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What is Artificial Intelligence?
AI is a branch of computers that creates intelligent machines that can perform tasks and think like humans. AI does this through learning, problem-solving, and improvisation. Some common examples are speech recognition, text recognition, planning, decision management, etc.
Moreover, AI uses machine learning and deep learning techniques to achieve accurate results on various business cases and problems. The first person to envision AI was Alan Turing, who asked, "Can a computer convince a human they're communicating with another human?"
Why should you learn AI?
AI engineers are in high demand, and AI is one of the top-paying jobs in the computer industry. They apply various AI techniques, like machine learning, deep learning, expert systems, etc., to make better systems daily.
An AI engineer's average pay scale ranges from $171,715 to $257,530. Not only that, but the work of an AI engineer also is never dull – you will have one of the most analytical, challenging, and skillful jobs as an AI expert. The best example is a self-driving car, which is on the track of constant development and betterment.
There are a lot of skills, but you don't have to have them all when you join; you can learn some through experience. The following are some essential skills:
- Mathematics, probability, and statistical knowledge
- Ability to understand and write algorithms
- Programming skills – C/C++/Java/Python/R
Other than technical skills, the following are some essential soft skills:
- Analytical approach to problem-solving
- Patience to handle failure and try till you achieve desired results
- Keep oneself updated with the latest AI developments
- Ability to pick up new concepts and scenarios quickly
- Out-of-the-box thinking and creative approach
- Curiosity and openness to new approaches and ideas
Top Artificial Intelligence Interview Questions and Answers
Most Artificial Intelligence Interview Questions and answers are technical and based on various simple and complex concepts. However, some questions will test your judgment and reasoning too. These questions are subjective and based on your understanding of artificial intelligence. We have divided the entire set of artificial interview questions into three levels: Beginner, Intermediate, and Advanced.
Beginner-Level Artificial Intelligence Interview Questions
1. Is AI good or bad?
Well, you can argue for both sides. AI is good because it can do repetitive or dangerous tasks that humans perform. Or you can say that it is terrible because it makes us humans depend greatly on technology. There are justifications for both sides, and there is no right or wrong.
2. Do you think machines will surpass humans? What will happen if this becomes true in the future?
Again, you can answer yes or no. Yes, machines can surpass humans if they think like humans, which is a reality considering technological advancements. However, since humans control AI, machines can only work in tandem and never surpass humans. However, since humans control AI, machines can only work in tandem and never surpass humans. However, since humans control AI, machines can only work in tandem and never surpass humans. If this happens, many tasks can be performed without errors that humans make otherwise. On the other hand, if machines start making all the decisions, there could be chaos in the existing systems.
3. What are the AI technologies that you use every day?
Robots are used in many places to perform repetitive tasks. Drones are used to capture images from any height and direction. Facebook performs face and image recognition. We use many AI technologies knowingly or unknowingly; for example, we use Alexa/Siri, which uses Natural Language Processing. We use many AI technologies knowingly or unknowingly; for example, we use Alexa/Siri, which uses Natural Language Processing. We use many AI technologies knowingly or unknowingly; for example, we use Alexa/Siri, which uses Natural Language Processing. Recently, the news was that UK researchers developed an AI tool (still in the research phase) to detect various brain injuries based on 600 different CT scans.
4. Do you believe self-driving cars will work like human driving someday?
With the current advancements in AI, it is only a matter of time before this happens – this could be one of the answers accurately; there have been tremendous improvements in computer vision and image classification technologies. However, you could also argue that it is a far-fetched dream and can't be as dynamic or accurate as human driving.
5. Which one would you like to pursue – AI or data science – as a career?
The answer to this wouldn't change anything in your technical interview; the interviewer wants to know what you think of both fields. AI would be more research-based work, which will require a lot of technical expertise, whereas, for data science, you would need but also need business knowledge interviewer asks you subjective and opinion questions like these; remember that there is no right or wrong answer as long as you can justify your point with proper reasoning.
6. What do you understand about AI? Give a simple example.
As we discussed above, AI is the intelligence possessed by machines by imitating humans. AI gives the power to machines to perform specific tasks and think the same way humans would in certain situations. For example, through Natural language processing AI technology, we can communicate with machines via speech – like Alexa/Cortana/Siri can understand our queries and respond accordingly.
