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There is usually something wrong with the ontologies approach as it rarely works. There is roughly two decades of evidence for this for anyone who cares to look

by bazqux2 10y ago

There is usually something wrong with the ontologies approach as it rarely works. There is roughly two decades of evidence for this for anyone who cares to look. Five decades if you loosen the definition to include the family of logic and constraint programming - see AI Winter. There is nothing new about these ideas. It always looks and feels like it's going to work which is why humanity has persisted with it for so long and will likely continue to persist for some time to come. 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.

dredmorbius 10y ago
Is this ontologies within the field of AI not working, or more generally? 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.)
- bazqux2 10y ago
  I meant generally they are generally not useful. Sometimes they are. It depends on the purpose and what you want to build and who it's for. 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.
  - dredmorbius 10y ago
    An ontology of technlogical mechanisms (or dynamics): https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww 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.
    - nickpsecurity 10y ago
- nickpsecurity 10y ago
  What bazqux2 said is accurate. I'll go further to say that the kinds of work Palantir is involved in is mostly probabilistic. Especially intelligence work. So, use of models requiring certainty or straight logic in areas rife with uncertainty & degrees of truth seems set up to fail outside easy inferences. One can encode the logical stuff in probability models but harder to do reverse. Hence, their underlying tech should be probabilistic, fuzzy logic, or something similar for best results instead of just some results. 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: http://www.missioncriticalit.com/development.html http://www.missioncriticalit.com/development.html Also, Franz Inc, of Allegro Common LISP, covers many of the same use cases as Palantir with their ontological tooling. http://allegrograph.com/solutions-by-use/ http://allegrograph.com/solutions-by-use/ 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. ;)
argonaut 10y ago
Again, Palantir is not an AI company. They are a data visualization and analytics company. So all your perfectly fine points about ontologies and AI winter are not relevant.
- bazqux2 10y ago
  It is an ontology company - see their website. This is how they derive their analytics and visualizations. So my points are relevant.
  - willalden 10y ago
    Hey -- I wrote that BuzzFeed article in May. I'm always looking to learn new things. If anyone on here wants to chat and compare notes, off the record, please don't hesitate to reach out: will.alden@buzzfeed.com

by bazqux2 10y ago

dredmorbius 10y ago

Is this ontologies within the field of AI not working, or more generally? 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.)

bazqux2 10y ago
I meant generally they are generally not useful. Sometimes they are. It depends on the purpose and what you want to build and who it's for. 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.
- dredmorbius 10y ago
  An ontology of technlogical mechanisms (or dynamics): https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww 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.
  - nickpsecurity 10y ago
nickpsecurity 10y ago
What bazqux2 said is accurate. I'll go further to say that the kinds of work Palantir is involved in is mostly probabilistic. Especially intelligence work. So, use of models requiring certainty or straight logic in areas rife with uncertainty & degrees of truth seems set up to fail outside easy inferences. One can encode the logical stuff in probability models but harder to do reverse. Hence, their underlying tech should be probabilistic, fuzzy logic, or something similar for best results instead of just some results. 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: http://www.missioncriticalit.com/development.html http://www.missioncriticalit.com/development.html Also, Franz Inc, of Allegro Common LISP, covers many of the same use cases as Palantir with their ontological tooling. http://allegrograph.com/solutions-by-use/ http://allegrograph.com/solutions-by-use/ 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. ;)

argonaut 10y ago

Again, Palantir is not an AI company. They are a data visualization and analytics company. So all your perfectly fine points about ontologies and AI winter are not relevant.

bazqux2 10y ago
It is an ontology company - see their website. This is how they derive their analytics and visualizations. So my points are relevant.
- willalden 10y ago
  Hey -- I wrote that BuzzFeed article in May. I'm always looking to learn new things. If anyone on here wants to chat and compare notes, off the record, please don't hesitate to reach out: will.alden@buzzfeed.com