Large Language Models – From Banality to Originality

Looking at the insanely popular Large Language Models (LLMs) like ChatGPT with an AGI researcher’s eye, what I see are some potentially interesting components for future AGI systems. 

Economically, I see the seed of a family of technologies that, over the next few years, is very likely to obsolete a majority of white-collar jobs, wreaking incredible disruption and ultimately moving us further toward a world in which people are liberated from the necessity to work for a living.

On the other hand, looking at them from an end user’s view, what I see are tools that are already pretty useful for some things – but are marred by a few really major and frustrating flaws.

The first major flaw is a bizarre lack of reality discrimination – LLMs are so full of shit it’s almost unbelievable, given how intelligent they are in some ways.

The second is a mind-numbing boringness and banality – such powerful cliché machines have never previously even been imagined.

I do believe, though, both of these issues can be overcome with a moderate amount of R&D effort. Which is probably going to be put in by various parties during the next couple years.

To fully overcome these weaknesses will require a true breakthrough to Artificial General Intelligence. But I suspect they can be remedied to a significant degree even without AGI, as a parallel track of development.

Generative AI vs. AGI

As an AGI guy, the first thing I need to clarify when talking about LLMs is that they are certainly not AGIs. They achieve a fair degree of generality in their intelligence, but it’s not because they generalize beyond their training and programming – it’s because their training data is so huge it covers almost everything. A tremendous variety of queries can be answered via minor variations and combinations on things in the training data. 

A generally intelligent artificial mind will have lots of uses for statistically recognizing patterns in huge datasets, and synthesizing new forms via merging these patterns together in a manner appropriate to some narrow context – the things that LLMs are good at. But I don’t think these capabilities can form the core of an AGI mind. That needs much more abstract thinking, bound up with agency, will, self-reflection, modeling of self and other. If AGI systems have these core functions fulfilled (by the OpenCog Hyperon AGI architecture my colleagues and I are working on, or something else), I think LLMs could add a lot of wonderful peripheral functions.

Toward LLMs with Reality Discrimination

LLMs trained to produce text currently have serious problems with truth-telling – they basically can’t distinguish truth from falsehood. I’ve gathered a few funny or instructive examples in a blog post on ChatGPT and AGI. There are many others easy to dig up online. I have a particular liking for the conversation where ChatGPT calls me the founder of OpenAI, the company that created it (in which I have no direct role).

A variety of approaches are being pursued to overcome this problem and make LLMs less full of shit, and I think a bunch of them will probably succeed to some degree. As an example, we at SingularityNET and TrueAGI are trying to address this with a neural-symbolic architecture: LLMs are hybridized with an OpenCog-based symbolic reasoning engine. 

This symbolic reasoning engine can extract logical relationships from an LLM, and put the LLM’s knowledge in a more structured form. It then compares the relationships between concepts with each other to identify inconsistencies. Where possible, it also compares these relationships with existing background knowledge – common-sense truths about the world like ‘water is wet’ or ‘Ben Goertzel is not the founder of OpenAI’. Inconsistencies can then be used as negative evidence to fine-tune the model – the model can be explicitly trained to unlearn the lies it contains.

I suspect these hybrid symbolic-LLM structures can greatly increase the generality and truthfulness of things like ChatGPT, even without taking us all the way to AGI.

The Banality of LLMs

But LLMs also have other problems besides their rampant BS generation – like the fact that so much of what they produce is boring clichés. Eric Hoel has called this “the banality of ChatGPT“. 

As a so-bad-it’s-good example of this banality, consider the following excerpt from a recent dialogue I had with the system:

BEN: How would you advise to manage and coordinate the project of creating a replica of the planet Jupiter with 1000x human intelligence? 

ChatGPT: For managing and coordinating a project of creating a replica of Jupiter with 1000x human intelligence, I would suggest the following steps:

  1. Define the project scope: Clearly define the goals, objectives, and deliverables of the project.
  2. Assemble the team: Recruit individuals with relevant skills and experience to form a diverse and effective team.
  3. Develop a project plan: Determine the tasks, resources, timelines, and budgets required to complete the project.
  4. Set up project management systems: Establish tools and processes to track project progress, communicate with team members, and manage risks.
  5. Monitor progress and adjust as needed: Continuously assess the project’s status and make changes as needed to ensure it stays on track.
  6. Ensure communication and collaboration: Encourage regular communication among team members and stakeholders to ensure everyone is on the same page.
  7. Anticipate and manage risks: Identify and plan for potential risks and obstacles to minimize their impact on the project.
  8. Celebrate success: Recognize and celebrate milestones and achievements along the way to keep team morale high.

