01 Artificial Intelligence programming: tomorrow's technology today
02 Python - a top programming language for Artificial Intelligence
a 1. An amazing ecosystem of tools and libraries
b 2. It's platform-independent
c 3. It's easy to write and share code
Artificial Intelligence (AI) is an undying buzzword in the IT industry and beyond. While the debates about its safety and ethical implications escalate, and governments introduce new ways to regulate the use of technologies such as image recognition, the tech industry is busy researching new methods for expanding its capacities.
Today, AI isn't a vision of the future anymore. Many products and services we use every day feature AI-powered components that analyze massive volumes of data and help businesses to be more proactive.
When it comes to the practical side of implementing Artificial Intelligence in software solutions, Python emerged as one of the best programming languages for the job. IBM claims that Python is the best language for AI and machine learning.
According to the TIOBE index at the time of writing this article, Python is the 3rd most popular programming language in the world today. One of the reasons behind this rank is that the language supports the most innovative areas of software development, such as AI, machine learning, and deep learning.
Read this article to find out how Python has become a critical technology for building AI-powered software today.
Artificial Intelligence programming: tomorrow's technology today
Even if Artificial Intelligence seems like something straight out of a science fiction movie, in reality, it's a technology that finds many implementations today. That's because AI comes in many shapes and sizes.
Different forms of Artificial Intelligence:
- Robotics – this area of engineering focuses on designing and manufacturing robots. Companies use them to perform tasks that are repetitive, difficult, or even impossible to accomplish by human workers.
- Robotic Process Automation (RPA) – in this context, robots are specialized computer programs (software robots) that automate and standardize tasks that are high-volume, repetitive, and generally boring when done manually by human employees.
- Machine learning – this exciting subfield of AI focuses on building software solutions capable of performing actions without being explicitly programmed to do so. By using data science and machine learning approaches, developers can build software that predicts the future behaviors of customers and their outcomes without any human intervention.
- Natural Language Processing – this area of AI programming concentrates on mapping and building on the interactions between computers and human languages. In particular, researchers study how we can program computer systems to analyze, interpret, and manipulate natural language data with specific goals in mind.
- Machine vision – this field focuses on developing systems able to provide imaging-based automated inspection and analysis with the help of a camera. Other key problems include analog-to-digital conversion and digital signal processing.
How AI is used in different industries
Autonomous mobile robots
Companies in sectors such as shipping and transportation or manufacturing are beginning to deploy autonomous mobile robots (AMRs) for higher-payload materials. These robots come equipped with AI capabilities that result in improved efficiency in path planning and environmental interaction. Instead of reacting similarly to all obstacles, such as mobile robots can learn as they go. For example, they can distinguish between human workers and a forklift, and adapt their driving patterns, avoid high-traffic areas during specific times (like during delivery of goods when many workers are present).
Optimizing manual and often paper-based processes is a priority of companies operating across many industries. In particular, businesses in consumer-facing sectors such as retail are looking for solutions that would help them become more responsive to the rapidly evolving customer trends. AI-powered solutions allow automating more complex tasks, leaving the human workforce to focus on high-value, mission-critical activities that rely on creativity and strategic decision-making.
- Learn more: Machine learning use cases in enterprises
AI software solutions can predict user behaviors on the basis of the existing data to make accurate recommendations about movies, TV shows, and other content. We all know that such recommendation engines work in practice – all it takes is browsing Amazon or Netflix. Companies use AI solutions to gain more insights about their customers and use it to their advantage. One example is advertising, where AI allows personalizing content based on the user's preference, increasing the chances of making a sale.
In the future, AI would be used in applications such as automated transportation, space exploration, and even cyborg technology. All in all, the AI software market is expected to grow from $1.4 billion in 2016 to $59.8 billion in 2025 indicates that the technology is here to stay (and change our lives!).
Python - a top programming language for Artificial Intelligence
Developing an AI software solution is a process riddled with challenges. Since the system needs to analyze massive volumes of data, the AI product needs to be able to handle high-loaded processes effectively in a reasonable time. That's why choosing the right technology stack is so important here. The best programming language for AI needs to have a relatively simple syntax, be able to handle advanced processes, and be easy to support. Luckily, the language fulfills all of these requirements and brings even more advantages to the table.
