This still happens! Silicon Valley gets a lots of long-term oriented funding from taxpayers via DARPA and other government agencies. Siri (named after SRI International where Engelbart did his work) and autonomous driving (DARPA Grand Challenge) are just a couple of recent examples.

In fact PARC still does too. Although owned by a private company, PARC does government-funded research.[1]

I would say generally people in SV are less consciously aware of the major role government funding plays in it to this day. It's probably because of the mythos of private entrepreneurship and the uncomfortable narrative of all this so-called "free market capitalism" being supported by billions in govt funds, with only a fraction of the profits returned to the public coffers via taxes.

[1] https://www.parc.com/news-release/97/parc-awarded-up-to-2-mi...

The DARPA model is also not big science because the researchers on the outside do not pick the problems, the PMs at DARPA do. The model works when a visionary becomes a PM and is given a "big" (20-80 mil) bucket of money to disperse to researchers to enact their big ideas. If the PMs are truly visionary, then this works. If they are not, you wind up with a sort of soup of crap.

Additionally, since it's applied, DARPA will do frequent (once a quarter and once a year) check-ins, measurements, and progress reports. If you don't measure up during those, your funding gets cut. So as a researcher, it is a challenge if what you want to do is a little bit off the path of what the PM wants to do.

I have much more limited exposure to IARPA but it seems to be the same way there.

I have more experience with NSF, which is the total opposite: researchers propose their own projects and there are infrequent touchpoints, and no cut points really. However, NSF will give you one to two orders of magnitude less money than DARPA.

The programs (and their vision, and their success) depend quite a bit on the PM, and on their ideas and engagement. To a lesser extent success depends upon the SETAs as well.

Eh. You can get like a $200/hr rate. If you're a small company with low overhead, you can get paid quite competitively with the private sector in base comp. Difficult to match big G stock grants, though, no one is becoming a millionaire off of this work.

Unless of course what you do on the grant can be turned into a billion dollar company, because (generally) contractors walk away from research programs with liberal rights to the IP they create, with the government retaining some rights to use, but very rarely (IME) retaining direct ownership. Of course, it's probably not that likely that what you do can turn into a billion dollar company but hey, one can dream...

Request: can you give an AMA on how one actually proposes/gets funding from darpa or at least an ELI5 on the topic...

What sort of idea should one have where you say to yourself "gee, I should hit up darpa with this!"

The truth, is that you should look at the Broad Agency Announcements (BAAs) that DARPA, specifically the Information Innovation Office (I2O) makes. There will be big PDFs that have lots of boiler plate and look really boring, but these are basically documents written by a program manager (PM) that describes what their envisioned research program is, how they see it being divided up, and what kind of researchers they see doing work in each area of their research program. A research program is split up into one of many (between 3 and 8) technical areas (TA) and there will be language in the BAA describing how many TAs one performer might propose to.

Overall a TA can be described at a high level as one of a few roles: a blue team, a red team, and a white team. There will usually be more than one blue team, at most one red team, and precisely one white team. A blue team does the R&D on the program. Each blue team can have their own unique approach to addressing the research challenges laid out by the PM. Usually the blue teams have competing or complimentary approaches, it is seen as a waste of money to pay two blue teams to do exactly the same thing. A red team evaluates the work done by the blue team. Exactly what this means depends on what the blue team builds. The white team "holds the room together" by fitting the PMs vision together with what the blue team does. The white team usually has a much closer relationship with the PM and usually the white team is pre-determined by the time the BAA is made public.

Blue teams are usually teams of 2-4 companies / universities. These teams usually form before a BAA comes out due to people "in the know" getting a rumor about an upcoming program, or just after it comes out, based on prior relationships. It's kind of rare to have a blue team that is only one entity, but depending on the entity and the size of the program, it can happen.

The path to a successful proposal is demonstrating that your team has ideas that are relevant to the PM and that you can successfully execute them. You show this by having a stack of prior work, good ideas, and a well formed and coherent proposal. Think of the proposal as an audition - if you as a team can't get it together for a month and write a 30 page document describing what you want to do, you probably can't keep it together for 4 years working on something. Your team reads the BAA, does some analysis, figures out how your ideas and past work can apply to the research program proposed by the BAA, and then you write the story up. If you do a good job, and your story is more compelling than other people, you get the money.

