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Artificial Intelligence is at the very epicentre of all technological innovations, coming together as an apex union between human ingenuity and machine intelligence. Its applications resound in industries, bringing about a change in the way we exist and work or interact with technology. The global market for artificial intelligence grew to touch a remarkable value of USD 150.2 billion in 2023 and is expected to rise at an outstanding CAGR of 36.8% during the forecast period from 2023 to 2030. This incredible growth of valuation in the AI market gives evidence of the indispensable role that it will play in modern society.
While AI pushes industries to adopt its potential, AI increases the demand for skilled professionals in this domain. It’s not just a career path-it’s opening the doorway to shaping the future. One looks forward to finding employment in this dynamic field; therefore, arming oneself with answers to the most frequently asked Artificial Intelligence interview questions becomes quite important.
These AI interview questions can be your passport to myriad opportunities lying inside this ever-evolving landscape.
TensorFlow is an open-source library platform developed by the Google Brain team. Basically, it’s a maths library used for various machine learning applications. Using tensor flow, we can easily train and deploy the machine learning model in the cloud.
These include reactive machines, limited memory, theory of mind, and self-aware AI. Each represents growth in terms of more complexity and capability, ranging from simple reaction-based machines to those that understand and develop consciousness.
Traditional programming thus requires the explicit writing of codes to make logical results from input data. Machine learning algorithms learn patterns and make decisions by themselves, with very minimal interference by humans.
Here are some of the most common ways to use AI in the business world:
Market-basket analysis is one of the common techniques used to find the association between items. Large retailers use this technique frequently in order to attain maximum profit. Here, we need to identify various item combinations that are most often bought together.
Because of this, for example, if someone buys bread, he will buy butter. Showing such a correlation will enable the retail business to grow by offering their customers things that are relevant to them.
A CNN is a useful deep-learning technique, especially for processing input images. Using learnable weights and biases, the network grants a score for various features or objects in the image to select which one is more important. This gives the network substantial power to distinguish between them.
NLP is, therefore, part of artificial intelligence and computer science, which deals with the relationship between computers and human beings in processing natural languages. It is related to techniques in computer science that use mechanisms of processing and examining the human language in combination with linguistics and mathematics.
The GAN consists of two neural networks that collaborate: a generator and a discriminator. The generated data should resemble the training data and get classified by the discriminator. Both components compete with each other to improve, thereby making GAN capable of producing highly realistic data.
Decision Trees represent a type of supervised learning used in classification and regression problems. They model decisions usually with the possible consequences that include a representation like trees, with choices as branches and results as leaves, intuitively used for the making of decisions.
The famous algorithm that works in reinforcement learning is Q-learning. Basically, it is based on the Bellman equation. In this algorithm, the agent tries to learn policies that can provide the best actions in order to maximise rewards under particular circumstances. The agent learns these optimal policies from past experiences.
Q in Q-learning is used to indicate the quality of actions to be performed in every state. The agent ultimately aims at maximising Q.
Bayesian networks are the graphical models used to show the probabilistic relationship between a set of variables. This is a directed cycle graph with multiple edges, where each edge describes conditional dependency.
Bayesian networks are probabilistic because these networks are built from a probability distribution and use probability theory in prediction and anomaly detection. The Bayesian network provides one of the most important conceptions in AI, as it is based on Bayes’ theorem and Bayes’s formula, which can be used to deduce answers to probabilistic questions.
The minimax algorithm is a backtracking algorithm for game theory to make decisions in game theory. This algorithm provides the optimal moves of a player, supposing that another player is also playing optimally.
This algorithm is based on two players: one is called MAX, and the other is called MIN.
Following are some terminologies used in the Minimax Algorithm:
It is that part of AI that deals with thinking by AI agents. It is used to represent the knowledge about the real world to the AI agents so that they can understand it and utilise the information to solve the complex problems in AI.
