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Intelligent Apps

Intelligent Apps

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Table of Contents

 

Contents                                                                                                                            Page

Executive Summary. 3

Introduction. 4

Literature review.. 5

Artificial intelligence. 5

Machine learning. 6

The data flywheel 7

The Algorithm Economy. 7

Cloud-Hosted Intelligence. 8

Deep neural networks. 8

Speech recognition. 8

Artificial intelligence in intelligent applications. 9

Report Body. 12

Drivers of intelligent application development 12

Organizational benefits and challenges of intelligent applications. 14

Conclusions. 16

References. 18

 

 

 

 

 

 

 

 

 

 

Executive Summary

Intelligent applications are an emerging technology that promises to automate enterprise processes and operations while facilitating the making of pertinent organizational decisions that can inform business strategy. Intelligent applications would miniaturize artificial intelligence technology while leveraging its robust capabilities of powering smart applications and solutions for enterprises in an increasingly complex and highly competitive business environment. Therefore, the study undertaken aimed at understanding intelligent applications as an emerging technology that was poised to pervade many enterprises in the next five years, its benefits and limitations, and applications in business enterprises. To this end, a review of existing literature was undertaken using an internet search approach and the information derived from the publications summarized as findings. The study revealed that intelligent applications would be highly dependent on the advancement s in artificial intelligence and big data analytics, which were technologies that were undergoing rapid development and testing in the business environment currently. Although intelligent applications promised to provide start solutions to decision making and directing business strategy, their application may be limited by the unavailability of high quality and sufficient data and the accompanying cost of data management, and the lack of expertise in the existing business enterprises, which may reverse the gains made in cost effectiveness. Therefore, it was recommended that organizations begin embracing advance enterprise systems early to develop technological capacity among their human resource in readiness for intelligent applications in the near future.

 

 

 

 

Intelligent Apps

Introduction

Artificial intelligence is pervading many aspects of daily living including business by facilitating the automation of many human activities, with the enhancement of efficiency being a major advantage. The development of new technologies based on artificial intelligence is poised to dominate the strategic technologies trends in 2018 as observed by Gartner (2017). In their identification of the top 10 strategic technology trend for the year 2018, Gartner (2017) predicted that intelligent applications would pervade almost every applications, apps, and services in the coming years through the incorporation of artificial intelligence and machine learning. In their view, intelligent applications can transform the structure of workplaces and the nature of work therein by forming a smart intermediary interface between systems and people. Already, much development activity was being observed in the addition of business value using artificial intelligence through innovations in intelligent processes, advanced analytics, and advanced user experiences.

Intelligent applications is an emerging technology that is leveraging artificial intelligence advancements while availing automation to businesses thus promising them improved efficiency in complex environments. Intelligent applications promise to revolutionize various aspects of business due to their ability to utilize a large amount of data and algorithms. Intelligent applications will enable the performance of human-like activities such as speech and image recognition, communication, personal assistance and decision making in future. In addition, technologies related to machine learning and deep neural networks promise to facilitate the development of intelligent applications will continue to be developed with the aim of bridging the gaps found in business activities and undertakings.

The ensuing discussion interrogates intelligent applications as an emerging technology that is able to revolutionize the manner in which workplace activities will be undertaken in future. As such, the discussion will explain the intelligent applications and the benefits it would proffer to organizations. In addition, the limitations that an organization would encounter while adopting this technology, the kind of organizations that would be suited for this technology, and the challenges that the enterprise would experience while integrating intelligent applications will be discussed. Further, the companies in the United Arab Emirates that would be recommended to adopt intelligent applications is highlighted and justified.

To undertake this discussion, a comprehensive literature review I undertaken to elucidate the existing knowledge evidence related to artificial intelligence, big data and analytics, and packaged software that is currently being employed to develop intelligent applications and the innovations direction being pursued. To this end, an internet search for published peer-reviewed journal articles, organizational publications and expert opinions was undertaken using search engines like Google Scholar and Microsoft Academic Search. The key works employed to interrogate online databases included intelligent applications, artificial intelligence in business, machine learning in business applications, and automation of business using artificial intelligence among others. The information obtained is then discussed thematically under intelligent applications as an emerging technology, benefits and limitations of intelligent application to an organization, and the challenges of integrating intelligent applications in the enterprise. Finally a recommendation of the organizations, particularly those located in the United Arab Emirates, that are suited for intelligent applications is made and justified.

