Boolean logic is a subset of neural networks. A single neuron can implement any logic gate. Assume the output is clamped between 0 and 1.
A and B = A + B - 1. A or B = A + B. Not A = -A + 1. But first order logic is not so simple. We also know from 35 years of experience (beginning with Cyc) that logic based knowledge representation is not a path to AGI, in spite of what seems to be a straightforward approach not requiring a lot of computing power. I hope you understand why this is the case. You can't train logic models. Language evolved to be learnable on slow, massively parallel networks. Semantics comes before grammar. If you want to know what works, study text compression. On Mon, Sep 30, 2019, 8:13 AM YKY (Yan King Yin, 甄景贤) < [email protected]> wrote: > On 9/27/19, Steve Richfield <[email protected]> wrote: > > YKY, > > > > The most basic function of neurons is process control. That is where > > evolution started - and continues. We are clearly an adaptive control > > system. Unfortunately, there has been little study of the underlying > > optimal "logic" of adaptive control systems. > > > > I strongly believe that a different sort of "logic" is at work, and that > > what we call "intelligence" is simply a larger adaptive control system > > working according to that "logic". We are clearly more intelligent than > > ants, but that is more quantatative than qualitative. > > > > Learning logic seems like a good idea, but you might want to reconsider > the > > logic you are learning. > > > > Steve > > > Thanks for the comment. It is a very common objection indeed, and also > has some good reasons behind it. > > From cognitive psychology most people tend to believe that the brain uses > *model-based* reasoning instead of *rules-based* reasoning. We don't > fully understand the brain's mechanism, but we may guess at some general > principles. I think the brain uses some sort of neural representations, > which are composed of neural "*features*", ie, certain patterns of > neurons' activations. > > Each neuron is either ON or OFF or may be regarded to activate with a > fuzzy truth value. Thus we can view each neural feature as a "micro" *logic > proposition*. That creates a rough correspondence between neural > representations and logic representations. > > Indeed, it is not so surprising, as we can express our thoughts into > natural language with relative ease, and natural language has the structure > of logic propositions. > > For example, the visual cortex can recognize images such as "cat" and > "dog". Through a dynamical process, it recognizes the situation of "cat > chases dog". This is likely represented by a juxtaposition of "cat", > "chase", "dog" neural features. This is very similar to the > *predicate-logic* expression: chase(cat, dog). > > So there may not be a huge gap between neural and logical > representations. The next question is: How does the brain jump from one > neural representation state to the next? In logic, this is achieved via > *rules > with variables and quantifiers*. > > At this point I am not so sure if the brain's mechanism is really similar > to the logical mechanism. That's why I think you raised a good question, > and I still don't have a good answer 😊 , but it is a good place to start > thinking. > > So I assume the brain jumps from one neural representation to the next, > and that such neural states are formed via juxtaposition of "*concepts*" > (which are some neural activation patterns). One way that this may be > different from logic rules is that the neural representations can be > "distributive". > > I need to think about this more, but there may exist a rough > correspondence between logic rules and neural state-transitions. > > > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + delivery > options <https://agi.topicbox.com/groups/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/T77af318d4abfa8a8-M5869542b8bf0c880a6a68f3e> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T77af318d4abfa8a8-M76095de8167921d265cefbe0 Delivery options: https://agi.topicbox.com/groups/agi/subscription
