Artificial Intelligence Interview Questions | Eklavya Online

Artificial Intelligence Interview Questions

In a planning system, the function of the third component is to detect when a solution to problem has been found.

In lists, elements maintain their order unless they are explicitly commanded to re-order. These can be made up of any data type that can be all the same or mixed. However, elements in lists can only be accessed via numeric, zero-based indices.

In a dictionary, the order isn’t guaranteed. However, each entry will be assigned a key and a value. As a result, elements within a dictionary can be accessed by using their individual key.

So whenever you have a set of unique keys, you have to use a dictionary. Whenever a collection of items are in order, you can use a list.

It’s difficult to predict how an AI interview will unfold, so if they follow up by asking you how to get a list of all the keys in a dictionary, respond with the following:

To obtain a list of keys in a dictionary, you’ll have to use the following function keys():

mydict={‘a’:1,’b’:2,’c’:3,’e’:5}
mydict.keys()
dict_keys([‘a’, ‘b’, ‘c’, ‘e’])

In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.

  • The advantages of an expert system are:
  • Easy availability
  • Low production costs
  • Greater speed and reduced workload
  • They avoid motions, tensions, and fatigue
  • They reduce the rate of errors.

A career in this can be realized within a variety of settings including : private companies public organizations education the arts healthcare facilities government agencies and the military.

Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.

A Turing test allows you to check your machine’s Intelligence in comparison to human Intelligence. In a Turing test, a computer would challenge human Intelligence, and if it passes the test, only then can you term it as intelligent. Even a smart machine might not be able to replicate humans also though it passes the test.

  • Fuzzy logic is a subset of AI. It is a way of encoding human learning for artificial processing. It is represented as IF-THEN rules. Some of its important applications include:
  • Facial pattern recognition
  • Air conditioners, washing machines, and vacuum cleaners
  • Anti Skid braking systems and transmission systems
  • Control of subway systems and unmanned helicopters
  • Weather forecasting systems
  • Project risk assessment
  • Medical diagnosis and treatment plans
  • Stock trading

Tower of Hanoi essentially is a mathematical puzzle that displays how recursion is utilised as a device in building up an algorithm to solve a specific problem. The Tower of Hanoi can be solved using a decision tree and a breadth-first search (BFS) algorithm in AI. With 3 disks, a puzzle can essentially be solved in 7 moves. However, the minimal number of moves required to solve a Tower of Hanoi puzzle is 2n − 1, where n is the number of disks.

‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.

  • Gates (forget, Memory, update, and Read)
  • Tanh(x) (values between −1 and 1)
  • Sigmoid(x) (values between 0 and 1)

The goal of Artificial intelligence is to create intelligent machines that can mimic human behavior. We need AI for today’s world to solve complex problems, make our lives more smoothly by automating the routine work, saving the manpower, and to perform many more other tasks.

Knowledge representation techniques are given below:

  • Logical Representation
  • Semantic Network Representation
  • Frame Representation
  • Production Rules

AI covers lots of domains or subsets, and some main domains are given below:

  • Machine Learning
  • Deep Learning
  • Neural Network
  • Expert System
  • Fuzzy Logic
  • Natural Language Processing
  • Robotics
  • Speech Recognition.

a. Design Domain
Basically, we use expert systems in designing of camera lens and automobile.
b. Monitoring Systems
Generally, in this data is compared with observed system
c. Process Control Systems
We have to control physical process based on monitoring
d. Knowledge Domain
Finding out faults in vehicles, computers
e. Finance Commerce
Also, an expert system is used to detect possible fraud.

Knowledge representation is the part of AI, which is concerned with the thinking of AI agents. It is used to represent the knowledge about the real world to the AI agents so that they can understand and utilize this information for solving the complex problems in AI.

Following elements of Knowledge that are represented to the agent in the AI system:

  • Objects
  • Events
  • Performance
  • Meta-Knowledge
  • Facts
  • Knowledge-base

Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered. Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.

is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.

When you’re dealing with a non-random sample, selection bias will occur due to flaws in the selection process. This happens when a subset of the data is consistently excluded because of a particular attribute. This exclusion will distort results and influence the statistical significance of the test.

Other types of biases include survivorship bias and undercoverage bias. It’s important to always consider and reduce such biases because you’ll want your smart algorithms to make accurate predictions based on the data.

Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.

Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas, model performance is based on the datasets we feed as inputs to the algorithm.

Just like research, you should be up to date on what’s going on in the industry. As such, if you’re asked about use cases, make sure that you have a few examples in mind that you can share. Whenever possible, bring up your personal experiences.

You can also share what’s happening in the industry. For example, if you’re interested in the use of AI in medical images, Health IT Analytics has some interesting use cases:

  • Detecting Fractures And Other Musculoskeletal Injuries
  • Aiding In The Diagnosis Neurological Diseases
  • Flagging Thoracic Complications And Conditions
  • Screening For Common Cancers

Learning rate: The learning rate is how fast the network learns its parameters.
Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent.
Number of epochs: The number of times the entire training data is fed to the network while training is referred to as the number of epochs. We increase the number of epochs until the validation accuracy starts decreasing, even if the training accuracy is increasing (overfitting).

A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.

ML is geared toward pattern recognition. A great example of this is your Facebook newsfeed and Netflix’s recommendation engine.

In this scenario, ML algorithms observe patterns and learn from them. When you deploy an ML program, it will keep learning and improving with each attempt.

If the interviewer prods you to provide more real-world examples, you can list the following:

Amazon product recommendations

  • Fraud detection
  • Search ranking
  • Spam detection
  • Spell correction

Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.

