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

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about 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:

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

Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of 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.

This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.

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 important applications.

Artificial Intelligence (AI) programming languages:

Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in the development and designing of different software. It is up to a developer to choose which of the AI languages will gratify the desired functionality and features of the application requirement.

Python :

Python is among developers’ favorites 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 as compared to C++ and Java. It also a very portable language as it is 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.

Also, Python is a Multi-paradigm programming supporting object-oriented, procedural and functional styles of programming. Python supports neural networks and development of Neural language programming (NLP) solutions thanks to its simple function library and more so ideal structure.

Advantages of Python:

·        Python has a rich and extensive variety of library and tools.

·        Supports algorithm testing without having to implement them.

·        Python supporting object-oriented design increases a programmer’ productivity.

·        Compared to Java and C++, Python is faster in development.

Drawbacks of Python:

·         Developers accustomed to using Python face difficulty in adjusting to completely different syntax when they try using other languages for AI programming.

·         Unlike C++ and Java, python works with the help of an interpreter which makes compilation and execution slower in AI development.

·         Not suitable for mobile computing. For AI meant for mobile applications, Python unsuitable due to its weak language for mobile computing.

C++:

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

Another important factor is that C++ supports 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 a multi-paradigm programming that supports object-oriented principles thus useful 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, highly complex making it hard for newbies developers at using it for writing AI programs.

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 the need for recompilation.

Java :

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

 Advantages of Java:

·        It is easy to implement on different platforms because of Virtual Machine Technology.

·        Unlike C++, Java is simple to use and also easy in terms of debugging.

·        Has an automatic memory manager which eases the work of the developer.

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 on software and hardware to facilitate.

·         Java is also a generally immature programming AI language as there are still some developments ongoing such as JDK 1.1 in beta version.

Powerful Examples of Artificial Intelligence (AI) in use today:

What are today called AI systems are simply an advanced machine learning software with extensive behavioral algorithms that adapt to what we like and dislike.

1) Siri:

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

2) Alexa:

When Amazon first introduced Alexa, it took much of the world by storm. However, it’s 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 smart homes and be a conduit for those that might have limited mobility.

2) Tesla:

If you don’t own a Tesla, you have no idea what you’re missing. This is quite possibly one of the best cars ever made. Not only for the fact that              it’s received so many accolades, but because of its predictive capabilities,     self-driving features and sheer technological “coolness.” Anyone that’s into technology and cars needs to own a Tesla, and these vehicles are only getting smarter and smarter thanks to their over-the-air (OTA) updates.