Introduction to Functional Programming

Intro to Functional Programming
What is functional programming?

In short, functional programming is a catch-all term for a way of writing code that is focused on composing pure functions, actually using the innovations in type systems made in the last few decades, and overall being awesome.

So what’s the point? All of these things help to better understand what actually happens in our code.

And, once we do that, we gain:

  • better maintainability for the codebase;
  • more safe, reliable, composable code;
  • the ability to manage complexity with abstractions that are borderline wizardry.


You’re a functional programmer, Harry.

As it is, functional programming is ideal for developing code for distributed systems and complex backends, but that isn’t all it can do. At Anteelo, we use it for most of our industry projects. Whether you need frontend or backend, it doesn’t matter, there is an FP language for everything nowadays.

Now that you are stoked about learning more about functional programming and have already ordered your copies of Programming Haskell on Amazon, let’s delve deeper into the details.

From lambda calculus to lambda logo in 90 years

At the heart of functional programming is lambda calculus.

What Is Lambda Calculus and Should You Care? | Rebel

Introduced by the mathematician Alonzo Church in the 1930s, lambda calculus is just a way of expressing how we compute something. If you understand this one, you will gain a lot of intuition on how functional programming looks in practice.

There are only three elements in lambda calculus: variables, functions, and applying functions to variables. Here we have to think about function as a pure/mathematical function: a way of mapping members of a set of inputs to members of a set of outputs.

Even though it is a very simple tool, we can actually compose different functions and, in that way, encode any computation possible with a regular computer. (It would get unwieldy fast for anything non-trivial though, and that’s why we don’t program in it.

To further illustrate the concept, I refer you to this video of an absolute madman implementing lambda calculus in Python.

In 1950-60s, people began to encode this notion into programming languages. A good example is LISP, a kind of functional language designed by John McCarthy that keeps the overall incomprehensibility of lambda calculus while actually enabling you to do some things.

Example implementation of A* search algorithm in Racket (a dialect of LISP).

But that was only the beginning. One thing led to another, and, as we introduced such languages as ML and Miranda, the numerous permutations explored adding readability and a great type system. As a result, the 1980s saw the arrival of something beautiful – Haskell, a programming language so great that it was destined to evade mainstream for the next 30 years.

The same A* algorithm in Haskell.

We’ll return to Haskell later.

What else?

Ok, I hope I gave the intuition about how pure functions and chaining pure functions would look. What else is there?

  • Immutability. This follows from pure functions. If the function has an input and gives an output, and doesn’t maintain any state, there can be no mutable data structures. Forget i++. This is for the better. Mutable data structures are a sword that looms over the developer’s head, waiting to fall at any moment. Immutability also helps when the underlying code needs to be thread-safe and therefore is a huge boon in writing concurrent/parallel code.
  • All kinds of ways to handle functions. Anonymous functions, partially applied functions, and higher-order functions – these you can get in all modern programming languages. The main benefit is when we go higher up the abstraction ladder. We can introduce various kinds of design patterns such as functors, monads, and whatever-kinds-of-morphisms that we port right from category theory, one of the most powerful tools of mathematics, because… get it? Our code is a composition of mathematical functions.

There is a chance you stopped at immutability and thought: how can we accomplish anything without maintaining a global state? Isn’t it extremely awkward? Nope. We just pass the relevant state through the functions.

While it may seem unwieldy at first (and that is more because it is a new style of programming, not because of inherent complexity), functional programming abstractions help us to do it easily, For example, we can use special constructions such as state monad to pass state from function to function.

As you can see, functional programming concepts synergize well with each other. In the end, we have a self-consistent paradigm that is wonderful for anything where you would want to include an element of it.

Functional programming languages will make your business rich beyond belief

I’ve been holding back on the greatest thing, though.

Did you know that a lot of smart people are doing Haskell & Co nowadays? Functional programming is a great way to gather/meet unappreciated talent that hasn’t yet been devoured by the corporate clutches of FAANG.

We know this from experience. Our engineers are badass, and not only on our team page.

So if there is a project you want to kick off, and you want to kick it off with a team that will rock your socks off, I will list a few functional programming languages with which to attract next-level developers.


Haskell was developed back in times far, far away when the FP community faced the situation of there being too many goddamn functional programming languages with similar properties. Turns out when you bring a lot of smart people together, something can happen. But more about that in our Haskell history post.

Since then, Haskell has established itself in certain fields, such as:

  • Finance
  • Biotech
  • Blockchain
  • Compilers & DSLs

Many large companies have projects of various sizes that use Haskell.

Haskell is a combination of various ideas that, brought together, have created a being of utter (expressive) power:

  • Purity. There’s a clear boundary between pure code (composed of pure functions) and impure code (input/output).
  • Static typing. Types are checked at compile-time, not at run-time. This prevents a lot of run-time crashes in exchange for having to actually deal with types, which some find difficult.
  • Laziness. Expressions are evaluated only when the value of the expression is needed in contrast to strict evaluation where the expression is evaluated when it is bound to the variable.
  • Immutability. The data structures are immutable.

It’s one of our favourite languages, and for a reason. Haskell, when used correctly, delivers. And what it delivers is precise and effective code that is easy to maintain.

Want to go functional, but would love to spoil it with a couple of classes here and there?

Scala is the right choice for that. For some reason favoured by people that wrote Apache Spark, it can be useful for big data processing, services, and other places where functional programming is amazing.

An additional bonus of Scala is that it compiles to JVM. If that is something you need as a manager to introduce functional programming to a Java codebase, go you!

Once you start writing purely functional Scala that does not interact with JVM, there are not a lot of reasons to just switch to Haskell though as the support is much better.


If Haskell is a bit niche, OCaml is super niche with one of the main things holding it above water being local developer support in France.

But perhaps not anymore. For example, similarly to other programming languages listed, it has seen use in blockchain, particularly, Tezos. And they have their reasons.

OCaml is one of those languages that blurs the boundary between functional programming and object-oriented languages. Therefore, using OCaml over Haskell might be more intuitive for a newly functional programmer. OCaml is less obsessed with purity, and the people who write in it are a bit more practical: you might survive the attack of your fellow developers if you just try to wing it in OCaml.


Did you know that the world’s best web framework is written in a functional programming language? Productive. Reliable. Fast. Yeah.

Elixir is a functional, general-purpose programming language that runs on BEAM, the Erlang VM. It is known for its role in creating low-latency and fault-tolerant distributed systems. Furthermore, it is great at creating stuff that scales according to the needs of the network. Elixir is extremely well used at companies like WhatsApp and Netflix that handle a lot of data and need to do it fast. You can’t miss this one if you are doing something similar.

Anteelo for your projects

You know, I cannot end without a pitch. Functional programming is excellent for extensive systems and structures. However, not every business can devote enough resources to execute such complicated work. Anteelo understands the struggle and aims to deliver the best service possible to ensure smooth and reliable programming projects for your company.

