Article | July 20, 2022
Progressive Web Apps (PWAs) enable cross-platform interoperability by giving users access to open web technologies. PWAs give your users an app-like experience that's tailored to their particular device.
PWA development is a collection of best software development practices for making a web application behave like a mobile or desktop app. PWAs work similarly to mobile apps providing push notifications and a home screen icon. Progressive web applications, on the other hand, are more straightforward and faster than standard mobile apps, and they can be shared via a URL.
According to Statista, there are around 3.5 billion smartphone users globally who utilize a variety of devices with various connection speeds and constantly changing conditions.
Web App Vs. Progressive Web App
The number of mobile users has expanded dramatically in recent years, necessitating the development of a better mobile web experience. Smartphones are utilized for a variety of purposes, such as reserving a cab or finding the nearest restaurant. Users expect a positive experience with their mobile devices in order to complete these tasks quickly.
However, traditional websites are unable to give that experience for a variety of reasons, ranging from poor loading speeds to ambiguous user interfaces. Here's where web apps come in, which provide a better user experience regardless of the device or browser you're using.
A typical web app is a website that can be accessed using a URL. Furthermore, these web apps contain several features that give them the appearance and feel of native apps, but they cannot be installed on any device. A progressive web app is the next step that can be installed on a device and appears and feels like a native app. You don't need to use a URL; just install a PWA on your device and run it like any other native app by clicking its icon on the home screen.
Pinterest built its new mobile web experience from the ground up as a PWA, with an eye toward worldwide expansion. However, due to poor mobile performance, the social network discovered that only 1% of its mobile users converted into sign-ups, logins, or app installs.
Realizing that there was a big chance to boost conversion, they rebuilt the mobile web utilizing PWA technology, which resulted in a number of excellent outcomes: when compared to the prior mobile web, time spent is up 40%, user-generated ad income is up 44%, and core engagements are up 60%.
Users of Alibaba's app were dissatisfied with their experience and refused to download or install the app. As a result, Alibaba had to make a progressive web application to make the experience much better for both first-time users and regular customers.
As a result, seasoned users were re-engaged thanks to a significantly better experience for first-timers, push alerts, and home screen shortcuts. Alibaba could swiftly improve its re-engagement rate to match that of its native apps.
To Sum It Up
Developers may create the most excellent progressive apps using standard frameworks like React, Angular, Vue, Polymer, Ionic, and others, which have several advantages over native mobile apps and regular web pages in terms of functionality, user offline experience, and device space usage. Because of their superiority to native desktop and mobile apps in recent years, Progressive Web Programs have emerged as a wonderful alternative to those apps. PWAs take up less space on consumers' smartphones than native apps, encouraging people to install them. Because of caching capabilities, PWAs can work offline because some content is cached during the first visit to the app. Developing a great progressive web app necessitates a strong working relationship with a technical partner. Larger companies can also tap into the PWA industry by giving their users limited access to a PWA and then letting them pick how they want to continue. Overall, you should invest in PWA in 2022 since user experience is critical in this competitive market.
Article | February 3, 2022
As Artificial Intelligence becomes more popular, it’s essential to know the best programming languages AI uses.
If you’re trying to code an AI software solution and don’t know which programming language(s) to use, it can hinder – even hurt – your development process.
However, there are a lot of choices when it comes to programming languages you could use to develop an AI solution. So if you’re wondering what the best programming languages for AI are, you’re at the right place!
Top 12 The Best Artificial Intelligence Programming Languages
Python development is one of the most popular programming languages globally and a top choice for AI developers.
It’s easy to learn, runs on multiple platforms, and provides an interactive environment that allows you to quickly test ideas and make changes without waiting for a compile or deployment phase.
Python development is also one of the fastest-growing languages in popularity and employment opportunities. You can use Python development in various AI applications, including natural language processing (NLP), machine learning, deep learning, and robotics.
Python development has also been used in data science applications such as web scraping, web crawling, and scraping data from websites.
LISP stands for List Processing Language or LISt Processor. It is a functional programming language that makes it easy to create functions that can manipulate lists of data items. John McCarthy created this language at MIT in 1958.
It remains in use today because it is still an excellent vehicle for research in logic programming and because of its continued wide use in artificial intelligence applications such as natural language processing for semantic web applications.
LISP is also great for creating neural networks because it can easily manipulate the network’s weights, which is usually the most challenging part of building a neural network.
R is a free software environment for statistical computing and graphics that runs on multiple platforms, including Windows, Unix, macOS X, and Linux operating systems. Ross Ihaka created R and Robert Gentleman at the University of Auckland, New Zealand, in 1993, originally under the name “S.”
R provides different analysis methods such as linear regression, generalized linear models, time series analysis, classification, and clustering.
The main advantage of using R is that it allows users to develop complex algorithms without having to write complicated code or spend time debugging the code.
Its popularity has increased significantly over time due to its ease of use and flexibility from simple linear regressions to multi-level modeling approaches.
