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6 DevOps trends you can’t miss in 2023

DevOps trends you can't miss in 2023

Research suggests that the global DevOps market is expected to reach USD 51.18 billion by 2030 growing at a CAGR of 24.7%. DevOps has been gaining a lot of importance among new-age enterprises and tech leaders. In this blog, we shall cover some of the top DevOps trends in 2023. But first, let’s understand what exactly DevOps means. Read on.

What is DevOps?

DevOps is defined as a set of tools, practices, and cultural philosophy that tend to automate and integrate different software development practices. The development and operations teams are not siloed under a DevOps model. It helps you innovate better and optimize business productivity.

Gartner defines DevOps in the following way:

“DevOps represents a change in the IT culture, focusing on rapid IT service delivery through the adoption of agile, lean practices in the context of a system oriented approach.”

Latest trends in DevOps

Serverless computing

It is certain that the DevOps market will witness a rise in the usage of serverless computing. Now, serverless architecture is the method of offering backend services on an as-used basis. It enables flexible use of resources that can be scaled in real-time.

Serverless computing

Some of the leading cloud service providers including Google, Microsoft , and others offer serverless architecture to users

With serverless computing, businesses can streamline all DevOps operations – right from software development to deployment. Simply put, serverless computing brings Dev and Ops teams together.

DevSecOps

Research suggests that the DevSecOps market is predicted to reach USD 23.16 billion by 2029 at a CAGR of 31.50%.

DevSecOps is the short form for development, security, and operations. It tends to automate security operations at every phase of the software development cycle. It prevents the DevOps workflow from slowing down by automating security gates. In a nutshell, DevSecOps is all about in-built security. Leveraging DevSecOps in DevOps enables developers to protect their code from cyber attacks. With DevSecOps tools, developers can easily identify security vulnerabilities and code issues right in the CI/CD pipeline.

GitOps

GitOps is a code-based infrastructure and is a combination of two powerful technologies – Git and Kubernetes. It helps you solve challenges like server outages, failed deployments, and much more.

Simply put, GitOps is a software development framework. It tends to manage IT infrastructure by utilising Git as a single source of truth. With GitOps, deployments happen within the source code.

One of the best benefits of leveraging GitOps is that it facilitates faster and reliable collaboration between developers and various other stakeholders.

Kubernetes

Kubernetes is also known as K8s. It is an open source system which automates the deployment and management of containerised applications. One of the striking benefits of Kubernetes is that it offers you the platform to schedule and run containers on clusters of physical/virtual machines. It enables you to develop your entire infrastructure as code.

With Kubernetes’ rolling updates and automated rollback features, developers can easily deploy updates to their cloud-based apps. Thanks to its role-based access controls, Kubernetes facilitates better collaboration.

Microservices architecture

Microservices architecture has become one of the most emerging trends in DevOps. It helps businesses break down applications into smaller services. When you leverage microservices along with DevOps capabilities, you can easily replace the traditional monolithic architecture.

Microservices can fix a bug issue without affecting the SDLC of other services. When a microservice faces an error, it never affects the entire application. This is because software development is divided into independent teams. Leveraging DevOps and microservices architecture together can help make your business more productive and agile.

Artificial intelligence and machine learning

AI and ML are fast-evolving concepts in the DevOps industry. New age machine learning systems can streamline data generated from various sources and organize it effectively. AI enables smarter software testing.

Now, DevOps involves various testing types and all of them produce huge amounts of data. With AI, you can easily identify data patterns and coding methods that led to the error. AI helps developers make intelligent decisions based on real-time data.

Wrapping Up

We hope our article helped you understand the latest trends in the DevOps market. However, one thing that you need to understand is that DevOps transformation is a never ending process. Every trend will help enhance your business ability to develop, launch, and handle high-quality software. So, delay no more! Implement the latest trends available in the DevOps market and take your step towards improved business agility.

At AlignMinds, we can assist you in effective DevOps adoption. We evaluate your infrastructure and design possible mitigation strategies. We empower developers with automation and enable them to synchronize their work.