Remember, creating a replica of Jupiter with 1000x human intelligence is a complex and challenging project, so it’s important to be organized, communicate effectively, and continuously adjust as needed to ensure success.

Not exactly wrong. But, shall we say, not really grappling with the particulars of the question…

Sometimes boring clichés are exactly what you want in life. If you’re writing a cover letter for a job application, or an essay for university English class… originality and quirkiness can sometimes be dangerous.

But one worries about a near-future in which boring clichés become even more common than they are now, because they’re so quick and cheap to generate using AI models. A boring dystopia.

Credit: Edward Keller

The Glory and Horror of Music LLMs

Google’s recently announced MusicLM neural model provides an intriguing case study of the intersection between “fascinatingly impressive” and “horrifyingly boring.”

You can give it a query like –

We can hear a choir, singing a Gregorian chant, and a drum machine, creating a rhythmic beat. The slow, stately sounds of strings provide a calming backdrop for the fast, complex sounds of futuristic electronic music.

– and it will generate music that fits the bill. Amazing stuff.

Except the effect is a bit like having a workaday lounge band improvise a musical passage for you. It’s very rarely artistically thrilling.

Given how impressive the functionality is, you might say this is a pretty lame complaint.

However, if such technology was used to generate music for people to listen to, the result would be an even more cliché-ridden and repetitious music sphere than record execs have already inflicted on us! Dentist’s office muzak++ forever!

Credit: Tesfu Assefa

The problem here is that averaging together everybody’s art produces art that is itself average. For some commercial purposes – e.g. background music for ads or video games – average, passable, competent music may be fine.

As a lifelong composer and improviser, I’ve generally been more interested in creating sounds that diverge from the average and the expectation – even if they get a little jarring or discordant in the process.

Of course, current neural models can be jarring and discordant too – but they will tend to do it in a way quite similar to something from their training dataset, or combining surface-level features of a few things in their training datasets.

Music is the domain in which I’ve thought most about how to overcome the banality of LLM output – because as a musician, I would really love to have an AI musician to improvise with. We already have a robot singer in our Jam Galaxy Band, and some AI-composed riffs, but a real-time AI improviser jamming alongside me is what I dream of. I don’t want boring lowest-common-denominator MusicLM-style in my band, not at all…

One approach that can be taken here is to formally introduce a theory of ‘interestingness’ – make a mathematical model of what constitutes interesting music, and then condition a MusicLM-type model to bias it to produce outputs meeting this interestingness criterion. This is not that far off from work I did in the 1990s using genetic algorithms to evolve music maximizing a ‘fitness function’ encoding a theory of musical interestingness. But LLMs allow the evolved music to incorporate patterns of various sorts from human music in a much more refined way than was possible back then.

LLMs vs. Hmmmmmm

Credit: Tesfu Assefa

Of course, this would still be a very different thing from how an AGI system would approach music. 

AGI and music could intersect in a variety of ways, but one way or another, it would involve an AGI system creating and understanding music in the context of its experience of being an agent in the world, like when the AGI in the 2013 film Her says, “I’m trying to write a piece of music that’s about what it feels like to be on the beach with you right now.”

Steven Mithen’s book The Singing Neanderthals presents an hypothesis about the origin of language and music. He posits that human communication began with a communication system he refers to as “Hmmmmm” because it had the following characteristics: it was Holistic, manipulative, multi-modal, musical and mimetic. Basically Hmmmmm combined sound and gesture and action and imitation – somewhat like the pre-verbal/semi-verbal communication one sees in one-year-old children, but with more adult-level cognitive sophistication underlying. His proposal is that Hmmmmm came first and then spawned both language and music, which evolved from Hmmmmm in their own different directions.

Cambridge Archeological Journal did a fascinating feature presenting various criticisms on the hypothesis along with Mithen’s responses. 

An interesting and fairly difficult challenge would be to coax AI agents living in a virtual world – let’s say Minecraft enhanced with plugins, or the in-process Sophiaverse virtual world – to invent language and music along the lines of the Hmmmmm theory. This could be an interesting and valuable thing for AGI researchers to do regardless of how fully accurate Mithen’s theory of evolution is. 

