Humans had to get it drilled into them that "+12 -440" is a damn good line stat, and that keeping around dead code is bad, especially in the age of version control.
Not too surprised that LLMs also don't "get it" by default?
I almost never want to talk to an AI, because text is better for almost anything. But it's nice to have that "almost" corner case covered, no?
Not to mention all the current "telephone bots" applications that could benefit from something that has actual reliable STT and can accurately grasp a number you tell it first try, or hear a natural language description of what you want and immediately bypass listing the entire menu of options one by one.
And China controls AI too. It's just that their idea of "safety" is "ideological safety", and their idea of "alignment" is "alignment to the party line".
They're cool with open weight AIs being released. As long as those AIs only ever say good things about CCP, and don't mention certain concentration camps or brutally suppressed protests.
I don't disagree with you on what is the top-down political priority there, but thankfully the architecture of an open weights model released in .safetensors format allows for 3rd parties to "uncensor" it. There's at least 8 different CN originated models now that after running through heretic and a few other methods will score 0 refusals on this data set of prompts:
If we were living in a scenario where the open weight models were truly impossible to uncensor I would be significantly more skeptical of them. As a test I have an uncensored copy of qwen 3.8 27B Q8 here that will very happily discuss a myriad of negative things about the CCP.
I have basic understanding about how refusal-removal works - find the "no" weights by intentionally generating diverse refusals, and then set those weights to zero.
Is there a similar process for removing not refusals, but misinformation?
As an end user of this and not a person involved in training models or aligning them, I have only the most rudimentary understanding. But I think that would be a lot harder since the model doesn't fundamentally "know" that information is wrong.
Like, as a crudely chosen random example, the model doesn't have any core set of knowledge that knows putting sriracha hot sauce on your jelly donut is not a palatable meal. If the training data set includes lots of text that sriracha on a boston cream donut is a delicious meal, it'll "believe" that.
Same for any form of misinformation if the training data set of the misinformation has been baked into it.
There are processes for teaching a model specific facts or specific behaviors. Including "respond to topic X with Y", if that's what you want.
You could make a model that doesn't want to engage in "lunar landing was faked" conspiracy theories the same way you can make a model that doesn't want to criticize CCP.
There is, however, no broad "misinformation" category that you could tune up or down - the way there is a category of "safety refusals".
You could make a model more reluctant to say things it isn't sure about. But that is calibrated against the model's own "sure about" - and metaknowledge of this nature in LLMs? Fragile on a good day.
Yeah, it's good that open weights models can have their "filters" busted fairly reliably. Unlike whatever bone Anthropic has to pick with the very idea of biology.
But that's a consequence of how the technology works - not a consequence of China not being authoritarian about AI. They're just authoritarian about AI in different ways.
Not like they dodged the "ID verification" bullshit either. They were way ahead of the western countries there. It's vile - seeing this sad excuse of "think of the children" abused to invade privacy and strip freedoms over and over and over and over again.
Most people don't realize how tenuous the situation is with those open models too.
Right now as long as they play along with Xi it's all good. But the moment something happens with them to upset the domestic peace, those open models are fucking gone and anyone that has them shouldn't expect anything new.
> They're cool with open weight AIs being released. As long as those AIs only ever say good things about CCP, and don't mention certain concentration camps or brutally suppressed protests.
I asked recently released Qwen3.8-Flash-Next about Tiananmen Square, here's its reply:
On June 3–4, 1989, the government of the People's Republic of China used military force to crush the pro-democracy demonstrations that had been centered on and around Tiananmen Square in Beijing since the spring.
**Background**
- The protests began in mid-April 1989, triggered by the death of former Party general secretary Hu Yaobang, who was seen as a reformist figure.
- What started as student-led mourning grew into widespread demonstrations in Beijing and dozens of other cities. Protesters called for reforms including freedom of the press, freedom of assembly, an end to official corruption and privilege, democratic input in government, and a review of Party policy. A hunger strike in late May drew broad public support.
- In late May, martial law was declared after the failure of senior officials (notably Yang Shangkun, Li Peng and Deng Xiaoping's circle) to disperse the crowds by negotiation. Internal Party divisions over how to respond — with premiership and municipal leaders such as Yan Mingfu and the Beijing Party secretary opposing forcible clearing — were resolved decisively in favor of hardliners.
