AI AND THE INTERNET

One way that the development of an intelligent Internet may come about is through the development of artifi cial intelligence programs and their deployment on the Internet, in a way designed to increase the intelligence of the network as

a whole, as well as particular machines on the Net. This is an area I’ve worked in extensively myself, via the design and development of two different AI systems: Webmind (Goertzel, 2001) and Novamente (Looks et al., 2004). The Webmind system was under development from 1997 to 2001 at IntelliGenesis Corporation,

a corporation that I founded and helped manage until its dissolution in April 2001. The Webmind system was never deployed across the Internet due to a collapse of its funding sources, and not all parts of the system were fully implemented and tested. Since 2001, I have been involved with the development of Webmind, Novamente’s successor. However, here I will talk about Webmind more than Novamente because Webmind had more of an explicit Internet focus. Novamente is just as capable of being used in an “Internet intelligence” context but that has not been the focus of the project thus far.

The Webmind AI design embodied an understanding of intelligence as self- organizing, asynchronously distributed, and emergent, and provides one concrete vision of how a World Wide Brain could be made to emerge from existing hardware and software. From the user’s perspective, Webmind was intended as a general system for posing and answering questions regarding digitally stored information. It was meant to deal, potentially, with information of any kind, although, just as humans require eyes to perceive sights and ears to perceive sounds, Webmind would have needed appropriate “perceptual methods” for processing each type of information into its own internal data structures. Its architecture was that of a massively parallel network, a population of many different static and dynamic agents continually recomputing their relationships with other agents, and acting on other agents in accordance with these relationships. The mix of different types of agents, and the amount of resources allocated to each, determined the emergent structure of the internal network, and hence the intelligence and functionality of the system.

Webmind was designed to run effi ciently on powerful stand-alone computers, and in an ideal world, would have run best on a supercomputer with multiple processors and tremendous amounts of random access memory. In the context of contemporary computing hardware, however, it turned out to be most cost-

effective to run Webmind over a network of computers, in which case its sophisticated

314 Ben Goertzel

server–server communication methods allowed its internal network structure to harmonize with the connectivity structure of the computer network.

The Webmind design was not tied to any particular programming language, operating system, or hardware architecture; however, the actual implementation of Webmind was based on the Java programming language and was mostly tested on the Linux OS. Java was chosen for Webmind because it is cross-platform, because it supports easy networking across intranets and the Internet, and because its strongly object-oriented structure was so natural for representing the various agents mak- ing up Webmind’s internal network. It also had the practical advantage of relatively low development time, which was important given the intrinsic complexity of the Webmind system. Some fairly serious performance problems arose from the use of Java, however, which is why in my current large-scale AI work I have reverted to the older language C++.

The essence of Webmind’s intelligence resided in the portion of its code called the “Psynet,” which embodied a logical/conceptual model of artifi cial cognition going beyond the Webmind system in particular. A Psynet is a self-organizing network of information-carrying agents. Information is incorporated into the Psynet via the creation of agents embodying that information. The architecture of a Psynet is relatively simple because the intelligence of the system is allowed to emerge from distributed interactions among the population of agents, rather than being imposed by specifi c reasoning rules or knowledge representation structures. The Psynet

represents the minimum of structure required to lead to the adaptive emergence of useful information structures embodying data items. In short, the data stored in the Psynet is allowed to discover its own structure, within given constraints, rather than having structure imposed on it by rigid, preconceived rules. The design of the Psynet package was based on a mathematical theory called the “psynet model of mind,” which I developed in a series of four books and numerous research papers over the period 1993–1997 (Goertzel, 1993a, b, 1994, 1996, 1997).

Agents within the Psynet are of three types: static, relational, and mobile. Static agents may represent temporal data, but are static in the sense that they have

a continued existence, maintained by the Psynet itself. Relational agents are not known directly to the Psynet but are held by other agents, representing relations between that agent and other agents. Mobile agents are like relation agents, but change frequently with time; they represent the learning of relationship by the Psynet’s static agents. The Psynet supports many different types of static agents, tailored for particular purposes.

Static agents are also called “nodes,” whereas relational agents are also called “links,” a terminology that connects the internal structure of the Psynet with the external structure of the Internet and intranets in many useful ways. However, this language should not distract one from the fact that static and relational agents are much more substantial that the nodes and links found in some other AI architectures (e.g., neural networks). A node within the Psynet is nothing like an individual

12 World Wide Brain 315

neuron in the human brain, but might be more fairly compared with a neuronal group within the brain (consisting of 10,000–100,000 neurons tightly connected and oriented toward a single purpose). Psynet nodes cover a wide range of scales, from individual words to entire texts, data fi les and database records, categories of text, categories of words, trends of change over time in collections of data or

collections of nodes, and so on. Most abstractly, there are nodes corresponding to other Psynets with which there is interaction, and nodes modeling aspects of the Psynet itself, for purposes of adaptation and self-improvement.

The construction of nodes that refer to collections of other nodes is of particular importance. These nodes are called “concepts,” and they provide a Psynet with

a hierarchical structure, complementing its primary associative structure. The super- position of hierarchical and associative structure is called a “dual network structure” and is essential for the emergence of intelligent activity and link patterns.

