BluePes Blog: Insights & Trends
Data Science Usage in Natural Disasters Predictions
Millions of people are affected by natural disasters each year. Wildfires, floods, tornadoes, volcanic eruptions, are just the beginning of a long list of potential disasters. Some can last a few seconds, while others can last for weeks. However, their effects can be felt for decades or even longer, and impact the global economy, infrastructure, agriculture, and human health. The worst part is that the future impact of disasters will grow dramatically due to climate change. Some regions, which previously rarely suffered floods or wildfires, now regularly experience the effects of these natural disasters. Researchers have collected a large amount of data and developed models that predict disasters, but most of these models are far from perfect. For instance, the amount of data that is monitored by satellites and various ground sensors all over the world each minute is incredibly large, and therefore presents a major challenge for researchers. Having lots of information can be an asset, but data requires computational resources. As more data is collected, computational models become increasingly complex and slow. Furthermore, since just a few minutes’ notice in advance of a flood or wildfire can save people’s lives, predictive models must be able to work and do corrections in real time. Artificial Intelligence (AI) techniques and approaches, like data mining, machine learning, and deep learning, can assist in disaster prediction. It is possible for AI to find hidden dependencies in data, which can be a basis for better understanding the mechanism of disasters, and, as a result, making better predictions. Good predictions and warnings reduce economic losses and save lives. We can’t stop most disasters, like floods, hurricanes, volcano eruptions, but we can be prepared for them. In this article, we will illustrate how data science can help in predicting different natural disasters.
- Mykola Lavrskyi
- Apr 20, 2020
- 9 min
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
- Apr 13, 2020
- 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
- Apr 06, 2020
- 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
- Mar 16, 2020
- 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
- Mar 02, 2020
- 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
- Dec 09, 2019
- 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
- Nov 11, 2019
- 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
- Sep 30, 2019
- 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
- Sep 26, 2019
- 6 min