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How to cite. This is a preview of subscription content, log in to check access. Aberer K. The Chatty Web: emergent semantics through gossiping. In Proc. World Wide Web Conference, , pp. Google Scholar. Bar-Yam Y. Dynamics of Complex Systems. Perseus Books Group, Cudre-Mauroux P. Viewpoints on Emergent Semantics. Data Seman. CrossRef Google Scholar.

Grosky W. Emergent Semantics and the Multimedia Semantic Web. Herschel S. The material mode, dealing with objects and facts, suited science inasmuch as it had an empirical import. Yet, the elucidation of both logic and the whole of knowledge, the task of philosophy according to Carnap at the time, required the formal mode. Philosophy reframed as the meta logic of science could at long last find a proper place to set in.


The unity of language was henceforth broken as the rules of syntax no longer obeyed any univocal representational imperative and could be engineered at will to fit the needs of a formal articulation of the content of science. All that is required of him is that, if he wishes to discuss it, he must state his methods clearly, and give syntactical rules instead of philosophical arguments. This is far too convoluted a story to tell in its entirety, but a few words will suffice. The elimination of the picture theory of language based on atomic sentences had entailed the elimination of meaning in Syntax in favor of syntax and the formal mode of speech of the logic of science.

Semantics continued to evolve after Carnap. When artificial intelligence AI researchers turned to model-theory they did it in a spirit which was much more reminiscent of Carnap than it was of Tarski. In that vein, Nicola Guarino, a scholar known for having established bridges between philosophy and ontology engineering, defines an ontology in the following way:. By building on top of these axioms the goal of the nascent field of artificial intelligence in its early days was to build real-world applications to interact with their environment and take concrete action therein based on logical inference.

Quite the contrary. Carnap really stands out as the forefather of AI and knowledge engineering. Knowledge engineering is a branch of AI that, building on ideas elaborated by Carnap, took its inspiration from philosophical ontology and certain strands of metaphysics especially the Aristotelian school, Husserlian mereology, and analytic metaphysics. Philosophically-inspired knowledge engineering which does not accounts for the whole discipline has long espoused realist views in metaphysics to fulfill its own need to formalize axioms with higher-order principles, thereby setting a hierarchy between formal descriptions of domains and top-level ontologies inspired by previous philosophical work.

With the advent of the Semantic Web, the principles of decentralization could be applied to knowledge engineering. The vision was that the Semantic Web would decentralize knowledge engineering, allowing data from everything from spreadsheets to databases to seamlessly connect on the Web via formal ontologies that would organically grow over time. Despite its promising decentralized vision and strong foundations in knowledge engineering, the Semantic Web effort stalled in terms of practical uptake.

While the hypertext Web had within a few years produced an exponential growth in Web sites, the Semantic Web mostly produced an exponential growth in academic papers with little real-world impact.


Simultaneously, a number of German Ph. As it was available to the research community and smaller companies, DBpedia inspired a wave of revived research on knowledge engineering. However, what was less noticed was that Google quietly acquired Freebase, and then soon began hiring experts in knowledge engineering, including R. Guha, one of the key designers of RDF at the W3C and pioneer in applying knowledge engineering to artificial intelligence. However, Yahoo! Other search engines soon followed, including Google with Google Rich Snippets.

This led to an explosion of structured data on the Web, as Webmasters thought that adding structured data would help their search engine optimization. The editor of HTML5, Ian Hixie, created yet another incompatible general purpose standard for embedding data called microdata. Using the considerable clout of Google, other search engines such as Yahoo! Although not an open standard and controlled informally by a small group of search engines, schema.

For the most part ignoring top-level ontologies based on metaphysical distinctions and even formal semantics, Google designed various lower-level ontologies for domains of interest to search engines, such as e-commerce, movie, and music information.

A social process based on mailing list discussions was put in place to add new ontologies, and soon schema. Thus ironically, much of the academic work on logical inference and formal semantics thought to be needed by the decentralized Semantic Web ended up being ignored, while a human-powered yet centralized web of knowledge began taking off. Furthermore, Google was using schema. At the same time, other companies such as Yahoo! The use of these knowledge graphs started becoming increasingly common in new products.

