Tag Archives: superhuman

Invest in Biotech and Information Sciences

If we invest heavily in biotechnology and information services companies (especially genomics, networked centralized computing, neurology, neural network predictive applications, and nerve regeneration) in the next 50 years, many currently living people may have an opportunity to achieve substantially improved and lengthened quality of life and indefinitely extended sentience.

It’s more than a financial return, but it can still be evaluated financially. The return on these investments should be calculated as the return on the securities themselves, plus the return on your other investments over the period of time that your life and investment horizon are extended. It is possible, then, that the net return on biotech and information science investments may be substancially higher than the direct value change for those investment securities.

Distributed Processing and Biological Approximation

The complexity of biological thinking is impossible is impossible to replicate today when constrained by the limitations of a single machine. With the ability to define and exchange standard objects and a standard interface, this barrier could be broken. The hurdle of coding models that approximate the function of the brain has also been insurmountable as long as development was dictated, managed, and organized within a company. The upper limit on programs of this origin seems to be in the range of 10 million lines. The growth of open source development opens the door to the integration of many codes to form the billions of lines necessary to approach human capacity for thought.

Building Intelligent systems: biological and software

There are amazing analogies between computer processors and the brain, and it is just a matter of time before the algorithms that define electronic processors mirror the functionality of the chemical processors of our brains. The biological neural systems rely on inputs and pattern recognition to learn. Computers are excellent at storing (remembering) facts, and are becoming proficient at recognizing relationships as defined by statistical patterns and neural networks. However, computers cannot yet create metaphors or learn how to independently process new information.

Metaphors are an important part of how humans think. We understand new information as we draw parallels and connections between it and prior information. Over-simply put, we learn by recognizing relationships between new information and old. Computers could do the same when their sets of data include enough relevant fields to be able to computationally identify systems that are described by similar dynamics. In other words, when a computer has data about how things works, it can find systems that work similarly to each other. The leap from there to drawing metaphors is a data-mining process: it can be solved by computational rote, where statistical relationships are identified, prioritized, and used for prediction.

This process also leads to “learning” about how to process new information. By recognizing the metaphors, new data can be classified and described according to how it is understood. And just like in our brains, there will be errors. Misunderstanding will occur as metaphors are calculated based on incomplete data sets. As more data is input, systems will have to be able to make corrections and re-calculate all of the other metaphors that included the corrected data. New corrections will be made and a cascade of corrections will result in a modified historical data record. A large number of calculations and recalculations will occur with each new input, and the storage of historical data (and calculated results) will require substantial processing and storage.

Recognizing metaphors will allow machines to output statements like: “It appears that ABC is driven in many similar ways to XYZ. The result we are seeking might be accomplished by A because a similar result was achieved in XYZ when X was applied.” Put more simply, computers will be able to express creative suggestions.

Interestingly, storage could be massively reduced by deleting large volumes of data that support the relationships that are strong enough to overcome some threshold level of certainty. For example, if everything falls, then we don’t have to keep all that data, just the relationship that everything falls. This may be analogous to forming intuitions.

Human Responsibility

The Human Rights movements of the 20th century will evolve into the Human Responsibility movements of the 21st. Just as the moral masses rose to fight battles of freedom, representation, protection, and equality, new moral questions of responsibility will arise as paramount. We will be forced to confront and socially decide upon subjective and highly contested issues in the use of technologies, preservation of environments, and rules of trade and labor. Harold T. Shapiro *64 is an early hero in this movement, speaking to thousands:

In the 21st Century, scientists and engineers will continue to inform us regarding what we can do with our ever-expanding knowledge base, but it is our shared responsibility to decide what we should do. And deciding what we should do is the greatest responsibility we all bear as we move forward together.

It will be a moral call to arms. Factions will grow in much the same ways that they have around abortion questions. Large numbers of issues will arise, and be grouped by medical, moral, philosophical, religious, technical, and other similarities. Specialized factions will fight for ultimate personal freedom to act, at least upon themselves, without restraint. While others will fight for the protection of others, even to the great restraint of personal freedoms. And there will be a majority in between.

Communities will form, and governments will be organized around the constituents’ answers to these questions. Those countries that embrace the most freedoms, particularly for businesses, will have financial advantages over those that embrace the most protections of others. This imbalance will allow particular countries to benefit for decades at the detriment of the whole, as their own incentives are not aligned with the benefit of the whole, but instead with their own short term economic benefit.

Learning Biofeedback Input System

By recording and analysing biofeedback and keyboard inputs, an analysis of the concurrent data could identify correlations to yield a biofeedback powered input system. The system would “learn” to replace the keyboard over time with better and better accuracy, faster input speed, and extended breadth of input types. Biofeedback devices could initially include medical biofeedback devices