Overfitting can be prevented by using the below-mentioned methods:

  • Early stopping
  • Ensemble models
  • Cross-validation
  • Feature removal
  • Regularization

Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.

FOPL stands for First Order Predicate Logic, Predicate Logic provides

a) A language to express assertions about certain “World”

b) An inference system to deductive apparatus whereby we may draw conclusions from such assertion

c) A semantic based on set theory

  • Natural language processing
  • Chatbots
  • Sentiment analysis
  • Sales prediction
  • Self-driving cars
  • Facial expression recognition
  • Image tagging

Overfitting is avoided in neural nets by making use of a regularization technique called ‘dropout.’

By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesn’t overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.

Overfitting is avoided in neural nets by making use of a regularization technique called ‘dropout.’

By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesn’t overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.

Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.

  • Image, speech, and face detection
  • Bioinformatics
  • Market segmentation
  • Manufacturing and inventory management
  • Fraud detection, and so on

Conferences are great places to network, attend workshops, learn, and grow. So if you’re planning to stick to a career in artificial intelligence, you should be going to some of these. For example, Deep Learning World has a great one every summer.

This year’s event in Las Vegas will feature keynote speakers like Dr. Dyann Daley (founder and CEO of Predict Align Prevent), Siddha Ganju (solutions architect at Nvidia), and Dr. Alex Glushkovsky (principal data scientist at BMO Financial Group, and others).

Some compelling examples of AI applications are:

  • Chatbots
  • Facial recognition
  • Image tagging
  • Natural language processing
  • Sales prediction
  • Self-driving cars
  • Sentiment analysis

As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network.

A hybrid Bayesian network contains both a discrete and continuous variables.

Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.

a) A set of constant symbols

b) A set of variables

c) A set of predicate symbols

d) A set of function symbols

e) The logical connective

f) The Universal Quantifier and Existential Qualifier

g) A special binary relation of equality

The directions along which a particular linear transformation compresses, flips, or stretches is called eigenvalue. Eigenvectors are used to understand these linear transformations.

For example, to make better sense of the covariance of the covariance matrix, the eigenvector will help identify the direction in which the covariances are going. The eigenvalues will express the importance of each feature.

Eigenvalues and eigenvectors are both critical to computer vision and ML applications. The most popular of these is known as principal component analysis for dimensionality reduction (e.g., eigenfaces for face recognition).

  • Facial pattern recognition
  • Air conditioners, washing machines, and vacuum cleaners
  • Antiskid braking systems and transmission systems
  • Control of subway systems and unmanned helicopters
  • Weather forecasting systems
  • Project risk assessment
  • Medical diagnosis and treatment plans
  • Stock trading
  • Univariate Selection
  • Feature Importance
  • Correlation Matrix with Heatmap

Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. Agent includes Robots, Programs, and Humans etc.

If you’re interested and heavily involved within this space, this question should be a no-brainer. If you know the answer, it’ll demonstrate your knowledge about a variety of ML methods and how ML is applied to autonomous vehicles. But even if you don’t know the answer, take a stab at it as it will show your creativity and inventive nature.

Google has been using reCAPTCHA to source labeled data on storefronts and traffic signs for many years now. The company also has been using training data collected by Sebastian Thrun, CEO of the Kitty Hawk Corporation and the co-founder (and former CEO) of Udacity.

Such information, although it might not seem significant, will show a potential employer that you’re interested and excited about this field.

The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance.

Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.

Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.

Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.

In online search, it will first take action and then observes the environment.

An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:

  • High performance
  • Adequate response time
  • Reliability
  • Understandability

The idea here is to standardize the data before sending it to another layer. This approach helps reduce the impact of previous layers by keeping the mean and variance constant. It also makes the layers independent of each other to achieve rapid convergence. For example, when we normalize features from 0 to 1 or from 1 to 100, it helps accelerate the learning cycle.

A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.

A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system.

In partial order planning , rather than searching over possible situation it involves searching over the space of possible plans. The idea is to construct a plan piece by piece.

If you talk about AI projects that you’ve worked on in your free time, the interviewer will probably ask where you sourced your data sets. If you’re genuinely passionate about the field, you would have worked on enough projects to know where you can find free data sets.

TensorFlow is an open-source framework dedicated to ML. It’s a comprehensive and highly adaptable ecosystem of libraries, tools, and community resources that help developers build and deploy ML-powered applications. Both AlphaGo and Google Cloud Vision were built on the Tensorflow platform.

  • LSTM: Long Short-term Memory
  • GRU: Gated Recurrent Unit
  • End-to-end Network
  • Memory Network

Perl language is not commonly used programming language for AI

RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.

Some algorithm techniques that can be leveraged are:

  • Learning to learn
  • Reinforcement learning (deep adversarial networks, q-learning, and temporal difference)
  • Semi-supervised learning
  • Supervised learning (decision trees, linear regression, naive bayes, nearest neighbor, neural networks, and support vector machines)
  • Transduction
  • Unsupervised learning (association rules and k-means clustering)

First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.

a) Add an operator (action)

b) Add an ordering constraint between operators

  • Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory.
    Example: Deep Blue
  • Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
  • Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
  • Self Aware AI: AIs that posses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
  • Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual assistants like Siri.
  • Artificial General Intelligence (AGI): Also known as strong AI. An example is the Pillo robot that answers questions related to health.
  • Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

Game theory, developed by American mathematician Josh Nash, is essential to AI because it plays an underlying role in how these smart algorithms improve over time.

At its most basic, AI is about algorithms that are deployed to find solutions to problems. Game theory is about players in opposition trying to achieve specific goals. As most aspects of life are about competition, game theory has many meaningful real-world applications.

