The Right Tools and Platforms

There are many tools for helping create AI models, and most of them are open source. Even though it’s good to test them out, it is still advisable to first conduct your IT assessment. By doing this, you should be in a better position to evaluate the AI Tools.

Something else: You may realize that your company is already using multiple AI Tools and platforms! This may cause issues with integration and the management of the process with AI projects. In light of this, a company should develop a strategy for the tools. Think of it as your AI Tools stack.

OK then, let’s take a look at some of the more common languages, platforms, and tools for AI.

Python Language

Guido van Rossum, who got his master’s degree in mathematics and computer science from the University of Amsterdam in 1982, would go on to work at various research institutes in Europe like the Corporation for National Research Initiatives (CNRI). But it was in the late 1980s that he would create his own computer language, called Python. The name actually came from the popular British comedy series Monty Python.

So the language was kind of offbeat—but this made it so powerful. Python would soon become the standard for AI development.

Part of this was due to the simplicity. With just a few scripts of code, you can create sophisticated models, say with functions like filter, map, and reduce. But of course, the language allows for much sophisticated coding as well.

Van Rossum developed Python with a clear philosophy:¹¹

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.

These are just some of the principles.

What’s more, Python had the advantage of growing in the academic community, which had access to the Internet that helped accelerate the distribution. But it also made it possible for the emergence of a global ecosystem with thousands of different AI packages and libraries. Here are just some:

• NumPy: This allows for scientific computing applications. At the heart of this is the ability to create a sophisticated array of objects at high performance. This is critical for high-end data processing in AI models.
• Matplotlib: With this, you can plot datasets. Often Matplotlib is used in conjunction with NumPy/Pandas (Pandas refers to “Python Data Analysis Library”). This library makes it relatively easy to create data structures for developing AI models.
• SimpleAI: This is an implementation of the AI algorithms from the book Artificial Intelligence: A Modern Approach, by Stuart Russel and Peter Norvig. The library not only has rich functionality but also provides helpful resources to navigate the process.
• PyBrain: This is a modular machine learning library that makes it possible to create sophisticated models—neural networks and reinforcement learning systems—without much coding.
• Scikit-Learn: Launched in 2007, this library has a deep source of capabilities, allowing for regression, clustering, and classification of data.

Another benefit for Python is that there are many resources for learning. A quick search on YouTube will show thousands of free courses.

Now there are other solid languages you can use for AI like C++, C#, and Java. While they are generally more powerful than Python, they are also complex. Besides, when it comes to building models, there is often little need to create full-fledged applications. And finally, there are Python libraries built for high-speed AI machines—with GPUs—like CUDA Python.