Info563 Paper - Explain & Analyze Emerging Technology.docx - Artificial Intelligence(AI is the ability of a computer or machine to simulate human

Info563 Paper - Explain & Analyze Emerging Technology.docx...

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Unformatted text preview: Artificial Intelligence (AI) is the ability of a computer or machine to simulate human intelligence. It is dependent upon three elements: massive amounts of data, sophisticated algorithms and high performance parallel processors (Bookbinder, 2017). Utilizing these elements, computer systems learn to identify and classify input patterns, probabilistically predict and operate unsupervised. AI is classified into two fundamental groups – applied and generalized. Applied AI is commonly defined as an application of AI that enables a high functioning system to replicate, and perhaps surpass, human intelligence for a dedicated purpose. Generalized AI is an application of AI that enables general purpose systems to think with an intelligence comparable to that of a human mind. It is intended to demonstrate understanding and reasoning skills with a breadth and depth of knowledge that allows it to easily transverse between vastly unrelated topics and use cases just as humans can (Young, 2017). Generalized AI has led to the development of machine learning and deep learning. Both machine and deep learning use neural networks to classify information the same way a human brain does. Neural networks can be taught to recognize, for example, images and classify them according to elements they contain. Essentially, they work on a system of probability – based on data it is fed, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables learning – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future (Marr, 2016a). Deep learning differentiates itself by focusing even more narrowly on a subset of machine learning tools and techniques and applying them to solving any problem that requires thought – human or artificial. Because deep learning is focused on further developing neural networks, they become what are known as Deep Neural Networks (DNNs) – logic networks of the complexity needed to deal with classifying datasets as large as, say, Google’s image library (Marr, 2016b). Some examples of how machine and deep learning are being used today are the navigation of selfdriving cars, precision medicine and fraud detection. Disruptive Innovation Theory Over the past year, AI has transformed at a rapid pace. Machines are now capable of processing and analyzing data sets larger than we once thought possible. This can be attributed to a significant increase in processing speeds, the progression of machine learning algorithms and diminishing technology costs. This has had a significant impact on industries that rely heavily on data, such as financial, retail and transportation industries. According to research, the financial industry spends approximately 50% of their time processing and collecting data. The ability of AI to significantly improve this processing time will inevitably disrupt this industry and those alike (HG, 2018). AI’s ability to simulate, and potentially surpass, human intelligence has transformed the way businesses in almost every industry operate. Thanks to cloud computing, massive amounts of data are able to be stored and processed at a fraction of the cost. This allows AI applications to interpret increasingly rising data volumes at manageable costs. Because of this, I believe AI represents a disruptive technology. At this time, I do not believe there are any technologies that might prove disruptive to AI. However, as computing costs continue to shrink and machine learning algorithms advance, the pace of innovation will accelerate. With that being said, new applications of AI have the potential to disrupt those already in existence. Innovation Attributes Relative Advantage AI has the potential to add value to every industry. From finance to healthcare, its ability to enhance efficiency and make faster, more informed decisions is transforming the way industries operate. Tasks previously performed by humans can now be performed by machines faster and more accurately. In addition, machines do not experience emotions or require breaks – allowing them to make rational decisions 24 hours a day, 7 days a week. Through the use of machine learning patterns in large data sets can not only be identified, they can also be acted upon. Lastly, AI is not prone to decision fatigue, allowing an infinite number of decisions to be made with inherent accuracy (Chou, 2018). Compatibility The evolution of cloud computing is allowing companies to develop and use AI applications at a manageable cost. The cloud can help provide AI with the information which they need to learn, while AI can provide the cloud with more data. In addition, increased processing power has made it possible for AI to execute tasks at speeds once unimaginable – at a cost that has fallen rapidly. Lastly, mobility and bandwidth ubiquity have made it possible for workers to access applications from most remote locations (Sallomi). All of these technological enhancements have made it easier for AI to be implemented. Complexity Despite AIs rapid growth, there is still a widespread lack of familiarity. According to West and Allen (2018), “When 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.” As companies continue to experiment with AI, effective ways to implement it will become clearer. Trialability Organizations in almost every industry have begun