This account is being kept for the posterity, but it won’t see further activity past February.
If you want to contact me, I’m at /u/lvxferre@mander.xyz
Yup. Google consistently gets rid of features or services that it deems unprofitable. And that’s fine, really - as long as you don’t pretend that you’re doing it for the users.
To be fair in modern phones there are some features that if removed would make the user experience better.
I hear ya - for example, the SIM toolkit being able to send you pop-ups (phone providers use that to spam the users).
We’re removing some underutilized features in Google Assistant to focus on delivering the best possible user experience.
Is this the non sequitur used nowadays to explain removal of features? “We’re removing it to give you a better experience”??? That’s bloody hilarious.
Be honest at least dammit. If you don’t want to maintain a feature, because it’s against your best interests, say so. Users are not stupid, and should not be implied to be stupid with this idiotic “it’s for you lol” discourse.
(I don’t even use Botnet Assistant.)
4chan was always called the asshole of the internet, but it’s more like the mouth of an extremely drunk internet ready to vomit on you.
I agree too much with the text to comment anything meaningful about it. So let’s see the comments…
One aspect of the spread of LLMs is that we have lost a useful heuristic. Poor spelling and grammar used to be a signal used to quickly filter out worthless posts. […]
Although I agree with the title, I also don’t think the internet is that significantly different from before GPTs 4, 3, or 2. Articles written by interns or Indian virtual assistants about generic topics are pretty much as bad as most AI generated material […]
Both comments reminded me a blogpost that I wrote more than a year ago, regarding chatGPT-3. It still applies rather well to 2024 LLMs, and it shows what those two tech bros are missing, so I’ll copypaste it here.
###The problem with GPT3.
Consider the following two examples.
Example A.
GPT3 bots trained on the arsehole of the internet (Reddit), chatting among themselves:
The grammar is fine, and yet those messages don’t say jack shit.
Example B.
Human translation made by someone with not-so-good grasp of the target language.
Captain: What happen ?
Mechanic: Somebody set up us the bomb.
Operator: We get signal.
Captain: What !
Operator: Main screen turn on.
Captain: It's you !!
CATS: How are you gentlemen !!
CATS: All your base are belong to us.
CATS: You are on the way to destruction.
The grammar is so broken that this excerpt became a meme. And yet you can still retrieve meaning from it:
What’s the difference? It’s purpose. In (B) we can give each utterance a purpose, even if the characters are fictional - because they were written by a human being. However, we cannot do the same in (A), because the current AI-generated text does not model that purpose.
And yes, assigning purpose to your utterances is part of the language. Not just what tech bros are able to see, namely: syntax, morphology, and spelling.
Personal take: suck it up, Somalia; if the population of Somaliland has effective control of the region, and desires it to be independent, then there isn’t much that you could (or should) do. And from that, if both Somaliland and Ethiopia reach an amicable agreement over the ports, so be it.
Also, let us drop all that babble about territorial integrity. Even if you believe in this sort of political superstition, Somalia’s territorial integrity went kaboom in 1991.
Lunix sucks so much that it got stuck into the version 2 for years.
Aaaaah. I really, really wanted to complain about the excessive amount of keys.
(My comment above is partially a joke - don’t take it too seriously. Even if a new key was added it would be a bit more clutter, but not that big of a deal.)
The source that I’ve linked mentions semantic embedding; so does further literature on the internet. However, the operations are still being performed with the vectors resulting from the tokens themselves, with said embedding playing a secondary role.
This is evident for example through excerpts like
The token embeddings map a token ID to a fixed-size vector with some semantic meaning of the tokens. These brings some interesting properties: similar tokens will have a similar embedding (in other words, calculating the cosine similarity between two embeddings will give us a good idea of how similar the tokens are).
Emphasis mine. A similar conclusion (that the LLM is still handling the tokens, not their meaning) can be reached by analysing the hallucinations that your typical LLM bot outputs, and asking why that hallu is there.
What I’m proposing is deeper than that. It’s to use the input tokens (i.e. morphemes) only to retrieve the sememes (units of meaning; further info here) that they’re conveying, then discard the tokens themselves, and perform the operations solely on the sememes. Then for the output you translate the sememes obtained by the transformer into morphemes=tokens again.