7. If you were to build an AI system, what would you like to build?
Your answer can be based on what is the one job that you probably would want to automate. For example, a system would enable people to maintain social distancing norms wherever they are and alert the police if someone violates the same.
Drones are already doing that, but someone needs to monitor them. This could be a system where users can appreciate the benefits of maintaining social distancing. Or you could ask for a home management system that can alert you if there is a shortage of groceries, vegetables, and other essential items at home, set up appointments, read out daily news, etc. A robot that can clean the house, chop vegetables, wash and fold clothes, etc., seems like a reasonable system too!
8. How is AI different from machine learning?
Machine learning is one of the branches of artificial intelligence. Therefore, we can say that AI encompasses machine learning. Machine learning algorithms are used by AI technologies to solve complex problems and make decisions. For example, recommendation systems, traffic predictions, fraud detection, marketing automation, etc., are all AI technologies based on machine learning algorithms.
9. What is the association between AI and deep learning?
Deep learning is another branch of AI, just like machine learning, that uses various techniques to solve complex problems. Deep learning techniques like neural networks are used in customer research, data forecasting, pattern recognition, image classification, and recognition.
Here is a simple diagram that helps picture the relationship between AI, ML, and deep learning (DL) .
10. What is deep learning?
Deep learning is a subset of machine learning. In deep learning, computers can predict the output by extracting multiple layers of non-linear features from the input and then passing the same to a classifier. Note that it is essential to transform the input into multiple layers, as a single layer wouldn't be able to produce many features (it would perhaps give a blob, which isn't a handy feature). Some examples of deep learning are classifiers, neural networks, convolution, inception, etc.
11. How is AI related to data science?
AI and data science are correlated because of machine learning and deep learning algorithms used in data science for finding patterns in data and making predictions. Both machine learning and deep learning are branches of AI. Check out our AI vs. Data Science article for more details.
12. Explain the layers of a neural network.
There are three layers in a neural network –
- Input: This layer consists of input values. Input can be loaded from different data sources like files, databases, web services, etc.
- Hidden: There can be many hidden layers that transform the input data received. For most problems, a single hidden layer is sufficient. The activation function propagates the input data through the hidden layers in the forward direction.
- Output: This layer gives the final output. It computes the output through the information received from the last hidden layer. The output layer has only one node.
Source - KDNuggets
13. Explain the different branches (domains) of AI.
The following are the six branches or capabilities of AI
- Machine learning – ML helps machines to understand large input datasets, apply models to the same using various algorithms, and get insights and predictions about the data.
- Neural network – NN tries to mimic a human brain and perform tasks using cognitive science and machines. The neural network uses the concept of neurology. The neural network uses the concept of neurology. The neural network uses the concept of neurology. Neural networks have three layers – input, hidden, and output.
- Robotics – Robotics deals with designing, creating, operating, and using robots. Robots can perform otherwise tricky, dangerous, or monotonous tasks for humans.
- Expert systems – expert systems can mimic the intelligence of a human brain to make decisions. Expert systems are based on reasoning and logic created using if-then-else statements and rules.
- Fuzzy logic – Fuzzy logic is used to modify uncertain information by measuring the degree to which a hypothesis is valid. The degree of truth can lie between 0 and 1, and there are more states than just 0 and 1.
- Natural language processing (NLP) – NLP enables humans to communicate with machines using human language (natural language). Through NLP, the computer can also identify how a user would behave in specific scenarios, such as analyzing the text's tone and recognizingnize speech and sentiments.
14. How many types of AI are there? Which is the most popular one?
The following are the different types of A I:
- Reactive Machines AI (Narrow AI or Weak AI) – machines can work on present data and present the outcome based on only current situations. Such machines can perform specific predefined tasks. Such machines can perform certain predefined tasks. Such machines can perform specific predefined tasks. These systems cannot forecast the future based on past events or data.
– Strong AI is also known as general AI or Artificial General Intelligence (AGI). Such a system can think, act, and respond like humans. Strong AI is sub-categorized as:
- Limited Memory AI – the system has Memory – but is limited, i.e., short-lived. Based on this, it can take immediate decisions for the future based on recent past actions. For example, self-driving cars.