One of the most long-standing problems of knowledge engineering was how to dynamically add new knowledge. Although schema. After all, it seemed infeasible to pay people to identify those in every single photo on Facebook, and users identified people explicitly in a minority of photos.

Also, as reality changed so did knowledge itself, and there were simply not enough knowledge engineers to manually update various knowledge graphs to take into account every single change. The answer was obvious: Computers had to be able to learn knowledge themselves, both with and without human supervision. Also coming out of artificial intelligence, the field of machine learning had been developing quietly in parallel to the more traditional knowledge engineering approaches. Machine learning, while it had some early successes in the work of AI pioneers such as Selfridge, had always suffered from a lack of data Machine learning itself found in the form of the Web a massive input data set that was increasingly updated in real-time.


Although the techniques behind machine learning seemed deceptively simple, by virtue of having as input data a massive representation of collective human existence, these simple techniques could tackle problems beyond knowledge engineering, with machine translation as the example par excellence. The same general approach of relying on real human data rather than formal rules and logical inference also applied to varied fields from speech recognition to search engines. With the amount of data on the Web skyrocketing into the millions of terabytes, what ended up mattering for the future of the Web was the ability to handle data that was larger than could be fit on a single machine, which in turn required large distributed — but centralized — data centers to handle.

The foremost company in this space was Google, a self-declared AI company. In his book, he claimed that AI was impossible as human intelligence required the full process of growing up in a human body.

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This book was not only a philosophical riposte against using knowledge engineering techniques to create a human-level artificial intelligence, but the book was based on a RAND report, Alchemy and artificial intelligence Dreyfus, This report — and others like the Lighthill one — was influential in determining if AI should continue to receive massive national government funding of the kind the Internet was receiving.

The answer of Dreyfus was a resounding negative. Therefore it should not be surprising that the reading of Division I of Being and Time given by Dreyfus defined not only a generation of philosophers but also AI researchers, given that Heidegger helped destroy their research funding. Of the number of clearly Heideggerian critiques Dreyfus makes of artificial intelligence, the one that was most thoroughly taken to heart by many in the artificial intelligence community was the non-representational nature of knowledge and problem-solving.

Early work attempted to replace explicit logical frameworks for representing knowledge with numerical computation over connected graphs of nodes. Staying close to the neural level was a difficult task, given that if anything neuroscience shows that the electrochemical process in the brain is quite removed from number crunching, so eventually AI researchers simply gave up on modeling neurons and generalized neural networks into more generic machine learning algorithms, based on anything from pure ad hoc design to a more principled Bayesian framework. In a sense, machine learning as a field is a strange offspring of the influence of Heidegger Dreyfus, The overwhelming importance of the practical task in defining the very world we live in was taken up in a Heideggerian context not only by Winograd, but by the forefathers of ubiquitous computing such as Mark Weiser, and so slowly but surely became second nature to computer engineering Weiser, Still, Heidegger was clear that what matters about embodiment was not the sheer presupposition of having a physical body that can interact with the world, but that embodiment enables having a world , and it is this worldhood Weltheit that is defining of being Heidegger, It was this insight that ended up transforming AI and machine learning more than any other insight from Heidegger, even if the insight was perhaps altered beyond recognition to Heidegger himself in transmission.

Under the influence of the early Heideggerian metaphysics from Being and Time as channeled into the Anglophone world by Dreyfus, as well the strange idiosyncratic cybernetics of autopoiesis from Maturana, Winograd and Flores attacked the logical foundations of artificial intelligence one of which we saw comes from Carnap and explicitly gave a new metaphysics for computing. Reinterpreting Heidegger and Merleau-Ponty in terms of a distinctly computational framing, Winograd and Flores began integrating the human into the heart of the computational system itself. Rather than attempting to create a third-person scientific perspective or an autonomous artificial intelligence based on logic, Winograd and Flores turned to a metaphysics of human-centered design, where the central task was transformed from representing human knowledge to using a machine to better enable the implicit and embodied knowledge of humans.

