Semantic Technologies for Disaster Management: Network Models and Methods of Diagrammatic Reasoning

Abstract:

The Chapter will provide a brief and informal introduction to diagrammatic reasoning (DR) and network modelling (NM) using string diagrams, which can be shown to possess the same degree of rigor as symbolic algebra, while achieving greater abbreviative power (and  pedagogical insight) than more conventional techniques of diagram-chasing. This review of the research literature will set the context for a detailed examination of two case-studies of semantic technologies which have been applied to the management of emergency services and search-and-rescue operations. The next section of the Chapter will consider the implications of contemporary and closely related developments in software engineering for disaster management. Conclusions will follow.

Introduction

This Chapter is concerned with developments in applied mathematics and theoretical computing that can provide a formal and technical support for practices of disaster management. To this end it will draw on recent developments in applied category theory , which inform semantic technologies. In the interests of brevity, it will be obliged to eschew formal exposition of these techniques, but to this end, comprehensive references will be provided. The justification for what might at first seem to be an unduly narrow focus, is that applied category theory facilitates translation between different mathematical, computational and scientific domains.

For its part, Semantic Technology (ST) can be loosely conceived as an approach treating the World-Wide-Web as a “giant global graph”, so that valuable and timely information can be extracted from it using rich structured-query languages and extended description logics. These query languages must be congruent with pertinent (organizational, application, and database) ontologies so that the extracted information can be converted into intelligence. Significantly, database instances can extend beyond relational or graph databases, to include Boolean matrices, relational data embedded within the category of linear relations, and that pertaining to systems of differential equations in finite vector space, or even quantum tensor networks within a finite Hilbert space.

More specifically, this chapter will introduce the formalism of string diagrams, which were initially derived from the work of the mathematical physicists, Roger Penrose (1971) and Richard Feynman (1948). However, this diagrammatic approach has since been extended and re-interpreted  by category theorists such as Andre Joyal and Roy Street (1988, 1991). For example, Feynman diagrams can be viewed as morphisms in the category Hilb of Hilbert spaces and bounded linear operators (Westrich, 2006, fn. 3: 8), while Baez and Lauda (2009) interpret them as “a notation for intertwining operators between positive-energy representations of the Poincaré group”. Penrose diagrams can be viewed as a representation of operations within a tensor category.

Joyal and Street have demonstrated that when these string diagrams are manipulated in accordance with certain axioms—the latter taking the form of a set of equivalence relations established between related pairs of diagrams—the movements from one diagram to another can be shown to reproduce the algebraic steps of a non-diagrammatic proof. Furthermore, they can be shown to possess a greater degree of abbreviative power. This renders an approach using string diagrams extremely useful for teaching, experimentation, and exposition.

In addition to these conceptual and pedagogical advantages, however, there are additional implementation advantages associated with string diagrams including: (i) those of compositionality and layering (e.g. in Willems’s 2007  behavioural approach to systems theory, complex systems can be construed as the composites of smaller and simpler building blocks, which are then linked together in accordance with certain coherence conditions); (ii) a capacity for direct translation into functional programming (and thus, into propositions within a linear or resource-using logic); and, (iii) the potential for the subsequent application of software design and verification tools. It should be appreciated that these formal attributes will become increasingly important as the correlative features of what some have described as the digital economy.

This chapter will consider the specific role of string diagrams in the development and deployment of semantic technologies, which in turn have been developed for applications of relevance to disaster management practices. Techniques based on string diagrams have been developed to encompass a wide variety of dynamic systems and application domains, such as Petri nets, the π-calculus, and Bigraphs (Milner, 2009), Bayesian networks (Kissinger & Uijlen, 2017), thermodynamic networks (Baez and Pollard, 2017), and quantum tensor networks (Biamonte & Bergholm, 2017), as well as reaction-diffusion systems (Baez and Biamonte, 2012). Furthermore, they have the capacity to encompass graphical forms of linear algebra (Sobociński, Blog), universal algebras (Baez, 2006), and signal flow graphs (Bonchi, Sobociński and Zanasi (2014, 2015), along with computational logics based on linear logic and graph rewriting (on this see Mellies, 2018; and Fong and Spivak, 2018, for additional references).