- Theory Of Mind AI – As the name suggests, this type focuses on the mind and emotional intelligence. Much research is happening in this field, and advancements would mean capturing and mimicking human behavior and thoughts.
- Self-aware AI (super AI) – if machines become self-aware, they will be able to compete with human beings and even surpass them. Many Sci-fi movies show the same. However, in reality, this is far-fetched.
The most common type of AI is weak, also known as Artificial Narrow Intelligence (ANI).
15. What is fuzzy logic? Give some applications of fuzzy logic.
Generally, we interpret situations with a 2-state answer – yes/no, true/false, good/bad, etc. However, not all real problems have a yes/no solution. There are more states than 0 and 1 (truth and false). For example, if something is true, the degree of truth is determined using fuzzy logic. This value could be anywhere between 0 and 1.
One of the most critical applications of fuzzy logic is in medical decision-making , which helps physicians in diagnosis, image analysis, biomedical signal analysis, feature extraction, etc. Fuzzy logic is also used in an underwater defense target recognition, mining and metal processing, and decision systems for security trading.
16. How is natural language processing (NLP) useful? Give some examples.
With NLP, text can be analyzed to understand human speech. This helps in topic extraction, stemming, sentiment analysis, text summarization, speech extraction, etc. Voice assistants like Alexa, Cortana, and Siri use NLP to analyze human speech. Whatever a human speaks is converted to text, analyzed, and the best response is found accordingly, then sent back to the device. The device then converts the text into speech, which we hear in a human-like voice.
17. What are Artificial Neural Networks (ANN)?
ANN is a model that imitates how a human brain analyses any information and processes the same. ANN is made of input, output, and hidden layers. In an ANN, all the neurons are connected through nodes. The hidden layer (or set of functions) processes the input information and generates an outcome. ANN has a set of rules called backpropagation which helps to go back to the hidden layer if the outcome is different from the expected one.
18. What are the different types of ANNs?
The following are the different types of artificial neural networks (ANNs):
- Feedforward neural network
It is the simplest type, where data passes through different input nodes until it reaches the output node. Data can move only in a forward (single) direction and consist of multiple hidden layers. They are used in AO technologies like computer vision and face recognition.
- Radial basis function neural network
This function (radial basis) calculates the relative distance between the center and a point. These NNs have two layers and are widely applied for power restoration systems to resume power as soon as possible.
- Multilayer perception
This type has three or more layers and is used to classify data that cannot be linearly separated. Each node in one layer is connected to each node in the next. This type of NN is quite popular in speech recognition and machine translation technologies.
- Convolutional Neural Network
CNN uses a variation of multilayer perceptron and contains one or more convolutional layers. These layers are either pooled or interconnected. CNN is very effective for image classification, NLP, video recognition, signal processing, and recommendation as they apply the convolution operation on the input before passing it to the next layer. This enables the network to be much deeper but still has fewer parameters.
- Recurrent Neural Network (RNN)
In this type, the output of a particular layer can be saved and fed to the input. This way, we can predict the outcome of the layer, and if it is wrong, the system can self-learn till the prediction is correct. The first layer is the same as the front propagation. Each node acts as a memory cell. This type is also called Long Short-Term Memory (LSTM).
- Modular NN
In modular NN, independent networks perform sub-tasks through individual computation. If there are complex computations, those can be broken down into independent network components, thus increasing the speed of computation. Each network is disconnected from the other.
- Sequence-to-sequence models
These consist of two runs. Encoder and decoder process the input and output, respectively. Both the encoder and decoder can use the same or different parameters. These models are extensively used in chatbots, Q&A systems, and machine translation.
19. What is AIOps?
AIOps, or Artificial Intelligence for IT operations, combines human intelligence and algorithmic intelligence to provide more visibility and improve the performance of IT operations and systems. AIOps can automate workflows and perform algorithmic filtering to reduce noise levels.
20. What is the T-test?
The Turing test (T-test) is named after Alan Turing. It is a test of a machine's intelligence and whether or not it can think and respond like a human. This test is a reference for scientists and researchers to check their progress in building AI systems.
21. Do you know about reinforcement learning? How does it work?
Reinforcement learning is a learning algorithm in which the machine is self-trained to make a sequence of decisions. The computer does a lot of trial and error to achieve the required solution to a problem. There is a system of reward and punishment (penalty) for each action the computer takes.
