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How Silicon Valley's Palantir Wired Washington

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Re: How Silicon Valley's Palantir Wired Washington

#61

Post by bazqux2 »

nickpsecurity wrote: post #53
bazqux2 wrote: post #30

Earlier quoted context omitted.

Sure, I'll give a few examples. A guy from BP told me in 2013 that they forked iPython, replaced all iPython references with Palantir and tried to sell it to them for $500K p.a. For me; back in the day (2010) they were less secretive about their technology which was essentially an ontological reasoner. This was pre the Big Data hype boom - and AFAIK Palantir has never been about Big Data. Ontological reasoners have p…

They apparently say it right here about the ontology approach: https://www.palantir.com/palantir-gotham/technologies/ That's especially hilarious given that approach's failures are what led to investment in machine learning in the first place. Such approaches tend to assume precise information, variables, and rules about the world. Most problems Palantir wants to address... the hard ones... are imprecise with hidden…

Huh, so they are open about it now. They definitely were not when I talked to them. I guess enough time has passed that people have forgotten the hard won lessons of the past.

I worked on scaling and generalizing ontologies at university and had already switched to working with Big Data / ML at a big company when Palantir tried to recruit me. I talked to some of their senior engineers about their tech and made the point that their tech sounded just like ontologies. I tried to get them to admit what it was so I could be sure I was having an honest conversation with them. They flatly denied it and made it out like the whole thing was their great new idea. I was unimpressed.

I was still interested in working for them. Access to hard interesting problems can be hard to come by. In the end I couldn't take their legendary arrogance and insecurities - to me these are bright red flags of a toxic corporate culture. And they low balled me. I would have temporarily put up with the toxic culture for large piles of money.

Re: How Silicon Valley's Palantir Wired Washington

#62

Post by [deleted] »

ajb wrote: post #52
jgalt212 wrote: post #39

I look at consultants like home improvement, if you can spend $100 and improve the value of your home by $70, you've basically won. As an aside, there should be a JP Power rankings for consultancies. Is there?

Maybe this is a typo, but the way you wrote it, you lost $30.

[deleted]

Re: How Silicon Valley's Palantir Wired Washington

#63

Post by nickpsecurity »

bazqux2 wrote: post #61
nickpsecurity wrote: post #53

Earlier quoted context omitted.

They apparently say it right here about the ontology approach: https://www.palantir.com/palantir-gotham/technologies/ That's especially hilarious given that approach's failures are what led to investment in machine learning in the first place. Such approaches tend to assume precise information, variables, and rules about the world. Most problems Palantir wants to address... the hard ones... are imprecise with hidden…

Huh, so they are open about it now. They definitely were not when I talked to them. I guess enough time has passed that people have forgotten the hard won lessons of the past. I worked on scaling and generalizing ontologies at university and had already switched to working with Big Data / ML at a big company when Palantir tried to recruit me. I talked to some of their senior engineers about their tech and made the po…

"I was still interested in working for them. Access to hard interesting problems can be hard to come by. In the end I couldn't take their legendary arrogance and insecurities - to me these are bright red flags of a toxic corporate culture. And they low balled me. I would have temporarily put up with the toxic culture for large piles of money."

Smart decision. Far as ontologies, the Cyc project to create common sense in machines was my favorite at the time. Used ontological, knowledge base if my broken memory is accurate. I was and still am firmly convinced that finding an architecture suitable to solve that problem is a pre-requisite for the AI's we really want. Deep-learning is approximating it but closer to how brain does vision than common sense. Minsky noted at one point he could count number of researchers doing common sense on a single hand or so. That's a hard problem if you want one. Also unbelievably hard to get funded. (sighs)

Re: How Silicon Valley's Palantir Wired Washington

#64

Post by bazqux2 »

bane wrote: post #58
tptacek wrote: post #22

Earlier quoted context omitted.

This is the kind of Palantir criticism I want to hear more of on HN. Could you flesh it out? What are the open or free alternatives?

I think this is the most surprising thing, from my understanding, Palantir's user-facing tech is an ancient c.2007 era Java web start tool backed by a fairly ho-hum enterprise ontology system, some free-text search and fairly basic geospatial map server. It sounds like a 3-6 month, 3 person project to replicate with modern tech. I think all of the pieces of a Palantir-like system exist with open/free alternatives, bu…

That matches my understanding of their tech stack as well. I also understand why people don't do an open sourced version - it doesn't pay nearly as well as having a job.

What I find interesting about this is that in effect Palantir is acting as a consulting company while pretending to be a software tooling company. This allows them to claim a higher earnings multiple to inflate value and extract more money out of VCs and offer a lower percentage of equity to employees. This is a very old trick. The problem is that consulting companies are much harder to scale than software companies and the inevitable disappointment will lead to a loss of equity.

There is good money in consulting (I am one) but it's hard to build a large consulting company when the 'tooling' companies can poach your talent away with cheap VC money and fairy tales about future piles of cash. It spoils the market. VC powered tooling companies masquerading as consulting companies are a real problem right now.

Re: How Silicon Valley's Palantir Wired Washington

#65

Post by bazqux2 »

nickpsecurity wrote: post #63
bazqux2 wrote: post #61

Earlier quoted context omitted.

