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Tag: Machine Learning

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Top 10 Programming Languages for AI Development

Recent studies show that businesses are investing more in artificial intelligence.

According to studies conducted by DataProt, 37% of businesses employ AI in their operations. The industry will be earning $126 billion by the year 2025 and the market value is expected to cross $267 billion by 2027.

Another study, conducted by Oberlo, states that 91% of top businesses have already invested in Artificial Intelligence. Also according to them, around 62% of customers are ready to share data if it improves their experiences with a business.

As artificial intelligence and related development are becoming more popular, the programming languages used for developing such software are also becoming popular.

If you are someone who has an interest in developing AI solutions, understanding the programming languages used for AI development will be compelling to you.

Best programming languages for AI development

When it comes to AI development, there are several programming languages you can choose from. Among them, here are major 10 programming languages that are used extensively in AI and machine learning development in 2022.

1. Python

Python is a high level, general-purpose programming language. It uses significant indentation to improve its code readability. Using language constructs and an object-oriented approach Python helps with developing clear and logical code for small to large scale AI projects.

Python was released in the year 1991 by Guido van Rossum. Ever since its inception, the language has been used in desktop apps, web apps, networking apps, scientific computing, machine learning apps and data science applications.

The libraries offered by Python such as Tensorflow, Keras, PyTorch, Scikit-learn, PyBrain and MXNet etc. make it one of the popular choices for AI development. Since Python offers rich text processing tools and uses modular architecture for scripting, it has also become a popular choice for Natural Language Processing (NLP).

A few of the leading companies that use Python include Google, Amazon, NASA, Reddit, Instagram, Intel, IBM, Facebook, Netflix, JP Morgan Chase and so on.

2. R programming language

R is a very popular programming language for statistical programming, especially data analysis and statistical computing. The language was created by statisticians Ross Ihaka and Robert Gentleman in 1993. As of March 2022, R is ranked at 11th position in the TIOBE index.

R is primarily written in C, Fortran and R itself. It comes with a command-line interface and offers support for multiple third-party user interfaces like RStudio and Jupyter.

R’s S heritage enabled it to have best-in-the class object-oriented programming facilities. R supports procedural programming with the use of functions and object-oriented programming with generic functions.

Due to its advantages, R is considered the primary programming language for statistical computations in domains such as biology, sociology, finance and medicine. The advantages of R can be extended through user-created packages that offer statistical techniques, graphical devices, import/export, reporting etc. The packaging system allows researchers to organize data, code and files in a systematic way for sharing and archiving. It is the ease of using such packages that drives the popularity of R as the best programming language for data science.

The alternatives to R programming language are SPSS, Stata and SAS, However, they are commercial statistical packages while R is a free software under the GNU General Public License.

Renjin and FastR used in the Java Virtual machines is a Java implementation of R programming language. The runtime engine “TERR” that is part of “Spotfire” is developed in R.

R is used by most of the leading companies including Facebook, Twitter, Google, Microsoft, Uber, Airbnb etc.

3. Java

Java is one of those programming languages that everyone has heard of.

This high-level, class-based, object-oriented programming language was designed in the year 1995 by James Gosling and became popular in the industry due to its write once, run anywhere (WORA) principle. WORA simply means that a compiled Java code can be run on all platforms that support Java without recompiling.

Java is one of the most used programming languages for client-server web applications. Even though it shares similarities with C and C++ in terms of the syntax used, Java has fewer low-level facilities than both.

Apart from web applications, Java is also used in Android apps, Artificial Intelligence and machine learning applications, search algorithms, server-side programming, neural networks and multi-robot systems. Its scalability, low dependencies, platform independence and support for Java Virtual Machines have made Java a popular general-purpose programming language.

When it comes to AI development, Java offers several libraries and frameworks such as Apache OpenNLP, Java Machine Learning Library, Neuroph, Deep Java Library, MLlib and so on.

Java is used by several companies including the noteworthy ones such as Google, Uber, Netflix, Airbnb, Instagram, Amazon, Spotify, Slack etc.

4. Rust

Designed by Graydon Hoare in 2010, Rust is multi-paradigm, a general-purpose programming language designed for performance and safety. Even though it is syntactically similar to C++, Rust guarantees memory safety unlike the former. Another benefit of Rust is that it offers memory safety without garbage collection and reference counting is only optional.

Rust offers low-level memory management as well as high-level features such as functional programming. Since it offers speed, performance and safety, Rust is gaining increased popularity day by day. It has been adopted and implemented by mainstream companies like Amazon, Dropbox, Facebook, Google, Microsoft and Discord. Google announced Rust as an alternative to C/C++ for their Android open-source project.

Here is a list of recent adoptions we have seen in the industry.

  • Microsoft Azure IoT Edge, a platform used to run Azure services and artificial intelligence on IoT devices, uses rust to create some of its components.
  • The blockchain platform Polkadot is written in Rust.
  • TerminusDB is written in Prolog and Rust
  • AWS’s Firecracker and Bottlerocket use Rust.
  • Google Fuchsia, an operating system by Google, uses Rust for its components.
  • Figma is written in Rust.
  • The Servo parallel browser engine developed by Mozilla in collaboration with Samsung is written in Rust.