Consider reinforcement learning as gameplay. The machine tries to understand how to perform a task to get the maximum rewards. An example is the training of the model to control autonomous cars.
Intermediate-Level Artificial Intelligence Interview Questions
22. What is Markov's decision process?
In real life, we come across situations that are partly in our control and partly not. For example, arranging a cricket match – we can make all efforts to block the stadium, sell tickets, get organizers, etc. However, there is still uncertainty due to rain or other natural factors beyond our control.
For such cases, the Markov Decision Process (MDP) provides a mathematical framework for modeling decision-making. It is a memory-less random process based on the Markov property that states that the future is independent of the past when we know the present or current state.
23. Which AI mechanism is used by self-driving cars?
Self-driving cars use the mechanism of autonomous driving. Autonomous vehicles have multiple sensors, like cameras, radars, radio antennas, ultrasound, and other equipment, that help capture the surroundings and make the right move. These generate a lot of data that enable self-driving cars to make decisions.
24. What are parametric models? How are they different from non-parametric models?
A fixed set of parameters is used to build a probability model in parametric models. On the other hand, parametric models have a flexible number of parameters. The following table highlights some other differences:
|A fixed set of parameters.
|There are no fixed parameters.
|Makes strong assumptions about data and depends on the population distribution.
|Significantly fewer assumptions and the methods are not dependent on population distribution.
|Used to test group means.
|Example – logistic regression, Naïve Bayes
|Example – decision tree, K nearest neighbor
25. What is reward maximization?
Reward maximization is a concept in reinforcement learning where suitable action is taken to maximize the reward in a particular situation. It is done to ensure that the machines follow the best path particular scenario. Think of a game where a person has to collect many coins to move to the next level.
To maximize his reward, the gamer must take the right path. Each coin will help him move towards the next (reward), and so on. If the gamer takes the right path, he can reach the next level; however, if he doesn't, he faces the wrath of harmful elements like monsters that can eat him.
26. Can overfitting occur in Neural networks? If yes, how can you avoid it?
Yes, overfitting can occur in neural networks. The following are some of the significant ways to avoid overfitting:
- Simplifying the model
- Early stopping
- Data augmentation
Check this detailed article from KDNuggets to know what these methods are.
27. What is a hidden layer in the neural network? How many nodes are there in a hidden layer?
It is a layer between input and output where artificial neurons take weighted inputs, transform them, and use an activation function to produce the output. The number of nodes depends on the problem, and there is no single formula to calculate the same.
28. What is the difference between grid search and random search?
Grid and random search are the two methods to tune a machine learning model's settings (hyperparameters). The following table draws a detailed comparison between grid search and random search:
|researchers set up a grid of the hyperparameter values and trained the model for each of the multiple combinations
|from the grid of hyperparameter values, random combinations of values are selected to train the model.
|The very time-consuming and expensive method
|controls the number of parameters to test, thus reducing the time and cost
|All possible data combinations are tested
|less number of iterations that depends on resources available and time
29. Do you know about Bayesian optimization? How is it done?
Bayesian optimization is done for global optimization of black-box functions, i.e., functions that do not assume any functional forms. Since there is no form and the objective function is unknown, the Bayesian optimization strategy is to treat it as a random function. So, we place a prior over the function that captures beliefs about its behavior.
These function evaluations (beliefs) are treated as data; based on this data, the prior is updated to form posterior distribution, which is used to construct a sampling criterion that determines the next query point. The two most common methods to define posterior distribution are Gaussian processes and the Parzen-Tree estimator. Information source – Wikipedia
30. What are the model parameters?
Model parameters are configuration variables internal to a machine learning model that help to increaincrease the model's accuracycide how to modify input data for the output to change. The value of a model parameter can be estimated from the data through estimation or learning.
31. Do you know about any Deep learning frameworks? Explain any one framework.
There are many deep learning frameworks like PyTorch, Keras, Tensorflow, etc. They help in implementing deep learning models quickly and easily. You can explain briefly ny of the deep learning frameworks that you know ample; TensorFlow supports multiple languages to create models like Python, Java, C++, and R. TensorFlow has a flexible architecture and is used for time series analysis, video analysis, image recognition, text-based applications, sound recognition, etc.