We could stock the virtual world with a few easy-to-use musical instruments, let’s say –

  • drums that make rhythms when hit
  • flutes that they breathe into (modulating volume and timbre with breath) while pushing buttons to make notes
  • Piano-type instruments that make notes when their keys are hit

One would then ultimately want these virtual-world proto-AGI agents – I like to think of them as “Neoterics” (new people) – to do things like:

  • Discover that dancing to music is pleasurable and creates a feeling of togetherness which fosters collective action and communication
  • Discover that drumming enthuses a group to carry out physical tasks together
  • Discover that listening to melodic music puts the mind in a state conducive to creativity

Given that the Neoterics’ emotion models will be similar to ours, yet different in the particulars, it may be that the music they create to express their own emotions and influence each others’ emotions will be significantly different from human music. Perhaps one could then train music LLMs on music made by Neoterics and get a fascinating sort of hybrid – a truly new genre of music!

Whether or not this Neoterics experiment ever gets done in precisely this form, it does highlight the big difference between an LLM approach and an AGI approach – to music or anything else. LLMs are munging and merging data patterns, and with cleverness one can work around the most immediate issues that emerge from this approach, issues such as tendencies to hallucinate or converge on clichés. AGI, however, requires a totally different approach.

Narrow AI systems like LLMs may be useful for feeding patterns into the core cognition of AGI systems, or for helping them speak fluently in the lingo of a given domain. But at core, AGI systems will necessarily be very different from LLMs – they will be what Weaver has called Open-Ended Intelligences – complex self-organizing systems that engage richly with their environments, driven by complementary-and-contradictory drives to individuation and self-transcendence. AGI systems will achieve generalization via abstraction, and the need to balance individuation and self-transcendence, while working with limited resources, will drive them to intelligent abstract understanding. When they generate language or make music, they will build it on this abstract understanding, formed from their experience, and as a result will be imaginative and truth-telling naturally – organically – rather than with an additional trick.

LLMs and other deep neural nets are going to have a big impact on society, disrupting the business models of today’s tech giants and potentially eliminating a significant percentage of human jobs. But their biggest contribution ultimately may be waking up the world to the potential of AI and thus attracting more cognitive, financial and cultural resources toward the development of AGI, which will have a far larger direct impact than even the coolest narrow AI systems.

The Mindplex Awakens!

So what the f**k is a “Mindplex”?

Well, firstly — as you can see — Mindplex is a fun/funky new future-oriented magazine and media site that some of my crazy colleagues and I are in the process of launching as a spinoff of SingularityNET, our AI-meets-blockchain project.

Read, view, comment, contribute, enjoy! Expand your mind and help expand ours and everybody else’s. …

Enter the Mindplex …

Yes, yes, an online magazine. And we’re aiming for an insanely great one — news articles, stories, images, videos and more … comments on these creations, comments on comments, and so forth. 

We have a great editorial team, led by Senior Editor Amara Angelica, who edited for 18 years and helped Ray Kurzweil create The Singularity Is Near and other works. AGI, life extension, nanotech, blockchain, robotics, consciousness, you name it. …

New open-source tools

But we’re also aiming at something more: To use the zine as a platform for gradually rolling out and experimenting with a series of exciting new tools, leveraging blockchain or AI or both.

Most of these tools will also be mde available open-source for others to use and made available via decentralized SingularityNET hosting for other media sites to use via API.

The goal is to make Mindplex a showcase for new processes and dynamics, with the capability of shifting the online mediaverse as a whole from its current convoluted and largely counterproductive condition into a majorly amazing force for both human and Singularitarian good.

A reputation system

One of the first things we’ll be experimenting with is a complex-systems-theory-based reputation system that leverages crypto tokens to manage creator and user reputations. 

Interaction between reputation and rating is important here, as is complete transparency to users regarding how and why the system’s algorithmic judgments are being made.   

Existing media sites like Reddit and Steemit are object lessons in how the nitty-gritty of reputation and rating algorithms can very concretely impact the nature of content in an online ecosystem. It’s essential that — with the active involvement of the community — we get this some kind of right.

New AI-based experiments

Beyond this, you can expect an ongoing flow of new tech/human experiments. Part of the fun will be the surprise of learning and reinventing as we go, along with the community, But to mention just a few things we’ve been playing with:

  • AI dialogue systems for chatting with readers and answering questions about media posted on the site.
  • AI-based interactive content-creation tools — focused initially on creating interactive science fiction that explores the potential implications and uses of new technologies described and discussed on the site.
  • AI-generated memes and comics
  • Collaboration with SophiaDAO, another amazing SingularityNET ecosystem project, on immersive virtual-world scenarios related to Mindplex content.
  • Leveraging TWIN Protocol technology, allowing users to create their own digital twins and allowing others to experience their twins’ reactions to Mindplex content.
  • Unique mathematical tools that allow the user to create visual and music art, based on the user’s own DNA sequence.

