Exponential productivity through optimization of knowledge yield

AI systems create value by converting human knowledge to digital form that can then be converted to other forms of energy, including kinetic. At the atomic level knowledge created by human intelligence and augmented by machine learning can be viewed and expressed as an extension of relativity discovered by Einstein more than a century ago.

Our concepts and laws of space and time can only claim validity insofar as they stand in a clear relation to our experiences; and that experience could very well lead to the alteration of these concepts and laws. By a revision of the concept of simultaneity into a more malleable form, I thus arrived at the special theory of relativity.” – Albert Einstein[i]

When knowledge is converted from neurons in the brain to digital form to be processed and exchanged over computer networks the energy can then be measured in many ways. For example, we can express the net amount of work within a system that equals the change in the quantity. We can also measure the total energy and time-evolution of a distributed network and apply equations to describe the changes in the system over time from quantum effects.[ii]

The importance of relativity becomes apparent when considering knowledge converted to AI is a form of focused energy that offers potential to be harnessed for any endeavor, which can be measured in BTU, joule, and other SI units.[iii]  A similar process can be used to measure quality, performance, productivity, financial and other values in systems that have the capacity to collect the information necessary to assign accurate values.

Yield management of knowledge:

The output from knowledge optimization processes as determined by the specific needs of the subject entity. If the system is the sum of the separate parts, has the functionality to capture highly specific particle value, and the ability to tailor to specific needs of each entity, we can then manage the yield curve of knowledge for specific missions in near real-time. Relevance is further defined by specific location and time.

exponential-productivity-knowledge-yield

Figure 1:Three outcomes are expressed in USD representing the impact of compounded productivity resulting from organizational knowledge yield curves. Outcomes are based on a $100,000,000 investment over a ten-year period, compounded annually with a monthly net profit of $500,000 reinvested on a continuous basis. The red line represents a fixed base rate of 2% increase in productivity at the actual Fed target rate for inflation (not inflation adjusted, rather constant current USD). The lower line in turquoise represents a variance below base rate (-8%) in an organization that is rapidly falling behind. At the end of the ten-year period the base rate productivity outcome (red) is $187 million while the lower line (turquoise) outcome is less than half at $86 million. The top line (blue) represents a variance above base rate (+12%) in an organization that is leaving their two competitors behind, ending the ten-year period with $416 million. While the top line may seem unreasonable to expect for organizations that are not operating at competitive levels, leaders in knowledge systems are producing a much greater exponential rate than displayed.

Achieving a CALO with the Kyield OS

AI applications are primarily limited by a combination that includes but is not limited to imagination, talent, training, physics, economics, rule of law, and system design. The more beneficial outcomes tend to target highly specific entity missions. For example, an improved image recognition algorithm can significantly improve the safety of autonomous vehicles, perhaps saving tens of thousands of lives annually. Similarly, organizational missions require a precise type of system designed for the task. Our standard Kyield OS includes highly specific organization-wide functionality for AI-enhanced governance, discovery, productivity, security, and prevention. The broader goal of the Kyield OS is to achieve yield management of knowledge for each entity resulting in a continuously adaptive learning organization (CALO). Additional functions can be plugged-in by customers or approved third parties with APIs (open standards).

The human element (cerebellum)

Although machine-learning algorithms have dramatically improved in recent years, autonomous super intelligence is decades in the future. AI systems are therefore still completely dependent upon human masters and our knowledge contributions. Data quality is critically important to AI system outcomes. The combination of cyber security breaches, lack of privacy, regulatory uncertainty, network-effect monopolies, technology architectures and other factors has resulted in a complex myriad of disincentives for the creation and sharing of original knowledge work. Misinformation and disinformation has become common in public and private networks.

It is for these reasons knowledge currency (code name cerebellum) was part of the early stages of our R&D. Organizations or individuals can use cerebellum to acquire, license, or sell digital products and intellectual property, or hire other firms and contractors. Our currency can be integrated with blockchain through Ethereum, IOTA, or other platforms, and/or be converted to national currencies. The rules-based formula employed has the capacity to provide accurate valuation due to the integrity of the data collected within the Kyield OS, which allows for precision analytics. Quantitative and qualitative factors in the formula are tied to actual performance metrics, providing strong incentives for transactions and long-term accumulation rather than speculative trading.

Conclusion

It is my hope that with this brief glimpse of advanced knowledge systems the reader can now better understand why augmented learning within a highly refined executable AI system is critically important. The compounding effect of improved productivity on real-world outcomes is profound, literally determining the difference between winners and loser in the modern network economy.

About Kyield

Kyield founder Mark Montgomery conceived the theorem ‘yield management of knowledge’ in 1997.[iv]The theorem posits that knowledge creation and transfer within distributed networks is a dynamic and complex undertaking consisting of physical and psychological properties with potential for a malleable yield curve. Two decades of R&D resulted in a distributed artificial intelligence operating system (Kyield OS) designed to provide optimal knowledge yield at the confluence of human and machine intelligence over networks. The now patented modular AI system core is fully adaptive and tailored to the unique profiles of each entity with a simple natural language interface.[v]

[i]A. Einstein,Princeton Press Volume 2: “The Swiss Years: Writings, 1900-1909” #264http://einsteinpapers.press.princeton.edu/vol2-doc/300

[ii]M. Montgomery, “Yield Management of Knowledge” 15 March, 2018 (unpublished)

[iii]A. Einstein, “Does the Inertia of a Body Depend on its Energy-Content?” 27 September, 1905 https://www.fourmilab.ch/etexts/einstein/E_mc2/e_mc2.pdf

[iv]See “20 year trek in AI systems” https://kyield.wordpress.com/2017/11/23/a-20-year-trek-in-ai-systems/

[v]M. Montgomery, ‘Modular system for optimizing knowledge yield in the digital workplace’, USPTO #8,005,778, 8/23/2011