If there was to be some kind of technical breakdown, the technological apparatus was to become reshaped based on human feedback with the ultimate goal of re-establishing its own self-organization that continuously improved in the face of the messiness of the world. In Heidegger as well as the theory of autopoiesis, there was no meaning outside of the phenomenological world, and so formal semantics and the rest of the Carnap-inspired apparatus was thrown out, with a new emphasis being placed on learning and human-computer interaction. Technology aimed for ever smoother, and eventually invisible, integration with the human.

In other words, Winograd and Flores had laid the metaphysical foundations for Google. In fact, Heidegger and Carnap had met and had cordial discussions in the s and Carnap had carefully read Being and Time Friedman, The latter deal with what exists in a given linguistic framework numbers in mathematics, atoms in physics, etc. On the other hand, Carnap still contends that an evaluation of the frameworks themselves is possible, but only a practical one because no theoretical evaluation of a linguistic framework is available within the same linguistic framework.

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Although, properly speaking, the evaluation may be both theoretical and practical, for weighing the consequences of formal apparatuses may be part and parcel of the contribution of other disciplines, each defining a different linguistic framework. As regards computer ontologies, those disciplines include HCI for instance, to which Winograd himself contributed after his turn to Heidegger. When AI researchers turned to HCI and machine learning after reading Heidegger, they betrayed a tendency which could have stemmed from Carnap himself due to his focus on practical efficacy!

The problem was how to connect the unstructured data and classification tasks that machine learning excelled at with the kinds of complex structures embedded in knowledge representations. Taking image recognition as a paradigmatic example, an image contains not only figures, but also these figures contain faces, which in turn contain eyes and mouths.

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Emergent Semantics

Or in the case of speech, it was from recognizing elementary phonemes that a machine learner could build entire words, then named entities, and then phrases, sentences, and paragraphs — and finally to place the text in some library-like hierarchy of subjects. These kinds of features that involved multiple and hierarchical features were at first impossible for machine learning.

However, due to the work of pioneering AI researchers like Geoffrey Hinton now at Google , layers of neural networks were hooked together, where each layer could recognize specific features and guide the learning of not only itself, but other layers via feedback LeCun, et al. These cascades of machine learning algorithms became known as deep learning algorithms due to their ability to learn at many different levels of abstraction simultaneously.

The numbers of inputs to these machine learners started scaling to billions, far more than what could be handled without a data center. As an aside, it must be noted that Carnap devoted the most important part of his career from the s to to elaborating an inductive logic, a project that remains quite obscure especially when compared to his previous efforts.

Interoperability of Information Systems Managed and Used by the Local Health Departments

Heidegger himself would have recognized a strangely familiar and monstrous return of his philosophical enemy, enframing Gestell , in the knowledge graph. In Heideggerian terms, knowledge graphs are a formalism to represent not the properly ontological, but the merely ontic — the world as facts. This attempt to define the world as entities with properties and concepts to be calculated over by machine learning algorithms, with Being somehow being at the top of the hierarchy, is for Heidegger an ontotheology par excellence that attempts to enframe a particular conception of the world as historically eternal, and so squarely violates the metaphysical stance of his later years.

Regardless of this misreading, a bizarre if unrecognized neo-Heideggerian ambiance pervades Silicon Valley, from the emphasis on user experience to the disappearance of the interface into an array of sensors that are directly placed on the body. Due to this invisibility, the mobile phone, and so Google, becomes a literal extension of our own knowledge, and it becomes unclear how we would even function without it Halpin, Only when the phone is absent or malfunctioning do we notice how utterly dependent we have become on the Internet for our knowledge.

Where does the rise of deep-learning and proprietary knowledge graphs leave us in terms of decentralization and autonomy? As a few companies currently control the massive amounts of computing power, closed algorithms and massive data sets needed to fuel the machine learning algorithms that operate behind the scenes in these new applications like Google Maps and Siri, so likewise only a small elite can truly harness the potential power of data on the Web.