Literature review

Artificial intelligence

Artificial intelligence is one area of technological development that was pitted to permeate many technological products in the near future due to its ability to enable machines and computers imitate intelligently the behavior of human beings. Artificial intelligence is the application of computer systems to undertake tasks with the same ability and precision as a human would only with minimal or no human intervention. As such, artificial intelligence can be summarized as the emulation of human behavior by intelligent machines and computer systems. Although artificial intelligence was inspired by the development of an electronic brain back in the 1950s, research activities in this area have been reinvigorated recently as the understanding of neurons, advancements in mathematics and computer systems became cheaper yet highly advanced in computational speeds, data storage spaces and ability to handle large volumes of data. Currently, a frenzy to develop advanced programs and apply them in various human situations is underway such that many researchers were optimistic of the pervasiveness of computer systems that could undertake numerous business tasks and other vital human activities to the convenience of users.

Gartner (2017) predicted that the rapid advancements in artificial intelligence in recent years would see new products emerging by 2020 that would revolutionize organizational processes. The pervasiveness of artificial intelligence would be spurred by innovations in technologies such as machine learning, image and speech recognition, deep neural networks, and natural language and conversational capabilities (Gartner, 2017).

Machine learning

According to Domingos (2012), machine learning was the ability of computer programs to automatic learn from the data inputted to them. Intelligent machines are able to use data to learn new behavior without necessarily having to be programmed afresh. The learning process relies on the automatic evaluation of feedback to produce new or refined behavior in machines. The current trends in machine learning included data flywheels, the algorithm economy, and cloud-hosted intelligence.

The data flywheel

According to Briegel and De las Cuevas (2012), the data flywheel or the data network effects is premised on the incremental improvement of products through the leveraging of better algorithms built from increasing amounts of data. The rate of data increase is dependent of availability of digital data and the amount of cloud data storage as stipulated by Moore’s law, which posits that the amount data available globally doubled and the cost of data storage halved every 2 years. The availability of large amounts of data facilitated the development of better models of machine learning that had more features, Therefore, the ability to generate data of the highest quality advantaged services that leveraged their data flywheels while enabling the development of intelligent applications alongside. For instance, the data flywheel of Tesla had advantaged it over Google in the development of cars with autonomous drive because Tesla had accumulated driving data for 780 miles and adding an extra one million of data after every ten hours, compared to the 1.5 million miles worth of driving data accumulated by Google.

The Algorithm Economy

Algorithms were central to enhancing the usefulness of data and their combination and stacking enabled improved manipulation of data and subsequent extraction of meaningful insights. Increasing amounts of data were demanding more algorithms that would give meaning to the data in a situation that had invited the collaboration of engineers, researchers and organizations. Such collaboration enables the creation, sharing and reconfiguration of algorithmic intelligence thus creating an algorithm market place, which sets the foundations for an algorithm economy. The concept of an algorithm market place could be likened to an app marketplace in which different kinds of people are able to develop, distribute and sell applications independently and seamlessly (Gartner, 2017). As such, the development of intelligent apps would benefit from the stacking of software developed by numerous individuals operating in an algorithm economy.

Cloud-Hosted Intelligence

Cloud technology had improved the ability to develop machine learning models and shortened the time required to create new models as well. With the easy access to cloud computing that was facilitated by application computer interfaces, the lowing of storage costs and the rapid expansion of storage space, development of machine learning models had been simplified greatly. Currently, a new model developer needs to develop a fragment of a customized code and insert it into models available in the cloud thus eliminating the need to develop machine-learning models from scratch. Frameworks for machine learning and deep learning whose codes were open source were readily available in the cloud and included NLTK, Torch, TensorFlow, Theano, Caffe, Scikit-Learn and Numpy (Gartner, 2017).        

Deep neural networks

Deep neural networks resembled neuron networks in the brain and can be considered as deep learning technologies. Various applications employing deep neural networks were already available commercially and were being employed to solve the processing of complex signals and recognize complex patterns. Specific examples of applications employing deep neural network technology that have been in existence since the year 2000 included facial recognition, weather perdition, data analysis for oil exploration, transcription of speech to text, and check processing in banks (Gartner, 2017).

Speech recognition

Speech recognition is a technology that is helping users interact with systems and machines without having they delve into the complexities of the machines and their operations. Speech recognition systems employ natural language processing to translate words in normal language and apply artificial intelligence to understand the meaning and intentions of the words as well. According to Deng and Li (2013), automatic speech recognition systems employ machine-learning models to enable a use to instruct a computer system using voice while attending to other activities simultaneously. Current research is heavily focused on integrating automatic speech recognition into the internet of things in which humans interact with regular daily equipments such as houses, cars, office equipment among others.