1.  Applied Category Theory

Category theory and topos theory have taken over large swathes in the field of formal or theoretical computation, because categories serve to link together the structures found in algebraic topology, and with the logical connectives and inferences to be found in formal logic, as well as with recursive processes and other operations in computation. The following diagram taken from Baez and Stay (2011), highlights this capability.

John Bell (1988: 236) succinctly explains why it is that category theory also possesses enormous ormous powers of generalization:

A category may be said to bear the same relation to abstract algebra as does the latter to elementary algebra. Elementary algebra results from the replacement of constant quantities (i.e. numbers) by variables, keeping the operations on these quantities fixed. Abstract algebra, in its turn, carries this a stage further by allowing the operations to vary while ensuring that the resulting mathematical structures (groups, rings, etc) remain of a prescribed kind. Finally, category theory allows even the kind of structure to vary: it is concerned with structure in general.

Category theory can also be interpreted as a universal approach to the analysis of process, across various domains including: (a) mathematic practice (theorem proving); (b) physical systems (their evolution and measurement); (c) computing (data types and programs); (d) chemistry (chemicals and reactions); (e) finance (currencies and various transactions); (f) engineering (flows of materials and production).

This way of thinking about processes now serves as a unifying interdisciplinary framework that researchers within business and the social sciences have also taken up. Alternative approaches to those predicated on optimizing behaviour on the part of individual economic agents include the work evolutionary economists and those in the business world who are obliged to work with computational systems designed for the operational management of commercial systems. However, these techniques are also grounded in conceptions of process

Another way of thinking about dynamic processes is in terms of circuit diagrams, which can represent displacement, flow, momentum and effort—phenomenon modelled by the Hamiltonians and Lagrangians of Classical Mechanics. It can be appreciated that key features of economic systems are also amenable to diagrammatic representations of this kind, including asset pricing based on notion of arbitrage, a concept initially formalized by Augustin Cournot in 1838. Cournot’s analysis arbitrage conditions is grounded in Kirchoff voltage law (Ellerman, 1984). The analogs of displacement, flow, momentum and effort are depicted below for a wide range of disciplines.

Applied Category Theory: in the US, contemporary developments in applied category theory (ACT) have been spurred along and supported by a raft of EU, DARPA and ONR Grants. A key resource on ACT is Fong and Spivak’s (2018) downloadable text on compositionality. This publication explores the relationship between wiring diagrams or string diagrams and a wide variety of mathematical and categorical constructs, including as a means for representing symmetric monoidal preorders, signal flow graphs, along with functorial translation between signal flow graphs and matrices and other aspects of functorial semantics, graphical linear algebra, hypergraph categories and operads, applied to electric circuits and network compositionality. Topos theory is introduced to characterise the logic of system behaviour on the basis of indexed sets, glueings, and sheaf conditions for every open cover.

2. Diagrammatic Reasoning

Authors such as Sáenz-Ludlow and Kadunz (2015), Shin (1995), Sowa (2000), and Stjernfelt (2007), who have published research on knowledge representation and diagrammatic approaches to reasoning, tend to work within a philosophical trajectory that stretches from F. W. Schelling and C. S. Peirce, through to E. Husserl and A. N. Whitehead, then on to M. Merleau-Ponty and T. Adorno. Where Kant and Hegel privileged symbolic reasoning over the iconic or diagrammatic, Peirce, Whitehead, and Merleau-Ponty followed the lead of Schelling for whom ‘aesthetics trumps epistemology’! It is, in fact, this shared philosophical allegiance that not only links diagrammatic research to the semantic (or embodied) cognition movement (Stjernfeld himself refers to the embodied cognition theorists Eleanor Rosch, George Lakoff, Mark Johnson, Leonard Talmy, Mark Turner, and Gilles Fauconnier), but also to those researchers who have focused on issues of educational equity in the teaching of mathematics and computer science, including Ethnomathematics and critical work on ‘Orientalism’ specialized to emphasize a purported division between the ‘West and the Rest’ in regard to mathematical and computational thought and practice.

As such, insights from this research carry over to questions of ethnic ‘marginalization’ or ‘positioning’ in the mathematical sciences (see the papers reproduced in Forgasz and Rivera, eds., 2012 and Herbel-Eisenmann et al., 2012). In a nutshell, diagrammatic reasoning is sensitive to both context and positioning and, thus, is closely allied to this critical axis of mathematics education.

