What is an artificial intelligence Neural Networks?
Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.
What is Prolog in AI?
In AI, Prolog is a programming language based on logic.
Give an explanation on the difference between strong AI and weak AI?
Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.
Artificial Intelligence Interview Question
Mention the difference between statistical AI and Classical AI ?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
What is alternate, artificial, compound and natural key?
Alternate Key: Excluding primary keys all candidate keys are known as Alternate Keys.
Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence. This is known as artificial key.
Compound Key: When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.
Natural Key: Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.
What does a production rule consist of ?
The production rule comprises of a set of rule and a sequence of steps.
Advance Artificial Intelligence Interview Question
Which search method takes less memory?
The “depth first search” method takes less memory.
Which is the best way to go for Game playing problem?
Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.
Mention the difference between statistical AI and Classical AI ?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
Artificial Intelligence Interview Question
Name some popular programming languages in AI.
Some commonly used programming languages in AI include:
- Python
- R
- Lisp
- Prolog
- Java
List some AI Applications and Common uses.
AI-powered tools are applied in various spheres of the economy, including:
- Natural Language Processing
- Chatbots
- Sentiment analysis
- Sales prediction
- Self-driving cars
- Facial expression recognition
- Image tagging
Mention Some Popular Domains of AI.
The most popular domains in AI are:
- Machine Learning
- Neural Networks
- Robotics
- Expert Systems
- Fuzzy Logic Systems
- Natural Language Processing
Advance Artificial Intelligence Interview Question
What is an expert system? What are its characteristics?
An expert system is an Artificial Intelligence program that has an expert-level knowledge about a specific area of data and its utilization to react appropriately. These systems tend to have the capability to substitute a human expert. Their characteristics include:
- High performance
- Consistency
- Reliability
- Diligence
- Unbiased nature
What are the Advantages of an Expert System?
The advantages of an expert system are:
- Easy availability
- Low production costs
- Greater speed and reduced workload
- They avoid motions, tensions, and fatigue
- They reduce the rate of errors.
What are the Hyper Parameters of ANN?
- Learning rate: The learning rate implies how fast the network learns its parameters.
- Momentum: This parameter helps in coming out of the local minima and smoothening the jumps while gradient descents.
- Number of epochs: This shows the number of times the entire training data is fed to the network. Here, the training is referred to as the number of epochs.
Artificial Intelligence Interview Question
What is the Tower of Hanoi?
Tower of Hanoi essentially is a mathematical puzzle that displays how recursion is utilized as a device in building up an algorithm to solve a specific problem. The Tower of Hanoi can be solved using a decision tree and a breadth-first search (BFS) algorithm in AI. With 3 disks, a puzzle can essentially be solved in 7 moves. However, the minimal number of moves required to solve a Tower of Hanoi puzzle is 2n − 1, where n is the number of disks.
What is the Turing test?
The Turing test is a method that tests a machine’s ability to match human-level intelligence. It is only considered intelligent if it passes the Turing test. However, a machine can be considered as intelligent even without sufficiently knowing how to mimic a human, in specific scenarios.
What is an A* Algorithm search method?
A* is a computer algorithm in AI that is extensively used for the purpose of finding paths or traversing graphs – to obtain the most optimal route between nodes. It is widely used in solving pathfinding problems in video games. Considering its flexibility and versatility, it can be used in a wide range of contexts. A* is formulated with weighted graphs, which means it can find the best path involving the smallest cost in terms of distance and time. This makes A* an informed search algorithm for best-first search.
Advance Artificial Intelligence Interview Question
What is a breadth-first search algorithm?
A breadth-first search (BFS) algorithm is used to search tree or graph data structures. It starts from the root node, proceeds through neighboring nodes, and finally moves towards the next level of nodes. Till the arrangement is found and created, it produces one tree at any given moment. As this pursuit is capable of being executed by utilizing the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.
What is a Depth-first Search Algorithm?
Depth-first search (DFS) is an algorithm that is based on LIFO (last-in, first-out). Since recursion is implemented with LIFO stack data structure, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.
What is fuzzy logic? List its Applications.
Fuzzy logic is a subset of AI. It is a way of encoding human learning for artificial processing. It is represented as IF-THEN rules. Some of its important applications include:
- Facial pattern recognition
- Air conditioners, washing machines, and vacuum cleaners
- Anti Skid braking systems and transmission systems
- Control of subway systems and unmanned helicopters
- Weather forecasting systems
- Project risk assessment
- Medical diagnosis and treatment plans
- Stock trading
Artificial Intelligence Interview Question
Mention some popular Machine Learning Algorithms?