I believe that this would have two big benefits:
And it might be an additional layer, but the whole approach is considerably simpler than what’s being done currently - pretending that the tokens themselves have some intrinsic value, then playing whack-a-mole with situations where the token and the contextually assigned value (by the human using the LLM) differ.
[This could even go deeper, handling a pragmatic layer beyond the tokens/morphemes and the units of meaning/sememes. It would be closer to what @njordomir@lemmy.world understood from my other comment, as it would then deal with the intent of the utterance.]
Not quite. I’m focusing on chatbots like Bard, ChatGPT and the likes, and their technology (LLM, or large language model).
At the core those LLMs work like this: they pick words, split them into “tokens”, and then perform a few operations on those tokens, across multiple layers. But at the end of the day they still work with the words themselves, not with the meaning being encoded by those words.
What I want is an LLM that assigns multiple meanings for those words, and performs the operations above on the meaning itself. In other words the LLM would actually understand you, not just chain words.
Complexity does not mean sophistication when it comes to AI and never has and to treat it as such is just a forceful way to make your ideas come true without putting in the real effort.
It’s a bit off-topic, but what I really want is a language model that assigns semantic values to the tokens, and handles those values instead of directly working with the tokens themselves. That would be probably far less complex than current state-of-art LLMs, but way more sophisticated, and require far less data for “training”.
Oh “great”, more crap between Ctrl and Alt.
[Grumpy grandpa] In my times, the space row only had five keys! And we did more than those youngsters do with eight, now nine keys!
My favourite insult in English - “you muppet”.
…I’m going to agree with @db2 here - sailing the seven seas will be likely the way to expose the new generations to this old content. I remember Muppet Babies from my childhood (albeit poorly, I was too young), and it’s fun for young kids. If nobody is streaming it nobody is losing value if you copy it, right?
It sounds more like a piece of protest than an actual license. Any arsehole could interpret the situation as “since this is software and not a human being, it isn’t ackshuyally learning anything. So our usage of your content doesn’t violate your license lol lmao”.
I think that people are correctly angry at big tech calling dibs on whatever it wants, but misblaming it on AI. There are worse cases out there; have you heard about targeted advertisement, for example?
The article mentions what the author sees as two trends behind retrocomputing (reusing the old and escaping the modern world), but I’d argue that the second one should be further split, since there are two modernities that people are running away from:
For further info, the link mentions this article. If I got it correctly:
Higher pressure compresses the orbitals of the sodium atoms, making them more cluttered together. As a result, the outer electrons - that “should” be in the 3s orbital, surrounding the nucleus like a bubble - are repelled to more energetic orbitals, like 3p and 3d. Those orbitals have “lobes” reaching far from the nucleus, so further away from the other electrons.
But since the sodium atoms are not isolated, and all those sodium atoms are doing this at the same time, the 3p and 3d orbitals from multiple atoms overlap. Orbitals overlapping form a chemical bond. And, since it’s damn hard to remove electrons from those bonds to send them elsewhere, electrical conductivity goes down. Sodium becomes first a semiconductor, then an isolating material.
So it’s a lot like your usual macromolecules (like, silicon dioxide or diamond), except that those bonds are shared by multiple atoms, not just two. And I don’t think that it’s a coincidence that all three are transparent, given that those electrons “stuck” in specific molecular orbitals suck major balls at absorbing photons and releasing them back.
Personal predictions:
Ah, got it. My bad. Yeah, not providing anything is even lazier, and unlike “lazy” bash scripts it leaves the user clueless.
I like them, even for software installation. Partially because they’re lazy - it takes almost no effort to write a bash script that will solve a problem like this.
That said a flatpak (like you proposed) would look far more polished, indeed.
Frankly in this case even a simple bash script would do the trick. Have it check your distro, version, and architecture; if you got curl and stuff like this; then ask you if you want the stable or beta version of the software. Then based on this info it adds Mullvad to your repositories and automatically install it.
I have this extension; it works great, and it has support for other engines (I use mostly DuckDuckGo), and if you really need the results from one of the blocked sites you just click a link and it shows them again. Give the link a check for the list of supported search engines.
It’s hard for Google to claim that they’re focusing resources (e.g. dev time), given the list of features being removed. As one of the HN comments said, quite a few of them “seem to fall under the umbrella of “features that actually make the assistant an assistant”/connecting the assistant to other apps”. In other words, integration - that’s core functionality for an assistant and they likely know it.