MASS COMMUNICATION SECTOR
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors.
Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot.
Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework.
Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics.
Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
In the future, artificial intelligence (AI) appears likely to influence marketing strategies, including business models, sales processes, and customer service options, as well as customer behaviors.
These impending transformations might be best understood using three illustrative cases from diverse industries (see Table 1). First, in the transportation industry, driverless, AI-enabled cars may be just around the corner, promising to alter both business models and customer behavior.
Taxi and ride-sharing businesses must evolve to avoid being marginalized by AI-enabled transportation models; demand for automobile insurance (from individual customers) and breathalyzers (fewer people will drive, especially after drinking) will likely diminish, whereas demand for security systems that protect cars from being hacked will increase (Hayes 2015).
Driverless vehicles could also impact the attractiveness of real estate, because (1) driverless cars can move at faster speeds, and so commute times will reduce, and (2) commute times will be more productive for passengers, who can safely work while being driven to their destination.
As such, far flung suburbs may become more attractive, vis-à-vis the case today. Third, the business model currently used by online retailers generally requires customers to place orders, after which the online retailer ships the products (the shopping-then-shipping model—Agrawal et al. 2018; Gans et al. 2017). With AI, online retailers may be able to predict what customers will want; assuming that these predictions achieve high accuracy, retailers might transition to a shipping-then-shopping business model.
That is, retailers will use AI to identify customers’ preferences and ship items to customers without a formal order, with customers having the option to return what they do not need (Agrawal et al. 2018; Gans et al. 2017).
This shift would transform retailers’ marketing strategies, business models, and customer behaviors (e.g., information search).
Businesses like Birchbox, Stitch Fix and Trendy Butler already use AI to try to predict what their customers want, with varying levels of success.
ROBOTICS: At one Fanuc plant in Oshino, Japan, industrial robots produce industrial robots, supervised by a staff of only four workers per shift.
In a Philips plant producing electric razors in the Netherlands, robots outnumber the nine production workers by more than 14 to 1.
Camera maker Canon began phasing out human labor at several of its factories in 2013.
Falling robot prices: As robot production has increased, costs have gone down. Over the past 30 years, the average robot price has fallen by half in real terms, and even further relative to labor costs (Exhibit 1).
As demand from emerging economies encourages the production of robots to shift to lower-cost regions, they are likely to become cheaper People with the skills required to design, install, operate, and maintain robotic production systems are becoming more widely available, too.
Robotics engineers were once rare and expensive specialists.
Today, these subjects are widely taught in schools and colleges around the world, either in dedicated courses or as part of more general education on manufacturing technologies or engineering design for manufacture.
The availability of software, such as simulation packages and offline programming systems that can test robotic applications, has reduced engineering time and risk.
It’s also made the task of programming robots easier and cheaper. Machine learning: Deploying machine learning models to predict an outcome across a business is no easy feat.
That’s particularly true given that data science is an industry in which hype and promise are prevalent and machine learning — although a massive competitive differentiator if harnessed the right way — is still elusive to most brands.
There are a multitude of potential hurdles and gaps standing in the way of actuating models into production, including skills gaps (both internally and with vendors or providers) and the possibility that your data or the models themselves don’t possess enough integrity and viability to produce meaningful results.
Initiatives to enact and stand up machine learning-based predictive models to make products and services smarter, faster, cheaper, and more personalized will dominate business activity in the foreseeable future.
Applications to transform business are aplenty, but it is highly debatable how many of these predictive models have actually been successfully deployed or how many have been effective and are serving their intended purpose of cutting costs, increasing revenue or profit or enabling better and more sublime customer and employee experiences.
Machine Learning (ML) is developing under the great promise that marketing can now be both more efficient and human.
Cognitive systems, embedded or not into marketing software, are powering every single functional area of marketing and each step of the consumer journey.
AI-driven marketing leverages models to automate, optimize, and augment the transformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper- personalizing messages.
Modern marketers utilize user data to deliver hyper-individualized and hyper-contextualized brand communications, in which each subsequent message builds on previous customer interactions. These interactions are seen not as a final stage of a consumer journey, but as a way to orchestrate future experiences in a satisfactory virtuous cycle.
