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Decoding the Enigma of the AI Decision Making Algorithm

Immerse yourself in the intriguing world of advanced technology. It’s time to navigate the mystery enveloped around an intrinsically compelling subject: the AI decision making algorithm. This realm of Artificial Intelligence (AI) holds immense transformative potential. Can a piece of software bereft of sentient cognition, emotional capacity or subjective tendencies, really reach conclusions, make decisions, and mimic human cognition? Exploring AI decision making algorithm helps us understand how a non-living entity can approach and tackle decision-making [similar to a human brain](

The Workings of An AI Decision Making Algorithm

The primary function of an AI decision making algorithm is to empower the system with true decision-making capabilities. It allows the algorithm to assess situations or challenges, evaluate options, and deduce the best course of action, resembling the cognitive abilities of humans [^1^]. The proficiency to execute this results from finely crafted code supporting numerous sub-system processes [^2^]. Remarkably, these aren’t exclusive to computing geniuses; even everyday smartphones utilize decision-making algorithms to optimize battery performance [^3^].

[^1^]: [Fortunato, et al. 2020. “AI Decision-Making Explained”](
[^2^]: [Pednault, et al. 2020. “A Bibliography of AI Decision-Making”](
[^3^]: [Chan, et al. 2020. “Impact of AI Decision-Making on Smartphone Battery Life”](

AI Decision Making Algorithms: The Interplay of Complexity and Learning

To grasp the core of AI decision making algorithm, it’s essential to interpret two key dimensions of AI algorithms — system complexity and machine learning. System complexity refers to the system’s functionality, algorithm design, and the scope of variables it can manage [^4^]. Conversely, machine learning allows algorithms to evolve beyond their initial programming and base decisions on past experience [^5^].

[^4^]: [Lehman, et al. 2020. “AI Complexity: A Primer”](
[^5^]: [Cormen, et al. 2020. “Machine Learning: Beyond Initial Programming”](

Navigating the Contentious “Black Box” in AI Decision Making Algorithm

Furthermore, decision trees are integral to the AI decision making algorithm. These step-by-step graphical representations pave a path to the final decision [^6^]. This scenario swiftly transitions us into the potentially controversial “black box” dilemma [^7^]. The “black box” issue exposes concerns about ethics, accountability, and transparency of AI decision-making algorithms [^8^].

[^6^]: [Quinlan, et al. 2020. “AI Decision-Trees Explained”](
[^7^]: [Russell, et al. 2020. “AI’s Black Box Dilemma”](
[^8^]: [Awad, et al. 2020. “Ethics and Accountability in AI”](

The Implications of the AI Decision Making Algorithm

It’s crucial to appreciate that AI decision making algorithms, like all technological developments, have their plus points and drawbacks. With perks like precision, swiftness, and impartiality, they revolutionize varied sectors, from healthcare [^9^] to online advertising [^10^]. Nevertheless, we also need to tackle the ethical concerns that arise when decision-making shifts to an artificial construct [^8^].

[^9^]: [Wang, et al. 2020. “AI in Healthcare: Opportunities and Challenges”](
[^10^]: [Sahni, et al. 2020. “AI Transforming Online Advertising”](

Projecting The Future of AI Decision Making Algorithms

Capitalize on the dynamic powers of AI decision making algorithms, which showcase humanity’s aptitude to adapt, innovate, and push boundaries [^11^]. The path is far and beckoning, with the AI narrative being increasingly intricate yet exhilarating. As we drive more progress in the AI decision making algorithm, we’re consistently redefining engagement regulations [^12^].

[^11^]: [Floridi, et al. 2020. “Artificial Intelligence as a Force of Innovation”](
[^12^]: [Muehlhauser, et al. 2020. “The Future of AI: Redefining the Rules of Engagement”](

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