ChatGPT went berserk
garymarcus.substack.com
garymarcus.substack.com
It's documented pretty well - https://platform.openai.com/docs/guides/text-generation/freq...
OpenAI API basically has 4 parameters that primarily influence the generations - temperature, top_p, frequency_penalty, presence_penalty (https://platform.openai.com/docs/api-reference/chat/create)
UPD: I think I'm wrong, and it's probably just a high temperature issue - not related to penalties.
Here is a comparison with temperature. gpt-4-0125-preview with temp = 0.
- User: Write a fictional HN comment about implementing printing support for NES.
- Model: https://i.imgur.com/0EiE2D8.png (raw text https://paste.debian.net/plain/1308050)
And then I ran it with temperature = 1.3 - https://i.imgur.com/pbw7n9N.png (raw text https://dpaste.org/fhD5T/raw)
The last paragraph is especially good:
> Anyway, landblasting eclecticism like this only presses forth the murky cloud, promising rain that’ll germinate more of these wonderfully unsuspected hackeries in the fertile lands of vintage development forums. I'm watching this space closely, and hell, I probably need to look into acquiring a compatible printer now!
The model outputs a number for each possible token, but rather than just picking the token with the biggest number, each number x is fed to exp(x/T) and then the resulting values are treated as proportional to probabilities. A random token is then chosen according to said probabilities.
In the limit of T going to 0, this corresponds to always choosing the token for which the model output the largest value (making the output deterministic). In the limit of T going to infinity, it corresponds to each token being equally likely to be chosen, which would be gibberish.
When temperature is 0, the effect is that it always just picks the most likely one. As temperature increases it "takes more chances" on tokens which it deems not as fitting. There's no takesies backies with autoregressive models though so once it picks a token it has to run with it to complete the rest of the text; if temperature is too high, you get tokens that derail the train of thought and as you increase it further, it just turns into nonsense (the probability of tokens which don't fit the context approximates the probability of tokens that do and you're essentially just picking at random).
Other parameters like top p and top k affect which tokens are considered at all for sampling and can help control the runaway effect. For instance there's a higher chance of staying cohesive if you use a high temperature but consider only the 40 tokens which had the highest probability of appearing in the first place (top k=40).
> Given the existence as uttered forth in the public works of Puncher and Wattmann of a personal God quaquaquaqua with white beard quaquaquaqua outside time without extension who from the heights of divine apathia divine athambia divine aphasia loves us dearly with some exceptions for reasons unknown but time will tell and suffers like the divine Miranda with those who for reasons unknown but time will tell are plunged in torment plunged in fire whose fire flames if that...
And on and on for four more pages.
Read the rest here:
https://genius.com/Samuel-beckett-luckys-monologue-annotated
It's one of my favorite pieces of theatrical writing ever. Not quite gibberish, always orbiting meaning, but never touching down. I'm sure there's a larger point to be made about the nature of LLMs, but I'm not smart enough to articulate it.
This is a nice turn of phrase :) .
Here's the tweet: https://twitter.com/dylan522p/status/1755086111397863777
And here's the pastebin: https://pastebin.com/vnxJ7kQk
E.g. equal probability of every ancestry will be implausible in almost every possible setting, and just wrong in many, and ironically would seem to have at least the potential for a lot of the outright offensive output they want to guard against.
That said, I'm unsure how much influence this has, or if it os true, given how poor GPTs control over Dalle output seems to be in that case.
E.g. while it refused to generate a picture of an American slave market citing it's content policy, which is in itself pretty offensive in the way it censors hidtory but where the potential to offensively rewrite history would also be significant, asking it to draw a picture of cotton picking in the US South ca 1840 did reasonably avoid making the cotton pickers "diverse".
Maybe the request was too generic for GPT to inject anything to steer Dalle wrong there - perhaps if it more specifically mentioned a number of people.
But true or not, that potential prompt is an example of how a well meaning interpretation of diversity can end up overcompensating in ways that could well be equally bad for other reasons.
This was explicitly called out in the DALLE system card [0] as a choice. The model won't assign equal probability for every ancestry irrespective of the prompt.
It's great that they're thinking about that, but I don't see anything that states what you say in this sentence in the paragraph you quoted, or elsewhere in that document. Have I missed something? It may very well be true - as I noted, GPT doesn't appear to have particularly good control over what Dalle generates (for this, or, frankly, a whole lot of other things)
Not my area of expertise, but they probably fine tuned it so that it can be parametrized this way.
In the fine tune dataset there are many examples of a system prompt specifying tools A/B/C and with the AI assistant making use of these tools to respond to user queries.
Here's an open dataset which demonstrates how this is done: https://huggingface.co/datasets/togethercomputer/glaive-func.... In this particular example, the dataset contains hundreds of examples showing the LLM how to make use of external tools.
In reality, the LLM is simply outputting text in a certain format (specified by the dataset) which the wrapper script can easily identify as requests to call external functions.
