Artificial Intelligence (AI) and Machine Learning (ML): An Overlap but With Distinct Differences

Artificial Intelligence (AI) and Machine Learning (ML): An Overlap but With Distinct Differences

In the realm of technology, Artificial Intelligence (AI) and Machine Learning (ML) are concepts that have revolutionized the way machines operate, mimicking human-like intelligence and decision-making capabilities.

AI, the broader concept, aims to replicate human-like intelligence, enabling machines to perform tasks such as decision-making, speech recognition, and language understanding [1][3][4]. Machine Learning, a subset of AI, is the method by which computers learn from data using algorithms and statistical models without explicit programming for each task [4][5].

ML contributes to AI by providing the techniques that enable machines to adapt to new situations and make intelligent decisions based on data, reducing the need for manual programming and addressing the "knowledge acquisition bottleneck" in AI development [4][5]. In essence, AI is the overarching goal of creating intelligent systems, and ML is one of the primary approaches that drive AI by enabling learning from data [1][2][5].

The emergence of the internet and the increase in digital information have been crucial for the development of machine learning. Today, machine learning is a current application of AI, where machines are given access to information or data so they can learn for themselves [1]. This learning allows a computer to recognize patterns, such as a machine learning system recognizing common denominators in photos of dogs [7].

Machine learning also allows neural networks to adjust factors of significance on their own, without human intervention, for improved precision [8]. This autonomous learning is key to the development of AI, as it allows machines to make decisions and exhibit intelligent behaviour, such as a voice-recognition system like Apple's "Siri" [9].

However, understanding AI and machine learning can be complex, even for technical people, and more so for the average person [2]. AI is a concept that mimics human intelligence and includes anything from a computer program playing a game of chess to a voice-recognition system [9]. It is implemented in a system and is not a system itself. AI is "intelligent" behaviour exhibited by a machine when it executes a task based on a set of specified rules that solve problems (algorithms) [10].

In the past, computers took up an entire room to perform basic calculations. Today, computer science is not just about computers, but a booming market full of innovative technology and learning opportunities [6]. As AI and machine learning continue to evolve, they are set to have a significant influence on the consumption of time, shaping the future of technology and the way we interact with machines.

References: [1] https://www.forbes.com/sites/bernardmarr/2019/04/16/the-difference-between-artificial-intelligence-ai-and-machine-learning-ml/?sh=7f0c0c89337c [2] https://www.techopedia.com/definition/28890/artificial-intelligence-ai [3] https://www.investopedia.com/terms/a/artificial-intelligence.asp [4] https://www.ibm.com/watson/artificial-intelligence/what-is-artificial-intelligence/ [5] https://www.ibm.com/watson/artificial-intelligence/what-is-machine-learning/ [6] https://www.bbc.co.uk/bitesize/articles/z3v6kqt [7] https://www.wired.com/2015/07/how-machines-learn/ [8] https://www.ibm.com/watson/artificial-intelligence/what-is-machine-learning/ [9] https://www.ibm.com/watson/artificial-intelligence/what-is-artificial-intelligence/ [10] https://www.wired.com/2015/07/how-machines-learn/

Machine learning, a significant component of AI, utilizes algorithms and statistical models to learn from data without explicit programming [4][5]. The development of neural networks in machine learning allows them to self-adjust and optimize controlled impedance for improved precision [8].

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