Rob So much of what you're saying makes so much sense, that it's almost scary. The years of theoretical discussions I had with Prof Honeycutt bears out what you're saying. Why I constantly say it remains an issue of design, is exactly what I see in your communications. It is because if one designs and implements, one simply becomes stuck with that design and trying to make it work. Very few social groups are willing to throw away a design that is clearly inappropriate - even after a few years' of investment - to use that learning for a radical redesign. Karl Mannheim referred to such elitist practices as being rooted in Ideological and Utopian thinking. Clearly, a society of developers need new ways of thinking about AGI.
My contention is; to develop AGI, one must first become AGI. To my mind, the artifact of such a transformation would be represented by an AGI blueprint. A strong truth is often most unpopular. I know my perspective is disturbing a number of staid veterans in the field, but it does not conclude that what I'm saying has no relevance. Rob, I'd like to venture to say that even what you're discussing, I have theoretically researched beyond with my mentor. There are a number of levels still above what you propose, which we have scant theory for. However, you are already thinking about what the integration and outcomes of such integration would mean. Such is systems thinking. I'm grateful for those who are diligently clawing away - with seemingly bare hands at times - at the AGI coalface, but I think it would've been much better had "we" first agreed on a the continuous development approach of an appropriate toolkit. There remains a chronic absence in consensus and I see no end to it. A failure of society to organize? One cannot use methodology, which has proven to be limited (eventually) to try to address futuristic solutions that we have to still develop "new" theory for. As such, I propose, at first, a radical redesign, supported by a pragmatic, next-step approach. We need case-based successes to learn from. In its absence, a number of us would simply follow our own idea of what an AGI system would become. Last, a critical thought. I have no idea why anyone would spend a useful life on building submarines, when those have already been perfected and are not what is needed in the world. To my mind, that is just playing it safe. What it is not, is assuming industry leadership and stepping out to define what AGI should become. To do so takes very-specific personality, and character. It requires a historical bigness, not petty nit picking and ridicule. This, I'm pointing to at our learned friend (and similar others who I have encountered here) who seem to think no one else in this whole, wide world has much use to contribute to their version(s) of AGI. I think they're sorely mistaken, but only time would tell. Rgds Robert Benjamin ________________________________ From: Rob Freeman <[email protected]> Sent: Thursday, 21 February 2019 12:38 AM To: AGI Subject: Re: [agi] openAI's AI advances and PR stunt... OK, that makes sense Ben. So long as you have a clear picture of how to progress the theory beyond temporary expediency, temporarily using the state-of-the-art may be strategic. So long as you are moving forward with some strong theoretical candidates too. If we get trapped without theory, we're blind. There are too few people with any broad theoretical vision for how to move forward. Too many script kiddies just tweaking blindly, viz, the "important step" this thread began with. I'm encouraged that it now appears you are deconstructing grammar and resolving it to a raw network level. That Linas is seeing the relevance of maths like category theory, which is motivated by formal incompleteness, speaks to this realization. (Though he may not be aware of the full import.) Deep learning does not realize this. It does not realize that formal description above the network level will be incomplete. I'm sure that is the key theoretical failure holding it back. I wish there were more people talking about it. If deep learning realized this they wouldn't still be trying to "learn" representations, whether in intermediate layers or other. (What was that article recently about the representation "bottle neck" idea in deep learning needing to be revised?) It's actually ironic that deep learning does not realize this idea that formal description (above the network) must always be incomplete, because it is also the key to the success of deep learning! The whole success of distributed representation is due to this. The field moved to distributed representation blindly, without theory, just because things started working better that way! But you still see articles where people say no-one knows why distributed representation works better! The failure of theoretical vision is extraordinary. But if you've deconstructed your dictionaries (throwing out your hand coded dictionaries?) and arrived back at the level of observation in a sequence network. And done it because of the theoretical realization that complete representation above the network level is impossible (or was it just an accident, trying to deconstruct symbolism to connectionism, and then accidentally noticing the relevance to variational theories of maths?) Then your group would be the only ones I've come across who have done (I think the Oxford thread of variational formalization, around Coecke et al. Grefenstette, were also seduced away by the short term effectiveness of deep learning on GPUs.) We need to keep (or get!) the theoretical vision. Even given a vision of formal incompleteness, you (and Pissanetzky?) may still be lacking a totally clear conception that the key problem is assembling elements in new ways all the time. Still, some focus on assembling elements in different ways (from a sequence network) is encouraging. There is scope to move forward. As a concrete, immediate, idea to explore moving forward, I hope you'll look at the idea of using oscillations to structure your sequence network representations. For it to be meaningful your networks will need to be connected in ways which directly reflect the ideas behind embedding vectors (without their linearities.) I don't know if that is true for your networks. But given that, implementation should be simple, if practically slow without parallel hardware. -Rob On Thu, Feb 21, 2019 at 12:03 AM Ben Goertzel <[email protected]<mailto:[email protected]>> wrote: It's not that it's hard to feed data into OpenCog, whose representation capability is very flexible It's simply that deep NNs running on multi-GPU clusters can process massive amounts of text very very fast, and OpenCog's processing is much slower than that currently... 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