investing in AI tools and techniques to improve business operations. One of the most well known company’s utilizing AI is Amazon. With its algorithms refined more and more with each passing year, the company has gotten acutely smart at predicting what customers are interested in purchasing based on their online behavior. Because of reductions in technology costs, smaller companies now have the opportunity to utilize AI in a similar manner. Observability Despite its numerous benefits, adoption of AI remains relatively low. One of the biggest barriers to implementation is unclear examples of success among similar businesses (HG, 2018). For the most part, many of today's AI systems are merely advanced machine learning software with extensive behavioral algorithms that adapt themselves to our likes and dislikes. While extremely useful, these machines aren't getting smarter in the existential sense, but they are improving their skills and usefulness based on a large dataset (Adams, 2017). As companies gain an understanding of how AI can successfully be integrated into their business process, observability will increase. Technology S Curves The concept of AI has been around for the better part of half a century. One of the best known early applications of AI was in 1959, when Arthur Samuel, an IBM scientist, published a solution to the game of checkers. For the first time, a computer could play checkers against a human and win. Other early AI attempts used computers to mimic human behavior through rule-based methods, which applied logicbased algorithms to tell a computer to “do this if you observe that.” Today, logic based machine learning is being replaced with a data based approach. In a data based approach a computer learns directly from the data it is fed. Using this approach, answers to problems are achieved through recognition of patterns and common associations in the data (Bauguess, 2017). This new data based approach can be considered a disruption to the original logic based approach. For a technology to have a dominant design it must have a stable set of features and attributes. As far as AI software, there are currently many different products offered by a range of vendors. Big players such as Google and IBM continue to add major components in an attempt to be recognized as the dominant design for the future. Looking at AI applications, use cases are being developed in various settings with various stages of maturity and success (Roelands, 2018). This experimentation of different product designs indicates that AI is in the fluid phase of the S-Curve technology cycle. As computing costs continue to diminish and machine learning algorithms advance, the pace of AI innovation will accelerate. As companies are able to successfully implement applications of AI within their business processes, the adoption rate will begin increasing. In addition to observable success, the ability of companies to build trust among consumers will be critical in increasing the adoption of AI. Technology Diffusion Despite the number of benefits AI offers, its adoption rate remains relatively low. This can be attributed to competing investment priorities and unclear examples of success among similar businesses (HG, 2018). According to a recent survey conducted by MIT and the Boston Consulting Group, there is a significant gap that exists between an organization’s AI ambitions and its ability to execute on them, with just one in five companies merging AI solutions with their processes. Another survey conducted by Forrester found that 58% of business and tech professionals are researching AI systems but only 12% are currently using them (as cited in Gordon, 2018). Because of the small percentage of businesses actually utilizing AI, I would place it within the Early Adoptors phase of Roger’s diffusion model. Early adoptors are those who represent opinion leaders. They are aware of the need to change and comfortable adopting new ideas. Although AI is ready to use and has been shown to meet customer demands, there is still a great deal of skepticism among consumers. For example, a survey of more than 1,000 car buyers in Germany showed that only 5% would prefer a fully autonomous vehicle. With that being said, I believe trust must be built among consumers before AI can cross the chasm. Three factors will be crucial in gaining this trust: performance, the application performs as expected; process, there is an understanding of the underlying logic of the technology; and purpose, there is faith in the design’s intentions (Enkel, 2017). After thorough analysis, I believe AI is likely to emerge as a technology within the next decade. As automation becomes increasingly sophisticated and technology costs continue to diminish, AI will continue to transform the operations of almost every industry. The use of AI in determining customer preferences has transformed our shopping and entertainment experiences, with no signs of slowing down anytime soon. This application of AI has been proven successful by large companies such as Amazon and Netflix. This success, in conjunction with reduced technology costs, has encouraged small and medium retail businesses to begin adopting similar applications of AI. AI is capable of far more than predicting customer preferences. Large financial institutions have invested billions of dollars in AI over the last year in order to upgrade their systems, protect their data and improve mobile banking and other digital customer experiences. Banks are now utilizing AI for things such as fraud