These problems tend to be dynamic. Some game theory problems are natural candidates for AI algorithms. So, whenever game theory is applied, multiple AI agents that interact with each other will only care about utility to itself.

Data scientists within this space should be aware of the following games:

  • Symmetric vs. asymmetric
  • Perfect vs. imperfect information
  • Cooperative vs. non-cooperative
  • Simultaneous vs. sequential
  • Zero-sum vs. non-zero-sum
  • Consistency
  • Memory
  • Diligence
  • Logic
  • Multiple expertise
  • Ability to reason
  • Fast response
  • Unbiased in nature

An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:

  • Data denoising
  • Dimensionality reduction
  • Image reconstruction
  • Image colorization

In AI, Prolog is a programming language based on logic.

In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.

First, you have to develop a “problem statement” that’s based on the problem provided by the business. This step is essential because it’ll help ensure that you fully understand the type of problem and the input and the output of the problem you want to solve.

The problem statement should be simple and no more than a single sentence. For example, let’s consider enterprise spam that requires an algorithm to identify it.

The problem statement would be: “Is the email fake/spam or not?” In this scenario, the identification of whether it’s fake/spam will be the output.

Once you have defined the problem statement, you have to identify the appropriate algorithm from the following:

  • Any classification algorithm
  • Any clustering algorithm
  • Any regression algorithm
  • Any recommendation algorithm
    Which algorithm you use will depend on the specific problem you’re trying to solve. In this scenario, you can move forward with a clustering algorithm and choose a k-means algorithm to achieve your goal of filtering spam from the email system.
  • Sliding window methods
  • Recurrent sliding windows methods
  • Hidden Markov models
  • Maximum entropy Markov models
  • Conditional random fields
  • Graph transformer networks

“Attachment” is considered as not a desirable property of a logical rule based system.

Feedforward Neural Network

  • The simplest form of ANN, where the data or the input travels in one direction.
  • The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.
    Convolutional Neural Network
  • Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
  • Mainly used for signal and image processing

Recurrent Neural Network(RNN) – Long Short Term Memory

  • Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
  • Here, you let the neural network to work on the front propagation and remember what information it needs for later use
    This way each neuron will remember some information it had in the previous time-step.

Autoencoders

  • These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
  • The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
  • Typically for the purpose of dimensionality reduction and for learning generative models of data.

Many AI-related misconceptions are making the rounds in the age of “fake news.” The most common ones are:

  • AI will replace humans
  • AI systems aren’t safe
  • AI will lead to significant unemployment
  • While these types of stories are common, they’re far from the truth. Even though some AI-based technology is able to complete some tasks—for example, analyzing zettabytes of data in less than a second—it still needs humans to gather the data and define the patterns for identification.
  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Transduction
  • Learning to Learn

A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.

Components of GAN:

Generator
Discriminator
Deployment Steps:

Train the model
Validate and finalize the model
Save the model
Load the saved model for the next prediction

A breadth-first search (BFS) algorithm, used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes.

Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.

Step 1: Allocate weights (x,y) with random values and calculate the error (SSE)

Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized

Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized

Step 4: Use new weights for prediction and calculating the new SSE

Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error

Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.

You should update an algorithm when the underlying data source has been changed or whenever there’s a case of non-stationarity. The algorithm should also be updated when you want the model to evolve as data streams through the infrastructure

  • Require less formal statistical training
  • Have the ability to detect nonlinear relationships between variables
  • Detect all possible interactions between predictor variables
  • Availability of multiple training algorithms

In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, process the data and gives the output based on the algorithm and empirical data.

  • Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained.
  • They are used to define the number of hidden layers that must be present in a network.
  • More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause underfitting.

From the perspective of systems theory, a good knowledge representation system will have the following:

  • Acquisition efficiency to acquire and incorporate new data
  • Inferential adequacy to derive knowledge representation structures like symbols when new knowledge is learned from old knowledge
  • Inferential efficiency to enable the addition of data into existing knowledge structures to help the inference process
    Representation adequacy to represent all the knowledge required in a specific domain

For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.

Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy.

  • Logistic regression
  • Linear regression
  • Decision trees
  • Support vector machines
  • Naive Bayes, and so on

Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.

TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.

Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.

When you have underfitting or overfitting issues in a statistical model, you can use the regularization technique to resolve it. Regularization techniques like LASSO help penalize some model parameters if they are likely to lead to overfitting.

If the interviewer follows up with a question about other methods that can be used to avoid overfitting, you can mention cross-validation techniques such as k-folds cross-validation.

Another approach is to keep the model simple by taking into account fewer variables and parameters. Doing this helps remove some of the noise in the training data.

AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data.

Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).

The intermediate tensors are tensors that are neither inputs nor outputs of the Session.run() call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.

Sessions can own resources, few classes like tf.Variable, tf.QueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory) are released when the session is closed, by calling tf.Session.close.

An algorithm is said completed when it terminates with a solution when one exists.

Grid Search
Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.

Random Search
It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.

Bayesian Optimization
This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.

There are a variety of keys in a relational database, including:

Alternate keys are candidate keys that exclude all primary keys.
Artificial keys are created by assigning a unique number to each occurrence or record when there aren’t any compound or standalone keys.
Compound keys are made by combining multiple elements to develop a unique identifier for a construct when there isn’t a single data element that uniquely identifies occurrences within a construct. Also known as a composite key or a concatenated key, compound keys consist of two or more attributes.
Foreign keys are groups of fields in a database record that point to a key field or a group of fields that create a key of another database record that’s usually in a different table. Often, foreign keys in one table refer to primary keys in another. As the referenced data can be linked together quite quickly, it can be critical to database normalization.
Natural keys are data elements that are stored within constructs and utilized as primary keys.
Primary keys are values that can be used to identify unique rows in a table and the attributes associated with them. For example, these can take the form of a Social Security number that’s related to a specific person. In a relational model of data, the primary key is the candidate key. It’s also the primary method used to identify a tuple in each possible relation.
Super keys are defined in the relational model as a set of attributes of a relation variable. It holds that all relations assigned to that variable don’t have any distinct tuples. They also don’t have the same values for the attributes in the set. Super keys also are defined as a set of attributes of a relational variable upon which all of the functionality depends.

