Article from The Atlantic, archive link: https://archive.ph/Vqjpr
Some important quotes:
The tensions boiled over at the top. As Altman and OpenAI President Greg Brockman encouraged more commercialization, the company’s chief scientist, Ilya Sutskever, grew more concerned about whether OpenAI was upholding the governing nonprofit’s mission to create beneficial AGI.
The release of GPT-4 also frustrated the alignment team, which was focused on further-upstream AI-safety challenges, such as developing various techniques to get the model to follow user instructions and prevent it from spewing toxic speech or “hallucinating”—confidently presenting misinformation as fact. Many members of the team, including a growing contingent fearful of the existential risk of more-advanced AI models, felt uncomfortable with how quickly GPT-4 had been launched and integrated widely into other products. They believed that the AI safety work they had done was insufficient.
Employees from an already small trust-and-safety staff were reassigned from other abuse areas to focus on this issue. Under the increasing strain, some employees struggled with mental-health issues. Communication was poor. Co-workers would find out that colleagues had been fired only after noticing them disappear on Slack.
Summary: Tech bros want money, tech bros want speed, tech bros want products.
Scientists want safety, researchers want to research…
The best part of Open AI’s self professed goal to make an AGI is that the more we learn about LLM’s the more it becomes clear that they inherently can never bridge the gap to AGI.
One would almost think the constant complaining about mythical dangers of AGI might be a distraction from the real more mundane dangers LLM’s pose here and now like exasperating bias, making mass misinformation easy, and of course shielding major companies from accountability.
Or the other option is that it’s just marketing, look at how scary our totally real product is, look how fast it improved when we went from a medium sized dataset to the largest that will ever be possible, don’t ask questions like why would a autocomplete that has been feed the entire internet actually help our business, just pay us and bolt it on to whatever you can.
LLM’s ability to replace jobs is honestly more terrifying than so called AGI.
At least with AGI, if they really can think like human, is that they may actually think about the implications of their actions…
Oh, I’m sure they will. That is not, in the slightest, the same as caring about said implications in ways that mean that the species won’t get murked, though.
I expected as much, I had this feeling about Altman, too. The draw of profit became too much for him, and the board called him on it and let him go.
Which makes it even worse that now they’re groveling at his feet to return.
Ugh.
Well the idea to ask for him to return came from MS and not from the board themselves. At least that plan failed according to media report.
His return deal totally capsized, he’s out as CEO still. The old CEO of Twitter, Emmet Shear, is now in charge.
[Resource] sacrificed for profit under [CEO].
Nothing about this is safe. It’s easily the worst misinformation tool in decades. I’ve used it to help me at work, GPT-4 is built into O365 corp plans, but all the jailbroken shit scares the hell out of me.
Between making propaganda and deepfakes this shit is already way out of hand.
Many members of the team, including a growing contingent fearful of the existential risk of more-advanced AI models, felt uncomfortable with how quickly GPT-4 had been launched and integrated widely into other products.
GPT-4 and anything similar isn’t going to pose an existential threat to humanity.
Eventually, yeah, there is probably a possibility of existential risk from AI. I don’t know where that line ultimately is, and getting an idea of that might be something important for humanity to figure out, but I am pretty confident that whatever OpenAI is presently doing isn’t it.
Same reason that Musk and his six month moratorium on AI work doesn’t make much sense. We’re not six months away from an existential threat to humanity.
I think that funding efforts to have people in the field working on the Friendly AI problem is a good idea. But that’s another story.
The apps using GPT4 without regards to safety can be though. Example: replacing human with chatbot for suicide prevention.
Being an existential threat is a much higher bar – that’s where humanity’s continued existence is at threat.
There are plenty of technologies that you could hypothetically put somewhere where a life might be at stake, but very few that could put humanity’s existence on the line.
It’s the same situation, just writ large. Dumb human decisions to put AI where it shouldn’t be. Heck, you can put it in charge of the nuclear missles now if you want to. Don’t. Though. That’d be really, really stupid.
Part of my knee-jerk dislike of the AI hype is that it’s glorified text completion. It doesn’t know shit. It only knows the % chance of your saying the next word. AGI is not happening anytime soon and all this is techbro theatre for the sake of money.
Anyone who reads a wall of bland generated text and thinks we’re about to talk to god is seriously mistaken.
I’m much more worried about the social implications. Namely, the displacement of workers and introduction of new efficiencies to workflows, continuing to benefit only those who are rich and in power, and driving more of us towards poverty.
It’s not an immediate existential threat, but it’s absolutely a serious issue that we aren’t paying enough attention to.
Displacement of workers isn’t necessarily a bad thing as long as it’s spread out over a long enough time for people to adjust.
I suspect(/hope) we’re not going to see people losing jobs, but rather jobs in certain industries will just be created at a slower rate. Workflows take a long time to change in larger companies. I suspect a lot of value will be realized by smaller/just-starting companies who could more easily afford, say a $500/mo AI “task helper” service vs. hiring a $60k/yr position.
How did the industrial and information revolutions work out for us? Sure we live lives of convenience, but our entire existences have been manipulated into making the rich richer.
Looking at long and short term trends in the wealth gap, I have absolutely no faith that this will go well.
I agree the wealthy only became more wealthy during these revolutions, but the average standard of living for the lower classes also increased as well for both movements.
