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Artificial Intelligence And Data Science Assignment Sample
Introduction of Artificial Intelligence And Data Science Assignment
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Artificial Intelligence and data science is the study of a field that deals with new scientific methods, techniques, and processes that are drawn from various domains such as computing, cognitive science, information science, and different statistics which helps in extracting knowledge from data that are structured and unstructured. The knowledge of Artificial Intelligence can be applied in business also for making intelligent decisions. Data Science and Artificial Intelligence focus on strategizing, collecting, analyzing, interpreting, and categorizing data. This also deals with the concept of deep learning and machine learning in order to solve various real-world and computation problems. The main target of Artificial Intelligence is of helping human capacities by assisting humans with the resolution of data with huge outcomes. Artificial Intelligence can help people carry a lot of information without much hard work (Anwer et al. 2022). Artificial Intelligence in organizations is utilized for improving and developing efficiencies in interaction, making considerable agreements, and for making forecasts in business that are dependent on information more than apprehension.
Barriers faced during implementing AI400
On one hand, the use of Artificial Intelligence and deep science has several uses and advantages, on the other hand, there are disadvantages too if it is not used effectively and properly. In business, PESTLE Analysis in the industry can demonstrate that. Pestle Analysis is also known as PEST analysis and is a concept that is used in marketing principles. This concept is used in industry as a tool for keeping track of the factors that affect the organization. PESTLE stands for Political, Economic, Social, Technological, Legal, and Environmental. Entrepreneurs and businessmen use these analytical tools for making decisions for their farms. The political component of Artificial Intelligence involves dangers while voting for the organization, calculating through AI, may cause a ripple effect mainly in issues which are legislative. If the AI cannot provide support to the potential misgivings, AI cannot be helpful in organizations merging, belief systems, and in implementing strategies. Artificial Intelligence in financial matters can be very helpful in solidifying information fully (Dangi et al. 2022). The algorithm of AI can track media headlines and has the ability to influence the financial market in the wrong direction if not used in the correct way. Although the concept of Artificial Intelligence implementation can save lots of time, it may cite hurdles too with the full digitization environment. Artificial Intelligence and deep science have a huge impact on the environment as the data centers need to stay cool, and the amount of heat produced because of it affects the climate. Other barriers which are faced by industries while implementing Artificial Intelligence are the Cultural barrier, Fear, Shortage of Talent, and lack of any strategic approach to AI adoption. The cultural barriers are all about resistance to any change that happened in the organization.
Human beings get habituated to a particular way of work, so they tend to avoid changes in the industry which may change the way of work and make the work to get completed efficiently and also can save time. With the implementation of Artificial Intelligence, decisions are mainly made through the algorithms in computers and decrease the human workforce this may give a feeling of fear and insecurity in the human mind that they are losing control over things. Most of the businesses face challenges while adopting AI is that there is a shortage of knowledge and talent for implementing digital transformation in the organization (Han and Liu, 2019). It is seen that even if the business organizations are aware of the fact of adopting the concept of AI they still lag to understand the importance of adopting a strategic approach from a particular standpoint. So, there is a need to overcome these barriers so that AI can make a huge impact on our society effectively.
Enablers during the implementation of AI
There are several enablers that can be considered while implementing Artificial Intelligence is that: Customer and Business benefits, Technology for processing data, AI ecosystem, AI micro services, skilled people and education, technology for processing data, data-copious amount of it, Governance, and regulation. The business and the customer's benefits are not just critical measurements of AI implementation but also it is about potential enabler which helps in the generation of the virtuous cycle. Data is a very important factor in Artificial intelligence implementation, data volumes are continuously growing over 25-fold worldwide in the past 10 years or more (Martínez-Álvarez et al. 2018). The growth in volume has helped in accelerating the data furthermore rapidly with increasingly advanced digitizing. In order to get an efficient Artificial Intelligence, the focus should not be always on the volumes, it should divert towards the data cleaning and being described well, which may be time-consuming for larger businesses. If the data are in large volumes then it requires transmission, processing, and storage capacity. The processing of data does not involve only transformation and moving of data but also it is about organizing the data, discovering, selecting, and extracting it for more purposeful use within the organization. Developing AI technology requires a large technology that is beyond moving, storing, and processing data. The platforms and tools of AI involve deep learning, machine learning, virtual agents, biometrics, speech recognition, and optimized hardware.
