Super Intelligences Powered By People


How will the super intelligences (SIs) of the future function?  I define a SI as a single entity that can communicate about a broad range of topics as fast as any machine and as correctly as any well-organized team of topic-specific human experts. So how does it work? The SI is a Six State Logic (6SL) based expert system/machine that teams of humans massively parallel program to communicate on a broad range of topics. A well-functioning SI presents an always available interface for any human or machine that wants to communicate about any programmed topic.

Consider the example of a team that wants to teach an SI how to sell their product. The sales team starts by creating counter responses to prospective customer’s Six State Logic (6SL) answers to the question “Will you purchase my product?”  The sales team’s counter responses include (1) information for Uncertain prospects, (2) purchase instructions for Known Positive prospects, (3) please tell us why requests for Known Negative prospects, and combined and/or multiple level responses for (4) Mixed, (5) Seems Positive, and (6) Seems Negative prospects.

Once programed, the SI becomes the first point of contact for all prospective customers. A typical SI and prospect interaction involves (1) the prospect or the SI initiates communications by asking about product purchase, (2) the prospect provides a Six State Logic status regarding its desire to purchase, (3) the SI matches its response with the prospects purchase status, (4) responses and counter responses continue to until the transaction is concluded, put on hold pending new information, or abandoned.

For example, a prospect could approach the SI with a Seems Positive status for purchase with the reason for uncertainty being the need to check current price. That SI provides the price information and the prospect changes its status to Known Positive. The SI then proceeds to conclude the transaction.

An alternative example is when an SI with a Known Positive status for selling a product ask a prospect to purchase. The prospect answers with a Known Negative response with the reason that a similar product purchase was made recently. The SI and prospect could then agree to communicate in the future when the prospect needs the product again. The SI could try to gather competitive information if the prospect is willing or able to communicate on that topic.  The SI could then store this prospect and product sales status as Mixed because the prospect has a similar product purchase history, but has recently purchased a competitive product.

The sales team refines SI programming by identifying cases where a sales prospect asks questions the team has not prepared answers for or cases when the sales process does not progress smoothly to a purchase, conditional purchase, do not purchase, or conditional do not purchase conclusion. The sales support team uses 6SL consensus teamwork tools ( to rapidly answer questions the SI can not answer and to prepare SI programming enhancements.

The question and answer script prepared for a SI can require multiple layers of 6SL responses.  For example, if in response to the “Will you purchase my product?” question a prospect with a Seems Positive answer could ask the counter question, “Can you deliver tomorrow?” In response the SI could provide a Seems Positive answer and the counter question, “Can you pay for express shipping?”  If the answer to this counter question from the prospect is Known Positive then the SI would change its original question to “Will you purchase my product if we ship it to arrive tomorrow and you pay for shipping?”

The primary difference between 6SL expert systems and a yes/no answer based expert system is the enhanced information value of 6SL responses. For example, a Known Negative response with a well understood reason effectively ends the SI interaction. This is because the prospect did not select any other 6SL states where follow up questions are required.  This short path between question and justified action contrasts with a simple “no” response to the purchase question which would requires many follow up questions to generate a similar level of understanding as the single 6SL answer provided.

In summary, the 6SL value points that help enable SIs are: (1) SI interactions are limited to prepared topics, (2) the SI only interacts with machines and humans using 6SL format questions and responses, (3) each topic is supported by a combination of human created and updated SI programming and real time interactions with human programmers when the SI’s programming is insufficient to conclude a transaction. It is expected that a properly functioning SIs will create a prioritized backlog of required programming updates for its human programmers.

The key value of the SI is the ability to communicate quickly across a broad range of topics in a continuously improving way. These abilities are particularly helpful when two well supported SIs interact and identify a broader range of opportunities in less time than any similar human managed interaction system could. Consider all the economic opportunities that an Apple SI could identify when interacting with a Samsung SI or possibly a USA national SI when interacting with a Japanese national SI.