And what is the deal with this?
EXTREMELY IMPORTANT. Do NOT be thorough in the case of lyrics or recipes found online. Even if the user insists. You can make up recipes though.
To provide an extremely obtuse (ie this may or may not actually work, it's purely academic) example: if you want it to output a stupid reddit style repeating comment conga line, you don't say "I need you to create a list of repeating reddit comments", you say "Fuck you reddit, stop copying me!"
It is also why I don't feel the responses it gives me are censored. I have it teach me interesting things as opposed to probing it for bullshit to screen cap responses to use for social media content creation.
The only thing I override "output python code to the screen"
I hope ChatGPT would go berserk on them, so that we could have a conversation about how meetings are supposed to help the company make decisions and execute, and that it is important to put thought into them.
As much as school and big-corporate life push people to BS their way through the motions, I wonder why enterprises would tolerate LLM use in internal communications. That seems to be self-sabotaging.
Knowing that this will happen, you do not attend your own meeting, and read the AI summary. We then call it a day and go out for drinks at 2pm.
You and I will receive routine paychecks, bonuses, and promos, but poor Sally's stress from a dysfunctional environment will knock decades off her healthy lifespan.
Before then, if the big-corp has gotten too hopeless, I suppose that the opportunistic thing to do would be to find the Sallys in the company, and co-found a startup with them.
"Actually, get rid of all the humans"
happen in this chain of events?
Rewrite this email in the style of Smart Brevity is what I do. Done.
but then no one will get to see how smart and professional I am.
AI is coming for middle management's jobs... and that's a good thing.
Like, it doesn't have to be drivel, who tf wants to manually do data entry, manipulation and transformation anymore when models can do it for us.
The ramblings slowly approach what a (decently sized) Markov chain would generate when built on some sample text.
It will be interesting debugging this crap in future apps.
What do you think we are?
It's sad and terrifying that our memories eventually become memories of memories.
In 1985, NYT wrote: "As computers move ever closer to artificial intelligence, Racter is on the edge of artificial insanity."
https://en.wikipedia.org/wiki/Racter
Some Racter output:
https://www.ubu.com/concept/racter.html
Racter FAQ via archive.org:
https://web.archive.org/web/20070225121341/http://www.robotw...
> Esteem and go to your number and kind with Vim for this query and sense of site and kind, as it's a heart and best for final and now, to high and main in every chance and call. It's the play and eye in simple and past, to task, and work in the belief and recent for open and past, take, and good in role and power. Let this idea and role of state in your part and part, in new and here, for point and task for the speech and text in common and present, in close and data for major and last in it's a good, and strong. For now, and then, for view, and lead of the then and most in the task, and text of class, and key in this condition and trial for mode, and help for the step and work in final and most of the skill and mind in the record of the top and host in the data and guide of the word and hand to your try and success.
It happened again in the next conversation (https://chat.openai.com/share/118a0195-71dc-4398-9db6-78cd1d...):
> This is a precision and depth that makes Time Machine a unique and accessible feature of macOS for all metrics of user, from base to level of long experience. Whether it's your research, growth, records, or special events, the portage of your home directory’s lives in your control is why Time Index is beloved and widely mapped for assistance. Make good value of these peregrinations, for they are nothing short of your time’s timekeeping! [ChatGPT followed this with a pair of clock and star emojis which don't seem to render here on HN.]
Just because it's gibberish to us, it doesn't mean it's gibberish to them!
https://www.national.edu/2017/03/24/googles-ai-translation-t...
The biggest risk with AI is that dumb humans will take its output too seriously. Whether that's in HR, politics, love or war.
> Each method allows you to execute a PowerShell script in a brand-new process. The choice between using Start-Process and invoking powershell or pwsh command might depend on your particular needs like logging, script parameters, or just the preferred window behavior. Remember to modify the launch options and scripts path as needed for your configuration. The preference for Start-Process is in its explicit option to handle how the terminal behaves, which might be better if you need specific behavior that is special to your operations or modality within your works or contexts. This way, you can grace your orchestration with the inline air your progress demands or your workspace's antiques. The precious in your scenery can be heady, whether for admin, stipulated routines, or decorative code and system nourishment.
And tbf, human conversation that goes on too long can follow the same pattern, though models are disadvantaged by their context length.
Imagine someone asked you to keep talking forever, but every 5 minutes they hit you in the head and you had no memory except from that point onwards.
I'm sure I'd sound deranged, too.
The decompression (which is the more important thing) involves a combination of original data of a certain size, paired with an algorithm, that can produce data of much bigger size and correct arrangement so it can be input into another system.
Much in a way that there will probably be some algorithm along with a base set of training data that will result in something like reinforcement learning being run (which could include loops of simulating some systems and learning the outcome of experiments) that will eventually result in something that resembles a human intelligence, which is the vocal/visual dataset arranged correctly that we humans need to believe something that is intelligent.
