Attention Business Owners, Decision Makers, and Professionals
Fundamentals
The incorporation of AI has become a crucial element in a multitude of solutions, a reality that is apparent in every product, whether digital or physical, that a company produces. Consequently, it has become increasingly evident that proficiency in developing, creating, utilizing, or at least understanding AI is essential for employment in the fields of applied and natural sciences, as reflected in job advertisements and related platforms. Despite considerable progress in AI and its crucial significance, misunderstandings and improper uses of AI capabilities are widespread. Moreover, there is a lack of a clearly defined path for professionals wishing to delve into AI development. This article seeks to address these issues by first offering a clear definition of AI from both theoretical and practical perspectives, and then delineating a definitive pathway for students, professionals, and enthusiasts eager to pursue a career in AI development.
AI Definition- Theoretical and Practical Perspectives
The acronym AI stands for two terms: ‘artificial‘ and ‘intelligence.’ The first term refers to the act of imitation, meaning something made or produced by humans rather than occurring naturally, typically as a replica of something that exists in nature. The second term denotes the capacity to learn and comprehend, specifically the ability to acquire and utilize knowledge and skills.
When AI acronym is linked to machines, it prompts the question of whether both terms accurately describe the machine. For the first term, it’s conceivable to agree that machines can artificially replace certain things. For example, a heating device might serve as an artificial substitute for the sun, a camera may function as an artificial version of the eyes, and sensors could be seen as artificial analogs to human senses.
In the second term, it is reasonable to assert that machines may never acquire or utilize knowledge in the same manner as humans. However, given their computational power, one could argue that machines can process vast amounts of data at speeds unattainable by humans and produce results based on that processing. This constitutes an artificial form of human intelligence, but it does not mean that the machine possesses true intelligence.
This comprehensive explanation of the AI acronym may shed light on the root of the confusion and the misuse of AI capabilities among professionals in the field. The media also significantly contributes to this misunderstanding. Headlines in technology news often highlight the ‘intelligence’ aspect of AI. Specifically, they assert that recent advancements in AI have enabled models to make near-perfect predictions, suggesting that an artificial brain, akin to a human one, could be created and embedded in a robot that would behave like a human being (Cremer & Kasparov, 2021; Egan, 2024; Gudilko, 2023; Urwin, 2024; Dahman, 2023).
Consequently, there is a risk that AI could replace humans in many tasks. The author of the article agrees to some extent and believes that from a technical standpoint, the advancements in AI and machine computational power make it conceivable that tasks once thought to be exclusively human can now be performed by machines.
The discussion in this article focuses not on the pros and cons of such a transformation but rather on its technical aspects. Specifically, it aims to:
- Emphasize the importance for business owners and decision-makers to grasp what is happening behind the scenes of AI systems, not necessarily from a technical standpoint but at least conceptually. This understanding can prevent them from making ill-informed decisions based on the belief that AI systems will provide a competitive edge, when in reality, it may lead to an over-reliance on systems controlled by the original developers of the AI model.
- Assist professionals with an applied science background who aspire to become AI developers by guiding them on the correct path to develop genuine skills, rather than merely replicating what is proposed by existing models and frameworks
Business owners and decision-makers considering the adoption of AI systems must grasp two critical concepts: firstly, AI systems are essentially mathematical functions that process input data and produce an output. Secondly, the quality of the output is largely dependent on the input data. This highlights a rigid aspect; there is no inherent intelligence, merely a technical process. Misunderstanding these leads business owners to assume that hiring AI professionals and obtaining advanced equipment is sufficient for optimal AI performance, which is not the case. To truly harness the ‘intelligence’ of AI, one must know how to craft the input data not just as a set of numbers from a technical standpoint, but also in a way that encapsulates the essence of the problem, including factors like cause, culture, norms, and potential impacts.
