#ChatGPT 14-109

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Trends in the evolution of transports

The distinctions of physical, data and semantic transports may seem somewhat forced, as would any other simple subdivision of a complex process. The fact remains that exo-biological systems take an ever increasing role in storing, transporting and processing structures valuable for biological individuals, and the involvement of the technology in these functions is ever more active and intelligent. A wheelbarrow filled with stone tablets already represents an external mobile transport of abstract references, although the message is still too deeply engraved in the medium, and the whole transport is passive and unaware of its function. During the course of history, value transports become increasingly abstract, changing physical carriers without human involvement, testing their own integrity and assuring delivery, processing the entrusted values, and ultimately, accepting a greater role in deciding what should be shared, where it should be sent, and how it should be processed. To build this new semantic transport, we need to create the global information infrastructure, with open standards for semantically rich descriptions of objects and their relations allowing for diversity of opinions on the same subjects and access control for different parties. We also need comprehensive feature and interest ontologies, to describe the structure of the environment, a variety of algorithms that would form the brain of this system, and an extensive amount of knowledge loaded into it by multiple participants.

The development of technology in any area goes through a similar set of stages. At each stage a new level of human abilities first becomes explicitly expressed in non-biological constructs, then independently carried, then carried out (all things that are started by humans and continue functioning on their behalf, from campfires to mousetraps to stock trading programs), then combined and processed, then get taken over. This has already happened, consecutively, with the handling of physical materials, energy, and raw information. Pursuit of AI, though a worthy goal, may have been an attempt to leapfrog the natural cooperative/mixed/synthetic stage in the area of object semantics. Now we are filling this gap. Human judgments have been expressed in artificial media for a long time, communication industry gave us efficient tools for transporting them. Automated processing of opinions is still in a very primitive stage (e.g., ballot counting systems), but ACF technology promises to significantly raise its level. After this is accomplished, the next layer of human functionality to be taken over will probably be semantic analysis of objects. The work here has already begun, with pattern recognition and signal processing techniques, and commonsense information analysis tools on the higher end. Existence of semantic transport infrastructure will allow immediate utilization of advances in automated semantic analysis, which should greatly accelerate its development. An analog here is text analysis tools that after the emergence of the World Wide Web became crucial for daily information searches of millions of people, and turned information retrieval from an area of orderly academic activity into an arena of intense commercial competition.

Economic promises of semantic transport
Strictly speaking, ACF, as well as data communications, starts with transporting references to existing values, rather than the values themselves. Reference transport optimizes the flows of underlying values. This allows references themselves to be recognized as values, and then the new mechanism turns into a value transport in the full sense. In the current society, though money can be easily passed from one agency to another, many crucial areas of economic and social activity are under-funded, as their importance is not recognized (though all necessary information may be available somewhere, it can remain unknown or ignored by people with money). In many cases, people have no incentive to share their knowledge with others, as in many areas there are no mechanisms to compensate them for sharing the knowledge. So instead of just telling other people what they know, which would be the least expensive way to provide the maximal social benefit, they attempt to realize that knowledge in something non-transportable, so that they can control the distribution of benefits and profit from it (though these implementations often could be realized faster and cheaper by other people if they knew about them). The creation of reward mechanisms for knowledge distribution, in terms of enhanced social reputation of the individual, as well as direct payment for advice based on the utility of previous opinions expressed by the same person, may give people incentives to share some of the knowledge – e.g., observations of the economy, or ideas for inventions – in the areas where they tend to keep information proprietary today. This would result in less antagonistic relationships between an individual and society, conservation of resources, and acceleration of progress.

More Sasha:

Applications of ACF in the near future
ACF provides multiple functions. Let us examine them and see how they may be used.

Finding objects of desire
People spend a lot of time trying to find the best objects to satisfy their interests. In the process, they waste a lot of effort looking through junk, find some occasional surprises (serendipity is a side benefit of bad searches), and miss many gems that might have made their lives much better. Email and Web access dramatically increased the efficiency of information searches and distribution, but did not modify the nature of the mechanisms. Improving this search process would increase quality of life and accelerate the adoption of new and worthy products and ideas. This would save resources and increase the rate of social and economic progress.