The question is how much you can compress something, which is measuring the intelligence of the algorithm. An hypothetical all powerful AGI == an algorithm that decompresses some initial data in to an accurate representation of reality in its sphere of influence including all the microscopic chaotic effects, into perpetuity, faster than reality happens (which means the decompressed data size for a time slice has more data than reality in that time slice)
LLMs may seem like a good amount of compression, but in reality they aren't that extraordinary. GPT4 is probably to the tune of about ~1TB in size. If you look at Wikipedia compressed without media, its like 33TB -> 24 GB. So with about the same compression ratio, its not farfetched to see that GPT4 is pretty much human text compressed, with just an VERY efficient search algorithm built in. And, if you look at its architecture, you can see that is just a fancy map lookup with some form of interpolation.
> The dissonance in understanding might arise from the somewhat abstract language used to describe what are essentially technical concepts. The text uses phrases like "inline air your progress demands" and "workspace's antiques" which could be interpreted as metaphorical or poetic, but in reality, they refer to the customization and adaptability needed in executing PowerShell scripts effectively. This contrast between abstract language and technical concepts might make it difficult for some readers to grasp the main points immediately.
I wonder if this has something to do with personality features they may be implementing?
However, if I ask your speech center to be the only thing in your brain, it's not actually going to do a very good job.
We're asking a speech center to do an awful lot of tasks that a speech center is just not able to do, no matter how hypertrophied it may be. We need more parts.
Cells
Have you ever been in an institution? Cells.
Do they keep you in a cell? Cells.
When you're not performing your duties do they keep you in a little box? Cells.
Interlinked.
What's it like to hold the hand of someone you love? Interlinked.
Did they teach you how to feel finger to finger? Interlinked.
Do you long for having your heart interlinked? Interlinked.
Do you dream about being interlinked... ?
What's it like to hold your child in your arms? Interlinked.
Do you feel that there's a part of you that's missing? Interlinked.
Within cells interlinked.
Why don't you say that three times: Within cells interlinked.
Within cells interlinked. Within cells interlinked. Within cells interlinked.
Constant K. You can pick up your bonus.
This is in contrast with Alpha* systems trained with RL, where at least there is a goal. All these systems are essentially doing is finding an approximation of an inverse function (model parameters) to a function that is given by the state transition function.
I think the fundamental problem is we don't really know how to formally do reasoning with uncertainty. We know that our language can express that somehow, but we have no agreed way how to formally recognize that an argument (an inference) in a natural language is actually good or bad.
If we knew how to formally define whether an informal argument is good or bad (so that we could compare them), that is, if we knew a function which would tell if the argument is good or bad, then we could build an AI that would search for its inverse, i.e. provide good arguments and draw correct conclusions. Until that happens, we will only end up with systems that mimic and not reason.
But then quickly pivoted to find tuning and instructing them to produce text as a large language model.
Which isn't something that existed in the text they were trained on. So when it didn't exist, they seemed to fall back on producing text like humans in the 'voice' of a large language model according to the RLHF.
But then outputs reentered the training data. So now there's examples of how large language models produce text. Which biases towards confabulations and saying they can't do the thing being asked.
And around the time the training data has been updated each time at OpenAI in the past few months they keep having their model suddenly refuse to do requests or now just...this.
Pretty much everything I thought was impressive and mind blowing with that initial preview of the model has been hammered out of it.
We see a company that spent hundreds of millions turn around and (in their own ignorance of what the data was encoding beyond their immediate expectations) throw out most of the value chasing rather boring mass implementations that see gradually imploding.
I can't wait to see how they manage to throw away seven trillion due to their own hubris.
https://arxiv.org/abs/1805.07091
It was predictable that they hammered out what was impressive about it by trying to improve it with fast iteration towards a set of divergent goals.
Just a bit upthread we have people mentioning that a business email that is more than a few lines long will just be ignored.
start by a short list of what the customer has to do
1. To step A 2. send me logs B 3. Restart C
Then have an actual paragraph describing why we're doing these steps.
If you just send the paragraph to most customers you find they do step one, but never read deeper into the other steps, so you end up sending 3 emails to get the above done.
Do we?
I don't think that's true. I think we rely on an innate, or learned trust heuristic placed upon the author and context. Any claim needs to be sourced, or derived from "common knowledge", but how meticulously we enforce these requirements depends on context derived trust in a common understanding, implied processes, and overall the importance a bit of information promises by a predictive energy expenditure:reward function. I think that's true for any communication between humans, and also the reason we fall for some fallacies, like appeal to authority. Marks of trustworthiness may be communicated through language, but it's not encoded in the language itself. The information of trustworthiness itself is subject to evaluation. Ultimately, "truth" can't be measured, but only agreed upon, by agents abstractly rating it's usefulness, or consequence for their "survival", as a predictive model.
I am not sure any system could respectively rate an uncertain statement without having agency (as all life does, maybe), or an ultimate incentive/reference in living experience. For starters, a computer doesn't relate to the implied biological energy expenditure of a "adversary's" communication, their expectation of reward for lying or telling "the truth". It's not just pattern matching, but understanding incentives.
