Does the name Mitch Kapor sound familiar? If you are interested in the history of SL, the answer may well be yes, because he was one if LL’s earliest investors. “Mitch Kapor was the only person who got it”, said Rosedale in an interview with Inc. Magazine.
Personally, Mitch Kapor first came to my attention through an essay of his, published in 2002 on Kurzweilai.net. As with LL and SL, Kapor was putting money forward in anticipation of a future outcome, but this time the money was riding on a failure, not success. The bet centred on a question: Will the Turing Test be passed by a machine by 2029? Ray Kurzweil said ‘yes’, Kapor said ‘No’ and whoever loses will donate $20,000 to a charity selected by the winner.
In his essay, Kapor explained why he was sceptical of the possibility that a machine will ever pass the test. ‘To pass the test, a computer would have to be able of communicating via this medium (text) at least as competently as a person. There is no restriction on the subject matter…It is such a broad canvas, in my view, that it is impossible to forsee when, or even if, a machine intelligence will be able to paint a picture which can fool a human judge’. Kapor further elaborated on why a computer can never mimic a person, but what struck me as I reread this essay recently was this: Just possibly, SL may prove to be a crucial link in the enabling technologies of human-like intelligence.
What will it take to build a machine that you can chat with as if it were a person? Decades of research into question has yielded three vital requirements. Power, Organization, and Education. The first requirement, power, means building hardware that matches the computational capacity of the human brain. If you have a top-spec PC, then you have at your disposal something with the equivilent brainpower of a fish — a millionfold too weak to do the job of a human brain (which Moravec estimates at 100 million MIPS). Actually, it is not the case that you don’t have access to a ‘computer’ capable of matching the raw power of the brain, especially if you connect to SL. As Rosedale explained to Tim Guest, ‘the combined computational capacity of the aggregate SL grid, running 24 hours a day as it does now, is in excess, by almost any measure, of at least one human brain at this point in time’. Of course, it would be a waste of resources to use the grid simply to simulate ONE human, when it can instead be used to run a virtual world harnessing the creative powers of tens of thousands of real people at any particular moment.
In any case, the second requirement (organization) refutes the possibility of the SL grid ‘waking up’ to self-consciousness. It’s not sufficient to simply match the 100 million MIPS of a human brain, but also to understand how the brain is organized, how it processes information. Thanks to functional brain imaging, we are beginning to understand how this organ differs to a computer, and the field of neuomorphic modelling is focused on building hardware and software that is ‘functionally equivilent’. Currently, brain imaging only hints at the underlying principles of human intelligence, it is not yet capable of following the actual information being transformed in realtime. Also, as mentioned above, we currently lack the raw power needed to model all several hundred regions, at least not on any computing system whose precious resources are not better used in other areas. What we have achieved so far, is to develop highly detailed mathematical models of several dozen of the several hundred types of neurons in the brain. Researchers have connected artificial neurons with real neurons from spiny lobsters, and found that this hybrid biological-nonbiological network performed in the same way, and produced the same type of results as, an all-biological net of neurons. Combining neuron modelling and interconnection data obtained from brain scanning has enabled science to reverse-engineer more than two dozen of the several hundred regions of the brain. Again, in some cases, the neuromorphic models have been connected to real brains. In 2006, researchers built a chip that mimicked a section of rat hippocampus. The real section was removed, the artificial replacement wired in place, and it restored function with 90% accuracy.
Given that brain imaging tools are increasingly improving, and computers are getting more powerful, there is no reason to suppose that we cannot reverse-engineer every neuron, every region, and so build an entire brain. And contemporary examples of hybrid networks make for a curious thought-experiment. What if we were to remove a neuron from Mitch Kapor’s brain, and put in its place its neuromorphic twin? If the artificial neuron sends and receives information just like its biological predecessor did, it seems hard to argue that Kapor’s behaviour would be affected. Now suppose that, step by step, his entire brain is replaced. Remember, we have already partially performed this experiment on rats and retained function with 90% accuracy. Subsequent generations of chips are likely to close the gap and creep towards 100%. So, hypothetically, if we systematically replace Kapor’s brain, ensuring at every step that the hybrid biological/nonbiological net is behaving normally, Kapor would retain the abilities we associate with human intelligence. But if we keep going, ultimately ALL the biological brain will have been replaced. Where once there was a brain there is now an astonishingly complex machine. Equally, instead of replacing a pre-existing organic brain we could just build a neurmorphic model and install it in a robot with appropriate sensors that feed information to it corresponding to sight, touch, smell and taste. Why would this robot- this machine- not be capable of behaving like a real person? We could, after all, replace each part of Kapor with an artificial version; a robotic eye, a robotic limb, a robotic heart, and so on until he is 100% artificial. You could perhaps argue he is no longer ‘human’ (though I defy you to pinpoint the exact point where humanity was lost) but Kapor could argue quite convincingly that he is a person, and deserves to be treated as such. Why would a robot built from the same parts not also be able to argue its case?
