I briefly describe here the morphic computing, developed by Massoud Nikravesh and Germano Resconi in 2010, because I think it is a good example of a bio-mimetic method, although we have not yet discovered how living beings proceed to process information that comes to them from their environment[1]. Morphic computing is based on field theory, particularly morphic fields. These were first presented by R. Sheldrake based on his hypothesis on formative causality via the notion of morphogenetic fields. Then, Sheldrake developed his famous theory of morphic resonance based on the work of the French philosopher Henri Bergson.

It is assumed that in morphic computing, computation[2] is not always related to symbolic entities such as numbers or words, because fields, as entities, are more complex than any symbolic representation of knowledge. Morphic fields include universal databases for both organic (living) and abstract (mental) forms. Morphic computing can therefore compute conceptual fields. It is the natural extension of the holographic computing, cellular/molecular computing, quantum computing and soft computing. It can therefore be affirmed that all these computing methods are part of the more general method of morphic computing in which form is associated, among other things, with the notions of holism, geometry, field, and superposition. …


The current artificial intelligence model is based on a data-driven model, the Big Data with which artificial neural networks (ANNs) are fed to train them in order to provide very convincing and even increasingly accurate and relevant results. ANNs can also train on their own, depending on their type. Big Data has thus a bright future ahead — even if experts are currently recommending to switch to Small Data — since ANNs are working wonders in almost every field: machine translation, reconstructing paintings, real or dreamlike image creation, cancer detection, home automation, autonomous vehicles, etc.

But, let’s admit that, even if the results of the ANNs are impressive and despite the important mathematical background that underlies them, their operating model remains primitive: it’s like feeding a goose to make a high-quality foie gras. So, the question is: Is there a way to build an intelligent artificial system worthy of that name? …


The Chatbot phenomenon is on the rise. Companies are being urged to create ones to build customer loyalty but also to solve and enhance business problems: boosting marketing, improve brand image, hire, etc. Chatbots seem to be the next big revolution for the way people engage with organisations online, for what will drive the next wave of digital commerce, and, above all, for what concerns a real virtual personal assistant.

With the new version of chatbots, we have moved from stilted binary conversations to the understanding of not only a spoken or a typed word, but also to the analyzing of the context in which it is used. The great progress of NLP technology currently allows chatbots to conduct almost human conversations. They can even be assigned a personality that matches the brand’s need while enriching the customer’s experience. Many companies provide their chatbots with a unique character that attracts customers and strengthens relationships with them. If chatbots are destined for such a bright future, it is because they will converse with us in an increasingly sophisticated way, which will make life a little easier for us: we will no longer have to type on the keyboard for anything; we’ll just ask James and it will serve us on the spot. …

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