Huh, so they are open about it now. They definitely were not when I talked to them. I guess enough time has passed that people have forgotten the hard won lessons of the past. I worked on scaling and generalizing ontologies at university and had already switched to working with Big Data / ML at a big company when Palantir tried to recruit me. I talked to some of their senior engineers about their tech and made the po…

"I was still interested in working for them. Access to hard interesting problems can be hard to come by. In the end I couldn't take their legendary arrogance and insecurities - to me these are bright red flags of a toxic corporate culture. And they low balled me. I would have temporarily put up with the toxic culture for large piles of money." Smart decision. Far as ontologies, the Cyc project to create common sense…

Thanks. There is some interesting work being done with Deep Learning mixed with Bayesian Models and external databases of facts. The training apparently ungodly slow though. My work these days is visual so CNN works well enough. I'm not an authoritative expert in this field though so I'm taking 6 months off to study it intensely, after which I hope to be able to make some meaningful contributions.

Re: How Silicon Valley's Palantir Wired Washington

#66

Post by hueving »

tptacek wrote: post #49
gaur wrote: post #46

Earlier quoted context omitted.

Why do people replace transparent English phrases with opaque foreign abbreviations? We're not writing with quills on vellum anymore. Even "$500k/yr" is an improvement on "$500k p.a.".

Complaining about p.a. in a finance context makes about as much sense as complaining about "etc".

Well it makes sense to complain about etc when we have etcd now. Embrace the future!

Re: How Silicon Valley's Palantir Wired Washington

#67

Post by gaur »

trengrj wrote: post #51
gaur wrote: post #46

Earlier quoted context omitted.

Why do people replace transparent English phrases with opaque foreign abbreviations? We're not writing with quills on vellum anymore. Even "$500k/yr" is an improvement on "$500k p.a.".

I'd argue "per annum" or p.a. isn't a foreign term and just standard English. English is full of Latin words, transparent, opaque, foreign, those are all words of Latin origin.

> I'd argue "per annum" or p.a. isn't a foreign term and just standard English.

Did you notice that a fluent English speaker had to ask what "p.a." meant? Do you think that would have happened if it had been written "per year" instead?

Re: How Silicon Valley's Palantir Wired Washington

#68

Post by bane »

bazqux2 wrote: post #64
bane wrote: post #58

Earlier quoted context omitted.

I think this is the most surprising thing, from my understanding, Palantir's user-facing tech is an ancient c.2007 era Java web start tool backed by a fairly ho-hum enterprise ontology system, some free-text search and fairly basic geospatial map server. It sounds like a 3-6 month, 3 person project to replicate with modern tech. I think all of the pieces of a Palantir-like system exist with open/free alternatives, bu…

That matches my understanding of their tech stack as well. I also understand why people don't do an open sourced version - it doesn't pay nearly as well as having a job. What I find interesting about this is that in effect Palantir is acting as a consulting company while pretending to be a software tooling company. This allows them to claim a higher earnings multiple to inflate value and extract more money out of VCs…

What's pretty interesting is that my understanding of their licensing scheme is that it is not cheap per seat per year. Something on the order of $10-12k. There was a pricing sheet that floated around a few months ago that was released through a FOIA request. So it sounds like they covered their initial development R&D costs through licensing, led with that tool into consulting.

My understanding is that many of the tooling companies who became consulting firms mostly jettisoned the tools along the way. Palantir has just let theirs stagnate.

Re: How Silicon Valley's Palantir Wired Washington

#69

Post by adenadel »

gaur wrote: post #67
trengrj wrote: post #51

Earlier quoted context omitted.

I'd argue "per annum" or p.a. isn't a foreign term and just standard English. English is full of Latin words, transparent, opaque, foreign, those are all words of Latin origin.

> I'd argue "per annum" or p.a. isn't a foreign term and just standard English. Did you notice that a fluent English speaker had to ask what "p.a." meant? Do you think that would have happened if it had been written "per year" instead?

The same could be argued for any jargon outside of one's own field.

Re: How Silicon Valley's Palantir Wired Washington

#70

Post by argonaut »

nickpsecurity wrote: post #53
bazqux2 wrote: post #30

Earlier quoted context omitted.

Sure, I'll give a few examples. A guy from BP told me in 2013 that they forked iPython, replaced all iPython references with Palantir and tried to sell it to them for $500K p.a. For me; back in the day (2010) they were less secretive about their technology which was essentially an ontological reasoner. This was pre the Big Data hype boom - and AFAIK Palantir has never been about Big Data. Ontological reasoners have p…

They apparently say it right here about the ontology approach: https://www.palantir.com/palantir-gotham/technologies/ That's especially hilarious given that approach's failures are what led to investment in machine learning in the first place. Such approaches tend to assume precise information, variables, and rules about the world. Most problems Palantir wants to address... the hard ones... are imprecise with hidden…

There is nothing wrong with the ontologies approach. Palantir is not aiming for a fully automated approach. Palantir's core product is a an entity graph mostly used and manipulated by humans for analysis. Basically their software is only meant to augment human analysis.

That being said, from my conversations with them, they also have a traditional machine learning team for whenever that approach is needed for a product. But their core product is meant to only help analysis that is mostly done by humans.

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