Following are the elements of Knowledge which are represented by the agent in the AI system:
Computer vision is the field of artificial intelligence that is used to train computers so that they can interpret and obtain information from the visual world, such as images. Hence, computer vision uses AI technology to solve complex problems such as image processing, object detection, etc.
The A* algorithm has become very important in AI because it is an efficient and effective method by which paths are found either in graph traversing or through pathfinding. This algorithm executes its functions through the use of heuristics for an approximate determination of the cost required to reach the goal from each node in order to optimise its search process for the shortest path.
The hidden Markov model is a statistical model that represents probability distributions over a chain of observations. A Hidden Markov Model is one that assumes that the state of a process generated at a particular time is hidden from the observer, and it assumes it satisfies the Markov property. The hidden Markov model finds most of its applications in temporal data.
In machine learning, there are two main types of models: parametric and non-parametric. Here, parameters are the predictor variables that are used in building the machine learning model. The explanation of the models is given below.
Parametric Model: Unlike a non-parametric model, the parametric model uses only one fixed number of parameters for developing an ML model. It makes strong assumptions about data. Examples of parametric models are Linear regression, Logistic Regression, Naïve Bayes, Perceptron, etc.
Non-Parametric Model: Non-parametric model utilises flexible numbers of parameters. In other words, the model considers a few assumptions about the data. These models are good for higher data and no prior knowledge. Examples of non-parametric models include Decision Tree, K-Nearest Neighbour, and SVM with Gaussian kernels, amongst others.
In machine learning, hyper-parameters are those that determine and control the complete training process. The parameters related to this are hidden layers, hidden units, activation functions, and so on. They are those parameters that are external to the model. The choice of good hyper-parameters makes a better algorithm.
The problems to address are context processing, sarcasm, idiomatic expressions, ambiguity of words, and precision to produce exactly the same meaning in various languages and dialects. This complexity depends on whether robust models can accurately capture the meanings of or synthesise human language.
Ensuring that AI models operate within the boundaries of ethics and have no bias employs rigid testing across a wide variety of diverse datasets, continuous monitoring for bias, ethics embedded into the AI development process, and model decisions being transparent.
Ethical issues include concerns about privacy, job loss to automation, accountability of decision-making processes, bias in artificial intelligence, and lastly, misuse of artificial intelligence.
There have been several misconceptions related to artificial intelligence since the start of its evolution. Some of them are given below:
The ethical consideration involves fairness, transparency, privacy, and accountability of the AI systems. In relation to ethical deployment, AI technologies have to ensure that bias is avoided, user consent is respected, and the societal implications of automated decisions are envisaged.
It would be quite influential when AI improves performances in all areas of life, opens up new opportunities to innovate, improves health outcomes, and, at the same time, can become a source of increasing social inequalities or job losses in some cases.
Bias in machine learning is the error that results from the model due to over-simplification, assumptions, or prejudices in training data. It is important because it may result in some inaccuracies in prediction or decision-making and especially hurt any considerations for fairness or ethics.
AI in cybersecurity automates complicated processes of cyber threat detection and response, analyses huge volumes of data for threat detection, and gives predictions about where vulnerabilities may occur.
The various ways in which AI is being developed and used in the healthcare sector include diagnostic algorithms, personalised medicine, patient monitoring, and operational efficiencies. It can help in the analysis of complex medical data and improve diagnosis accuracy, thereby optimising treatments for predictive outcomes, which greatly improves health care.
AI in intelligent vehicles encompasses perception, decision, and navigation. It processes sensor data to understand the environment, predicts the behaviour of other participants in traffic, and makes decisions in real time about how to navigate in a way that is safe and efficient.
AI model explainability is the ability to understand and explain the decisions taken by the AI model. The importance of this feature is that it brings transparency and builds trust, helping models make decisions for a reason.
The solution of a new problem using AI includes problem domain comprehension, data collection and pre-processing, model and algorithm selection, model training, and iterative improvements with performance metrics.