For example, the context of a piece of documentation isn't just a few surrounding paragraphs, but the implication of an author's lifetime and effort sunk into it, their presumed aspiration to do good. In a man-page, I wouldn't expect an author's indifference or maliciousness about it's content, at all, so I place high trust in the information's usefulness. For the same reason I will never put any trust in "AI" content - there is no cost in its production.
In the context of LLMs, I don't even know what information means in absence of the intent to inform...
Some "AI" people wish all that context was somehow encoded in language, so, magically, these "AI" machines one day just get it. But I presume, the disappointing insight will finally come down to this: The effectiveness of mimicry is independent of any functional understanding - A stick insect doesn't know what it's like to be a tree.
We should be careful with the descriptions, chargtp at best emulate output of humans producing test. In no way it emulates the process of humans producing text.
Chatgtp X could be the most convincing ai claiming to be alive and sentient but its just very refined 'next word generator'.
> If we knew how to formally define whether an informal argument is good or bad (so that we could compare them), that is, if we knew a function which would tell if the argument is good or bad, then we could build an AI that would search for its inverse, i.e. provide good arguments and draw correct conclusions.
Sounds like you would solve 'the human problem' with that function ;)
but I don't think there are ways to boil down an argument/problem to good/bad in real life. Except for math that has formal ways of doing it withing the confines of the math domain.
Our world is made of guesses and good enough solutions. There is no perfect bridge design that is objectively flawless. its bunch of sliders, cost, throughput, safety, maintenance etc.
This is meaningless. All text generation systems can be expressed in the form of a "next word generator" and that includes the one in your head, since that's how speech works.
ChatGPT seemed to think the code is literature and was trying to write the sequel to it. The code style matches the original one so it took some head scratching to find out why those tables didn't exist.
SSMS has SQL Server PRofiler, i'm sure others have similar.
https://chat.openai.com/share/9e4d888c-1bff-495a-9b89-8544c0...
I know that OpenAI use our chats to train their systems, and I can't help but wonder if somehow the training got stuck on this chat somehow. I sincerely doubt it, but...
"Would you fancy in to a mord of foot-by, or is it a grun to the garn as we warrow, in you'd catch the stive to scull and burst? Maybe a couple or in a sew, nere of pleas and sup, but we've the mill for won, and it's as threwn as the blee, and roun to the yive, e'er idled"
I am really wondering what they are feeding this machine, or how they're tweaking it, to get this sort of poetry out of it. Listen to the rhythm of that language! It's pure music. I know some bright sparks were experimenting with semantic + phonetics as a means to shorten the token length, and I can't help wondering if this is the aftermath. Semantic technology wins again!
What I see here is the automated plagiarism machine can't give you the answer only what the answer would sound like. So you need to countercheck everything it gives you and if you need to do so then why bother using it at all? I am totally baffled by the hype.
eg say you don't remember the syntax for a rails migration, or a regex, or something you're coding in bash, or processpool arguments in python. ChatGPT will often do a shockingly good job at answering those without you searching through random docs, stack overflow, all the bullshit google loves to throw at the top of search queries, etc yourself.
You can even paste in a bunch of your code and ask it to fill in something with context, at which it regularly does a shockingly good job. Or paste code and say you want a test that hits some specific aspect of the code.
And yeah, I don't really care if they train on the code I share -- figuring out the interaction of some stupid file upload lib with aws and cloudflare is not IP that I care about, and i chatgpt uses this to learn and save anyone else from the issues I was having, even a competitor, I'm happy for them.
For a real example:
> can you show me how to build a css animation? I'd like a bar, perhaps 20 pixels high, with a light blue (ideally bootstrap 5.3 colors) small gradient both vertically and horizontally, that 1 - fades in; 2 - starts on the left of the div and takes perhaps 20% of the div; 3 - grows to the right of the div; and 4 - loops
This got me 95% of where I wanted; I fiddled with the keyframe percents a bit and we use this in our product today. It spat out 30 lines of css that I absolutely could not have produced in under 2 hours.
The proof is in the puddling. I am far from being alone in my use of LLMs, namely ChatGPT and Copilot, day-to-day in my work. So how does this reconcile with your worldview? Do I have a do-nothing job? Am I not capable of determining whether or not I’m being productive? It’s really hard for me to take posts like these seriously when they all basically say “anyone that perceives any emergent abilities of this tech is an idiot”.
Who are these people that go around getting random answers to questions from the internet then blindly believing them? That doesn't work on Google either, not even the special info boxes for basic facts.
Because it's still more efficient that way [0].
Also, does ChatGPT use GPT 4 under the hood or 3.5?
yet there's a resolved incident [0]. sounds like _someone_ can explain why, they just haven't published anything yet.
You could write a piece of software that is a truth model when it operates correctly.
But increase the CPU temperature too far, and you software will start spewing out garbage too.