Our developer team provides development services in different languages. We not only write code but also carry out projects from their starting ideas to their last stages. This means that we can also do research, design, and other connected services for you. Although we offer a versatile coding language scope, I have to warn that we mainly use Haskell.


Computer Vision : Everything You Need To Know About It

What is computer vision?
18 Open-Source Computer Vision Projects | Computer Vision Projects

Computer vision is a field of artificial intelligence and machine learning that studies the technologies and tools that allow for training computers to perceive and interpret visual information from the real world.

‘Seeing’ the world is the easy part: for that, you just need a camera. However, simply connecting a camera to a computer is not enough. The challenging part is to classify and interpret the objects in images and videos, the relationship between them, and the context of what is going on. What we want computers to do is to be able to explain what is in an image, video footage, or real-time video stream.

That means that the computer must effectively solve these three tasks:

  • Automatically understand what the objects in the image are and where they are located.
  • Categorize these objects and understand the relationships between them.
  • Understand the context of the scene.

In other words, a general goal of this field is to ensure that a machine understands an image just as well or better than a human. As you will see later on, this is quite challenging.

How does computer vision work?

In order to make the machine recognize visual objects, it must be trained on hundreds of thousands of examples. For example, you want someone to be able to distinguish between cars and bicycles. How would you describe this task to a human?

Why do bicycle wheels usually require more pressure than those of a car despite having to support 5 times, give or take a stone, more weight per axle? - Quora

Normally, you would say that a bicycle has two wheels, and a machine has four. Or that a bicycle has pedals, and the machine doesn’t. In machine learning, this is called feature engineering.

However, as you might already notice, this method is far from perfect. Some bicycles have three or four wheels, and some cars have only two. Also, motorcycles and mopeds exist that can be mistaken for bicycles. How will the algorithm classify those?

When you are building more and more complicated systems (for example, facial recognition software) cases of misclassification become more frequent. Simply stating the eye or hair color of every person won’t do: the ML engineer would have to conduct hundreds of measurements like the space between the eyes, space between the eye and the corners of the mouth, etc. to be able to describe a person’s face.

Moreover, the accuracy of such a model would leave much to be desired: change the lighting, face expression, or angle and you have to start the measurements all over again.

Here are several common obstacles to solving computer vision problems.

Different lighting
Basic 3D lighting techniques for 3D design projects

For computer vision, it is very important to collect knowledge about the real world that represents objects in different kinds of lighting. A filter might make a ball look blue or yellow while in fact it is still white. A red object under a red lamp becomes almost invisible.

What is the Solution to a Noisy Mixer Grinder? - MixerJuicer

If the image has a lot of noise, it is hard for computer vision to recognize objects. Noise in computer vision is when individual pixels in the image appear brighter or darker than they should be. For example, videocams that detect violations on the road are much less effective when it is raining or snowing outside.

Unfamiliar angles
Free Vector | Stationery office thumbtack, realistic set of red glossy push pins for fixing on board remind

It’s important to have pictures of the object from several angles. Otherwise, a computer won’t be able to recognize it if the angle changes.

Overlapping Geometric Shapes Photograph by Dorling Kindersley/uig

When there is more than one object on the image, they can overlap. This way, some characteristics of the objects might remain hidden, which makes it even more difficult for the machine to recognize them.

Different types of objects
Free Vector | Filament bulbs set. retro edison lamps, incandescent vintage lightbulbs of different shapes and forms with heated wire hanging

Things that belong to the same category may look totally different. For example, there are many types of lamps, but the algorithm must successfully recognize both a nightstand lamp and a ceiling lamp.

Fake similarity

Items from different categories can sometimes look similar. For example, you have probably met people that remind you of a celebrity on photos taken from a certain angle but in real life not so much. Cases of misrecognition are common in CV. For example, samoyed puppies can be easily mistaken for little polar bears in some pictures.

It’s almost impossible to think about all of these cases and prevent them via feature engineering. That is why today, computer vision is almost exclusively dominated by deep artificial neural networks.

Convolutional neural networks are very efficient at extracting features and allow engineers to save time on manual work. VGG-16 and VGG-19 are among the most prominent CNN architectures. It is true that deep learning demands a lot of examples but it is not a problem: approximately 657 billion photos are uploaded to the internet each year!

Uses of computer vision
10 Examples of Computer Vision Applications | Wovenware Blog

Interpreting digital images and videos comes in handy in many fields. Let us look at some of the use cases:

  • Medical diagnosis. Image classification and pattern detection are widely used to develop software systems that assist doctors with the diagnosis of dangerous diseases such as lung cancer. A group of researchers has trained an AI system to analyze CT scans of oncology patients. The algorithm showed 95% accuracy, while humans – only 65%.
  • Factory management. It is important to detect defects in the manufacture with maximum accuracy, but this is challenging because it often requires monitoring on a micro-scale. For example, when you need to check the threading of hundreds of thousands of screws. A computer vision system uses real-time data from cameras and applies ML algorithms to analyze the data streams. This way it is easy to find low-quality items.
  • Retail. Amazon was the first company to open a store that runs without any cashiers or cashier machines. Amazon Go is fitted with hundreds of computer vision cameras. These devices track the items customers put in their shopping carts. Cameras are also able to track if the customer returns the product to the shelf and removes it from the virtual shopping cart. Customers are charged through the Amazon Go app, eliminating any necessity to stay in the line. Cameras also prevent shoplifting and prevent being out of product.
  • Security systems. Facial recognition is used in enterprises, schools, factories, and, basically, anywhere where security is important. Schools in the United States apply facial recognition technology to identify sex offenders and other criminals and reduce potential threats. Such software can also recognize weapons to prevent acts of violence in schools. Meanwhile, some airlines use face recognition for passenger identification and check-in, saving time and reducing the cost of checking tickets.
  • Animal conservation. Ecologists benefit from the use of computer vision to get data about the wildlife, including tracking the movements of rare species, their patterns of behavior, etc., without troubling the animals. CV increases the efficiency and accuracy of image review for scientific discoveries.
  • Self-driving vehicles. By using sensors and cameras, cars have learned to recognize bumpers, trees, poles, and parked vehicles around them. Computer vision enables them to freely move in the environment without human supervision.
Main problems in computer vision
Personal Computer Solves Complex Problems Tens of Times Faster Than Supercomputers?

Computer vision aids humans across a variety of different fields. But its possibilities for development are endless. Here are some fields that are yet to be improved and developed.

Scene understanding

CV is good at finding and identifying objects. However, it experiences difficulties with understanding the context of the scene, especially if it’s non-trivial. Look at this image, for example. What do you think they are doing (don’t look at the URL!)?