The following illustration of the elements and flows associated with diagrammatic forms of reasoning comes from Michael Hoffman’s (2011) explication of the concept first outlined by the American philosopher and logician, Charles Sanders Peirce.

The above Figure depicts three stages in the process of diagrammatic reasoning: (i) constructing a diagram as a consistent representation of key relations; (ii) analysing a problem on the basis of this representation; and (iii) experimenting with the diagram and then observing the results. Consistency is ensured in two ways. First, the researcher or research team develop an ontology specifying elements of the problem and the relations holding between these elements, along with pertinent rules of operation. Second, language is specified in terms of both syntactical and semantic properties. Furthermore, in association with this language, a rigorous axiomatic system is specified, which both constrains and enables any pertinent diagrammatic transformations.

3a. Case-Study One:

A 2010 paper by SAP Professors, Paulheim and Probst reviews an application of STs to the management and coordination of emergency services in the Darmstadt region of Germany. The aim of the following diagram, reproduced from their work, is to highlight the fact that, from a computational perspective, the integrative effort of STs can apply to different organizational levels: that of the common user interface, shared business logics and that of data sources.

In their software engineering application, the upper-level ontology DOLCE is deployed to link a core domain ontology together with a user-interface interaction ontology. In turn, each of these ontologies draws on inputs from an ontology on deployment regulations and various application ontologies. Improved search capabilities across this hierarchy of computational ontologies, are achieved through the adoption of the ONTOBROKER and F-Logic systems.

3b. Case-study Two:

An important contribution to the field of network modelling has come from the DARPA-funded CASCADE Project (Complex Adaptive System Composition and Design Environment), which has invested in long-term research into the “system-of-systems” perspective (see John Baez’s extended discussion of this project on his Azimuth blog). This research has been influenced by Willems’s (2007) behavioural approach to systems, which in turn, is based on the notion that large and complex systems can be built up from simple building blocks.

Baez et al. (2020) introduce ‘network models’ to encode different ways of combining networks both through overlaying one model on top of another and by setting each model side by side. In this way, complex networks can be constructed using simple networks as components. Vertices in the network represent fixed or moving agents, while edges represent communication channels.

The components of their networks are constructed using coloured operads, which include vertices representing entities of various types and edges representing the relationships between these entities. Each network model gives rise to a typed operad with an associated canonical algebra, whose operations represent ways of assembling a more complex network from smaller parts. The various different ways to compose these operations characterize a more general notion of an operation, which must be complemented by ways of permuting the arguments of an operation a process yielding a permutation group of inputs and outputs).

In research conducted under the auspices of the CASCADE Project, Baez, Foley, Moeller, and Pollard (2020) have worked out how to combine two formalisms. First, there are Petri nets, commonoly used as an alternative to process algebras as a foralism for business process management. The vertices in a Petri net represent collections of different types of entities (species) with morphisms between them used to describe processes (transitions) that can be carried out by combining various sets of entities (conceived as resources or inputs into a transition node or process of production) together to make new sets of entities (concived as outputs or vertices are positioned after the relevant transition node). The stocks of each type of entity that is available is enumerated as a ‘marking’ specific to each type or colour together with the set of outputs that can be produced by activated the said transition.

Second, there are network models, which describe processes that a given collection of agents (say, cars, boats, people, planes in a search-and-rescue operation) can carry out. However, in this kind of network, while each type of object or vertex can move around within a delineated space, they are not allowed to turn into other types of agent or object.

In these networks, morphisms are functors (generalised functions) which describe everything that can be done with a specific collection of agents. The following Figure depicts this kind of operational network in an informal manner, where icons represent helicopters, boats, victims floating in the sea, and transmission towers with communication thresholds.

By combining Petri nets with an underlying network model resource-using operations can be defined. For example, a helicopter may be able to drop supplies gathered from different depots and packaged into pallets, onto the deck of a sinking ship or to a remote village cut off by an earthquake or flood.

The formal mechanism for combining a network model with a Petri net relies on treating  different type of entities as catalysts, in the sense that the relevant species are neither increased nor decreased in number by any given transition. The derived category is symmetric monoidal and possesses a tensor product (representing processes for each catalyst that occur side-by-side), a coproduct (or disjoint union of amounts of each catalyst present), and within each subcategory of a particular catalyst, an internal tensor product describes how one process can follow another while reusing the pertinent catalysts.

