- cross-posted to:
- technology@lemmy.world
- software
- cross-posted to:
- technology@lemmy.world
- software
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.
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.
Just yesterday I was faced with someone complaining because something that was “supposed” to work didn’t work. They proceeded to describe a function they wanted to use that didn’t exist. Finally it came out that it was what GPT says to do… Sigh…
It’s still LLM, so it’s still breaking input into tokens and generating answers based on their relations.