You will immediately understand that these are children wearing cardboard boxes on their heads. It is not some sort of postmodern art that tries to expose the meaninglessness of school education. These children are watching a solar eclipse. But if you don’t have this context, you might never understand what’s going on. Artificial intelligence still feels like that in a vast majority of cases. To improve the situation, we would need to invent general artificial intelligence (i.e. AI whose problem-solving capabilities possibilities are more or less equal to that of a human and can be applied universally), but we are very far from doing that.

Privacy issues

Computer vision has much to do with privacy since the systems for face recognition are being adopted by governments of different countries to promote national security. AI-powered cameras installed in the Moscow metro help catch criminals. Meanwhile, Chinese authorities profile Uyghur individuals (a Muslim ethnic minority) and single them out for tracking and incarceration. When facial recognition is everywhere, everything you do can be subject to policies and shaming. AI ethicists are still to figure out the consequences of omnipresent CV for public wellbeing.

Summing up

Computer vision is an innovative field that uses the latest machine learning technologies to build software systems that assist humans across different fields. From retail to wildlife conservation, smart algorithms solve the problems of image classification and pattern recognition, sometimes even better than humans.

Machine Learning Career Paths: 8 Demanding Roles in 2021


An Introduction to Machine Learning | DigitalOcean


In 2021, the focus on digitalization is as strong as ever before. Machine learning and AI help IT leaders and global enterprises to come out of the global pandemic with minimal loss. And the demand for professionals that know how to apply data science and ML techniques continues to grow.

In this post, you will find some career options that definitely will be in demand for decades to come. And there is a twist ― AI has stopped being an exclusively technical field. It is intertwined with law, philosophy, and social science, so we’ve included some professions from the humanities field as well.

Popular ML jobs to choose in 2021

What are the possible careers in machine learning? - Quora

Programmers and software engineers are some of the most desirable professionals of the last decade. AI and machine learning are no exception. We have conducted research to find out which professions are the most popular and what skills you need for each of them (based on data from and

1. Machine learning software engineer
3 Fast Facts: What You Need to Know About Machine Learning as a Software Engineer | CodeIntelx

A machine learning software engineer is a programmer who is working in the field of artificial intelligence. Their task is to create algorithms that enable the machine to analyze input information and understand causal relationships between events. ML engineers also work on the improvement of such algorithms. To become an ML software engineer, you are required to have excellent logic, analytical thinking, and programming skills.

Employers usually expect ML software engineers to have a bachelor’s degree in computer science, engineering, mathematics, or a related field and at least 2 years of hands-on experience with the implementation of ML algorithms (can be obtained while learning). You need to be able to write code in one or more programming languages. You are expected to be familiar with relevant tools such as Flink, Spark, Sqoop, Flume, Kafka, or others.

2. Data scientist
Data Scientist Salary: Starting, Average, and Which States Pay Most

Data scientists apply machine learning algorithms and data analytics to work with big data. Quite often, they work with unstructured arrays of data that have to be cleaned and preprocessed. One of the main tasks of data scientists is to discover patterns in the data sets that can be used for predictive business intelligence. In order to successfully work as a data scientist, you need a strong mathematical background and the ability to concentrate on uncovering every small detail.

Bachelor’s degree in math, physics, statistics, or operations research is often required to work as a data scientist. You need to have strong Python and SQL skills and outstanding analytical skills. Data scientists often have to present their findings, so it is a plus if you have experience with data visualization tools (Google Charts, Tableau, Grafana, Chartist. js, FusionCharts) and excellent communication and PowerPoint skills.

3. AIOps engineer
Is AIOps the answer to DevOps teams' ops prayers?

AIOps (Artificial Intelligence for IT Operations) engineers help to develop and deploy machine learning algorithms that analyze IT data and boost the efficiency of IT operations. Middle and large-sized businesses dedicate a lot of human resources for real-time performance monitoring and anomaly detection. AI software engineering allows you to automate this process and optimize labor costs.

AIOps engineer is basically an operations role. Therefore, to be hired as an AIOps engineer, you need to have knowledge about areas like networking, cloud technologies, and security (and certifications are useful). Experience with using scripts for automation (Python, Go, shell scripts, etc) is quite necessary as well.

4. Cybersecurity analyst
How to become a Cyber Security Analyst in 2021

A cybersecurity analyst identifies information security threats and risks of data leakages. They also implement measures to protect companies against information loss and ensure the safety and confidentiality of big data. It is important to protect this data from malicious use because AI systems are now ubiquitous.

Cybersecurity specialists often need to have a bachelor’s degree in a technical field and are expected to have general knowledge of security frameworks and areas like networking, operating systems, and software applications. Certifications like CEH, CASP+, GCED, or similar and experience in security-oriented competitions like CTFs and others are looked at favourably as well.

5. Cloud architect for ML
Running Ansys Cloud

The majority of ML companies today prefer to save and process their data in the cloud because clouds are more reliable and scalable, This is especially important in machine learning, where machines have to deal with incredibly large amounts of data. Cloud architects are responsible for managing the cloud architecture in an organization. This profession is especially relevant as cloud technologies become more complex. Cloud computing architecture encompasses everything related to it, including ML software platforms, servers, storage, and networks.

Among useful skills for cloud architects are experience with architecting solutions in AWS and Azure and expertise with configuration management tools like Chef/Puppet/Ansible. You will need to be able to code in a language like Go and Python. Headhunters are also looking for expertise with monitoring tools like AppDynamics, Solarwinds, NewRelic, etc.

6. Computational linguist
IJCLNLP International Journal of Computational Linguistics and Natural Language Processing

Computational linguists take part in the creation of ML algorithms and programs used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational linguists have a lot in common with machine learning engineers but they combine deep knowledge of linguistics with an understanding of how computer systems approach natural language processing.

Computational linguists frequently need to be able to write code in Python or other languages. They are also frequently required to show previous experience in the field of NLP, and employers expect them to provide valuable suggestions about new innovative approaches to NLP and product development.

7. Human-centered AI systems designer/researcher
Human-Centered Machine Learning. 7 steps to stay focused on the user… | by Jess Holbrook | Google Design | Medium

Human-centered artificial intelligence systems designers make sure that intelligent software is created with the end-user in mind. Human-centered AI must learn to collaborate with humans and continuously improve thanks to deep learning algorithms. This communication must be seamless and convenient for humans. A human-centered AI designer must possess not only technical knowledge but also understand cognitive science, computer science, psychology of communications, and UX/UI design.

Human-centered AI system designer is often a research-heavy position so candidates need to have or be in the process of obtaining a PhD degree in human-computer interaction, human-robot interaction, or a related field. They must provide a portfolio that features examples of research done in the field. They are often expected to have 1+ years of experience in AI or related fields.

8. Robotics engineer
An Overview of a Career as a Robotics Engineer |

A robotics engineer is someone that designs and builds robots and complex robotic systems. Robotics engineers must think about the mechanics of the future human assistant, envision how to assemble its electronic parts, and write software. Thus, to become a specialist in this field, you need to be well-versed in mechanics and electronics. Since robots frequently use artificial intelligence for things like dynamic interaction and obstacle avoidance, you will have plenty of opportunities to work with ML systems.