While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.

  • Logistic regression
  • Linear regression
  • Decision trees
  • Support vector machines
  • Naive Bayes, and so on

Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.

TensorFlow Installation Guide:

CPU : pip install tensorflow-cpu

GPU : pip install tensorflow-gpu

Alternate Key: Excluding primary keys all candidate keys are known as Alternate Keys.

Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence. This is known as artificial key.

Compound Key: When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.

Natural Key: Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.

An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks.

In the beginning, it might not be so great at performing a task, but it will improve over time.

When we first run the tf.Variable.initializer operation for a variable in a session, it is started. It is destroyed when we run the tf.Session.close operation.

A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.

Overfitting can be prevented by using the following methodologies:
Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model.

More training data: Feeding more data to the machine learning model can help in better analysis and classification. However, this does not always work.

Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis. Such features only increase the complexity of the model, thus leading to possibilities of data overfitting. Therefore, such redundant variables must be removed.

Early stopping: A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs. But after a certain number of iterations, the model’s performance starts to saturate. Further training will result in overfitting, thus one must know where to stop the training. This can be achieved by a mechanism called early stopping.

Regularization: Regularization can be done in n number of ways, the method will depend on the type of learner you’re implementing. For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.

Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. This is one of the best ways to prevent overfitting. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting.

Python wasn’t built for data science. However, in recent years it has grown to become the go-to programming language for the following:

  • Machine learning
  • Predictive analytics
  • Simple data analytics
  • Statistics
    For data science projects, the following packages in the Python standard library will make life easier and accelerate deliveries:

NumPy (to process large multidimensional arrays, extensive collections of high-level mathematical functions, and matrices)
Pandas (to leverage built-in methods for rapidly combining, filtering, and grouping data)
SciPy (to extend NumPy’s capabilities and solve tasks related to integral calculus, linear algebra, and probability theory)

If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.

  • Independent component analysis
  • Principal component analysis
  • Kernel-based principal component analysis

Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning.

There are three literals in (top-down) inductive learning:

  • Arithmetic literals
  • Equality and inequality
  • Predicates
    In deductive learning, the smart algorithms draw conclusions by following a truth-generating structure (major premise, minor premise, and conclusion) and then improve them based on previous decisions. In this scenario, the ML algorithm engages in deductive reasoning using a decision tree.

Abductive learning is a DL technique where conclusions are made based on various instances. With this approach, inductive reasoning is applied to causal relationships in deep neural networks.

In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.

  1. Constants
  2. Variables
  3. Placeholder
  4. Graph
  5. Session

The production rule comprises of a set of rule and a sequence of steps.

The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior.

Its popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.

There’s a chance that the interviewer might keep the conversation going and ask you for more examples. If that happens, you can mention the following:

  • Java
  • Julia
  • Haskell
  • Lisp

Yes, logical inference can easily be solved in propositional logic by making use of three concepts:

  • Logical equivalence
  • Process satisfaction
  • Validation checking

Generality is the measure of ease with which the method can be adapted to different domains of application.

  • Keras is an open source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks.
  • TensorFlow is an open-source software library for dataflow programming. It is used for machine learning applications like neural networks.
  • PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing.

Collaborative filtering can be described as a process of finding patterns from available information to build personalized recommendations. You can find collaborative filtering in action when you visit websites like Amazon and IMDB.

Also known as social filtering, this approach essentially makes suggestions based on the recommendations and preferences of other people who share similar interests.

Inductive logic programming combines inductive methods with the power of first order representations.

  • Data collection
  • Data preparation
  • Choosing an appropriate model
  • Training the dataset
  • Evaluation
  • Parameter tuning
  • Predictions

You have to first split the data set into training and test sets. You also have the option of using a cross-validation technique to further segment the data set into a composite of training and test sets within the data.

Then you have to implement a choice selection of the performance metrics like the following:

  • Confusion matrix
  • Accuracy
  • Precision
  • Recall or sensitivity
  • Specificity
  • F1 score
    For the most part, you can use measures such as accuracy, confusion matrix, or F1 score. However, it’ll be critical for you to demonstrate that you understand the nuances of how each model can be measured by choosing the right performance measure to match the problem.

The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.

The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.

A cost function is a scalar function that quantifies the error factor of the neural network. Lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.

The “depth first search” method takes less memory.

in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.

Face verification is used by a lot of popular firms these days. Facebook is famous for the usage of DeepFace for its face verification needs.

There are four main things you must consider when understanding how face verification works:

Input: Scanning an image or a group of images
Process:
Detection of facial features
Feature comparison and alignment
Key pattern representation
Final image classification
Output: Face representation, which is a result of a multilayer neural network
Training data: Involves the usage of thousands of millions of images

A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.

  • In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.
  • In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.

There are many disadvantages to using linear models, but the main ones are:

  • Errors in linearity assumptions
  • Lacks autocorrelation
  • It can’t solve overfitting problems
  • You can’t use it to calculate outcomes or binary outcomes

The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied.

Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.

Whenever data is missing or corrupted, you either replace it with another value or drop those rows and columns altogether. In Pandas, both isNull() and dropNA() are handy tools to find missing or corrupted data and drop those values. You can also use the fillna() method to fill the invalid values in a placeholder—for example, “0.”

