anjc

Your litmus test isn't correct and your assumption of personalisation isn't correct either. All of the criteria that you see as fine are controlled under the relevant legislation and are considered personalisation, requiring transparency etc.

Furthermore, bills have been brought to EU parliaments that have erroneously attempted to ban all forms of ranking, which would include even the most basic information retrieval algorithms. So it isn't obvious at all what is meant by 'algorithm'.

Yeah in any case they're great pics. Will definitely try the flocking suggestion.

Nice video. Same exposure/stacking/filtering/conditions in the before and afters? Cave nebula shows a big difference in results

Gemini gives instant, adaptive, expert solutions to an esoteric and complex problem, and commenters here are still likening LLMs to junior coders.

Glad to see the author acknowledges their usefulness and limitations so far.

Yes I agree, I don't think that it's good at all. It's just fascinating to me that people are criticising current LLMs using information they heard about LLMs 3 years ago, when right in front of their eyes are sci-fi-like results from the field.

> I don't want to live in a world where reality doesn't exist

Perhaps the end result is that the world turns away from digital completely and goes back to reality :) We see already that some universities are going back to written and oral assessments, for example.

It seems that people can't grasp the exponential rate of developments here. They're stuck in the GPT2 LLM narrative. Even with the amazing Veo 3 videos this week, people are still nitpicking and seemingly unable to remember the state of the art 2 weeks ago, 6 months ago, 1 year ago, etc.

I don't mean to say that scores on evaluation metrics will remain exponential but rather the developments, uses, integrations will (e.g., web search in ChatGPT), and people can't conceive or keep track of this, and therefore discussions on the area are always behind the times.

For example, I think it's inevitable now that TV/movie production will not exist as we know it in a short time, except as niche work, like fine art in the age of digital. It's also inevitable that fully personalised media will be predominant. I think this is obvious, but yet people are zooming in on the background of essentially perfect videos to spot minor and irrelevant coherence aberrations.

Nitpicking will also inevitably become a niche hobby, like people who complain about the colour grading on a movie remaster, while the rest of the world just watches the movie and doesn't notice or care about the issues.

I did just mean AI and Computer Science per OP. By "head of steam" I mean to say that much research is built on it, think the likes of "Attention is All you Need". There isn't quite an equivalent of this in public policy in my experience.

Conversely, computer science/AI doesn't have an equivalent of the rigor that public policy research tends to go through. CS has e.g., benchmark datasets, typical evaluation metrics, but these are more like norms rather than requirements, whereas in public policy, instruments for validations are far more rigorously tested and enforced. Depending on the area.

I agree that outright fraud would be detrimental, but I think OP overblows this issue completely and should apologise to his co-authors.

> Should we focus on the 3 amazing papers this year, cited by hundreds, that resulted in clear progress or should we complain that 100 papers are useless?

Agree, and it seems that this is how fields naturally evolve anyway.

Overly pessimistic, and doesn't acknowledge that heads of steam only build behind promising findings, while the deficient (or 'fraudulent') work die on the vine, published or not. In other words the system tends to work.

Secondly, there are many ingredients required to successfully publish, communicate science, foster collaboration, etc., beyond technical brilliance. I'm sure we all know many technically brilliant people whose career never advanced because they lacked in some necessary area. People shouldn't be discouraged from improving in all areas because OP's delicate genius is offended by their technical ability.

Speaking of discouragement, it's a shame and a disgrace that you publicly called your colleague's work bullshit, including a first author that isn't yourself.

Seems more like a typical Chopin mazurka than a waltz

On reflection, it depends on what was meant by 'express' in the previous post; if an expression is a constructive discussion then that may be good. If an expression is smashing things or shouting, that isn't good.

Aggressive expressions don't work long term because they reinforce the behaviour of outwardly and rapidly being angry.

> Expressed anger/emotion is okay

No it's not

> bottled up anger/emotion is not good

Yes it is

It is tautologically obvious why expressing anger does not result in reduced anger in the long term, and why practicing subduing your anger is beneficial. Cathartic expression of anger is a long-debunked approach to coping with it.

Great idea. Americans are so funny.

You're pointing out a single inconsistency and calling it a basic mistake, but are ignoring the thousands of advanced consistencies evident in the image, i.e., creativity that is beyond the ability of humans generally. Given this, the nose poking out is a trivial issue and isn't worth focusing on.

Would be interesting to see if the perceptive abilities of generative models are superior to human perception, when tested on optical illusions that humans are fooled by. E.g., do they correctly assess depth in a Ponzo illusion scenario

There wasn't something that needed covering up. If he'd reported the methodology in the paper there would have been no issues.

