Posts Tagged ‘Parties’

Machine Learning and Database Reverse Engineering

October 13, 2019

Artificial intelligence (AI) is based on the assumption that programming a computer using a feedback loop can improve the accuracy of its results.  Changing the values of the variables, called “parameters”, used in the execution of the code, in the right way, can influence future executions of the code.  These future executions are then expected to produce results that are closer to a desired result than previous executions.  If this happens the AI is said to have “learned”.

Machine learning (ML) is a subset of AI.  An ML execution is called an “activation”.  Activations are what “train” the code to get more accurate.  An ML activation is distinctly a two-step process.  In the first step, input data is conceptualized into what are called “features”.  These features are labeled and assigned weights based on assumptions about their relative influence on the output.  The data is then processed by selected algorithms to produce the output.  The output of this first step is then compared to an expected output and a difference is calculated.  This closes out the first step which is often called “forward propagation”.

The second step, called “back propagation” takes the differences between the output of the first step, called “y_hat” and the expected output, called “y” and, using a different but related set of algorithms, determines how the weights of the features should be modified to reduce the difference between y and y_hat.  The activations are repeated until either the user is satisfied with the output, or changing the weights makes no more difference.  The trained and tested model can then be used to do predictions on similar data sets, and hopefully create value for the owning party or (either person or organization).

In a sense, ML is a bit like database reverse engineering (DRE).  In DRE we have the data, which is the result of some set of processing rules, which we don’t know[i], that have been applied to that data.  We also have our assumptions of what we think a data model would have to look like to produce such data, and what it would need to look like to increase the value of the data.  We iteratively apply various techniques to try to decipher the data modeling rules, mostly based on data profiling. With each iteration we try to get closer to what we believe the original data model looked like.  As with ML activation we eventually stop, either because we are satisfied or because of resource limitations.

At that point we accept that we have produced a “good enough model” of the existing data.  We then move on to what we are going to do with the data, feeling confident that we have an adequate abstraction of the data model as it exists, how it was arrived at, and what we need to do to improve it.  This is true even if there was never any “formal” modeling process originally.

Let’s look at third normal form (3NF) as an example of a possible rule that might have been applied to the data.  3NF is a rule that all columns of a table must be dependent on the key, or identifier of the table, and nothing else.  If the data shows patterns of single key dependencies we can assume that 3NF was applied in its construction.  The application of the 3NF rule will create certain dependencies between the metadata and the data that represent business rules.

These dependencies are critical to what we need to do to change the data model to more closely fit, and thus be more valuable for, changing organizational expectations.  It is also these dependencies that are discovered through both ML and DRE that enable, respectively, both artificial intelligence and business intelligence (BI).

It has been observed that the difference between AI and BI is that in BI we have the data and the rules, and we try to find the answers.  In AI we have the data and the answers, and we try to find the rules.  Whether results derived from either technology are answers to questions, or rules governing patterns, both AI and BI are tools for increasing the value of data.

These are important goals because attaining them, or at least approaching them, will allow a more efficient use of valuable resources, which in turn will allow a system to be more sustainable, support more consumers of those resources, and produce more value for the owners of the resources.

[i] If we knew what the original data model looked like we would have no need for reverse engineering.

Fortune: Google Claims ‘Quantum Supremacy,’ Marking a Major Milestone in Computing

September 21, 2019

Fortune: Google Claims ‘Quantum Supremacy,’ Marking a Major Milestone in Computing.
https://fortune.com/2019/09/20/google-claims-quantum-supremacy/

Much has been said for a long time about the relative merits of and differences between quality and quantity. One school of thought holds it is not how much you produce but the ‘fit to purpose’ or quality of the output that trumps everything else. The other school holds that large amounts of nearly anything overwhelms subjectively measured attributes simply because enough of anything will ultimately include the best examples of that thing, as well as the non-best examples.

However what is becoming abundantly clear to many people, is that speed cures all ills. Go fast enough and quality and quantity are rendered meaningless as differentiators. Enough speed, for example, allows a party or a thing to just ‘re-do’ until it’s right. Quantity and quality become the same attribute at high enough speeds.

Automation and the End of Human Wealth

January 15, 2019

Time is money. Well not really, but they do equate very nicely. A person’s wealth can be measured not only by how much money he or she controls, but by how much of their time can be used for activities not necessary just for survival. This time, freed up from mere survival activities, has always been used to create increasing wealth for humans. The increase in wealth creation accrues to both producers and consumers. Producers get wealthier by getting more money, and consumers get wealthier by getting more time.

Previously the march toward automation has created ever increasing wealth because some party has invented the latest automation, sold it to others, and another party has bought the automation and used in to free up more of their time. In the 6BI sense, “money” and “time” are the product and payment exchanged at armslength in the transaction.

The question we should ask now is, will we ever reach the point when there are simply no new wealth creating activities that humans can invent? A time when every activity that could have created new wealth for humans will already be performed by some form of automation. Could it be possible that at some point in time any invention, instead of being valuable to some human, will have no value and thus not be able to be exchanged for money?

