GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time(opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.
Prior to GPT-4o, you could use Voice Mode to talk to ChatGPT with latencies of 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4) on average. To achieve this, Voice Mode is a pipeline of three separate models: one simple model transcribes audio to text, GPT-3.5 or GPT-4 takes in text and outputs text, and a third simple model converts that text back to audio. This process means that the main source of intelligence, GPT-4, loses a lot of information—it can’t directly observe tone, multiple speakers, or background noises, and it can’t output laughter, singing, or express emotion.
GPT-4o’s text and image capabilities are starting to roll out today in ChatGPT. We are making GPT-4o available in the free tier, and to Plus users with up to 5x higher message limits. We’ll roll out a new version of Voice Mode with GPT-4o in alpha within ChatGPT Plus in the coming weeks.
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I can’t wait till someone dose this, but open source and running on not billionaire hardware.
If you already didn’t know, you can run locally some small models with an entry level GPU.
For example i can run Llama 3 8B or Mistral 7B on a 1060 3GB with Ollama. It is about as bad as GPT-3 turbo, so overall mildly useful.
Although there is quite a bit of controversy of what is an “open source” model, most are only “open weight”
you can run locally some small models
Emphasis on “small” models. The large ones need over a terabyte of RAM and it has to be high bandwidth (DDR is not fast enough).
And for most tasks, smaller models hallucinate way too often. Even the largest models are only just barely good enough.
Look up oobabooga, and then play with all the fun extensions.
I have this running at home on a used r630 (CPU only). oobabooga/automatic1111 for LLM/SD backends, vosk + mimic3 for tts/stt. A little bit of custom python to tie it all together. I certainly don’t have latency as low as theirs, but it’s definitely conversational when my sentences are short enough.
Maybe this is wishful thinking but this, at first glance, seems like a sign that we’re already entering the LLM plateau. Like when they got the point with phones that each new version is just more cameras, smoother UI, and harder glass.
It’s an end-user plateau for the moment, but there are still tons of things going on underneath the hood. From the outside it may not look like things are moving, but we’ve gone from Model-T to Chevy Bel Air fairly quickly, and while the difference is huge, the engineers are still trying to get us to Bugatti Veyron level. Until then, we are going to have a long “80 and 90s” period of sameness.
18 months ago, chatgpt didn’t exist. GPT3.5 wasn’t publicly available.
At that same point 18 months ago, iPhone 14 was available. Now we have the iPhone 15.
People are used to LLMs/AI developing much faster, but you really have to keep in perspective how different this tech was 18 months ago. Comparing LLM and smartphone plateaus is just silly at the moment.
Yes they’ve been refining the GPT4 model for about a year now, but we’ve also got major competitors in the space that didn’t exist 12 months ago. We got multimodality that didn’t exist 12 months ago. Sora is mind bogglingly realistic; didn’t exist 12 months ago.
GPT5 is just a few months away. If 4->5 is anything like 3->4, my career as a programmer will be over in the next 5 years. GPT4 already consistently outperforms college students that I help, and can often match junior developers in terms of reliability (though with far more confidence, which is problematic obviously). I don’t think people realize how big of a deal that is.
There’s a basic problem with replacing human experts with AI. Where will they get their info from with no one to scrape? Other AI generated content?
They can’t learn anything and are just “standing on the shoulders of giants”. These companies will fire their software developers, just to hire them back as AI trainers.
“they can’t learn anything” is too reductive. Try feeding GPT4 a language specification for a language that didn’t exist at the time of its training, and then tell it to program in that language given a library that you give it.
It won’t do well, but neither would a junior developer in raw vim/nano without compiler/linter feedback. It will roughly construct something that looks like that new language you fed it that it wasn’t trained on. This is something that in theory LLMs can do well, so GPT5/6/etc. will do better, perhaps as well as any professional human programmer.
Their context windows have increased many times over. We’re no longer operating in the 4/8k range, but instead 128k->1024k range. That’s enough context to, from the perspective of an observer, learn an entirely new language, framework, and then write something almost usable in it. And 2024 isn’t the end for context window size.
With the right tools (e.g input compiler errors and have the LLM reflect on how to fix said compiler errors), you’d get even more reliability, with just modern day LLMs. Get something more reliable, and effectively it’ll do what we can do by learning.
So much work in programming isn’t novel. You’re not making something really new, but instead piecing together work other people did. Even when you make an entirely new library, it’s using a language someone else wrote, libraries other people wrote, in an editor someone else wrote, on an O.S someone else wrote. We’re all standing on the shoulders of giants.
Worth listening to a podcast by Ed Zitron who covers this exact same thing.
I work in analytics consulting and aside from some relatively straight forward text classification and parsing there really are so few instances where AI is useful. He actually made the case that AI is useful to people selling AI. But these chatbots are mostly useless bullshit.
Why would you wish for technology to stop improving?
5x usage limits (if they last) is the real news here. The current limits make it unusable for any meaningful projects.
I just dropped chatgpt plus because of those limits…
I disagree. The real news is the free model will now search the internet for up to date answers, and for calculations it will write and execute a python script, then show you the result.
Paid users of ChatGPT have had those features for months, and they were a massive step forward in terms of how often the AI provides accurate answers.
The demo showcasing integration with BeMyEyes looks like an interesting way to help those who are blind.
I hate everything about the AI trying to be funny and faking laughter. Insanely creepy.
Other than that, seems like a good step forward from GPT-4. Cheaper API will make a lot of people happy.
Future robots will make a joke before they shoot you in the head for resisting arrest. What’s not to like?
Just before overriding the safety brakes on your elevator and popping the cable release:
Sorry to give you the shaft...
This is pretty impressive and hella creepy!
But can it respond intelligently? Does it actually think to look up information to answer questions? Or does it still hallucinate answers? Because if it does it’s still useless for all of the things people seem to think it’s good for. We need the idiot proof this damn thing because all of the idiots are using it.
It’s still LLM, so it’s still breaking input into tokens and generating answers based on their relations.
How will this go wrong … really ?
With GPTs’ help :
1st draft :
1- Privacy Violations:
Unrestricted use of AI for surveillance will lead to widespread invasion of privacy as individuals’ activities will be monitored without their consent.2-Discriminatory Practices:
Biases in AI algorithms will result in discriminatory practices, targeting specific groups unfairly based on factors like race, gender, and religion.3-Abuse of Power:
Governments or other entities will abuse AI surveillance capabilities to suppress dissent, control populations, and violate human rights.4-Security Risks:
As AI surveillance systems will be perverted, they willbe manipulated, leading to further privacy breaches and misinformationlead to absolute despotism.5-Lack of Accountability:
no one will ever be held accountable for all of this.And those still are understatements.
2nd draft :
Question : Describe the worst despotic society facilitated by artificial intelligence.
Answer : (( … bad things … ))
🥱
think you can do better tough guy?
Who says I want to do better?
Edit: who says I want it at all?
