May 2017
Text by Ray Gallon, Neus Lorenzo and Michael Josefowicz

Image: ©

Ray Gallon is co-founder of The Transformation Society. He has over 40 years of experience as a communicator, first as an award-winning radio producer, then in the technical content industries. Ray is a speaker at events throughout the world, and has contributed to many books and periodicals, most recently as editor of The Language of Technical Communication (XML Press).

Twitter: @RayGallon, @TransformSoc


Dr. Neus Lorenzo is co-founder of The Transformation Society and Inspector of Education in Barcelona, Spain. She is an expert in training and learning theory, and has been involved in research and thought leadership around educational innovation, teacher training, education and technology, leadership, and school management for 20 years.

Twitter: @NewsNeus, @TransformSoc


Michael Josefowicz is a retired printer who graduated from Columbia College in 1967 with a degree in Sociology. After 40 years in the print project management business, he has now returned to his love of the sociology of knowledge as a passionate amateur.

Twitter: Toughloveforx

Of humans and robots – Communication challenges in Industry 4.0

We have long succumbed to the fact that Artificial Intelligent (AI) beats our humble human brains at many kinds of activities – not just in a game of online chess. But we’re still in control, right? While we cannot fight the rise of AI, we need to learn to communicate and interact with our hyper-digital, interconnected environment in a way that goes well beyond human interactions.

As interconnected media platforms became more and more diverse in the early 1990s, a new concept was introduced: "Transmedia", a term first used by Marsha Kinder in 1991, describes a new media supersystem, using intertextuality and diverse sources with different levels of interaction. The concept is open enough to incorporate media that had not been invented then, such as wearables, bionic implants, or Augmented Reality. Industry 4.0, a term coined by the German government, extends this idea beyond media into the realm of hybrid communications in a world of autonomous, interconnected objects mediated by artificial intelligence.

Back in the 1960s, media theorist Marshall McLuhan identified the fragmentation of content as a characteristic of mass media. Four decades later, social media added to its complexity with what has been described as the "transmedia narrative": Content was now spread across many platforms with varying degrees of interaction among multiple authors and multiple audiences. When machines are added into the mix as intelligent agents in these dynamic interactions, we add another layer of complexity: Part of the cross-media content is now not directly readable by humans. Eventually, much of these connections and messages will be unknown, untracked, and invisible to human beings.

So how can we function in this hybrid communication environment? What skills will we need to work in Industry 4.0? Models based on the "Nemetic" system are useful to analyze, track, and represent hybrid interactions in extremely digitalized environments. But first, let’s take a look at this new communication environment.

For people to function in Industry 4.0, they will need skills well beyond traditional listening and reading, and even beyond the new skill of “transliteracy”, understood as the ability to communicate across a range of platforms, tools and media. They will need to be able to determine appropriate modalities and strategies for coding and decoding different types of discourse:

  • Human-human
  • Human-machine/machine-human
  • Machine-machine

Our new hybrid ecosystem

In 2003, Henry Jenkins described "transmedia storytelling" as a collection of fragments in which "each medium does what it does best, so that a story might be introduced in a film, expanded through television, novels, and comics, and its world might be explored and experienced through game play."

And this multiplatform narrative is now expanding to include human/machine communication, in all present and future configurations. These hybrid communications introduce not only new codes, but new behaviors that emerge from the new ecosystem.

But what is this new ecosystem we live in? We understand it as a complex network of networks that integrates Industry 4.0 and is powered by Artificial Intelligence (AI) and the new relationships that humans and machines will develop. In this landscape, objects interact continuously, exchanging data they have picked up via sensors, and adding them to the global pool of Big Data. The future vision is an extremely complex hybrid reality where humans and machines develop communities and networks in dynamic clusters of interests, acting both individually and collectively, embedding their experiences in a constantly changing communicative context.

To understand the complexity of this ecosystem, we can no longer merely depend on traditional scientific disciplines for analyzing language, communication and conduct. Our existing institutional structures outlining legal rights and duties are not sufficient for defining ethical behaviors and interactions. What we will need in the future are models that take into account the superposition of three main levels of complexity:

1. Data and information – Dealing with Artificial Intelligence agents

Software agents work with data and metadata they extract from databases, human agents, context sensors, and other devices to produce adaptive information exchanges that function like a personal assistant. They have some capacity to learn as they acquire more data and compile it into information, and use written or spoken natural language interfaces. Current examples include chatbots, SIRI, Google Assist, or Amazon Echo. Linked together in the Internet of Things, these agents will aggregate Big Data to determine hierarchies of content, context states, and visualization tools. They will also create priority protocols for network access based on the importance of different communications.