The preprocessing of data involves cleaning, normalising, and structuring raw data into a form that could be usable in machine learning algorithms. It enhances the accuracy of any given model, ensuring that the inputted data is consistent and relevant while filtering out the noise and irrelevant information.
Reward maximisation is the terminology used in reinforcement learning, and it is the objective of the reinforcement learning agent. The reward is positive feedback by action for a transition from one state to another. In the case of taking an optimal policy, applying a good action provides a reward to the agent, and in the case of a bad action, one reward is subtracted. The goal of the agent is to maximise these rewards by applying optimal policies, which is termed reward maximisation.
It just involves transferring a pre-trained model that was trained on a very large dataset to apply to another similar problem but of a smaller scale. This reduces training time and data requirements, therefore bringing about better performance of the model, especially for tasks where limited data exists.
While symbolic AI or rule-based AI works on explicitly defined rules and logic for decision-making, connectionist AI learns through neural networks from patterns in data. While symbolic AI shines on clear-cut, well-defined tasks, connectionist AI does much better on pattern recognition or predictive tasks.
The two major concepts of Linear algebra are Eigenvectors and eigenvalues.
Eigenvectors are unit vectors that possess a magnitude equal to 1.0.
Eigenvalues are the coefficients applied to the eigenvectors, or these are the magnitude by which the eigenvector is scaled.
Feature vectors in machine learning represent objects with n-dimensional vectors of numerical features. Each dimension in a vector forms a feature relevant to an object for the algorithms to analyse or predict. They form the bedrock that the models need to comprehend patterns or classifications in the data.
When an AI model is too complicated and just fits the training data too closely. It would start to memorise the outcome of training without knowing the real structure, patterns, and relations. That means such a model will yield good results on the training data but poor results on new, previously unseen data.
This generally applies to all machine learning algorithms, which, when the learned model becomes too complex considering the number and quality of training data, might fall into the case of overfitting. Sometimes, it even starts fitting the noise of the data rather than the true pattern. That degrades the performance and accuracy when used for the prediction or classification of new data.
The regularisation, cross-validation, and early stopping techniques during training avoid overfitting. All these help in the simplification of a complex model and, therefore, can generalise well on new unseen data.
Cross-validation: One of these methods is cross-validation, where there are a number of folds the data could be divided into and then trained and tested on different folds. This prevents overfitting, which is essentially a model memorising the training data and performing generalisation poorly on new data.
Regularisation: This is one of the techniques of including a penalty term in the model’s objective function. It assists in ensuring the model doesn’t over-depend on a single feature. This will also prevent the model from fitting the noise present in the training data.
Early stopping: This technique involves stopping the training before the model’s performance starts to degrade on the training data. This is useful when one trains the model with several iterations.
Ensemble methods: Typically, these involve training more models and then combining their predictions for a single prediction. This helps reduce the variance and, hence, increases the robustness of the model.
Pruning: It’s a technique in which the complexity of any model is reduced by the removal of unimportant features or nodes.
Dropout: It is a technique that drops out random subsets of neurons during network training. This prevents the network from relying too heavily on any one neuron.
Bayesian approaches: In these approaches, prior information is combined and updated with new information using Bayes’ theorem in model parameters.
Bias-variance tradeoff basically tries to find that optimum level where the error due to bias balances the error due to variance. High bias and high variance result in problems of underfitting or overfitting, which in turn affect the performance of the model.
Classification and regression represent two types of supervised machine learning used to predict the output variable based on one or more input variables.
By classifying data into groups, classification is utilised to predict discrete responses. Regression analysis is used to anticipate numerical values and predict continuous responses.
Classification is a kind of supervised learning wherein the aim is to predict any given input with a categorical label or class. The output needs to be discrete and finite, such as “spam” or “not spam” in an email classification problem. Thus, in this case, the input data is labelled by a class, while the model learns to predict the class based on features.
