Artificial Intelligence Interview Questions and Answers for 2023 Data Science

AI may be employed to create more effective and devastating weapons. AI is a tool that cybercriminals can employ to hack humans. Numerous end-of-the-world prophets have even envisioned a situation where artificial intelligence will rule, and humans will be reduced to slaves.

The entire world must work together to establish a council and define AI ethics, which addresses all moral questions and problems pertaining to AI systems. These challenges, situations, or worries are those that prompt people, societies, and organizations to consider what is good and wrong. These AI ethics should develop a transparent data privacy policy that gives users some degree of control.

Machine learning is a subset of artificial intelligence that uses mathematical models to enable a system to keep picking up new skills and improve based on experience. On the other hand, artificial intelligence imitates human cognitive processes like learning and problem-solving using logic and math.

I believe that the employment of AI for a noble human cause will make it more significant than anything else. AI can aid in developing vaccinations and treatments for diseases that are now incurable. Additionally, it can aid in developing robotic arms that can aid in sensitive surgeries that are difficult for humans to do. It can assist in developing systems that enable especially abled persons to lead normal lives.

There are billions of linked neurons in the human brain. Interconnections are extremely complicated with billions of connections among billions of neurons. Numerous millions of additional neurons are connected to each link. The cell body is what makes up each neuron in the brain. There is only one axon and one cell body, yet many dendrites are linked. Information from the outside is sent to the cell by the dendrites. The axons in the brain cell help to link the neurons. Two neurons can interact with one another due to these connections or edges. The weight connected to an edge determines how many interactions there are between two neurons. A larger edge weight would indicate higher interactions, and a lower edge weight would indicate lower interactions.

An artificial neural network is designed to mimic the human brain. This design comprises an input layer, one or more hidden layers, and an output layer. There are one or more nodes in each layer. These nodes can accept many inputs and outputs. These nodes are trainable because they have adjustable weights and thresholds. The neuron must adjust its threshold or weights if the intended output is not produced. The data is fed into the artificial neural network through an input layer. Data is transmitted from the input layer to one of the hidden layers. The hidden layer processes the data and, if another hidden layer is available, transmits the output to it. Otherwise, it sends it to the output layer. The output layer then provides the final output. There may be one or more nodes in each layer of the network.

An AI system has a wide range of skills or features. The capacity to make precise decisions by being given enough data to identify patterns is one of these features. Several decision-makers and several different criteria make complex decisions. The systems can reason logically, much like the human brain. Your experiences grow with each new learning encounter. Your experiences can improve your selections, which can also open more favorable prospects. We can use Google Photos as an illustration because it can tag people in the pictures. The user may occasionally be prompted to describe the person in the image. With the information you provide, it can learn and further enhance its capabilities.

Expect to come across this popular question in AI interview questions for freshers. We may require coffee or breakfast to increase the effectiveness of our mornings. Similarly, the input layer might need to be activated in a certain way to get precise output. The artificial neural network's output is determined by computing this activation function over the net input. The input is linked to an integration function that combines external evidence, information, and activation. To guarantee that a neuron's response is bounded, non-linear activation functions are applied. When a signal is fed through a multilayer network with linear activation functions, the output obtained remains the same as that could be obtained using a single-layer network. Due to this, nonlinear activation functions are used in multilayer networks compared to linear functions.

In contrast to rule-based systems, learning-based systems apply general Al through the systems' capacity for learning. A learning system is envisioned to have an infinite capacity to simulate intelligence. It is said to possess adaptive intelligence, or the capacity to learn. It aids in changing the volume of currently held information. It also makes it easier to learn new things. That is what distinguishes learning from rule-based testing. An artificial neural network is an example of a learning system.

The rule-based approach defines a set of rules to arrive at a particular outcome. These rules need to be altered manually. Machines are not expected to learn from events. Instead, it will only work with established rules. Therefore, in situations where no rules have been formulated, it gets stuck and is not able to solve it. It may not even understand the problem. These systems are hard to maintain due to the manual efforts required to define rules for unseen events. Also, they are not very useful in solving complex problems.

The main distinction between rule-based systems and learning-based systems is that rule-based systems are manually programmed, whereas self-learning systems are automatically trained by computers. In other words, instead of receiving explicit instructions from humans, self-learning systems learn through their own experiences. Self-learning systems analyze a lot of historical data and decide depending on what they've learned from it.

