GitHub vs GitLab: Which is the Best in 2024?

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

What Is Artificial Intelligence (AI)?

The term "Artificial intelligence" describes software algorithms and systems that are aimed at solving problems typical to the human mind – such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Programming Languages ​​Most Often Used for AI in 2024

It is worth noting that AI-powered solutions may not be based on any software language – this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are seven languages that are most often used in AI programming.

Python

This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. In addition to compatibility with popular tools for ML and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project) .

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. It is also worth noting that Julia projects can be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

‍

If you are working on a digital solution, one of its integral stages will be UX/UI design. While trivial projects usually have “beaten” paths for creating navigation and visuals, any deviation from the standard can lead to dissatisfaction among end users and increase the entry threshold for them. In this regard, no matter how sophisticated the logic of your project is, it is important to understand the usability principles maintained by human psychology to ensure its excellent user experience.

Theories of Perception in UX/UI Design

Generally speaking, there are several popular theories of perception in UX/UI design:

• Gestalt psychology aimed at gaining an understanding of how people associate individual objects with things that are meaningful to them. The main gestalt principles include figure-ground (according to which some objects, depending on their visual appeal, are perceived as key navigation elements, while others serve as background), similarity (according to which elements with identical visual characteristics are perceived as interconnected), proximity (according to which closely placed objects are also perceived as related to each other), and closure (which states that incomplete objects, if sufficiently detailed, can be perceived by users as complete).
• Affordance theory, claiming that to ensure intuitiveness, users should receive some cues from the interface from the very beginning on how to deal with a particular element – one way to do this is to use skeuomorphism;
• Hick's Law, which states that the time it takes a particular user to make a decision is logarithmic to the number of available options (which means that to maximize intuitiveness, the user's choice should always be limited);
• Fitts' Law, stating that the time it takes a user to reach a desired part of the interface depends on its size and distance (which means that to ensure a great user experience, you need to place interactive elements closer to the user's starting point);
• Signal Detection Theory, claiming that users can subconsciously distinguish between meaningful cues (calls to action) and background noise, meaning that they should be visually separated.

Cognitive Load: Its Effects on UI Design

Now let's move from theory to practice and consider individual insights extracted from the above theories of psychology in UX. 

In particular, the first insight is the minimization of cognitive load, that is, the efforts of the human brain that must be applied to perform a specific target action. Thus, the importance of reducing the amount of information or tasks provided to the user becomes clear – otherwise, there is a high probability that the user will begin to act erroneously. 

In practice, such brevity can be achieved by correctly grouping interrelated elements and removing elements whose functionality duplicates each other.

Emotional Response & UX Design

Another valuable insight from UX design and psychology is to establish an emotional connection with the end user through your interface. This is a global task that begins with user behavior analysis and identifying their desires, pain points, and intentions and ends with transforming the obtained information into design elements that resonate with them. 

Ultimately, you will have to walk in the shoes of the average consumer of your software solution to ensure the proper level of empathy with your emotional design. In terms of practical implementation of the emotional response, such user empathy can be achieved through the integration of “user experience stories” that can be presented by a service provider to users as they get acquainted with the digital solution, as well as the use of the right color palette, forms, audio, pop-ups, and text.

The Role of Memory in User Interaction

Since, according to cognitive psychology in design, the average person can concentrate on one object for up to 20-30 minutes, it is important to take this aspect into account if your software solution involves long-term interaction with it. 

Specifically, you can reduce the load on your users' memory by implementing elements and scenarios that work and occur in a way that is familiar to them. Otherwise, if something does not function as expected, it can lead to users’ confusion and erroneous actions. 

