Solving Business Problems with Artificial Intelligence and Machine Learning

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Machine learning (ML), a branch of artificial intelligence (AI), enables computers to make predictions and decisions from data without explicit human instructions, distinguishing it from traditional programmed software. It automates decision-making more swiftly and efficiently by learning from experience, thus improving over time. A particular focus within machine learning is deep learning (DL), which uses complex neural networks to enhance problem-solving capabilities (see Figure 1). At the core of machine learning lies the algorithm — a mathematical formula that processes inputs to produce outputs and adaptively updates its understanding of data. Additionally, data science often intersects with machine learning; it involves data preparation and analysis and frequently contributes to training machine learning models, although it encompasses more than just machine learning.

Figure 1: Relationship between AI, ML, DL and Data Science

The Data Hierarchy and Relationship

The Data, Information, Knowledge, Wisdom (DIKW) hierarchy is a framework to conceptualize the transformation of data into progressively more useful forms which can be conceptualize through the The Input-Process-Output (IPO) model providing a structured approach to understanding how data is transformed into wisdom in the DIKW hierarchy (Figure 2):

Figure 2. The DIKW Hierarchy

Input: Raw data is collected which is often not very useful in its raw form until it is combined with other data and organized in a meaningful way.

Process: Data is processed into information and refined into knowledge through analysis and integration with contextual insight

Output: Knowledge is applied and contextualized over time to develop wisdom which is actionable intelligence — combined with experience and insights that suggest how the information should be dealt with, that is knowing what to do based on the data provided.

Applied AI and ML

The field of artificial intelligence (AI) and machine learning (ML) offers practical applications in business context:

1. Application Over Theory: While AI is fundamentally an academic discipline with much ongoing research, the focus for business professionals is primarily on applying AI and ML principles to solve practical problems. This means that understanding the theoretical aspects is important, but the priority is to leverage this technology to create products and solutions that meet specific organizational needs and customer expectations.
2. Project-Centric Work: AI/ML solutions are typically developed as part of broader projects with defined goals. This emphasizes the importance of contextualizing individual work within the larger objectives of the organization. Understanding how the work fits into and contributes to the overarching goals of a project can enhance the relevance and impact of the solutions developed.
3. Team Collaboration and Communication: AI/ML projects often require collaboration among various team members who might have diverse expertise. Effective communication is crucial in such environments to ensure that ideas and data are properly shared and integrated across the team which would include investigating user requirements feasibility, consulting industry experts, participating in community, communicating results to an audience amongst others. Good communication skills are therefore essential for machine learning practitioners.
4. Exposure to Diverse Applications: Gaining experience across a variety of AI/ML applications, even those outside one’s immediate industry, is beneficial. This exposure not only broadens a practitioner’s skill set but also enhances their attractiveness to potential employers. Diverse experience can lead to more innovative solutions by enabling practitioners to draw on a wider array of ideas and methodologies.
5. Creativity and Problem Solving: A broad knowledge base can stimulate creative problem-solving and prevent the limitations that come with a narrow focus. Exploring different applications of AI and ML can provide new perspectives and techniques that might be adaptable to one’s primary field of work.
6. Understanding Stakeholder Influence: A project stakeholder is anyone who has a significant interest in the success of a project or who participates in its execution. Stakeholders can be internal (within the organization) or external, and their involvement can significantly influence the project’s outcome and its likelihood of success. They have varied roles and responsibilities, impacting the project in multiple ways.
7. Addressing Ethical Risks: When developing AI/ML applications, it’s important to address ethical risks that may not be immediately apparent. You’ll need to clearly identify these risks and propose ways to mitigate them. For example, in the case of self-driving cars, safety is a major concern. It is essential to explain how your solution reduces the potential for harm, ensuring that the application is developed with ethical considerations and safety as priorities. Considerations like privacy, accountability, transparency, explainability, fairness, non-discrimination, and safety and security should be established.

These insights underscore the importance of practical, hands-on experience in AI/ML and highlight how integral soft skills like teamwork and communication are to succeeding in this field. They also suggest that being open to learning from various sectors and applications not only enhances professional development but also fosters innovative thinking.

Solutions for Commercial Problems

The following table lists some common problems in commercial organizations, with examples of how AI/ML can solve those problems.