  • Data acquisition
  • Ground truth acquisition
  • Cross validation technique
  • Query type
  • Scoring metric
  • Significance test

At present, a lot of work within the AI space is research-based. As a result, many organizations will be digging into your background to ascertain what kind of experience you have in this area. If you authored or co-authored research papers or have been supervised by industry leaders, make sure to share that information.

In fact, take it a step further and have a summary of your research experience along with your research papers ready to share with the interviewing panel.

However, if you don’t have any formal research experience, have an explanation ready. For example, you can talk about how your AI journey started as a weekend hobby and grew into so much more within a space of two or three years.

  1. Linear neuron
  2. Binary threshold neuron
  3. Stochastic binary neuron
  4. Sigmoid neuron
  5. Tanh function
  6. Rectified linear unit (ReLU)

Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.

There are three literals available in top-down inductive learning methods they are

a) Predicates

b) Equality and Inequality

c) Arithmetic Literals

The difference between the two is just like the terms sound. Strong AI can successfully imitate human intelligence and is at the core of advanced robotics.

Weak AI can only predict specific characteristics that resemble human intelligence. Alexa and Siri are excellent examples of weak AI.

Strong AI

  • Can be applied widely
  • Extensive scope
  • Human-level intelligence
  • Processes data by using clustering and association
    Weak AI
  • Can be great at performing some simple tasks
  • Uses both supervised and unsupervised learning
  • The scope can be minimal

There are many algorithms that are used for hyperparameter optimization, and following are the three main ones that are widely used:

  • Bayesian optimization
  • Grid search
  • Random search

These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.

Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.

A game can be defined as a search problem with the following components:

  • Game Tree: A tree structure containing all the possible moves.
  • Initial state: The initial position of the board and showing whose move it is.
  • Successor function: It defines the possible legal moves a player can make.
  • Terminal state: It is the position of the board when the game ends.
  • Utility function: It is a function which assigns a numeric value for the outcome of a game.

A feature vector is an n-dimensional vector that contains essential information that describes the characteristics of an object. For example, it can be an object’s numerical features or a list of numbers taken from the output of a neural network layer.

In AI and data science, feature vectors can be used to represent numeric or symbolic characteristics of an object in mathematical terms for seamless analysis.

Let’s break this down. A data set is usually organized into multiple examples where each example will have several features. However, a feature vector won’t have the same feature for numerous examples. Instead, each example will correspond to one feature vector that will contain all the numerical values for that example object.

Feature vectors are often stacked into a design matrix. In this scenario, each row will be a feature vector for one example. Each column will feature all the examples that correspond to that particular feature. This means that it will be like a matrix, but with just one row and multiple columns (or a single column and multiple rows) like [1,2,3,5,6,3,2,0].

AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:

  •  Participant design: Game theory is used to enhance the decision of a participant to get maximum utility.
  •  Mechanism design: Inverse game theory designs a game for a group of intelligent participants, e.g., auctions.

A chatbot is Artificial intelligence software or agent that can simulate a conversation with humans or users using Natural language processing. The conversation can be achieved through an application, website, or messaging apps. These chatbots are also called as the digital assistants and can interact with humans in the form of text or through voice.

The AI chatbots are broadly used in most businesses to provide 24*7 virtual customer support to their customers, such as HDFC Eva chatbot, Vainubot, etc.

Karl Pearson’s correlation coefficient is a measure of the strength of a linear association between two variables.
It is denoted by r or rxy (where x and y being the two variables involved).
This method of correlation draws a line of best fit through the data of two variables.
The value of the Pearson correlation coefficient (r) indicates how far away all these data points are to this line of best fit.

Formula –

Artificial Intelligence formula
Where,
* cov(X, Y): is the covariance between X and Y

Generally, ES users and ES itself uses User interface as a medium of interaction between users. Also, the user of the ES need not be necessarily an expert in Artificial Intelligence.
Although, at a particular recommendation, it explains how the ES has arrived. Hence, the explanation may appear in the following forms −

  • Basically, the natural language displayed on a screen.
  • Also, verbal narrations in natural language.
    Further, listing of rule numbers displayed on the screen. The user interface makes it easy to trace the credibility of the deductions.

This strategy doesn’t require any domain-specific knowledge. Thus it’s so simple strategy. Hence, it works very smoothly and fine with a small number of possible states.

Requirements for Brute Force Algorithms

a. State description

b. A set of valid operators

c. Initial state

d. Goal state description

is a field of computer science wherein the cognitive functions of the human brain are studied and replicated on a machine or a system. Artificial Intelligence today is widely used in various sectors of the economy including science and technology, healthcare, telecommunications, energy and so on. AI has three different levels:

  • Narrow AI: AI is narrow when the machine performs a specific task better than a human. The current research of AI is taking place at this level.
  • General AI: AI reaches the general state when it can perform any intellectual task equivalent to the accuracy of that of a human.
  • Active AI: AI is active when it can completely beat humans in all performed tasks.

Turing test is one of the popular intelligence tests in Artificial intelligence. The Turing test was introduced by Alan Turing in the year 1950. It is a test to determine that if a machine can think like a human or not. According to this test, a computer can only be said to be intelligent if it can mimic human responses under some particular conditions.

In this test, three players are involved, the first player is a computer, the second player is a human responder, and the third player is the human interrogator, and the interrogator needs to find which response is from the machine on the basis of questions and answers.