For example, with the Industrial Revolution, newly created industrial jobs led to generally increased pay over rural jobs, improved transportation access, and started a focus on education.
That isn’t to say workers weren’t abused in this system, though.
I don’t think the wealth inequality problem is something that will get better or worse with an “AI Revolution”. There are plenty of jobs available to keep wages where they are. This could only be solved with tremendous government action or an incredible accident.
You do realize that a lot of people are already being displaced by AI right? These are not “unskilled” jobs either. For e.g. the illustrators who used to get jobs probably spent thousands of hours to get to that level
AI is already taking video game illustrators’ jobs in China
https://restofworld.org/2023/ai-image-china-video-game-layoffs/
CNET used AI to write articles. It was a journalistic disaster. - The Washington Post
https://www.washingtonpost.com/media/2023/01/17/cnet-ai-articles-journalism-corrections/
They believed that the AI safety work they had done was insufficient.
Considering that every new model seems to be getting worse for anything but highly sanitized corporate usage, I’m not sure that I want more AI safety …
For my usage, I use Chat GPT 3.5 turbo with the march checkpoint because I can’t get the current one to stop moralizing about bullshit instead of doing what it’s supposed to (I run two twitch bots with it). GPT4 used to be okay there, but the new preview is now starting to have the same issue with more frequent “I can’t do that Dave”-style answers, though it’s still mostly circumventable with enough prompt massaging, but it is getting harder.
In a year, I don’t see anything but self-hosted models usable for anything not corporate glitz if trajectories hold, so fuck all that AI safety.
On top of all of this, those efforts to tame and control outputs from the developer side could be abused to simply appease investors or totalitarian markets. So we might see a Disneyfication like we‘re seeing on other platforms like Youtube with their horrendous filters, spawning ridiculous terms like „unlifed“. And just imagine the level of censorship we‘d see if they ever try to get into the Chinese market because clearly, the ‚non‘ in non-profit is becoming more and more silent.
It’s already easy to self host and we’ve optimized LLMs to run locally on not much serious hardware after we’ve trained them; I have GPT4ALL set up on my machine and it runs everything locally with my processor, no GPU or anything. Some of those datasets are uncensored, and I’ve seen what Stable Diffusion can do for image generation.
I tend to use the GPT-4 built into Edge with my O365 corporate plan, because it suits my needs better for day-to-day challenges. It can still audit code and summarize things, which is all I really need it to do here and there.
Nothing that runs on my GPU / CPU comes even close to GPT 3.5, GPT4 is not even in the same universe, and that’s with them running far more slowly.
In my tests, the self hosted options that have access to a 30xx or 40xx graphics card return results far faster than gpt4
Which model are you talking about?
Mistral for chatgpt, and i’m not saying it gives better answers, just that it’s much faster than my web portal to gpt4
Oh, faster is easy. GPT 3.5 is also far faster than GPT 4. Faster at quality replies is the issue.
Pulled up a self hosted option last week to try it out. It’s not gpt4 level, but it’s damn close and I don’t worry giving access to my local documents
PrivateGPT for anyone interested
That’s an interface for models. Which model did you use?
Mistral-7B-Instruct-v0.1 is the default, i’m downloading the Llama2 model to test it with now, but many models on HuggingFace should still work
I do not believe any 7B model comes even close to 3.5 in quality. I used LLama V1 64B, and it was horrible in comparison. Are you really telling me that this tiny model gives better general answers? Or am I just misunderstanding what you are saying?
I didn’t say better, I said comparable
And faster, without handing over my data and conversations for monetizationGiven the locally hosted benefits, and the ability to go to chatgpt for any answer minstrel gives that doesn’t satisfy you, makes it strong competition to chatgpt as the default tool
Hosting it yourself also means you can swap llm’s out based on context and what they’re trained on. Highly tuned models perform better than chatgpt at the things they are meant to excel in.
Prompt:
I’m currently trying to show on the Website Beehaw, that certain LLMs are far superior in writing than others. Examples of what bigger models do better than smaller ones: *
Mistral-7B-Instruct-v0.1
- ntire articles* vs Headlines Descriptions vs Product titles *Bul
GPT 3.5-Turbo doesn’t support completion as it’s for chat, so I used an even worse one, text-davinci-003 which is far behind state of the art.
Bigger models are able to handle more complex and detailed tasks with ease
- Bigger models are better suited for natural language understanding and text processing
- Bigger models are able to learn more accurate representations of context, thus improving the precision of the output
- Bigger models can process data more quickly and efficiently, saving time and processing power when large volumes of data are used
- Bigger models can better recognize more subtle nuances in language, which allows them to produce more accurate results
- Bigger models are able to use more sophisticated algorithms, resulting in a more comprehensive and deeper understanding of the data being used
Mistral 7B might be okay for some very specific cases, but it’s not comparable to proper models at all.
edit: gave it a second chance, it’s a bit better (at least no complete nonsense anymore), but still terrible writing and doesn’t make much sense
Paraphrasing The ability of a language model to generate text that has a similar meaning to the original text is called paraphrasing. This is a very common problem in natural language processing, and many LLMs are designed to be able to paraphrase text. However, there are some LLMs that are particularly good at paraphrasing, and these models are often preferred over smaller models because of their ability to generate more varied and unique text. Examples of LLMs that are known for their paraphrasing abilities include GPT-2 and transformers. These models