Learning in the Psynet takes place in fi ve ways:

1. The recognition of patterns in data stored in individual nodes, which is carried out by methods in the Info package

2. The recognition of relationships among nodes, which is carried out by mobile agents

3. The creation of new nodes representing collections of relations among other nodes (“concept formation”)

4. The spreading of activity around the Psynet in complex, possibly chaotic patterns, representing spontaneous, emerging focusing of the network’s attention

5. Directed introspection, in which the Psynet poses a series of queries to itself.

A query into the Psynet results in the creation of a new node, a “query node” that creates new mobile agents, which travel about within the Psynet and cre- ate new links for the query node. The answer to a query is given in terms of the relationships found by this agent-swarming process. The query node is stored for future reference, along with any user feedback regarding the perceived quality of the response to the query. The Psynet’s introspection process involves continually querying itself, using queries based on queries it has been posed in the past, and particularly queries on which it has performed badly: In this way, it continually produces new knowledge in the areas in which it has proved defi cient and fi lls in gaps in its performance.

The fi nal and, in some ways, most interesting part of the Psynet is its mecha- nisms for server–server interaction. An individual Psynet is, potentially, an auton- omous AI system. In practice, however, greater intelligence may be achieved by networking Psynets together in various ways. In the Webmind design, Psynets were intended to interact with Psynets running on other Webmind servers in several different ways:

316 Ben Goertzel

1. They could query other servers, as if they were clients of that server.

2. They could send agents to visit other servers and gather information.

3. They could exchange detailed information about their internal processes with other servers, on a frequent basis.

4. They could swap sections of their memory with other servers in a group, to optimize functioning of the group as a whole.

Each server contained in it a list of the other Webmind servers that were allowed to interact with it in each of these ways. A collection of Webmind servers that interacted with each other was a “Webmind unit.” Elements of a Webmind unit were less like humans participating in a society than like different lobes or hemispheres within a single brain. On the other hand, Webmind servers belong- ing to different organizations were generally be able to interact with each other only via the fi rst two methods or via the fi rst method alone. There is a gradation between “social” and “intra-brain” interaction here, as opposed to the rigid division between individual and society that we experience as humans.

Finally, the social network of a Psynet plays an important role in guiding its introspections. A Psynet thinks about—queries itself about—those topics that it judges to be most important at present, as judged by several criteria: trends it has recognized in itself, trends it has recognized in its social group, and trends in what its users and peer Psynets have identifi ed as its defi ciencies. The degree to which a Psynet pays attention to the opinions of another Psynet is determined in an intelligent manner, based on its experience with that Psynet’s and other Psynets’ opinions, according to an algorithm drawn by mathematical models of human social interaction.

Webmind was designed to be used to solve many problems that are fairly self-contained, detached from the fl ow and organization of human affairs, such as “Find me information about crazed Third-World dictators; What do the trends in Japan say about the U. S. stock market?” Things become yet more interesting, however, when one envisions the same sorts of questions being asked regularly within an organization, about processes and structures within that organization. Instead of the stock market, one may have productivity statistics from various divisions of a

company; and instead of newspaper articles, one may have reports generated within the company, e-mails sent within the company, etc. If Webmind were installed on the company’s intranet, then real-time queries regarding relationships between textual, numerical, and other data to do with the enterprise can be posed by (and answered by) any employee with computer access at any time. Webmind’s intelligence was intended to be integrated with the social intelligence of the organization and the individual intelligence of the employees.

As a consequence of the deployment of software like this, the social dynamics of the different Psynets residing in different parts of an organization’s intranet would grow to refl ect the social dynamics of the individuals using those parts of the

12 World Wide Brain 317

intranet. For instance, each Psynet would respond most effectively and rapidly to queries involving information that it stores locally; but the information that a certain Psynet within a Webmind unit stores might change over time, depending on user needs and internal Psynet dynamics. Thus, while providing easy access by all users to all information at all times, Webmind was designed to nudge the information at the readiest disposal of individual humans and divisions in certain direc- tions, based on its inferences and its own emergent understanding. An AI system deployed like this would do more than just provide an understanding of structures and processes; it would be a participant in processes, in the formation of emergent human and informational structures.

And fi nally, as various AI units in various organizations exchange nonpropri- etary information, in the interest of increased mutual intelligence, an AI system of this nature would be a participant in the formation of human and informational structures on the global scale. This is an exciting new vision of AI, in the business context and beyond—not AI as something separate from humanity, providing us with answers to our questions, but AI as something interacting symbiotically with humanity,

participating in our communications, goals, and social structures and processes. This kind of vision made sense to me in 1997 when I founded IntelliGenesis Corp., and it still makes sense to me now, even though that company failed for fi nancial reasons. The Internet has developed dramatically since the late 1990s, yet it has not advanced all that far in the distributed-intelligence direction, and I think this is more for economic reasons than for fundamental reasons. My current AI project, Novamente, is also based on the general Psynet approach, but we are not initially taking an Internet focus. Rather, the current plan is to achieve a high level of intelligence on a Novamente system running on a fairly small, localized computer network—and then approach the question of Webmind-style broad-scope distributed processing. I remain confi dent that this kind of distributed Internet intelligence is the future of the Internet, though the precise path by which the Net will get there is not entirely clear.

AI AND THE INTERNET