A token represents a security or utility that a company has and they usually give it away to their investors during a public sale called ICO (Initial Coin Offering), in the case of utility tokens, and STO (Security Token Offerings), in the case of security tokens.
While utility and security tokens might seem similar on the surface, there’s actually some pretty complex differences behind them. Are they regulated or unregulated? Who can they be sold to? What is the purpose of the token? All of these questions can decided what is or isn’t a utility token. What about the Howey Test?
In 1946, the Supreme Court handled a monumental case. The case was SEC vs Howey which would lay down the foundation for the, now infamous Howey Test. The case was about establishing a test of whether a particular arrangement involves an investment contract or not.
The SEC and Swiss Financial Market Supervisory Authority (FINMA) have broken down tokens into two broad categories:
Utility Tokens
Security Tokens
Because most of the ICOs are investment opportunities in the company itself, most tokens qualify as securities. However, if the token doesn’t qualify according to the Howey test, then it classifies as utility tokens. These tokens simply provide users with a product and/or service. Think of them like gateway tokens.
Developed by Brendan Eich, the Basic Attention Token is a project that aims to make the online advertising space better for everyone.
The Basic Attention Token (BAT) is a digital advertising token which is built on the Ethereum blockchain. The purpose of BAT is to create an ad exchange marketplace which will connect advertisers, publishers and users in a decentralized manner so as to monetize user attention and remove all the other needless expenditure related to ad networks.
In brief, users get paid to provide their attention to ads. The system has to be fair to all the participants: users, advertisers and content publishers.
BAT works in the Brave browser which gets rid of all ads, but if you turn them on, you get paid for watching ads.
MetaX is attempting to solve the problem of fraud in the digital advertising world by establishing a common language and universal data set called adChain on blockchain, in this case Ethereum.
For nearly 40 years, Geoff Hinton has been trying to get computers to learn like people do, a quest almost everyone thought was crazy or at least hopeless – right up until the moment it revolutionized the field.
Digital technologies are transforming the ways in which content and creative are developed. Heat President, Mike Barrett and Deloitte Digital Chief Creative Officer, Alan Schulman discuss the use of AI to forecast future trends and generate creative content based off of these predictions.
Quantum computing can solve problems that would take classic computer a lifetime to process. Quantum computers use quantum bits, or qubits, that researchers believe will be able to process information exponentially faster than any computer we have today. Researchers also believe that quantum computing will help humanity solve some of the toughest problems facing our existence. This video also discusses topological qubits, which are considered to be more stable.
Andy Chan is a Product Manager at Infinia ML, an artificial intelligence company that builds custom algorithms and software for Fortune 500 companies.
He talks about the 30 years AI winter and the false start of the promise of AI, after it was initially anticipated to be significant in the 1950s.
He cites autonomous cars as an example where safe performance is better than humans.
Chan also discusses how AI can lead to wide-scale unemployment. However, he also outlines three areas that humans excel: curiosity, communication and empathy.
Humans will be required to define new problem spaces and work with AI to solve them.
He discusses how gamers, hipsters and angel investors revitalized the AI movement of today.
Many tech industry experts believe the idea of a superintelligent or sentient AI is greatly exaggerated and many years away. But there’s AI already in the works to help solve real-world problems that can improve people’s lives.
ADOHM- this new age marketing platform is powered by Artificial Intelligence to help you automate the entire advertising process, deliver impactful campaigns with minimal wastage of valuable budgets, lesser manual interventions and ultimately higher ROI on ad spend.
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Capacity building (step 2 of the ELLab), is actually an integral part throughout all the steps of the ELLab process. The participants (all stakeholders) are building capacity (informal training) in systems thinking, interconnectedness and model construction, using Causal Loop Diagrams (this chapter) and Bayesian Belief Network (BBN) Modelling (Chapter 6) in order to achieve:
The integration of various mental models into a systems structure
‘Ownership’ of the systems model(s) through direct involvement and informal training
An understanding of the inter-connectedness between and amongst different stakeholders (government departments and sectors in the organisatio respectively) to improve communication
The necessary links and needs for effective cross-sectoral collaboration.
People who are intrinsically involved are doing all the modules of the training, while some end-users (e.g. women in rural areas), are only involved informally in certain modules (e.g. for awareness) to help identify themes, discuss leverage points, rank the important variables, evaluate and refine the models and develop ways to reflect on outcomes to maximise co-learning benefits.
We have helped to build the capacity of various people (relevant stakeholders) in different places where ELLabs have been/are being established.
The stakeholders have been/are closely involved in all the different steps of the establishment of their respective ELLabs.
This close involvement has enabled a shared vision amongst stakeholders and helped them to understand complexity and be able to identify the root causes of problems, rather than merely treating the symptoms.
It has also helped them to develop solutions collaboratively over time, ‘experiment’ with them and be able to adapt when required through knowledge sharing and discussions with others.
In addition, the close involvement has enabled the relevant stakeholders to take ‘ownership’ of the ELLab and to know how to operate it.
Having a ‘champion’ is another important lesson learned through our work. We have been fortunate to work with a champion (a key person in a leading position, who understands and supports the approach) in every site where an ELLab has been established. This is essential for the successful implementation and operation of the ELLab.
INTEGRATING THE MENTAL MODELS BY DEVELOPING A SYSTEMS STRUCTURE MODEL
The process of developing a systems model provides stakeholders with a shared understanding and a big picture of the system they are dealing with. While no model represents a ‘true’ or complete representation of reality, a systems model can usefully unravel important dynamics of a complex system.
Decision makers, managers and relevant stakeholders often find it difficult to ‘see’ the big picture and account for all relationships and interdependencies between different components of their system. Therefore, it is essential to have an overall picture of the system to show the interconnectedness and roles of various players and agencies and their impacts. For example, the systems model represents a ‘big picture’ of the Cat Ba Biosphere system and provides a powerful platform for learning, collaboration and collective decision making for various stakeholders including policy makers, managers, and community representatives.
