BluePes Blog: Insights & Trends

Chatbots in NLP

Chatbots or conversational agents are so widespread that the average person is no longer surprised to encounter them in their daily life. What is remarkable is how quickly chatbots are getting smarter, more responsive, and more useful. Sometimes, you don’t even realize immediately that you are having a conversation with a robot. So, what is a chatbot? Simply put, it is a communication interface which can interpret users’ questions and respond to them. Consequently, it simulates a conversation or interaction with a real person. This technology provides a low-friction, low-barrier method of accessing computational resources.

• Mykola Lavrskyi
• 2020-04-13
• 5 min

Sentiment Analysis in NLP

Sentiment analysis has become a new trend in social media monitoring, brand monitoring, product analytics, and market research. Like most areas that use artificial intelligence, sentiment analysis (also known as Opinion Mining) is an interdisciplinary field spanning computer science, psychology, social sciences, linguistics, and cognitive science. The goal of sentiment analysis is to identify and extract attitudes, emotions, and opinions from a text. In other words, sentiment analysis is a mining of subjective impressions, but not facts, from users’ tweets, reviews, posts, etc.

• Mykola Lavrskyi
• 2020-04-06
• 6 min

Why Businesses Choose Self-Hosted Jitsi for Secure Video Communication

With more and more people spending time at home in recent years, finding ways to organize work well and be in touch with work teams is a top priority. There are many specialized services like Skype, Google Hangout, or Microsoft teams here to help us. But there is an interesting alternative: Jitsi, a set of open-source projects that allows you to quickly build and deploy secure video conferencing solutions for your company.

• Mykola Lavrskyi
• 2020-03-16
• 4 min

How to Choose a Software Development Partner in Ukraine?

Nowadays, Ukraine is considered one of the best countries to work from if you are in the IT sector. While the current level of general economic growth is modest, the software industry has been blooming for the past decade, attracting more talent and creating a stable network of professionals.

• Mykola Lavrskyi
• 2020-03-02
• 6 min

Reinforced Learning

Artificial Intelligence uses three basic methods for machine learning: supervised learning, unsupervised learning, and reinforcement learning. In general, these methods are called learning paradigms. The learning paradigm chosen is determined by the specific task at hand. We choose supervised learning for classification and regression tasks. Cluster identification or anomaly detection are typical tasks that can be solved within the unsupervised learning paradigm. The primary goal of reinforced learning is to create software agents that can automatically interact with an environment, learn from it, and determine the optimal behavior in order to optimize its performance. In this article, we will discuss reinforced learning paradigms in detail.

• Mykola Lavrskyi
• 2019-12-09
• 7 min

How Can Data Science Help My Organization?

Nowadays, there is a tendency to hire data scientists or even form data science groups in companies. This does not only apply to specific activity sectors or large organizations. Small and midsize businesses are more frequently involving data scientists, in order to get actionable insights from collected information. So, how does data help to run and grow everyday businesses? There are several areas where collected data and the insights drawn from that data can have a significant impact on business.

• Mykola Lavrskyi
• 2019-11-11
• 6 min

Emotion Recognition

It is obvious that emotions are peculiar to humans and some social animals, like apes, wolves, crows. Emotion recognition is an important part of the communication between people. The efficiency of humans’ interactions depends on how we can predict the behavior of the other person we are interacting with, and, as a result, adjust or change our behavior. Fear can indicate danger; satisfaction indicates that the conversation is successful. Emotion recognition is not an easy task, as the same emotion may be shown differently by different people. With this being said, most people have no trouble distinguishing basic emotions such as fear, anger, disgust, happiness, or surprise, to list a few examples. The question that arises here is whether we can teach a computer to recognize emotions. Because of the advancements made in recent years, the answer is yes. Automatic emotion recognition is a field of study in AI. It is a process of identifying human emotion by leveraging techniques from multiple areas, such as signal processing, machine learning, computer vision, natural language processing. But before we discuss automatic emotion recognition in detail, it is important to explore why this technology is necessary at all. Well, as we already mentioned above, emotions are a powerful source of information. Different surveys said that verbal components convey one-third of human communication, and nonverbal components convey two-thirds. So, successful human-computer interaction needs this channel of communication.

• Mykola Lavrskyi
• 2019-09-30
• 6 min

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) focuses on using computers to understand and derive meaning from human languages. In this formulation, the challenge for NLP is an extremely difficult one. The average 20-year-old native speaker of American English knows 42,000 words (from 27,000 words for the lowest 5% to 52,000 for the highest 5%) and thousands of grammatical concepts. We need a large volume of linguistic knowledge for communication in a professional context, as well as writing books and articles, which we spend decades developing. On the other hand, in everyday life, our language needs are less complex; using a vocabulary of 3000 words is enough to cover around 95% of common texts, such as news items, blogs, tweets, and learning from a text context. This facilitates the process of meaning extraction for computers, especially in terms of performing “simple” tasks like summarization, relationship extraction, topic segmentation, etc.

• Mykola Lavrskyi
• 2019-09-26
• 6 min

Fraud Detection

Fraud losses are the subject of constant interest by organizations and individuals alike. Interest in this area is justified, given that in 2018, 49% of organizations said they had been victims of fraud and economic crime according to PwC. Worldwide card fraud losses totalled $24.26 billion in 2017 according to The Nilson Report. Fraud is a widespread, global issue. Organizations should always monitor their data in order to be fraud resistant. The automatization of this process can reduce costs and detect fraud faster. A powerful helper in fraud detection and understanding how fraud works is Data Science. In addition to detecting known types of fraud, data analysis techniques help to uncover new types of fraud.

• Mykola Lavrskyi
• 2019-09-02
• 4 min