You can install the pip command (pip install TensorFlow ). Tensorflow is built on NumPy but has lazy initialization, i.e., the entire graph is built but runs only when the session calls it.
32. How is NLP done? Explain in detail with one example.
NLP techniques rely on machine learning algorithms to infer insights from human inputs. The most common example is Alexa/Siri, virtual assistants that take the human audio input and respond accordingly.
Here is how Alexa responds to human queries:
- Humans ask a query, let us say, "What is the capital of India?"
- The machine (Alexa) captures the audio and sends it to the Alexa voice service on the cloud.
- The voice is converted to text, and the text is processed
- The appropriate response is formulated and converted back to audio
- This is played by the machine (Alexa)
Not only queries but we can also ask Alexa to play songs, read the news, and many more. For every request, the process is the same.
33. Suppose you have blogs/articles about a topic from various learned users. Which AI technology will you use to get valuable insights from it? How?
Through text, we can allow to easily and quickly relationships and facts about data easily and quickly. This can be done through text analytics (text mining). It uses Natural language processing features to transform text (raw or unstructured) into a structured form so that data can be analyzed using Machine Learning algorithms. This information can then be presented in tables, maps, and charts for easy viewing.
34. Give one example of how fuzzy logic is helpful in real-life.
Most real-life problems involve some uncertainty, and the outcome depends on many factors, so fuzzy logic is helpful in most problems. It is used extensively in facial pattern recognition, weather forecasting systems, medical diagnosis, stock trading, decision-making systems, etc. Many scientific and engineering applications use fuzzy logic too.
35. What are hyperparameters used for?
Hyperparameters help determine the model parameter. They cannot be inferred directly from the data but are manually specified by a practitioner when we tune the model parameters for a machine-learning algorithm .
36. What are the components of fuzzy logic control (FLC)?
The central controller or FLC are the components fuzzified, fuzzy inference rules, and the fuzzified. Other thaBesidese, knowledge base, and rule bases are essential components of FLC.
- Fuzzifier – this converts the crisp input values to fuzzy values.
- Fuzzy knowledge base – knowledge about all the fuzzy relationships between input and output is stored in this. From the fuzzifier, the input variables are fed to inference rules.
- Inference rules engine – this simulates human decisions through reasoning and approximation. It takes in the input from the fuzzifier and applies rules from the rule base.
- Rule base – contains the knowledge about the process
- Defuzzifier – converts the fuzzy output from the inference engine into crisp output values that can be fed to a plant or feedback system.
37. How are image processing and computer vision related?
The following are the key differences between image processing and computer vision:
|Various transformations, like smoothing, stretching, sharpening, contrasting, etc., are done to the image to produce an output.
|The goal is to find information and insights from the input video or image
|Aims at improving the quality of the image or enhancing it for further processing
|Applies image processing techniques and algorithms to emulate human vision like automatic driving, object recognition, etc.
|No need for machine learning or comprehending the image; image processing includes cosmetic improvements to the image
|computer vision is used to enable machines to process the data (image and video) and make decisions like a human by applying machine learning techniques
38. What are expert systems? List the components.
An expert system is a machine or system that can make human-like decisions. These systems can solve complex problems using rules and reasoning through a knowledge base. The expert system consists of two main components, the inference engine (rules) and the knowledge base (reasoning). Another component is the user interface through which the expert system interacts with a user, for example, through a command prompt, dialog box, etc.
39. What is face verification/detection? Which algorithm is commonly used for the same?
Face recognition/verification/detection means finding a face based on its features and similarities by comparison and analysis and representing the key patterns. It is a crucial AI interview question as this AI technology is used in many places – for example, Facebook uses face detection for tagging and other purposes, some smartphones use this technology to unlock the phone, etc.
The LBPH (Local Binary Pattern Histogram) is the most straightforward algorithm for face detection, which can recognize the front and side of the face. Some other popular algorithms are PCA (Principal Component Analysis), Linear Discriminant Analysis, and the hidden Markov model.
40. What is the difference between weak AI and strong AI?
The following table highlights the differences between weak AI and strong AI:
|The response is pre-programmed, and only known responses are possible.
|There are no set answers to a particular question; the responses can vary.
|The machine does not make any decisions or perform human-like thinking, for example, comprehending what a human speaks.
|The machine can think like a human and use clustering and association to process data.
|Every output Y is a function of X, f(X).
|A single function cannot define how the machine will respond to specific queries.
|For example, voice assistants like Alexa, Siri, and a game of chess with the computer.
|AI-based games that can self-improve and give results that are not programmed in the system.