The following diagram taken from Baez et al. (2020), illustrates the overlaying process which enables more complex networks to be constructed from simpler components. The use of the Grothendieck construction in this research ensures that when two or more diagrams are overlayed there will be no ‘double-counting’ of edges and vertices. When components are ‘tensored’ each of the relevant blocks would be juxtaposed “side-by-side”.

Each network model is characterized by a “plug-and-play” feature based on an algebraic component called an operad. The operad serves as the construct for a canonical algebra, whose operations are ways of assembling a network of the given kind from smaller parts. This canonical algebra, in turn, accommodates a set of types, a set of operations, ways to compose these operations to arrive at more general operations, and ways to permute an operation’s arguments (i.e. via a permutation group), along with a set of relevant distance constraints (e.g. pertinent communication thresholds for each type of entity) .

One of Baez’s co-authors, John Foley, works for Metron, Inc., VA, a company which specializes in applying the advanced mathematics of network models to such phenomena as “search-and-rescue” operations, the detection of network incursions, and sports analytics. Their 2017 paper mentions a number of formalisms that have relevance to “search-and-rescue” applications, especially the ability to distinguish between different communication channels (different radio frequencies and capacities) and vertices (e.g. planes, boats, walkers, individuals in need of rescue etc.) and the capacity to impose distance constraints over those agents who may fall outside the reach of communication networks.

In related research paper, Schultz, Spivak, Vasilakopoulou, Wisnesky (2016) argue thay dynamical systems can be gainfully thought of as ‘machines’ with inputs and outputs, carrying some sort of signal that occurs through some notion of time”. Special cases of this general approach include discrete, continuous, and hybrid dynamical systems. The authors deploy lax functors out of monoidal categories, which provide them with a language of compositionality. As with Baez and his co-authors, Schultz et al. (2016) draw on an operadic construct so as to understand systems that result from an “arbitrary interconnection of component subsystems”. They also draw on the mathematics of sheaf theory, to flexibly capture the crucial notion of time. The resulting sheaf-theoretic perspective relates continuous- and discrete-time systems together via functors (a kind of generalized ‘function of functions’, which preserves structure). Their approach can also account for synchronized continuous time, in which each moment is assigned a specific phase within the unit interval.

4. Related Developments in Software Engineering

This section of the Chapter examines contemporary advances in software engineering that have implications for ‘system-of-sytems’ approaches to semantic technology. The work of the Statebox group at the University of Oxford and that of Evan Patterson, from Stanford University, who is also affiliated with researchers from the MIT company, Categorical Informatics, will be discussed to indicate where these new developments are likely to be moving in the near future. This will be supplemented by an informal overview of some recent innovations in functional programming, which have been informed by the notion of a derivative applied to an algorithmic step. These initiatives have the potential to transform software for machine-learning and the optimization of networks

The Statebox team based at Oxford University have developed a language for software engineering that uses diagrammatic representations of generalized Petri nets. In this context, transitions in the net are morphisms between data-flow objects represent terminating functional programming algorithms. In Statebox (integer and semi-integer) Petri nets are constructed with both positive and negative tokens to account for contracting. Negative tokens represent borrowing while positive tokens represent lending and, likewise, the taking of short and long positions in asset markets. This allows for the representation of smart contracts, conceived as separable nets. Nets are also endowed with interfaces that allow for channelled communications through user-defined addresses. Furthermore, guarded and timed nets, with side-effects (which are mapped to standard nets using the Grothendieck construction), offer greater expressive power in regard to the conditional behaviour affecting transitions (The Statebox Team, 2018).