Some of the popular Machine Learning algorithms are:
Logistic regression
Linear regression
Decision trees
Support vector machines
What is the role of frameworks of Sickest-learn, Kera’s, Tensor Flow, and PyTorch?
Scikit-learn is an open-source and standard library for Machine Learning. It is like one umbrella under which all the data preprocessing, feature selection, machine learning algorithms are included. It expands on the two most important libraries: NumPy and SciPy, where Numpy is used for scientific computing and data analysis and SciPy is used in statistical, mathematics, scientific, engineering, and technical computing.
Keras is an open-source library written in Python for artificial neural networks It is designed to enable fast experimentation with deep neural networks.
Tenser Flow is an open-source software library with a focus on data flow and differential programming. It is used for machine learning applications.
PyTorch is an open source machine learning library based on the Torch library. It is used for applications such as computer visioning and natural language processing. It was primarily created by Facebook’s AI Research Lab.
Are AI and ML the same? If yes, how, and if not then why so?
No, Artificial intelligence and Machine Learning are not the same. Artificial Intelligence is a field of computer science concerned with building machines that are capable of thinking like humans and performing tasks that typically require human intelligence. AI is a rule-based if-else programming input approach that is static and hardcoded.
M is a subset of AI wherein the machines are not specifically or explicitly programmed to perform certain tasks but rather the machines learn by themselves and improve automatically through experience. In contrast to AI, ML is dynamic where the rules are not predefined and are not known beforehand.
Advance Artificial Intelligence Interview Question
What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?
Artificial intelligence is the all-encompassing branch of computer science to build machines that are capable like humans. Example: Robotics
Machine learning is the subset of AI. It is the practice of getting machines to make decisions without being programmed. It aims to build machines learning through data so that they can solve problems. Example: churn prediction, detection of disease, text classification.
Deep Learning is the subset of Machine Learning. It has neural networks that can perform unsupervised learning from unstructured data. They learn through representation learning, and it could be unsupervised, supervised, or semi-supervised. Deep learning aims to build neural networks that automatically discover patterns for feature detection. Example: uncrewed cars and how they can recognize stop signs on the road.
What is Supervised vs. Unsupervised Learning?
In supervised learning, the algorithm learns on the labeled dataset, where the response is known. This acts as a ‘supervisor’ to train the model that provides an answer key that the algorithm can use to evaluate its accuracy on training data. This is used to predict the values for future or unseen data. Examples of Supervised learning are: predicting the sales price of products, how much loan to grant, churn prediction, predicting survivors from Titanic.
In unsupervised learning, the model infers the hidden structure, pattern on the unlabeled data. There is no response or the target variable present in such data to supervise the analysis from what is right or what is wrong. The machine tries to identify the pattern and gives the response. In the absence of the desired output, the data is categorized or segmented using clustering. The algorithm learns to differentiate correctly between a human’s face to the face of a horse or cat. Examples of unsupervised learning: customer segmentation, image segmentation, market basket analysis, delivery store optimization, identifying accident-prone areas.
Explain Reinforcement Learning. How does it work?
Reinforcement Learning, a type of Machine learning algorithm, which is based on the feedback loop where an agent and environment is set up.
The way it works is the agent learns to behave in an environment, by performing certain actions and observing the rewards and results which it gets from those actions. Hence This technique is behavior-driven and is based on the reinforcements learned via trial-and-error. Example: to learn how to ride a bicycle. This method can be used to optimize the operational productivity of systems including supply chain logistics, manufacturing and robotics.
Artificial Intelligence Interview Question
Differentiate between Text Mining and NLP.
| Differentiation | Text Mining | Natural Language Processing |
| What is it? | Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from textual data (semi-structured and unstructured data). | Natural Language Processing (NLP) is the science of teaching machines how to understand the language we humans speak and write |
| How it does? | Text mining can be done using text processing languages such as Perl, statistical models. | Using advanced machine learning models, deep neural networks |
| Outcome: | PatternsCorrelationsFrequency of words | Sentiment AnalysisSemantic meaning of textGrammatical structure |
What is NLP, what are its applications and its components?
