Today, when companies utilize artificial intelligence (AI) programs, they are likely using machine learning. Often, people use the terms machine learning and AI interchangeably. However, no matter how closely they may be related, machine learning vs AI are both different in their own ways.
IBM defines machine learning as:
“A branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn.”
IBM
Artificial intelligence, on the other hand, is the application of computer science and engineering to create intelligent human machines that are capable of performing human-like tasks.
Machine Learning vs AI
In 1962, Arthur Samuel, IBM investigator of machine learning watched Robert Nealey, a self-proclaimed checkers master, play a checkers game against the computer and lose to it. This event became a major milestone in the field of artificial intelligence.
Machine learning was later used in other games such as chess and blackjack.
Machine learning is a subset of AI. AI enables machines to imitate human behavior and the goal of machine learning is to use the existing data and teach machines to produce accurate results.
This means that the machines can understand a text written in natural language, perform an action, and read and process an image.
Examples of Machine Learning
- Face detection in images — detect human faces in digital photos
- Map route recommendations — quickest route based on traffic, arrival time, best alternative route.
- Agriculture — provide farmers insights on crops, soil water levels, improve quality, and pest detection.
Examples of AI
- Search engines — recommendations based on keywords and highlighting answers in specific parts of video or text
- Smart word recommendations — emails offering predictive text, tools such as Grammarly.
- Video games — realistic looking game graphics and characters, AI-powered bots in PUBG and Fortnite.
How to Make an AI?
Building an AI is not impossible in today’s time and age. You need a couple of skills along with some passion to properly make an A. Here are steps on how to make an AI system:
Understand the need and identify the problem
The first step in building any AI system is to understand the need at hand. For example, Typecast is an AI software that offers hundreds of AI-generated voice templates that can be used to create videos. Creating manual voice scripts can be time consuming and expensive, hence, with Typecast YouTubers and content creators can create their content fast.
An AI software understands the need, identifies a problem, and creates a solution to solve that problem.
OpenAI GPT-3 Playground is another example of AI software that allows developers to use the application without using code. In fact, you can provide the software prompts to ChatGPT in plain English.
Google text to speech is also an efficient example of AI converting text into natural-sounding speech with 100+ voices available in multiple languages and variants.
Collect the data
Data is the meat of AI. Images, text, and audio are examples of data that is fed to the AI software for it to work. You also need to clean the data and fix errors before training your AI model.
Create and train the algorithms
Using the data at hand, you need to create algorithms and train the AI to behave and work in a certain way. For example, Netflix’s algorithm is trained to suggest users shows based on their viewing history.
Choose a programming language
While you can use most modern languages to create codes, R and Python are ideal choices for creating AI models. This is because both languages have extensive machine learning libraries that developers can use to build AI models.
Run on a platform
Lastly, you need a platform to run your AI models on. Examples include Microsoft Azure Machine Learning and Google Cloud Prediction API. These platforms allow developers to build, deploy, and manage their models efficiently.
Where to Get AI Training?
If you are an aspiring developer, a data scientist, or simply an AI enthusiast who wants to learn and develop skills in AI, you can enroll in various AI training courses.
Google Cloud
Google Cloud offers a training course on machine learning and artificial intelligence based on specific roles, such as data scientist, ML engineer, and contact center engineer.
You also have access to AI machine learning videos from Google Cloud. The platform also offers documentation for AI and machine learning.
Google AI
Google AI also provides various courses, training, documentation, and interactive guides for business makers, developers, students, and curious cats exploring AI.
Some of the courses include Introduction to Machine Learning and Problem Framing, Machine Learning Crash Course With TensorFlow APIs, and Intro to Fairness and Machine Learning Module.
The platform also has hands-on AI guides such as People+AI Guidebook and Responsible AI practices.
IBM
IBM has a vast library of AI & Machine Learning courses designed for different learning levels — basic, intermediate, and advanced.
Courses include: The AI Ladder: A Framework for Deploying, Exploratory Data Analysis for Machine, Supervised Learning: Regression, and more.
Coursera
Coursera also offers a large number of AI training courses. Machine Learning course by Standford University and AI for Everyone by DeepLearning.AI are some of the courses on the platform