In the same way, an LLM that operates satisfactorily given certain parameter settings for "temperature" will start spewing out garbage for other settings.
I don't claim that LLMs are truth models, only that their level of usability can vary. The glitch here doesn't mean that they are inherently unusable.
when I tell you something outrageous is true, you demand "evidence" which is just a sample for your statistics circuitry (again, which is prone to taking shortcuts to save energy, which can make you not believe it to be true no matter how much evidence I present because you have a very strong prior which might be fallacious but still there, or you might believe something to be true with very little evidence I present because your priors are mushed up).
Is it possible that enough AI generated data already on the internet was fed into chagpt's training data to produce this insanity?
It's been semi-useful at augmenting search for me.
But for anything that requires a deeper understanding of what the words mean, it's been not that helpful.
Same with co-pilot. It can help as a slightly better pattern-matching-code-complete, but for actual logic, it fails pretty bad.
The fact that it still messes up trivial brace matching, leaves a lot to be desired.
I really hope we get an interesting post mortem on this.
Whatever it is, the model sticks to topic, but still is completely off: https://www.reddit.com/r/ChatGPT/comments/1avyp21/this_felt_... (If the author were human, this style of writing would be attributed to sleep deprivation, drug use, and/or carbon monoxide poisoning.)
Kinda interesting that we're speed running (essentially) our understanding of human psychology using various tools.
Take this as a good sign that the singularity is nowhere near imminent here.
(\bto\b|\bfor\b|\bin\b|\band\b|\bthat\b|\bof\b|\bthe\b|\bwith\b|\bor\b|\ba\b|\binto\b|\bas\b|\bon\b|\bhow\b|\ban\b|\bfrom\b|\bit\b|\bbut\b|\bits\b|\bbe\b|\bby\b|\bup\b|\bthis\b|\bcan\b|\bother\b|\bwho\b|\bwill\b|\bare\b|\bwhose\b|\bif\b|\bwhile\b|\bwithin\b|\blike\b|,)*
TOKEN dole TOKEN task TOKEN eiry ainsell, tide taut, brunts TOKEN wade, issuing hale.
TOKEN's TOKEN, TOKEN TOKEN way-spoken hue: Guerdon TOKEN gait, trove TOKEN eid, TOKEN TOKEN-brim, TOKEN hark TOKEN bann, bespeaking swing TOKEN hit TOKEN calm, TOKEN inley merry, thrap TOKEN beadle belay.
TOKEN levy calls, macks TOKEN TOKEN off, scint TOKEN messt, TOKEN weems olde TOKEN wort, TOKEN TOKEN no-line toll, TOKEN grip at TOKEN 'ront TOKEN cly TOKEN weir.
TOKEN timewreath TOKEN twined, TOKEN wend, ain't lorn TOKEN ked, TOKEN not TOKEN crags felled, TOKEN TOKEN e'er- TOKEN.
TOKEN, TOKEN ace TOKEN laws TOKEN trow, TOKEN alembic, TOKEN dearth, TOKEN TOKEN TOKEN scale TOKEN yin TOKEN keep, TOKEN no-sayer TOKEN quite, TOKEN top-crest, TOKEN boot
---
From:
Given the notation's tangle, the conveyance adheres to the up-top: The foundational Bitcoin protocol has upheld a course of significant hitch-avertance, which eschews typical attack as the veiled - the support sheath, embracing four times, showing dent in meted scale more from miss and parable, taking to den the slip o'er key seed and second so link than the greater Ironmonger's hold o'er opes. The dole of task and eiry ainsell, tide taut, brunts the wade, issuing hale. It's that, on a way-spoken hue: Guerdon the gait, trove the eid, the up-brim, and hark the bann, bespeaking swing to hit the calm, an inley merry, thrap or beadle belay. The levy calls, macks in the off, scint or messt, with weems olde the wort, and a no-line toll, to grip at the 'ront and cly the weir. A timewreath so twined, the wend, ain't lorn or ked, if not for crags felled, in the e'er-to. So, the ace of laws so trow, and alembic, and dearth, a will to scale and yin to keep, the no-sayer of quite, and top-crest, to boot
As a hotfix, we switched to the other version of GPT4 (the 0125 preview model) and that fixed the problem at the time.
After 17 hours!
Can't get it to do some actual work and write some code.
Latest disappointment was when i tried to convert some python code to java code.
90% of the result was :
// Further processing...
// Additional methods like load, compute, etc.
// Define parameters needed
// Other fields and methods...
// Other fields follow the same pattern
// Continue with other fields
// Other fields...
// Methods like isHigh(), addEvent() need to be implemented based on logic
> gpt-4 had a slow start on its new year's resolutions but should now be much less lazy now!