5. Prolog

Prolog, which derived its name from “Programming in Logic”, is a logic programming language mainly used in artificial intelligence and computational linguistics. It was designed by Alain Colmerauer and Robert Kowalski in 1972.

Unlike many other programming languages, Prolog inherits first-order logic and is intended mainly as a declarative programming language. The logic is declared in the form of relations represented by facts and rules.

Even though Prolog was one of the first logic programming languages, hence one of the oldest, it still holds its position in the industry. There are several free and commercial adoption of Prolog such as Tabling (used in systems like B-Prolog, XSB, SWI-Prolog, YAP, and Ciao), hashing (used in WIN-PROLOG and SWI-Prolog) and Tail Call Optimization (TCO). 

Prolog has been mainly used as a primarily logic language for expert systems, term rewriting, type systems, automated planning, theorem proving and natural language processing. This programming language is best suited for an AI solution that features rule-based logical queries such as searching databases, voice control systems, and filling templates. IBM Watson is one such system. 

IBM Watson. Image credit: Pbs.org

6. C++

C++ is one of the well-known programming languages due to the popularity of C, the programming language it inherits from.

Designed by Bjarne Stroustrup as a general-purpose programming language in 1985, c++ has seen significant expansion over the years. Now it supports object-oriented, generic, and functional features besides low-level memory manipulation. 

The main advantage of C++ is its performance, efficiency, and flexibility as it was designed as a programming language for building resource-constrained software and large systems. The language is used extensively in building desktop applications, servers (mainly for e-commerce, web search and databases), video games and performance-critical applications such as telephone switches and space probes.

C++ offers several AI and ML libraries such as Caffee, Microsoft Cognitive Toolkit (CNTK), TensorFlow, DyNet, OpenNN, FANN, Shogun and mlpack library.

The popular companies that use C++ include Walmart, Google, Accenture, Twitch, Telegram and Lyft.

7. Lisp

Lisp, a name derived from “LISt Processor”, is the second-oldest high-level programming language still in use and is only one year younger than Fortran. Designed by John McCarthy in 1958, this family of programming languages has a long history with the presence of several distinctive dialects such as Racket, Scheme, Common Lisp and Clojure.

Designed primarily as practical mathematical notation for computer programs, Lisp later became the most favoured programming language for Artificial Intelligence. Several inventions in the field of programming are pioneered by Lisp and they include tree data structure, dynamic typing, conditionals, automatic storage management, recursion, self-hosting compiler, higher-order functions and read-eval-print loop. Apart from these, Lisp offers several features such as rapid prototyping, dynamic object creation, flexibility, garbage collection and information process capabilities.

CLML (Common Lisp Machine Learning Library), mgl, Antik and LLA are the popular AI and ML libraries offered by Lisp.

iRobot, The Mimix Company, NASA (in their PVS), Rigetti Quantum Computing, Grammarly, Mind AI, Carre Technologies, NuEcho, Kina knowledge, Emotiq and Anaphoric are a few examples of companies that are using Lisp in their products or operations.

Logo of Lisp programming language

8. Julia

Julia is a high-level, high-performance, dynamic programming language well suited for AI solutions that deal with numerical analysis and computational science.

Designed by Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral B. Shah in 2012, Julia supports concurrent, parallel and distributed computing.

Advantages of Julia include

  • It can direct call “C” and “Fortran” libraries without glue code.
  • Compiles all code by default to machine code before running it.
  • Automatic memory management/garbage collection
  • It uses easter evaluation
  • Offer libraries for floating-point calculations, random number generation, linear algebra, and regular expression matching.

Julia offers several packages for Artificial Intelligence and machine learning. Few of them are Flux.jl, Knet.jl, Mocha.jl, TensorFlow.jl, ScikitLearn.jl, TextAnalysis.jl, MXNet.jl, DecisionTree.jl, Merlin.jl, and LossFunctions.jl. Find the complete list here.

9. Haskell

Named after great logician Haskell Curry, Haskell is a general-purpose, statically-typed, purely functional programming language. Primarily designed for research, teaching and industrial application, Haskell boast of pioneering innovative features like type classes that enable type-safe operator overloading.

According to the number of Google searches conducted for tutorials, Haskell was the 28th most popular programming language in 2021.

The several features offered by Haskell include lazy evaluation, pattern matching, lambda expressions, list comprehension, type classes and type polymorphism. Since Haskell is purely a functional language, functions have no side effects.

Popular applications of Haskell include Agda (proof assistant), Cabal, Darcs (revision control system), Git-annex, Pandoc, TidalCycles, Cryptol, Facebook’s anti-spam programs and Cardano blockchain platform.

10. Smalltalk

This object-oriented programming language was specifically designed for constructionist learning. It is a dynamically typed reflective language which means that using Smalltalk, developers can create software programs that have the ability to examine, introspect and modify their own structure and behaviour.

