I'd really like to hear what you think about Nvidia's approach to self-driving, especially using supercomputing + simulation + backtesting to bootstrap the process. We keep going back and forth on this topic, but how can you develop a self-driving platform without a bunch of NNs in production, running on the Nvidia Saturn V supercomputer?

> How you can use 8 bits and integers for inference for example. Said to me these guys are trying to catch up.

I think it's interesting that you presume that only Google came up with the idea first, rather than 'reducing precision' to be a rather obvious idea that any chip designer or ML practitioner would have brought up. Again, can you please justify that?

I think where we're at a disconnect is that you equate AI leadership with publishing and patents, while looking at Nvidia, they are an extremely secretive organization that would probably avoid publishing what they see as a competitive advantage. This is similar to how Apple operates.

I used to work at finance, and the culture was the same way—banks had state-of-the-art models internally but would never share it. Published papers in academia were probably ~5 years behind what the banks had.

I do believe that Google (mostly Deepmind) is the leader in the research field, but note that they had to go out and buy that expertise.

> Google on R&D will spend 2x Nvidia 2017 sales!

Yes, but it's not all going into AI for sure, and definitely not into bankrolling the TPU effort. We should compare apples to apples here, surely?

> The entire dynamics of the chip business have changed. You will see Amazon do their own just like Google has.

So what about Nvidia's self-driving efforts? I've talked about it for about 3-4 posts now, with references to presentations and videos, and heard more or less crickets from you about it. I don't see how you can repeatedly say that Nvidia has no access to data when they clearly have a working product (Drive PX2) already, plus more (Drive Xavier) ready to be deployed in cars within the next ~18 months.

> Google deploys both, TPUs and Nvidia, for a number of reasons I suspect.

> The biggest is they want TF to be the canonical framework for AI and they MUST show not favoring their own solution until it is a done deal which is getting close.

Yes, but for those exact same reasons, the TPU will not be a strategic edge for Google and lower the ROI of working on the project.

You can't have it both ways: either the TPU is the secret sauce that drives Google Cloud adoption and gives them a big leg up in AI (in which case, they would want to leverage TF and make it 'run better' on the TPU than on other hardware), or else TF is a neutral platform and it doesn't benefit either party (which I actually agree with).

> It is like saying Android would run as well as iOS on the Apple processors. It is all about controlling the entire stack like Apple has done and Nvidia is just not in a position to be able to.

I think the analogy here is really apt, but also shows why I don't believe in Google's success here long-term.

The iPhone basically invented the smartphone market; its product was 10x better than any other competitor when it was introduced, and it was probably the majority of volume (and definitely profit) for years before Android was able to compete.

The TPU is not heads and shoulders above the competition. The Volta came out literally ~1 year after Pascal and had 10X the tensor throughput; you say that Google isn't standing still, but certainly neither will Nvidia.

Basically, Google is not starting from a 'commanding lead' position like Apple did. And we see today that even though Apple still leads in profits, Samusng is very close, and, Android is the vast majority of the market.

Larger ecosystems tend to beat fully vertical stacks in the long term. We see this across many markets and products. So why do you think this will be the exception?