41. What is a heuristic approach in gameplay? Give an example.
The heuristic approach uses intelligent guesswork techniques, making it the best gameplay approachristic technique is not necessarily always accurate but is the most practical method to satisfy immediate goals.
For example, a game of chess, where there are thousands of pre-programmed moves and the computer can only guess what can move based on the opponent, can also be incorrect sometimes.
42. What is robotics? Do you think robots are posing a danger to humans?
Robotics is a branch of AI that involves the design of robot construction, and the working of the robots are intelligent machines that can assist humans in performing routine tasks or tasks that can be dangerous for humans.
Machines taking over humans is still a far-fetched dream! A recent use for robots was to supply food and medicines in COVID hospitals, to avoid contact with humans, and avoid getting infected. Today, robots are controlled by humans and do not pose any danger to humans.
43. What is collaborative filtering?
It is a valuable technique for processing massive data sets for detecting patterns, predicting user preferences, building automatic recommender systems, etc. In a narrow sense, these systems can make predictions about the likes and dislikes of a user based on the interests of other similar users.
For example, if persons A & B both like the genre 'comedy', and person B likes a particular comedy movie M1, it is likely that person A will also like the same movie. In general, collaborative filtering can filter patterns or information that collaborate between multiple data sources, agents, viewpoints, and more. Besides recommender systems, collaborative filtering is used for sensing and monitoring data, finding the nearest neighbors of a data point, etc.
44. What is the Hidden Markov Model? Where is it used?
It is a probabilistic framework where the data is modeled as a series of outputs generated by any hidden internal states. The probability of each state in the data is then determined using inference algorithms. This model has many applications, such as speech recognition systems, molecular biology, etc.
45. What is the difference between inductive and deductive reasoning?
The following table draws a detailed comparison between inductive and deductive reasoning:
|Moves from a particular case (observation) and goes on to generalize the principle.
|A general principle, statement, or hypothesis is applied to particular cases through tests.
|Moves from observation to idea
|Moves from idea to observation.
|The process is observation -> pattern -> hypothesis -> theory.
|The process is theory -> hypothesis -> pattern -> observation.
|Inductive reasoning makes observations into generalizations for how a particular thing works.
|Deductive reasoning has theories that can prove an outcome
|As the world is full of uncertainties, probabilities, and partial knowledge, inductive reasoning is easy to apply.
|Hard to apply as it applies a set of facts that must be true.
Advanced-Level Artificial Intelligence Interview Questions
46. When you play tic-tac-toe with the computer, which algorithm does the computer use to plan its next moves?
The algorithm for a tic-tac-toe game is the minimax algorithm, which evaluates moves that lead to a terminal state. The computer (AI) chooses a move with the maximum score when it has to play and moves with the minimum score when the human has to play to avoid losing to a human player. If the machine wins, it returns +10; else returns -10. If it is a draw, 0 will be returned. The minimax algorithm recursively goes to more profound levels until it finds a terminal.
47. A company receives 1000's of resumes per day. How can AI help in the faster and more accurate selection of resumes to take the next step in the interview process?
Using text recognition and analysis, machine learning algorithms can scan for specific keywords in the resume that will help them filter candidates who do not match the requirements. Further, voice-based assistants can ask a standard shortlisted candidates d set of questions to the calls and select or reject them for the next level based on specific responses.
48. Explain some ways in which AI is helping essential workers to deal with COVID-19.
Answer: The following are different ways in which AI is helping essential workers:
- Drones can capture and monitor various areas to ensure social distancing and supply essentials in containment zones
- Robots can take food and medicines to the patients admitted, ensuring a contactless transfer
- Early diagnosis and detection of the infection
- Drug research
- Apps like Arogya Setu use AI to identify potential cases and help curb the spread of the disease.
49. Which AI technology helps detect and prevent fraud?
Frauds can be detected and prevented using biometric data like fingerprints, DNA, retina scans, and facial patterns. Advanced biometric systems can collect data about user usage patterns, typing speed, and other information and look for deviations to prevent fraud by alerting the user of suspicious activity.