For instance, if a company wishes to use an AI system to assist the marketing department in promoting a product, it would be a mistake to assume that the IT department should create magical AI tools that generate leads from various sources to achieve this goal. In reality, while the IT department can develop the tools, it is up to the AI developer to design, craft, and create an input vector that aligns with the task’s objectives. This input must consider factors such as market size, company culture, vision, mission, values, customer profile, production factors, and more. If this vector is meticulously designed, the AI system is more likely to yield the desired results. Understanding and accepting this approach will eliminate unrealistic expectations from business owners towards AI developers. The second part of this article will propose a pathway for AI developers who aim to create such a precise vector and will guide business owners on what to look for in the skill set of AI developers beyond technical abilities
Guidance Path for AI Developers
Essentially, it is crucial for professionals aspiring to pursue a career in AI development to understand that AI development is not a profession in itself, but rather a suite of skills. It’s a complex system consisting of numerous critical elements that one must acquire and build over time. These elements can be categorized into two groups: the technical suite and the personal suite. The technical suite requires a variety of skills, such as strong mathematical and algorithmic understanding and application, modeling and engineering abilities, extensive knowledge of existing tools, frameworks, and solutions pertinent to the field, a solid grasp of various scientific domains, and programming expertise. In the personal suite, it is vital for the professional to possess certain characteristics, personality traits, and values, such as extreme patience, a strong passion for learning and development, an analytical and curious nature that drives one to observe and question rather than accept things at face value, independence from societal and normative constraints, a set of principles and ethics that foster self-control and prevent arrogance and ego from overshadowing the spirit, determination, and importantly, communication and interpretation skills.
While the first skill set can be acquired and learned in schools and workplaces, the second is not so easily mastered through training and coaching programs. The latter, as the article suggests, is challenging for professionals to develop over time before they can consider themselves AI developers. This is because AI systems mirror human behavior; think of the AI system as a child and the professional as a parent. If the parent lacks quality parenting skills, the child’s upbringing and personality may be unstable. Similarly, if a professional lacks personal development, the AI system they build may be deficient, leading to inaccurate performance. For example, when developing an AI system for a marketing department, if the AI developer lacks personal development, their focus might be solely on the applied aspects, such as algorithms, tools, and frameworks. However, the task often requires the AI developer to conduct comprehensive research, starting with forming and articulating the right research questions, communicating with business owners and team members across different departments, designing tools to collect necessary data, and then conducting the appropriate analysis with a sense of inquiry and curiosity. Following this comprehensive process, the AI developer can then apply their skills in building and creating to design, craft, and generate the input vector for the AI system based on accurate data, ensuring the AI system produces the correct outcomes.
This article outlines a pathway for professionals to follow while developing skill sets, introduced through a digital educational resource titled “The Big Bang of Data Science.” The material, available in two editions, encompasses five primary fields of study presented sequentially. The first field is “research,” covering the entire process from inception to conclusion, offering comprehensive guidance on conducting professional research, including design, creation, and implementation. It emphasizes the relationship between research and data, positing that the quality of data depends on solid research foundations. Following this, the material addresses “analysis,” detailing the necessary skills and tools required to develop, implement, and apply appropriate analyses to the correctly gathered data. The third field, “prediction,” delves into the technical skills needed to create AI systems. The first edition then introduces “coding” of AI systems, demonstrating how AI models, developed in the previous stages, can be translated into software products like desktop apps. The second edition shifts focus to deploying AI models in forms other than software, such as integrating them into physical devices, leading to intelligent machines like robots. Additionally, it explores the abstract concepts of emerging research fields like “quantum mechanics,” discussing its potential, challenges, and future prospects. The material adopts a distinctive style and delivery method, reflecting the dual skill sets discussed in this section
Conclusion
The article provided a detailed explanation of the AI acronym and the implications of the terms that constitute it. It addressed the common misunderstandings and misuses of the term ‘AI’ among business owners and professionals. Additionally, it offered guidance to business owners on the correct interpretation of AI system solutions and advice on choosing the appropriate AI developer for their needs. The second part of the discussion outlined two essential skill sets required to be a competent AI developer, delved into the components of each set, and concluded by recommending a digital resource containing five key elements that assist professionals in pursuing AI development as a career.
References
Cremer, D., & Kasparov, G. (2021, March). AI Should Augment Human Intelligence, Not Replace It. Harved Business Review. Retrieved from https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it
Dahman, D. (2023, May). The Big Bang of Data Science. Retrieved from GitHub Repository: https://github.com/dahmansphi/big_bang_of_data_science_project
Egan, M. (2024, June). AI is replacing human tasks faster than you think. CNN. Retrieved from https://edition.cnn.com/2024/06/20/business/ai-jobs-workers-replacing/index.html
Gudilko, A. (2023, Dec). Can Artificial Intelligence Replace Humans? An Engineering Perspective. Forbes. Retrieved from https://www.forbes.com/sites/forbestechcouncil/2023/12/27/can-artificial-intelligence-replace-humans-an-engineering-perspective/
Urwin, M. (2024, Feb). AI Taking Over Jobs: What to Know About the Future of Jobs. Builtin. Retrieved from https://builtin.com/artificial-intelligence/ai-replacing-jobs-creating-jobs
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