Patterson (2017) begins his paper with a discussion of description logics (e.g. OWL, WC3), which he interprets as calculi for knowledge representation (KR). These logics, which are the actual substrates responsible for the World-Wide-Web (WWW), lie somewhere between propositional logic and first-order predicate logic possessing the capability to express the (∃,∧,T,=) fragment of first-order logic. Patterson highlights the trade-off that must be made between computational tractability and expressivity before introducing a third knowledge representation formalism that interpolates between description logic and ontology logs (see Spivak and Kent, 2012, for an the extensive description of ologs, which express key constructs from category theory, such as products and coproducts, pullbacks and pushforwards, and representations of recursive operations using diagrams labelled with concepts drawn from everyday conversation). Patterson (2017) calls this construct the relational ontology log, or relational olog, because it is based on, Rel, the category of sets and relations and, as such, draws on relational algebra, which is the (∃,∧, , T,⊥,=) fragment of first-order logic. He calls Spivak and Kent’s, 2012, version, a functional olog to avoid any confusion, because these are solely based on Set, the category of sets and functions. Relational ologs achieve their expressivity through categorical limits and colimits (products, pullbacks, pushforwards, and so forth

The advantages of Patterson’s framework are that functors allow instance data to be associated with a computational ontology in a mathematically precise way, by interpreting it as a relational or graph database, Boolean matrix, or category of linear relations. Moreover, relational ologs are, by default, typed, which he suggests can mitigate the maintainability challenges posed by the open world semantics of description logic.

String diagrams (often labelled Markov-Penrose diagramsby those working in the field of brain science imaging) are routinely deployed by data-scientists used to represent the structure of deep-learning convolution neural networks. However, string diagrams can also serve as a tool for representing the computational aspects of machine-learning.

For example, influenced by the program idioms of machine-learning, Ghica and Muroya (2017) have developed what they choose to call a ‘Dynamic Geometry of Interaction Machine’, which can be defined as a state transition system operating whose transitions not only account for ‘token passing’ but also for ‘graph rewriting’ (where the latter can be construed as a graph-based approach to the proving of mathematical hypotheses and theories). Their proposes system is supported by diagrammatic implementation based on the proof structures of the multiplicative and exponential fragment of linear logic (MELL). In Muroya, Cheung and Ghica (2017), this logical approach is complemented by a sound call-by-value lambda calculus inspired, in turn by Peircean notions of abductive inference. The resulting bimodal programming model operates in both: (a) direct mode, with new inputs provided, new outputs obtained; and, (b) learning mode, with special inputs applied for which outputs are known; to achieve optimal tuning of parameters to ensure desired outputs approach actual outputs. The authors contend that their holistic approach is superior to that of the TensorFlow software package developed for machine-learning, which they describe as a ‘shallow embedding’ of a domain specific language (DSL) into PYTHON” rather than a ‘stand-alone’ programming language.

Adopting a somewhat different approach, Cruttwell, Gallagher and MacAdam (2019) extend Plotkin’s differential programming framework, which is itself a generalization of differential neural computers, where arbitrary programs with control structures encode smooth functions also represented as programs. Within this generalized domain, the derivative can be directly applied to programs or to algorithmic steps and, furthermore, can be rendered entirely congruent with categorical approaches to Riemannian and Differential geometry such as Lawvere’s Synthetic Differential Geometry.

Cruttwell and his colleagues go on to observe that, when working in a simple neural network, back-propagation takes the derivative of the error function, then uses the chain rule to push errors backwards. They point out that, for convolution neural networks, the necessary procedure is less straightforward due to the presence of looping constructs.

In this context, the authors further note that attempts to work with the usual ‘if-then-else’ and ‘while’ commands can also be problematic. To overcome these problems associated with recursion, they deploy what have been called ‘join restriction tangent categories’, which express the requisite domain of definition and detect and achieve disjointness of domains, while expressing iteration using the join of disjoint domains (i.e. in technical terms, this is the trace of a coproduct in the idempotent splitting). The final mathematical construct they arrive at, is that of a differential join restriction category along with the associated join restriction functor which, they suggest, admits a coherent interpretation of differential programming.

It should be stressed that each of these category-theoretic initiatives to formalize the differential of an algorithmic step will become important in future efforts to develop improved, yet diagrammatically-based forms of software for machine learning that have greater capability and efficiency than existing software suites. The fact that both differential and integral categories can be provided with a coherent string diagram formalism (Lemay, 2017) provides a link back to the earlier discussion about the role of diagrammatic reasoning in semantic technologies.

It is clear that techniques of this kind could also be applied to a wide variety of network models (e.g. for the centralized and decentralized control of hybrid cyber-physical systems), where optimization routines may be required (including those for effective disaster management).

