Frequently asked AI (Artificial intelligence) interview questions – part 1

Machine learning and Artificial Intelligence (AI) are closely related but distinct fields within the broader domain of computer science. AI includes not only machine learning but also other approaches, like rule-based systems, expert systems, and knowledge-based systems, which do not necessarily involve learning from data. Many state-of-the-art AI systems are built upon machine learning techniques, as these approaches have proven to be highly effective in tackling complex, data-driven problems.

Artificial Intelligence (AI) is a field of Computer Science focuses on creating systems that can perform tasks that would typically require human intelligence, such as recognizing speech, understanding natural language, making decisions, and learning. We use AI to build various applications, including image and speech recognition, natural language processing (NLP), robotics, and machine learning models like neural networks.

Artificial Narrow Intelligence (ANI), also known as Weak AI, refers to AI systems that are designed and trained to perform a specific task or a narrow range of tasks. These systems are highly specialized and can perform their designated task with a high degree of accuracy and efficiency. This type of technology is also known as Weak AI.

TensorFlow is an open-source platform developed by Google designed primarily for high-performance numerical computation. It offers a collection of workflows that can be used to develop and train models to make machine learning robust and efficient. TensorFlow is customizable, and thus, helps developers create experiential learning architectures and work on the same to produce desired results.

Neural networks are one of many types of ML algorithms that are used to model complex patterns in data. They are composed of three layers — input layer, hidden layer, and output layer.

Deep learning is a subfield of machine learning that focuses on the development of artificial neural networks with multiple layers, also known as deep neural networks. These networks are particularly effective in modeling complex, hierarchical patterns and representations in data. Deep learning is inspired by the structure and function of the human brain, specifically the biological neural networks that make up the brain. Learn more about AI vs ML vs Deep Learning here.

LSTM stands for Long Short-Term Memory, and it is a type of recurrent neural network (RNN) architecture that is widely used in artificial intelligence and natural language processing. LSTM networks have been successfully used in a wide range of applications, including speech recognition, language translation, and video analysis, among others.

A data cube is a multidimensional (3D) representation of data that can be used to support various types of analysis and modeling. Data cubes are often used in machine learning and data mining applications to help identify patterns, trends, and correlations in complex datasets.

There are three main components to NLP: 1. Language understanding - This defines the ability to interpret the meaning of a piece of text 2. Language generation - This is helpful in producing text that is grammatically correct and conveys the intended meaning. 3. Language processing - This helps in performing operations on a piece of text, such as tokenization, lemmatization, and part-of-speech tagging.

Cognitive computing is a type of AI that mimics human thought processes. We use this form of computing to solve problems that are complex for traditional computer systems. Some major benefits of cognitive computing are: • It is the combination of technology that helps to understand human interaction and provide answers. • Cognitive computing systems acquire knowledge from the data. These computing systems also enhance operational efficiency for enterprises.

Some examples of weak AI include rule-based systems and decision trees. Basically, those systems that require an input come under weak AI. On the other hand, a strong AI includes neural networks and deep learning, as these systems and functions can teach themselves to solve problems.

Natural Language Processing (NLP) and Natural Language Understanding (NLU) are two closely related subfields within the broader domain of Artificial Intelligence (AI), focused on the interaction between computers and human languages. Although they are often used interchangeably, they emphasize different aspects of language processing. NLP deals with the development of algorithms and techniques that enable computers to process, analyze, and generate human language. NLP covers a wide range of tasks, including text analysis, sentiment analysis, machine translation, summarization, part-of-speech tagging, named-entity recognition, and more. The goal of NLP is to enable computers to effectively handle text and speech data, extract useful information, and generate human-like language outputs. While, NLU is a subset of NLP that focuses specifically on the comprehension and interpretation of meaning from human language inputs. NLU aims to disambiguate the nuances, context, and intent in human language, helping machines grasp not just the structure but also the underlying meaning, sentiment, and purpose. NLU tasks may include sentiment analysis, question-answering, intent recognition, and semantic parsing

There are many sectors where data mining is applicable, including: Healthcare -It is used to predict patient outcomes, detection of fraud and abuse, measure the effectiveness of certain treatments, and develop patient and doctor relationships. Finance -The finance and banking industry depends on high-quality, reliable data. It can be used to predict stock prices, predict loan payments and determine credit ratings. Retail- It is used to predict consumer behavior, noticing buying patterns to improve customer service and satisfaction.

Data mining is the process of discovering patterns, trends, and useful information from large datasets using various algorithms, statistical methods, and machine learning techniques. It has gained significant importance due to the growth of data generation and storage capabilities. The need for data mining arises from several aspects, including decision-making.