It's likely that this was omitted from the paper as an oversight; such imputation likely wouldn't have affected the outcome of peer review.

This reads to me like codified psychopathy. Maybe it's profitable to assess people's insecurities or "experience caring for a dying parent" when hiring, but I wouldn't be advertising it.

I bought one of the cheapish (€300) Super 8/8mm scanners on Amazon. It scans quite quickly while displaying the results on a small screen.

It's a nice convenient device, but I can't now unsee the artifacting and compression arising from it. If I were to do it again I'd just pay a service to scan properly, or build a rig to photograph the frames.

On the other hand, I'm very pleased to have scanned and archived the films given that they've been unseen for so long and can now be shared easily.

I also restored some Super 8 footage recently and had great success. The biggest win I had wasn't resolution, but slowing down the speed to be correct in DaVinci, and interpolating frames to make it 60fps using the RIFE algorithm in FlowFrames. I then used Film9 to remove shake, colour-correct, sharpen and so on.

Correcting the speed and interpolating frames added an amazing amount of detail that wasn't perceptible to me in the originals (albeit it was there).

All of this processing does remove some of the charm of the medium, so I'll be keeping the original scans in any case.

> They act as if they are paid by amount of time you spend watching movies

At least up until 2015 (last published paper that I'm aware of) this actually is how they optimised their recommender:

"However, we have observed that improving engagement—the time that our members spend viewing Netflix content—is strongly correlated with improving retention. Accordingly, we design randomized, controlled experiments, often called A/B tests, to compare the medium-term engagement with Netflix along with member cancellation rates across algorithm variants"

Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.

It's probably a reasonable approach and has clearly been successful

> Computers can't read minds. They can't analyze the content of the movie and have the emotional experience that a person can

No but you can read minds by proxy, via ratings, which is what the dataset consisted of.

That's called collaborative filtering (https://en.wikipedia.org/wiki/Collaborative_filtering) and is perhaps the most battle-hardened and most effective approaches in recommender systems. Even now, novel deep learning approaches implement the concept, but simple naive approaches are still as/more effective. The first paper published on it specifically in the field was in the 90s but the seeds of it go back to the 70s. It would have made a good thesis in the 80s :)

Part of why it's so effective are for the reasons you outline, in that it can find items that you'll probably like, that aren't similar to items you already like, based on people that have similar tastes to you.

I have to wonder about a business model where you sell one inexpensive device and it works so well that the user never has to buy one of your products again.

I'm in the same boat as others here, I bought a laser Brother about 20 years ago - €100 or so - and it hasn't missed a beat ever since. Probably the best purchase I've made and it still "sparks joy" for me.

Tascam and Zoom still have a modern range of x-track recorders that are standalone and don't need PC.

> That obviously P!=NP ... but your difficulty increases exponentially as n increases

> I am pretty sure there is something in this problem that makes it a not legal P/NP problem

It's not in the space of P and so isn't relevant to the problem, if I'm reading your post right, as it's exponential complexity

Is daydreaming as maladaptive as it's presented? For me it's more like scenario planning than nonsense dreaming. Even if 90% is fruitless, 10% have fed into real life decisions that have had real life outcomes.

An extreme example is that "call of the void" feeling one gets when standing beside a large drop. Is it daydreaming to engage with this feeling in this scenario, or is it an intuitive way to reinforce positive decisions?

Disappointed by the commentary here. Most commenters are focussed on their view of Musk's personal attributes, and think he was stupid all along, and that he had successes only through luck. Many bizarre comments seem to be able to read his mind and understand his inner motivations too.

I'd expect more nuance and less bias from Hacker News contributors.

> and whether or not it has been reproduced.

Nothing would ever get done if this was the bar for citation. Real advancements take place on the back of fraudulent results in widely cited papers. Actually, even within papers that have fraudulent results, advancements happen.

The motivation for such fraud is because work generally wont get published without positive results, even if the work was excellent. The fraud doesn't take the place of hard/great work. I'm not defending the practice.

> You would be forced to fix the car, or sell it to someone who will fix it, or remove the plates and deregister it, or scrap it. If you don’t do anything the police will remove the plates.

> I would advice fixing the car or selling it to someone who will fix it because it has some value.

Your comments were quite confident when you weren't actually familiar with the basics of the topic. Most countries have relaxed road worthiness tests or complete exemptions for classic cars, and I think this stands to reason.

It's interesting to read how many comments here underestimate the sophistication and expertise of mechanics and car thieves, and, all of the poor suggestions to overcome theft. This despite the userbase being technical.

I wonder if there's a mechanics forum somewhere in which posters are confidently proposing Caesar ciphers and so on.