Depth-first search (DFS) is an algorithm that is based on LIFO (last-in, first-out). Since recursion is implemented with LIFO stack data structure, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.

a. Communication

  • Basically, a computer is a medium to communicate with users. Also, to learn a new language we can’t force users. Although, for casual users, it’s most important. Such as Managers and children. As they don’t have time and inclination to learn new skills to learn new interaction skills.
  • Basically, in natural language, it’s having a vast store of information. Also, we have to access via computers. Although, we have to generate information constantly. Also, it’s in the form of books, business, and government report.
  • Generally, in natural language processing, problems of AI arise in a very clear and explicit form.
  • Moreover, there are three major aspects of any natural language understanding theory:
    b. Syntax
    Basically, we use it to describe the form of the language. Also, grammar is used to specify it. further, we use natural language for the A.I languages of logic and computer programs. Also, these language is more complicated than other formal languages.

c. Semantics
Generally, utterances meaning provided with the of semantics. Although, if we want to build this understanding, general semantic theories exist for it.

d. Pragmatics
Basically, we use this component to explain how the utterances relate to the world.

In iterative deepening DFS algorithms, the search process of level 1 and 2 takes place. It continues the exploration until it finds the solution. It generates nodes until it finds the goal node and saves the stack of nodes it had created.

In artificial intelligence, the inference engine is the part of an intelligent system that derives new information from the knowledge base by applying some logical rules.

It mainly works in two modes:

Backward Chaining: It begins with the goal and proceeds backward to deduce the facts that support the goal.
Forward Chaining: It starts with known facts, and asserts new facts.

In speech recognition, Acoustic signal is used to identify a sequence of words.

Artificial intelligence can be divided into different types on the basis of capabilities and functionalities.

Based on Capabilities:

  • Weak AI or Narrow AI: Weak AI is capable of performing some dedicated tasks with intelligence. Siri is an example of Weak AI.
  • General AI: The intelligent machines that can perform any intellectual task with efficiency as a human.
  • Strong AI: It is the hypothetical concept that involves the machine that will be better than humans and will surpass human intelligence.
    Based on Functionalities:
  • Reactive Machines: Purely reactive machines are the basic types of AI. These focus on the present actions and cannot store the previous actions. Example: Deep Blue.
  • Limited Memory: As its name suggests, it can store the past data or experience for the limited duration. The self-driving car is an example of such AI types.
  • Theory of Mind: It is the advanced AI that is capable of understanding human emotions, people, etc., in the real world.Self-Awareness: Self Awareness AI is the future of Artificial Intelligence that will have their own consciousness, emotions, similar to humans.

a. High Cost Its creation requires huge costs as they are very complex machines. Also, repair and maintenance require huge costs.

b. No Replicating Humans As intelligence is believed to be a gift of nature. An ethical argument continues, whether human intelligence is to be replicated or not.

c. Lesser Jobs As we are aware that machines do routine and repeatable tasks much better than humans. Moreover, we use machines instead of humans. As to increase their profitability in businesses.

d. Lack of Personal Connections We can’t rely too much on these machines for educational oversights. That hurt learners more than help.

When a plan specifies all the actions you need to perform but specifies the order of the steps only when necessary, it’s called a partial-order plan.

Basically, it’s Popular Search Algorithms. Also, a prospective solution. Further, moves to a neighboring solution. Moreover, returns a valid solution.
a. Hill-Climbing Search Algorithm

We can start this algorithm with an arbitrary solution to a problem. Also, it’s an iterative algorithm. Hence, the algorithm attempts to better solution by a single element of the solution. Although, we take an incremental change as a new solution. As if the change produces a better solution. Moreover, we have to repeat until there are no further improvements.
b. Local Beam Search Algorithm

In this algorithm, we have to hold k number of states at any given time. In the beginning, we have to generate states randomly.
Moreover, with the objective function, we have to compute successors of these k states. Also, this stop, if any of these successors is the maximum value of the objective function.
Otherwise, we have to put the (initial k states and k number of successors of the states = 2k) states in a pool. Also, a pool is then sorted numerically. Further, we have to select highest k states as new initial states. This process continues until a maximum value is reached.

The process is of heating and cooling a metal to change its internal structure. Although, for modifying its physical properties is known as annealing. As soon as the metal cools, it forms a new structure. Also, metal is going to retain its newly obtained properties. Although, we have to keep the variable temperature in a simulated annealing process.
First, we have to set high temperature. Then, left it to allow “cool” slowly with the proceeding algorithm. Further, if there is high temperature, algorithm accepts worse solutions with high frequency.

Start
Initialize k = 0; L = integer number of variables;
From i → j, search the performance difference Δ.
If Δ random(0,1) then accept;
Repeat steps 1 and 2 for L(k) steps.
k = k + 1;
Repeat steps 1 through 4 till the criteria matches.

Basically, we have noticed that no technology can offer an easy and complete solution. Also, large systems are too costly. Although, they require significant development time and computer resources.
Also, ESs have their limitations which include −

  • Limitations of the technology
  • Difficult knowledge acquisition
  • ES are difficult to maintain
  • High development cost

Basically, we have to start searching for the root node. And continue through neighboring nodes first. Further, moves towards next level of nodes. Moreover, till the solution is found, generates one tree at a time. As this search can be implemented using FIFO queue data structure. This method provides the shortest path to the solution. FIFO(First in First Out). If the branching factor (average number of child nodes for a given node) = b and depth = d, the number of nodes at level d = bd. The total no of nodes created in worst case is b + b2 + b3 + … + bd.

DL is a subset of ML, which is the subset of AI. Hence, AI is the all-encompassing concept that initially erupted in computer science. It was then followed by ML that thrived later, and lastly DL, that is now promising to escalate the advances of AI to another level.