Smalltalk’s reflective features help developers with advanced debugging in the most user-friendly way. In fact, Smalltalk ranked second in the list of “most loved programming languages” in the Stack Overflow Developer Survey in 2017.

Designed by Alan Kay, Dan Ingalls and Adele Goldberg in 1972, Smalltalk has influenced so many programming languages such as Python, Ruby, Java and Objective-C.

Even though it was created mainly for AI-related studies, Smalltalk lost its position in front of other popular AI programming languages such as Python and R. However, Smalltalk is picking up the pace by introducing more libraries for AI and ML development and natural language processing. For example, Pharo has a numerical package called PolyMath that is almost equal to NumPy of Python.

Due to its conciseness, object purity, simplicity and better OOP implementation, Smalltalk has started regaining the attention it always deserved as an AI language.

Conclusion

The adoption of artificial intelligence and machine learning is growing at a fast pace. There are several programming languages used in AI and ML development. However, languages like Python and R are the most popular. To meet the growing demand of the industry, there are several other programming languages that are expanding their capabilities to become the best AI programming language of tomorrow.

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The Fundamentals of Machine Learning

Have you ever wondered what machine learning is?

Even I had no idea about machine learning a few months ago. My interest in machine learning developed when I saw a documentary on the latest trends in robotics. Ever after, my idea of machine learning keeps on getting simpler.

What is Machine learning?

Wikipedia says

“Machine learning is the ability of a computer to learn and act accordingly without being explicitly programmed”.

Well, that is just the technical explanation of machine learning.

Let’s investigate a much simpler one.

Humans learn everything from their past experiences whereas computers follow instructions for doing the same task. For a computer to acquire such instructions a human should have knowledge about the same. Think about a situation where computers can also learn from past experiences and act faster!!! This precisely is called machine learning.

Machine learning is an application of Artificial Intelligence. Machine learning concentrates on the development of computer programs that can teach themselves to grow and change when exposed to new data. Since we are living in a technically emerged era, there are plenty of examples of machine learning in our daily life.

For example, let’s take the case of Google Maps. Google maps help you to analyse the time required to reach your destination based on current traffic. Also, in case of heavy traffic, Google Maps redirects you through another route which helps you to reach your destination at lesser time. This can be considered as the best example of machine learning. Let’s have look at how machine learning works with google map.

Google maps use a combination of people currently using the app, historical data of the route collected over time and a few other tricks. Everyone using maps is providing their location, the average speed and the route in which they are travelling which in turn helps Google collect massive data of traffic which helps them predict the upcoming traffic and adjust your route accordingly.

Above depicted is a graph which is plotted based on the number of users at a location versus speed of the user. When the number of users using maps are on one specific location and the speed of the user is slow, we can conclude for heavy traffic and redirects another route. Google maps keep on analysing such situations and keeps on improving their data.

Types of Machine learning

Machine learning can be mainly categorized into three different types.

Supervised Learning

Supervised learning is the simplest form of machine learning.

Supervised learning uses labelled data to train the modal. This type of learning always has an input variable X and an output variable Y. We figure out an algorithm to get a  mapping function from the input to output. In simple words, y = f(x)

Whenever you get a new input data x, the machine can easily predict the output y for the data. The result of supervised learning can be continuously predicted by the machine.

For example, let’s take Siri, Alexa or Google Assistant. Each one of these is a voice automated system which collects your voice and starts working based on this collected data.

Biometric attendance is another common example of supervised data from our day to day life. Here, the system first collects data of our fingerprints, retina scan or even face recognition and trains the machine with this data. And hence, it will validate our biometrics.

Unsupervised Learning

Unsupervised learning always has input X but we cannot directly predict the output Y.

They have unlabelled data for output calculation. They are important because they allow the machine to self-analyse and develop an output from the collected bulk data.

Unsupervised learning clusters input data into classes of statistical properties. Clustering and Association are the two most important concepts in unsupervised learning.

For example, consider cases of online shopping sites like Amazon, Myntra or Flipkart. When we add an item like mobile to their cart, they will suggest products people brought together with that mobile and also its similar product recommendations. This is possible by continuous observing of order details of customers and clustering such data.

Another example of unsupervised learning is Google maps which we already discussed earlier. Google maps also form two clusters where one is with high traffic and other normal traffic.

Reinforcement Learning

Reinforcement Learning works on the principle of feedback. This type of learning is all about taking decisions sequentially. There should be an initial state of input which leads to output and the next input depends on the output of the previous input.

Google Survey in Google Photos is the best example of Reinforcement Learning. Google photos identify a face and groups all photos of that same face together. For this, Google photos first collect all of the images of that face and ask the user if they are all of the same people. Thus, it gets into a conclusion and groups all photos with the same face.

Summary

Machine learning is now the hottest trend. This will provide enormous hopes for building Artificial Intelligence. Sophia, the first social humanoid robot developed by Hong Kong is one of the first major achievements of machine learning and artificial intelligence. Hopes everyone had a good time reading this blog and have figured out more about machine learning.

– Sharoon Shaji