A brief overview of Machine learning: How do the machines learn? - 0 views
started by ai_ar_services on 08 Feb 22
started by ai_ar_services on 08 Feb 22
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A brief overview of Machine learning: How do the machines learn?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Machine learning is a form of artificial intelligence wherein a machine “learns” by looking for patterns among massive data loads, and when it sees one, it adjusts the program to reflect the “truth” of what it found. The more data you expose the machine to, the “smarter” it gets. And when it sees enough patterns, it begins to make predictions. Unlike humans, however, machines cannot generalize knowledge or transfer learning from one application to another. Due to this, the idea that Artificial intelligence will take over the world and decide the fate of humanity which many people hold is largely a misconception as AI in itself cannot formulate opinions or emotions. However, this does not reduce the AI’s intelligence as it can still perform computations that will take data scientists months to come up with. When we talk of learning we have to realize its context in the modern world, before learning took place, problem-solving tasks relied on writing algorithms.
A brief overview of Machine learning: How do the machines learn?
An algorithm is simply a set of rules that takes an input and returns an output as a solution for the problem. Consider the following: Given a list of numbers, you are asked to sort them in increasing order. This problem is solved by algorithms. On the other hand, some problems were not so easy to solve by algorithms. A notable example of this problem is classification, where it is very difficult to write an algorithm that classifies unknown objects without an already assembled database which makes the task moot. In this regard, machine learning can prove to be very useful. According to the, a computer learns “from previous computations to produce reliable, repeatable decisions and results”. In simpler terms, machine learning is the act of a computer creating its own database through the observation of existing data and therefore evolving the ability to make predictions on that data over time, essentially learning about said data. The idea of machine learning is not new. The term was first defined back in 1959. However, we only recently started realizing its potential when technology became capable of gathering massive amounts of data. By marrying that data to affordable computers with tremendous processing power and inexpensive storage, the age of machine learning was born. Now, within machine learning, there are two further types that define exactly how a machine learns. These classifications are supervised learning and unsupervised learning. Supervised learning is the type of machine learning in which machines are trained using well "labeled" training data, and on the basis of that data, machines predict the output. The labeled data means some input data is already tagged with the correct answer. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept used when a student learns under the supervision of the teacher. Unsupervised learning, on the other hand, uses machine learning algorithms to analyze and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.
A brief overview of Machine learning: How do the machines learn?
Some common applications of machine learning include the teaching and creation of AI chatbots and facial recognition software. Here, we will further explore what a chatbot is and how it works in order to gain a deeper understanding of the uses of machine learning. A chatbot, in essence, is a computer program that simulates a natural human conversation. Users communicate with a chatbot via the chat interface or by voice, like how they would talk to a real person. Chatbots interpret and process users’ words or phrases and give an instant pre-set answer. Similar to regular apps chatbots have an application layer, a database, APIs, and Conversational User Interface. Within all this, we can find the crux of this example, which is the training process by which it answers questions. Chatbots work based on three classification methods. Pattern Matches where bots utilize pattern matches to group the text and it produces an appropriate response from the clients. “ Markup Language (AIML), is a standard structured model of these Patterns. Natural language understanding is When examining a sentence, it doesn’t have the historical backdrop of the user’s text conversation. This implies that, if it gets a response to a question, it has been recently asked, it won’t recall the inquiry.
A brief overview of Machine learning: How do the machines learn?
Finally, Natural language processing, where the chatbot finds a way to convert the user’s speech or text into structured data. Which is then utilized to choose a relevant answer. In conclusion, I hope this article solved the mystery behind learning. Learning is all about discovering the best parameter values (a, b, c …) for a given model. These values enable the model to output good results based on previous. Machine learning is now possible due to the advances in computer hardware, and the drop in their prices and we certainly hope that it continues to permeate its way into society!