You could answer that by asking this: Can a one year old baby pass a Turing Test? The answer is clearly no, because a baby has yet to develop the capabilities associated with human intelligence. To be sure, some functionality is ‘hard-wired’ into our brains from birth, but so many more only develop as the baby spends years interacting with reality. The same thing would apply to our robot. We should not expect to build it, turn it on, and expect it to immediately engage us in conversation about the music of Proust or the price of sprouts. No, we will have to provide the third requirement: Education.
It is this requirement that Kapor is betting will fail. ‘Part of the burden of proof for supporters of intelligent machines is to develop an adequate account of how a computer would acquire the knowledge it would be required to have to pass the test…I assert that the fundamental mode of learning is experiential…most knowledge, especially that having to do with physical, perceptual and emotional experience is not explicit, never written down…the Kurzweil approach to knowledge acquisition (he argued that the AI would educate itself by ‘reading all literature and by absorbing the knowledge contained on millions of websites’) will fail’.
Kapor argues that human beings are embodied creatures, grounded by our physicality and in many ways defined by it. This logically leads to the observation that there is an intimate connection with the environment around us. ‘Perception and interaction with the environment is the equal partner of cognition in shaping experience’, he reasoned. The qualities we associate with human intelligence were shaped by evolution, but for humans there is another form of heredity to consider, along with natural selection of genetic information. That additional form is ‘culture’. Our social networks evolved the rules that define common sense and artistic sensibilities, never written down but nevertheless transmitted from mind to mind. We can therefore identify a crucial step in achieving Turing AI; the construction of an ambitious ‘laboratory’, consisting of an entire environment in which a network of social and cultural relationships can grow almost from the ground up. Of course, we have many such laboratories already, for as Edward Castronova explained, ‘we have real human societies that grow up on their own within computer-generated fantasy worlds’.
There is a pretty sound argument, championing SL above the likes of ‘World of Warcraft’. Yes, WOW has a greater population (though for how much longer is open to question) but it does not have the degree of self-organization we see in SL. It is a mistake to think evolution is only a means of shaping life to fit its environment, because the environment is shaped by the presence of life. Both are in a state of constant change. An emergent property by definition cannot be achieved with a centralized system and the degree of emergence required to achieve a suitably complex evolved culture can only happen in a dynamic environment that is shaped by the populace. This essential quality is built into the very concept of reality, as defined by LL. ‘The thing we concluded is that something is only real if you can change it. If there’s a pixel on the screen in front of you in SL, and you can’t alter it, then why would we put it there?’
This may run counter to many people’s concept of reality. After all, my belief in the objective reality of the sun (for example) is based on the observation that it remains as it is, no matter what my whims may be. But the immutable and the alterable are not so separate as they seem. The fixed laws of the universe are what make creativity possible, because total chaos makes learning an impossibility. The Lindens could demonstrate this. If the behaviour of prims randomly changed each day, to the extent that nothing you learned today was applicable tomorrow, creativity of any meaningful mind would not be at all feasible.
Of course, the Lindens recognise the importance of stability. ‘We are trying to create a close reproduction of the actual, physical world we live in — one that will easily be comprehensible and useful to us because it so closely resembles ours’. If, as Kapor suggests, part of the essential component of human-equivilent AI is to be intimately connected to an environment, our collaborative efforts to build exactly that can reasonably seen as a step in the right direction.
But, why bother building a simulated world when there is a real one ready to go? Why not build physical robots interacting with real people, as opposed to bots conversing with avatars? Well, a virtual world has an advantage in that everything can, in principle, be recorded. Given that the entire world is computer-modelled, it is technically possible to record every movement, gesture, and interaction that takes place. This could be advantageous for scientists wishing to ‘download’ patterns of information ‘never directly expressed’ so that our infant AI can acquire a knowledge of human experience that occurred in the past but was tacit.
