From everything I've heard I fear Google is next up on this Ballmer path. They are turning off R&D and looking to capitalize on what they have after a decade of crazy growth from new things that fell out of R&D. It's bizaar to watch them do this, but this at least explains their motivation. As to how they let this start happening to themselves... oh well. Slippery slope
Definitely doesn't seem that way from the inside. We're constantly productizing new research that comes from R&D. Self-driving cars, Glass, DistBelief, etc.
In fact, one thing that's unusual about Google is how much R&D actually makes it into products (unlike, say, Microsoft, whose research group turns out great stuff but rarely does anything with it). In that sense, it's less pure blue-sky, but more applied. I think it's more akin to the Manhattan Project, Apollo program, etc - scientists and engineers working closely together to redefine what's possible. (We even internally refer to the ambitious projects as moon shots). SpaceX and Tesla are doing work in a similar vein - an virtuous cycle of innovations and research.
This is mostly (edit: completely -> mostly) inaccurate (edit: untrue -> inaccurate). Just because we aren't vocal about what gets tech transferred doesn't mean it doesn't happen (disclosure, I work for MSRA, one of the MSR labs; I assume you work for Google).
Google differs from MSR in that almost all of their researchers work in product teams on products. Their contributions are highly visible as a result, but they don't really get to take the risks that we get to in a research lab setting. Much of the work that Google capitalizes on comes out of universities: Google then hires the researchers and allows them to continue working on the research as a product. For example, Google did not take the big initial risks on self driving cars (DARPA funded that), but they've bought into it and think its product ready (which is a risk in itself, but a different kind). And if your researchers are working on products (emitting their experience), they don't exactly have time to do new research (collecting new experiences for later emission). Google's 20% time was supposed to help fix that, but its not clear that this works.
I (and many of my colleagues I think) have a lot of respect for the Google way. At the same time, it is quite clear that the results of both systems are very different.
Wasn't MSRA behind that amazing live English-to-Chinese translator [0]?
It's interesting to contrast Google's and Microsoft's approach with ours at Wolfram Research (at 0.3% their size, of course [1]).
We've come to see that it is very fruitful to live in a murky space somewhere between "commercial product" and "research project".
I'm thinking specifically about Wolfram|Alpha.
It's an enormous and daunting project. We've tackled a lot of very hard problems, and obviously have a long way still to go.
It has the weird property of being a thing that ships every week, but not one that the parent company depends on financially.
And so we kind of do our own thing, encoding ___domain knowledge, adding content, curating data, and designing frameworks, even if some if it won't ever make us any money (e.g. who will ever pay for dog
vision [2]). We mostly just do things because they're cool or fun.
But it's been incredibly useful to drive innovation elsewhere in the company.
For example:
1. Alpha's unit system (the best in the world, the authors claim) is already in Mathematica 9.
2. Alpha is inspiring us to bring high-level semantic data and reasoning to Mathematica 10.
3. The automatic analysis in Pro is being souped up and will form part of Mathematica's predictive interface (automatic suggestions to "perform logistic regression", and so on).
4. We're working on taking the natural language understanding frameworks we've built for Alpha and using them to translate natural language queries into structured SQL or hierarchical document queries.
5. The ___domain specific languages we've invented to represent things in the real world are going to be exposed in a soon-to-be open format we're calling the Wolfram Data Format (which I hope will succeed where RDF is failing).
We wouldn't have though of any of this stuff without Alpha. And it wasn't explicitly driven by either commercial or basic research, but rather some kind of eccentric blend of the two.
Yes, a semi-research project with lots of peel offs works well. It is nice that Wolfram has a nice product with good profits that can bankroll something like Alpha. Seemingly inefficient (not directly profitable) experimentation is a great way of driving innovation and invention. Contrast with Apple, who has a razor sharp focus on profitable products: they don't make many mistakes, and they aren't taking any risks these days.
As an aside, I don't think the mostly constructed knowledge representation approach taken by Alpha will scale in the long run. You guys might want to look at playing around more with machine learning. On the other hand, there are gaps in machine learning that are best filled by DSLs and explicit construction.
We _are_ taking machine learning more seriously (I claim some credit here). My team is integrating ML into Mathematica 10 as we speak. And we've experimented with deep learning. I think some interesting things will come out of that in the future.
I think a hybrid approach is pragmatic. There is much computational knowledge that can't be assembled from web scrapes, as Google's purchase of Metaweb indicates (anyone remember Google Squared?).
Perhaps their product R&D has slowed down a bit ("more wood behind fewer arrows"), but their academic research is still strong. Their collaborations with Hinton, Thrun, and Ng are just the most obvious and recent examples.
On the other hand, Microsoft has for a long time made a serious investment in basic research, both on the programming language side and on algorithms, but that hasn't stopped them from misfiring in multiple product categories.
Not arguing with Google's strategy, but has anyone ever really thought about the "more wood, fewer arrows" analogy?
If you look at the battles that were won by archery (Crecy, Agincourt, Mongol domination of Chinese, Moslem & European armies) it was the volume of arrows that won them, not the weight of the arrows.
There were some battles where arrow weight was mildly important (eg, at Agincourt where the French armour had advanced enough since Crecy that it gave some reasonable protection against arrows). But even in these battles the archers used heavier heads on the arrows (which were also shaped differently) - not more wood.
Anyway - totally off topic, and like I said I think Google's strategy is good. But their analogy breaks down when we actually look at history.
To continue discussing the analogy, there are some things that threw lots of wood behind very few arrows to great effect. They just weren't fired by archers anymore: http://en.wikipedia.org/wiki/Ballista
Microsoft Research is still one of the most influential and visible industry research labs in Computer Science, publishing more great papers than most top universities.
However, this work rarely seems to make it into their consumer products. I suspect it's mostly an issue of the organisational structure of Microsoft, rather than the type of research, which makes it difficult to jump the gap from research to products.
Where is the evidence for this? Anecdotally, all the evidence seems to point in the opposite direction: Self-driving cars, Google Glass, Project Loon.
Even in front-end web (the area I'm most familiar with) they are doing lots of exciting and experimental stuff with R&D driven frameworks like AngularJS and Polymer.
I think the evidence that most (outsiders) see is that "labs.google.com" seems kind of bare. One item that I found useful in there was something that let you type in a few related words / phrases, and it would complete the list for you. Useful for finding other items in a particular category when you knew of a few examples, but didn't know the specific category name (example: what comes after primary, secondary, tertiary). Now it's gone.
Open up a google doc spreadsheet, type the words in a column, then use the little pull-down at the bottom right. Lists of languages, ponies, whatever will autocomplete.
Possibly I'm misunderstanding your directions, but it doesn't seem to actually do what the parent said. Using the little expand-and-fill thing just repeats the contents of the cells selected. (Unless you have an obvious sequence of numbers, in which case it continues.)
Not exactly, but they seems to be way more aggressive about optimizing their profits over whatever values they previously had, such as privacy or search neutrality. Google is in immensely powerful position as they can basically control the world's economy by a simple algorithmic changes in the search engine. Now, everyone trusts them on search neutrality, but for how long? Everyone loves them for their technology, that's part of the equation. That's why we use Google's services and are willing to give up anything for that.
