Token Economics 1: Context

Economic forces are everywhere. They shape and structure our everyday lives. It’s how we organize people, resources, and technology to create and exchange value within society.

During the modern era those forces came to be channeled and structured within a particular set of centralized bureaucratic institutions based around the nation-state and the enterprise.

But today the proliferation of information networks is unleashing constrained economic forces.

Through ledgers and blockchain technology, the most fundamental rules governing our society are now open for redefinition, as was prior to the industrial age.

As advanced economies move out of industrial production and into a new form of global services and information economy, the economic model of the industrial age is becoming eroded.

This emerging global information and services economy will be coordinated through the internet running on an updated set of protocols that provides the secure distributed infrastructure for this emerging global token economy.

This transition builds upon major trends that began in the late 20th century which are today converging in powerful new ways. Privatization and globalization, financialization and the rise of online platforms are all converging as blockchain networks merge economics and information technology to take us into a new economic paradigm.

  • Privatization opened up more spheres of activity to markets.
  • Globalization expanded those market around the world.
  • Financialization connected up our real economy into an integrated information-based financial system.
  • The platform economy created new forms of user-generated networks.

Blockchain brings these trends together in synergistic, powerful new ways.

Hence, a new economic system is being established; one that is truly global, that reflects the underlying logic of services. This will be an economic model that is for the first time in harmony with its underlying technology of information.

These emerging token networks offer the potential to unleash a massive wave of creativity and innovation.

With trillions of dollars set to migrate to this global cloud computing and blockchain infrastructure in the coming decades, the stakes are high.

Financial and economic sovereignty appear to be slipping out of the fingers of nation states.

And the tensions are mounting.

Are we moving into a lawless chaos? Or are we moving to a historically new level of economic organization?

That will be decided by our capacity to understand this new economic paradigm coupled with our ability to design and develop new token networks of synergistic incentives.

Rethinking how we organize economic production and exchange is one of the major challenges and opportunities. Token economics is an opportunity to revisit the foundations of economic organization.

It’s an opportunity to reconstruct a new form of economy that is different from the industrial model that we know so well.

It’s an exercise that is of critical importance to the development of a sustainable model to economic development in the age of information, globalization and billions of people wishing to join a worldwide economic system, which is already showing major signs of stress.

This is no longer about politics, policies or protesting, the technology is reaching a maturity. We now stand at a point where we can design economic systems from the ground up. The success or failure of such systems does not rest with the actors in the network, but squarely with the design of the system.

Possibly for the first time ever, if we don’t like the prevailing economic system, we now have the option to design a better one.

What is so powerful about this revolution is that it is not really driven by idealism or politics but rather economic incentives. The global economy will switch to being based upon distributed blockchain networks with each actor seeing it as in their economic interest to do so.

This revolution does not require large-scale political coordination. It bypasses it. Instead, it employs a highly modular and granular transition.

Specific parts of the existing economic institutions can be upgraded and integrated into a new economic model.

Yet, this will be a profoundly disruptive transformation.

Enterprises will be automated. Whole industries will be upended by powerful new blockchain ecosystems. National governments will face mounting pressures from a global information services architecture.

This new economic paradigm promises the potential to build new forms of economic organization to deliver what people value.

It promises a more open and inclusive model that harnesses the efforts of the many instead of the few.

Token Economics 0: Course Intro

This video is an overview of a course on token economics, or crypto-economics, which is the study and design of economics based on blockchain technology.

The course touches upon distributed ledger technology and triple-entry accounting as well as the two primary categories of tokens: utility tokens and security tokens.

The course also addresses decentralized organizations, game theory and the design of incentive structures to align the interests of the individuals of the whole organization within user-generate networks.

As a result, token networks can be used to remove the centralized management structure of organizations while better aligning the incentive structures of producers and end users.

The course also addresses the formation of large-scale blockchain networks that span across organizations and industries to create powerful new ecosystems. Inn other words, blockchain as an infrastructure for a global-services economy.

Finally, the course addresses token networks as a way to fund their own establishment and guide their future development.

Neural Network 3D Simulation

This neural network 3D simulation demonstrates how different models read visual images of hand-written numbers to translate and identify the images as their respective characters.

The iterative process of computational data comparison and pattern recognition is animated via 4 models:

  • Perceptron
  • Multilayer Perceptron
  • Convolutional Neural Network
  • Spiking Neural Network

The animated simulations offer an opportunity to observe the relatively abstract operations of algorithmic data processing.


Blockchain Art Exchange

https://youtu.be/Jd7ZZqwMk2Y

The Blockchain Art Exchange, based in London, is seeking to establish a “more democratic art market.”

The idea is that when a digital artist sells work on the Blockchain Art Exchange they will receive Blockchain Art Exchange (BAE) tokens.

Holders of BAE will receive royalty payments from every artwork sale that happens on the BAE exchange. Hence, the more sales an individual accrues, the larger the share of royalties received. In theory, emerging artists will be supported by royalties from the sales of more established artists.

The sequence is as follows:

1) Submit digital artwork for analysis.

