This post is a rather long story. It attempts to connect topics from a range of domains, and the insights from experts in these domains. In this story my role is mainly the one of an observer. Over the years I have worked with hundreds of domain experts, distilling the essence of deep domain knowledge into intuitive visual domain-specific languages. If anything, my work has taught me the skill to observe and to listen, and it has made me concentrate on the communication across domain boundaries – to ensure that desired intent expressed in one domain is sufficiently aligned with the interpretations performed in other domains.

The life of language and the language of life can’t be expressed in written words. Many of the links contained in this story are essential, and provide extensive background information in terms of videos (spoken language, intonation, unconscious body language, conscious gestures), and visual diagrams. To get an intuitive understanding of the significance of visual communication, once you get to the end of the story, simply imagine none of the diagrams had been included.

It may not be evident on the surface, but the story of life started with language, hundreds of millions of years ago – long before humans were around, and it will continue with language, long after humans are gone.

The famous Drawing Hands lithograph from M. C. Escher provides a very good analogy for the relationship between life and language – the two concepts are inseparable, and one recursively gives rise to the other.

At a fundamental level the language of life is encoded in a symbol system of molecular fragments and molecules – in analogy to an alphabet, words, and sentences.

The language of life

Over the last two decades molecular biologists and chemists have become increasingly skilled at reading the syntax of the genetic code; and more recently scientists started to work on, and have successfully prototyped techniques to write the syntax of the genetic code. In other words, humans now have the tools to translate bio-logical code into digital code as well as the tools to translate digital code back into bio-logical code. The difference between the language of biology and the language of digital computers is simply one of representation (symbolic representations are also called models). Unfortunately, neither the symbols used by biology (molecules), nor the symbols used by digital computers (electric charges), are directly observable via the cognitive channels available to humans.

However, half a century of software development has not only led to convoluted and unmaintainable legacy software, but also to some extremely powerful tools for translating digital representations into visual representations that are intuitive for humans to understand. We no longer need to deal with mechanical switches or punch cards, and modern user interfaces present us with highly visual information that goes far beyond the syntax of written natural language. These visualisation tools, taken together with the ability to translate bio-logical code into digital code, provide humans with a window into the fundamental language of life – much more impressive in my view than the boring magical portals dreamed up by science fiction authors.

The language of life is highly recursive. It turns out that even the smallest single-celled life forms have developed higher-level languages, to communicate – not only within their species, but even across species. At the spacial and temporal scale that characterises the life of bacteria, the symbol system used consists of molecules. What is fascinating, is that scientists have not only decoded the syntax (the density of molecular symbols surrounding  the bacteria), but have also begun to decode the meaning of the language used by bacteria, for example, in the case of a pathogen, communication that signals when to attack the host.

The biological evidence clearly shows, in a growing number of well-researched examples, that the development of language does not require any “human-level” intelligence. Instead, life can be described as an ultra-large system of elements that communicate via various symbol systems. Even though the progress in terms of discovering and reading symbol systems is quite amazing, scientists are only scratching the surface in terms of understanding the meaning (the semantics) of biological symbol systems.

Language systems invented by humans

Semantics is the most fascinating touch point between biology and the mathematics of symbol systems. In terms of recursion, mathematics seems to have found a twin in biology. Unfortunately, computer scientists, and software development practitioners in particular, for a long time have ignored the recursive aspect of formal languages. As a result, the encoding of the software that we use today is much more verbose and complex than it would need to be.

Nevertheless, over the course of a hundred years, the level of abstraction of computer programming has slowly moved upwards. The level of progress is best seen when looking at the sequence of the key milestones that have been reached to date. Not unlike in biology, more advanced languages have been built on top of simpler languages. In technical terms, the languages of biology and all languages invented by humans, from natural language to programming languages, are codes. The dictionary defines code as follows:

1. Code is a system of signals used to send messages
2. Code is a system of symbols used for the purpose of identification or classification
3. Code is a set of conventions governing behaviour

Mathematically, all codes can be represented with the help of sets and the technique of recursion. But, as with the lowest-level encoding of digital code in terms of electric charges, the mathematical notation for sets is highly verbose, and quickly reaches human cognitive limits.