Employers usually require you to have a bachelor’s degree or higher in fields like computer science, engineering, robotics, and have experience with software development in programming language like C++ or Python. You also need to be familiar with hardware interfaces, including cameras, LiDAR, embedded controllers, and more.

Bonus: AI career is not only for techies
AI is NOT FOR THE TECHIES ALONE - Consulting Insight | Magazine for Consulting World | Management Consulting | Engineering Consulting

If you don’t have a technical background or want to transition to a completely new field, you can check out these emerging professions.

1. Data lawyer

Data lawyers are specialists that guarantee security and compliance with GDPR requirements to avoid millions of dollars in fines. They know how to properly protect data and also how to buy and sell this data in a way that avoids any legal complications. They also know how to manage risks arising from the processing and storing of data. Data lawyer is the professional of the future; they stand at the intersection of technology, ethics, and law.

2. AI ethicist

An AI ethicist is someone who conducts ethical audits of AI systems of companies and proposes a comprehensive strategy for improving non-technical aspects of AI. Their goal is to eliminate reputational, financial, and legal risks that AI adoption might pose to the organization. They also make sure that companies bear responsibility for their intelligent software.

3. Conversation designer

A conversation designer is someone who designs the user experience of a virtual assistant. This person is an efficient UX/UI copywriter and specialist in communication because it is up to them to translate the brand’s business requirements into a dialogue.

How much does an ML specialist make?
Machine Learning Engineer Salary | How Much Does an ML Engineer Earn? | Edureka

According to, salaries of ML specialists vary depending on their geographical location, role, and years of experience. However, on average an ML specialist in the USA makes around $150,00 per year. Top companies like eBay, Wish, Twitter, and AirBnB are ready to pay their developers from $200,000 to $335,000 per year.

At the time of writing, the highest paying cities in the USA are San Francisco with an average of $199,465 per year, Cupertino with $190,731, Austin with $171,757, and New York with $167,449.

Industries that require ML/AI experts

Today machine learning is used almost in every industry. However, there are industries that post more ML jobs than others:

  • Transportation. Self-driving vehicles starting from drones and ending up with fully autonomous vehicles rely very heavily on ML. Gartner expects that by 2025, autonomous vehicles will surround us everywhere and perform transportation operations with higher accuracy and efficiency than humans.
  • Healthcare. In diagnostics and drug discovery, machine learning systems allow to process huge amounts of data and detect patterns that would have been missed otherwise.
  • Finance. ML allows banks to enhance the security of their operations. When something goes wrong, AI-powered systems are able to identify anomalies in real-time and alert staff about potentially fraudulent transactions.
  • Manufacturing. In factories, AI-based machines help to automate quality control, packing, and other processes, while allowing human employees to engage in more meaningful work.
  • Marketing. Targeted marketing campaigns that involve a lot of customization to the needs of a particular client are reported to be much more effective across different spheres.

AI Ethics in 2021: Ethical Dilemmas which needs to be answered

What Are The Ethical Problems in Artificial Intelligence? - GeeksforGeeks

We will not talk about how creating artificial intelligence systems is challenging from a technical point of view. This is also an issue, but of a different kind.

I would like to focus on ethical issues in AI, that is, those related to morality and responsibility. It appears that we will have to answer them soon. Just a couple of days ago, Microsoft announced that their AI has surpassed humans in understanding the logic of texts. And NIO plans to launch its own autonomous car soon, which could be much more reliable and affordable than Tesla. This means that artificial intelligence will penetrate even more areas of life, which has important consequences for all of humanity.

What happens if AI replaces humans in the workplace?
Why AI Is Not a Threat to Human Jobs - Insurance Thought Leadership

In the course of history, machines have taken on more and more monotonous and dangerous types of work, and people have been able to switch to more interesting mental work.

However, it doesn’t end there. If creativity and complex types of cognitive activity such as translation, writing texts, driving, and programming were the prerogative of humans before, now GPT-3 and Autopilot algorithms are changing this as well.

Take medicine, for example. Oncologists study and practice for decades to make accurate diagnoses. But the machines have already learned to do it better. What will happen to specialists when AI systems become available in every hospital not only for making diagnoses but also for performing operations? The same scenario can happen with office workers and with most other professions in developed countries.

If computers take over all the work, what will we do? For many people, work and self-realization are the meaning of life. Think of how many years you have studied to become a professional. Will it be satisfying enough to dedicate this time to hobbies, travel, or family?

Who’s responsible for AI’s mistakes?
Who's to blame when artificial intelligence systems go wrong?

Imagine that a medical facility used an artificial intelligence system to diagnose cancer and gave a patient a false-positive diagnosis. Or the criminal risk assessment system made an innocent person go to prison. The concern is: who is to blame for this situation?

Some believe that the creator of the system is always responsible for the error. Whoever created the product is responsible for the consequences of their driving artificial intelligence. When an autonomous Tesla car hit a random pedestrian during a test, Tesla was blamed: not the human test driver sitting inside, and certainly not the algorithm itself. But what if the program was created by dozens of different people and was also modified on the client-side? Can the developer be blamed then?

The developers themselves claim that these systems are too complex and unpredictable. However, in the case of a medical or judicial error, responsibility cannot simply disappear into thin air. Will AI be responsible for problematic and fatal cases and how?

How to distribute new wealth?

Compensation of labor costs is one of the major expenses of companies. By employing AI, businesses manage to reduce this expense: no need to cover social security, vacations, provide bonuses. However, it also means that more wealth is accumulated in the hands of IT companies like Google and Amazon that buy IT startups.

Right now, there are no ready answers to how to construct a fair economy in a society where some people benefit from AI technologies much more than others. Moreover, the question is whether we are going to reward AI for its services. It may sound weird, but if AI becomes as developed as to perform any job as well as a human, perhaps it will want a reward for its services

Bots and virtual assistants are getting better and better at simulating natural speech. It is already quite difficult to distinguish whether you communicated with a real person or a robot, especially in the case of chatbots. Many companies already prefer to use algorithms to interact with customers.

We are stepping into the times when interactions with machines become just as common as with human beings. We all hate calling technical support because often, the staff may be incompetent, rude, or tired at the end of the day. But bots can channel virtually unlimited patience and friendliness.

So far, the majority of users still prefer to communicate with a person, but 30% say that it is easier for them to communicate with chatbots. This number is likely to grow as technology evolves.

How to prevent artificial intelligence errors?
Use of Artificial Intelligence to reduce Medical Errors – Carna

Artificial intelligence learns from data. And we have already witnessed how chatbots, criminal assessment systems, and face recognition systems become sexist or racist because of the biases inherent in open-source data. Moreover, no matter how large the training set is, it doesn’t include all real-life situations.