Regression is a type of supervised learning whereby, for an input provided, it tries to predict a continuous value. The output will be real value – the price of a house or temperature. Input data are labelled with a continuous value, and the model learns from them in order to predict values according to features in the input.
Game theory is an academic study of strategic decision making; it involves the study of the possible actions of players in a situation where the outcome for one depends not just on one’s own actions but also on the actions of others. Game theory is viewed as a mathematical methodology for analysing situations of conflict or cooperation between intelligent and rational decision makers. As a tool for analysing real life, game theory is applied in such diverse areas as auctions, bargaining, and the evolution of social norms.
Game theory is a logical and scientific study that forms a model of the possible interactions between two or more players. Here, rational implies that each player thinks others are equally rational and equally informed. In game theory, players deal with the given set of options in a multi-agent situation; it means the choice of one player affects the choice of the other or opponent players.
Game theory and AI are closely related; game theory is useful to AI and vice versa. In AI, game theory is being applied to broaden the ability to enable some of the key functionalities that are definitely required in this multi-agent environment where multiple agents try to interact with each other to achieve certain goals.
Different popular games, such as poker and chess, are considered to be logical games with certain specified rules. So, to play such games online or digitally, like on Mobile, laptop, etc., one has to create algorithms for such games. These algorithms are applied with the help of artificial intelligence.
An agent is said to be rational if it possesses well-defined preferences, has model uncertainty, and always executes the best action. In other words, an agent can never fail to take the best possible action in any given situation.
Rationality simply refers to the status of being reasonable and sensible with good sound judgement.
The Turing Test provides the basis for ascertaining the ability of a machine to display intelligent behaviour indistinguishable from human intelligence. Its importance is derived from the benchmark it sets for measuring the capability of systems in carrying out objectives concerned with intelligence similar to those of human beings.
An expert system mainly consists of three components:
Partial Keys: The set of attributes that differentiates the weak entities within the same owner entity
Alternate Keys: All candidate keys other than the primary key are called alternate keys
Compound Key: It contains more than one field that allows the user to identify any particular record distinctly.
Artificial neural networks are a statistical model inspired by functioning human brain cells called neurons. These neural networks include various AI technologies, such as deep learning and machine learning.
An Artificial neural network or ANN consists of multiple layers such as the Input layer, Output Layer, and hidden layers.
ANN, through several deep learning techniques, is one of the AI tools that can solve various complex problems like pattern recognition, facial recognition, and so on.
Some commonly used Artificial neural networks:
The measure of the performance for classification problems may be based on accuracy, precision, recall, F1 score, and AUC-ROC, while for regression problems, it is mostly MSE or MAE. These different measures tell us how the model will predict or classify new data.
Current limitations of AI are that it lacks an understanding of context and common sense, is bound by rather high demands on data, could be possibly biassed in training, there are ethical concerns, and explanation of the decisions made by AI is a big challenge. Since this is an ongoing process, comprehensive research and development will be required for some time to come.
To be up-to-date about AI, the necessary things to do are: taking courses, continuously learning, attending conferences, reading research articles or papers, being part of an AI community, and practising all AI technologies.
Go through the following tips to ace your Artificial Intelligence interview:
Familiarise yourself with the interview platform
Set your interview desk up for success
Dress to impress
Prep yourself like a traditional in-person interview
Practice active listening
Pay attention to your non-verbal cues
Reference relevant keywords
Emphasise your soft skills
Manage your time
Check your digital presence
What are the Common Uses and Applications of AI?
What are Intelligent Agents, and How are They Used in AI?
What is Tensorflow, and What is It Used For?
What is Machine Learning, and How Does It Relate to AI?
What are Neural Networks, and How Do They Relate to AI?
In top-tier roles, AI engineers may command salaries reaching 50 lakhs, with an average annual income exceeding $100,000 for the profession.
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