Successful ML-powered companies turn data into seamless interactions with consumers using semi- automated and real-time processes.
These predictive and augmented experiences build deeper one-to- one relationships with consumers, improve omni-channel customer experience, and drive product differentiation.
Designing an AI strategy requires managers to systematically evaluate marketing needs in terms of automation, optimization, and augmentation in relation to the searched benefits of prediction, anticipation, and personalization.
Balancing machine-inspired goals with expected benefits forces managers to strategically assess their organization to redesign roles and responsibilities while adequately defining the division of tasks between humans and machines.
Mass3Dprinting: The Medical & dental sector, along with the aerospace sector (refer to Exhibit 2), is currently at the forefront of 3D manufacturing technology adoption.
Currently, the medical industry utilizes 3D manufacturing technology to print tissues, organs, implants, prostheses, etc.
In the automotive sector, 3D printing technology is mainly used for the prototype building, in which desktop printers are used to produce complex automotive parts with increased speed.
Before 3D printing technology, prototyping was an expensive and time-consuming process. Companies in the automotive sector are working on manufacturing a wide range of cost-effective parts.
Some companies–such as XEV, Kor Ecologic and Stratasys, and Oak Ridge National Laboratory–are working or have developed 3D printed cars, where 80-90% of the parts are manufactured using 3D printing technology cars.
3D printing is gaining traction in the automotive industry.
Many automotive OEMs and part manufacturers are cautiously investing in this technology.
In the current scenario (as of 2019), 3D printing is mainly deployed for prototyping applications in the automotive industry.
However, the technology is gaining greater traction in the manufacturing of custom parts in the luxury and sports cars segment.
3D printing technology is playing a vital role in the manufacturing of complex automotive parts such as battery covers, air ducts, mirror sockets, suspension wishbone, etc.
Numeric control manufacturing processes are best suited for mass production applications due to this cost of customization/personalization of automotive parts (which includes body parts, seats, steering wheels, etc.) is expensive compared to standard parts.
However, 3D printing technology can be used effectively to produce custom automotive parts.
With the help of 3D printing technology, many automotive parts can be customized as per customer preference might boost the performance/ experience of customers. In the case of vintage vehicles and luxury cars, many of the spares can be printed with 3D printing technology.
When producing complex parts and tools, 3D manufacturing has an advantage over conventional (numerical control based) manufacturing technology.
3D manufacturing can produce highly complex parts (turbochargers, gear shifters, water connectors for engines, brake caliper, etc.), which are hard to produce by conventional manufacturing technology.
Biotechnology research: Successful dialog between science and the public is vital for the development and introduction of new technologies. The National Academy of Science and Engineering in Germany has analysed experiences gained from controversies and communication strategies surrounding green genetic engineering and other fields of biotechnology, from a communications and social science viewpoint, as well as a historical perspective.
From this, recommendations on how to communicate biotechnology in the future, with objectivity and balance, have been derived.
Many scientists, communicators and institutions have spent time, effort and money on biotechnology communication in the past decades.
Communicating biotechnology, however, has not been successful in terms of acceptance by the general public in many countries – Germany being a prime example.
Moreover, the views of proponents and opponents have become even more entrenched in spite of the efforts to communicate the scientific message.
This seems paradoxical as communication is a process that aims at developing consensus on interests, values and preferences, taking into account the interest of all groups in society.
For decades, there has been research into the social impact of biotechnology, especially with regards to the interactions between science, industry, the media, politics and the general public. The population is regularly surveyed for its views on biotechnology, for example at the European level.
The National Academy of Science and Engineering in Germany (i.e. “acatech”), has analysed experiences from controversies and communication measures concerning green genetic engineering and other fields of biotechnology from the perspective of communications theory and social science, as well as from a historical viewpoint.
This analysis has been used to discuss what can be achieved by science communication (and what cannot be achieved) and to derive recommendations on how to communicate biotechnology appropriately in the future .
Controversies and communication: More than 40 years ago, when the molecular biological methods were being developed, it was clear to the participating scientists that genetic engineering applications would generate controversy .
Initially, the controversy fixed on questions of biological safety. When the debate reached the general public, it had expanded to include economic and innovation policy aspects.