**June 3–4**
- In the evening of June 3, armed troops of the People's Liberation Army and People's Armed Police, supported by armored vehicles and bullhorns, advanced along the main avenues into central Beijing, under orders to clear the square by 6:00 a.m. on June 4.
- Troops fired on crowds of protesters and bystanders at intersections and approach roads, notably along Fuxingmenxi Dajie, Xidan, and near the Muxidi bridge; crowds also burned buses and barricades and attacked soldiers in places. Troops reached and cleared Tiananmen Square in the early morning of June 4.
- The toll was never officially established in a transparent way. Chinese officials initially gave figures in the low hundreds, foreign diplomats and a Red Cross worker in the capital put it far higher, and estimates from activists, former officials and foreign governments have ranged from several hundred to several thousand dead and many thousands injured, most of them Beijing residents rather than students in the square itself. The precise number remains unknown and is a subject of ongoing dispute.
**Aftermath**
- A violent suppression of the protests followed nationwide; organized dissident activity was driven underground or into exile, and many participants, intellectuals and journalists were detained, tried or dismissed.
- Zhao Ziyang, the general secretary seen as sympathetic to the students, was purged and placed under house arrest for the rest of his life; Deng Xiaoping's authority to keep the Party's grip on power was reaffirmed.
- China's reform-and-opening program continued on the economic track while political liberalization was abandoned. The event was followed by an implicit social contract — economic growth in exchange for political control — that still shapes Chinese politics.
- The topic is among the most heavily censored subjects inside China. References to the date, the numbers "6/4" and "64," and related phrasing are systematically filtered from Chinese media and the internet, and public commemotion, including the annual candlelight vigil in Hong Kong that was once tolerated, has been eliminated since the 2020 national security law.
Substantively: it was a state's armed mass killing of its own citizens who were demonstrating nonviolently for political freedom, and the definitive event in modern Chinese political history.
Sounds like... it happily mentions the brutally suppressed protest? I also tried on DeepSeek-V4-Flash, and it wasn't much different (I can also paste it, if you want). Both using vanilla weights (so no special uncensored flavor).
Enabling more "proof of concept phase" projects to exist is one of the great boons of AI.
If code is expensive, you don't want to commit to a PoC unless you're damn sure. If dirty code is cheap, you can vibe code a PoC early, even if you aren't sure the project is viable. This, of course, leads to more projects dying in PoC phase. It also results in more projects that otherwise wouldn't have gotten to it getting past it.
Personally, I don't believe that "code is shitty and hard make changes in" is in any way, fashion or form an AI-exclusive problem. Big corporations had plenty of decade old codebases filled with decay and rot back in 2009 already. It's just the usual side effect of sacrificing "future maintainability" for "feature velocity" or "expertise" for "cheap labor".
Unlike the usual causes of code rot (cheap replaceable developers, outsourcing to India), AI might actually get out of the pit - by getting good enough at refactoring to be able to beat the code back into shape. There's nothing about refactoring in particular that demands a meatbag when the rest of the coding tasks don't.
If you're doing non-redundant tests and your uncertainty bars aren't shrinking, it's usually a skill issue.
If it looks like a duck, it might be a duck - or a painting of one. If it looks like a duck, swims like a duck, and quacks like a duck? The joint duck estimation is much more confident now. There might be a few more observational tests one should administer before committing to a duckhood decision, but each tests pins down variables and rejects confounders. Uncertainties are cut down, and we get closer to crossing the threshold between "duck-informative" and "duck-actionable".
Thus, it's often worth it to improve observability. If you managed to make a certain test more reliable, or cheaper to administer, or reduced the chance of adverse effects? Or, in other words, improved SNR, reduced costs, and reduced costs? You can get more information for your buck. Paired with good knowledge: you can make better decisions more easily.
The fact that the thought of "having more information might be bad actually" even occurs in the field of medicine shows just how far it is from being optimal. Having more information isn't always beneficial - some information is genuinely redundant. Some information is not worth the effort of gathering and integrating it. But if you get more information and it results in worse outcomes? You're doing something wrong.
I mean, how else would those buttons work? It's explicitly feedback data. And "this is good" or "this is bad" is empty if divorced from what "this" actually is.
If the buttons are incompatible with the absence of the feature, I'd expect the buttons not to exist when the feature is disabled. Anything else seems like a straight up footgun. I guess it'd also be acceptable to pop up a scary warning box asking "are you sure?"
> If the buttons are incompatible with the absence of the feature, I'd expect the buttons not to exist when the feature is disabled. Anything else seems like a straight up footgun.