Humans make decisions and see patterns in their daily lives. We can achieve the same with neural networks. Everywhere, AI is being used increasingly. For instance, Siri and Alexa are becoming increasingly popular in automating some manual duties like setting alarms, reading notifications, turning on or off lights, and playing music. We have made some progress in bridging the gap between languages thanks to language translation software and programs like Google Translate. AI-based suggestions and recommendations, which we frequently see in e-commerce platforms and other applications, have the potential to increase our spending by considering our preferences. With the development of AI, targeted advertising has become more popular and profitable for businesses.

This is a frequently asked question in Artificial Intelligence interviews questions. Each neuron in an artificial neural network has a direct communication link with every other neuron, and each of these links carries weight. The weights have data about the input signal that the network uses to solve a problem. If we are looking at ‘m’ input nodes and ‘n’ output nodes, then all m x n weights can be represented by a weight matrix of ‘m’ rows and ‘n’ columns.

One way to think of bias is as a constant. For instance, the initial compensation may be viewed as a bias in a salary increase every year by a certain percentage. The constant in the straight-line equation acts like a bias. The bias included in the network has an impact on calculating the net input. Bias is included by adding a component to the input vector. It can be considered another weight that plays a major role in determining the network's output. The bias can be of two types: positive bias and negative bias. The positive bias helps increase the net input of the network, and the negative bias helps decrease its net input.

The threshold is used in activation functions based on which the neural network's final output is calculated. The threshold value defines a limit that is compared against the net input of the network based on which the final output is calculated. For example, in a ramp function, we have thresholds defined at 0 and 1. Based on the comparison of the net input value ‘x’ and the threshold limits, the output is generated. The threshold is usually represented as ‘θ.’

Consider that in the multi-input architecture, and we have 3 input variables or neurons present in the input layer. The output layer will consist of a single neuron representing a single output network. We have one hidden layer in the network with three neurons. Each input neuron is connected to the hidden layer's other neurons. Let w11, w12, and w13 be the weights connected from input neuron x1 to neurons z1, z2, and z3 present in the hidden layer, respectively. Similarly, we have w21, w22, and w23 from input neuron x2 to neurons z1, z2, and z3, respectively. Followed by, w31, w32, and w33 from input neuron x3 to neurons z1, z2, and z3, respectively. The weights of the links present between the hidden layer and the output layer are v1, v2, and v3, connecting the respective neurons from each of these layers. The output is also connected to a bias term ‘b.’ The neurons from the hidden layers can also be connected to some biased term.

The model is trained on a labeled dataset in supervised learning. It means that both the raw input data and the outcome are present. We separate the data into a training dataset and a test dataset. Our network is trained using the training data, and the test data is used to act as new input data for output prediction or to assess the model's correctness. The model learns from the predetermined outcomes in this sort of learning. Due to the short training period, this model performs at a rapid rate. The algorithm is aware of the patterns of input that lead to the desired results. Once trained, the system can predict the right answer from a new input. Predicting future sales using historical data is an example of supervised learning.

In unsupervised learning types, the training input is neither classified nor labeled. The objective of unsupervised learning is to understand the patterns hidden in the data. Once a model learns to understand the patterns, it can predict them for any new dataset. The system does not understand the correct output but explores the data and can draw inferences from the given datasets to describe the latent structures from unlabeled data. If we feed the images of a cycle, car, and helicopter as raw inputs, our model will easily differentiate among the three. However, it will not be able to tell whether a given cluster is of a cycle or not, as it is unlabeled. Finding customer segments based on common characteristics such as demographics or behaviors is an example of unsupervised learning.

There are no labeled datasets or results tied to the data in reinforcement learning. Because of this, the accurate target output values are unknown. Therefore, the only way to successfully predict a given input is to gain experience. In this case, the exact information is unavailable; only the critical information is. An algorithm receives positive reinforcement for each wise move or choice it makes.

On the other hand, negative reinforcement is given for every incorrect action. It gains knowledge of the types of actions that must be taken. Industrial automation can benefit from this kind of learning.

This is one of the most frequently asked AI questions and answers. Neural networks learn through an element of feedback. The feedback allows the network to evaluate the model output. It is like the feedback mechanism that we see in humans. If we touch something hot, we immediately take back our hands and, next time, stay cautious of hot substances. Our senses provide us with feedback during our first encounter with hot substances that might hurt us. Artificial neural networks have similar learning patterns through feedback. Based on the feedback, the threshold and the weights are modified. This process is repeated with every input, and the system continues to learn.

The McCullouch-Pitts neuron or M-P neuron is one of the most basic neural networks. The network has a binary activation function and suits for binary tasks. The M-P neurons are connected by directed weighted links that may be positive or negative. There is a fixed threshold for each neuron, and if the net input to it is greater than the threshold, the neuron output is positive. For the net input less than the threshold, the neuron's output is negative. These are mostly used in logic functions.

