You’re lucky he even took time to respond to you tbh
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He did just try and explain.. I don't think you're trolling but being nice is free..
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On a technical level, no. GraphRAG is one way to leverage KGs in LLMs but only sometimes the best way. If the KG is a Hyperon Atomspace, one can do better by translating the natural language in an LLM prompt into pattern-matching and unification queries against the Atomspace using semantic parsing, then executing these queries against the Atomspace and using the results to expand the prompt. However that is indeed "jargon" and to explain it in a nontechnical way would require elaborating a bunch of examples, which I don't have time for right now, it's midnight and I have a couple more hours of work to do before crashing. Good night all 😉
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Isn’t that GraphRag as you explained earlier?
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The only thing that I read from you is jargons after jargons. The only thing you did to differentiate GraphRag with Hyperon is to add more jargons.
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If you cannot explain it, you don’t know it. The lack of a product from your team speaks volumes. And name calling? How classy.
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I think 90% of our new community is unaware of your work, @bengoertzel. Please let your team create more blogs about this and also make the Twitter community aware of your work, specifically the vision you just explained.
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Thank you for showing up here and these explanations. We need this kind of stuff in the roadmap or blogs with future plans for ASI. This is what everywant need to read beside this small telegram group.
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Both sound exactly the same to me. The only difference is that Microsoft has a working product
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Microsoft's GraphRAG simply takes a knowledge graph and transforms it into a big text that can be used to fine-tune a transformer or similar.
This is nothing like the Hyperon system which does probabilistic higher order predicate logic reasoning, probabilistic evolutionary program learning, algorithmic chemistry and a whole bunch of other AI algorithmics in a dynamic in-RAM knowledge graph spread across multiple machines in a decentralized network.
The commonality is that graph knowledge representations (though of very different sorts) are involved in both.
I think what ben says is that there are 2 approaches to ASI: full power but limited (o3), less power but unlimited (Hyperon). We can't compete with someone who only focus one power while our goal is power AND decentralization.
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What you are doing sounds suspiciously like the one already released open source by Microsoft, the GraphRag
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Are you depending on centralized big tech such as Meta to come up with a satisfactory OSS llm model before your Hyperon will even start working?
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It sounds like you are waiting for someone else to develop the OSS model before you start your work.
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Especially as the rewards become greater and greater as decentralised finance becomes more mainstream, and the reach of DeASI expands into more and more humanity benefiting areas. We really have a strong community of early tech adopters, familiar with DeFi. I love the thought of ASI buying back our time from corporate endeavours, to family and societal orientated activities. You're absolutely right, what a time to be alive!!🍾🥂
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Hey, I just wrote a long appreciation post that seems to have been automatically deleted (with the TG disintegration animation on the comment).
If moderators can revive it, that would be appreciated! 🙏
If not: general positive sentiments! 👌
Have a nice time running away from more difficult questions.
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I will take it as a worthwhile TODO to create a simple explainer of some sort for Hyperon, which clarifies the core nature of the system for folks with no tech background. Thanks for the nudge.
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Indeed the recently released roadmap focused on products to be released in the next 6-9 months, and on that time-frame our more advanced AGI R&D stuff will only play a supporting role...
But we are designing ASI:Create so that
-- initially it will focus on making it easy for AI devs to connect fine-tuned LLMs to knowledge graphs
-- however, we will then add more reasoning capability to the knowledge graphs themselves (which is straightforward for KGs in Hyperon Atomspace format)
-- As our scalable Hyperon codebase matures, the center of gravity will switch, and Create will become essentially a platform for devs to connect Hyperon instances to their applications, with LLMs serving to feed and guide the Hyperon instances...
So the ASI:Create roadmap, post its launch, will involve it gradually. morphing into being mostly a decentralized Hyperon front end...
There may end up being a chatbot comparable to a ChatGPT type system as well, but it will be implemented as just one among the various dapps running against ASI:Create ...
Sometime early next year we will release a roadmap looking ahead into 2026 which will then focus more on actual AGI rather than just near-term product plans ... both are important!
Dude... both sound exactly the same to my toy poodle as well, but so what?
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You're exiting. You need to guide the marketing team with the blogs and roadmaps. This is so exciting and people need to read this.
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My SNet/ Hyperon colleagues and I have been around the AI field quite a long time. We know the players. One of the two DeepMind technical founders worked for me and learned about AGI from me in the first place. I was at the OpenAI unconference back before they became ClosedAI. I personally know (in some cases well, in some cases casually) a pretty sizable fraction of the lead AI researchers at every Big Tech company in US and China (I lived in HK for 10 yrs). I've only been doing blockchain seriously since 2017 but I've been doing AI seriously since the late 1980s... I have organized the annual AGI research conference each year since 2008 ...
