Programming

Programming Sections Series (2) | Artificial Intelligence (AI) programming

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Artificial Intelligence programming

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Programming Sections Series (2) | Artificial Intelligence (AI) programming

Artificial intelligence (AI) allows machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.



Artificial Intelligence History
Artificial Intelligence History

Artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and computing power and storage improvements.

Early AI research in the 1950s explored topics like problem-solving and symbolic methods. In the 1960s, the US Department of Defense became interested in this work and began training computers to mimic basic human reasoning.

For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa, or Cortana were household names.

Artificial Intelligence Technology
Artificial Intelligence Technology

While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn't that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading this article for more information about artificial intelligence, the programming languages it relies on, and its most critical applications.



Artificial Intelligence Programming Languages
Artificial Intelligence Programming Languages

Java, Python, Lisp, Prolog, and C++ are primary AI programming languages used for artificial intelligence that can satisfy different needs in developing and designing other software. It is up to a developer to choose which AI languages will gratify the desired functionality and features of the application requirement.

Python

Python is among developers' favorite programming languages in AI development because of its syntax simplicity and versatility. Python is very encouraging for machine learning for developers as it is less complex than C++ and Java. It is also a portable language used on platforms including Linux, Windows, Mac OS, and UNIX. It is also likable from its features such as Interactive, interpreted, modular, dynamic, portable, and high level, which make it more unique than Java.

Advantages of Python

  • Python has a comprehensive and extensive variety of libraries and tools.
  • Supports algorithm testing without having to implement them.
  • Python supporting object-oriented design increases a programmer's productivity.
  • Compared to Java and C++, Python is faster in development.

Drawbacks of Python

  • Developers accustomed to using Python face difficulty adjusting to completely different syntax when using other languages for AI programming.
  • Unlike C++ and Java, Python works with an interpreter's help, making compilation and execution slower in AI development.
  • Not suitable for mobile computing. For AI meant for mobile applications, Python is unsuitable due to its weak language for mobile computing.

++C

C++ is the fastest computer language; its speed is appreciated for time-sensitive AI programming projects. It provides speedier execution and has less response time which is applied in search engines and the development of computer games. In addition, C++ allows extensive use of algorithms and efficiently uses statistical AI techniques.

Another critical factor is that C++ supports the re-use of programs in development due to inheritance and data-hiding, thus efficient in time and cost saving. C++ is appropriate for machine learning and neural network.

Advantages of C++:

  • Good for finding solutions for complex AI problems.
  • Rich in library functions and programming tools collection.
  • C++ is multi-paradigm programming that supports object-oriented principles and is thus sound in achieving organized data.

Drawbacks of C++

  • Poor in multitasking; C++ is suitable only for implementing core or the base of specific systems or algorithms.
  • It follows the bottom-up approach, thus, is highly complex, making it hard for new developers to use it for writing AI programs.

Java 

Java (official website) is another programming language to answer "Which computer language is used for artificial intelligence?" Java is also a multi-paradigm language that follows object-oriented principles and the principle of Once Written, Read/Run Anywhere (WORA). It is an AI programming language that can run on any platform that supports it without recompilation.

Advantages of Java

Java is one of the most commonly used and not just in AI development. It derives a significant part of its syntax from C and C++ and lesser tools. Java is not only appropriate for Neural language programming (NLP) and search algorithms but also for neural networks.

  • It is easy to implement on different platforms because of Virtual Machine Technology.
  • Unlike C++, Java is simple to use and also accessible in terms of debugging.
  • It has an automatic memory manager, which eases the developer's work.

Drawbacks of Java

  • Java is, however, slower than C++; it has less speed in execution and more response time.
  • Though highly portable, on older platforms, Java would require dramatic changes in software and hardware to facilitate.
  • Java is also a generally immature programming AI language as some developments, such as JDK 1.1 in beta version, are ongoing.


Powerful Examples of AI Used Today
Powerful Examples of AI Used Today

What are commonly referred to as artificial intelligence systems today are advanced machine learning programs with wide-ranging behavioral algorithms that adapt to what we like and dislike. While these machines are handy, they do not increase existential intelligence but instead, work on improving their skills and usefulness based on large datasets. These are some of the most common examples of artificial intelligence used today.

  • Siri

Everyone is familiar with Apple's assistant, Siri. It's the friendly voice-activated computer that we interact with daily. It helps us find information, add ends to our calendars, and send messages. Siri is a semi-intelligent digital personal assistant. It uses machine-learning technology to get smarter and better predict our natural-language questions and requests.

  • Alexa

When Amazon introduced Alexa, it took much of the world by storm. However, its usefulness and its uncanny ability to decipher speech from anywhere in the room has made it a revolutionary product that can help us scour the web for information, shop, schedule appointments, set alarms, and a million other things, but also help power intelligent homes and be a conduit for those that might have limited mobility.

  • Tesla

If you own a Tesla, you will know what you're missing. This is one of the best cars ever made. Not only because it's received so many accolades but because of its predictive capabilities, self-driving features, and sheer technological "coolness." Anyone into technology and cars needs to own a Tesla, and these vehicles are only getting more intelligent and wiser thanks to their over-the-air (OTA) updates.


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