced cost, all that - local AI is a massive market amplifier.
Let me speculate - we are going in the weird direction of no private property unless you're an overlord that rents his property to peasants. I like to call it the revenge of communism. See how the market behaves in the llm space - it's more viable to share infrastructure than to own it. Imagine the private car revolution in the US was a bus revolution.
All they need to do is "randomly" label 99,999 of every 100,000 as AI and they'll be right 99.999% of the time.
Cryptographically verifiable provenance and chain of custody is going to be necessary to get to the human only stuff, before long, but the good AI stuff will be better. Just a matter of time, at this point.
Not sure why you appear to be downvoted. Cryptographic provenance is indeed the only solution to humanities digital woes. But only the government could make that a rule so it's not going to happen - at least not in my lifetime.
It's strange - like someone went for brevity, but without the usual exercise of packing meaning into each sentence. There's a lot of fluff in the shape of serious writing, lol.
Recent studies have demonstrated using fMRI data to reconstruct the images of what the person being scanned is seeing. There's enough information there to produce a highly plausible reconstruction - if someone is seeing a picture of a zebra, the software shows a zebra, but it's not going to get the stripe patterns exactly right.
fMRI provides a great proxy and noisy set of signals. Fortunately, the brain is redundant enough that a bunch of regions getting activated creates a sufficiently differentiable pattern at large that you can get enough good information to do things like MindEye and so on. Fortunately, recent AI breakthroughs have allowed extremely high dimensional geometry to be handled relatively simply, with millions or billions of dimensions being processed into semantically useful tools.
The low bar for human quality makes this a more or less nonsensical endeavour. Trivial edits like introducing deliberate misspellings, common transposes, and an occasional autocorrect candidate breaks the semantic patterns that LLMs are designed to produce. Throw in things like humanizing skills, a good, stylometricly comprehensive prompt framework, and a systematic approach to the task of producing human-like text, and you can defeat the detectors completely.
The false positive rate in identifying human writing as AI nullifies any particular advantage in systematic detection.
At best - at the absolute best, ideal, perfect case scenario - a system like this will be suitable to flag a piece of writing for review, and additional evidence, context, and reasoning will be required.
A majority of the time, this will be used in a lazy, cover-your-ass corporate fashion to arbitrarily "detect" and penalize users, students, or other targets.
The fundamental issue is that the false positive rate is so high as to make the statistical value of any particular detection nearly null. It doesn't matter if it detects 99.99999% of AI writing if it also deems 15% or more of human writing to be AI as well.
I don't know that it's 15%. I suspect it could easily be that high. Even if it's 2%, that's unacceptable in any situation for which there are significant consequences for a false positive - derailing an academic career, automated rejection of resumes, etc.
The moral purview of peddling this sort of detection as a service is somewhere deep on the wrong side of the line between neutral and evil.
People need to sue the ever loving pants off of companies that sell this shit to schools and companies and universities, because a handful of ignorant administrators have nowhere near the competence and understanding of how to properly mitigate the damage they will inevitably cause through the gratuitous use of this sort of automation.
Company 1: Imagine you have a drug test and you randomly test employees. It's 100% accurate at detecting meth use. It has a 15% false positive rate.
Company 2: You randomly drug test employees. The test is 95% accurate at detecting meth use. It's got a .000015% false positive rate.
See the issue? Let's say the bosses mandate that there's a zero tolerance policy and that any indication of meth use means termination on the spot.
If the incidence rate of meth use is a standard .5%, of 1000, and they randomly test 2 people per week for a year, how many people does company 1 fire, and subsequently expose themselves to liability for wrongful termination? What about company 2?
The base rate fallacy, or false positive paradox, is a huge problem with AI detectors. Company 1 would fire 16 people, all of whom would be overwhelmingly unlikely to be actual meth users. Company 2 would fire 1 person every other year, and they'd be almost entirely certain that the detection was legitimate.
Software like this might be good at detecting one-shot, lazy, rewrites. If you're a big AI platform, you might have some clever steganographic tricks up your sleeve to watermark text. The second someone puts effort into it, they become completely indistinguishable from the majority of human writers, to the extent that the false positive rate becomes unacceptable for use in any real world scenario. Throw in the fact that kids are enthusiastically learning their vocabulary, writing styles, and textual mannerisms from ChatGPT, Claude, and Gemini, and it makes the commercial use of detection software an outright ignorant, twisted, and evil thing to do.
The problem has been differing narratives from different sources with different biases, motives, and objectives. The solution is a thorough interrogation of different sources, cross-checking and validating novel claims, using a Bayesian approach to maintaining a model of the world. Not rigid, but roughly scientific.
Most people can't afford to do that, so they pick a proxy from among the many individuals that do the work of sorting and filtering and comparing and validating news from a wide spectrum.
Some proxies are decent, some are not, and come with their own biases and skew.
The solution is high intelligence local AI that maintains a world model for you, providing you with updates based on your interests and cross-validated world events, with a rigorous record of sources and reliability. Anything short of that is just repackaged proxy games.
On the plus side, Asmongold or Hasan Piker are the low bar to beat. Haha. People are so well informed and educated now that they have access to the interwebs.
Make the local AI competent enough to do good image generation and editing, realtime voice and music generation, handle agentic tasks with a framework like Hermes, and you can take your AI places to do tasks in contexts that are inaccessible to or inappropriate for cloud.
Frontier big platform models will be the best, but there's a level of "good enough" for local uses that we're already seeing flourish, and "good enough" for the average joe is almost here.
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