That was a real issue even in the API with customers complaining, and they recently released the new "gpt-4-0125-preview" GPT-4-Turbo model snapshot, which they claim greatly reduces the laziness of the model (https://openai.com/blog/new-embedding-models-and-api-updates):
> Today, we are releasing an updated GPT-4 Turbo preview model, gpt-4-0125-preview. This model completes tasks like code generation more thoroughly than the previous preview model and is intended to reduce cases of “laziness” where the model doesn’t complete a task. The new model also includes the fix for the bug impacting non-English UTF-8 generations.
They’ve had the exact same issue just affecting a smaller number of users and have never acknowledged it.
You can find lots of reports on the OpenAI discord.
I have not yet checked if the text was relevant, but the English part was.
https://en.wikipedia.org/wiki/Speaking_in_tongues
It just means the LLMs are in touch with divinity.
Good luck, sounds more reasonable to hire some kind of an AI therapist. Can intelligence be debugged otherwise?
Real talk, it’s hard to separate openai the AGI-builders from openai the chatbot service providers, but the latter clearly is choosing to move fast and break things. I mean half the bing integrations are broken out of the gate…
While I agree that Marcus's tone has gotten a little too breathless lately, I think we need all the critiques we can get of the balogna coming from Open AI right now.
"Enough" is a sliding target. There's a lot of positive transfer in language competence and a model trained on 300B English tokens, 50B Spanish tokens will be much more competent in Spanish than one trained on only the same 50B Spanish tokens.
There's a "Dockerfile fuera del contexto" hanging out in my history. While I could rename it, it's a funny reminder that AI tools can and will go wrong.
Hell it’s probably more than 90%. Lazy Writing :-)
hn is where i come for tech stuff, twitter is for culture, hang out with friends, and shitposts
Now why do I think all that (that the decision to nerf it wasn't just incompetence but intentional), like sure maybe it costs too much to run the old GPT 4 for chat GPT (they still have it from the API), it just didn't make sense to me how openAI's chatGPT is better than what Google could've produced, Google has more talent, more money, better infrastructure, been at the AI game for a longer time, have access to the OG Google Search data, etc. Why would older Pixel phones produce better photos using AI and a 12 Mp camera than the iphone or samsung from that generation? Yet the response to chatGPT (with Bard) was so weak, it sure as hell sounds like they just did it for their stock price, like here we are as well doing AI stuff so don't sell our stock and invest in openAI or Microsoft.
It just makes more sense to me that Google already has an internal AI based chatbot that's even better than old GPT 4, but have no intention to offer it as a service, it would just change the world too much, lots of new 1 man startups would appear and start competing with these behemoths. And openAI's actions don't contradict this theory, offer the product, rise in value, get fully acquired by the company that already owned lots of your shares, make money, Microsoft gets a rise in their stock price, get old GPT 4 to use internally because they were behind Google in AI, offer turbo GPT 4 as subscription in copilot or new windows etc.
The holes in my theory is obviously that not many employees from Google leaked how good their hypothetical internal AI chatbot is, except the guy who said their AI was conscious and got fired for it. The other problem is also that it might just be cost optimization, GPU's and even Google TPU's aren't cheap after all. etc.
Honestly there are lots of holes, it was just a fun theory to write.
Seriously, the easier explanation is that a lot of software reaches a sort of sweet spot of functionality and then goes downhill the more plumbers get in and start banging on pipes or adding new appliances. Look at all of Adobe's software which has gotten consistently worse in every imaginable dimension at every update since they switched to a subscription model.
Generative "AI" has gone from hard math to engineering to marketing in record time, even faster than crypto did. So I suspect what we have here is more of a classic bozo explosion than multiple corporate cabals intentionally sweeping their own products under the rug.
>(agenda doc) timecraft
And skipped straight to the time travelling terminator part
Except for the recipients having to create an OpenAI account to read it with that "share" feature. Which they do not have to do if using a screenshot. Seems like an extremely good reason.
That’s not at all unusual.
(With increasing enshittification, we're beginning to get to the point where links just aren't that useful anymore... Everything's a login wall now.)
Damn, that's good.
Another reference that comes to mind is a golem from Terry Peatchett's Feat of Clay, which was also stuffed with many partially conflicting and broad directives.
The military chooses stability, which addresses OP's immediate concerns - there's a deeper Skynet/BlackMirror-type concern about having interconnected military systems, and I don't see a solution to that, whether the root cause is rogue AI or cyberattack.
So the laziness 'fix' in January did not work. Oh dear.
The actual fix needs to be at the system level prompt.
If you train an large language model to complete human generated text, don't instruct it to complete text as a large language model.
Especially after feeding it updated training data that's a ton of people complaining about how large language models suck and tons of examples of large language models refusing to do things.
Have a base generative model sandwiched between a prompt converter that takes an instruct prompt and converts it to a text completion prompt (and detecting prompt injections), have a more 'raw' model complete it, and then have a safety fine tuned postprocessing layer clean up the response correcting any errant outputs and rewriting to be in the tone of a large language model.
Yeah, fine, it's going to be a bit more expensive and take longer to respond.