The super-truth is that it's an old boys club with a lot of luck and nepotism.

The way people get "in" is when their peers go to DARPA to be a PM. Then you, the researcher, can think "Well Dr. Smith knows about X and likes X+Y, I do some Y, I have an idea that could use some funding, what would Dr. Smith like to read about." Then you write it up and e-mail it to them.

Luck is a big factor here because PMs get e-mails like this all the time. It's almost essential to already be in the PMs rolodex/friend group to get some of their cycles. They like to say that they want to have people outside their circle approach them with new ideas but that's kind of a white lie, there are lots of people in the world and a finite amount of the PMs time.

So it's no surprise who you know is important - the brain is mostly about connectivity, not about the individual neuron.

Just a model for thought, obviously not a complete description ("every model is wrong"). I think the value of this model, if you can warm up to it, is that you stop worrying that "it's all about the connections" - because it really is and it's useful that way because that's a major point of how a network works. So do work on your connectivity! And also just like in the brain, a few high-quality connections are worth much more than a thousand low-quality ones.

> 2. Fund people not projects — the scientists find the problems not the funders. So, for many reasons, you have to have the best researchers.

So yes, the total amount may be there but is now diverted.

Reminds me of a (probably the) reason we're doing standardized testing in schools. A decent teacher is able to teach and test kids much better than standardized tests, but the society needs consistency more than quality, and we don't trust that every teacher will try to be good at their job. That (arguably, justified) lack of trust leads us as a society to choose worse but more consistent and people-independent process.

I'm starting to see this trend everywhere, and I'm not sure I like it. Consistency is an important thing (it lets us abstract things away more easily, helping turn systems into black boxes which can be composed better), but we're losing a lot of efficiency that comes from just trusting the other guy.

I'd argue we don't have many PARC and MIT around anymore because the R&D process matured. The funding structures are now established, and they prefer consistency (and safety) over effectiveness. But while throwing money at random people will indeed lead to lots of waste, I'd argue that in some areas, we need to take that risk and start throwing extra money at some smart people, also isolating them from demands of policy and markets. It's probably easier for companies to do that (especially before processes mature), because they're smaller - that's why we had PARC, Skunk Works, experimental projects at Google, and an occasional billionaire trying to save the world.

TL;DR: we're being slowed down by processes, exchanging peak efficiency for consistency of output.

It's more about it being way to hard to fire bad teachers and standardized tests, in theory at least, are an attempt at providing objective proof of bad teaching that won't be subject to union pushback and/or lawsuits.

I think a lot of the desire for standardized testing would dissolve if principals could just make personnel decisions like normal managers. And, of course, it's up to their superiors to hold them accountable for being good at that job.

> ...we're losing a lot of efficiency that comes from just trusting the other guy.

I think people have been trusting public schools a lot. That generally worked out great for people in affluent suburban districts. And it worked out horribly for people in poor, remote, and urban districts.

> I think a lot of the desire for standardized testing would dissolve if principals could just make personnel decisions like normal managers.

That could maybe affect the desire coming from the bottom, but not the one from the top - up there, the school's output is an input to further processes. Funding decisions are made easier thanks to standardized tests. University recruitment is made easier thanks to standardized tests. Etc.

> I think people have been trusting public schools a lot. That generally worked out great for people in affluent suburban districts. And it worked out horribly for people in poor, remote, and urban districts.

That's the think I'm talking about. I believe it's like this (with Q standing for Quality):

  Q(affluent_free) + Q(poor_free) > Q(affluent_standardized) + Q(poor_standardized)
  Q(affluent_standardized) < Q(affluent_free)
  Q(poor_standardized) > Q(poor_free)

http://www.balancedscorecard.org/Resources/About-the-Balance...

A similar issue was just discussed yesterday in a thread on Jeff Bezos' letter to shareholders:

"A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp."

https://news.ycombinator.com/item?id=14107766

I remember in middle school, I had a couple teachers who would say "ok, this is the standardized stuff I have to say" and then go into a very boring lecture of what we just had been learning except the way we first learned it was interesting and engaging. They were simply dotting their i's to make sure that they did their job in conveying the material the state wanted us to know.