When the machine learning algorithm tries to capture all the data points, and hence, as a result, captures noise also, then overfitting occurs in the model. Due to this overfitting issue, the algorithm shows the low bias, but the high variance in the output. Overfitting is one of the main issues in machine learning.

Methods to avoid Overfitting in ML:

  • Cross-Validation
  • Training With more data
  • Regularization
  • Ensembling
  • Removing Unnecessary Features
  • Early Stopping the training.

Some of the popular Machine Learning algorithms are:

  • Logistic regression
  • Linear regression
  • Decision trees
  • Support vector machines

We can understand the advantage of natural language programming in an easy way as we consider two statements:
“Cloud computing insurance should be part of every service level agreement”

“A good S.L.A ensures an easier night’s sleep — even in the cloud.”
Generally, if an individual is used to of NLP, in an entity, a person will recognize cloud computing program. Also, a cloud is an abbreviated form of cloud computing.
Basically, in human language, these type of vague elements appears frequently. Although, machine learning algorithms are historically bad at interpreting. Moreover, many improvements take place in deep learning and artificial intelligence. And interpret them effectively.

A bidirectional search algorithm runs two simultaneous searches. The first go forward from the initial state, and the second goes backward from the goal state. They both meet at a common point, and that’s when the search ends—the goal state links with the initial state in a reverse manner.

Bayesian networks are the graphical models that are used to show the probabilistic relationship between a set of variables. It is a directed cycle graph that contains multiple edges, and each edge represents a conditional dependency.

Bayesian networks are probabilistic, because these networks are built from a probability distribution, and also use probability theory for prediction and anomaly detection. It is important in AI as it is based on Bayes theorem and can be used to answer the probabilistic questions.

Biagram model gives the probability of each word following each other word in speech recognition.

Machine Learning can be mainly divided into three types:

  • Supervised Learning: Supervised learning is a type of Machine learning in which the machine needs external supervision to learn from data. The supervised learning models are trained using the labeled dataset. Regression and Classification are the two main problems that can be solved with Supervised Machine Learning.
  • Unsupervised Learning: It is a type of machine learning in which the machine does not need any external supervision to learn from the data, hence called unsupervised learning. The unsupervised models can be trained using the unlabelled dataset. These are used to solve the Association and Clustering problems.
  • Reinforcement Learning: In Reinforcement learning, an agent interacts with its environment by producing actions, and learn with the help of feedback. The feedback is given to the agent in the form of rewards, such as for each good action, he gets a positive reward, and for each bad action, he gets a negative reward. There is no supervision provided to the agent. Q-Learning algorithm is used in reinforcement learning.

a. Virtual Personal Assistants

Basically, it is processed in which we have to collect a huge amount data. That is collected from a variety of sources to learn about users. Also, one needs to be more effective in helping them organize and track their information. For Example There are various platforms like iOS, Android, and Window mobile. We use intelligent digital personal assistants are like Siri, Google Now, and Cortana. AI plays an important role in this apps. If you demand they use to collect the information. And this information is used to recognize your request and serves your result.

b. Smart Cars

There are two examples: That are featured Google’s self-driving car project and Tesla’s “autopilot”. Also. the artificial intelligence is been used since the invention of the first video game.

c. Prediction

We call it as the use of predictive analytics. Its main purpose is potential privacy. Also, we can use in many ways. As its also sending you coupons, offering you discounts. That is close to your home with products that you will like to buy. Further, we can call it as the controversial use of artificial intelligence.

d. Fraud Detection

We use AI to detects fraud. As many frauds always happen in banks. Also, computers have a large sample of fraudulent and non-fraudulent purchases. As they asked to look for signs that a transaction falls into one category or another.

There are two best Hyperparameter in a tree-based model

  • Measure the performance over training data
  • Measure the performance over validation data
    We have to consider the validation result while comparing with the test results, so the answer is B
  • Computer Science
  • Cognitive Science
  • Engineering
  • Ethics
  • Linguistics
  • Logic
  • Mathematics
  • Natural Sciences
  • Philosophy
  • Physiology
  • Psychology
  • Statistics

Perl Programming language is not commonly used language for AI, as it is the scripting language.

It includes:
a. Expert System Development Environment

Basically, hardware and tools are included in it. They are −
Minicomputers, workstations, mainframes.
LISt Programming (LISP) and PROgrammation en LOGique (PROLOG).
Large databases.
b. Tools

Generally, tools are used to reduce the effort and cost.
Powerful editors and debugging tools with multi-windows.
They provide rapid prototyping.
Have Inbuilt definitions of a model, knowledge representation, and inference design.

It is based on the concept of LIFO. As it stands for Last In First Out. Also, implemented in recursion with LIFO stack data structure. Thus, It used to create the same set of nodes as the Breadth-First method, only in the different order. As the path is been stored in each iteration from root to leaf node. Thus, store nodes are linear with space requirement. With branching factor b and depth as m, the storage space is bm.

Reactive Machines AI: Based on present actions, it is not capable of using previous experiences to form current decisions whilst simultaneously updating their memory.
Limited Memory AI: This type of AI is used in self-driving cars – they detect the movement of vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced levels of AI have the ability to understand emotions and people.
Self Aware AI: This type of AI possesses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
Artificial Narrow Intelligence (ANI): This type of AI is a general-purpose AI, essentially used in building virtual assistants like Siri or Alexa.
Artificial General Intelligence (AGI): AGI is also known as strong AI. Example: Pillo robot – that answers questions related to health.
Artificial Superhuman Intelligence (ASI): This is the AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

Dropout Technique: The dropout technique is one of the popular techniques to avoid overfitting in the neural network models. It is the regularization technique, in which the randomly selected neurons are dropped during training.

Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, and function approximation of any model.

Basically, it can be implemented in systems with various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in artificial intelligence.