2. Interaction and singularity – Recognizing Artificial Intelligence personas

More than agents, these are real robots – software only or a combination of hardware and software. They are powered by deep learning engines such as IBM's Watson or Google’s DeepMind. These robots are capable of making independent decisions and learning from their environment and context. This means that each robot is an individual with different characteristics that can be likened to a personality. The logic of this leads to the notion that such robots have a status in society with duties and rights. They form relationships and participate almost as what we could call "robot citizens". This was echoed in a resolution of the European Parliament, which suggested

creating a specific legal status for robots, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons with specific rights and obligations, including that of making good any damage they may cause, and applying electronic personality to cases where robots make smart autonomous decisions or otherwise interact with third parties independently.

         European Parliament, 2017, resolution of 16 February 2017 article 59(f)

The resolution did not include a recommendation that robots pay taxes and social charges.

These robots acquire social knowledge, and exist as parts of a variety of communities that include human and non-human members. Their decision-making power implies that machines and networks must also respond to, and be responsible for, ethical principles. The "Charter on Robotics" proposed in the 2017 European Parliament resolution defines a code of ethical conduct in the field:

The framework must be designed in a reflective manner that allows individual adjustments to be made on a case-by-case basis in order to assess whether a given behaviour is right or wrong in a given situation and to take decisions in accordance with a pre-set hierarchy of values...

Special emphasis should be placed on the research and development phases of the relevant technological trajectory (design process, ethics review, audit controls, etc.). It should aim to address the need for compliance by researchers, practitioners, users and designers with ethical standards, but also introduce a procedure for devising a way to resolve the relevant ethical dilemmas and to allow these systems to function in an ethically responsible manner.

          European Parliament (2017), Annex to the Resolution


3. Accepting Artificial Intelligence collectivities

The hybrid-connected networks of intelligent objects in Industry 4.0 are weaving interconnections with people, whether or not they are connected via mobile terminals, wearables, implants, or prostheses. These networks will cluster together to form very complex networks of networks that make today's Internet seem simple. These clusters will be highly dynamic with continuously emerging information that is shaped and reshaped every moment as a function of the person, object, or situation it is addressing. Responses and responsibility will be determined by the environment and context, according to the ethical, social, economic, and personality strategies that different entities have acquired through programming or learning.

In their article for tcworld magazine Issue 4/16, Ray Gallon and Andy McDonald provided an example of how this can work involving a jogger in a shopping center:

You pass a shoe store (part of a national chain) in a shopping center – Sam's Shoes. Your terminal knows that you bought your running shoes six months ago and, based on your time spent running and wear calculation, deduces you could buy a new pair. Correlating with the store, it finds your brand and model on sale there, and alerts you.  If you are jogging, it will have the store send an email, and the store decides to include a voucher.

It's not going to alert you about Sam’s Shoes national sale. It triggers THIS Sam's Shoes to suggest you buy the SAME shoes, on sale NOW, because your phone deduced YOUR CURRENT SHOES ARE ABOUT TO WEAR OUT.

This level of personalization makes marketers salivate – but it will be a reality before we notice.

This kind of collaboration can, on a small scale, provide a great deal of convenience, and on a large scale, help us manage large, complex, "wicked" problems. But it can also violate our privacy, be used to spy on us, or simply provide an isolating bubble in which we know a lot of mass data but nothing about our specific situations.

To illustrate, Big Data connections could give you not only large amounts of raw data about the performance of 15-year-olds around the world in the PISA school assessments, but also intelligent analysis that explains why, in one country or situation, kids do better than in others. But you won’t learn why your 15-year-old is doing well or poorly from that information. You would need your own set of parameters, programmed just for you, to extract that information. It would also require a human to merge the emotional support your kid might need in a particular moment with the interpretation of unexpected data or occurrences that describe the particular needs of your child.

