OpenAI releases GPT-4o, natively interfacing with text, voice and vision

Agreed. Note that they don't say what Martin claim they say, but they only say

We’ve evaluated GPT-4o according to our Preparedness Framework

I think it's reasonably likely to imply that they broke all their non-evaluation PF commitments, while not being technically wrong. 

I believe this is likely a smaller model rather than a bigger model so I wouldn't take this as evidence that gains from scaling have plateaued.

What's your setup? Are you using it via ChatGPT interface or via API and a wrapper?

I was a bit surprised that they chose (allowed?) 4o to have that much emotion. I am also really curious how they fine-tuned it to that particular state and how much fine-tuning was required to get it conversational. My naive assumption is that if you spoke at a merely-pretrained multimodal model it would just try to complete/extend the speech in one's own voice, or switch to another generically confabulated speaker depending on context. Certainly not a particular consistent responder. I hope they didn't rely entirely on RLHF.

It's especially strange considering how I Am A Good Bing turned out with similarly unhinged behavior. Perhaps the public will get a very different personality. The current ChatGPT text+image interface claiming to be GPT-4o is adamant about being an artificial machine intelligence assistant without emotions or desires, and sounds a lot more like GPT-4 did. I am not sure what to make of that.

Specifically, their claim is "2x faster, half the price, and has 5x higher rate limits". For voice, "232 milliseconds, with an average of 320 milliseconds" down from 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4) on average. I think there are people with API access who are validating this claim on their workloads, so more data should trickle in soon. But I didn't like seeing Whisper v3 being compared to 16-shot GPT-4o, that's not a fair comparison for WER, and I hope it doesn't catch on. 

If you want to try it yourself you can use ELAN, which is the tool used in the paper they cite for human response times. I think if you actually ran this test, you would find a lot of inconsistency with large differences between min vs max response time, average hides a lot vs a latency profile generated by e.g HdrHistogram. Auditory signals reach central processing systems within 8-10ms, but visual stimulus can take around 20-40ms, so there's still room for 1-2 OOM of latency improvement.

LLM inference is not as well studied as training, so there's lots of low hanging fruit when it comes to optimization (at first bottlenecked on memory bandwidth, post quantization, on throughput and compute within acceptable latency envelopes), plus there's a lot of pressure to squeeze out extra efficiency given constraints on hardware.

Llama-2 came out in July 2023, by September there were so many articles coming out on inference tricks I created a subreddit to keep track of high quality ones, though I gave up by November. At least some of the improvement is from open source code making it back into the major labs. The trademark for GPT-5 was registered in July (and included references to audio being built in), updated in February, and in March they filed to use "Voice Engine" which seems about right for a training run. I'm not aware of any publicly available evidence which contradicts the hypothesis that GPT-5 would just be a scaled up version of this architecture.

Gemini also supported audio natively.

It seems to be able to understand video rather than just images from the demos, I'd assume that will give it much better time understanding too. (Gemini also has video input)

Are you saying this because temporal understanding is necessary for audio? Are there any tests that could be done with just the text interface to see if it understands time better? I can't really think of any (besides just doing off vibes after a bunch of interaction).