The learning rate of an artificial neural network determines the rate at which we want the algorithm to perform the training of the network. Learning refers to the weight adjustment at each step of training. This can be controlled using the learning rate. It is represented using ‘α.’ The value of the learning rate ranges from 0 to 1. It is determined at each step during the training process. A smaller learning rate might give the most accurate results but requires more time and computing resources. A higher learning rate will train the network quickly but might not produce the best results. Therefore, deciding on a learning rate will provide the right balance is important. This can be decided after running through certain iterations of training using a random learning rate and then adjusting it accordingly.

An AI system processes the data we input into it, performs intricate mathematical calculations, and outputs a result. It is impossible for humans to comprehend the algorithms and intricate mathematics required to carry out this procedure manually, and it would take a lot of time. A situation like this is referred to as "black-box learning." Black-box models come in many forms, including random forests, neural networks, and others. Models with simple logic and straightforward interpretation include linear regression, KNN, decision trees, etc. These kinds of models are known as white box models since the underlying logic or algorithm is simple to comprehend.

Python is the most widely used language in Data Analytics, Machine Learning, and Artificial Intelligence. The different frameworks and libraries make the python programming language the most versatile library for these applications. Some of these libraries include: 

• Scikit-Learn is a free python library that includes all the basic and intermediate models required for building supervised and unsupervised models. It is built on top of the SciPy library and comes with a BSD license. 
• Pytorch is a machine-learning framework built on top of the Torch library. It also comes with a BSD license and is used mostly for predictive modeling and computer vision applications. 
• TensorFlow – It is one of the popular deep learning frameworks created by Google. It consists of pre-trained models and even the ability to perform transfer learning. There is an excellent community that provides support, which becomes an added advantage. 
• NLTK - NLTK stands for Natural Language Toolkit. It is one of the most powerful NLP libraries, which helps make machines understand human language and respond to it appropriately. It contains all the necessary functions to perform text processing and even create models for text-specific use cases. 
• OpenCV – It is a synonym for real-time computer vision. This open-source library helps with image processing and computer vision applications. 

The different applications of Artificial Intelligence are: 

• Machine learning 

In this type of learning, the system automatically identifies patterns and tries to make estimates without requiring clear programming instructions. 

• Robotics 

Robotics entails the creation, maintenance, use, and operation of robots. Robotics aims to create devices that can aid and support people. 

• Self-driving cars 

A self-driving car often referred to as an autonomous car, is a vehicle that has been automated to the point where it can sense its surroundings and move safely with little to no human intervention. Tesla is renowned for building driverless vehicles. 

• Natural Language Processing 

The ability of computer software to comprehend spoken and written human language is known as natural language processing (NLP). It is a part of artificial intelligence. For instance, the typing assistant Grammarly employs AI to check for spelling, grammatical, and punctuation errors. 

• Speech Recognition 

Speech recognition is an AI technology that enables the recognition and translation of spoken language into text by computers Services such as Amazon Alexa and Google Assistant is an examples of speech recognition. 

• Personalization 

Personalized experiences on websites and apps that are determined by a user's location, search history, etc., are an application of AI. Amazon, Flipkart, Google, Instagram, Facebook, etc., can provide AI-generated recommendations based on each user's experience. 

Data is the core aspect of any Artificial Intelligence system. Data for AI systems can be categorized as follows:  

• Structured Data 

This sort of data is typically kept in relational databases in tabular form and has a specified data schema. Train timetables, stock information, and sales data from a certain retailer are a few typical examples of this type of data. 

• Unstructured Data 

There is no fixed structure for this kind of data. It can appear in any way. Most data in the world is available in this format. Audio data, video or image data, textual data, etc., can be categorized as unstructured data. 

• Semi-structured data 

The structure of this type of data combines the advantages of both structured and unstructured data. This information is not rationally arranged. Nevertheless, it features a few different types of tags and markers that give it a recognizable structure. JSON file formats are an example of semi-structured data. 

Common interview questions for artificial intelligence, don't miss this one. The back propagation-based artificial neural networks follow the backpropagation algorithm, which aims to generalize the training set to achieve the network’s ability to generate the desired output.  

• The back propagation learning algorithm is applied to a multi-layer feed-forward network. A multi-layer feed-forward network consists of an input layer hidden layer, and an output layer. 
• It uses a monotonically increasing differentiable activation function.  
• It also uses the gradient-descent-based approach to modify the weights during network training. The weights are updated by computing the errors generated due to the difference in actual output and desired output.  
• The gradient descent approach is very slow to train the network if the learning rate is small. With a high learning rate, the gradient descent bounces too much, which speeds up the training process with less accurate output. 
• The network can contain one or more hidden layers. With more hidden layers, the training gets complex. Once the training is complete, the network can produce output quickly. 
• A bias term acts as a weight for the neurons present in the hidden layer and the output layer. 

