Artificial Intelligence and Neural Networks

NLP (Natural Language Processing) is a more advanced form of Artificial Intelligence. It focuses on applying AI and Smart Algorithms to analyze and interpret natural language. Virtual assistants like Siri and Alexa are good examples of NLP technology . One of the numerous natural language processing applications they build is an environment for human-like conversations. Instead of using word-to-word translation, Google Translate uses natural language processing (NLP) to translate languages. Natural Language Processing is used by word processors to assess grammatical accuracy. To respond to typical questions, customer care centers use natural language processing (NLP) in chatbots. NLP applications can be found all around the world. Natural Language Processing has Use Cases that Serve Multiple Industries. These include – Predictive text Language translations Virtual assistants Text processing Data analytics Text filtration Smart Search  

The word " Artificial Intelligence " is sometimes misinterpreted, yet the technology is more beneficial than harmful. Although past developments in the field of AI may no longer be relevant, the method has evolved significantly over time. Despite the fact that AI is one of the newest branches of intellectual inquiry its roots may be traced back thousands of years. Strong AI is a speculative form of machine intelligence that argues that machines can develop human consciousness on par with humans. Strong AI refers to computers or programs that have their own mind and can think and complete complicated tasks without the need for human intervention. Strong AI uses complicated algorithms to assist systems operate in a variety of scenarios and strong AI-powered robots can make judgments without the need for human intervention. Artificial intelligence with restricted capability is known as weak AI or narrow AI. Weak AI is the application of advanced algorithms to specific probl

In general, data science can be defined as the process of extracting information and insights from data. It is a combination of several tools, algorithms and machine learning techniques that uncover hidden patterns in raw data. With the introduction of Artificial Intelligence and Deep Learning, the notions of Data Science have reached a pinnacle in recent years. The basic cornerstone of Data Science is the ability to extract significant insights from unstructured and raw data. We will present you with in-depth knowledge about Data Science and its importance in this blog. Because data science can help transform raw data into actionable insights, it has become a must-have for businesses. A Data Scientist is an expert in the fields of statistics and computer science and he employs his analytical skills to address a variety of business challenges. Role of Data Scientist Each firm uses data in a unique way, and the way data is used also differs from one organization to the next.

In gaming artificial intelligence (AI) refers to responsive and adaptive video game experiences. These AI-powered interactive experiences are typically created by non-player characters or NPCs that act intelligently or creatively as if they were commanded by a human gamer. The AI engine is what governs how an NPC behaves in the gaming world. While AI has long been present in video games in some form or another, it is now regarded a burgeoning new frontier in terms of how games are created and played. AI games are gradually handing up control of the gaming experience to the player, whose actions contribute to the game's creation. AI procedural generation, also known as procedural storytelling, in game design refers to game data being produced algorithmically rather than every element being built specifically by a developer. The goal of artificial intelligence in gaming is to improve the player's experience. It's especially critical now that game creators are deliver

  Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analyzing the data. COVID-19 has affected more than 100 countries in a matter of no time. This will affect people all across the planet in the future. The development of a control system that can detect the coronavirus is critical. The detection of disease with the use of various AI tools could be one option to control the current chaos. Using classical and ensemble machine learning techniques, we categorized textual clinical reports into four types in this research. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. Traditional and ensemble machine learning classifiers were given these features. Logistic regression and Multinomial Naïve Bayes showed better results than other