Artificial intelligence has various definitions and meanings. It is completely based on the person who defines self-driving cars, robots that impersonate humans, machine learning, and more. Artificial Intelligence is a computer system that performs tasks that ordinarily require human intelligence. This basically includes tough calculations and manipulations of data.
Artificial Intelligence includes Machine Learning and Deep Learning as parts of it. Machine Learning is basically the usage of statistics to enable machines to improve with their own experience automatically. Deep Learning makes the computation of multi-layer neural networks feasible.
Examples of these tasks are visual perception, speech recognition, decision-making, and translation between languages.
COMPONENTS OF AI
AI has four main components
1. Expert systems
2. Heuristic problem solving
3. Natural Language Processing
The expert system handles the situation and gives a performance. Problem-solving is meant to evaluate a small range of solutions, may involve some guesswork to find a near-optimal solution. Natural language processing provides natural language communication between humans and machines. Vision is the ability to recognize shapes and features etc. automatically.
DOES AI NEED PROGRAMMING?
Artificial Intelligence is based on various algorithms that can finally implement human behavior in a robot. Effectively what this means is that the robot does what a human would’ve done in that same scenario.
There might be two parts to Artificial Intelligence, the actual application of the concept and we can see how things actually work out, and there is a more theoretical and mathematics-based part, where you look for better algorithms and present proofs as to why your method is better.
One issue Artificial Intelligence faces is, there is no theoretical proof for what we have planned to implement, and this can only be seen by the actual implementation.
Artificial Intelligence involves a lot of testing and research, and that involves applying algorithms on a real system and tinkering with the parameters to get better results, and that definitely has to do with coding the algorithm.
Regardless of which side of the Artificial Intelligence spectrum we look into programming is a must in Artificial Intelligence.
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FACTORS TO CONSIDER BEFORE SELECTING PROGRAMMING LANGUAGE
There are various factors that should be taken into consideration before choosing a programming language.
- Short development time
- Supporting pre-built libraries
- Ease of learning
- Platform Independent
- Mass community support
WHY PYTHON IS THE BEST CHOICE FOR AI?
Python is one of the most developers’ favorite popular programming languages used today. Guido Van Rossum created it in 1991 and ever since its availability, this has been one of the most widely used languages along with C++, Java, etc. Python has taken a big lead in our endeavor to identify what is the best programming language for neural networks and Artificial Intelligence. Let us look at why Artificial Intelligence with Python is one of the best ideas among developers.
ADVANTAGES OF PYTHON
It Has an Active and Supportive Community
Programming languages that lack documentation just don’t farewell. Python has neither of those problems. There is plenty of documentation, guides, tutorials, and more for python as it has been in the market for a long time. Plus, the developer community is incredibly active. Any time ready to support the users who are in need of some help. This active community ensures that developers of all skill levels from beginner to expert always have somewhere to find support. And, as any experienced programmer or developer knows, when you run into development issues in the middle of critical development, support is the only support we would need.
Great Corporate Sponsors
It helps when a programming language has a corporate sponsor. C# has Microsoft, Java had Sun and PHP is used by Facebook. Google adopted Python and they’ve used it for many platforms and applications since 2006. Why does this matter? If leading companies like Google want their future developers and team to work with their systems and apps, they need to provide resources. For working with Python Google has created a vast quantity of tutorials and guides. In the development world, it contributes to a growing list of documentation and support tools and provides free advertising for the language.
Big data and cloud computing solutions have helped Python skyrocket to success. It is one of the most popular languages used in data management and science, second only to R. It’s also used for machine learning and Artificial Intelligence systems and various modern technologies. Of course, it helps that Python is incredibly easy to analyze and organize into usable data.
When it comes to bigger projects, libraries can really help save ample time and cut down on the initial development cycle. Python has a large selection of libraries for scientific computing and for web development. There are a few libraries with a more specific focus, like nltk for natural language processing and scikit learn for machine learning applications. In other words, there are library tools that offer cross-platform support, which is a huge benefit.
Reliable and Efficient
Python has always been agreed by all developers to have been speedy, reliable, and efficient. Python applications can be deployed in nearly any environment, and there has not been any performance loss no matter which field it is. Because it is versatile, developers can work across several domains but not limited to web development, mobile applications, desktop applications, hardware. You’re not bound to a single domain or platform, and it offers the same experience in any platform.
Python is incredibly easy to learn and use, for any developer. In fact, it is the most available programming languages available. The reason is the simplified syntax, which helps in quick and easy learning, with an emphasis on natural language. But it’s also because you can write and execute Python code much faster. Whatever the case, it’s a beginner’s language, so it’s where young developers are getting their start. More importantly, experienced developers have plenty to do.
Python being an Interpreted language which means that it does not need to be compiled into machine language instruction before execution. It can be used directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or any virtual machine on top of the machine language which is what the hardware understands.
It is a High-Level Programing language which can be used for complicated scenarios. High-level languages deal with variables, objects, arrays, complex arithmetic or Boolean expressions, and other computer science concepts to make it more understandable thereby drastically increasing its usability.