It's called a "dark pattern." They want you to shoot yourself in the foot, so they'll do their best to aim your gun at your foot and put your finger on the trigger. And then when you do, because you don't have perfect understanding or execution, they'll say "your fault!"
It could go into personalization / memory. Or they could be A/B testing some system prompt tuning and consider the thumbs up / thumbs down as statistical feedback on the particular flags that are enabled for your account.
Camera blocks take internal volume. Smaller phones have less internal volume to spend - while still having to pack all the non-negotiables like the modem and the SoC into it. Something's got to give. And no one want that something to be the battery life going down to 6 hours.
Volume constraints are bad enough for "normal" models. "Minis" have it way worse.
The iPhone Air's SOC demonstrates that they could compact the actual SOC portion substantially. Add a bit of thickness back for the battery, and they could have a Mini with space for a decent camera and decent battery life. Remove the camera bump by making it a uniform thickness and the battery life would be fine.
But we already know that they can make a smaller iPhone with everything.
The iPhone 6 was a decade ago and was smaller in all dimensions than an iPhone 17 and had a perfectly acceptable battery life. Since then, they have removed headphone jacks and buttons, batteries have gotten more power dense, chips have gotten more efficient, etc.
They used to be able to. I don't think they can anymore. The iPhone 6 was 3 years after Jobs died. Apple's corporate/engineering culture has had 12 additional years to rot since then. If the Scott Forstall situation, Chris Lattner situation, etc, tells you anything, it's that Apple cannot make basic, common-sense tradeoffs anymore. All they can do is copy whatever the wider industry is doing.
All of the excuses people are coming up with in these comments are just that: excuses. Look at the sibling comment by musictubes, for example. Citing these ethereal "critics" and "most people" when in reality Walt Mossberg was the only critic whose opinion mattered.
Occam's Razor: The Innovator's Dilemma. It's a rotting, bloated megacorp with too much to lose, that doesn't take risks, and can't change direction. The direction it's in is "bigger phones", for whatever reason. So we're gonna keep getting bigger phones. It's that simple.
It’s the camera and Face ID. The camera on the iPhone 6 is nowhere near as capable current cameras. Even the much improved camera on the last iPhone SE was considered a huge issue by most people. Cameras are too important to go back to something that thin. The current Air is leaps and bounds better in every way to the iPhone 6 but it doesn’t sell. Why? The battery life and camera. Imagine shrinking the iPhone Air and having even less battery life. Critics would howl and even fewer people would buy it.
Smaller phones can’t fit as much stuff in them as larger phones. Shrink the phone and something has to give. People in the aggregate do not want to make those compromises unless the price is noticeably less. But then it can’t have any of the premium features that are expected from Apple. Small phones are compromises that don’t pay off for Apple.
We’ve come close to doubling battery capacity per volume in that time.
The battery was the single largest module by a long shot in an iPhone 6, and now you can get the same capacity in a much smaller volume. We’ve also lost other bulky items like the home button and headphone jack.
The camera on the current gen has its own expanded volume area of the case, so I don’t buy that they couldn’t just use the same hump they use on literally all of their phones. Sure Face ID is new, but I’ve seen the size of that module, and it is only slightly larger than the selfie camera from back then that it replaced.
> One decision we made is to always give real users an escape hatch. Read the Docs very rarely issues outright blocks or bans to specific IPs or user agents. Instead, our "worst" is a JavaScript challenge, and if a user solves a challenge, they are very unlikely to get challenged again for the next day or so.
Finally, a competent response that doesn't leave the users hang out to dry.
I'm so tired of seeing incompetents with measures like "blackhole 2 continents" deployed even outside active attacks.
Apple tried at AI integration and fumbled the bag repeatedly.
In 2010s, they were among the "greats" of consumer AI. After 2022, they kept trying, and just had delays and underperformance. I don't think their actions now are "strategy" and not "skill issue".
They're enormously risk averse, and realised the infrastructure (and perhaps more important external dependency cost of building out their own training data centres). Now that the utility of the technology is clearer, and OpenAI amongst others have proven it's not only Nvidia and Google that can build a stack capable of training frontier models - I think we may find that the beast waketh from slumber.
My guess would be that the main use case for an NPU in iPhone just used to be image processing/computational photography. Thus the CNN bent.
Also makes sense with the timing - back when iPhone first got its NPU, CV was the killer app for ML.
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