Artificial Intelligence and Machine Learning

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About This Subject

Artificial Intelligence (AI) is a broad interdisciplinary field of study combining science, mathematics, neuroscience and more. AI refers to the development of programs capable of intelligent behaviour. The history of AI began with the creation of the electronic computer by mathematician Alan Turning, who’s theory of computation suggested that by shuffling 0 and 1 a machine could simulate any conceivable act of mathematical deduction. These technological advancements, combined with Neurology Information Theory and Cybernetics inspired the possibility of building an electronic brain. Through continuous efforts and decades of research, AI evolves. Computers are now capable of solving word problems in algebra, proving logical theorems, beating humans in checkers and even speaking English. AI can even accomplish tasks like schedule planning, and makes systematic choices for itself. The main goal of AI, is to create a software that thinks much like a human brain. Just like the human brain learns from its environment and makes decisions based on experience, so too does AI machine learning try and make sense of its environment in order to make decisions through artificial neural networking.

Machine Learning: Machine learning is a field of computer science that gives computer systems the ability to "learn" with data, without being explicitly programmed.

Artifical Intelligence: Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.

Performance Enhancement of Information Retrieval via Artificial Intelligence (BRENNOR JACOBS)

Topics

Analytics, Machine Learning And The Internet of things

Due to our massively connected world, combined with low-cost sensors and distributed intelligence, a popular question asked by scholars today is whether or not businesses and current organizations will be able to adapt and evolve quickly enough to maintain their place in this new competitive landscape.

Articles

Earley, S. (2015). Analytics, machine learning, and the internet of things.. IT Professional, 17(1), 10-13 (JOSHUA ERDMANN)

Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors (BRENNOR JACOBS)

Braun, A., Zweck, A., & Holtmannspötter, D. (2016). The ambiguity of intelligent algorithms: job killer or supporting assistant. (JOSHUA ERDMANN)

Parnas, D. L. (2017). The Real Risks of Artificial Intelligence: Incidents from the early days of AI research are instructive in the current AI environment. (JOSHUA ERDMANN)

Machine Learning and AI Impacting Modern Transportation

Modern Transportation and more specifically self-driving autonomous vehicles have been an important topic in the past decade. More and more researchers are looking into the possibility of self-driving cars and trying to make a dream a reality.

Articles

Greengard, S. (2017). Gaming machine learning. Communications of the ACM, 60(12), 14-16. (JOSHUA ERDMANN)

Machine Learning affecting the Work Force

Articles

Brynjolfsson, E. and Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), pp.1530-1534.

Antons, D. and Breidbach, C. (2017). Big Data, Big Insights? Advancing Service Innovation and Design With Machine Learning. Journal of Service Research, 21(1), pp.17-39.

Popenici, S. s., & Kerr, S. s. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research & Practice In Technology Enhanced Learning SUNNY ANSARI

Acoustic Modeling in Speech Recognition

Speech recognition software has become faster, more accurate, and as a result more prevalent. The technology is literally everywhere, constantly following consumers through their phones, cars, watches, and every other smart technology they've integrated into their lives. These articles will explore how computers are increasingly capable of understanding human language in context, thinking and learning in human language, and answering complex questions in human language.

Articles

Dahl, G. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82-97. (WARREN BUZANKO)

Variani, E., Bagby, T., Mcdermott, E., & Bacchiani, M. (2017). End-to-End Training of Acoustic Models for Large Vocabulary Continuous Speech Recognition with TensorFlow. Interspeech 2017. (WARREN BUZANKO)

Books

Author, A. A. (2018). Title of work: Capital letter also for subtitle.

Enhancing Digital Marketing Strategies with Machine Learning

Artificial Intelligence is capable of discovering themes and often overlooked aspects of consumer behaviour in order to develop better market design strategies. These articles will explore how utilizing big data and AI can help business in different markets make more timely, well informed, and capable decisions.

Articles

How Artificial Intelligence and Machine Learning Can Impact Market Design (BRENNOR JACOBS)

Convergent Innovation in Food through Big Data and Artificial Intelligence for Societal-Scale Inclusive Growth (BRENNOR JACOBS)

Li, S., & Li, J. Z. (2010). AgentsInternational: Integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making. Expert Systems With Applications, (3), 2580. (WARREN BUZANKO)

Lainez, J. M., Reklaitis, G. V., & Puigjaner, L. (2010). Linking marketing and supply chain models for improved business strategic decision support. Computers And Chemical Engineering, 34 (12), 2107-2117. (WARREN BUZANKO)

Machine Learning and Sentiment Analysis

Sentiment analysis is used in digital marketing in order to gauge consumers reactions towards a brand through computer-mediated communication. While it has been done manually for years, algorithms are being developed to accurately portray consumers comments via online mediums with the usage of machines to amalgamate the data.

Articles

Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O. and Provost, F. (2013). Machine learning for targeted display advertising: transfer learning in action. Machine Learning, 95(1), pp.103-127.

Dhaoui, C., Webster, C. and Tan, L. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), pp.480-488.

Artificial Intelligence and Its Impact On Machine Vision

Hardin, W. (2017, April). Artificial intelligence and its impact on machine vision SUNNY ANSARI.

The Future of Artificial Intelligence

The future of artificial intelligence as predicted by experts within the field.

Articles

Berke, A. (2016). The future of artificial intelligence. Strategic Studies Quarterly, (3), 114. (MACKENZIE STENGER)

Popenici, S. s., & Kerr, S. s. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research & Practice In Technology Enhanced Learning, 12(1), 1-13. doi:10.1186/s41039-017-0062-8 (MACKENZIE STENGER)

Artificial Intelligence and Machine Learning Effects on Employment

As artificial intelligence continues to grow and become integrated within the workforce, employment and jobs are evolving as well. These articles look at how advancements in artificial intelligence will affect the employment rate.