There is now widespread concern that this vast power may be abused, and there is spreading among the general population a fear of these companies and a distrust of the Internet Morozov, Can the Web return to being a tool of empowerment? In the transition to the Web as a universal space of information, the truly necessary tool is the universal abstract machine, the Turing Machine that executes any computable algorithm. As through education and literacy our ancestors learned how to autonomously extend their physical capabilities with modern tools and learned how to autonomously organize in a larger complex social fabric than simple face-to-face meetings, through programming humans can learn how to communicate with the machines necessary to autonomously understand and control the complex technological world we have inherited.

What is necessary is for the generalized skillset of scientific, logical and algorithmic thinking that underlies programming to be spread throughout the population. This does not mean it should in any way supersede our previous languages and modes of thinking, just as writing did not absorb non-verbal tool-use and the visual arts, but that it is necessary in order to maintain autonomy in the era of the Internet.

Rather than a valence of description of the world or a technique for controlling the world, it would be far better to think of algorithmic thinking as yet another capacity that can be developed and nurtured in future generations due to its own limited yet powerful capacity: A meta-language for controlling the general abstract machines — computers — that currently form the emerging global infrastructure of much of our inhabitation of the planet.

Without the ability and freedom to navigate through these programs, autonomy would be lost.

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Tim Berners-Lee has, via the Semantic Web, long advocated for open data. It is now obvious that open data is necessary but not sufficient for the development of autonomy. The ability to think algorithmically and program is useless in terms of the decentralization of knowledge unless the proper infrastructure is provided. Decentralized open data and even open versions of the knowledge graph like DBpedia are rather meaningless in terms of human knowledge if only a small minority controls the data centers and machine learning algorithms needed in order make sense of the data.

The Semantic Web should encompass more than just open data! If the key to the autonomy of future generations is control over knowledge, then not only must there be open access to the data such as provided by Wikipedia and DBpedia, but there must also be control of the data centers and algorithms by ordinary people. Data centers are already becoming increasingly cheap to deploy due to the Cloud, but they are still fundamentally controlled by corporations rather than people. Likewise, the machine learning algorithms that appear currently as radically opaque trade secrets need to become open algorithms that can be inspected and deployed by anyone.

Decentralization must mean seizing back control not only of data, but of algorithms and data centers from centralization, which is a political task for the future. For better or worse, our immediate survival is evermore tightly knit to the infrastructure which truly has a second nature. It makes little to no sense now to simply let go of it despite the fact that it has become avowedly unsustainable and its functioning increases the strain on the planet, a decaying infrastructure whose current energy-intensive trajectory leaves it to endure the same fate as more ancient dwellers of the biosphere: Extinction.

What we do with other beings which face a common threat, those which contribute to futuring as well as those who contribute to defuturing Fry, , remains to be seen; however, we can imagine that data centers under popular control can be decentralized, and ultimately made more ecologically sound. Daniel DeLaurentis [14] and his co-researchers at Purdue University. Systems of systems, while still being investigated predominantly in the defense sector, is also seeing application in such fields as national air and auto transportation [23] and space exploration.

Other fields where it can be applied include health care , design of the Internet , software integration, and energy management [19] and power systems. Collaboration among wide array of organizations is helping to drive development of defining system of systems problem class and methodology for modeling and analysis of system of systems problems. There are ongoing projects throughout many commercial entities, research institutions, academic programs, and government agences.

For example, DoD recently established the National Centers for System of Systems Engineering [25] to develop a formal methodology for system-of-systems engineering for applications in defense-related projects.

What is interoperability?

Bush in the Vision for Space Exploration. A number of research projects and support actions, sponsored by the European Commission , are currently in progress. This objective has a specific focus on the "design, development and engineering of System-of-Systems". From Wikipedia, the free encyclopedia. Information, Knowledge, Systems Management.