It is one of the most popular and widely used programming languages for developing AI solutions. It is a general-purpose language used to create software applications, operating systems, and other programs.
Bjarne Stroustrup originally developed C++ to enhance C with object-oriented features. Its popularity has increased due to its ability to support multiple paradigms, including object-oriented programming (OOP), imperative and procedural programming techniques, generic programming, and others.
Java is one of the most popular programming languages in the world. As a platform, it has excellent support for AI development. You can use Java for developing machine learning algorithms, deep learning frameworks, and other tools.
Many of the most popular libraries and frameworks are written in Java, including Hadoop and TensorFlow.
Java is also very popular with enterprises because it has excellent performance, scalability, and portability across different operating systems and hardware platforms.
Haskell is an excellent choice for AI development because it allows for the use of a functional programming paradigm.
It means that it will enable you to write your code in a way that focuses on composing functions together instead of focusing on variables and data structures.
While this may not seem like a big deal, it makes it easier to reason about your code and make changes without introducing errors.
Haskell also has strong type inference capabilities, which allow you to focus on what your program does rather than how it does it. It also makes writing tests and ensuring that your code works as expected without manually testing every possible case.
Julia is a high-level, high-performance programming language that makes coding fast and efficient. It’s great for AI development because it’s flexible and easy to learn. Julia combines the speed of C with the usability of Python, making it ideal for numerical computing.
Julia is a multi-paradigm programming language that allows you to write code in numerous styles – from functional to imperative, object-oriented to metaprogramming – all within the same program.
You can hire an AI developer to take advantage of the best tools for each task instead of forcing everyone down a single path.
One of the most popular programming languages for developing AI solutions is Prolog. It was developed in 1972 and used for decades as a powerful logic programming language.
Prolog uses an underlying theory called predicate logic based on facts, rules, and inference.
Prolog became popular due to its ability to represent knowledge in a form that can be queried and reasoned about. It makes it ideal for solving problems that require reasoning about the world.
Another reason Prolog is so famous for developing AI solutions is because it allows you to hire AI developers to combine logic programming with declarative programming through pattern matching and unification.
Scala is a general-purpose programming language that runs on the Java virtual machine. It can be used in distributed and parallel systems, developing scalable software applications.
Scala is easy to learn because it integrates object-oriented languages with functional programming features.
It was developed by Martin Odersky in 2001 at École Polytechnique Fédérale de Lausanne in Switzerland as an academic research project. Many companies have used Scala to develop AI applications.
It has an advantage over other programming languages because it allows to hire AI developers to get the best of both worlds, i.e., object-oriented and functional programming paradigms.
Scala is statically typed and uses JVM for execution. You can use Scala for building web applications and big data applications.
The Rust programming language is one of the most popular languages for developing AI solutions. It was developed by Mozilla and was released in 2010.
The main goal of Rust is to provide a safe, fast, productive, and memory-safe language.
Performance is one of the main features of the rust programming language because it can work on a low level with C/C++ and other systems programming languages.
Rust is also suitable for machine learning because you can use it to write efficient code without sacrificing safety or performance.
Wolfram is a programming language and environment for technical computing, data science, and machine learning.
Students, teachers, researchers, and commercial developers worldwide develop applications that range from technical and scientific to financial analysis, music generation, and art creation.
Wolfram is a great place to start if you’re new to programming because it has a clean syntax that makes it easier to understand than other languages.
It’s also inherently visual – you can build your code by dragging blocks together on a canvas. So you don’t need to spend time learning how to write code from scratch before getting started.
Article | February 8, 2022
Web 3.0, or as it is commonly known as Web3, is the future avatar of the world of the internet and it is based on decentralization. The present-day internet, or Web 2.0 as it should correctly be referred to, has contributed a lot more to transforming the way we live than any other tool in the universe. Web2 has been a key part of building the digital world we live in today, from Snapchat and Instagram filters to using Augmented Reality (AR) to catch Pokémon.
Web3 and Metaverse are the most popular buzzwords today.
With the decentralized network, Web 3.0 means that instead of having a few large companies control all the data, users will have equal access to control their data. Web3 also promises to enhance data and network privacy.
What does Web 3.0 Mean for Businesses?
Web 3.0 presents enormous opportunities for businesses. Let’s explore some of the ways in which Web3 will benefit from this new technology.
Enhance Transparency and Trust
Web 3.0 technology may help organizations and their consumers boost transparency and trust. Blockchain technology, for instance, can contribute to the creation of a tamper-proof record of transactions. This would help businesses increase customer trust by giving customers a clear picture of how they do business.
Presently, in Web 2.0, all of your data is saved in one spot in a centralized system. As a result, it is prone to exploitation. However, nothing is more crucial to a company than data security. Web 3.0 technologies decentralizes your data and assists in increasing its security.
Deeper Customer Insights
Web 3.0 can also help organizations acquire customer insights. How?