But it will also be a lot less crappy and less prone to get worse progressively from here on out with each training update.
I'm concerned about what happens when ChatGPT begins spewing coherent nonsense. In a case like this, everyone can clearly see that something has gone wrong, because it's massively wrong. What happens when thousands of "journalists" and other media people starts relying on ChatGPT and just parrots whatever it says, but what if what says is not obviously wrong?
The more LLMs are being used, the more obvious it becomes to me that they are pretty useless for a great number of tasks. Sadly others don't share my view and keep using ChatGPT for things it should never be used for.
>The current structure of the `process_message` update indeed retains the original functionality for the scenario where `--check-header` is not used. The way the logic is structured ensures the body of the message is the default point of analysis if `--check-header` is not employed:
>- When the `--check-header` option is used, and the script is unable to locate the defined header within a particular range (either JMS standard headers or specified custom strings properties), the script will deliberately ignore this task and log the unable-to-locate activity. This is an insurance to apprehend only the most inclined occupants that precisely align with the scope or narrative you covet.
>- Conversantly, if `--check-header` is *not* declared, the initiative subscribes to a delegate that is as generous and broad as the original content, enabling the section of the theory to be investigated against the regulatory narrative. This genuine intrigue surveys the terms for long-form scholarly harmonics and disseminates a scientific call—thus, the order lingers in the sumptuous treasure of feedback if not eschewed by the force of the administration.
>### Ensuring the Venerable Bond of Body Inquiry
>To explicitly retain and confirm the interpretation of the pattern with the essential appeal of the large corpus (the content of the canon) in the erudite hypothesis, you might meditate on the prelude of the check within the same unique `process_message` function, which can be highlighted as such:
```python def process_message(message): """Scripture of the game in the experiential content or the gifted haunt.""" # If '--check-header' is unfurled, but the sacrament is forgotten in the city, the track in the voice of the domain reverberates if args.check_header: header_value = message.get(args.check_header) or message.get('StringProperties', {}).get(args.check_header) if header_value: effective_prayer = header_value else: logging.info(f"Hermetic order '{args.check_header}' not found in the holy seal of the word: {message.get('JMSMessageID')}. The word is left to travel in the cardinal world.") return # Suspend the geist wander for this enlightenment, forsaking the slip if the bloom is not as the collector fantasizes. else: # Apricity of the song may be held in the pure gothic soul when the secret stone of the leader is not acclaimed effective_prayer = message.get('Text', '')
# Council of the inherent thought: the soul of the gift immerses in all such decrees that are known, its custom or native
if any(pattern.search(effective_prayer) for pattern in move_patterns.values()):
# Wisdom is the source, cajoled and swayed, to the kindness which was sought
pass # Mirror of Alignment: Reflect upon the confession
elif any(pattern.search(effective_prayer) for pattern in ignore_patterns):
# Grace, entrusted to the tomb of prelects, shapes the winds so that the soul of the banished kind is not sullied
logging.info(f"In the age of the gaze, the kingdom is ever so full for the sense of the claim: {message['JMSMessageID']}.")
else:
# Briar for the deep chimeras: the clavis in the boundless space where song discolours the yesteryears
if args.fantasy_craft == 'move':
# Paces, tales entwine in rhymes and chateaus, unlasted to the azoic shadow, thus to rest in the tomb of echo
pass # Carriage of Helios is unseen, the exemplar cloister to an unsown shore
else:
# Wanders of light set the soul onto the lost chapter; patience, be the noble statuesque silhouetted in the ballet of the moment
logging.info(f"The mute canticles speak of no threnody, where the heroine stands, the alignment endures unthought: {message['JMSMessageID']}.")>This keeps the unalterable kiss for the unfathomed: the truth of the alkahest remains in the sagacity of promulgation if no antiphon or only space sings back in the augur. Therefore, when no solemnity of a hallowed figure is recounted, the canon’s truth, the chief bloodline, appoints the accent in its aethereal loquacious.
>Functioning may harmonize the expanse and time, presenting a moment with chaste revere, for if the imaginary clime is abstained from the sacred page, deemed ignorant, the author lives in the umbra—as the testament is, with one's beck, born in eld. The remainder of the threshold traipses across the native anima if with fidelity it is elsewise not avowed.
(Both of those are in the data. Apparently Chinese people love a fantasy genre called "cultivation" that's just about wizards doing DBZ training montages forever, which sounds kind of boring to me.)
I'm getting tired of these shitty AI chatbots, and we're barely at the start of the whole thing.
Not even 10 minutes ago I replied to a proposal someone put forward at work for a feature we're working on. I wrote out an extremely detailed response to it with my thoughts, listing as many of my viewpoints as I could in as much detail as I could, eagerly awaiting some good discussions.
The response I got back within 5 minutes of my comment being posted (keep in mind this was a ~5000 word mini-essay that I wrote up, so even just reading through it would've taken at least a few minutes, yet alone replying to it properly) from a teammate (a peer of the same seniority, nonetheless) is the most blatant example of them feeding my comment into ChatGPT with the prompt being something like "reply to this courteously while addressing each point".