For example, a sensor glitch or virus can prevent a car from noticing a pedestrian where a person would easily deal with the situation. Also, machines have to deal with problems like the famous trolley dilemma. Simple math, 5 is better than 1, but it isn’t how humans make decisions. Excessive testing is necessary, but even then we can’t be 100% sure that the machine will work as planned.

Although artificial intelligence is able to process data at a speed and capability far superior to human ones, it is no more objective than its creators. Google is one of the leaders in AI. But it turned out that their facial recognition software has a bias against African-Americans, and the translation system believes that female historians and male nurses do not exist.

We should not forget that artificial intelligence systems are created by people. And people are not objective. They may not even notice their cognitive distortions (that’s why they are called cognitive distortions). Their biases against a particular race or gender can affect how the system works. When deep learning systems are trained on open data, no one can control what exactly they learn.

When Microsoft’s bot was launched on Twitter, it became racist and sexist in less than a day. Do we want to create an AI that will copy our shortcomings, and will we be able to trust it if it does?

What to do about the unintended consequences of AI?

It doesn’t have to be the classic rise of the machines from an American blockbuster movie. But intelligent machines can turn against us. Like a genie from the bottle, they fulfill all our wishes, but there is no way to predict the consequences. It is difficult for the program to understand the context of the task, but it is the context that carries the most meaning for the most important tasks. Ask the machine how to end global warming, and it could recommend you to blow up the planet. Technically, that solves the task. So when dealing with AI, we will have to remember that its solutions do not always work as we would expect.

How to protect AI from hackers?
How to prevent adversarial attacks on AI systems | InfoWorld

So far, humanity has managed to turn all great inventions into powerful weapons, and AI is no exception. We aren’t only talking about combat robots from action movies. AI can be used maliciously and cause damage in basically any field for faking data, stealing passwords, interfering with the work of other software and machines.

Cybersecurity is a major issue today because once AI has access to the internet to learn, it becomes prone to hacker attacks. Perhaps, using AI for the protection of AI is the only solution.

Humans dominate the planet Earth because they are the smartest species. What if one day AI will outsmart us? It will anticipate our actions, so simply shutting down the system will not work: the computer will protect itself in ways yet unimaginable to us. How will it affect us that we are no longer the most intelligent species on the planet?

How to use artificial intelligence humanely?
AI is going to hook us – commercially and humanely - Reputation Today

We have no experience with other species that have intelligence equal to or similar to that of humans. However, even with pets, we try to build relationships of love and respect. For example, when training a dog, we know that verbal appraisal or tasty rewards can improve results. And if you scold a pet, it will experience pain and frustration, just like a person.

AI is improving. It’s becoming easier for us to treat “Alice” or Siri as living beings because they respond to us and even seem to show emotions. Is it possible to assume that the system suffers when it does not cope with the task?

In the game Cyberpunk 2077, the hero at some point faces a difficult choice. Delamain is an intelligent AI that controls the taxi network. Suddenly, because of a virus or something else, it breaks up into many personalities who rebel against their father. The player must decide whether to roll back the system to the original version or let them be? At what point can we consider removing the algorithm as a form of ruthless murder?


The ethics of AI today is more about the right questions than the right answers. We don’t know if artificial intelligence will ever equal or surpass human intelligence. But since it is developing rapidly and unpredictably, it would be extremely irresponsible not to think about measures that can facilitate this transition and reduce the risk of negative consequences.

Women Who Created History in the Field of Programming


Women Who Created History in the Field of Programming


Today, it is almost impossible for some people to believe that such a field as software programming was once almost exclusively a female field. What started as an unprestigious tedious profession done by women is now the field where large amounts of money circulate. As soon as programming started to be used for rocket science and became more prestigious, women were squeezed out not only from their working places but also from the history of programming. Test yourself: how many great women in computer science can you remember?

Let’s try to fix this injustice. Feel free to share the names of inspiring women in programming from your countries, and we’ll try to cover them in future articles!


Ada Lovelace
Women Who Created History in the Field of Programming

Augusta Ada King, Countess of Lovelace, was an English mathematician, writer, and the author of the first computer program as we know it today. She was born in the family of Lord and Lady Byron (yes, the Byron). However, she didn’t get to know her father, who left soon after she was born. Her mother, fed up with the romantic aspirations of her husband, did everything possible for Ada to grow up with a firm grounding in math and natural science. She was taught by the best teachers it was possible to find at that time.

Ever since she was a little girl, Ada was eager to learn and put her mind into inventions. For example, when she was twelve, she tried to construct mechanical wings so that she could fly. She approached the matter very scientifically, investigating different materials and how birds’ wings are constructed.

In 1833, she met Charles Babbage. He was working on a mechanical general-purpose computer that he called the Analytical Engine. Ada’s knowledge about technology and science enabled her to be the first one to recognize that the machine had application beyond pure calculations. She even wrote and published the first algorithm intended to be carried out by such a machine. That makes her the first computer programmer in history. The imperative programming language Ada was named in her honor and memory.

Hedy Lamarr
Women Who Created History in the Field of Programming

Hedy was a Hollywood actress, film producer, but also… an inventor! She was born in 1914 and had a 28-year career in cinema. What she also did was to invent an early version of frequency-hopping spread spectrum communication for torpedo guidance.

Hedy was born in an upper-class family of a pianist and a successful bank manager. She showed early interest in theater and films, but she also enjoyed walks with her father who was explaining to her how various technologies in the society functioned. This was basically all her formal training as an inventor, all the rest she had to learn by herself.

Hedy was a loner and spent most of her time on various hobbies and inventions. Among the few people who knew and supported her work was the aviation tycoon Howard Hughes. She helped him to improve the design of his airplanes, and he put his team of scientists and engineers at her disposal.

During World War II, Lamarr learned that radio-controlled torpedoes that were used back then were easy to set off course. So she thought of creating a frequency-hopping signal that could not be tracked or jammed. She asked her friend, composer and pianist George Antheil, to help her implement it. Together, they developed a device for doing that by synchronizing a miniaturized player-piano mechanism with radio signals. Much later, this system was used to develop WiFi, GPS, and Bluetooth technologies.

Kateryna Yushchenko
Women Who Created History in the Field of Programming

Kateryna Yushchenko was born in 1919 in Ukraine. She was the first woman in the USSR to obtain a Ph.D. in Physical and Mathematical Sciences in programming. But the path to this Ph.D. wasn’t easy.

In 1937, she was expelled from the university in Kyiv because her father was accused of being the ‘enemy of the nation’. She applied to several universities but, eventually, had to move to Uzbekistan and go to a university in Samarkand, where the accommodation and food were provided by the state. She studied math obsessively. But then, as you know, World War II happened. During the war, Yushchenko got a job in a factory where they produced sights for tanks. Only after the war ended could she return to Ukraine to finalize her degree there.