Here are the key reasons behind the rise of Python on the AI programming scene.
1. An amazing ecosystem of tools and libraries
One of the main reasons why the language is the most popular programming language used for AI is its fantastic collection of libraries. They allow developers and data scientists to take advantage of pre-written pieces of code that build functionality or perform an action. That way, they can accelerate the development process and help developers avoid the need to build base-level items from scratch every time.
Where can you find Python AI libraries? The PyPI repository is the best starting point for developers looking for libraries that help in areas such as machine learning, data mining, and Natural Language Processing.
If you'd like to learn more, explore these lists of libraries prepared by our expert team members:
- 24 machine learning libraries for data science projects
- 8 best Natural Language Processing (NLP) libraries
- A guide to the history, uses and opportunities with natural language processing
2. It's platform-independent
The language is easy to use and versatile. It can run on any platform, be it Windows, macOS, Linux, Unix, or others. If you need to transfer the process from one platform to another, all it takes is implementing a few small-scale changes and modifying some lines of code. That's how you create an executable form of code for your chosen platform. Moreover, developers can use packages like PyInstaller to prepare code for running on different platforms. You can only imagine how much time and money that saves for testing the software on various platforms. This feature also leads to speeding up the overall development process.
3. It's easy to write and share code
Python is very easy to read. Every developer is bound to understand the code written by other developers – as well as change, copy, or share it with others. The chances of any confusion or misunderstanding creeping into the development process are very low.
Python has a simple syntax that allows working with complex systems more easily, as it helps to clarify the relations between the system elements. This is especially important for sophisticated fields like machine learning, where you need the team to be able to exchange tools, algorithms, and ideas. Moreover, the immense ecosystem of tools features solutions like IPython, an interactive shell that offers extra features for testing, debugging, tab-completion, and many others – all to facilitate the work process.
4. Gentle learning curve
AI applications require teams to deal with plenty of data that needs to be processed efficiently. The low entry barrier to learning Python allows data scientists to quickly pick up the language and start using it for development without wasting too much time on learning the ins and outs of the language first.
The gentle learning curve also means that replacing or finding new team members will be easier and faster, especially if you're looking to scale your product quickly. The Python community is very large and includes many professionals with skills in the field of AI.
5. Lively community
The community is another advantage of using this language in complex projects that center on building AI solutions. The language and its tools come with ample documentation that helps teams to make the most of them. Moreover, the community is always ready to provide developers with advice or help. Solving a problem is much easier when one can rely on the collective knowledge of the global community of Pythonistas.
6. It's flexible
Developers don't need to recompile the source code, so they can see results when implementing changes. Moreover, it's possible to combine Python with other languages easier. This flexibility also allows developers to choose the programming styles they prefer and with which they're fully comfortable – or even combine the styles for the best effects. Another result of this flexibility is the lower rate of errors since developers get to work in the most comfortable environment.
7. It offers great visualization options
Python offers a variety of libraries – and some of them are fantastic visualization tools. In developing AI solutions, it's essential that developers can represent data in a format that is understandable to humans. And libraries help to do just that. For example, libraries such as Matplotlib help to build charts, histograms, and plots a more effective presentation of data. Another factor that facilitates the visualization process is the variety of application programming interfaces (APIs) that make it easier for development teams to create clear reports.
Here’s a case study that shows data visualization in practice: Data visualization in Python, Part 1: Cellular Automata
The growing popularity of Python means that the demand for it in the data science community is only going to grow. Choosing a technology in high demand is a smart move – since the entire ecosystem and the language itself are constantly developed, they will allow even more functionalities in the future. And judging by its current areas of Python application, many of them will be directly related to AI programming.
Are you looking for a team of seasoned Python developers to help you jump-start your Artificial Intelligence project? Get in touch with us.
We have to build AI-powered solutions for companies across various industries. We just knew that Python is the best programming language for AI right from the beginning!