Sounds good? Connect today.

The Biggest AI Trends in 2023

Artificial Intelligence

If AI is what you are planning to transition to in 2023, then you need to see these AI trends that will revolutionize technology to a whole another level.

Artificial Intelligence is going to be a big deal this year. AI trends are a commodity to look forward to for Artificial Intelligence oriented functionality in 2023. And let us review the top AI trends for 2023.

 

These are the AI trends that will review technology in the time 2023.

Rule of Predictive Analytics

Predictive Analytics

With operations in multitudinous academic sectors, advancements in Predictive Analytics have become one of the most fascinating areas of artificial intelligence. It predicts the future based on the former data available, statistical algorithms, and machine literacy. The thing is to directly anticipate the future using data from history. Predictive analysis didn’t simply appear overnight; rather, it has been around for quite a number of years before getting popular recently.

 

Growing Hyper Automation

The term Hyper Automation is used to describe the extension of conventional business process robotization beyond the bounds of specific procedures. Combining artificial intelligence( AI) tools with robotic processes gives rise to the dynamic discovery of business processes, and the construction of bots to automate them ( RPA). Hyper Automation will become more significant in the forthcoming times since it’ll be necessary for any business that wants to keep up with the advancement of digital technology, according to Gartner.

 

AI and Cybersecurity

The coming natural development in automated defenses against cyber pitfalls is the growing operational need of AI in security operations. Beyond the capabilities of its precursor, robotization, artificial intelligence ( AI) is used in cybersecurity to perform routine data storehouse and protection conditioning. Still, cybersecurity artificial intelligence goes beyond this and supports more delicate jobs. One use is the discovery of ongoing assaults or other suspicious trends using advanced analytics. Still, not all of the news is good. Cybercriminals will be using AI to their advantage, and organizations will be engaged in a no way – ending game of cat and mouse with them. Thus, enterprises who are concerned about staying in business must begin integrating AI into their cybersecurity as soon as possible.

Stoked Process and AI

The role of AI and data innovation in invention and robotization will increase in 2023. Data ecosystems are suitable to gauge, drop waste, and give timely data to a variety of inputs. But laying the foundation for change and fostering invention is pivotal. With the use of AI, software development processes can be optimized, and further advantages include lesser collaboration and a larger body of knowledge. We need to foster a data- driven culture and go past the experimental stages in order to change to a sustainable delivery model. This will really be a significant advancement in AI.

 

The Rising of AIOps

Rising of AIOps

Over the years, IT systems have become more sophisticated. So merchandisers will seek results that offer visibility across multitudinous monitoring disciplines, including operation, structure, and networking, according to a new Forrester study. IT operations and other brigades can ameliorate their most pivotal procedures, judgements, and conduct with the use of AIOps results and better data analysis of the enormous volumes of incoming information. To encourage cross-team collaboration, Forrester advised IT leaders to look for AIOps merchandisers who integrated the IT operations toolchain, handed end- to- end digital inputs, and identified data.

 

Machine Literacy and robotization ( AutoML)

The automatic revision of neural net topologies and better tools for data labeling are two promising areas of automated machine literacy. When the selection and enhancement of a neural network model are automated, the cost and time to vend for new results for artificial intelligence ( AI) will be reduced. According to Gartner, perfecting the PlatformOps, MLOps, and DataOps processes will be crucial to operationalizing these models in the future. These sophisticated features are pertained to as XOps by Gartner as a whole.

 

Expansion of Natural Language Processing

NLP is constantly expanding as a result of the need for computers to comprehend mortal languages more. Startups give NLP- grounded systems that can identify words, expressions, and speech parts. They’re employed by businesses to enhance consumer commerce and carry out expansive exploration.

 

For example, NLP- grounded smart assistants are being used by businesses in the HR, trip, and consumer goods sectors to speed up response times and offer information related to their products. NLP also makes it possible for machines to speak to people in their own languages. In turn, this scales other language – related jobs into numerous languages, such as digital phone calls, and text analytics.