5. Conclusion

In conclusion, the innovations in software engineering described above, have obvious implications for those attempting to  develop new semantic technologies for the effective management of emergency services and search-and-rescue operations in the aftermath of a major disaster. Hopefully, the material surveyed in this Chapter should serve to highlight the advantages of a category-theoretic approach to the issue at hand, along with the specific benefits of adopting an approach that is grounded in the pedagogical, computational, and formal representational power of string-diagrams, especially within a networked computational  environment charactrised by Big Data, parallel processing, hybridity, and some degree of decentralized control.

While a Chapter of this kind cannot go into too much detail about the formalisms that have been discussed, it is to be hoped that enough pertinent references have been provided for those who would like to find out more about the mathematical detail. Of course, it is not always necessary to be a computer programmer both to understand and to effectively deploy powerful suites of purpose-built software. It is also to be hoped that diagrammatic reasoning may assist the interested reader in acquiring a deeper understanding of the requisite mathematical techniques.

Author: Professor Dr. James Juniper – Conjoint Academic, University of Newcastle; PhD in Economics, University of Adelaide

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Artificial Intelligence – An Avalanche of Business Opportunities

“Artificial intelligence is the future and the future is here.”
~ Dave Waters

AI or Artificial intelligence, today’s most innovative technology, is all about creating intelligent machines that do tasks usually done only by human intelligence. In simple words, it is the brainpower validated by machines and computers that are automated through codes to impersonate the natural intelligence demonstrated by human beings.

Not many of us know that Artificial intelligence is impacting our day-to-day life immensely. Yes! Have you ever wondered how your smartphone unlocks with your face ID or how social media feeds are personalized & how google gives recommendations when you search for a term on google search? You guessed it right. It’s AI!

AI is creating an avalanche of business possibilities today. It has different categories such as Mundane to Formal and also Expert tasks! Let’s look at some domains where Artificial Intelligence proves to be highly lucrative.

Travel, Tourism & Hospitality Industry

Personalization guarantees guest satisfaction. The travel & tourism industry and sectors like the hotel industry, airline industry, restaurant industry, and travel agents within it have adopted AI for several assistances, some of them are: 

  • Chatbots and Online Customer Service

AI chatbots offer a relevant response to the customers by understanding their queries & give them related information, just like a human does. But unlike humans, it is very prompt & can function 24/7 without breaks or pays yet it provides a pleasant experience to the guests.

  • Data Processing and Data Analysis

Apart from customer service AI is used in this sector to gather and interpret data about their customers. AI can also sort this data more rapidly and precisely than a human can, and that too without errors. 

  • Personalized Recommendations

AI is applied to offer personalized travel recommendations & options by using the data like the interests, budgets, and past search history of the customers. This helps the customers to effortlessly make their travel choices which in turn improves the profits of the company. 

  • Tracking and booking trips

AI-based booking apps track prices & recommend the customers the best times to book flights as well as make hotel reservations, by accurate prediction of prices, well in advance.

Fitness Industry

Yes, AI is revolutionizing the Fitness Industry in several ways and transforming home workouts into a smarter, better, and less expensive method to keep people’s health on track.

  • AI-Based Personal Trainers

People desire fitness but with their hectic schedules & time shortage, do not go to the gym. Also, hiring a personal trainer is not affordable at all. AI comes as a huge rescue for such people. AI-based fitness app provides the luxury of personalized trainers to guide & monitor the accuracy and the pace of the exercise, at any time and any place. 

  • Smart Wearables and Exercise Equipment

Wearables assist their users in tracking their fitness

activities, counting the calories burnt, detecting irregular heartbeats and signs of diabetes, etc. AI fortified exercise equipment when fed with some personal details, and offers recommendations to their users to exercise competently. 

  • Sales promotion

AI integrated fitness apps help fitness companies to find their prospective customers and collect & sort their data. Companies use this data to boost their sales and improve profits.

Healthcare Industry

AI plays a significant role in the Healthcare Industry by accomplishing tasks that are usually done by humans only, and that too faster, more precisely & cost-effectively than humans. 

  • Medical Diagnosis

Artificial Intelligence (AI) has been synonymous with competence in the medical field. It has grown to become the second pair of eyes that never need to rest. AI-based medical diagnoses are automated & and can detect diseases like cancer even if the symptoms are not explicitly evident. Such diagnoses are mostly accurate.

  • Symptoms examination 

When patients mention their symptoms & health complaints in symptoms examining AI Chatbots, it uses its algorithm to precisely diagnose the disease. It also guides the patients toward appropriate health care.