Sure , some teachers could use the freedom to teach better(and create new teaching methods) , but statistically , won't most teachers perform better when driven by above average methods ?

And as for research , sure we need those big breakthroughs , but we also need many people to work on the many small evolutionary steps on each technology . Surely we cannot skip that ?

Basically, think of why you build layers of abstraction in code, even though it almost always costs performance.

--

[0] - e.g. one teacher focusing on quality math skills, another on quality outdoors skills, etc.

And as for education, i don't think standardization always leads for performance loss. For example ,the area of reading has seen a lot of research, and one of the results is "direct instruction", one of the best methods to teach reading, and is a highly standardized method.

But maybe what's your saying is true for standardized tests in current implementation.

To use your example - more modularized technologies are more flexible and can be developed faster, but they no longer use resources efficiently. A modular CPU design makes design process easier, but a particular modular CPU will not outperform a hypothetical "mudball" CPU designed so that each transistor has close-to-optimal utilization (with "optimal" defined by requirements).

Or compare standard coding practices vs. the code demoscene people write for constrained machines, in which a single variable can have 20 meanings, each code line does 10 things in parallel, and sometimes compiled code is itself its own data source.

--

The way I see it, building abstractions on top of something is shifting around difficulty distributions in the space of things you could do with that thing.

But on bigger design spaces, when you let hundreds of thousands of people collaborate, create bigger markets faster, and grab more revenue - you enable a much more detailed exploration of the design space.

And often, you discover hidden gold. But also - if you've discovered you've made a terrible mistake in your abstractions - you can often fix that, maybe in the next generation.

I think this is a really bad straw man. There's broad bipartisan support for funding research into good ideas, assuming it's done the right way. The problem, though, is that the billions in government research spending we already allocate are subject to incredible amounts of lobbying and earmarking.

We'll have a NASA allocation, but it has to take place in the state of a particular Senator. Or the research will be funneled through the department of defense and received by defense contractors.

And, besides, for every DARPAnet, we have many "Where does it hurt most to be stung by a bee?" (1).

It's hard to evaluate whether we would still end up in a net positive situation. I don't have hard feelings against people who are both enthusiastic and skeptical about government funding grants.

(1) https://www.flake.senate.gov/public/_cache/files/ef6fcd58-c5...

From the link:

METHODOLOGY Specific dollar amounts expended to support each study were not available for the projects profiled in this report. Most were conducted as parts of more extensive research funded with government grants or financial support. The costs provided, therefore, represent the total amount of the grant or grants from which the study was supported and not the precise amount spent on the individual studies. This is not intended to imply or suggest other research supported by these grants was wasteful, unnecessary or without merit.

It's an incomplete and distorted picture of how bureaucracy funds research. I brought it up to add counterpoints and context to the implication that government is actually good at this sort of thing if we'd just get over free market capitalism (or something).

Did they invest the same amount in these two?

I was saying that just pointing out that programs like DARPAnet might not make up for all the other nonsense.

And we can flip that around as well, maybe PARC was spun off from some successful grants, but we're not taking into account these sorts of failures when attaching the price tag to those initial grants.

PARC really wasn't an R&D center for Xerox copiers (this stayed in Rochester), and Gary was a great engineer who could do the work of making a laser and its optics and scanner work with a standard copying engine pretty much by himself.

Fantastic read, "Sketchpad: A man-machine graphical communication system" ~ https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-574.pdf

So in that case imagine there is a PARC bis somewhere out there... what kind of out-of-regular-folks wallet things they might be working on right now?

I want to be surprised today like Jobs was when he visited them first time.

Also Decoupled Neural Interfaces: https://deepmind.com/blog/decoupled-neural-networks-using-sy...

And AlphaGo of course.

There are certain moments in time where such innovation is possible and this was one of them. In this respect they are like The Beatles--the right talent just at the right time.

There isn't anything like Xerox PARC at the moment and won't be until the conditions are once again ripe for another Cambrian Explosion (as another commentor aptly described it) of innovation.