Understanding User Behavior Through Heuristics

Heuristics are specific UI design principles that can predict user psychology. Here are some of them:

• Visibility of system status, which implies the importance of informing end users about what is happening with your software solution at the moment through progress bars, loading animations, or status messages;
• Match between the system and the real world, which emphasizes the importance of correspondence between the system's behavior and the knowledge and experience that users received before they became familiar with your software solution;
• User control and freedom, which implies the need to provide end users with the ability to freely navigate your software solution and manage negative scenarios;
• Consistency and standards, which determine the significance of logical connections between elements, terminology, and layouts;
• Error prevention, which consists of preventing erroneous actions by end users by providing them with unambiguous and understandable instructions;
• Recognition over recall, which states that to ensure better intuitiveness, users should “recognize” familiar patterns from the real world in elements and scenarios of your interface.

Thus, you have to perform a heuristic evaluation to check whether your solution is good in terms of usability. Also, note that in reality, there are many more psychology UI design heuristics, but the above list will be sufficient for a basic understanding of what is meant by the term excellent user experience.

The Impact of Attention on User Experience

Many designers, in order to encourage the user to perform a particular action, abuse pop-up notifications and other features to attract attention, thereby increasing the cognitive load, the inadmissibility of which we have already discussed earlier. 

Instead, you can use such approaches as the correct building of a visual hierarchy, optimization of the color palette, breaking down complex processes into several simple ones, etc. And, of course, do not forget to appeal to the memory of users, which already contains some experience of interaction with other digital solutions and can automate some processes of dealing with them due to the familiarity of the mental models implemented.

User Motivation & Reward Systems in UX/UI Design

Finally, from the point of view of UX psychology, many UX/UI designs benefit from gamification, which motivates users to move toward the target action. In general, gamification is aimed at boosting user engagement and creating an exciting user experience that they don’t want to interrupt. Its main elements are rewards and recognition.

At the same time, it is crucial to note that some reward systems are too confusing for the average user and, thus, create unnecessary cognitive load instead of motivation. Moreover, gamification is not considered a universal approach that could be used in any project. Therefore, you have to always start with the appropriateness and the affordance of implementing this concept clearly and simply for the consumer.

Conclusion

We hope that with the help of this article, you were able to understand the importance of UX design psychology and how it is implemented in UI/UX principles like heuristics, emotional response, lowering cognitive load, user motivation, and others. If you are interested in creating a digital solution that fully meets all these psychological principles, feel free to contact us.

Creating simple yet effective versions of new products, known as Minimal Viable Products (MVPs), is a crucial strategy for improving services and making the most of resources. This method focuses on testing products gradually and getting feedback from those who matter most. In this article, we'll look at what MVPs are and give examples of how they've been successfully used to develop better final products.

What is MVP and Why Create It?

An MVP, or Minimal Viable Product, is an important step in creating new products. It uses a straightforward method where a product is built with just enough features to be usable. This approach allows for regular feedback from users, making it easier to adjust and improve the product over time. Starting with the basics, it helps to quickly see if an idea is worth pursuing, saves time and money, and ensures that the final product meets the needs of its users more accurately before it's fully developed and released.

Benefits of MVP

An MVP offers a cost-effective means of gaining insight into a product's potential early on. It comprises essential features, allowing for validation and subsequent addition of functionalities in the final version. Typically, these features are outlined in the product roadmap. Now, let's explore the advantages of introducing an MVP to the market:

• Time to Market. MVP enables a faster route to market, helping you stay ahead of competitors by launching quicker than with a fully developed product.
• Cost Savings. By creating a basic functional version with innovative features, MVP saves money, which is especially beneficial for startups.
• User Testing. MVP allows real user testing, providing valuable feedback to identify what works and what doesn't, enabling you to add only requested features and reduce market risks.
• Flexibility. MVP offers flexibility in product development, allowing you to adapt based on user feedback and market responses, ensuring the product meets evolving needs.
• Investor Appeal. Utilizing MVP demonstrates the product's real value to users, increasing the likelihood of securing funding from investors, even at the concept stage, before market entry.