Artificial intelligence in intelligent applications

Intelligent applications are increasingly emerging as developers develop smart software that is able to automate processes in the office, at home and outdoors as well. Artificial intelligence is at the heart of the development of intelligent applications, which are able to perform analytics processes and automate business intelligence (Kowalski, et al., 2012; Ruth, 2017). For instance, SAPA HANA is a database management application that collects data from access points of digital equipment across a business premise and among employees and analyses it to provide trends and peculiarities almost instantaneously (Ruth, 2017). In addition, Kowalski and colleague (2012) presented a supply chain management project recommender that was a reasoning system able to compare the similarities between collections of knowledge that had been presented in natural language. The project recommender was an intelligent standalone application that was able to access expert knowledge that had been accumulated from previous projects and preserve new knowledge to avail it to new employees in an organization that undertook international logistics in supply chain management projects. This application was able to demonstrate the use of reasoning that was based on ontology and cases that could be employed in international project related to logistics. This application was able to converge artificial intelligence and information systems that were able to compare the similarities between two projects whose knowledge had been presented qualitatively in a natural language. The architecture of the application consisted of open source code to facilitate managers and developers, and a back-end that consisted of a servlet that was java-based and run in a container.

Intelligent and virtual personal assistants were areas of technological development that were leveraging the existing machine learning capabilities and natural language interactions between systems and people, which had the potential to spurring innovations of natural interaction applications (Canbek & Mutlu, 2016; Hill, Ford & Farreras, 2015; Imrie & Bednar, 2013). Hill, Ford and Farreras (2015) observed that conversation that was mediated by computers was already ubiquitous in merchandising catalogues and airline reservation applications, which had demonstrated that people experience minimal or no challenges in transferring their language skills to computer software. However, various studies have indicated the direction this innovation would be likely to follow. Imrie and Bednar (2013) provided experimental evidence of the application of natural language processing that could be employed to develop a smart virtual personal assistant (VPA). The four software tested aimed at providing personal companionship by using algorithms to replicate the interaction between human beings that was as accurate as possible. The smart software were able to interact with users by framing their responses based of the communication from users using the processing of natural language.

Indeed, a chatbox was an emerging technological advancement under development that leveraged huge data quantities and a natural language algorithm to learn language skills from human input (Hill, Ford & Farreras, 2015; Imrie & Bednar, 2013). Such as program could be trained to function as a virtual personal assistant that could respond to a users natural language input. As such, the programs were able to demonstrate the possibility of providing emotional interactions that was either local based of simulate, which could be employed in the virtual personal assistants of the future. Imrie and Bednar (2013) observed that the future of this technology was premised on the ability to generate metadata and linking it to provide outputs that were contextual during the interaction between the program and the user. Hill, Ford and Farreras (2015) observed that Cleverbot was perhaps the most advanced program with close-to-human conversation abilities. However, Imrie and Bednar (2013) were of the opinion that advancing computer-mediated communication software required that the new program be able to create metadata by forming links between data it is given and provide contextual outputs during the interaction that the user finds useful. The programs named Siri, Hal, Kari, and Watson were able to demonstrate the progress that had been made in the machine learning capabilities of applications, at least in the acquisition of natural language that is accompanied with a level of emotional interaction. At least, Hill, Ford and Farreras (2015) felt that the internet and cloud computing was instrumental in the advancement of applications employing natural language interaction.

Canbek and Mutlu (2016) presented a more advanced form of virtual personal assistant that not only employed audio input but also visual and contextual information to provide responses in natural language as well as make recommendations and perform actions that were almost human-like. The researchers cited Siri, Neo and Cortana, that had been developed by Apple, Google and Mostrosoft as examples to intelligent personal assistants that enploed natural language programming to facilitate the interaction between computers and people using natural language. Advancements in this area were dependent on the ability to leverage artificial intelligence to make programs that were smarter, more intelligent, predictive, and with the ability to respond to lengthy and complex requests from their human users.