 

Prefacing of Virtual Agents

Virtual agents, also pertaining to as virtual assistants, automate routine chores so that staff members can concentrate on further pivotal jobs. Voice assistants with AI capabilities take over client and implicit client dispatches, enhance product discovery, and give product suggestions. Accordingly, they find use in a variety of functions, including retail and food assistance. They also help HR departments with onboarding, analyzing resumes, and opting the most good aspirants for the job. As a result, startups develop clever virtual assistants to automate relations with guests and cut down on time spent on executive conditioning.

 

Quantum Artificial Intelligence

Quantum AI

It’s pivotal to align vast quantities of information where it is a world of quick changes and judgements. The advancement of delicate task optimization results in the extent at which AI enhances marketable operations. High- performance AI is made possible by the immense processing capacity handled by large computers. High- speed data processing that outperforms the limitations of conventional computers is made possible by advances in AI. To broaden the use of AI across diligence, startups produce slice- edge amount algorithms and smart computers. The major demands for AI are in finance, industry, and life sciences.

  Edge Artificial Intelligence

Edge computing brings computations closer to data sources, reducing latency, bandwidth, and energy usage. Developers and enterprises can dramatically lower the infrastructure requirements for real-time data processing by using AI at the edge. In order to avoid system failure, smart cities, factories, and automobiles for autonomous driving systems, companies integrate this technology. Edge AI gives businesses

additional information to make wiser decisions in conjunction with other technologies, such as 5G and high-performance computing (HPC).

Wrapping Up

We have listed out the most popular AI trends that are set to rule the AI ecosystem this year. Which one do you think would be the one that is suitable for your business objectives?

World Is Moving Towards Automation, Where Are You?

Digital adoption has been accelerating across the globe ever since the pandemic. IT roles are becoming more prominent in organizations and routine tasks are permanently automated.

According to Gartner, by 2025 half of the world population will participate in at least one IoBs (Internet of Behaviours) programme formulated by private, commercial or government organisations.

Within the next year, 40% of all enterprise workloads will be managed by cloud infrastructure and platform services. Also, 40% of Product and platform teams will be using automated change risk analysis with the help of technology pipelines like DevOps to reduce downtime and risk mitigation.

By the year 2025, half of the total enterprises will have artificial intelligence (AI) orchestration platforms at their disposal.

New terms like IoB etc. are the best example of how technology is advancing into new fields and how automation is becoming a normal part of life. IoB programmes make use of big data and IoT to influence customer behaviour. Such programs can help retailers and manufacturers to find the right price for their products and increase sales.

Adoption of automation is not limited by industry. However, the IT, service, retail, marketing, and manufacturing industry are the major sectors that are adopting automation at a faster rate. In fact, by 2025, the majority of the workforce in the manufacturing industry will be replaced by machines, resulting in 20% of all products produced not being touched by any human being before they enter the market. It is estimated that such transformation in the operational process will help organizations to lower their operation cost by 30%.

If all these data points to one thing, it is that the future of businesses depends on automation. Today, digital transformation is not a convenience. It has become a necessity for businesses that want to survive the fearsome competition and changing market.

By 2025, 50% of enterprises will have artificial intelligence orchestration platforms at their disposal.

Should you adopt automation?

Automation helps you reduce manual and routine processes. As a business, it will help you save time and cost along with improving operational efficiency. The application of automation is not limited to cutting down costs and time.

Since technology is so advanced today, you can use automation to collect data on a large scale, analyse them and make real-time decisions. Such opportunities bring tremendous advantages to a business that is competitive and analytical with its long-term goals. If used in the right way, such strategies will help a business to understand their customers better and reach the right audience to promote their products and services.

Because it is handled by technology, the opportunity to scale with automation is limitless. Manual processes will not only slow you down, but they are also prone to trivial errors. Also, the security of data becomes a bigger concern if there are more people involved.

Automation can be adopted by any business since customer support, invoice generation, payroll management, documentation, record-keeping and tracking, reporting etc. are common to all businesses and there are already stacks and tools available in the market.