Natural Language Processing (NLP), a branch of data science and one of the principal areas of Artificial Intelligence, processes for analyzing, understanding, and deriving information from the text data in a smart and efficient manner.
Applications of NLP are: text classification, text summarization, automated chat bots, multilingual translation, entity detection, machine translation, question answering, sentiment analysis, intent analysis, speech recognition, and topic segmentation.
Natural Language Understanding includes:
- Mapping input to useful representations
- Analyzing different aspects of the language
Natural Language Generation includes:
- Text Planning
- Sentence Planning
- Text Realization
What is the difference between Stemming and Lemmatization?
Stemming: Stemming is a rudimentary rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word.
Lemmatization: Lemmatization, on the other hand, is an organized & step by step procedure of obtaining the root form of the word. It uses vocabulary (dictionary importance of words) and morphological analysis (word structure and grammar relations).
Advance Artificial Intelligence Interview Question
What is the difference between Information Extraction and Information Retrieval?
Information retrieval (IR): It is concerned with storing, searching, and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (stemming). Some current research and applications seek to bridge the gap between IR and NLP.
Information extraction (IE): It is concerned with the extraction of semantic information from text. This covers tasks such as named entity recognition (NER), coreference resolution, relationship extraction.
List the characteristics of the expert system.
The characteristics of an expert system include:
- High performance
- Adequate response time
- Reliability
- Understandability
- Consistency
- Memory
- Diligence
- Logic
- Multiple expertise
- Ability to reason
- Fast response
- Unbiased in nature
- Easy availability
- Low production costs
- Reduces the rate of errors
What are the components of the Expert system?
An expert system mainly contains three components:
- User Interface: It enables a user to interact or communicate with the expert system to find the solution for a problem.
- Inference Engine: It is called the main processing unit or brain of the expert system. It applies different inference rules to the knowledge base to draw a particular solution.
- Knowledge Base: The knowledge base is a type of storage area that stores the domain-specific and high-quality knowledge.
Artificial Intelligence Interview Question
What is an agent in AI?
In reinforcement learning, a domain of artificial intelligence uses agents that senses or perceives the environment by sensors that allow them to understand the settings. The agents have specific goals, and can learn and use the knowledge to achieve the goals.
What is the difference between breadth-first and depth-first search algorithms?
Both breadth-first search (BFS) and depth-first search (DFS) algorithms are used to search tree or graph data structures.
| Breadth-first search algorithm | Depth-first search algorithm |
| It is based on FIFO (first-in, first-out) method. | It is based on the LIFO (Last-in, Last Out) approach. |
| It starts from the root node, proceeds through neighboring nodes, and finally moves towards the next level of nodes. Till the arrangement is found and created, it produces one tree at any given moment | It starts at the root node and searches as far as possible along every branch before it performs backtracking. |
| The method strategy gives the shortest path to the solution. | The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement. |
Explain a bidirectional search algorithm. What is it?
In a bidirectional search algorithm, two searches are run simultaneously. The first search begins forward from the initial state, and the second goes backward in reverse from the goal state. Both the searches meet to identify a common state, and this way, the search ends. The initial state is linked with the goal state in a reverse manner.
Advance Artificial Intelligence Interview Question
How would you explain a uniform cost search algorithm?
In a uniform cost search algorithm, the search starts from the initial state and goes to the neighboring state to choose the ‘least costly’ state. It performs in increasing the cost of the path to a node and expands the least cost node. A breadth-first search algorithm will become a uniform cost search algorithm if it has the same cost in every iteration. It investigates ways in the expanding order of the cost.
What are iterative deepening depth-first search algorithms?
In iterative deepening DFS algorithms, the search process of levels 1 and 2 takes place. The search process continues until the solution is found. It generates nodes until it finds the goal node and saves the stack of nodes it had created.
What is partial order planning?
In partial-order planning, the search takes place over the space of possible plans rather than searching over all the possible situations. The partial-order plan specifies all actions that need to be undertaken but specifies the actions only when required. The goal is to construct a planned piece by piece.
When a plan specifies all the actions you need to perform but specifies the order of the steps only when necessary, it’s called a partial-order plan.
Artificial Intelligence Interview Question
What is FOPL?
First-Order Predicate Logic (FOPL) is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject. It provides:
- A language to express assertions about certain “World”
- An inference system to deductive apparatus whereby we may draw conclusions from such assertion
- A semantic based on set theory
What does FOPL consist of?
