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Comment by chimtim | Hacker News Reader

This is not how research works or a new fundamental technique is discovered. Any result needs to stand the rigor of peer-review before the said breakthrough can be conclusively proven (more unbiased minds trying to find the limitations of the approach and applying the said approach to various problems). Also this process is slow, and requires many PhDs, students building upon one another's work.

Also, going from 96-99 may not be really revolutionary, what really matters is how you got there e.g. was new research needed to detect new unicorn classes or scenes, or if it was simply solved by great engineering, more data, etc.

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This is a strange view of computer science research. In most of the cases of material advances outside the academic setting I am familiar with, the researchers always demonstrate or prove their result to a high degree of rigor themselves. None of this requires an army of PhDs (or even one) to rigorously inspect the result unless it is so badly represented as to be opaque. It is not like they sit around coming up with random ideas for how to solve a problem and leave it at that. Peer review is useful for checking the result but you aren't supposed to rely on peer review to filter garbage ideas.

I know a few professional computer science researchers, people who churn out new and better algorithms (some radically so) in multiple domains on a surprisingly consistent basis for private companies. At least half their job is actually the easily understandable explanation and proof of why the algorithm must have the claimed properties. Their job isn't to produce academic papers but to solve algorithm problems for software engineers writing real code, so a lot of focus is on reduction to practice and clear explanation of the mechanics.

>Any result needs to stand the rigor of peer-review before the said breakthrough can be conclusively proven

Not if you can easily verify the claim is true with sample images (in the situation given). Then you don't need to care for any "peer review".

In the same vein, if I come up with a data structure implementation that is 100x faster than common techniques, it just has to pass a suite of tests. No peer review needed. If some Intel engineer finds a revolutionary, 100% speed improving cache prediction method, ditto. And for lots of other cases. As soon as you check it works, you can start patenting and marketing pronto.

Sure this is the case sometimes but generally you need to check and make sure you aren't gaming some odd property of the tests somehow; does your algorithm give up speed somewhere in order to recover it somewhere else. Is there a worst case in the tests you're not noticing. It's easy to miss something small. Proprietary algorithms are by and large not fundamental general breakthroughs in the field, rather they are incremental upgrades or modifications or tradeoffs or completely non-general algorithms for a very specialized purpose that happens to align with the business goals of the company

By patenting though, you reveal your technique (that's the whole point: you get exclusive rights in exchange for disseminating knowledge). Once you file and receive your patent, you've basically published your results and everyone can benefit (although they'll have to pay you, of course).

Any result needs to stand the rigor of peer-review before the said breakthrough can be conclusively proven.

Apple aren't in the business of doing research for the sake of provable science though. They do research to make things they can sell. The goal is very different.

>The goal is very different.

The article agrees, and suggests that because of this, Apple will miss out on the top talent that prefers to publish

That is not how Google works. They keep their new fangled systems under wraps and only release the details after a few years (likely 10). Most definitely research is done through the rigor of peer reviews but in a commercial setting that is almost never the case.

More like 5 years, and shrinking.

My comment and the article are about AI research specifically, where there is lots of exciting new research due to the success of deep learning. And since the area is new, there is lot of low hanging fruit and challenging problems, which researchers are eager to solve and publish, and as a result Apple is suffering.

So it does not matter how Google research worked earlier or if other areas of Google research continue to wait for a 5 year timer.

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