What can you automate?

While customer support, invoice generation, payroll management, documentation, record-keeping and tracking, reporting etc. are the most common tasks that gave way to automation, there are other opportunities that vary depending on the objective and the nature of the business.

For example, here is a list of automation opportunities available to different businesses.

Automation opportunities

Batch Scheduling

A batch is a collection of tasks or jobs that are grouped together for execution. Since they are resource-intensive, batch processes are pre-planned and scheduled overnight when the number of other processes is very less. Ever since the technology advanced enough to have more powerful resources in hand, these batch processes are executed frequently by organizations.

The best possible examples are the banking industry and the stock market. Prior to automation, all the documentation and transactions were handled manually and “closing” was scheduled weekly, monthly or even quarterly. As the technology advanced, the interval between each schedule became shorter and nowadays the closing is executed almost daily.

Automating batch scheduling helps organisations to reduce workload and human interaction. It also helps organisations to ensure that the process is executed on time.

Examples of batch process automation tools include IBM workload automation, VisualCron, VisualCron and Redwood RunMyJob.

Business Process Automation (BPA)

Business Process Automation mainly deals with sales, marketing, finance etc. BPA tools are essential for competitive businesses since they help with scaling and growth through digital transformation. Since enterprises deal with file transfer, report generation and distribution, employee onboarding, document approval etc. at a large scale on a day-to-day basis, BPA has become essential in this day and age.

Robotic Process Automation (RPA)

Robotic Process Automation should not be confused with BPA. RPA executes predefined tasks via user interfaces. Each RPA program will have certain rules that it follows to complete the task. Usually, RPA software is trained by a human being to execute the tasks. When ready, such programs can completely replace a human being; hence they are implemented mostly to reduce workload. Recent developments in Machine learning and Artificial Intelligence have enhanced RPA capabilities. Website scraping, screen scraping etc. are the best examples of RPA.

Big Data Automation

Big data automation is implemented to collect, organize, and process large chunks of data in a noticeably short time. It is immensely helpful in dealing with anomaly detection and fraud detection as the data collected can be used to discover patterns. Big data automation along with other technologies like artificial intelligence and machine learning can be used to detect fraud in big casinos and the banking industry.

Digital Process Automation (DPA)

Digital process automation aims to facilities end to end flow of information in a business ecosystem. It integrates applications, business operations, workforce and customers of a business to ensure that data is available in real-time to all the parties involved and as a result customers have a better experience. Digital process automation is implemented knowingly or unknowingly by businesses when they undergo digital transformation. However, without due focus, the advantages of DPA will be limited in scope. The customer support portals offered by most businesses are the best examples of DPA tools.

Enterprise Automation

Business Process Automation and Robotic Process Automation etc. can be limited to a single department or workflow. However, enterprise automation aims to create a single, enterprise-wide automated environment. Enterprise automation is the integration of different automation workflows implemented within an organisation. When they work in tandem, the overall efficiency and productivity of the enterprise is improved.

IT Process Automation

IT Process Automation aims to replace manual IT processes with the help of automated platforms and orchestration. It helps IT staffs to deploy, run, manage, and monitor processes using a single platform. For example, a business may implement Business Process Automation to augment its digital transformation initiative. The required IT infrastructure and process can be automated using IT Process Automation.

Infrastructure & Operations Automation

Infrastructure & Operations Automation mainly deals with the hardware and middleware of a business environment. There is a need to regularly update operating systems, tweaks configuration, and maintain optimal resource usage to avoid downtime and ensure security. An automated platform can be used for this purpose.

Cloud Automation

Businesses are moving to cloud environments faster than ever before. However, operating and managing cloud resources can still be a challenge for businesses. They must maintain a team who understands the technology to regularly monitor and configure the cloud resources as the demand and traffic can change at any time. Cloud automation aims to automate these tasks and offer a dynamic cloud environment where any changes in demand will be handled in real-time without the need for human interaction. PaaS services like AWS Elastic Beanstalk can help you with cloud automation.

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.

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