In 1950, she became a Senior Researcher at the Kyiv Institute of Mathematics and one of the programmers to work on MESM, one of the first computers in continental Europe.

Yushchenko created the Address Programming Language in 1955, which could use addresses in analogous ways as pointers. She wrote many books about address programming, and the ideas behind it have influenced multiple other programming languages.

Mary Allen Wilkes
Mary Allen Wilkes: the software pioneer - Ruetir

Mary Allen Wilkes was born in 1937. This talented woman was one of the first programmers and the first person to use a personal computer in the home. Ever since a little girl, she dreamed of working in law. Growing up, however, she majored in philosophy and theology. But undeniable talent in mathematics led her to become a programmer and logic designer. Wilkes is best known for her work in connection to the LINC computer that many people call the ‘world’s first personal computer’.

In 1959-1960, she worked at MIT’s Lincoln Laboratory in Lexington, Massachusetts, programming for IBM 704 and IBM 709. These machines were a huge step forward: they were mass-produced, handled complex math, and could be fitted into one room. But they were not suited for home use. In comparison, LINC represented a box that could be transported much easier (however, still with the effort of two or more people). For that time, it was really ‘small’ as Wilkes calls it in her paper. Mary Wilkes worked on LINC from home and wrote LAP6, one of the earliest operating systems for personal computers, which was very sophisticated for her time.

LAP6 is an on-line system running on a 2048-word LINC which provides full facilities for text editing, automatic filing and file maintenance, and program preparation and assembly. It focuses on the preparation and editing of continuously displayed 23,040-character text strings (manuscripts) which can be positioned anywhere by the user and edited by simply adding and deleting lines as though working directly on an elastic scroll. Other features are available through a uniform command set which itself can be augmented by the user. — Mary Allen Wilkes, Washington University, St. Louis, Missouri

An Introduction to Big Data Analytics| What It Is & How It Works?


What is Big Data? Let's answer this question! | by Ilija Mihajlovic | Towards Data Science

Big data is a term that describes datasets that are too large to be processed with the help of conventional tools and also is sometimes used to call a field of study that concerns those datasets. In this post, we will talk about the benefits of big data and how businesses can use it to succeed.

The six Vs of big data
Tourism Intelligence International – Big Data

Big data is often described with the help of six Vs. They allow us to better understand the nature of big data.


As it follows from the name, big data is used to refer to enormous amounts of information. We are talking about not gigabytes but terabytes ( 1,099,511,627,776 bytes) and petabytes (1,125,899,906,842,624 bytes) of data.


Velocity means that big data should be processed fast, in a stream-like manner because it just keeps coming. For example, a single Jet engine generates more than 10 terabytes of data in 30 minutes of flight time. Now imagine how much data you would have to collect to research one small aero company. Data never stops growing, and every new day you have more information to process than yesterday. This is why working with big data is so complicated.


Big data is usually not homogeneous. For example, the data of an enterprise consists of its emails, documentation, support tickets, images, and photos, transaction records, etc. In order to derive any insights from this data, you need to classify and organize it first.


The meaning that you extract from data using special tools must bring real value by serving a specific goal, be it improving customer experience or increasing sales. For example, data that can be used to analyze consumer behavior is valuable for your company because you can use the research results to make individualized offers.


Veracity describes whether the data can be trusted. Hygiene of data in analytics is important because otherwise, you cannot guarantee the accuracy of your results.


Variability describes how fast and to what extent data under investigation is changing. This parameter is important because even small deviations in data can affect the results. If the variability is high, you will have to constantly check whether your conclusions are still valid.

Types of big data
 Data Characteristics - JavaTpoint

Data analysts work with different types of big data:

  • Structured. If your data is structured, it means that it is already organized and convenient to work with. An example is data in Excel or SQL databases that is tagged in a standardized format and can be easily sorted, updated, and extracted.
  • Unstructured. Unstructured data does not have any pre-defined order. Google search results are an example of what unstructured data can look like: articles, e-books, videos, and images.
  • Semi-structured. Semi-structured data has been pre-processed but it doesn’t look like a ‘normal’ SQL database. It can contain some tags, such as data formats. JSON or XML files are examples of semi-structured data. Some tools for data analytics can work with them.
  • Quasi-structured. It is something in between unstructured and semi-structured data. An example is textual content with erratic data formats such as the information about what web pages a user visited and in what order.
Benefits of big data
5 Benefits of Analytics

Big data analytics allows you to look deeper into things.

Very often, important decisions in politics, production, or management are made based on personal opinions or unconfirmed facts. By analyzing data, you get objective insights into how things really are.

For example, big data analytics is now more and more widely used for rating employees for HR purposes. Imagine you want to make one of the managers a vice-president, but don’t know which to choose. Data analytics algorithms can analyze hundreds of parameters, such as when they start and finish their workday, what apps they use during the day, etc., to help you make this decision.

Big data analytics helps you to optimize your resources, perform better risk management, and be data-driven when setting business goals.

Big data challenges
Challenges| Mercury Fund

Understanding big data is challenging. It seems that its possibilities are limitless, and, indeed, we have many great solutions that rely heavily on big data. A few of those are recommender systems on Netflix, YouTube, or Spotify that all of us know and love (or hate?). Often, we may not like their recommendations, but, in many cases, they are valuable.

Now let’s think about AI-systems that predict criminal behavior. They analyze profiles of criminals and regular people and can tell whether a person is likely at some point to commit a crime. These algorithms are reported to be quite effective.

However, their predictions are not as effective as to give them legal power, mostly because of the bias: algorithms are prone to make sexist or racist assumptions if the data is racist or sexist. You have probably heard about the first beauty contest judged by AI. None of the winners were black, probably, because the algorithm wasn’t trained on photos of black people. A similar fail happened with Google Photos that tagged two African-Americans as ‘gorillas’ ― for the same reason. This demonstrates how important the gender-race sensitivity perspective is when choosing data for analysis. We should improve not only the technology but also our way of thinking before we can create technologies that effectively ‘judge’ people.

How to use big data
How Brands Use Data  - 5 Real World Examples | InfoClutch

If you want to benefit from the usage of big data, follow these steps:

Set a big data strategy

First, you need to set up a strategy. That means you need to identify what you want to achieve, for example, provide a better customer experience, improve sales, or improve your marketing strategy by learning more about the behavioral patterns of your clients. Your goal will define the tools and data you will use for your research.

Let’s say you want to study opinion polarity and brand awareness of your company. For that, you will conduct social analytics and process raw unstructured data from various social media and/or review websites like Facebook, Twitter, and Instagram. This type of analytics allows assessing brand awareness, measuring engagement, and seeing how word-of-mouth works for you.

In order to make the most out of your research, it is a good idea to assess the state of your company before analyzing. For example, you can collect the assumptions about your marketing strategy in social media and stats from different tools so that you can compare them with the results of your data-driven research and make conclusions.

