AI 101: Everything You Need to Know

Curious about Artificial Intelligence but not sure where to start? You’re in the right place. This AI guide will give you a useful overview of what you need to know and help you get started if you want to learn how to use AI and even create your own AI tools. 

AI refers to non-human intelligence, or the intelligence of machines or software, that’s used to carry out a variety of tasks. Sometimes, AI can be used to refer to the machines themselves. It works by using algorithms and statistical models to process and analyze enormous amounts of data, recognize patterns and give predictions or make decisions. AI tools learn over time because they collect and process data from their users and then use that knowledge to carry out tasks like coming up with images, creating text-based output or translating content, just to name a few. 

Some of the most common AI tools for these tasks include ChatGPT4, Google Bard, Midjourney, and DALL-E. 

Types of AI 

AI can be broken down into three distinct categories, depending on its capabilities and uses: 

• Artificial Narrow Intelligence (ANI): the most common form of AI and what we typically use when we interact with AI tools. These tools are used to perform a single, specific task. 

• Artificial General Intelligence (AGI): can understand, learn, adapt and implement knowledge across tasks essentially at a human level. 

Artificial Super Intelligence (ASI): this is still a hypothetical form of AI and refers to something that could happen in the future. The scenario involves AI surpassing human intelligence and replacing all human work, but will likely remain merely speculative for a very long time.

How Do I Start Learning AI? 

If you want to learn how to use AI and how to master machine learning, this beginner’s guide is a great start. Beforehand, though, you should assess several factors to see how long your learning journey will take and what you’ll need to focus on. For example, if you already have some knowledge of math, science and statistics, you can likely skip the basic overview of algebra and calculus that are necessary for mastering AI down the road. You should also ask yourself why you want to learn AI, or what your career goals are. If you’re learning to pursue a job in the field, you’re going to want to seek out a more comprehensive education than if you simply want to add some knowledge to your current role or skills, or if you’re simply learning out of curiosity. Finally, it’s good to keep in mind that the technology field often uses complex jargon and covers concepts that take time to master. If you already have some background knowledge, this won’t prove as big an obstacle. 

Ready to get started? The first thing you should do is outline a learning plan for yourself. Keep in mind the factors we went over beforehand and ask yourself how much time you’ll be able to dedicate to your learning process, along with how much money you can spend. You can watch videos on YouTube or read up online, or you can take paid courses that will guarantee an immersive and comprehensive learning path. 

Next, make sure you have a strong foundation in statistics and math. The skills you’ll need to master AI are much easier to acquire once you have a firm grasp on data interpretation and concepts like significance, regression, distribution and likelihood. Calculus, probability, and algebra also pop up across different AI models and algorithms, so knowledge of these basic concepts is also necessary. 

Finally, keep in mind that AI is an ever-evolving field. You’ll need to read and learn constantly and keep up with the changes, and stay adaptable. 

Begin Learning AI Skills

Programming: This is definitely one of the basics. Knowing how to code is key to mastering and creating your own AI tools because you’ll use it to develop algorithms and models and work with data. One of the most well-known language models is Python, but there are several others that are widely used like Java and SQL. 

Data Structures: These formats, used for organizing, storing, retrieving and manipulating data, are necessary for programming as well. They’re used for writing the code that builds AI models and algorithms. Trees, lists and arrays are some of the most well-known types of data structures. 

Data Science: This broad category includes different tools and algorithms that are used to work with data, especially for extracting insights from extensive amounts of data. A knowledge of data science is important because it will help you get to the correct algorithms. 

Machine Learning: Machine learning is what’s behind many of the products and services that we use today. Machines learn how to predict and improve performance, and you’ll need to learn how different algorithms work and when to apply them to get the results you want. 

Deep Learning: This is a subset of machine learning that uses neural networks to find and understand data patterns. It’s used in the more advanced forms of AI that we know today. 

Add Some Tips to the Mix

With so many resources available today, starting your AI learning journey is actually easier than ever. Here are some things you can do to kickstart your learning path today: 

• Take a few courses. There are tons of great online courses, and many of them are free. Take a few that cover the basics, start mastering code and data manipulation, and get into some of the topics we covered above, like machine and deep learning, quickly and easily. 

• Set goals and complete projects. Identify some projects that you can complete once you have a certain grasp of the information you need. Make sure you have the right tools and understand how to manipulate your data correctly. If you need ideas for beginner projects, you can find lots of suggestions online that are tailored to your specific interests. 

• Read some books. Identify the topics that interest you most and/or that prove most difficult for you. Check out some books from your local library, see if you can find any online or purchase the ones you think will be most useful for your own learning journey.