A lot of semantic NLP tried this and it kind of worked but meanwhile statistical correlation won out. It turns out while humans consider semantic understanding to be really important it actually isn’t required for an overwhelming majority of industry use cases. As a Kantian at heart (and an ML engineer by trade) it sucks to recognize this, but it seems like semantic conceptualization as an epiphenomenon emerging from statistical concurrence really might be the way that (at least artificial) intelligence works
I don’t see the approaches as mutually exclusive. Statistical correlation can get you pretty far, but we’re already seeing a lot of limitations with this approach when it comes to verifying correctness or having the algorithm explain how it came to a particular conclusion. In my view, this makes purely statistical approach inadequate for any situation where there is a specific result desired. For example, an autonomous vehicle has to drive on a road and correctly decide whether there are obstacles around it or not. Failing to do that correctly results in disastrous results and makes purely statistical approaches inherently unsafe.
I think things like GPT could be building blocks for systems that are trained to have semantic understanding. I think what it comes down to is simply training a statistical model against a physical environment until it adjusts its internal topology to create an internal model of the environment through experience. I don’t expect that semantic conceptualization will simply appear out of feeding a bunch of random data into a GPT style system though.
I fully agree with this, would have written something similar but was eating lunch when I made my former comment. I also think there’s a big part of pragmatics that comes from embodiment that will become more and more important (and wish Merleau-Ponty was still around to hear what he thinks about this)
Indeed, I definitely expect interesting things to start developing on that front, and we may see old ideas getting dusted off because now there’s enough computing power to put them to use. For example, I thought The Society of Mind from Minsky lays out a plausible architecture for a mind. Imagine each agent in that scenario being a GPT system, and the bigger mind being built out of a society of such agents each being concerned with a particular domain it learns about.
Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.
I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.
A lot of semantic NLP tried this and it kind of worked but meanwhile statistical correlation won out. It turns out while humans consider semantic understanding to be really important it actually isn’t required for an overwhelming majority of industry use cases. As a Kantian at heart (and an ML engineer by trade) it sucks to recognize this, but it seems like semantic conceptualization as an epiphenomenon emerging from statistical concurrence really might be the way that (at least artificial) intelligence works
I don’t see the approaches as mutually exclusive. Statistical correlation can get you pretty far, but we’re already seeing a lot of limitations with this approach when it comes to verifying correctness or having the algorithm explain how it came to a particular conclusion. In my view, this makes purely statistical approach inadequate for any situation where there is a specific result desired. For example, an autonomous vehicle has to drive on a road and correctly decide whether there are obstacles around it or not. Failing to do that correctly results in disastrous results and makes purely statistical approaches inherently unsafe.
I think things like GPT could be building blocks for systems that are trained to have semantic understanding. I think what it comes down to is simply training a statistical model against a physical environment until it adjusts its internal topology to create an internal model of the environment through experience. I don’t expect that semantic conceptualization will simply appear out of feeding a bunch of random data into a GPT style system though.
I fully agree with this, would have written something similar but was eating lunch when I made my former comment. I also think there’s a big part of pragmatics that comes from embodiment that will become more and more important (and wish Merleau-Ponty was still around to hear what he thinks about this)
Indeed, I definitely expect interesting things to start developing on that front, and we may see old ideas getting dusted off because now there’s enough computing power to put them to use. For example, I thought The Society of Mind from Minsky lays out a plausible architecture for a mind. Imagine each agent in that scenario being a GPT system, and the bigger mind being built out of a society of such agents each being concerned with a particular domain it learns about.
Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.
I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.
and just ran across this https://arxiv.org/abs/2308.00352