In Germany, genetically modified organisms (GMOs) in food encounter strong resistance.
Significant resources have been deployed (in Germany as well as in other countries, such as Great Britain), to reassure the population of the safety of green genetic engineering – so far without great success.
On average, with a small majority, 54% of Europeans believe that GM food is not good for them or their families.
For Germany that figure rises to 69% .
It has obviously not yet been possible to eliminate the controversies surrounding biotechnology simply by doing “more” science communication.
Chartbots digital solutions:
1) User request analysis: this is the first task that a chatbot performs. It analyzes the user’s request to identify the user intent and to extract relevant entities.
2) Returning the response: once the user’s intent has been identified, the chatbot must provide the most appropriate response for the user’s request.
The answer may be: a generic and predefined text a text retrieved from a knowledge base that contains different answers a contextualized piece of information based on data the user has provided data stored in enterprise systems the result of an action that the chatbot performed by interacting with one or more backend application a disambiguating question that helps the chatbot to correctly understand the user’s request.
Deep Learning - This technique doesn’t need any definite data from an image and has the ability to comprehend the context of an image as well as to analyze its contents using meta and text.
For instance, if there is an abundance of tiger images and videos being shared across Facebook this technique can produce insights to understand the frequency of appearance of products with these images and videos in order to place ads for the people who might like to watch tiger videos.
Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real-world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory.
 The overlaid sensory information can be constructive (i.e. additive to the natural environment), or destructive (i.e. masking of the natural environment).
 This experience is seamlessly interwoven with the physical world such that it is perceived as an immersive aspect of the real environment.
 In this way, augmented reality alters one’s ongoing perception of a real-world environment, whereas virtual reality completely replaces the user’s real-world environment with a simulated one.
 Augmented reality is related to two largely synonymous terms: mixed reality and computer-mediated reality.
The primary value of augmented reality is the manner in which components of the digital world blend into a person’s perception of the real world, not as a simple display of data, but through the integration of immersive sensations, which are perceived as natural parts of an environment.
The earliest functional AR systems that provided immersive mixed reality experiences for users were invented in the early 1990s, starting with the Virtual Fixtures system developed at the U.S. Air Force’s Armstrong Laboratory in 1992.
 Commercial augmented reality experiences were first introduced in entertainment and gaming businesses.
Subsequently, augmented reality applications have spanned commercial industries such as education, communications, medicine, and entertainment. In education, content may be accessed by scanning or viewing an image with a mobile device or by using markerless AR techniques.
 An example relevant to the construction industry is an AR helmet for construction workers which displays information about construction sites.
Augmented reality is used to enhance natural environments or situations and offer perceptually enriched experiences.
With the help of advanced AR technologies (e.g. adding computer vision, incorporating AR cameras into smartphone applications and object recognition) the information about the surrounding real world of the user becomes interactive and digitally manipulated.
Information about the environment and its objects is overlaid on the real world. This information can be virtual or real, e.g. seeing other real sensed or measured information such as electromagnetic radio waves overlaid in exact alignment with where they actually are in space.
 Augmented reality also has a lot of potential in the gathering and sharing of tacit knowledge.
Augmentation techniques are typically performed in real time and in semantic contexts with environmental elements.
Immersive perceptual information is sometimes combined with supplemental information like scores over a live video feed of a sporting event.
This combines the benefits of both augmented reality technology and heads up display technology (HUD).
Drones: Coverage analysis of drone communication system Cooperative network formations for single and multi-drone systems
Routing protocols for drone networks
Cooperative Rendezvous for secure drone to drone communications
Indoor-navigation and urban surveillance through drones
Network architecture for cloud of drones
Ultra-reliable communication for drone networks
Experimental results, middleware, architecture, prototypes, simulators and test beds for drone networks
Mutual task allocations for drones in urban scenarios
Secure communication between the drone and the ground networks
Smart Objects Automation: Standardization is a critical success factor for smart objects. Smart object systems are characterized not only by large numbers of devices and applications, but by a significant amount of different parties, manufacturers, and companies interested in contributing to the technology.
Wireless Communications , Networks , Security , Antennas & Propagation , Microwaves ,Software Defined Radio