A Comparison of Machine Learning Applications Across Professional Sectors

Joffrion, Allison; Bailey, G.; Pearson, Megan

doi:10.2139/ssrn.3174123

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Artificial intelligence is a branch of computer science that "focuses on machines that learn" (FRANK et al, 2017 p. 48). AI is defined by its function, which means "the system actually makes the decision" (BOOBIER, 2018, p. 37), because AI can formulate hypotheses, conduct diagnoses, recommend treatments (BAILEY et al, 2018). AI makes a machine "make itself" and with machine-learning software, they improve over time.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Using smartphones, users can regularly access information about their health (BELL, 2014). Machine learning has been applied in oncology and mining Electronic Health Records (EHRs) (BAILEY; ALLISON; PEARSON, 2018). Decision tree learning is also used in making medical diagnoses in some cancer cases or congenital heart disease (Kumar & Kalra as cited in BAILEY et al, 2018).…”

Section: Uses Of MLmentioning

confidence: 99%

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The product manager in the artificial intelligence world

Singh¹

2019

Product

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Cloud applications, artificial intelligence, machine learning, data insights, rapid prototyping, design thinking, and faster decision making are becoming more and more significant in the daily life of the product manager. Basic physical, digital, and biological technologies are intersecting to create large scale system changes, altering the very fabric of our social system. More than ever, the product manager is faced with the challenges of proactively designing the systems of the future. This study will deal with the question: how can the product manager thrive in an artificial intelligence machine learning world?. With the advancement in machine learning, products can now significantly differ from the traditional style of product designs. One widely known example is how Google answers our questions with the best possible answers through ranking. Similarly, Netflix or Spotify suggest media to customers using the process of recommending, giving users things they may be interested in, without them explicitly searching. On the other hand, Gmail groups an email as spam or not spam through classifying. With these many possibilities, today's product manager must understand the actual problem that must be solved to grow customer value systems in line with the company's goals. And while product managers inspire through vision, decisions must roll downstream and be implementation-based like an assembly line. More than ever, it has become crucial for a product manager to set and manage the anticipations of users, gather measurable feedback frequently, communicate meticulously to engineers, and make sure products logically progress with market shifts.

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Section: Artificial Intelligencementioning

confidence: 99%

Section: Uses Of MLmentioning

confidence: 99%

The product manager in the artificial intelligence world

Singh¹

2019

Product

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Machine Learning Applications in Surface Transportation Systems: A Literature Review

Behrooz

Hayeri

2022

Applied Sciences

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Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements.

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A Comparison of Machine Learning Applications Across Professional Sectors

Cited by 2 publications

References 17 publications

The product manager in the artificial intelligence world

The product manager in the artificial intelligence world

Machine Learning Applications in Surface Transportation Systems: A Literature Review

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