Variations on the Theme of “Mindplex”

But why are we launching such a site at this time … what’s the underlying philosophy and mission?

Well, I’m glad you asked…

“Mindplex” is a term I introduced in 2003 or so when I was looking for a word to describe a mind that’s more unified than human society, but less unified than an individual human mind. 

Imagine a group of best friends in the year 2050 who are all supplied with brain-chip implants and share their every thought and feeling with each other via “Wi-Fi telepathy.” 

Such a bestie group will be almost — but not quite — a sort of “group mind” … something like a Borg mind, but with more loose/wild self-organization and without the fascist flavor.

Another kind of mindplex

My neuroscientist collaborator Gabriel Axel and I picked up the term again in 2019 when we were writing a paper on non-ordinary states of consciousness — including states where multiple people feel fused into a sort of mutually experiencing whole.

Fast forward to 2021: I found myself collaborating with an amazing team to create a tokenomics-and-AI-fueled futurist media project — and I realized that what we were aiming to curate was precisely another form of mindplex.   

Could we use AI and blockchain to create a self-organizing body of content, intelligence and understanding? One centered around the theme of transformational technology and the human and transhuman future? 

One that would include insights, respect, and reward for the autonomy of the individual people contributing, but also display an emergent vision and comprehension beyond the scope of any one human individual?

In a way, this is what any subculture does. But historical human cultures didn’t have access to the array of technologies at our present disposal, which have the potential to make communication and information dynamics more intense and emergence more emphatic.

The mind is only free for those who own their online selves

The Internet has been an unabashedly awesome thing for software programs and robots. For human beings, it’s been a mixed bag — with huge positives and chilling negatives, and a whole lot of aspects that are just plain confusing. This has been true for the impact of the internet on so many aspects of human life, and definitely in the areas of media and social interaction.

A. J. Liebling’s statement, “Freedom of the press is guaranteed only to those who own one,” is no longer so relevant. Instead, we have a situation where “the ability to reliably direct people’s attention is only available to those with a lot of money to feed AI marketing engines.” 

Almost anyone in the developed world — and an increasing subset of the developing world — can now post their thoughts, images, sounds or videos online … and their commentary on the works of others. But getting other folks to notice your productions or comments is a whole different matter.   

Getting paid to produce high-quality content is also increasingly difficult as the pool of freely offered content increases in size, depth and diversity. More of the money in the mediasphere goes to those offering services that sell advertisements to readers of said freely offered content.

Finding like-minded folks — or at least like-opinioned folks regarding particular classes of issues — gets easier and easier as the technology for clustering people into similarity groups gets more and more refined, due to its utility for driving targeted marketing. 

But finding interesting people whose mix of similar and divergent perspectives can drive one toward fundamental growth is much harder and has been a serious focus because it’s more difficult to monetize.

Tribal mind lifeforms

The proto-mindplexes that are most habitually forming on today’s internet are often troublingly tribal in nature — like narrow-minded, collective-mind lifeforms creating their own insular information metabolisms on the scaffolding of social media filter bubbles. 

The rare individuals able to earn a living creating media mostly do so by playing, in a highly focused way, to the most differentiating beliefs of particular online tribes.

Today’s slogan should perhaps be more like “the mind is only free for those who own their online selves” — i.e., they own their data, metadata and online persona. 

Because if you don’t own and control those aspects of your online presence, your mind is being constantly influenced and controlled by the information shown to you by whoever does have this ownership and control.   

Decentralized, democratized media

Of course, “freedom” in an absolute sense is a philosophical conundrum that mixes poorly with physical and social realities. But what we’re talking about here is practical causal agency over what one sees, does and thinks. 

Increasingly, this causal agency is being handed over to large corporations with their own financial growth agendas, rather than maintained individually or thoughtfully granted to mindplexes or other social groups we have intentionally entered into and co-formed.

Case in point: SingularityNET was formed to decentralize and democratize AI. And this provides a big piece of what’s needed to decentralize, democratize, and end Big Tech hegemony over media (social, anti-social and otherwise). But it’s not the whole story.    

Dynamic intelligent mindplexes

Decentralized democratized media needs decentralized, democratized AI, deployed appropriately alongside other media-focused tools and mechanisms. This is how we can create online media that accentuates the positive and palliates the negative aspects of the Internet we experience today. 

And it’s how we can move forward to foster the creation of dynamic, intelligent mindplexes that transcend tribalism, help us grow, connect and expand our consciousness, and self-transform in unexpectedly positive and glorious ways.