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 variables/relationships. The machine learning techniques did very well on those kind of mess problems. So, research shifted.
If Palantir is using ontologies for that stuff, then that would certainly be a sign for buyers to run. I still encourage academics to look into such approaches with probabilistic, simple methods in case any advances come up. Fuzzy logic was main one in my day. Just stumbled on a claim today a drone AI did human-level performance using that. Some corroboration for R&D in underdog solutions but not production apps. Haha.
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.
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)
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.
There is a whole generation of better techniques that have come out of machine learning that totally eclipses ontologies and I know Palantir isn't using them. Their corporate culture isn't set up for fostering that kind of applied research.
No-one is advocating for a fully automated approaches. I don't know where that notion came from.
In my view is that Palantir is a consulting company that is pretending to be a tooling company. And their consultants are not worth the money they charge. Just one of many Silicon Valley based frauds.
Do you have references to any specific discussions on this?
Curious as I'm doing some work of my own (well outside AI) in which developing ontologies strikes me as useful, though I'd prefer not falling into any well-worn traps.
(My use is largely comping up with useful descriptive models of otherwise hairy concepts.)
Far as ontologies in general, they have a mixed, track record. They take a lot of work to create. Then, they have to be mapped to real world inputs and outputs. One way they got applied is so called business rules engines or business process management. It's like a subset of ontology approaches of past. Here's a company that uses the real thing for enterprise software with Mercury language for execution part:
Also, Franz Inc, of Allegro Common LISP, covers many of the same use cases as Palantir with their ontological tooling.
So, there's definitely companies using it for long periods of time for real-world, use cases. Palantir just seemed to be mixing it with hype and secrecy to maximize their sale price later. ;)
Given that you're building a descriptive model it would depend if you're working with facts or with probabilities. If it's facts then Ontologies should work fine, for probabilities I'd recommend Bayesian techniques.
The input for these are usually small. From the sounds of it you're generating the input yourself so you should be safe.
Particularly in economic and policy discussion, technology is just "technology". A black box. In economics, Solow's Residual is described, by Solow, as "the measure of our ignorance" of factor productivity growth influences -- it's quite literally, statistically, what's left over after accounting for labour and capital.
I see a few quite evident classifications which strike me as useful:
1. Fuels. Apply more energy to something, it tends to happen faster. Wood, plant and animal oils, fossil fuels, nuclear fission, possibly fusion.
2. Material properties. Some things are highly dependent on specific material properties. Conductivity of gold, silver, copper, and aluminium. Ferromagnetism. Hardness of diamond. Softness of graphite. Semiconducting of silicon. Fertilising properties of nitrogen, phosphorus, and potassium. Many others. Point being, you're now locked into availablity and other properties of that material.
3. Specific process knowledge. What used to be called "arts". Most of what's now considered "technology", from agriculture to zymurgy (though zyumurgy's actually fairly close to agriculture...). These approach theoretical efficiency limits.
4. What seem to be dendritic or web structured aspects. Computer chips and Moore's law are today's classic example, but I'd count communications, transport, and trade networks, cities and urbanisations, knowledge itself, and other elements among these. What they have in common is an increasing rate of progress with greater accumulation, modulo retarding factors.
There are several other elements. Sensing and measurement increase various capabilities -- navigation and fine metal machining come to mind. Symbolic processing, from speech and writing to abstract maths and programming. Organisation -- of people, states, business, and finance.
The final element, and one which popped out at me whilst devising the ontology, was the concept of hygiene or pollution factors. They're a distinct class of phenomena which if not addressed tend to put a damper on further growth, everything from infectuous disease in cities to heavy metal pollution, salination of croplands, traffic congestion, spam and fraud in communications and business networks. It's a superset of common categories such as "pollution" or "disease" or "social breakdown".
Anyhow, that's what I'm working on. I find it a useful organising tool, still developing the idea.
2. This is true. It's worth noting such dependencies.
3. Elaborate on that.
4. That's true. There's a lot of work on that topic already that you can draw on. I remember some showing that how the cities grew was similar to how bacteria looked. Weird stuff.
Re waste. You can model it as a separate thing that goes up when certain actions happen, then starts bringing them down. Definitely should be considered.
Fuels feed processes in which energy is crucial. Food and metabolism, almost all ore refining and metalworking, heating and cooking, and transport. Air travel (at any significant level) and Earth-to-orbit space launch are both entirely dependent on fuel-driven processes.
I didn't mention energy transmission and transformation, which is another set of mechanisms, ranging from projectiles (force-at-a-distance) to the simple machines (lever, ramp, screw, pulley, gears), linear-to-rotary and rotary-to-reciprocating transforms. Electricity, in this this ontology, is for the most part an energy transmission and transformation mechanism: to heat, motion, light, sound, etc.
3. See the Ello link for a list. The key is that the understanding is of how to do a process, which approaches some theoretical maximum efficiency. There's probably a learning curve associated, see J. Doyne Farmer and Wright's Law (related to Moore's) of process improvement.
4. You're likely thinking of Geoffrey West. There's a lot of Santa Fe Institute thinking in this idea generally.
The hygiene factors are more than just waste.
An early realisation of this came when I was considering Metcalfe's Law and the Tilly-Odlyzko refutation, of network effects. What I realised was that while yes, additional nodes tended to produce lesser value, each node also had a tendency to impose a cost to others, that being roughly constant. In a message or information network, you could consider this to be the "is this worth reading or not" cost associated with any given message.
(If you have Reddit's RES installed, set to view images, as there's a set of graphs illustrating the cost function.)
Applying that to various group communication sets, you can estimate the cost constant, and it turns out that the maximum supportable group size is a function of that constant. Among other things, Facebook manages to scale to a billion or several members by keeping the negative cost constant really, really low.
That's just one instance.
More generally, there are other phenomena which show examples of cost:
1. The Silk Road increased trade but also created a "commerce" in disease from China to Europe and versa. Similar for interactions with the New World (smallpox, syphilus).
2. Greek and Roman city engineers were conscious of location especially as regarded water flow, with the associations with disease. Clean in, dirty out. And no deisel pumps.
3. Indoor fire gives heat and cooking, but contributes to air pollution. Chimneys help.
4. Disease and epidemics limited city sizes. ~1800 London could not sustain its own population through births given the death rate. Constant in-migration was essential. Life-expectency of new arrivals was frightfully low. This improved tremendously with creation of sewers. By the end of the 19th century, solid waste, sewage, and horse metabolites (solid and liquid) were a crisis for many large cities, which had populations of hundreds of thousands of horses alone. The automobile solved a crushing pollution problem. But you got sewage, freshwater, sanitation, etc.
5. Reducing costs of something inevitably increases the amount of undesirable activity enabled. You need highly differentiated reward/punishment systems to limit these. Highway congestion, cruising, fraud, spam, advertising, etc.
6. Systemic disruptions. Here, the issue is effects which operate in difficult-to-forsee, systemic ways. CO2 and global warming, CFCs and ozone, asbestos, endocrine disrupters, nonnative species introduction, light pollution and wildlife disruption, are all examples.
Some of this overlaps with various other areas -- pollution, ecological principles, health and sanitation, etc. But I think the concept may be more general than any of these, and in terms of a technological dynamic, it has its own space, where the factors act to limit growth unless themselves specifically addressed.