Reference
Appendix - Dr. Ben Goertzel Quotes from Press
We're living in a really pivotal time in terms of the development and rollout of new technologies and, I wanna spend time on things like that. Important and have real transformational value. Twin Protocol fits that very precisely. I wanna frame the project a little bit by explaining, you know, how it fits into the overall SingularityNet ecosystem from which it has come out and took the overall quest to create smarter and smarter AI systems and, machines and use them to, uh, help people and learn from people. So I'm a mathematician originally, but I've been doing AI, R and D since the 1980s, uh, long before AI became as popular as it is right now.
SingularityNET is building a decentralized infrastructure for AI sort of AI agent system. That any algorithms can run on top of and it has a blockchain below that it's been running on theory. We're reporting it to Cardano we're building our new card chain side chain called hypercycle SingularityNET as a decentralized AI platform has various AI tools and algorithms running on top of it. There's neural nets of various sorts, like the transformer nets we're using heavily in Twin Protocol. There's the Opencog Hyperon Neuro-Symbolic system.
Dr. Ben Goertzel provides his view on why human-like digital twins are important
Twin Protocol is one of the more amazing of these applications. AI that carries out highly specific tasks or general AIs that we're working toward from a research standpoint. If you look at what we need there, I mean, we need hard work, we need core AI algorithms, we need infrastructure like SingularityNet, decentralized infrastructure, we need knowledge and values. And where does AI get those from? It can get those from learning from its own experience.
One of the more interesting ways for AIs to learn from human beings while also gaining positive values is to basically become digital Twins of specific humans, right? It's also very important to use AI and other advanced technologies to prolong biological human life. The same old across the board for family members, colleagues, and even people who haven't passed away but they're just too busy to give their advice on things.
Dr. Ben Goertzel discusses one reason why Twin Protocol has been created.
To be able to train an AI to emulate a particular human, there's value to those who love and respect that human. There's value to the whole economy and society and people's knowledge can be leveraged more effectively because AI simulacrum can scale better than individual people. And there's also value for the emerging global AI mind as we transition from narrow AI mind towards general intelligence in that you're getting not just facts and knowledge and not just isolated chunks of know-how.
Like I would recognize the face of drive a car, but you're getting the whole integrated complex of knowledge. We want AI that can emulate the core of humanity from a practical and conceptual and ultimately even conscious standpoint So this was the grand motivation mind Twin Protocol and to employee knowledge retention for a company,
There are many reasons why human-like digital Twins of employees are beneficial for business.
A company has employees or contractors who learn so much about their business and that person goes away to do something else with their life. The knowledge is lost to the enterprise, but what if you had a twin to that person that the enterprise could consult for expertise after the person that left?
So he put his finger on a sort of enterprise business model for digital twins alongside the cosmic purpose and alongside the personal value. Each of us may have in training a digital twin just for our friends and family to have when we don't have time or after we're gone. That's really the crux of Twin Protocol, the core technological idea is to use AI to create a simulacrum emulating particular human beings from a person or a workforce perspective.
Twin uses transformer neural networks and Neuro-Symbolic systems to create human-like digital Twins. Twin is also a natural match for a token model.
We're working with transformer neural networks toward this purpose. We're also experimenting with neuro-symbolic systems putting OpenCog Hyperon together with transformer neural nets. We should be giving people incentive tokens for contributing data to train their models. People should then be earning tokens when they're their digital simulacrum delivers services to someone, right? So that this is an extremely natural match for a token model and that's where the protocol comes from.
So we have a tokenomic protocol incentivizing building and then utilization. AI trained digital simulacra of individual humans who are members of the Twin protocol. Network which uses SingularityNET as the, the base platform for running its AI and I think we're in the early stages here.
We've been experimenting with digital simulacrum. We started with actually the science fiction by there, Phil K. Dick, because we built a simulacra David Hanson and I with several others that built a simulation model of Philip K. Dick. A number of years ago to go behind the Philip K. Dick robot and with Bill and some of the SingularityNET and empirical AI team, we've been working on simulation models of myself and a couple of others.
There certainly don't yet replace humans. They're not generally intelligent, like human beings on the other hand, even for the simulacrum model of myself, you know, it's uncanny sometime if I probe that model with a question. I'll get an answer, which could be something I could have said or could have thought about, but hadn't yet at that point, like it is some of what it generates is not that brilliant or silly, but some of it is like second-guessing stuff that is in my sort of mind network, but I hadn't ever put together yet in that way, which is really quite fascinating.
So I think there's tremendous potential for this technology, both, you know, economically and product-wise and in a broader sense for helping infuse AI with human values and culture. And I'm really psyched that Bill Inman, uh, Michael Foley, a bunch of others have grabbed all this from a practical perspective.