Python is also a General-purpose programming language, it can be used across domains and technologies.
Python also features automatic memory management and dynamic system supporting a wide variety of programming types including object-oriented, imperative, functional, and procedural.
Python is available for all Operating Systems. It also has an open-source offering titled CPython which is garnering widespread popularity these days.
PYTHON FOR AI
The question that we need to encounter at this point is why we should choose Python for Artificial Intelligence over other programming languages.
Python offers the least code among others and is in fact 1 among 5 the number compared to other Object Oriented Programming languages. No wonder it is one of the most popular in the developers market today.
Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For Artificial Intelligence.
Python developers around the world always provide comprehensive support and assistance through forums and tutorials. This makes the job of any coder easier than any other popular language.
Python is a platform-independent and is one of the most flexible and popular choices. This is very often in use across different platforms and technologies with the least problems in very basic coding.
Python is the most flexible of all others with options to choose between scripting and OOPs approach. Any developer can also use the IDE to check for most codes and is a very huge advantage for developers struggling with different algorithms.
Python along with packages such as NumPy, scikit learn, iPython Notebook, and matplotlib form the basis to start any Artificial Intelligence project.
NumPy is used as a container for generic data comprising of an N-dimensional array object, tools for integrating C++ code, random number capabilities, Fourier transform, and other functions.
Another useful library is pandas which is an open-source library that provides users with easy to use data structure and analytic tool for Python.
Your next step will be to gather knowledge about decision trees, continuous numeric prediction, logistic regression.
Some of the most commonly used Python Artificial Intelligence libraries are AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning like PyBrain, MDP, scikit, PyML.
OTHER AI PROGRAMMING LANGUAGES
Artificial Intelligence is has been developing and growing. There are several languages that dominate the development world. There a list of programming languages that provide ecosystems for coders to build projects with Artificial Intelligence and machine learning.
C++ will not be any developer’s first choice when developing an Artificial Intelligence application, however when you need to wring every last bit of performance from the system. A scenario that becomes common as deep learning comes to the edge and you need to run your models on resource-constrained systems.
Thankfully, modern C++ can be pleasant to write. You have a choice of approaches. You can either dive in at the bottom of the stack, using libraries like Nvidia’s CUDA to write your own code that runs directly on your GPU, or you can use TensorFlow or PyTorch to obtain access to flexible high-level APIs. PyTorch and TensorFlow allow developers to load models generated in Python and run them straight in a CPP runtime. This helps to get closer to the bare metal for production, preserving flexibility in development.
In short, C++ becomes a critical part of the toolkit as Artificial Intelligence applications proliferate across all devices from the smallest embedded system to huge clusters. Artificial Intelligence at the edge means it’s not just enough to be accurate anymore; you need to be good and fast.
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R is generally applied to analyze and manipulate data for statistical purposes. R has packages such as Class, Gmodels, Tm, and RODBC that are commonly used for building machine learning projects. These packages allow developers to implement machine learning algorithms without extra steps to perform and let them quickly implement business logic.
R was created by statisticians for their needs. This language can give in-depth statistical analysis whether it’s analyzing financial models or handling data from an IoT device.
If the task requires high-quality graphs and charts, R is the best tool to use. R’s capabilities are greatly extended with ggplot2, ggvis, googleVis, Shiny, rCharts, and other packages, helping turn visuals into interactive web apps.
Compared to Python, R is slow and lags when it comes to large scale data products. It is better to use Python or Java, for actual product development.
Scala is invaluable to big data. It offers data scientists an array of tools such as Scalalab, Saddle, and Breeze. Scala has great concurrency support, it helps with processing large amounts of data. Since Scala runs on the JVM, it goes beyond all the limits hand in hand with Hadoop, an open-source distributed processing framework that manages storage for big data applications running in clustered systems and data processing. Scala is highly maintainable, despite fewer machine learning tools compared to Python and R.
If you need to build a solution for high-performance computing and analysis, you might want to consider Julia. Julia has a similar syntax to Python and was designed to handle numerical computing tasks. Julia provides support for deep learning via the TensorFlow.jl wrapper and the Mocha framework.
Many libraries are not included in the language. This doesn’t yet have a strong community like Python because it is comparatively new.
Another language worth mentioning is Java. Java is object-oriented, portable, maintainable, and transparent. It’s supported by numerous libraries such as WEKA and Rapidminer.
Java is spread across when it comes to search algorithms, natural language processing, and neural networks. Java allows the developer to quickly build large scale systems with fantastic performance.
But if you want to perform statistical visualization and modeling, then Java will be the last choice for any developer. Even though there are many Java packages that support visualization and statistical modeling, however, they are not sufficient. On the other hand, Python has advanced tools well supported by the community.
Python ecosystem is well suited for Artificial Intelligence-based projects. With its simplicity, large community and tools allow developers to build architectures with high perfection keeping the focus on business-driven tasks.