MVP Development Team

Creating innovative solutions requires a talented team. This team, made up of different experts, works together to make sure the products they develop are both useful and easy to use. Let's take a closer look at the key roles:

• Project Manager. Leads the team, providing guidance, motivation, and problem-solving support to keep the project on track. They also suggest ideas for making the product better and plan for its future.
• Business Analyst. Focuses on understanding what users need and how they use the product. They use data to figure out how to improve the product and make suggestions for changes or new features.
• UI/UX Designers. Create a user-friendly and attractive look for the product. They make sure it's easy to use and looks good, so people enjoy using it.
• App Developers. Build the product based on the design. They write the code that makes everything work properly.
• QA Engineers. Test the product to find any problems and make sure it works well. They help make sure the product is good quality and works as it should.

MVP Development Process

Crafting an MVP demands a carefully planned approach to ensure it meets the needs of users and aligns with market trends. Here's a detailed breakdown of the steps to help you navigate this process effectively:

• Market Research. Conduct thorough research to understand the MVP's purpose, target audience, and competition. Define the ideal customer and assess competitors to find opportunities. Clarify what makes the MVP unique and better than existing options.
• Define Product Value. Clearly explain the problem the MVP solves. Figure out why people would use it and make sure it meets their needs well. Make sure the business model, design, and services all add value for users.
• Feature Prioritization. Decide which features are most important based on what users need and want. Customize features focusing on what's essential for the MVP's success.
• Budgeting and Resourcing. Develop the MVP within a reasonable budget to save money. Decide whether to use your team or hire outside help. Consider hiring professional MVP developers for specific tasks.
• Development and Iteration. Build a skilled team, using outside help for convenience. Focus on making the MVP user-friendly and useful. Keep improving based on user feedback to make it even better.

MVP examples

Even the biggest names in the business world started small: launching a basic version of their product, known as a Minimal Viable Product (MVP). These early versions were their first steps, proving there was a real demand for their vision. By smartly using MVPs to engage early users and gather crucial feedback, they laid the groundwork for what would become global success stories.

Dropbox - Cloud Storage

Dropbox is a classic example of an MVP that used a simple video demonstration to validate consumer interest and market demand for cloud storage solutions. Without initially developing the technology, Dropbox focused on showing potential users the ease and functionality of file syncing across multiple devices. This approach allowed them to gather significant interest and feedback, for refining their product and scaling their user base effectively.

Airbnb - Accommodation Sharing

Airbnb started with a basic website that offered short-term living quarters, breakfast, and a unique business networking opportunity for attendees of large conferences who had trouble finding accommodation. Using their apartment as the first listing, the founders were able to test the market's response to their concept. This MVP proved a demand for such a service, paving the way for the platform's evolution into a global peer-to-peer accommodation service.

Zappos - Online Shoe Retail

Zappos began as a simple website testing the online demand for shoes. The founder, Nick Swinmurn, started by posting photos of shoes from local stores without actually holding inventory. When a pair was ordered, he would purchase the shoes from the store and ship them to the customer. This MVP approach validated the concept that people were willing to buy shoes online, leading to the development of a billion-dollar e-commerce platform.

Uber - Ride Sharing

Uber's MVP started in San Francisco as a simple mobile app that connected users with luxury car services. The initial focus was to solve a specific problem—finding a reliable ride in the city. By limiting the initial service to a small market and focusing on a premium service, Uber was able to refine their model, understand user needs, and eventually expand to offer various transportation options globally.

Spotify - Music Streaming

Spotify's MVP tackled the issue of illegal music downloads by offering a legal streaming service with a vast library of songs. The initial product was a desktop application that provided free, ad-supported music streaming. By addressing copyright issues and focusing on user experience, Spotify was able to demonstrate the viability of streaming as a business model, leading to partnerships with major record labels and a rapidly growing user base.

Conclusion

Creating an MVP demands careful planning and execution. MVPs offer advantages such as quicker market entry, cost-effectiveness, and user-focused design. By adhering to the MVP development process, organizations can drive innovation and enhance customer service in the dynamic industry. Ready to develop your MVP? Contact us to start right now.