Artificial intelligence (AI) is one of the fields in computer science. It involves tweaking code to enable computers to handle tasks normally done by human beings. The father of AI is Alan Turing, and is now a very famous field due to the advent of technology. Modern AI computers can even play chess well and even beat renowned champions. While others claim that AI is the savior of the world, some believe it will become humanity’s undoing. The applications of AI are still expanding, and its full limits are yet to be realized. Robotics is also another field in computer science that relies on AI to enable machines to perform otherwise human functions or tasks.
Artificial intelligence requires coding. The coding languages most often used in the field are Python, C++, and Java. Of these three, Python is used most frequently, and its most popular libraries for artificial intelligence are Tensorflow and PyTorch.
Artificial intelligence, machine learning, and robotics are among the most common examples of advanced computer software engineering practices.. These fields require knowledge in logic, technology, math, and engineering. You will also need good communication skills to apply your knowledge effectively in this field. So, does artificial intelligence requires coding?
You will need to understand programming to develop applications which use AI and simulate human behavior. In AI, math, science, and programming are key. Necessary programming knowledge includes popular languages such as Python, Java, C++, Prolog, and LISP. These languages will provide you with the technical foundation necessary for creating the functionalities and features in your AI models.
Why Coding is Important to AI
Coding is naturally designed to be unambiguous and high-level. And though non-developers can find it hard to code, most programming languages are concise. This means that writing your code using basic English will require more words than when you write with a programming language such as Python or Ruby.
Therefore, instructing your AI on what to do could take more work than if you decide to build your code. And even if you find coding a daunting task, you will need to code when developing AI applications. Instructing your machine to use AI to build even the basic applications can lead to disaster if you don’t indicate the variables and instructions well. But with coding, everything becomes easier.
Besides, AI programs require code that can continually produce outputs. And since writing an analysis on infinite space is daunting, it’s important to test your AI code with real trials. Therefore, when beginning your AI career, focus on building code from scratch instead of relying on AI. This early coding expertise will give you valuable insight into the way that a computer thinks and allow you to interpret its behaviors more effectively.
Python Programming Language and AI
Python is one of the best programming languages for artificial intelligence; it’s ideal for developing web applications, mechanization, and many other uses.
AI has been an important field, and among its more common applications are Netflix’s recommender systems, which suggest movies ad series to viewers, and Spotify, which recommends melodies and musicians on the platform.
There are many differences between traditional programming and artificial intelligence; this includes the innovation stack, profound research, and requirements for artificial intelligence-based experiments. Python is a steady, versatile, and resourceful programming language that enables engineers to design the best products. This language has consistency, is effortless, and enables access to numerous structures and libraries for artificial intelligence. It can also be easily adapted and has an extensive network of other libraries not mentioned here.
With Python, you can utilize modules from sources (such as PyPi) with pre-composed code, which you can utilize to perform your tasks. With Python libraries, you will have basic level programming, and you won’t need to start everything from scratch.
Some of the top Python libraries are:
- Pandas
- TensorFlow
- Keras
- Matplotlib
- PyBrain
- PyTorch
- TensorFlow
- Scikit
These Python libraries will help you to sift and combine information. Additionally, you will enjoy profound learning, prototyping, and also setup artificial neural systems using large datasets. They enable you to make 2D plots, graphs, and other visual representations to bring out a more appealing form of your data.
Why Python is a Simple Language
Python has short and easy-to-decipher code. However, you will encounter some complex calculations while applying AI. With Python, engineers can easily design modern frameworks without focusing on the complex parts of the language.
Most engineers prefer Python because it is simple and intuitive when compared to other programming languages, like Java and C++. Besides, it’s ideal for teams where different designers and engineers contribute to a project. And considering it’s a highly useful programming language, you can run complex errands and other AI tasks using Python language.
The two Python libraries which enable artificial intelligence to the greatest extent are: PyTorch and Tensorflow.
PyTorch & Tensorflow in AI
PyTorch and TensorFlow are open-source Python libraries which have proven themselves to be quite useful in the AI field, and they are commonly used in the production of commercial code and academic research. You can enhance their functionality them via different APIs, model repositories, and more.
TensorFlow was released in 2015 by Google and it has a basic data structure called a tensor. You can perform operations on the data in this library through flowcharts that can remember initial events. TensorFlow is ideal for training, deploying, and serving models and boasts of a very large user base. Though it declined in popularity in 2016 after the launch of PyTorch, it again sparked interest among the AI and machine learning community after the 2.0 version was released by Google in 2019. It also boasts of numerous tools including APIs, data, models, and built-in datasets to make your work easier.
PyTorch, on the other hand, was developed by Facebook and was first released to the public in 2016. It aimed to provide production optimizations and make them easier, unlike its competitor. Due to its ease of user, it attracted many Python programmers which made the TensorFlow developers adopt some of their features in their 2019 version. This library is more common in research than in commercial code production, and its users have been increasing exponentially from its release. It’s also popular for teaching deep machine learning.
According to a 2020 Stack Overflow Developer Survey, PyTorch has 4.1% usage among professional developers – less than half of the 10.4% of developers who use TensorFlow.
Bottom-line
If you’re looking to learn artificial intelligence, you should begin by learning Python and, eventually, Tensorflow. Though coding is not exactly a prerequisite to learn AI, machine learning, or robotics, knowledge of it will be very helpful especially when it comes to debugging your programs.