Artificial intelligence functions by combining significant volumes of data with rapid sequential processing and algorithms; the software then automatically learns the features from big data’s patterns and features. The whole process includes technologies and processes that involve machine learning, which uses a neural network and statistics to derive meaningful information from the data (Jackson, 2019). It also integrates deep learning, which employs numerous processing unit patterns from a big neural network. Additionally, it constitutes cognitive computing, which helps machines exhibit human-like image and speech interpretation and then rely on the information coherently; Cognitive computing relies on two aspects: computer vision and NLP (Ramesh et al., 2019).
Furthermore, apart from the processes, Artificial intelligence involves several technologies such as the graphical processing units, which provide the power needed to compute iterative processing. The internet of things provides a vast volume of data from the connected gadgets in unanalyzed form and helps a lot in AI systems (Reese, 2017). Additionally, advanced algorithms and APIs also complete AI’s whole process by analyzing data that are more complex faster and adding image recognition capabilities to the security systems respectfully.
There are numerous differences between machine learning and deep learning. As explained earlier, machine learning refers to a type of AI through which a computer is trained to perform automatic exhaustive or impossible tasks for human beings, such as analyzing, pattern identification, and making meaningful decisions. Deep learning, on the other hand, refers to a program that mimics the neurons of the human brain by using different layers in a model. Additional Machine learning performs excellently in a small dataset, while deep learning performs excellently in big datasets (Reese, 2017). The execution time of machine learning ranges between minutes to hours, while Deep learning takes weeks since it deals with colossal weights. Interpretation of some ML algorithms ranges from easy to impossible, while in DL, interpretation ranges from complicated to impossible. ML does not require complex hardware, but ML requires powerful complex machines since its matrix multiplication significantly (Ramesh et al., 2019).
Jackson, P. C. (2019). Introduction to artificial intelligence. Courier Dover Publications.
Ramesh. Delen Sharda (Dursun. Turban, Efraim.). (2019). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Global Edition. PEARSON Education Limited.
Reese, H. (2017). Understanding the differences between AI, machine learning, and deep learning. URL: https://www. techrepublic. com/article/understandingthedifferencesbetweenaimachine learninganddeeplearning.
AI’s Power Generation
The major driver in the process of generating the power of AI is big data (the internet of things). By utilizing AI algorithms and the technology computing power, AI analyzes the big data patterns, which makes it possible to automate tasks (Venkatesan, 2018). Integrating neural networks such as deep learning also enhances the power of AI as they allow the analysis of data with multiple layers.
Machine Learning and Deep learning
Although there is a significant difference in the function of machine learning and deep learning, it is crucial to realize that deep learning is an advanced form of machine learning. The main difference between machine learning and deep learning is machine learning models require human assistance in case of errors or inaccurate predictions. In contrast, deep learning utilizes algorithms and neural networks to determine if a prediction is accurate or not (Sharda, Delen & Turban, 2014). This is because machine learning focuses on generating algorithms to analyze data, gain insights from the data and make informed decisions. On the other hand, deep learning focuses on generating algorithms in multiple layers that develop artificial neural networks that can analyze the data and make informed decisions without human assistance. In the context of feature engineering, deep learning provides efficient and reliable services as it allows automatic analysis, prediction, and extraction of big data required in the process. On the other hand, feature engineering is challenging in machine learning as the features require high efforts for humans.
Machine learning excels at several functions such as face recognition, online recommendations, and email filtering. In contrast, deep learning is useful in recognizing self-driving cars’ environments, image recognition and labeling, and AlphaGo.
Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence and analytics. System for Decision Support.
Venkatesan, M. (2018). Artificial intelligence vs. machine learning vs. deep learning. Data science central.
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