  • Drug discovery and Development

This use of AI has been amassed in various sectors of humanity, especially in the pharmaceutical industry. AI help in discovering & designing new drugs and enhances R&D while speeding up the time and cutting off the extensive process involved in it. 

Logistics & Supply Chain Management 

AI has positively transformed the logistics and supply chain industry. It contributes a lot to reducing operating costs and is more efficient to use when it comes to responding to clients.

  • Accurate Inventory Management 

AI helps to prevent understocking and overstocking of inventory with its smart algorithms that can predict and determine consumer habits & seasonal demands.

  • Timely Delivery 

AI speeds up the warehouse processes by eliminating manual work, & operational shortcomings in the value chain. Thus, timely delivery goals can be smoothly accomplished. 

  • Warehouse Management 

AI manages warehouse security by tracking individuals who are entering and exiting the warehouse. Not just that it also tracks the goods in the warehouse with their barcodes and thus helps in keeping the inventory data updated. 

Marketing sector

  • Product recommendations 

AI recommends products & services to prospective customers based on their online search. AI understands & speculates people’s choices based on their behavior on the internet and recommends to them the products/services they are likely to purchase. More importantly, AI is effective in the Marketing Sector as speed is necessary. It empowers scalable growth, pushes profit, and customizes customer experience.

  • Dynamic pricing

AI automatically prices a product based on its demand & availability in an online market. This process needs no human intervention at all.

  • Targeting Ads

AI can be used to display ads to potential customers based on their relevant search on a search engine or social media. 

Cybersecurity Industry 

With increasing cyber-attacks & complications associated with them, the cybersecurity industry is applying AI in its operations to keep cyber threats at bay.

  • Threat exposure

AI-empowered security systems reveal the new trends hackers follow, worldwide as well as in a specific sector. This information can be used to make crucial decisions to protect against cyber danger. 

  • Phishing Detection

AI-based cybersecurity systems are capable of recognizing spam emails, determining if a website is genuine or fake and thus preventing phishing threats, breaches as well as data loss caused by malicious emails. 

  • Biometric Authentication

Biometric systems with AI make very precise and fail-proof verification with Face Recognition, Voice Recognition, Fingerprint Recognition, etc. 

Retail Industry

Just like the online marketplace, the retail industry also prefers the usage of AI for boosting sales and enhancing customer experience. AI supports retail systems to work together and enhance customer experiences, managing inventory, forecasting, and more.  

  • Smart Product Searches

Artificial intelligence simplifies product search for customers by allowing them to click a picture of any product online or offline and letting them search for the retailer who sells over the internet.

  • Personalization and Customer Insights 

Consumers can enjoy a personalized shopping experience with AI-based technology. It makes use of face recognition to spot a customer who is revisiting a shop and recommends products based on their preferences. 

  • Better In-Store Experience

AI-built system can cut down the operational cost of any retail store by taking away the need for a salesperson & a cashier, thus eliminating queues too. It also helps to monitor stocks & restock them instantly. 

In conclusion, AI has the power to improve the output and profits of any business. And so, companies are dynamically searching for new ventures to make the best of AI. Companies must create AI usage ideas for any specific sector to generate promising AI business opportunities. 

Can Content Automation Do Better Marketing?

“Technology, through automation and artificial intelligence, is definitely one of the most disruptive sources.” – Alain Dehaze

In the past, converting content into a marketing material was so tricky. It consumed our time and the cost of using various writing utensils and printing services. Once we started surviving in the digital life where content is king, the marketing industry entered the digital world and digital publishing services became a great boon for content marketers in every sector, thanks to the technology that changed the scenario and revolutionized the way every marketing industry followed earlier.

The new lock: where is the key?
Currently, the scenario comes with new challenges. When it comes to content marketing, present online content marketers face new marketing and production challenges because of a plethora of content in the digital world. On the one hand, content marketing is one of the essential parts of any industry, but on the other hand, it has currently become a complicated process since the variety of content goes unlimited and the way how the content is consumed by people has become unimaginable.

A large amount of the content generated today is consumed in its digital form. Digital content publishing has gradually entered into every sector such as IT, entertainment, business, and education. The form of content has evolved as variety of formats in different platforms and has created complex production processes that need special workflows for the variety of content produced. Every step in production of digital content requires customization and careful examination to suit the output required by the marketers.