The internet of things (IoT) was spurring innovative applications of artificial intelligence and big data that would facilitate the development of intelligent applications in future (Arsénio, et al., 2014; O’Leary, 2013). According to Arsénio and colleague (2014), intelligent sensors were already in deployment in the energy sector in the form of autonomous programs that were able to control energy harvesting applications. On the other hand, O’Leary (2013) observed that stock exchanges were already deploying artificial intelligence in the handling of the enormous amounts of data emanating from rapid and voluminous financial transactions. Artificial intelligence in stock trading had enabled the recognition of complex data patterns and learning from these patterns to make swift decisions that were computer-based. In his opinion, O’Leary (2013) has asserted that artificial intelligence-based systems were predominating humans in stock trading floors and increasing the velocities of the financial transactions undertaken therein. In this aspect, artificial intelligence was able to facilitate the capturing and structuring of big data, and when combined with analytics, artificial intelligence was able to provide valuable insights that could be used to make rapid decisions that were accurate. For instance, Google had developed MapReduce and used it to create scalable applications. The map function was able to convey numerous small problems to computer nodes while the reduce function undertakes the combination of the solutions derived from the diverse sub-problems. As such, the ReduceMap application was able to employ a bunch of computers to undertake numerous activities that involved machine learning and data mining. However, O’Leary (2013) projected that while major advancement had been realized in the area of text an audio data for natural language applications, the next horizon for innovators was to develop casual machine learning and natural visual interpretations abilities that leverage artificial intelligence.

Report Body

Drivers of intelligent application development

From the literature reviewed, the rate of development of technologies surrounding intelligent applications was evidence of the imminent permeation of artificial intelligence in the workplace and at home as the major driving force. Many researchers and technology experts believed that with the current rate of technological development, intelligent application would be commonplace at the workplace within the next five years with the innovative trend moving towards the development of intelligent things. For instance, Gartner (2017) 85 % of the interactions of organizations with their customers would be handled y intelligent application rather than human by 2020, with customer digital assistants being able to interact and provide services to customers based on their facial and voice recognition abilities. This optimism emanated from the high intensity of research and development activities that was being exhibited by the forerunners in the technical industry such as Google as illustrated in figure 1.

 

 

Figure 1. The number of projects using artificial intelligence at Google

Source: Kiser (2016).

Intelligent applications were exciting because they leveraged the already entrenched mobile technologies that had disrupted lives in all spheres and information explosion and the internet that had delivered advanced knowledge to the fingertips of users of technology. Therefore, adoption of intelligent technologies in the existing habits of people would not only be seamless but also welcome because people were already applying applications to monitor calorie intake, physical activity, navigation, budgeting, and other regular daily activities. Another trend spurring the development of intelligent applications was the cloud technology that had greatly reduced the price of data storage while enlarging the storage space as illustrated in figure 2.

 

Figure 2. Changes of data storage price and size between 2010 and 2015

Source: Kiser (2016).

The advancements in cloud technologies had not only changed the manner in which applications are developed but also revolutionized the way they are utilized and managed. Specifically, software models could be stored in the cloud in open source codes enabling collaborative development of customized applications without having to begin the process from scratch or expending many resources in the development process. In addition, the cloud offered a storage alternative that could be shared easily thus eliminating the burden of having to store or run the applications locally on devices or engaging managers of application infrastructure. All one needed was to have connectivity to the cloud to be able to run an application on any enabled device. Therefore, the cloud technology was vital to the advancements in intelligent applications which are more complex to develop, and bulkier to store and run.

Organizational benefits and challenges of intelligent applications

Intelligent applications may be employed in various organizations, particularly those that relied on data to make decisions regarding their processes and to achieve certain levels of performance. Organizations in the retail industry such as hypermarkets, in the tourism and hospitality industry such as restaurants, hotels and resorts, in the financial industry such as banks and stock traders generate and rely heavily on data from their customers or the activities and processes they undertake. For instance, the data obtained from the stock market can help a stock-trading firm advice its clients on investment decisions without having human interaction such that the organization can handle many clients efficiently depending on their investment preferences. In the United Arab Emirates, companies such as EFG-Hermes and Mena Corp Financial Services LLC are good candidates for enterprise intelligent applications due to the expansiveness of the brokerage services and the large number of clients and transactions they engage.

In addition, a retail outlet could employ intelligent applications to manage its customer relations by using customer data accumulated from various access points including their online shopping behavior and preferences. This can help the organization up-sell and cross-sell merchandise thus increasing sale revenues in the end. Souq and Namshi are retail outlets in the UAE that would from intelligent applications because of the large number of customers, their geographical dispersion and the wide variety of products they stock, that would provide enormous amounts of data that needs to inform business strategies.

Intelligent applications present these organizations with various benefits and challenges as well. Intelligent applications promises firstly, to deliver time as a major organizational resource advantage that would be manifested the reduced application development period and the increased performance speed of the smart applications. Secondly, intelligent applications promised the benefit of data storage space because they would be run mainly from the cloud. Thirdly, intelligent applications promised to enhance the organizational efficiency by the automation of complex organizational process.