Alan Turing is credited with deciphering Nazi codes and assisting the Allies in winning World War I. He is also credited for creating the Turing Test and becoming modern computers' founder. The test was first created to determine whether a conversation between humans and artificial intelligence, displayed simply in text, could trick a human. A machine is said to have human intelligence if it can have a conversation with a human without being recognized as a machine. However, it has since become shorthand for any Al that can fool a person into trusting they are witnessing or interacting with a real human.

Once an AI system has learned something, it can continue to build on its existing knowledge. Every artificial neural network requires massive amounts of data for effective training of the model. Therefore, it is not a good idea to train a neural network that requires massive amounts of data to be trained from scratch. Instead, we can always find a pre-trained model that can achieve similar tasks. The pre-trained model is reused in a new learning model. If the two models are developed to perform similar tasks, then generalized knowledge can be shared between them. We reuse the lower layers of the pre-trained model, which not only requires less training data but also speeds up the training time significantly. This idea of re-training on a pre-trained model instead of training it from scratch is known as transfer learning. Transfer learning is becoming increasingly popular with Google, Microsoft, Hugging face, etc., training models for a widespread use case on the already available big data with them. For instance, to train a text similarity model or sentiment analyzer model, we can make use of pre-trained models like BERT or MLNet and perform transfer learning on these models to generalize to a specific use case.

Natural language processing is a subfield of artificial intelligence concerned with the interactions between computers and human language, with a focus on how to design computers to handle and interpret massive volumes of natural language data.  

Following are some of the applications of Natural Language Processing 

• Machine Translation 

By using software to translate text or speech from one language to another, machine translation aids in overcoming language barriers. To translate text, documents, and webpages from one language into another, Google developed the machine translation tool known as Google Translate.  

• Automatic Summarization 

Automatic summarization is useful for summarizing the contextual meaning of documents and information while maintaining the emotional meanings hidden inside the information. Automatic summarization is particularly useful when we wish to get an overview of a news item or blog post while avoiding redundancy from multiple sources. 

• Sentiment Analysis 

Companies make use of NLP applications, like sentiment analysis, to identify opinions and sentiments online to understand what users feel about their products and services. 

• Text Classification 

Text classification enables us to assign predefined categories to a document to organize, structure, and filter the information. For example, an application of text categorization is spam filtering in email. 

• Virtual Assistants 

Virtual assistants use natural language processing (NLP) to understand user text or voice input and even respond to them or perform certain actions. For example, Siri by Apple and Alexa by Amazon is the most popular and widely used virtual assistants. 

It's no surprise that this one pops up often in artificial intelligence interview questions. The cost function is used to measure the performance of an AI model by computing the error between predicted and expected values. The gradient descent helps minimize this cost function by tuning the model parameters. Not every solution has a coinciding local and global minimum. There are cases where we have multiple local minimums, but there is always only one global minimum. The cost function is the minimum at the global minimum. However, it is not always possible to reach the global minimum, so the idea is to reach as much as close to it. This is controlled by the learning rate. The batch, stochastic, and mini-batch gradient descent are different techniques of gradient descent to reach a solution close to the optimal. 

At each stage, Batch Gradient Descent employs the complete set of training data. Its performance on exceptionally large training sets is, therefore, terrible. As a result, it is not preferred as we usually deal with Big Data regarding AI solutions. Every time a step is completed, stochastic gradient descent simply chooses a random instance from the training set and computes the gradients based solely on that one occurrence. This obviously speeds up the process because there is a lot fewer data for it to deal with at each iteration. Since only one instance must be kept in memory during each iteration, it also allows for training large training sets. Mini-batch gradient descent computes the gradients on small random groups of examples termed mini batches rather than computing the gradients based on the entire training set as in batch gradient descent or based on only one instance as in stochastic gradient descent. Performance can be improved, which is the fundamental benefit of mini-batch gradient descent over stochastic gradient descent. 

There are a lot of hyperparameters in a neural network that one can tweak. We can change the type of activation function, the number of hidden layers, the number of neurons in a hidden layer, weight initializations, etc. To start with, we can begin with a single or two hidden layers. A deep neural network contains more hidden layers. We can then increase the number of neurons in each hidden layer before trying to add another layer to the architecture. A common practice is to size them to form a funnel, with fewer and fewer neurons at each layer. Finding a perfect number of hidden layers and neurons in each layer is a difficult job and does require tuning. A simpler approach is to pick a model with more layers and neurons than you need, then use early stopping to prevent it from overfitting. However, we can use grid search with cross-validation to find the right hyperparameters.