FOPL consists of:
- A set of constant symbols
- A set of variables
- A set of predicate symbols
- A set of function symbols
- The logical connective
- The Universal Quantifier and Existential Quantifier
- A special binary relation of equality
Explain Hidden Markov Model.
The hidden Markov model (HMM) is a statistical model used to represent the probability distributions over a chain of observations. In the model, the hidden layer defines a property that assumes the state of a process is generated at a particular time and is hidden from the observer. It assumes that the process satisfies the Markov property. The HMM is used in various applications such as reinforcement learning, temporal pattern recognition in almost all the current speech recognition systems.
Advance Artificial Intelligence Interview Question
What is a Minimax Algorithm? Explain the terminologies involved in a Minimax problem.
Minimax algorithm is a recursive and backtracking algorithm. It is used for decision making in game theory. The algorithm is used to select the optimal moves for a player by assuming that another player is also playing optimally. It is based on two players, one is called MAX, and the other is called the MIN.
The terminologies that are used in the Minimax Algorithm are:
- Game tree: A tree structure encompassing all the possible moves.
- Initial State: The initial position of the board and showing whose move it is.
- Terminal State: It is the position of the board when the game finishes.
- Utility Function: The function that assigns a numeric value for the outcome of the game.
- Successor function: It defines the possible legal moves a player can make.
What is the difference between parametric and non-parametric models?
| Differentiation | Parametric Model | Non-Parametric Model |
| No of Features | Uses a fixed number of parameters to build the model. | Uses an unbounded number of parameters to build the model. |
| Assumptions | It considers strong assumptions about the data. | There are fewer assumptions about the data. |
| Algorithms | Linear Regression, Logistic Regression, Naive Bayes | KNN, Decision Trees,Support Vector Machines |
| Benefits | These take compute faster and require less data | Have higher flexibility, power and performance |
| Algorithms | Linear Regression, Logistic Regression, Naive Bayes | KNN, Decision Trees,Support Vector Machines |
| Limitations | These are constrained, have limited complexity and poor fit | These compute slower and require more data |
What is a hyperparameter and how is it different from the model parameter?
| Differentiation | Model Parameters | HyperParameters |
| What is it? | These are internal to the model and can be estimated from the data | These are external to the model and cannot be estimated from the data. |
| How does it work? | These are features of the training that will learn on its own from the training data. | These are the parameters that will determine the entire training process. |
| Manually set up? | These are often not set manually by the practitioner. | These are often specified and set manually by the practitioner. |
| Used in: | These are required by the model when making predictions. | These are used in processes to help estimate the model parameters. |
| Examples | Split points in Decision TreesWeights and biases | Learning Rate in gradient descentNumber of epochs in ANNNumber of layers in ANN |
Artificial Intelligence Interview Question
What are different algorithms used for hyperparameter optimization?
The three main hyperparameter optimization algorithms are:
- Grid Search: It is a way to detect the family of models parameterized by a grid of parameters. It trains the model for all the possible combinations from the value of hyperparameters provided.
- Random Search: In this, it randomly searches the sample space and evaluates the sets from a probability distribution. Here, the model is run only a fixed number of times.
- Bayesian Optimization: It uses Bayes theorem to direct the search to find the minimum or maximum objective function. It is most useful for objective functions that are complex, noisy, and/or expensive to evaluate.
What is Gradient Descent?
Gradient descent is an optimization algorithm used to minimize the cost function, which is the error term. It is an iterative method that converges to the optimum solution by moving in the direction of the steepest descent as defined by the negative of the gradient. The gradient descent technique has a hyperparameter called learning rate, α that specifies the jumps the algorithm takes to move towards the optimal solution.
What are ensemble techniques?
The general principle of an ensemble method is to train multiple models and combine those predictions with improving robustness over a single model. The technique trains multiple weak predictors on a dataset. These get slightly different results, some models learn some patterns better, and others learn other patterns and then combine their predictions to get overall better performance.
Advance Artificial Intelligence Interview Question
What are the two paradigms of ensemble methods?
The two paradigms of ensemble methods are:
- Parallel ensemble methods: these methods build the several estimators or models independently and then take average for regression or voting for classification problems. Example are: Bagging methods, Random Forest
- Sequential ensemble methods: These fall under the family of Boosting methods where the base estimators are built sequentially and then reduces this bias of the combined estimator. Examples: Ada Boost, Gradient Boost, XG Boost.