2) The digital work is graded and given an “objective price.”

3) The digital work is uniquely identified with a blockchain certificate to prove authenticity.

4) The digital work can be exchanged instantly on the BAE platform.

Can such an idea be executed for traditional (non-digital) art forms?

Top 5 Challenges in Machine Learning

This video represent a list of top 5 challenges that are common to many in the machine learning and data science community.

  • Scarcity of Data

The more data that can be used for modeling and predictions, the better.

This isn’t a problem for big companies, such as Facebook and Google. However, for many others, lack of sufficient data can limit their results rendering machine learning less productive.

  • Unclear Question

Vague questions will not result in substantive results. Data science is about recognizing patterns. So, clear questions are fundamental to defining what types of patterns to analyze.

  • Unclear Representation of Data

For a data scientist, the resulting work needs to be represented to the end user in meaningful ways. There is more room for libraries to make life easier to better represent data.

  • Expensive Resources

Computing millions of lines of code over and over can be expensive. But it should get less expensive in the future.

  • Machine learning Algorithm selection

Currently, the biggest challenge in data science is the selection of the right algorithms. It’s important to understand the algorithms as well as possible to determine which ones will most benefit your project.

Token Economics via Personal Devices

Artificial General Intelligence (AGI) is in the process of rewriting what it means to live on planet earth.

AGI refers to the intelligence of a machine that could successfully perform any intellectual task that a human being can.

AGI is a goal of some artificial intelligence research, as well as a science fiction topic.

Kimera Systems, Inc. offers artificial intelligence technology to observe user behavior, context, and derive a common sense set of actions to apply under specific circumstances.

Mounir Shita, Co-Founder and CEO of Kimera Systems, speaks about AGI and a vision of how to develop an economic model that benefits people rather than a handful of powerful organizations or governments.

In this brief video he discusses using token economics and blockchain to pay individuals via the devices they use, which hold data. He says it’s about financially rewarding people for living their lives.

Machine Learning – Defining and Understanding for Entertainment Production

Machine learning is recognizing patterns in data and can help people support decisions.

Some practical ways that media and entertainment companies can apply artificial intelligence and machine learning is with contract management processing, budgeting, on-boarding production freelancers and standardization of digital asset management.

In brief, machine learning can help make an institution’s accumulated knowledge accessible to stakeholders.

The future of AI: risks and challenges

The future of artificial intelligence (AI) encompasses risks and challenges.

The public perception of AI includes a fear that artificial intelligence might take away their job. And it’s true: AI is a tool people can use to operate business more efficiently. Of course some new jobs are created to support AI systems, but more people will lose jobs than new jobs are anticipated to be created.

Other risks for AI include data privacy concerns, government laws and regulations, and company stakeholders who don’t understand the technology.

AI is moving forward in spite of the challenges, but how smooth the transition will be for such an important shift in global technology and human labor is undetermined.

The Deep Learning Revolution

This NVIDIA video describes deep learning as the fastest-growing field in artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text.

Using multiple levels of neural networks, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Specific real-world deep learning applications include the ability to analyze in one month, what used to take 10 years; voice-to-text technology; robotics; autonomous cars; and winning chess against a world champion.

Every industry will be impacted by deep learning, and many businesses are already delivering new products and services based on this new way of thinking about data and technology.

Description of a Two-Token Economic Model

https://youtu.be/Tk-GcqZpr24

This video outlines a parallel between token economic models and business models. The central idea is that the way business models are designed is to provide value for customers while extracting value for shareholders. In a token model, the design is intended to create value in the token itself and therefore reward those supporting the business, including customers and shareholders.

However, part of the problem of a single-token model is the inherent tension between those who want the token to rise in value, like an investment, and those who simply want to pay for services, as with a currency that maintains a stable value.

Hence, the design of this two-token model is intended to satisfy the needs of two groups: customers and business supporters. This model is comprised of three key principles:

  1. Reward value creators
  2. Align actor incentives
  3. Support business sustainability and expansion

Often blockchains are thought of as a way to disintermediate middlemen. However, some of them add value. Hence, this system is designed to reward all those who are adding value to the business, whether they are creators or traditional middlemen.

This specific 2-token system is designed for music blockchain Emanate. The two tokens are designated as MN8 and MNX.

  • MN8 is a governance token
  • MNX is a stable, internal cryptocurrency

Customers don’t care about MN8. They will swap money for MNX and use MNX to pay for services, just like traditional currency.

Different actors aligned with supporting the business will extract value from the system by receiving MNX as part of their provided services, according to pertinent smart contracts. They can then swap MNX for whatever currency they desire through an internal exchange.

However, they will need to stake some MN8 to participate in the system and earn MNX. Hence, they are buying into part ownership. Alternatively, they could be investing in the business itself.

All of the MN8 stakeholders can vote on the direction of the business. Those who own the most MN8 will have the most votes and the greatest vested interest in the success of the business and MN8 token. As the business becomes more successful, the MN8 tokens would be anticipated to rise in value, in the same way the value of stocks rise in a traditional, publicly traded corporation.