Content + Automation process = ?
Content marketers need to overcome these challenges with innovative tools and ideas if they want to rule the kingdom of content. Here, automation, an obvious answer for many of the problems currently faced by publishing houses and content marketing industry, comes as an ultimate problem solver which makes content production smooth, uncomplicated, fast and easy. Content automation supports digital marketing strategy which integrates big data, blockchain, artificial intelligence, and natural language in order to accelerate the process of both production and distribution.

For the past few years, the ship of content marketing has definitely steered in the direction of automation. Around 51% of digital companies have started using marketing automation, according to statistics. If information is reliable, relevant, insightful and actionable with a proven and powerful method, customers will be attracted to marketing strategies. Here, technology plays a major role in providing fruitful data to customers.

With one-stop automation services processing in the cloud, all the processes in publishing which can be automated are scripted to work on the infrastructure of cloud computing. Workflows are developed by lining up different types of processes in the expected flow. The platform is exclusively designed to carry out a variety of automated tasks – composition, transformation and enhancement based on tailor-made automated workflows. Irrespective of at what stage your content production lines currently, automation can be applied at any phase of production, either the entire process or adopting it progressively, platforms and features that can be tailored to suit the expected demands.

In fact, recent content market demands have necessitated the inclusion of elements in content like info graphics, images, and GIFs, and interactive elements like videos and games. The content we produce is to be enriched with dynamic indexing, metadata, and semantic empowerment. In the method of content production with automation, the content is empowered with all the strategies that need for effective marketing. With the support of automation in content production, content marketers are able to streamline and accelerate the entire workflow, from the initial draft to the final output that they need.

Benefits of content automation in marketing
Content automation with a set of technologies supports for automating manual processes in content marketing. Its key aim is to automate the process of production and distribution in every stage and to keep the content up-to-date without the support of human intervention. Here let’s study some of the most important benefits of content automation in marketing.

  • Content automation improves the credibility of your product or service with content marketing strategies and can make your brand trendy.
  • Technically, it helps put sales on autopilot, sharing content across multiple digital platforms and optimizing content with SEO techniques. Your brands can catch a good place on Google search engine.
  • It converts content into other formats such as translated versions, audio or graphics, proofreading content to solve spelling and grammar issues and publishing content with reminders and notifications
  • Content automation supports your branding pages to receive more visibility and makes your social media engagement following.
  • With content automation, you can manage the entire process of your content strategy since you can virtually track every possible statistics on your campaign.
  • With the support of content automation, you can have the chances of converting high-quality qualified leads into sales. Content automation drives sales on specific products or services, empowering you control the way you sell.

Content automation tools for marketing

Content automation tools for marketing make a task a little more painless.  Here are some effective tools that you would help you streamline marketing functions.

  • io: It helps you send messages to targeted customers for specific products in a customer-friendly way.
  • Constant Contact: It is an email-marketing automation tool that helps you take your marketing to the next level.
  • Marketo: It is a sort of marketing software that lets you drive revenue with lead management.
  • Dialog Tech: It can be highly useful when you focus on voice-based marketing automation.
  • Oracle Eloqua: It lets marketers plan automated as well as personalized campaigns.
  • Bizible: Bizible is a tool that supports you to close the gap between sales and marketing.
  • Bremy: It lets you configure a customized content marketing package of database publishing, email newsletters and video editing.
  • Genoo: It enhances the success of your marketing plans.

Conclusion
With delivering excellence of designs and formats, automation services have become a better platform for content marketers to motivate the community with new words. The content is not only described words but also an art with alluring designs giving a new shape to the world. With a new perspective of content, we can imagine a better world and make reading fun for aspiring readers. Start-ups are increasingly turning to marketing strategies with content automation. The more marketing functions become automated, the more the marketing teams can focus on marketing strategies and digital marketing campaigns.

Content marketing is essential for a company to executing any long-term marketing strategy, but it is difficult to identify the most effective working content. In this case, automation will provide the data to answer your questions and enhance your content marketing processes. With the support of automation, you may:

  • Identify cost-effective and customer-friendly channels and campaigns.
  • Find out how your content influences buyer behaviour, and helps to increase leads on a particular content marketing campaign.