From transliteracy to global competences for Industry 4.0

Transliteracy – the ability to communicate across this complex range of platforms, tools and media – requires evolving beyond individual, linear human skills such as reading, writing, listening, speaking, or interacting. Complex hybrid communication demands intertextual abilities: from translation, correlation, or mental association to analogy, context awareness, synthesis, or connotational association. Emotional skills such as empathy and engagement are also required to add enriched contextual interpretation to the matrix.

Each analysis at any given moment is recursive, and we can analyze interactions at various levels of granularity. In Industry 4.0, the imperative is to produce real results and actions from this complexity. Software developers working with Agile methodologies are already familiar with this way of thinking. Priorities can change at any moment, and definitions of quality or completeness are dependent on immediate contingent needs that also evolve.

Like Agile development, hybrid transliteracy requires compound, intangible projective skills, which are strategically oriented. Problems are solved collectively, and social mediation skills such as negotiating, conciliating, or social media abilities are the added value that humans bring to the table.

We need new, specialized analytical tools to engage with this high level of complexity. Fractal models such as the Nemetic system offer a transversal approach that helps us understand the fragmentation.

Nemetics and hyper-connected networks

Complexity, understood as a collection of elements and processes in dynamic relationships, can be better understood when seen as a series of recursive patterns that can be modeled. The system known as Nemetics provides models that can help express co-creation in complex adaptive/creative environments, among humans and machines.

The Nemetic model provides quanta that help identify and analyze communicative routines. It includes dimensions of individual reflection, professional development, and organizational transformation. The analyses derived from it contribute to leadership and resource management, focus on integrated learning, and promote complex problem-solving.

Nemetics functions as a fractal meta-language that facilitates communication among researchers in different disciplines to debate about complexity.

The essentials of Nemetics can be summarized in a simple mnemonic acrostic, which describes learning in any context at any level. At its most effective it is:

  • Notice without preconceptions (N).
  • Engage without judgment (E).
  • Mull before communicating or acting (M).
  • Exchange in the appropriate way and time (E).

This basic path retrieves four action levels that may or may not be performed during interactions (after each verb, add the option, "or not").

The whole conversation is then conceptualized as a single identified process, a NEME that can be seen as a coherent unit, represented visually by the interactions that took place during the debate. The analysis of these NEMEs shows patterns and waves of exchange that offer extremely rich information (Big Data), about both the media environment and the participants.

In other words, the NEME is both a process and a communicative quantum – a unit of exchange that can be studied on its own. The recursive, self-similar nature of a NEME means that a NEME for communication between two agents can be nested inside a NEME for a network-wide communication, and so on, like a Russian doll.

The simplicity of the fractal Nemetic process makes it useful for developing Agile models that can be generalized to help bridge the human-machine communication gap, and to design strategies for complex communication at all levels in the Internet of Things.

Nemetics and Artificial Intelligence

In this article, we have shown that Artificial Intelligence (AI) is going to be driving many processes and making autonomous decisions that will affect us. If we humans want to maintain control over our own lives, and be good stewards of how AI interacts with us, we need ways to understand it that do not involve digging deep into digital code and trying to crack messages that are intrinsically unreadable to us. It will not serve for us to try and duplicate functions that AI will always do better than we can. Our role is to add value that only humans can provide.

Analyzing the NEMEs at different levels of granularity can help us do that. In the example we have given about Big Data, using the PISA results, we referred to the difficulty of extrapolating reasons for the results of one single child from the great mass of accumulated data.

If we look at the NEME for the global results, and examine it iteratively as an Agile software developer might do, we can begin to see patterns that emerge. We can see how the NEME for our country contributes to the global NEME. Our regional NEME, in turn, is part of, and also reflects, the country NEME. The local NEME carries characteristics of the region, and of the individuals in it, and finally, the NEME for your daughter or son relates to the local NEME, and their school’s NEME. The school’s NEME should provide enough information for you to understand the evolution of your child.

If we simply look at global or country results, we can’t know anything about one child. If we only look at our child's performance and environment, we are unable to generalize even to our local neighborhood. The recursive, fractal Nemetic view of data allows us to monitor both, and by drilling down or up, make relationships, deduce trends, and understand consequences that take into account the unexpected, and allow for creative variance. These are the added-value human elements that AI cannot provide.

Further reading