50. How does AI help in targeted marketing? Which algorithm can be used for the same?
In target marketing, specific segments of customers are targeted for focused marketing. For example, new fashion apparel would be trendier among people aged 20-35 years than among those who are less or more in age. So, companies can focus their campaigns and advertisements on this age group. There can be many other factors, like gender, likes, dislikes, etc., for targeted marketing. The following are various ML algorithms that help is targeted marketing:
- Recommender system
- Market basket analysis
- Text analysis, classification, and pattern search
51. Suppose you want to buy bread. You pick some butter and eggs along with it. Many customers do the same – pick bread, butter, and eggs, and some also pick milk. Then you realize that bread-butter, bread-eggs, and bread-milk make for a great combination, and that's why many customers purchase them together! What is this type of analysis called? How can machines do the same type of analysis?
This type of analysis is called the market basket analysis. It analyses the items frequently purchased together to find correlations and helps companies promote sales by offering discounts on such bundles. For example, if you buy two packets of bread, you get 10% off butter, etc.
Many supermarkets use this technique to place items on their shelves, where items bought together are placed together. The two main algorithms used for market basket analysis are association rule mining and the Apriori algorithm. Learn more from our article on Unsupervised learning.
52. Explain the Q-learning algorithm with an example.
Q-learning algorithm is a reinforcement algorithm involving an agent, a set of states, and actions per state. In this algorithm, the agent knows the action under each state by finding the optimal policy. It is a model-free, value-based algorithm. The Q-value or action value improves the behavior of an agent in each iteration. This value is computed using the temporal difference or TD-update rule.
53. Explain how image classification works.
Image classification can be done using deep neural networks. A neural network is fed with various images and classifies them into different categories. For example, dog, car, apple, and plant are the categories, and images about each can be fed to the NN.
TensorFlow is the most common library used to implement deep learning frameworks. Through supervised learning, we can train the network to understand what each of these items looks like (what does a dog look like? How to identify an apple? Etc…). The most accurate NN to classify images is the Convolutional Neural Network (CNN). Once the NN is trained, it can identify images with reasonable accuracy.
54. What is the difference between minimax and maximin algorithms?
Both algorithms are used for game theory. The following table will help you understand the differences between minimax and maximin algorithms:
|The Minimax method tries to mini mize the max imum loss in decision theory.
|The Maximin method tries to maxi mize the min imum gain in decision theory.
|Based on the concept of zero-sum games, where one player's gain is the same as the other person's loss in the game
|It is used for non-zero-sum games where a player tries to maximize his minimum gain.
|Used while referring to one's loss.
|Used while referring to one's gain.
55. What is alpha-beta pruning?
It is an adversarial search algorithm for two-player computer games like tic-tac-toe, chess, etc. It seeks to reduce the number of nodes evaluated by the minimax algorithm in its search tree by pruning the branches that would not possibly influence the final decision for the next move.
56. Explain the working of the minimax algorithm using an example.
The Minimax algorithm focuses on two-player games; one is the computer, and the other is a human. The algorithm creates a tree of all possible moves of all the players to a certain depth. Each position holds a heuristic value (+1, 0, or -1). The algorithm starts with the bottom of the tree and decides the best move that results in a +1 for self and -1 for the other player. It also prunes out all the wrong moves along the traversal path.
Check out this interactive YouTube video for an example of creating a stick game using this algorithm.
57. What is the A-star algorithm?
A-star is a path search algorithm that uses graph traversal, i.e., it starts with a particular starting node of a graph and finds the least distance/shortest time to reach the goal node. A* does this by maintaining a tree of paths that start at the beginning node and extend until the termination criterion is satisfied. The extension is decided at each iteration based on the cost of the path and the estimation of the overall cost required to reach the goal through that path.
58. Explain game theory with an example.
Game theory is used in various fields like economics, computer science, mathematics, engineering, biology, etc., to study strategy and conflict. In-game theory, an agent's success depends on what others choose. Game theory is used to understand various behaviors, such as market behavior, customer behavior, and the behavior of companies, and to make rational decisions when a conflict arises.
An example is the simple game of UNO, where there are multiple decision-makers, known as players, who have a set of strategies or actions that they can perform, which results in a set of outcomes of the game. Players get payoffs based on different outcomes.
