>I don’t get the idea that Apple dropped the ball on AI.

That's the public perception. Maybe due to them not getting in on a quick cash grab off the LLM hype wave?

I share this perception for two reasons:

1) Siri

2) Dearth of published AI research

I agree with 1. For 2, have they ever been a company big into research? They're very consumer focused and it can take time to integrate new tech into consumer products at scale. Especially the way Apple likes to do it: polished and seamlessly integrated into the rest of their ecosystem.

I would say not doing AI research (or buying another big company that does) is tantamount to dropping the ball on AI, if it turns out that AI is a capability they should have had and must have to succeed.

You could argue that publishing research is not the same thing as doing it. But they don't seem to have done much of it until fairly recently.

I agree that Apple does less research than other big tech companies. But they do it where they think it matters. Their M-series CPUs are more than just integration and polishing. And they have been doing some research in health AI as well, I think.

> Dearth of published AI research

https://machinelearning.apple.com/research seems to have too many publications to be considered a "dearth" IMO.

Dearth relative to Apple's size and relative to the amount of research that competitors have been doing.

But I think part of the problem is that Apple simply hasn't focused on the tasks and the methods and the people that have now turned out to be so impactful.

They have clearly been course correcting for a while now as some of the more recent papers show, and they have done successful research in areas such as health AI.

> They were fairly early with adding neural engine hardware to their chips

If that's all it takes to stay ahead of the curve, then Rockchip and Qualcomm are arguably right up there alongside them. Tons of vendors shipped their own AI silicon, and of those vendors, it seems like Nvidia is the only one that shipped anything truly usable. Medium-sized LLMs, Stable Diffusion and probably even stuff like OAI Whisper is faster run on Apple's GPUs than their AI coprocessor.

> and of those vendors, it seems like Nvidia is the only one that shipped anything truly usable. Medium-sized LLMs, Stable Diffusion and probably even stuff like OAI Whisper is faster run on Apple's GPUs than their AI coprocessor.

Be careful not to have NVIDIA-shaped tunnel vision. Performance isn't the whole story. It's very telling that approximately everybody making SoCs for battery powered devices (phones, tablets, laptops) has implemented an AI coprocessor that's separate from the GPU. NVIDIA may take exception, but the industry consensus is that GPUs aren't always the right solution to every AI/ML-related problem.

Ideally, you're right. Realistically, Apple has to choose between using their powerful silicon (the GPU) for high-quality results or their weaker silicon (the Neural engine) for lower-power inference. Devices that are designed around a single power profile (eg. desktop GPUs) can integrate the AI logic into the GPU and have both high-quality and high-speed inference. iPhones gotta choose one or the other.

There's not nothing you can run on that Neural Engine, but it's absolutely being misunderstood relative to the AI applications people are excited for today. Again; if chucking a few TOPS of optimized AI compute onto a mobile chipset is all we needed, then everyone would be running float16 Llama on their smartphone already. Very clearly, something must change.

> they don’t make their own search engine

Curious then, why they keep recruiting search engineers[1]. And why they run a web crawler[2]. And why typing "Taylor Swift" into safari offers a Siri Suggested website before Google.

I guess what people mean by search engine is "show ads alongside web search to as many people as possible"?

1: https://jobs.apple.com/en-us/details/200548043/aiml-senior-s... 2: https://support.apple.com/en-us/HT204683