Report on Big Data, Artificial Intelligence and Knowledge Management
Added on 2020-04-15
12 Pages4367 Words66 Views
Data Science and Big DataArtificial IntelligenceEnvironmental Science
|
|
|
HOW WILL BIG DATA, ARTIFICIAL INTELLIGENCE AND KNOWLEDGE MANAGEMENTPLAY OUT IN THE NEXT 3 YEARS? 1How will Big Data, Artificial Intelligence and Knowledge Managementplay out in the next 3 years?NameDate
HOW WILL BIG DATA, ARTIFICIAL INTELLIGENCE AND KNOWLEDGE MANAGEMENTPLAY OUT IN THE NEXT 3 YEARS? 2IntroductionInnovations in analytics, artificial intelligence (AI), digitization, and automation are creatingseveral productivity and performance opportunities for businesses and other industries such as agriculture, as well as the wider economy. Further, these technologies are reshaping the future of work and employment and how the most pressing challenges at present will be solved, including in medicine, agriculture and food security, and business. The business landscape has been reshaped significantly in the recent past, supercharging business performance and resulting in the emergence of new innovations in business as well as new forms of competitiveness. The technologies continue to advance, resulting in new waves of developments in robotics, AI, analytics, and especially, machine learning. Taken together, these technologies amount to a significant change and advancement in technical capabilities that will have profound implications for the economy, business, and in a broad sense, on the wider society. This paper discusses the projected advances on how the trends in AI, big data, and knowledge management in the next three years. Specially, this paper focuses on how these technologies will impact the fields of cancer treatment, their impact on decision making, agriculture, and banking. After discussing how the sectors will be impacted in the next three years, the paper will then track the trends and drivers in the context of the political, economic, social, and technological (PEST) framework, identifying and discussing the opportunities and threats for the concepts of big data, knowledge management, and AI. The paper then identifies the various uncertainties in light of the identified future trends and the PEST opportunities and threats. Finally, scenarios in the context of all issues discussed will be discussed for the two major identified uncertainties. Specifically, a scenario will be created in the issue of big data and its storage, as well as the associated costs and how big data is to be cleaned. How AI, big data and knowledge management will impact Cancer Treatment in the next three yearsAt present, one of the crucial areas for the survival of cancer patients through effective treatment is early diagnosis and detection of cancers. Research shows that screening in both high risk and healthy populations provides an excellent opportunity for the early detection of cancers andcreates greater opportunities and chances for successful treatment and cure of cancers (Schiffman, Fisher & Gibbs, 2015). However, at present, every screening test has its imitations, and improved methods of screening and managing knowledge for cancer is needed to attain better outcomes. Big data analytics and artificial intelligence offers much promise in this area. One area that has been signled out as a gap in effective cancer research and treatment is that most information hospitals
HOW WILL BIG DATA, ARTIFICIAL INTELLIGENCE AND KNOWLEDGE MANAGEMENTPLAY OUT IN THE NEXT 3 YEARS? 3record about patients is not being effectively used for better outcomes fr cancer patients (Guillen, 2017). cancer will be detected in far les shorter time in future using AI; present research shows that it is possible to accurately detect cancers in less tha a minute through the use of AI with very high levels of accuracy (Donnelly, 2017). Clinical decisions usually are made based on the results from the few persons that participate in these trials, usually just 3% of all patients so that the data collected about other 97% of patients is never used and this is an area big data, knowledge management (KM) and analytics can play a very huge role. The vast amounts of data collected on cancer patients can be productively used in enhancingtreatment, research and cancer patient outcomes by teaching machines AI to read and make sense ofall the notes doctors take about cancer patients and be stored in a central data base for research and information. So a breast cancer patient seeking treatment would prompt care givers to first check the cancer database to see what treatments were given to similar patients and their effectiveness anddecide on practically proven remedies for the present patient condition. Years of research has shownthat cancer is many different diseases (Guillen, 2017), (Kantarjian & Yu, 2015) with each form of cancer having its own causes and origins. As such, big data analytics and effective KM will in the near future (between 1-3 years) offer a