Artificial Intelligence and the Future of Work

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.

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Systems Practitioner 7: Building Stakeholders’ Capacity


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.


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.

Systems Practitioner 6: Stakeholders’ Mental Models

Mapping the system starts through exchanging the perceptions of a problem and exploring questions such as:

  • What exactly is the problem we face?
  • How did the problematic situation originate?
  • What might be its underlying causes?
  • How can the problem potentially be addressed?
  • What barriers exist to deal with the problem?
  • What or who are potential drivers in the system?


Running a workshop to elicit the mental models requires a good facilitator and some key ground rules such as:

  • All knowledge, opinions, information are regarded as valuable.
  • Allow for discussion in order for stakeholders to understand each other’s mental models.
  • Avoid conflict by respecting each other’s knowledge and recording all opinions.

It is important to remember that in order to communicate with another person, one does not need to think (construe) in the same way, but be able to construe how the other person is construing.

This means that while divergent views occur, the appreciation of one another’s views gained through ‘mapping the system’ helps stakeholders to converge on a common understanding of the management system.

Effective communication can also help to change perceptions and expectations to make them realistic and achievable.

Workshop settings with all stakeholders involved could often lead to a group of people that, say, work for the same organisation, but have different levels of seniority.

We have conducted many of these workshops, for example in sustainable tourism in Cambodia, where the stakeholders included the full range from top government officials, such as the Minister of Tourism and Director General, to officials responsible for implementing policies, young officials still in the lower ranks of government, hotel owners, and taxi drivers.

In this situation it was not possible to obtain honest and in-depth insights into the mental models of all the participants.

Changing the nature of the workshop to a ‘silent’ sharing of people’s perceptions and ideas solved the problem of domination by senior officials, while juniors and people with no power remained totally quiet.

All participants put their mental models (responses on the above questions) on notes that were then anonymously put into big containers. This process revealed a very rich picture of the tourism sector through full participation by all stakeholders.

The downside was that very little discussion took place that could improve the understanding of each other’s mental models.

However, once the mental models were obtained and integrated into a systems structure, much discussion and co-learning eventually did happen.


In the following example women small-scale farmers and relevant stakeholders (in a Gates Foundation’s project) identified the issues they see as the key to their problems, potential solutions, barriers and drivers.

Participants in the workshop were encouraged to share their mental models of how they viewed the circumstances under which they live and farm and to think about potential solutions towards achieving their main goal, namely to improve the quality of their lives.

The various ideas were written down on sticky notes and then pasted on a white board. Many of the notes were the same, in which case the participants put the notes on top of each other.

The participants were then asked to discuss the different variables and to identify the main themes that emerged. The notes were rearranged around these main themes to produce a visual map of the mental models that evolved.

This step provides an opportunity for the stakeholders to discuss their mental models with each other and develop an understanding of how different stakeholders construe. Even if people do not agree with each other, all the ideas are left on the board.

This is the first step towards co-learning and developing an understanding of what the system under consideration looks like.

It creates a basis for reaching consensus on the main goal, and initiates the process of thinking how the different variables relate to each other.

The grouping into main themes significantly helps to integrate the mental models into a systems structure/model.

How Blockchain Will Impact Marketing and Advertising in 2019 & Beyond?

Blockchain can make advertising more transparent. It can reduce ad fraud, which is a larger problem than many are aware.

Blockchain can help maintain better management of display advertising. Blockchain allows privatizing our own data and has the potential to allow individuals to control how their data is used in regards to ad engagement

Some of these startups are Blockstack, Brave and BAT that aim to decentralize and improve ad delivery, privacy and data exchange.

Artificial Intelligence and The 4P’s of Marketing

Tom Edwards, Ad Age Marketing Technology Trailblazer and Chief Digital Officer, Agency and Epsilon discusses how the 4 P’s of marketing; Product, Price, Place & Promotion will need to evolve as intelligent systems redefine how we advertise and connect with consumers.

Understanding Psychographics, Predictive APIs, working through Proxy’s and Pervasive intelligent environments represent a new framework for marketing in the near future.

Artificial Intelligence in Marketing

Use AI to help make decisions.

Audience targeting and automatic content creation are just a few of the many ways AI can be used to help grow your user base and increase sales.

This video presents how some startups are applying AI to the marketing space and then programmatically walk through some AI techniques like matrix factorization, SVD, and LSTM neural networks that help a marketer outperform the competition and get the optimal results for their business.

Some existing services to use: – Aims to provide artificial intelligence (AI) platforms to help enterprises solve their most challenging business problems, including audience prediction. – Connects, unifies, and supercharges customer data to create a complete view of the people you do business with, including predicting times types of users will be on a specific platform. – Delivers an AI-powered SaaS platform to guide sales teams to build better pipeline and close more of the right deals, including helping to find 20% of customer base that will convert – Persado applies mathematical certainty to words to help create phrases and words for content that drives action.