I’m working on a computer science discussion question and need an explanation to help me learn.
Powering AI and Machine learning and Deep learning
Artificial intelligence (AI) is a system that is computerized, and it is enabled to carry out tasks that require human intelligence. The artificial intelligence systems have their power generated by machine learning. However, there exist some that are powered by a deep understanding and a few that are powered by rules, which happens to be the most monotonous thing. Artificial intelligence can broadly be categorized in to; Narrow artificial intelligence and artificial general intelligence.
Narrow Artificial intelligence is a set of discovery and intelligence that tends to impersonate human intelligence. Narrow Artificial Intelligence is powered by progress in machine learning and deep learning. Narrow AI focuses on carrying out a single task and does it exceptionally well. Despite looking intelligent, they operate under many constraints and limitations compared to the most basic human intelligence. AGI is a computer system with general intelligence like a being and can use it to solve problems. AGI and other types of AI are powered by algorithms and techniques such as machine learning and deep learning in additions to rules.
Machine learning role is to feed a computer data and utilize statistical methods that help it learn on how to carry out tasks better in a progressive way; this is without having a program to that aid in the operation of a job, hence leaving aside the millions of lines with written code that are needed in programming. Machine learning entails supervised learning that operates with labeled data sets and learning without supervision that operates with unlabeled data sets in comparison to Deep learning which is a form of machine learning used to run inputs via a biologically-inspired neural network architecture. The machine can go deep in its education because it has neural networks that contain a variety of hidden layers that helps in data processing (Xin et al., 2018).
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., … & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365-35381.
Artificial Intelligence (AI), in a simple term, is a branch of modern computer science that focuses on the development of intelligence in machines and making them think and continue to enhance their decision making like a human does; this can be specified in the areas such as problem-solving, speech recognition, planning (Ashish Ahuja, 2020). Artificial Intelligence makes it possible for machines to learn from experience. Some of the examples for artificial intelligence applied today are self-driving technology, chess-playing computers. The steps for building the power of AI purely reside in data availability and locating the data source. The steps for building an ai model include preprocessing image data, training, validation, testing, and evaluation of the trained model’s performance (Ashish Ahuja, 2020).
Both machine learning and deep learning are part of artificial intelligence. Machine learning is a branch where the algorithm is created and modified without human interventions to create and produce the desired output through continuous feeding (Kapoor, 2020). On the other hand, deep learning is an artificial intelligence that imitates the human brain while processing and creating data. It is also known as deep neural learning. Deep learning is a subfield of machine learning concerned with algorithms inspired by the brain’s structure and a function called artificial neural networks (Kapoor, 2020).
Although machine learning and deep learning contribute to artificial intelligence, their working mechanisms and problem-solving techniques are different. To solve any specific problems, machine learning uses structured data to extract the data from the given problem. Then it will continue to learn and work on that data while classifying and referencing millions of other data at the same time. Once this has been completed, it concludes to solve the specific problem. While on the other hand, deep learning works differently (Browniee, 2020). As explained in the definition above, deep learning does not necessarily require structured data, and it takes the inputs from the problem. However, artificial neural networks to different layers, just like the human brain, define specific features through various concepts while passing through these networks. It relates that concept to the problem to find a solution.
Ashish Ahuja, D. K. (2020). Commentary: Artificial intelligence A game changer. Commentary, 405-406.
Browniee, J. (2020, August 14). What is Deep Learning? Retrieved from https://machinelearningmastery.com/what-is-deep-le…
Kapoor, A. (2020, October 27). Deep Learning Vs Machine Learning: A Simple Explanantion. Retrieved from https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08.
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