detection, data requests and fund transfers. The continued success of these developing AI applications will build trust among consumers which, in conjunction with diminishing costs, will encourage the adoption of AI in businesses large and small. Artificial intelligence is continuing to transform almost every industry, and the future remains seemingly limitless. Specifically, AI has had a significant impact on industries that rely heavily on data, such as the financial services industry. This is because the use of machine learning and deep learning to identify patterns is much easier to adopt in a data rich environment. Not to mention, half of the time spent by workers in the financial industry is allocated to collecting and processing data. AI is being utilized in the following aspects of financial services. Fraud Detection The financial services industry has long been plagued by the issue of fraud. A record 16.7 million US adults experienced identity fraud in 2017. The amount of fraudulent transactions, massive data breaches, and instances of identity theft continues to rise as hackers and fraudsters become more sophisticated. Artificial intelligence and its subsets of machine learning and deep learning make it possible to accurately process, verify, and authenticate identities at scale (Larson). For example, CitiBank has made a strategic investment in Feedzai, a leading global data science enterprise that works in realtime to identify and eradicate fraud in all avenues of commerce including online and in-person banking. Through its continuous and rapid evaluation of large amounts of data, Feedzai can conduct large-scale analyses. Fraudulent or questionable activity is identified and the customer is rapidly alerted. Feedzai is able to do so through the use of “machine-based learning” to evaluate “big data” and potentially fraudulent activities (Sennaar, 2018). Process Automation Robotic Process Automation (RPA) is one of the key drivers of process automaton in the financial services industry. Through the use of AI, RPA is evolving into a cognitive process automation that allows systems to perform more complex automation. JPMorgan Chase recently invested in a new technology called COiN that reviews documents and extracts data in much less time than it would take a human. This tool reviews about 12,000 documents (which, without automation, would require more than 360,000 hours of work) in just seconds (Maskey, 2018). Trading AI has become prominent in stock exchanges. High-frequency trading by machines has replaced much of human decision making. People submit buy and sell orders and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. That dramatically increases storage capacity and decreases processing times (West & Allen, 2018). As AI continues to evolve in the workplace, employees will have to evolve their own roles as well. Mundane tasks will be able to be performed by computers, freeing up time for employees to focus on more complex tasks. In an ideal world AI would only remain a supplement to employee roles, rather than a complete replacement. Time will tell the impact this great technology will have on the workforce. Citations Adams, R. (2017, January 10). 10 Powerful Examples Of Artificial Intelligence In Use Today. Retrieved December 12, 2018, from Bauguess, S. (2017, June 21). The Role of Big Data, Machine Learning, and AI in Assessing Risks: a Regulatory Perspective. Retrieved December 12, 2018 from Bookbinder, M. (2017, September 29). The Intelligent Trial: AI Comes To Clinical Trials. Retrieved December 11, 2018, from Chou, J. (2018, August 15). Artificial Intelligence Can Help Leaders Make Better Decisions Faster. Retrieved December 11, 2018, from Enkel, E. (2017, April 17). To Get Consumers to Trust AI, Show Them Its Benefits. Retrieved December 11, 2018, from Gordon, A. (2018, July 2). Can AI Really Improve Industrial Production Efficiency? Retrieved December 11, 2018, from HG, L. (2018, March 12). Why AI will be the World's most Disruptive Technology. Retrieved December 11, 2018, from Larson, G. (n.d.). How to use AI to fight identity fraud. Retrieved December 12, 2018, from Marr, B. (2016a, December 6). What Is The Difference Between Artificial Intelligence And Machine Learning?. Retrieved December 12, 2018 from Marr, B. (2016b, December 8). What Is The Difference Between Deep Learning, Machine Learning and AI?. Retrieved December 12, 2018 from Maskey, S. (2018, December 5). How Artificial Intelligence Is Helping Financial Institutions. Retrieved December 12, 2018 from Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017, September 6). Reshaping Business With Artificial Intelligence Closing the Gap Between Ambition and Action. Retrieved December 11, 2018, from Roelands, M. (2018, June 5). AI History and its Bright Future ahead. Retrieved December 11, 2018, from Sallomi, P. (n.d.). Artificial intelligence (AI) goes mainstream Revolutionizing businesses. Retrieved December 11, 2018, from Sennaar, K. (2018, December 12). AI in Banking – An Analysis of America’s 7 Top Banks. Retrieved December 12, 2018, from Waral, J., Rana, A., & Handrahan, S. (2016, September 29). Artificial intelligence: Disruption era begins. Retrieved December 12, 2018, from West, D. M., & Allen, J. R. (2018, April 24). How artificial intelligence is transforming the world. Retrieved December 11, 2018, from Young, R. (2017, September 12). Applied vs. Generalized Artificial Intelligence – What’s the Difference? Retrieved December 12, 2018, from ...
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