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Artificial Intelligence and Machine Learning Explained

This post is a comparison between Artificial Intelligence(AI) and Machine Learning(ML).

By siddhesh thakarePublished 3 years ago 5 min read
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Artificial Intelligence and Machine Learning Explained
Photo by Michael Dziedzic on Unsplash

Artificial Intelligence (AI) and Machine learning (ML) are the base of almost every technology available today, be it the virtual assistant on your phone or the supercomputers that process the most difficult physics simulations. Most people nowadays get confused between Artificial Intelligence and Machine Learning. They consider both of them to be the same while they have quite a lot of differences. Both are generally used in reference to robots. So, let's check out what Artificial Intelligence (AI) and Machine Learning (ML) exactly are.

Artificial Intelligence (AI)

How do we even begin to predict where artificial intelligence is going? With all the data that is being collected about us in the form of IoT devices, there is so much information to mine and make sense of. Even if you are not a consumer, your information is most likely being taken by a company and used to better profit from your information. If you are a developer with an interest in applying artificial intelligence in your projects, you must first understand the basics of AI. However, if you are not a developer, what should you do to stay informed about what is going on in the world of AI? I have compiled a list of sites and blogs that are great resources to get up to date with the latest developments in AI.

1. What is AI?

Artificial intelligence is an area of computer science and artificial intelligence research. The name speaks very well for itself. According to Wikipedia, Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Artificial Intelligence is the heart and core of almost every self-sustained computer system on the planet. Artificial Intelligence is a way of mimicking human intelligence using machines. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. We are all surrounded by AI systems every time. We have AI in our computers, in our phones and virtual assistants. In fact, AI has become an integral part of our lives.

2. The History of AI:

The search for a technique that can instill human intelligence in machines started in 1950. A researcher called Alan Turning published a research paper: Computing Machinery and Intelligence. He asked a small yet revolutionary question, "Can machines think?" This research along with concurrent research in neurobiology, information technology, and cybernetics led researchers to the possibility of building electronic brains. Thus started the quest to make machines intelligent like humans.

The term 'Artificial Intelligence' was coined by John McCarthy, whom we know as the father of Artificial Intelligence. The first use of AI was mostly to run simulations. Later, as technology grew and along with the introduction of the 'Internet of Things', the scope of AI rapidly expanded. Between 1961 and 1972, IBM launched the IBM Shoebox, the first speech recognition tool. Fast forward to 2011, Apple launched Siri, the first-ever virtual assistant. And then started a surge of AI systems like Amazon Echo, Google Assistant, Humanoid robots, and much more.

3. Important terms about AI:

a) Algorithm:

An algorithm is a set of rules provided to a computer system, which guides the computer through the steps it should undertake to complete a task. An algorithm consists of variables, which are bonded to each other by some formulae. An algorithm is one of the most crucial aspects of any AI system since it controls how the system behaves under certain conditions. An algorithm may also consist of 'if this-then that' condition to tweak the system such that it will provide a specific output corresponding to the input. Most of the time, an algorithm is considered the same as a program, but that is not the case. An algorithm is the set of rules, the skeleton of the program. Program is a medium to feed the algorithm to a computer. The program is written according to the steps provided in the algorithm.

b) Data Science:

Data Science is a term highly associated with AI. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. Modern AI systems have to deal with a huge amount of data. Data Science allows them to organize their data properly and use it efficiently. There are 4 main steps in working of any algorithm using data science:

1) Data acquisition: This step involves gathering of raw structured and unstructured data by either manual input or automatic input.

2) Preparing and maintaining: The data acquired from step 1 is now arranged and sorted using algorithms in a format that would be useful for analysis. This may include steps from cleansing, deduplicating, formatting the data to using ETL(extract, transform, load) and data integration technologies.

3) Processing the data: The data is examined by data scientists for biases, range, patterns and distribution of value to determine the data's compatibility for deep learning algorithms.

4) Analysis: Data scientists perform predictive and statistical analysis, regression and deep learning to extract insights from the data.

The data is then used to train the AI system using training algorithms.

c) Supervised and Unsupervised learning:

AI systems are constantly evolving and upgrading for better usability and features. To make them compatible with new upgrades, these systems need to be trained so that they can perform the tasks accurately. Training an AI system includes collecting data from various sources and teaching the system to manage it using algorithms so as to get the desired output. Now, there are two types of learning in AI. These are supervised learning and unsupervised learning. In supervised learning, the AI system is corrected by a human if it takes a wrong decision. The inputs are provided manually. Unsupervised learning is like artificial consciousness. When the system is left into an environment on its own, it analyzes the environment, takes the inputs, and accordingly makes decisions. It does this all by itself, or with very minimum human interference.

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About the Creator

siddhesh thakare

I am Siddhesh, a technology blogger. I am interested in gathering and sharing knowledge about different fields in technology. Check out my blog at: <a href="https://www.techxplained.software">TechXplained</a>

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