Definition of AI
Artificial Intelligence, also known as AI, is one of the broad branches of computer science. This field encompasses the entire process of building intelligent machines with cognitive abilities similar to humans. The goal is to have the AI replace humans to perform some tasks that require a certain amount of intelligence.
What is AI Programming?
Artificial intelligence is still considered a separate area of programming because it is different from logic programming. AI programming is done by humans on machine systems. Therefore, these machines will be able to simulate human-like intelligences when it comes to things like reasoning, solving puzzles, thinking, solving problems when they happen, speaking, writing, acting, etc.
Classifications of AI Programming
Currently, experts divide AI into 4 categories ranging from the simple to complex as follows:
- Limited memory AI
This type of AI system relies on accumulated past experiences to make decisions in the future. This technology is combined with an ambient sensor that is used to predict situations. From there, the most appropriate signals or decisions can be made.
- Reactive machines
This technology is one of the basic forms of AI. It is capable of analyzing its own main moves as well as its opponents. From there, it will select the most feasible strategic actions.
- Theory of mind
This type of programming usually creates the ability to understand the feelings, thoughts, expectations, and beliefs of the people around them. This gives the program the ability to interact with human society. Although theory of mind has improved a great deal, experts warn that this type of AI is far from perfect.
This is a conscious AI, capable of super intelligence, self-awareness, and sentimentality like a human. However, this type does not really exist. If it is developed, it will certainly be an important milestone in the field of AI.
Typical Applications of AI Programming
A few forms of AI already exist and are part of daily life; although, most consumers and users aren’t aware of them or don’t necessarily think of these things as AI.
- Google Search Engine
Most search engines are not capable of scanning the entire internet and providing you with what you want without the help of artificial intelligence. Also, the ads that appear around your search are all AI-enabled and based on your search history. These are personalized to match what it believes your goals are and deliver ads that it believes you will find useful or appealing.
Netflix is one of the most popular current entertainment tools. This recommendation engine is powered by AI that uses your search history to make suggestions that it believes are a good fit for you. These recommendations can be based on actors you seem to like, the length of program time you seem to prefer, movie genres you seem to like, etc. According to actual results, about 80% of what we are watching is driven by Netflix recommendations.
Some Important Steps to Learn AI Programming
Step 1: Learn about Python and SQL
Programming languages are considered one of the core elements for you to learn anything related to the information technology industry. Currently, there are many languages that you can start studying on your own, but if you choose Machine Learning as your field, you should focus on Python.
Python is ideal for a few different reasons, but the primary ones are that it has a large number of inbuilt libraries, many of which are for machine learning and artificial intelligence. It also has an intuitive syntax paired with basic control flow and data structures. Learning Python also allows you to easily practice using the data science tools Jupyer and Anaconda.
Step 2: Learn to perform data analysis along with manipulation and visualization for Pandas, NumPy, and Matplotlib
Pandas, a software library written for Python, specifically for data manipulation and analysis. It allows you to work with information in a data structure similar to Excel.
NumPy is another Python software library that allows you to apply operations to an entire set of values all at once. This helps you further analyze your data by letting you look for patterns more easily and quickly.
Matplotlib is a plotting library for Python and works in tandem with NumPy. It helps you create charts and visualize your data and makes it easier to communicate the results of your findings.
Step 3: Machine learning with scikit-learn
Once you have learned the skills related to data manipulation and visualization, then you need learning patterns. Among the tools available to you, scikit-learn is considered one of the Python libraries that possess the most useful machine learning algorithms that have many different functions built in, including classification, regression, clustering, and dimensionality reduction. This means you can use it to figure out your learning algorithm.
Step 4: Deep Learning and Neural Networks
Broadly speaking, neural networks are a subfield of machine learning. Neural networks then are backbone of deep learning algorithms. Neural networks imitate the human brain using deep learning algorithms. It doesn’t rely on human intervention to learn but instead uses data sets to “learn” and make decisions. Depending on your goals or those of the organization you work for, gaining a basic understanding of these two areas is beneficial.
Step 5: Practice
It should go without saying that hands-on practice throughout the learning process will deepen your own understanding. Working on your own projects to experiment and see for yourself how things work and fit together will put you that much further ahead when it comes time to hunt for your first job in this exciting new field.
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