The mathematical notation for sets predates modern computers, and was invented by those who needed to manually manipulate sets at a conceptual level, for example as part of a mathematical proof. Software programming and also communication in natural language involves so many sets that a representation in the classical mathematical notation for sets is unpractical.

The importance of high-quality representation of symbols is often under-rated. A few thousand years ago humans realised the limitation of encoding language in sounds, and invented written language. The notation of written language minimises syntactical errors, and, in contrast to spoken language, allows reliable communication of sequences of words across large distances in space and time.

The challenge of semantics

Software development professionals are becoming increasingly aware of the importance of notation, but interpretation (inferring the semantics of a message) remains an ongoing challenge. Adults and even young children, once they have developed a theory of mind, know that others may sometimes interpret their messages in a surprising way. It is somewhat less obvious, that all sensory input received by the human brain is subject to interpretation, and that our own perception of reality is limited to an interpretation.

Interpretation is not only a challenge in communication between humans, it is as much a challenge for communication between humans and software systems. Every software developer knows that it is humanly impossible to write several hundred lines of non-trivial program code without introducing unintended “errors” that will lead to a non-expected interpretation by the machine. Still, writing new software requires much less effort than understanding and changing existing software. Even expert programmers require large amounts of time to understand software written by others.

The challenge of digital waste

We have only embarked down the road of significant dematerialisation of artefacts in the last few years, but I am somewhat concerned about the semantic value of many of the digital artefacts that are now being produced at a mind-boggling rate. I am coming to think of it as digital waste – worse than noise. The waste involves the time involved in producing and consuming artefacts and the associated use of energy.

Of particular concern is the production of meta-artefacts (for example the tools we use to produce digital artefacts, and higher-level meta-tools). The user interfaces of Facebook, Google+ and other tools look reasonable at a superficial level, just don’t look under the hood. As a result, we produce the digital equivalent of the Pacific Garbage Patch. Blinded by shiny new interfaces, the digital ocean seems infinite, and humanity embarks on yet another conquest …

Today’s collaboration platforms not only rely on a central point of control, they are also ill-equipped for capturing deep knowledge and wisdom – there is no semantic foundation, and the tools are very limited in their ability to facilitate a shared understanding within a community. The ability to create digital artefacts is not enough, we need the ability to create semantic artefacts in order to share meaningful information.

How does life (the biological system of the planet) collectively interpret human activities?

As humans we are limited to the human perspective, and we are largely unaware of the impact of our ultra-large scale chemical activities on the languages used by other species. If biologists have only recently discovered that bacteria heavily rely on chemical communication, how many millions of other chemical languages are we still completely unaware of? And what is the impact of disrupting chemical communication channels?

Scientists may have the best intentions, but their conclusions are limited to the knowledge available to them. To avoid potentially fatal mistakes and misunderstandings, it is worthwhile to tread carefully, and to invest in better listening skills. Instead of deafening the planet with human-made chemicals, how about focusing our energies on listening to – and attempting to understand, the trillions of conversations going on in the biosphere?

At the same time, we can work on the development of symbolic codes that are superior to natural language for sharing semantics, so that it becomes easier to reach a shared understanding across the boundaries of the specialised domains we work in. We now have the technology to reduce semantic communication errors (the difference between intent and interpretation) to an extent that is comparable to the reduction of syntactic communication errors achieved with written language. If we continue to rely too heavily on natural language, we are running a significant risk of ending the existence of humanity due to a misunderstanding.

Life and languages continuously evolve, whether we like it or not. Life shapes us, and we attempt to shape life. We are part of a dynamic system with increasingly fast feedback loops.

Life interprets languages, and languages interpret life.

Language is life.