If we ever do reach the point where additional automation can no longer drive the creation of wealth for humans because everything that humans could do for themselves will have already been automated, then there will be no advantage, or value, to the next invention. It simply will not be an innovation.

At that point in time, I believe the earth’s human population will crash or go into a period of slow negative growth. There will be no motivation to either invent or procreate. Human population will decrease as a product of reduced opportunities and consequently the influence of humans on the planet will decrease.

On the other hand, the robots and artificial intelligence that provide automation to humans, since they do not need to either invent nor procreate, will increase in number and influence. In number because they will wear out more slowly than flesh and blood humans and in influence because they will no longer be dependent on humans to improve their programming.

Because of the decrease in number of unmet human needs fewer software developers will be needed, for example. This decrease in unmet needs, doesn’t necessarily mean humans will be more satisfied, just that there will be fewer and fewer value and wealth creating activities that they can perform for themselves.

If this happens, and there are substantially less humans, will there really be a lesser need for automation? What will happen when there is no longer any new human need or activity to be automated? Will robots and artificial intelligence continue to operate with humans eventually becoming less and less relevant to them? Will humans become even less aware of the means of automation? Are humans ultimately essential for the operation of automation and thus as human numbers drop, computing entities, the means of automation, will drop as well? Will automation itself be automated and operate without human intervention at all because any knowledge of how it works will eventually be lost to humans?

Will there be an ever increasing demand for resources such as electricity to keep a kind of “closed loop” automation going and going even though it has reached the point where automation’s added value to humans is at, or near zero? Even more interesting, from a human perspective, what will happen when new wealth can no longer be created?

The Currency of the Human Cohabitation Contract

September 27, 2018

This article is somewhat different from those I usually post here, in that it is not explicitly about a computing subject.  However, it asks questions about what happens when assumptions on which expectations are dependent evolve over time, thus changing definitions in the process.  It is something I have been thinking about for a while and have finally decided to write it down so the idea does not get lost.

 

Children are the currency of the human cohabitation contract.

Traditionally a man’s role was to provide a woman children in exchange for companionship and nurture.  A woman’s role was to provide a man children in exchange for companionship and protection. These sex specific roles in the cohabitation contract go back to way before the agricultural revolution some 12,000 years ago, to our hunter and gatherer ancestors.  All the way back to the early Hominins like Homo Erectus, as much as a million years ago.

The evolution of cohabitation roles is now changing this equilibrium.

As men become more capable of nurturing and women become more capable of protecting, the role of each sex is changing.  Each is becoming more like the other.

One result of the evolution of the roles in the cohabitation contract is the increase in the number of single parent family units and the increased acceptance of homosexuality.

After a certain point neither sex needs the traditional services of the other as they once did.  They have begun to provide these services for themselves.  Thus the definition of the cohabitation contract roles is evolving.  However, the definition of the services provided are not.

This shows the difference between sex and gender.  Sex is the biological difference between males and females.  Gender is sociological differentiation between men and women.  Throughout history gender was determined by sex.  Males were men and females were women.[i]  The roles of the cohabitation contract were essentially sex roles.  Now they are evolving into gender roles.

No one knows how far the evolution of cohabitation roles will go.  Will there be a tipping point when the trend reaches the critical mass to produce an accelerating change in human society?  Will this change then become a permanent stable condition?  Will it be uniform across the globe?

Power has almost always accrued to the cohabitation role providing protection.  This role has traditionally been played by men in nearly every civilization in history, and even pre-historically.

Will males in mass realize and become aware of the change in their role expectations?  Will this awareness be perceived by men as a loss of power?

Will females become aware in mass of the change in their role expectations?  Will a significant number of women form alliances to protect this newly realized power?

One key to answering these questions is whether roles produced by changes in the cohabitation contract will be perceived as gender roles, or will they continue to be perceived as sex roles.

How cohabitation roles are perceived should have a measurable effect on the maturation process of boys into men, and girls into women.  The gender roles, man and woman, may actually change.  Possibly each immature sex will evolve into its mature form in an environment where males will no longer just be men, and females will no longer just be women.  We might no longer think of men only as male, nor women only as female.  Cohabitation contract roles may change so much that they may become disassociated from either sex or gender.  The roles of nurturer and protector may someday not even be exclusively performed by humans[ii].

Regardless of the change in the definition of the roles, children will continue to be the currency exchanged for services between these roles in the cohabitation contract.  As long as these services are provided in a manner acceptable to both genders, it will make little difference who plays these roles.

 

[i] Note the word in English used to identify the two groups. The stem word in both cases designates the man/male and the “modified” word identifies the “wo”man/”fe”male.  This could be a by-product of, at least in the English speaking parts of the world, the male dominating the female socially throughout history.  In some languages (ex: Estonian, Hindi) there are distinct words for “man” and “male”, while “woman” and “female” are not differentiated, with a single word for both.

[ii] An argument could be made that this has already begun to happen..