It just so happens that many video codecs are based on image formats, so ffmpeg already has a lot of the complex machinery to do so available to also implement these image formats - internally it can just handle it as a single frame of video with specialized formats for that.
Imagemagick (and other tools) also work, but why use multiple pieces of software if what you already have is adequate? ImageMagick is also software, and can also have vurnabilities.
You would be inventing some style of UUID. Include a timestamp in front, so that it is sortable and you have Snowflake.
That’s fair. I actually don’t think we disagree that much - I just think I have trouble conveying what I am trying to say. Whenever someone talks about ‘shallow statistical predictions’, I think about older techniques like Statistical Machine Translation which even had trouble with things like word order, LLMs handle text on a higher level of abstraction (which I described as a form of textual understanding) - and hence handle things like word order better - but are still inherently statistical predictors. The model stores info about how words interact and relate to one another, but it does not ‘understand’ what the words actually (physically?) represent beyond these interactions nor does it ‘understand’ what it is doing. Albeit, those interactions are modeled well enough to give a convincing replica of doing so.
That is no more understanding the problem than my spelling or grammar checking understands the comment I’m writing.
My general point would be that even a grammar checking can have some form of understanding of the text, no matter how shallow. The checker probably has a rule for when a is used versus an, if this rule generalizes across new words that rule that were previously unseen is a form of ‘understanding’ of the language being used in my view, despite being overly simplistic, while rote memorization - having a list of words that are followed by an - may not be.
LLMs are a weird case, because their internal representations for many concepts generalize even across new settings / inputs - in that sense the model has a form of understanding of what is being given, while for many other concepts the patterns break down even in the simplest of cases. It may ‘know’ that the preceding text is the writing of an essay - and it should autocomplete accounting for that -as is defined in its weights, but this understanding is shallow - it does not know why it knows, or how it knows - it cannot self reflect as it does not see and understand its own internal workings, or account for that. Yet the internal representations represent a form of text understanding that can be useful nonetheless - it is a language model after all.
My comment was intended to show this duality, hence the duality between the two paragraphs.
And this is the crux of my beef, I think, because stochastic pattern matching is not understanding, it’s a mathematical representation of how the model processes your input tokens. The fact that it has to start over every time you provide it input, and uses the previous input/output tokens as context is why this is not ‘understanding’, it’s just fancy token prediction that gives a middling-to-passable facsimile to intelligence and understanding things.
The problems you note in your second paragraph fundamentally undermine the argument that there is any form of understanding to the AI, because those are basic mistakes that a trivial understanding of the problem would prevent.
I am not entirely grasping the point you are trying to make here. I am certainly not arguing that it is conscious, self-aware, or in any way not a mechanical procedure that is being performed (I would not argue for that!). My key point is that it is not a simple black or white it understands / it does not understand - it may have internal representations that relate many concepts together, allowing it to draw upon these links when generating text, giving it a certain semantic understanding of the language and text it is using, while simultaneously not having a bit of self-awareness.
These models do have some form of understanding though. There are features for bugs and typos, and general features that map descriptions and pieces of code. It understands the code in so far it helps with next token prediction.
The bigger problem is that these language models are inherently unreliable and stochastic in how they generate. You request a feature - and it destroys something else in the process - because a single incorrect prediction caused it to diverge and skip a portion of the original code. You request a small modification - and it decides to restart from scratch - because random sampling made a different way of doing something more likely rather than what was there already. Errors compound and the model has no way of fixing or correcting them.
I use passkeys via KeepassXC (on Windows & Linux) and KeepassDX (Android), in which case Passkeys are essentially an upgrade:
The only downside is that you need access to the database to login - unlike with passwords where it is cumbersome, but still reasonably possible to enter it manually.
I wouldn’t want my keys to be wholly linked to my device (problematic if I lose it, or it breaks) or be reliant on Google’s - or other big tech - password managers either.
It is not just him, but also the people he is trying to reach. Even if he gets assistance & the resources to set up an independent platform for this, he still has to get people to use it - which is a much higher bar to meet.
20 minutes for 4km would be 12km/h, which would really be taking your time. 15km/h is the slowest I would usually go when taking my time, and that would take about 16 minutes to cover a 4km distance.
A single plane emits a lot, but if you are not using a private jet - it enjoys the same benefit as other forms of ‘public transport’: emissions are shared among multiple passengers. Especially if you are in economy, in a packed plane there are a lot of people to correct for that.
Furthermore, emissions for planes are far from uniform for the distance travelled: a disproportionate amount of emissions occurs as take-off.
A short or long haul flight is more efficient than an ICE car [source]. This concerns efficiency though - not total emissions - so if you use a car, but travel a shorter distance, emissions will still be less. Also, this source is from 2023, things will probably have shifted around a bit.
Another indication that interacting with others from outside your bubble is the best vaccine against right wing rethoric regarding immigration: in rural areas and suburbs such interactions tend to be much rarer than within the city…