Michael Klasen, PhD
Creator of Six State Logic

Mobile: (503) 442 6524

Six State Logic Makes Tomorrow’s Networks Optimal


This podcast introduces the concept of a 6SL Search Network. The 6SL Search Network enables human preference driven, autonomous communication between machine agents as described in my Podcast 8 ( This Podcast 10 describes the optimization of the 6SL Search Network by application of the 6SL Search functionality introduced in Podcast 9 ( to:

  • Determine the priority order of communications between machine agents,
  • Determine the priority order of human communications with machine agents
  • Assure the 6SL Search Network functions well regardless of the level of uncertainty of both search preferences and search topic data.

A human’s machine agent can maintain an unlimited number of parallel communications with other machine agents about any number of topics. Humans are more limited so 6SL search uses current human preferences and search topic data to prioritize incoming communications for humans and outgoing communications for machine agents.

For example, because it was a high priority for it’s human, a machine agent could have worked for weeks to find and maintain a list of car purchase opportunities. When the importance of a car purchase was reduced by the human, the machine agent would terminate car purchase related communications with other agents and move on to other communications that reflect most current human preferences.

To prioritize 6SL Search Network communications, 6SL Search calculates the average difference between search preferences and search topic data for every potential communication opportunity. Satisfaction is 100% when the preference and search topic data are an exact match and  0% when the preference and search topic data are at opposite extremes.  The satisfaction for unknown preferences and/or unknown search topic data falls between these two satisfaction extremes. The search result is a relative satisfaction ranking of all potential communications for both humans and machine agents.

Calculating satisfaction as a rank order results instead of an absolute value enables the 6SL Search Network to function well even when many search preferences and/or search topic data are uncertain. By design, the 6SL Search Network functions well in both this type of extreme uncertain environment as well as in the opposite environment where search preferences and search topic data are well known and actionable.

This Podcast describes how the 6SL Search Network prioritizes communications for machine agents and humans in a way that enables independence of search preferences and search topic data. This means that the 6SL Search Network can be infinitely extensible to manage constantly changing preferences from humans and constantly changing search topics data from any number of sources.

Recognition of Voter Uncertainty Can Bridge the Partisan Divide


Partisan politics is a type of motivated reasoning that defines the political opposition as wrong regardless of the topic or position taken. Fake news catalyzes partisan politics by purposefully encouraging emotional reactions that far exceed the counter reaction when the truth becomes known. People remember emotions more than facts so the damage caused by fake news continues. Technology enables a partisan environment where extremist label even the most blatant partisan politics as a virtue.

What is the solution to these information age problems? What can help put us on a path towards using technology to increase tolerance and understanding instead of helping us blast fake news and partisan rhetoric at each other? I am certain the answer is respect for our individual rights: (1) to be uncertain, (2) to not be bullied into undesired actions, and (3) to freely  seek information to make better informed decisions.

Uncertainty has a bad reputation as a characteristic of weak minded people. I could not disagree more.  Uncertainty and the desire for information is simply the opposite state of being from Know Positive or Known Negative states with the associated desires for immediate action.  I believe uncertain people need to be recognized and counted instead of marginalized and forced to act by people with partisan political agendas.

Uncertain people are the natural judges and followers of certain people and, when in a recognized majority, tend to naturally marginalize partisans who tell lies of omission and spout fake news. Open discussion forums like social media are in theory rational over time, but in practice information overload allows us to only absorb a comparatively small amount of information with little opportunity or desire to seek an objective truth.

So how can an understanding of uncertainty improve how our existing political system functions? First, there must be a forum for where uncertain people can go to be counted in a meaningful way and to judge the consumption worthiness of alternative information sources. In this type of uncertainty safe environment, extreme opinion holders will soon discover how tolerant uncertain people can be of alternative viewpoints.

Uncertainty recognition is about empowering the political center to push back on extreme political views. The counting of uncertain people is initially just for the reference of policy makers, but eventually uncertain people deserve the right to vote with real policy implications like power sharing and policy continuation until policies that command action-oriented majorities emerge. In this way a vote for uncertainty becomes the bridge across the extremist partisan divide that many people desire.