better opportunity for personalized treatments, given the disease is also very personal for effective treatments. This will be done through genome analysis and sequencing. AI will also play a great role in the next three years in cancer treatment, considering current trends and research. Using algorithms, effective treatments for some cancers will be achieved through medicines research and development where information from healthy and cancerous tissues will be collected and an AI algorithm used to process the information and propose an effective treatment; this will work with big data analytics and KM. AI will be used increasingly to identify and interpret visual scans and identify healthy and cancerous tissue, using AI algorithms. The systems will increasingly learn how to identify potential cancerous tissues and using big data and KM, recommend the right course of action for the clinician for better outcomes (Gray, 2017). the development of drugs will effectively be done using big data and KM by enabling better targeted research based on individual and population data for novel treatments. Big data and KM will be used for real time monitoring of clinical quality measures, evaluating trends that will results in better outcomes, identifying patient groups that have similar characteristics but are anonymous, and better using EHRs (Donato, 2015). How AI, big data and knowledge management will impact Agriculture in the next threeyears
HOW WILL BIG DATA, ARTIFICIAL INTELLIGENCE AND KNOWLEDGE MANAGEMENTPLAY OUT IN THE NEXT 3 YEARS? 4Rising global populations, greater pressure on farmlands, new pests and diseases, and the challenges of global warming and climate change pose immense threats to agriculture and food security in the future. The agricultural industry is being taken over by digital innovations and advancements in adding value and enhancing production. Cognitive computing will become the biggest disruptive technology in agriculture, as big as the green revolution (Irimia, 2016). Technologies such as the IoT (Internet of Things) will be greatly used in agriculture for collection and transmission of real time data and information. Machine learning capabilities will be applied to sensors and drone data to transform management systems in real AI systems. Cognitive IoT technologies will be used to enable correlation of large amounts of unstructured and structured data form several sources; for instance social media posts, historical weather data, soil information, research notes, information from marketplaces and images contain vast amounts of information that can be extracted and used to give policy makers and researchers greater insights to improve food security. AI will be used for image recognition and to give insights such as predicted weather patterns, even pest infestations. AI and big data will be used increasingly to replace workforce, in monitoring, watering, an nutrient application, even harvesting of crops in future while farmers will increasingly use chatbots to obtain accurate and timely information (Irimia, 2016). Further, big data will be increasingly used in smart farming to ensure greater yields and better returns for farmers, as well as better quality products that guarantee health and food security. ICT will be used more in the cyber physical farm cycle of management through the incorporation of cloud computing and the IoT to leverage these benefits. There will be greater use of robots and AI in farming leveraging technologies such as big data and cloud computing. The various discrete devices such as weather sensors, satellite images, drone images, and chatbots as well as research repositories will together form large volumes of information (big data) that will be used for decision making, policy development, and resource allocation and utilization for better outcomes (Wolfert, Ge, Verdouw, & Bogaardt, 2017). Big data will also be used increasingly fro crop protection and weed control where the vast volumes of data collected on farming will be better analyzed, modeled, and shared and used for effective crop protection and weed control to improve yields and returns for farmers. By combining distributed information and data sources into a central big data repository and applying tools such as AI and machine learning and big data analytics as well as knowledge management, better insights can be obtained from such data and e used for better agricultural practices (Wolfert, Ge, Verdouw, & Bogaardt, 2017).
End of preview
Want to access all the pages? Upload your documents or become a member.
Related Documents
Recent trends in business analyticslg...
|6
|831
|9
Artificial Intelligence & Human Resourcelg...
|16
|846
|25
Impacts of Artificial Intelligencelg...
|6
|1106
|250
Artificial Intelligence And Robotics Applicationslg...
|25
|1141
|37
Role of Artificial Intelligence in Healthcare Industrylg...
|11
|2620
|96
Literature Review: Machine Learning in Cancer Diagnoseslg...
|13
|4745
|7