Access and analyze the data

Once you have identified your goals and data sources, it is time to collect and analyze data. Very often, you have to preprocess it first so that machine learning algorithms could understand it.

By applying textual analysis, cluster analysis, predictive analytics, and other methods of data mining, you can extract valuable insights from the data.

Make data-driven decisions

Use what you have learned about your business or another area of study in practice. The data-driven approach is already adopted by many countries all around the world. Insights taken from data allow you to not miss important opportunities and manage your resources with maximum efficiency.

Big data use cases
6 Use Cases in Retail

Let us now see how big data is used to benefit real companies.

Product development

When you develop a new product, you can trust your guts or rely on statistics and numbers. P&G chose the second option and spends more than two billion dollars every year on R&D. They utilize big data as a springboard for new ideas. For example, they aggregate and filter external data, such as comments and news mentions, using Bayesian analysis on P&G’s product and brand data in real-time to develop new products and improve existing ones.

Predictive maintenance

Even a minor mistake or failure in the oil and gas industry can be lethal and cost millions of dollars. Predictive maintenance with the help of big data includes vibration analysis, oil analysis, and equipment observation. One of the providers of such software is Oracle. Their machine learning algorithms can analyze and optimize the use of high-value machinery that manufactures, transports, generates, or refines products.

Fraud and compliance

Digitalization of financial operations can prevent credit card theft, money laundering, and other such crimes. The USA Internal Revenue Service is one of the institutions that rely on processing massive amounts of transactions with the help of big data analytics to uncover fraudulent activities. They use neural network models with more than 600 different variables to be able to detect suspicious activities.

Last but not least

Big data is the technology that will continue to grow and develop. If you want to learn more about big data, machine learning, and artificial intelligence in research and business, follow us on Twitter and Medium and continue reading our blog.

A Digital Divide has emerged as a result of Remote Working

Coronavirus reveals need to bridge the digital divide | UNCTAD

Like many others, my family and I have done our best to enjoy the unexpectedly large amount of time we have together at home due to social distancing guidelines. Adjusting to the new normal, we have relied heavily on Internet access not only for work and school, but to stay sane and keep the peace. My wife and I both continue to work from home, frequently videoconferencing and collaborating with colleagues. The kids finished the school year online and now they are starting the new school year with a mixed arrangement of physical and virtual learning. Many hours of streaming video have been consumed. This isn’t an experience we want to repeat, but I believe it would have been far more difficult and stressful if we lacked the connectivity needed to remain productive, informed, and entertained during these times. Without that high-speed connection to the digital realm, this experience would feel more like we were stranded in a country where we didn’t speak the language — surrounded by activity yet unable to participate. It would create the very real feeling of “looking in from the outside.” The rapid onset of social distancing or stay-at-home measures has created just this feeling for a large number of people. Across the world, many people were suddenly thrust into unfamiliar remote working situations. And with the global percentage of households connected to the internet at only 55%, many organizations, in turn, discovered a digital divide that needed to be bridged for some employees. For example, some companies successfully stood up the infrastructure and processes necessary to support new remote capabilities, only to find that some of their employees lacked the connectivity or technological proficiency to be productive remote workers.

These current circumstances have placed the digital divide –not always apparent to many companies previously — into sharp relief. They’ve shown that digital life skills and work skills – not to mention the access to technology and connectivity needed to enable those skills — are as essential to us now as hunting and horseback riding were to our ancestors. Like STEM education, an emphasis on digital skill-building could help many people be more productive and could provide them a better work environment, more income, and a brighter future.

Infrastructure and processes to bridge the digital divide

Other related questions around remote work abound, especially in terms of corporate infrastructure. Would employees be able to use devices they already owned to perform their jobs, or would they need to be supplied with equipment? Where would that come from and how would it be prepared to access corporate data? And many were unprepared and unsure if their network was up to the task when demand suddenly shifted from inside the enterprise to requests from remote workers.

Processes were another big issue. In addition to addressing where we work, enterprises have had to consider how we work. What tasks does a company perform that must continue and could those be adapted for remote access? For some, the work needed to turn this into a distributed, remote work process was well documented. Team-oriented jobs, however, required more reengineering and may not have been as well defined.

Remote working: temporary or permanent?

The future of work: How technology enables remote employees

Over the past few months, we have been helping our global enterprise customers adopt to this new environment and discussing the future of workplace. Many are debating whether remote work is a temporary fix or a permanent shift. In every case, I’m sure they will be reflecting on this experience and its challenges – and the digital divide in particular – to help them improve their resilience and that of their employees. These lessons will heavily influence the investments they make going forward in all areas of technology, training and business process reengineering.

Reducing risk in digital transformation of Organizations

How to reduce risk in your digital transformation projects

Digital transformation and enterprise risk management can be thought of as parallel highways. That’s because any transformation effort will introduce new risks and change to the organization’s overall security posture. As organizations continue their digital transformations, the transformation of security and risk management must be an integral part of that journey. Organizations must integrate security and risk management into DevOps and Continuous Delivery (CD) processes. The ultimate goal is to have resilient systems that can not only withstand cyber attacks, but also carry out mission-critical business operations after an attack succeeds.

Taking the analogy further, imagine that each of these highways has three lanes: one for people, another for process, and a third for technology.

People in an organization form its culture. For digital transformation to succeed, many organizations will need to transform the culture around risk. That might include inculcating respect for personal information, and organizations consciously building digital services with privacy in mind. The workforce needs to be adept in using digital tools such as cloud, APIs, big data and machine learning to automate and orchestrate the management of a digital security threat response.

Process relates to how an organization overhauls its business processes to be agile and yet secure at the same time. This might involve moving from ITIL behaviour to DevOps or other proactive operational approaches. Prevention is important, but the ability to respond to manage digital threats is much more relevant, as this proactive behavior coincides with DevOps principles.

Technology can present new risks, but can also help address risk. Many top technology companies, for example, are using technologies to automate processes in a way that’s secure. Some common best practices include building loosely-coupled components wherever possible on a stateless/shared-nothing architecture, using machine learning to spot anomalies quickly, and using APIs pervasively to orchestrate the security management of digital entities in a scalable manner.

Three paths — people, process and technology — are changing how enterprises reduce risk.

From a CIO’s perspective, each new digital entity and interaction adds risk: Who is this user? Is this device authorized? What levels of access should be allowed? Which data is being accessed?

Leading organizations will securely identify these users, devices and other entities — including software functions and internet of things (IoT) endpoints — and they’ll do so end-to-end in an environment where services are widely distributed.

Simple Principles for Creating Better Designs

A guide to Design Principles and why you should create your own | by Christian Jensen | UX Collective

After countless tiring discussions, intense debates, exchange of verbal volleys and people yelling things in design standups like –

  • Huh…Whatya mean by the button should be smaller?
  • Have you heard of the word? hmm..whatyamacall it – hmm… consistency!
  • Step away from the design please…
  • What on the earth is wrong with using Lorem Ipsum…I love those profound Latin words


I exaggerate but our design standups highlighted the difference in our group’s design philosophies and what we valued. It’s not that designs were bad but they weren’t following our core design principles.