Web 3.0 brings in data anonymization that allows businesses acquire customer data without fetching their personal data. It also helps businesses scale consumer data at large without any compromise on privacy. The data then gathered through customer insights can be utilized to enhance products and services.
Finer Data Searchability
Businesses can also deploy Web 3.0 technology to improve the searchability of their data. Using technologies for the semantic web can help make a data web that is easy for machines to search.
This would make it easy for companies to get the information they need quickly and without wasting money or time on manual data searches.
Better User Experience (UX)
By offering a more customized web, Web 3.0 technologies can assist in enhancing the user experience. Artificial intelligence and machine learning algorithms can assist in giving each user a personalized experience that is suited to their specific requirements. As a result, consumer satisfaction and loyalty will rise.
Web 3.0 Possible Business Applications
Some of the most common possible areas where Web3 will definitely have an effect are:
Web 3.0 transforms how app developers create social media apps. It returns ownership of data from platforms to end-users, making story-spinning and data exploitation impossible, as in the Facebook Cambridge Analytics Scandal.
Currency Exchange Service
Centralization fails in money exchange. Mt. Gox's $460 million bitcoin hack is a failure. Decentralized exchanges prevent hacker breeding grounds. Web 3.0 will see decentralized exchanges gain popularity for smooth user trades without hackers or lack of transparency.
Messaging has always played an essential role in our lives. At work, Slack and Telegram replace Facebook Messenger and WhatsApp.
Unsafe message transmission and centralized solutions make messaging systems exploitable.
Web 3.0 messaging applications like ySign, Obsidian, e-Chat, etc., leverage Blockchain to protect users' privacy and security.
The Internet's evolution has been substantial, and this trend will undoubtedly continue. Web 3.0 will revolutionize the way we engage with the digital world, and the transition will affect more than just individuals.
This will lead to more honest and transparent use of customer data, from tailored search results to cross-platform development tools and 3D graphics. A much more immersive and engaging web is on the way.
The new Internet will be around very soon! Let's welcome Web 3.0 with open arms!
Article | June 1, 2022
Natural Language Processing, also known as computational linguistics or NLP, is a branch of Artificial Intelligence (AI), Machine Learning (ML), and linguistics. It is a subfield of AI that enables computers or machines to understand, manipulate, and interpret human language.
Simply put, natural language is the natural method by which humans communicate with one another. We have now trained computers to interpret natural language. Communicating with computers has become simpler with voice queries such as "Alexa, what's the news today?" or "Ok Google, play my favorite songs."
Similarly, when you ask Siri, Apple's voice assistant, "What is the cheapest flight to New York later today?" It instantly searches airline and travel websites for flights from the user's location to New York. It also compares the prices and lists the one with the lowest fare first. So, even without specifying a date or the "lowest fare", Siri understands the inquiry and returns accurate results. This is the result of NLP in action.
Natural Language Processing: Business Applications
Natural language processing has a variety of applications, some of them are listed below.
Summarize text blocks to extract the most relevant and core concepts while excluding unnecessary information.
Develop a chatbot that makes use of Point-of-Speech tagging to enhance customer support. Chatbots are AI systems that use NLP to engage with people through text or voice.
Determine the type of extracted entity, such as a person, location, or organization.
Sentiment Analysis can be used to recognize the sentiment or emotions of a text string, ranging from highly negative to neutral to very positive.
HR teams can utilize NLP-based solutions to scan resumes based on keyword synonyms and swiftly shortlist candidates from a pile of resumes.
Extracting Text data from the data storage allows in extracting specific information from text. Text can be broken down into tokens, or words can be reduced to their root or stem.
Topic categorization helps users organize unstructured text. It's a great way for businesses to obtain insights from customer feedback.
How Can Businesses Prepare for the NLP-Powered Future?
NLP has evolved tremendously, and has benefited both companies and consumers. NLP technologies are assisting businesses to better understand how consumers perceive them through channels such as emails, product reviews, social media postings, surveys, and more.
AI technologies can be used not just to analyze online interactions and how people speak about companies but also to automate tedious and time-consuming operations, enhance productivity, and free up staff to concentrate on more meaningful duties.
When it comes to NLP the sky is the limit. As NLP technology is becoming more prevalent and greater advancements in ability are explored, the future will witness enormous shifts. Here are some of the ways in which businesses can prepare for the future of NLP.
Analyze your company's text data assets and evaluate how the most recent techniques can be used to add value.
Understand how you can use AI-powered language technology to make wiser decisions or rearrange your skilled labor.
Start implementing new language-based AI tools for a range of jobs in order to better understand their potential.
Prepare now to capitalize on transformative AI and to make sure that advanced AI contributes to society fairly.
Thanks to natural language processing technology, conversational commands and everything related to conversational AI in businesses have become faster and better. Natural language processing helps large businesses make flexible choices by revealing consumer moods and market movements. Smart companies now make decisions based not only on data but also on the intelligence derived from NLP-powered system data.