An interesting issue here is that recommendations delivered by ACF come from the person’s affinity groups, and are more geared towards the person’s interests than suggestions from mass-media channels, whose choice recommendations are linked to the power interests of a small number of commercial and political organizations. This could very well lead to decentralization and democratization of influential powers in the society, even for mainstream interests. I would like to stress that I divide interests into fringe and mainstream here, not people. For example, my interest in philosophy may be fringe, but my health and nutrition interests may be quite typical. I would expect that the ACF-enhanced system would be more likely to recommend apple juice over Coca-Cola than would commercial advertising. The effect on the economy should be a greater incentive for companies to tailor both their products and marketing messages to consumer interests rather than to manipulate people’s opinions. “Producer push” in value exchanges would give some space to “consumer pull”.

Implications for fringe interests are still more promising. People who are interested in rare objects or obscure issues usually get no social messages targeted at them at all. ACF may provide them with advice whose quality matches that of wider spheres of interest. ACF can also benefit people who have interests in the mainstream subjects, but who have opinions different from those of others – they may be trying to do something new, or have finer or exotic tastes. For these individuals, ACF can deliver advice corresponding to their preferences. Such functions will allow ACF systems to support the diversity of tastes and interests and encourage their development, while preventing alienation.

Some caveats exist here as well. If people’s expertise and tastes in some area are underdeveloped, a straightforward ACF system might provide them with the advice of their “laggard” affinity group that may give them support but not help in developing beyond their current level.

The hopes here are:

if people advance in their current interest faster due to ACF, and at the same time get better advice from other social channels, they can still evolve faster.
it is possible to design ACF algorithms so that they can track the development paths of other people interested in a particular area – noticing, for example, that people who study arithmetic or rap music today are likely to turn to algebra or rock tomorrow – and give them advice aimed at helping them move ahead.
Soon, ACF technology as an information search method will join the ranks of established information retrieval tools, and will make information retrieval systems more intelligent and adaptable. Increasingly semantics-rich information systems will include elements of ACF functionality as integral parts, and will use them to adjust their communications with humans and each other, building representations of the environment more suitable for their tasks. This includes negotiation of “consensus ontologies” for different subject areas, and building models of people’s interests that would help refine searches for information. Later, automated content analysis tools based on “common sense knowledge bases” may aid humans in evaluating item semantics.

The ACF methods should not necessarily be based on the straightforward matching of people’s interests. One group of people can be interested in materials that another group considers offensive or controversial; educators would pay special attention to things that students find too difficult or boring; researchers may be interested in problems that have puzzled their colleagues or have not received sufficient attention; social scientists and market analysts would study subjects experiencing rapid changes in levels of public interest or shifts of opinions; reviewers would look for new or unknown material in promising areas; fashion watchers may pay attention to things rising in popularity. As a result, the ecology of knowledge may establish productive information exchanges among groups that previously didn’t find much in common. (Let us hope that this information will not be used for hostile purposes too often).

The most important feature to Humans would be:

Finding like-minded people
This is a very important function of ACF, just as it is of many social mechanisms. Finding people who share your interests is important for finding further directions in life, starting social and economic activities, forming friendships and families, getting advice on important personal decisions, and feeling more confident and stable in the social environment. Many people abandon ideas of businesses or social undertakings, or feel depressed and alienated only because they cannot find other individuals who share their interests, even though those people may pass them in the street, or could be reached in a few seconds with a phone call. ACF can help you find kindred spirits wherever you go – just express your preferences, and you will find that the person in the next theater seat shares your interests in the arts, that your neighbor at a business banquet table is thinking of hiring somebody with your qualifications, and that the person in the airplane seat next to yours is single, fits all your dating criteria and shares your political affiliations and musical tastes. Some day, you might even be able to find people sharing such bizarre interests as the historical subject of social alienation in the pre-ACF era.

The social functions of ACF can be supplemented by other Automated Collaboration (AC) techniques, such as group scheduling, multi-user games, teleconferencing, groupware, etc., that would facilitate all kinds of activities among like-minded people.