The decision tree is one of the most basic techniques used today for data classification. It is a part of tree neural networks. We employ a decision-tree-like model that includes every potential outcome. Numerous nodes make up a decision tree. The Al system uses a test at each node to move the query down a node. Up until the query is sent to the terminal or leaf node, this process continues. There is a preassigned value for each leaf node. This value is regarded as the tree's output. This tree can alternatively be seen as a flowchart with the root node at the top and the decision-making root node at the bottom.

The following are some of the terms frequently associated with decision trees: 

• Root Node – It can be found at the top or the start of a tree. It refers to all the analyzed data. Based on the characteristics of the data, the flow from the root node to the other nodes is determined.  
• Splitting - A node gets split into two or more sub-nodes during the splitting process.  
• Decision Node - The Decision or Interior Node is where splitting happens. A sub-node is further subdivided into more sub-nodes in this location. 
• Leaf Node - The last node is called Leaf Node. There are no sub-nodes in this node. 
• Branch or Sub-Tree - A decision tree's subdivisions are called branches or subtrees. 
• Parent Node and Child Node - The nodes existing below the current nodes are known as the child or sub-nodes. The nodes that exist above a node are known as the parent nodes. 

In the process of Text Normalizations, we undertake several steps to normalize the text to a lower level from multiple documents. The textual data from all the documents altogether is called a corpus. The steps involved in text normalization are – 

• Sentence Tokenization 

Sentence tokenization is also known as sentence segmentation. In this step, a string of written language is divided into its component sentences. In human languages, we can split sentences into further smaller sentences whenever we see a punctuation mark. For example, the sentence “This is amazing! I won the competition.” Will be tokenized into two sentences – “This is amazing!” and “I won the competition.” 

• Word Tokenization 

Word tokenization is also known as word segmentation. In this process, each sentence is further divided into component words or tokens. In English and many other languages that are based on Latin alphabets, space is a good estimate of a word divider. For example, “interview questions artificial intelligence” when tokenized produces [‘interview,’ ‘questions,’ ‘artificial,’ ‘intelligence’]. 

• Text Lemmatization and Stemming 

For grammar rules, documents can have various forms of a word, such as run, runs, and running. Moreover, sometimes, we have related words with similar meanings, such as nation, national, and nationality. Two of the specialized cases of normalization are Stemming and lemmatization. However, they are different from each other. 

Stemming refers to a crude heuristic process that involves chopping off the ends of the words with the aim of achieving this goal correctly. Stemming algorithms work by cutting off the common prefixes or suffixes that can be found in a word. This cutting does not always produce a successful result, i.e., a meaningful word is not always obtained after stemming. For example, the stemming operation for collected will be collected, but for closed will be closed. 

Lemmatization refers to performing tasks properly with vocabulary and morphological analysis of words. It is done with the aim of removing inflectional endings only and obtaining the base or dictionary form of a word, known as the lemma. For example, the stemming operation for collected will be collected, and for closed will be closed. 

Bag of Words: 

Machine learning algorithms cannot perform with raw text. The text is first converted into vectors of numbers. This process is known as feature extraction. The bag of words is an extremely popular model which uses a feature extraction technique to work with the text. It explains the occurrence of each word within a document. We can use this model by designing a vocabulary of known words (also called tokens) and choosing a measure of the presence of the known words. Any information about the order or structure of words is rejected. That is why we call it a bag of words. This model attempts to find if a known word occurs in a document, but it does not know the location of the word in the document. The perception is that similar documents have similar content. We can also learn something about the meaning of the document from its content page. 

To implement a bag of words algorithm, we must follow the following steps: 

• Text Normalization: Collection of data and pre-processing of it 
• Creating Dictionary: Listing out all the unique words occurring in the corpus. (Vocabulary) 
• Creating document vectors: For each document in the corpus, find out the frequency of each word from the unique list of words. 
• Creating document vectors for all the documents. 

Term Frequency and Inverse Document Frequency (TFIDF) 

Term Frequency and Inverse Document Frequency (TFIDF) is a statistical measure that evaluates the importance of a word to a document in a corpus. The TFIDF scoring value increases in proportion to the number of times a word appears in the document, but it is equalized by the number of documents in the corpus that contains the word. 

The following formula is used to calculate a TF-IDF score for a given term x within document y.