However, adoption of intelligent application could be challenged by the lack of suitable human resource or organizational mindset that is required to facilitate the development and employment of the new technology in their organizations. A skill mismatch would need investment in human resource either through professional development or through engagement of talented and knowledgeable individuals. In addition, the quality and quantity of data availed to the intelligent system would influence the performance of the applications considering that the intelligent applications needed much high quality data to be able to learn the possible trends and behavior to identify and draw conclusions. For intelligent applications that have a predictive function, the data availed needs to be balanced and representative to avoid the adoption of bias during the application of the application. Further, the accurate discernment of the return on investment (ROI) of adopting the intelligent applications may pose a challenge to organizations that may have a difficulty with the articulation of the organizational objectives or expected outcomes. Evaluation of the performance of the intelligent system requires an accurate identification of the key performance indicators and procedures, which in turn would facilitate the optimization of the system. Finally, the affordability of enterprise intelligent applications would be unaffordable to new startups and small businesses considering that expertise such as data scientists were expensive to higher and maintain. In the United States, a data scientist could earn as much as 144,000 dollars making the employment of a data team a heavy organizational investment (King & Magoulas, 2015).

Conclusions

Intelligent applications are poised to permeate many organizations in various industries, particularly the organizations that generate huge quantities of data to enable the enterprises use the data to make pertinent business decisions. This trend will be driven by the reduction of cost and enlargement of data storage space presented by the cloud technology. intelligent applications shall leverage crowd-sourced software development to hasten the application development path from conception to commercialization, with minimal costs to the organizations. Therefore, intelligent applications provide a cost effective avenue of automating organizational processes while expanding the application of artificial intelligence to create smart applications. Enterprises that will embrace intelligent applications will benefit from enhanced and swift decision making capabilities, and improved business operational efficiencies. However, enterprises have to contend with the steep learning curve and skill upgrade required by the workforce and particularly the individual charged with the management of data generated in their organizations, which may require heavy financial investment initially. In addition, organizations will require to accumulate substantial amounts of data to be able to benefit from the intelligent applications. Therefore, it is recommended that organizations begin automating their organizational processes with existing technologies in readiness to adoption of intelligent applications when they become ubiquitous in the next five years, in a trend that appears irreversible. This way, organizational can begin consolidating their knowledge and human capital well in advance to ensure that they are not left out, which may compromise their competitiveness in future. Nonetheless, further development in intelligent applications should be focused on the development of mobile devises that can run such applications cost effectively such that the overall cost of technology implementation can be lowered to accommodate small business enterprises and startups.

 

 

 

 

 

 

 

 

 

 

References

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Briegel, H. J., & De las Cuevas, G. (2012). Projective simulation for artificial intelligence. Scientific Reports2, 1-16.

Canbek, N. G., & Mutlu, M. E. (2016). On the track of Artificial Intelligence: Learning with intelligent personal assistants. Journal of Human Sciences13(1), 592-601.

Deng, L., & Li, X. (2013). Machine learning paradigms for speech recognition: An overview. IEEE Transactions on Audio, Speech, and Language Processing21(5), 1060-1089.

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM55(10), 78-87.

Gartner (2017). Gartner identifies the top 10 strategic technology trends for 2018. Gartner. Retrieved 1 November 2017 from https://www.gartner.com/newsroom/id/3812063

Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior49, 245-250.

Imrie P, and Bednar P. (2013). ’Virtual Personal Assistant’, in: Martinez M. and Pennarolaecilia F. (editors). ItAIS 2013. Proceedings of 10th Conference of the Italian Chapter of AIS, ’Empowering society through digital innovations’, Università Commerciale Luigi Bocconi in Milan, Italy, December 14th, 2013.

King, J. & Magoulas, R. (2015). 2014 Data science salary survey: tools, trends, what pays (and what doesn’t) for data professionals. O’Reilly Media.

Kiser, M. (2016). Machine learning trends and the future of artificial intelligence. Retrieved 1 November 2017 from https://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html/

Makadia, M. (2017). Why is it difficult for businesses to adopt artificial intelligence? Business 2 Community. Retrieved 1 November 2017 from http://www.business2community.com/tech-gadgets/difficult-businesses-adopt-artificial-intelligence-01787000#BzXOFRZsicI5EL8C.97.

O’Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems28(2), 96-99.

Ruth, J. P. (2017). 6 examples of AI bin business intelligence applications. Tech Emergence. Retrieved 1 November 2017 from https://www.techemergence.com/ai-in-business-intelligence-applications/.

 

 

 

 

 

 

 

 

 

 

 

 

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