You can see what a tech company is working on R&D wise from the nature of their hires, among other things. OpenAI is a transformer-NN shop through and through. Meta is a DNN shop, transformers being a minor part of their story. DeepMind is way more heterogeneous, and they do have people doing some neural-symbolic stuff (as well as stuff in other directions like algorithmic information theory etc.), but not that much, and it's not the passion of their founders, Demis is really a bio-inspired guy....
No tech company has gone about systematically cherry-picking the best PhD students from profs doing neural-symbolic AI.... or evolutionary learning either for that matter.. not yet. They have done that for deep NNs pretty systematically...
The Chinese tech companies are pretty much copying the US ones, and the Chinese gov't (which controls the companies closely) has no desire to create systems with. more autonomy and initiative, they LIKE systems that stick close to the training data and are easy to control...
So no we can't know for sure what skunkworks projects are going on. But we know what major projects are going on.... It's pretty evident to anyone who is in the center of the modern AI field that there is no non-DNN-centered project of the size, sophistication or ambition of Hyperon going on in any Big Tech company or government agency.
Now, supposing we launch some super awesome open source neural-symbolic AGI system in late 2025 or mid 2026 or whatever ... how fast will Big Tech start to copy it? That's a quite different question. Probably very fast. On the other hand, at that point we will have no trouble getting immense resources for our own effort, and we will have a big head start in terms of having a dedicated team with their brains and hearts wrapped fully around our approach. And we will be fully decentralized, so ideally positioned to take advantage of a large global team crystallizing from universities in every country etc.
Interesting times are coming 😉
What does it mean by executing on the design?
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Indeed not, we are executing on this design pretty rapidly right now: https://arxiv.org/abs/2310.18318
We are also giving out $1M+ USD in grants to enable more OSS devs to join the effort ... and likely an additional batch of grants early next year...
https://deepfunding.ai/all-rfps/
We don't need LLMs to make Hyperon work. However they can be a quite interesting accelerant.
You know when you have a meeting at 7am, but it is currently 6:50am and you are about 40 minutes away from your destination. You know that you won't be there until 7:30am (30 minutes late), but you look at your car's clock, and you convince yourself to feel a level of ease because it's still 6:50am, and you are not actually late until the clock says 7am, so you have 10 more minutes to not go into a panic, because you know you'll have enough panicking about being late in just 10 minutes (when you are literally late)...
I hope no similar psychological concept is being unintentionally applied at Snet in that since "there hasn't been any big tech announcing that they working on symbolic reasoning and evolutionary learning, it stands that they are not working on those models, so we are still ahead of the game."
My point is, how can you be sure they are not working on the thing you say they are not working on?
I mean, Microsoft kept the surface computer line pretty well under wraps and they did so with the hololens as well. So how can we be sure they are not keeping an atomspace with opencog hyperon equivalent under wraps until the right time to announce?
I find it unlikely that big tech is not exploring all model options
You seem to have a better solution, please share.
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Is this a loaded question you are asking?
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When are you running this OSS model that doesn’t yet exist?
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Actually though o3 and any further developments in that direction are very encouraging regarding our own AGI efforts...
o3 is probably clever enough to reliably map English into uncertain predicate logic expressions. And while it may be ahead of OSS models now, that won't last, the rhythm of the LLM world is that the OSS models don't lag the proprietary models by thaaaat long.... So before too long we'll have an OSS model that can reliably map English into uncertain predicate logic expressions...
Then we can take whatever the best LLMs output (in response to any query or situation) and feed it into the scaled-up version of our Hyperon Atomspace (running spread across our decentralized networks...). We can use Hyperon's logical reasoning to go beyond what LLMs can do in terms of abstraction and generalization ... and we can use Hyperon's evolutionary and algorithmic-chemistry methods to do creative invention beyond what LLMs can do... fueled by the input from LLMs themselves, and using LLMs for guidance of the Hyperon method wherever helpful...
We will be using the f**k out of the best OSS LLMs, but not letting them run the show, rather using them as a resource within a system leveraging Hyperon for advanced reasoning and creativity, and also for running the basic cognitive cycle of values, goals/motivations, perceptions, plans and actions...
In terms of competition within the crypto sphere, honestly, no other decentralized-AI project has any sort of unique or original or differentiating approach to AGI ... they are just doing API calls to OpenAI behind the scenes, or running fine-tuned versions of the same OSS LLMs as anyone else... they may offer some additional decentralization and security over centralized AI services, but this is appreciated by only a pretty small segment of the AI market. We are really the only deAI project with any differentiating AGI-ish tech....
We are also probably the largest AGI R&D project on the planet doing anything besides variations on the same (amazing yet also limited) DNN approach that OpenAI and Google are using. (We have a great transformer NN team, but also a great team doing quite different stuff w/ Hyperon...). Yes R&D always has uncertainty but at least we are making a bet with a super super high upside potential and a plausible chance of success... [alongside our less adventurous bets like ASI: Train, ASI:Create etc.]
These next couple years are going to be insanely fascinating... What a time to be alive and working in the AGI space !!