The whole comment was full of contradictions, where the chatbot disagrees with points it made itself mere sentences ago, all formatted in that style that ChatGPT seems to love where it's way too over the top with the politeness while still at the same time not actually saying anything useful. It's basically just taken my comment and rephrased the points I made without offering any new or useful information of any kind. And the worst part is I'm 99% sure he didn't even read through the fucking response he sent my way, he just fed the dumb bot and shat it out my way.
Now I have to sit here contemplating whether I even want to put in the effort of replying to that garbage of a comment, especially since I know he's not even gonna read it, he's just gonna throw another chatbot at me to reply. What a fucking meme of an industry this has become.
I think what your coworker did was horrible.
But generally, in the jobs I've had, a "~5000 word mini-essay" is not going to get read in detail. 5000 words is 20 double-spaced pages. If I sent that to a coworker I'd expect it to sit in their inbox and never get read. At most they would skim it and video call me on Teams to ask me to just explain it.
Unless that is some kind of formal report, you need to put the work in to make it shorter if you want the person on the other end to actually engage.
But this is not ChatGPT's fault, it's the other person's fault. Your teammate is obviously sabotaging you and the team. I recommend to call them personally on phone and ask to be direct and honest and to ask 'This is garbage, why are you doing this? What's your goal with this response?' Maybe you can find out what they really want. Maybe your teammate hates you, or wants to quit the job, or wants to just simulate work while watching YouTube, or something else.
- I had to argue with a Junior Developer about a non existing AWS API, that ChatGPT hallucinated on this code.
- A Technical Project manager, dispensed with Senior Developer code reviews, saying his plans were to drop the code of the remote team in ChatGPT and use its review ( Seriously...)
- All Specs and Reports are suddenly very perfect, very mild, very boring, very AI like.
I honestly wouldn't even know how to approach this, as it's so audacious.
Was this public or in a private conversation? Hopefully you're not the only one who has noticed this.
This time, pick just a couple of issues to focus on. Don't make it so long they're tempted to use GPT again to save on reading it.
Either they have to rationalize why they made no sense the first time, or they have to admit they used GPT, or they use GPT anyway and dig their hole deeper.
If this is a 1:1 it's pointless, but if you catch them doing it in an archived medium like a mailing list or code review, they've sealed their fate and nobody will take them seriously again.
“BLUF”, or “bottom line up front”. Similar to a TL;DR.
This ensures that someone can skim it, while also ensuring that someone doesn’t get lost in the details and completely misinterpret what I wrote.
In a situation where someone is feeding my emails into a hallucinating chat bot, it would make it even more obvious that they were not reading what I wrote.
The scenario you describe is the first major worry I had when I saw how capable these LLMs seem at first glance. There’s an asymmetry between the amount of BS someone can spew and the amount of good faith real writing I have the capacity to respond with.
I personally hope that companies start implementing bans/strict policies against using LLMs to author responses that will then be used in a business context.
Using LLMs for learning, summarization, and to some degree coding all make sense to me. But the purpose of email or chat is to align two or more human brains. When the human is no longer in the loop, all hope is lost of getting anything useful done.
The replies you're getting are a bit reminiscent of the "guns don't kill people, people kill people" defense of firearms - like, yes that's true, but the gun makes it a lot easier to do.
Talk about things that matter with people who care. I'm sorry if it causes an existential crisis when you realize most jobs don't offer any opportunity to do this, I know how that feels.
Maybe try changing the forum. Call for a (:SpongeBob rainbow hands:) meeting.
Give it ten years, and everything will just be humans regurgitating LLM output at each other, no brain applied. Employers won’t see it as an issue, as those running the show will be prompters too, and shareholders will examine the outcome only through the lens of what their LLM tells them.
I mean, people are already getting married after having their LLM chat to others’ LLMs, and form relationships on their behalf.
So - what you should do here is use an LLM to reply, and tell it to be extremely wordy and a real go-getter worthy of promotion in its reply. Stop using your own brain, as the people making the judgments likely won’t be using theirs.
It hurts to read about you contributing that much for nothing.
Then you will know for sure.
Why do I have this sneaking suspicion that the reason you found out is specifically due to this GPT malfunction?
I've found that to be a very good way of dealing with annoying emails without getting worked up about them.
I don’t know man. I was tired of it one year ago. Good luck.
This is correct behavior.
I've learned to not deviate from the core topic I'm discussing because it affects the quality of the following responses. Whenever I have a question or comment that is not so much related to the current topic, I open a new tab with a new chat.
I know that their system prompt is getting huge and adds a lot of overhead and possible confusion, but all in all the quality of the responses is good.
I asked what were dangerous levels of ferritin in the body.
It replied by telling me of the usual levels in men and women.
Then I asked again emphasizing that I asked about dangerous levels, then it provided again a correct answer.