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Comment by Oculus | Hacker News Reader

$400M for a company I've never heard before is quite surprising. Could someone who participates in the machine learning community share any insights or facts they have on the company? Other than the founders' histories, the article doesn't do much justice to the pricetag.

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As far as I can tell, quite a few ML people can vouch for someone at the company (I know someone there second-hand myself), but the details of what precisely they're doing have been kept fairly well under wraps. The broad parameters are "something to do with Artificial General Intelligence (AGI)", which is a field that tries to straddle the fine line between contemporary AI and sci-fi AI, without erring too far in the direction of either too incrementalist to too pie-in-the-sky. More specifically on the "methods" side, they have people who are known in both statistical ML, and in the recent resurgence of neural-networks known as "deep learning".

You can discern some more about their general direction by looking at courses they've been involved with, talks they've given or sponsored, etc., but as far as I know (and I tried to probe a few months ago through a friend who knew someone there) their actual product / business / etc. hasn't really been leaked, or at least not leaked widely enough that I could find out about it.

This has seriously piqued my curiosity. Do you think Google bought the product or the team? $400M is a big chunk of change for ~3 people.

Edit: peaked --> piqued, thanks to spiderPig!

If the article is correct, they bought the team.

Could 3 people be worth that much? This ranks as one of the ten most expensive acquisitions by Google[1].

1: http://venturebeat.com/2014/01/14/where-nest-ranks-among-goo...

Those three are just the co-founders. The whole team seems to be much bigger. The arxiv article linked above cites seven names already.

Article has been updated; company is about 50 people.

400 millions for a 3 person startup that don't even have a proper web-site and is not in wikipedia seems crazy. But then again Google knows a little more about future value than me.

I think you're mixing up two types of acquisitions here.

There is a difference between Snapchat being worth $3 billion and Nest being worth $3 billion. The former gets the valuation based on users, the latter on talent and intellectual property.

Ditto here: $400 million is not buying you users, it's buying you raw talent and IP. Users can go off to another service in a blink of an eye - IP can't (talent can, but you can often structure the deal so that it won't for some time).

This could still be a terrible deal (I'm sure there are some people at Google still a little sore over Motorola, where the IP was valued far more than it ended up being worth), but for very different reasons.

Yeah, obviously if they don't have a trendy iOS app and a twitter presence, they couldn't possibly be worth anything! /s

Or some patents.

50 people should cost ~$50 million, if it's a very strict talent acquisition, maybe $200 if they're really good. I think as well that there is some hard IP involved, or that they were close enough to a breakthrough for there to be hard IP.

It looks to me like Google really wants to build a monopoly on the best AI people in the world, and is willing to pay out the nose to do it. Given that we're starting to get to the point where AI is going to be useful in a lot of fields, I think it's actually a really good strategy. No one competent out there to build a good competitor to completely autonomous self-driving cars at another company, for example, which will give Google a de facto monopoly on the tech behind something that almost everyone is going to want in their cars asap.

Not to mention the enormous number of innovations they'll likely be able to churn out. Hopefully it's like an AI focused PARC, but with a competent tech company at the helm :-)

And the update to the article says they were competing with Google, Facebook, Baidu... could be google was getting rid of the competition for talent; and gaining ability to attract more talent.

Though if they're expected to do wonderful things, and they've been doing things for years... it seems a certainty that they have already done some of those wonderful things. And hence have something concrete worth acquiring. Which would explain the valuation.

piqued* my curiosity. Sorry, had to :)

"peaked" does at least make some sense as a replacement. I'd wager that it'll be common usage in a decade or two.

I'm not so sure their technology is as futuristic as everyone thinks it is. If I had to take an educated guess, I would say it's some powerful AI that makes their knowledge graph smarter. Currently Google's Knowledge Graph uses more structured data sets and depends on a mechanism like this: http://www.zachvanness.com/nanobird_relevancy_engine.pdf

But the real challenge is to make the knowledge graph update in real time and take meaning from something as unstructured as a blog post or an email. And to do something like that requires some really unique AI.

--mjn - I totally agree!

Oh, I'm decidedly agnostic on whether it really is futuristic AI. Just there is some buzz around it, and some of the people involved in it are definitely legit, and have also involved themselves in the "AGI" community, which leads to such speculation (which they've pretty deliberately cultivated). That doesn't prove they've Solved AI for any sci-fi-ish notion of Solved AI. They could just have some good but in the big picture fairly modest knowledge-graph tech, or they could even have not-that-good knowledge-graph tech with great PR! Hard to say without knowing any details.

Deep learning is pretty impressive as a supplement to knowledge engineering approaches.

Google's Deep Learning team were the people who developed the alogithm that discovered cats on YouTube (without training). Presumably this team had something that impressed them.

The weakness to knowledge engineering approaches is that they tend to be fragile - they break badly with small holes in recorded knowledge. The IBM Watson team has a great video that showed how the different definitions of "fluid" and "liquid" meant a correct answer would have been missed if evidence collected in the answer verification phase of the DeepQA pipeline (no relation to Deep Learning) hadn't overridden it.

Edit: Your(?) paper on your (?) relevancy engine is interesting. It seems like an application of skip-grams (which, ironically enough are heavily used by the DeepQA answer verification phase mentioned above).

No facts about the company's work, but Yann LeCun says 3 of his former students + postdocs work at this company:

https://www.facebook.com/yann.lecun/posts/10151812982157143?...

If they're hiring his students, they probably have a high level of talent (speaking as a former -- and present, starting tomorrow -- student).

They have apparently been cornering the market for certain types of PhD and other high-end talent in a bigger-than-niche ML/AI category.

Google should buy Wolfram Research. Wolfram Alpha seems to be pretty decent functionality.

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