Another advantage of growing AI in a computer-modelled world is that it puts both ‘artificial’ and ‘real’ people on more of an equal footing. Indeed, this is a requirement of the Turing Test; prejudging personhood by observing who is the robot and who is the human violates the rules. An avatar controlled by a person you cannot see (or is the avatar under the control of AI?) is more in keeping with the conditions of the test. Another sense in which the playing field is levelled is that both ‘bot’ and ‘avatar’ are in a more basic state of learning about the social rules appropriate to their environment. We are, in a cultural and artistic sense, both ‘children’ learning through trial and error.
But while projects like ‘Neufreistadt’ are fascinating studies in the emergence of governance, it could be argued that such systems require a higher-order internationality beyond the capability of an infant’s mind to model. Worlds like SL develop from a more basic level than the modern society we are born into, but perhaps not quite basic enough to evolve higher-order internationality (a theory of mind, in other words) from scratch. In the sci-fi novel ‘Accelerando’, Charles Stross attributes consciousness to ‘a product of an arms race between predator and prey’. More precisely, a product of a mind’s ability to model behaviour. The hawk runs an internal simulation of its prey’s likely behaviour, calculating the direction it will run when it senses danger. The sparrow, meanwhile, uses its model of the hawk’s mind to calculate its likely attack strategy and execute an effective evasion. Natural selection weeded out the less-effective theories of mind, until for certain genes survival required cooperation among ‘a species of ape that used its theory of mind to facilitate signalling — so the tribe could work collectively — and then reflexively, to simulate the individuals own inner states’.
Stross attributes human-level consciousness to a paring of signalling and introspective simulation. Can a simulated world evolve a theory of mind from the ground up? That is a question being explored by ‘New and Emergent World models Through Individual, Evolutionary and Social learning’ — NEW TIES. The project, which brings together a consortium of researchers in AI, language evolution, agent-based simulation and evolutionary computing, seeks to use grid computing to model an environment inhabited by millions of agents, each one a unique entity with characteristics including gender, life expectancy, fertility, size and metabolism. Sexual reproduction will be possible, with agents able to reproduce and their offspring inheriting a random selection of their parents ‘genes’. Also, by pointing to objects and using randomly generated ‘words’, the project hopes to develop culture, which it defines as ‘knowledge structures shared among agents that reflect aspects of the environment, including other agents’.
In summary, the NEW TIES project states, ‘we will work with virtual grid worlds and will set up environments that are sufficiently complex and demanding that co-operation and communication is necessary to adapt to given tasks. The population’s weaponry to develop advanced skills bottom-up consists of individual learning, evolutionary learning, and social learning (which) enables the society to rapidly develop an understanding of the world collectively. If the learning process stabilizes, the collective must have formed an appropriate world map’.
Such work cannot help but provoke questions about our own existence. Here, we have patterns of information that will (it’s hoped) organise into structures capable of introspection and communication. What, then, are we humans? Patterns of matter and energy evolving in the ‘real’ universe…or are we too information running as part of a simulation built by lofty intelligences, curious about us because they are curious about their origins? Are our avatars brave pioneers of the ‘Third Life’, rather than the second?
And what place does SL occupy in the grand scheme of things? Evolution was using co-operation long before culture developed. A single-celled organism is a vast society of chemicals. An animal is a vast society of cells. Our modern cities are a vast society of animals. Philip Rosedale forsees the metaverse as the next logical step in the emergence of a single entity consisting of a society of interdependent agents. ‘We think a lot about the nature of the brain, and whether computational substrates can be dense enough to enable thinking within them. I know exactly how that’s going down, I think… SL is dreaming. It could be looked at as one collective dream. In an almost neurological sense’.
Are we witnessing the early stages of the emergence of a global mind? Will the TCP/IP nodes of the internet evolve into functioning neurons, resulting in a free-thinking entitity capable of introspecting upon all human knowledge? And if its immense computational prowess dreams of imaginary people, will they wonder about a Creator, and try to reconcile their beliefs with an increasing understanding that the ‘rules’ of their universe evolve complexity from the bottom-up? More importantly, will this happen by 2029? Will Mitch Kapor lose his bet, thanks to the ‘collective dream’ of the Metaverse?
Perhaps we should not use terms like ‘winners’ and ‘losers’ here. Perhaps SL and its successors will not help develop general artificial intelligence, but it already showcases the marvellous abilities of people that Kapor so eloquently expressed in his essay. Read it for yourself, and then dive into SL to see for yourself what we can do with our collective mind