The trick which plays a role in with these various technologies is that there is a need to select the tools of the right combination in order to obtain the required work. The AI ecosystem is comprised of various complementary roles for collaborating with the partner for delivering intelligent systems (Pennington et al. 2020). At the foundation level, it is observed that data engineering skill is crucial to obtain and processing data is considered to be the primary need for any AI ecosystem. The visualization of data and analysis of it requires familiarity with the analytic skillset and the relevant software. It was found that there is a requirement for high-quality talent in sufficient numbers for driving, managing, and executing the activities for building, deploying, testing, and maintaining the components of Artificial Intelligence.
Opportunities in AI implementation
Artificial Intelligence offers several opportunities for the integration and development of business technology and processes. It can help in making virtual agents, machine learning, and data analysis. Every small business and large businessman dreams of maximizing their profits and tries to focus more on market strategizing. The businessman wanted to learn a lot about the activities of marketing so that they can ge3t the highest return out of their investments made. This analysis and monitoring take a lot of time, for this, there was an introduction to the concept of artificial intelligence in marketing solutions. The Artificial intelligence-enabled platform helps in managing a lot of operations related to managing the market (Silvia et al. 2021). The machine learning of AI helps in analyzing the live campaign data along with the sentiment analysis algorithm and then suggests a marketing activities distribution for better results of the analysis. AI techniques can also be used in tracking the competitors in the same field of business. It is crucial for any businessman to keep a track of what other competitors are doing. As the businessman are highly busy with their work so they use the concept of Artificial Intelligence in analyzing the outputs of their competitors. The AI tracks the competitors by checking different channels like social media, apps, and websites. They also track any slight change in marketing plans of the competitors like in PR activities or if any modifications happen in messages or any change in the price of the product (Simonian et al. 2018). Overall, Artificial Intelligence and deep science are very helpful for the businessman for their growth.
Overcoming problems in the industry by AI
The challenges which are faced by the Artificial Intelligence industry can be overcome by adopting a few steps: A clear strategy of the AI process before implementation, efforts in making the machine models product-friendly, and keeping a keen focus on labeling, quantity, and quality of the product, ensuring scalability. It is not always the right thing to think about the technology development but the focus should also be on the hurdles caused to the data and the infrastructure. Sometimes, the business owners and the team gets disconnected from the AI-based app and that may create a mess for this a clear strategy for implementing the AI process is needed (Virkus and Garoufallou, 2019). The challenge that is most prominent in the AI process is that the moving part of the system does not work in the same way that conventional or traditional systems do. So, there must be the adoption of the product evaluation which includes availability (Wang, 2018). Machine learning and deep learning involve a huge amount of data for functioning and most of the time the data are used for real-time monitoring and streaming, so for this problem, AI needs to reconfigure the company's data structure in a more organized way for the ease of doing business.
References
Anwer, M.H., Alsolai, H., Al-Wesabi, F., Mohammed Abdullah Al-Hagery, Manar, A.H. and Duhayyim, M.A. 2022, "Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science", Computers, Materials, and Continua, vol. 70, no. 3, pp. 6289-6304.
Dangi, D., Bhagat, A. and Dixit, D.K. 2022, "Emerging Applications of Artificial Intelligence, Machine learning and Data Science", Computers, Materials, and Continua, vol. 70, no. 3, pp. 5399-5419.
Han, H. and Liu, W. 2019, "The coming era of artificial intelligence in biological data science", BMC Bioinformatics, vol. 20(Suppl 22), pp. 1-2.
Martínez-Álvarez, F., Troncoso, A. and Riquelme, J.C. 2018, "Data Science and Big Data in Energy Forecasting", Energies, vol. 11, no. 11.
Pennington, D., Ebert-Uphoff Imme, Freed, N., Martin, J. and Pierce, S.A. 2020, "Bridging sustainability science, earth science, and data science through interdisciplinary education", Sustainability Science, vol. 15, no. 2, pp. 647-661.
Silvia, C., Tania, C., Wrembel, R. and Daniele, Q. 2021, "Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science", Information Systems Frontiers, vol. 23, no. 1, pp. 1-7.
Simonian, J., López de Prado, M. and Fabozzi, F.J. 2018, "INVITED EDITORIAL COMMENT: Order from Chaos: 0RW1S34RfeSDcfkexd09rT2How Data Science Is Revolutionizing Investment Practice1RW1S34RfeSDcfkexd09rT2", Journal of Portfolio Management, vol. 45, no. 1, pp. 1-4.
Virkus, S. and Garoufallou, E. 2019, "Data science from a library and information science perspective", Data Technologies and Applications, vol. 53, no. 4, pp. 422-441.
Wang, L. 2018, "Twinning data science with information science in schools of library and information science", Journal of Documentation, vol. 74, no. 6, pp. 1243-1257.