For example, if the player's last card is 7 of red, and the person playing before him changes the color to something else, the game's outcome will change. However, if the color remains red, the player wins the game.
59. Which one is more accurate – supervised learning or unsupervised learning?
Supervised learning techniques learn from past experiences and data, that's why they are more trustworthy and accurate. Supervised learning techniques learn from past experiences and data, that's why they are more trustworthy and accurate. Supervised learning techniques learn from past experiences and data, that’s why they are more trustworthy and accurate. The supervised learning technique is considered more accurate because the output is already known. Hence we can train the model and improve it using the labeled data.
60. What are intelligent agents?
An intelligent agent is a program that makes decisions or performs services based on environment, experience, and user inputs. The programs autonomously gather information on a pre-programmed schedule or prompted by the user. We refer to intelligent agents as bots.
For Intelligent systems with AI, the user input is taken through sensors like cameras or microphones, and the agent output is delivered through actuators like a computer screen or speakers. A good example is the virtual assistant Alexa, which takes input through microphones and delivers output through the speaker. Read more.
61. What is the difference between breadth-first and depth-first search algorithms?
The following table highlights the differences between BFS and DFS:
|Uses queue data structure – FIFO or first in, first out.
|Uses stack data structure – LIFO or last in, first out.
|A vertex-based technique to find the shortest path in a graph.
|We may travel through more edges to reach the destination; visited vertices are stacked and popped out if no other vertices are there.
|Suitable for searching vertices closer to the source.
|Suitable for searching vertices that are far from the source.
|Firstly, all the neighbors are identified, and the decision is made for the shortest path.
|The decision is made first, and then all the paths are explored; whichever path satisfies the decision most is considered the best.
62. What is LSTM? What are its components?
LSTM (Long Short-Term Memory) is an improvement over the Recurrent Neural Network (RNN) used for deep learning. It is a complex mechanism that tries to mimic the human brain and can process the entire sequence of data rather than just single points (images). This means that LSTM has feedback connections.
Various applications of LSTM include speech recognition, text recognition, and handwriting recognition systems, amongst others applications. The components of LSTM are the various memory blocks, and we refer to them as cells. Each cell transfers two critical pieces of information to the next cell – the cell state and the hidden state.
The memory blocks remember facts and manipulate them using three following major regulators:
- Input gate: controls the flow of new values into the cell
- Forget gate: controls how long value should remain in the cell
- Output gate: controls how a value can be used for computation of activation function
63. Which is the best algorithm for image classification? Can we use other algorithms? Why or why not?
Convolutional Neural Networks (CNN) are one of the best and most widespread techniques for image classification. With CNN, feature extraction is automatic, and learning is more than other techniques like SVM, logistic regression, etc. This gives more accuracy and less error in the final output. With the other algorithms, much time was spent on feature selection (extraction). CNN selects both local and global features automatically for image classification.
64. Is there a way in which a program can improve itself? How?
Yes, programs can evolve through machine learning. The program keeps repeating itself until it reaches a specific limit. The program keeps repeating itself until it reaches a specific limit. The program keeps repeating itself until it reaches a specific limit. Such algorithms are called genetic algorithms, which can improve by selecting the best possible solution with a set of constraints. Genetic algorithms are said to be the programmatic implementation of the survival of the fittest.
65. Which programming language is ideal for robotics and why?
There are many languages, but C++ and Python are the most preferred. Both are ideal for the Robot Operating system. C++ has many features, like embedded programming, navigation, image processing, motor control, etc., that cater to robotics.
Python comes with loads of libraries for machine learning and deep learning. Other than that, MATLAB is an excellent language to learn for robotics because it supports embedded programming, rapid prototyping, and image processing tools.
AI is a complex topic, and there can be many more Artificial Intelligence Interview questions that an interviewer may ask you. Most Artificial Intelligence Interview questions depend on your answer to the previous question. However, the interviewer tests your aptitude, thinking, observation, and technical skills.
Through these top Artificial Intelligence Interview questions, you can get a fair idea of the question patterns and the common topics you may expect in most interviews. But this list is in no way exhaustive. There can be more questions based on your experience level, and if you have done any AI-related projects, an interviewer will ask you about them.
Preparing the above questions should give you a good level of confidence to clear AI interviews.
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