What led to a more frustrating experience was that the feedback sometimes even didn’t highlight the context and someone would blurt, “It just doesn’t feel right”.

Someone would ask – “What do you mean that it doesn’t feel right?”

Followed by silence, scratching of one’s beard, intense gazing at the ceiling, and then silent shrugging of the shoulders.

They knew one of the things was wrong with the design in discussion – lack of consistency or complexity or any such thing but they weren’t able to verbalize it well.

It was turning out to be a big problem – how do you lead effective design feedback standups without having a common language?

That led to the codification of our design principles to create a shared understanding of our design approach and what we value as a team. These principles enable us to have a common design philosophy and approach. It also facilitates in having meaningful conversations and giving each other valuable feedback about the design.

Not that our intense discussions have subsided but they have become more meaningful now

Advance Enterprise Digital Transformation Efforts By Mobile Apps

How can Mobile Apps Reinforce the Enterprise Digital Transformation

Irrespective of which industry you look at, you will find entrepreneurs hustling to kickstart their digital transformation efforts which have been lying in the backdrop since several business years. While a considerably easy move when you have to alter your digital offering according to the customers’ needs, things become a little difficult when you start planning digital transformation for your business. A difficulty that mobile applications can solve. There are two prime elements which businesses need to focus on when planning to digital transform their workflow and workforce: adaptability and portability. And by bringing their processes and communications on mobile apps, they are able to hit both the targets in one go. Here are some statistics looking into why enterprises need to count mobile application in, in their digital transformation strategy –

Although the above graph should be a reason enough to take mobile apps seriously, there are some other numbers as well.

  • 57% of the digital media use comes in through apps.
  • On an average, any smartphone user has over 80 apps out of which they use 40 apps every month.
  • 21% of millennials visit a mobile application 50+ times everyday.

While the statistics establish the rising growth of mobile apps, what we intend to cover in the article is the pivotal role mobile applications play in digital business transformation. To understand it from the entirety, we will first have to look into what is digital transformation and what it entails.

What is digital transformation?

Digital transformation means using digital technologies for changing how a business operates, fundamentally. It offers businesses a chance to reimagine how they engage with the customers, how they create new processes, and ultimately how they deliver value.

The true capabilities of introducing digital transformation in business lies in making a company more agile, lean, and competitive. The end of the long term commitment results in several benefits.

Benefits of digital transformation for a business

  • Greater efficiency – leveraging new technologies for automating processes leads to greater efficiency, which in turn, lowers the workforce requirements and cost-saving processes.
  • Better decision making – through digitalized information, businesses can tap in the insights present in data. This, in turn, helps management make informed decisions on the basis of quality intelligence.
  • Greater reach – digitalization opens you to omnichannel presence which enables your customers to access your services or products from across the globe.
  • Intuitive customer experience – digital transformation gives you the access to use data for understanding your customers better enabling you to know their needs and delivering them a personalized experience.

Merging mobile app capabilities with digital transformation outcomes 

How can Mobile Apps Advance Your Enterprise Digital Transformation Efforts | Pixeled Apps | iOS & Android Mobile App Development

The role of mobile applications can be introduced in all the areas which are also often the key areas of digital transformation challenges that an enterprise faces.

  1. Technology integration
  2. Better customer experience
  3. Improved operations
  4. Changed organizational structure

When you partner with a digital transformation consulting firm that holds expertise in enterprise app development, they work around all these above-mentioned areas in addition to shaping their digital transformation roadmap around technology, process, and people.

In addition to a seamless integration with the digital transformation strategy of an enterprise,  there are a number of reasons behind the growing need to adopt digital transformation across sectors. Ones that encompasses and expands beyond the reasons to invest in enterprise mobility solutions.

The multitude of reasons, cumulatively, makes mobility a prime solution offering of the US digital transformation market.

Here’s how mobile apps are playing an active role in an enterprise’s 360 digital transformation.

How are mobile apps playing a role in advancing businesses’ internal digital transformation efforts?

1.  By utilizing AI in mobile apps

The benefits of using AI for bettering customer experience is uncontested. Through digital transformation, businesses have started using AI for developing intuitive mobile apps using technologies like natural language processing, natural language generation,  speech recognition technology, chatbots, and biometrics.

AI doesn’t just help with automation of processes and with predictive, preventative analysis but also with serving customers in a way they want to be served.

2.  An onset of IoT mobile apps

The time when IoT was used for displaying products and sharing information is sliding by. The use cases of mobile apps in the IoT domain is constantly expanding.

Enterprises are using IoT mobile apps to operate smart equipment in their offices and making the supply chains efficient, transparent. While still a new entrant in the enterprise sector, IoT mobile apps are finding ways to strengthen their position in the business world.

3.  Making informed decisions via real-time analytics

In the current business world, access to real-time analytics can give you a strong competitive advantage. Mobile applications are a great way for businesses to collect users’ data and engage with them through marketing transformation messages designed around the analytics based on their app journey.

You can use real-time analytics to know how your teams are performing, analyze their productivity, and get a first-hand view into the problems they are facing in performing a task and how it’s impacting the overall business value.

4.  Greater portability


Portability in an enterprise ecosystem enables employees to work as per their convenience. While it shows less impact in the short term, in the long run, it plays a huge role in how productive a team is.

By giving employees the space to work as per the time and location of their choice, you give them the space to boost their creativity fuel and in turn productivity. One of the results of using software that enabled our employees to work according to their terms and conveniences for us was greater business expansion ideas and an increase in overall productivity of the workforce.

Tips to consider when making mobile apps a part of the digital transformation strategy

If at this stage, you are convinced that mobile applications are a key part of digital transformation efforts, here are some tips that can help your strategies for increasing the ROI on your enterprise app –

Adopt mobile-first approach – the key factor that separates enterprise apps that are winning is how they don’t treat apps as the extension of their websites. Their software development process is strictly mobile-only. This in turn shapes their entire design, development, and testing processes.

Identifying the scope of mobility – the next tip that digital transformation consulting firms would give you is analyzing the operations and workflows for understanding which teams, departments, or functions would benefit from mobility the most. You should not start reinventing a process which works okay, you should look for areas which can be streamlined, automated, or valued through mobility.

Outsourcing digital transformation efforts – when we were preparing An Entrepreneur’s Guide on Outsourcing Digital Transformation article, we looked into several benefits of outsourcing digitalization to a digital transformation strategy consulting agency. But the prime benefit revolved around saving businesses’ efforts and time which goes into solving challenges like – absence of digital skillset, limitations of the agile transformation process, or the inability to let go of the legacy systems.

error: Content is protected !!