In a uniform cost search algorithm, you start from the initial state and go to the neighbouring states to choose the ‘least costly’ state. From there, you’ll select the next least costly state from the unvisited neighbouring states and the visited states. You’d keep looking for the goal state in this manner, and even if you do, you’ll look for other potential states. If every iteration of a breadth-first search algorithm had the same cost, it would become a uniform cost search algorithm.

The heuristic function is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close the agent is from the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in a reasonable time. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.

To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.

Q-learning is a popular algorithm used in reinforcement learning. It is based on the Bellman equation. In this algorithm, the agent tries to learn the policies that can provide the best actions to perform for maximining the rewards under particular circumstances. The agent learns these optimal policies from past experiences.

In Q-learning, the Q is used to represent the quality of the actions at each state, and the goal of the agent is to maximize the value of Q.

The main goal of this problem is to find a low-cost tour. That starts from a city, visits all cities en-route exactly once and ends at the same starting city.
Start
Find out all (n -1)! Possible solutions, where n is the total number of cities.
Further, determine the minimum cost by finding out the cost of each of these (n -1)! solutions.
Finally, keep the one with the minimum cost.

  • Various level of math, including probability, statistics, algebra, calculus, logic, and algorithms.
  • Bayesian networking or graphical modeling, including neural nets.
  • Physics, engineering, and robotics.
  • Computer science, programming languages, and coding.
  • Cognitive science theory.

In general, there are certain algorithms that are mostly used, or we can say that they are the first one to approach to understand the complex scenarios. Here are some of them.

  • Neural Network
  • Generic Algorithms
  • Reinforcement Learning

According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Also, intelligence distinguish us from everything in the world. As it has the ability to understand, apply knowledge. Also, improve skills that played a significant role in our evolution. We can define AI as the area of computer science. Further, they deal with the ways in which computers can be made. As they made to perform cognitive functions ascribed to humans.

Reinforcement learning is a type of machine learning. In this, an agent interacts with its environment by producing actions, and learn with the help of feedback. The feedback is given to the agent in the form of rewards, such as for each good action, he gets a positive reward, and for each bad action, he gets a negative reward. There is no any labeled data or supervision is provided to the agent. In RL, the agent continuously does three things(performing actions, changing state, and getting the feedback) to explore the environment.

The popular reinforcement learning algorithms are:

  • Q-Learning
  • SARSA(State Action Reward State Action)
  • Deep Q Neural Network
  • Basically, robots have mechanical construction. That is to form or shape designed to accomplish a particular task.
  • Also, it contains electrical components. That is a use of power and control the machinery.
  • Basically, it contains some level of a computer program. Also, it determines what, when and how a robot does something.

Basically, starts searches forward from an initial state and backward from goal state. As till both meets to identify a common state. Moreover, initial state path is concatenated with the goal state inverse path. Each search is done only up to half of the total path.

Some commonly used programming languages in AI include:

  • Python
  • R
  • Lisp
  • Prolog
  • Java

NLP stands for Natural Language Processing, which is a branch of artificial intelligence. It enables machines to understand, interpret, and manipulate the human language.

Components of NLP:

There are mainly two components of Natural Language processing, which are given below:

  • Natural Language Understanding (NLU):
    It involves the below tasks:
    To map the input to useful representations.
    To analyze the different aspects of the language.
  • Natural Language Generation (NLG)
    Text Planning
    Sentence Planning
    Text Realization

AI is a branch of computer science that stresses and finds a way of creating an intelligent machine that has the ability to work, think and reacts like humans.

Generally, we use it for the practical as well as commercial purposes.

  • Basically, we can use it to consumer products and control machines.
  • Although, not give accurate reasoning, but acceptable reasoning.
  • Also, this logic helps to deal with the uncertainty in engineering.

Classical AI focuses on deductive thought, such as a group of constraints. On the other hand, Statistical AI focuses on inductive thought like a pattern or trend.

Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour. They are used in almost all current speech recognition systems.

Deep learning is a subset of Machine learning that mimics the working of the human brain. It is inspired by the human brain cells, called neurons, and works on the concept of neural networks to solve complex real-world problems. It is also known as the deep neural network or deep neural learning.

Some real-world applications of deep learning are:

  • Adding different colors to the black&white images
  • Computer vision
  • Text generation
  • Deep-Learning Robots, etc.

We use English language to communicate between an intelligent system and N.L.P. Processing of Natural Language plays an important role in various systems.
For Example:

  • A robot, it is used to perform as per your instructions. The input and output of an N.L.P system can be −
    Speech
  • Written Text
  • Software analysts and developers.
  • Computer scientists and computer engineers.
  • Algorithm specialists.
  • Research scientists and engineering consultants.
  • Mechanical engineers and maintenance technicians.
  • Manufacturing and electrical engineers.
  • Surgical technicians working with robotic tools.
  • Military and aviation electricians working with flight simulators, drones, and armaments.

This is the most popular Artificial Intelligence Interview Questions asked in an interview. Searching is the universal techniques used in AI problem techniques. This algorithm is used to search a particular position. Every search terminology has some components.

Problem space: this is the environment in which the search takes place.
Problem Instance: it’s a result of the Initial State + Goal state.
Problem Space Graph: This is used to represent a problem state.
The depth of a problem: Here we can define the length of the shortest path.
Space Complexity: We can calculate this by the maximum number of nodes that are stored in memory.
Time Complexity: It is defined as the maximum number of nodes that are created.
Admissibility: This is the property of the algorithms that are used to find the optimal solutions.
Branching Factors: This can be calculated by the average number of child nodes in the problem space graph.
Depth: it is the length of the shortest path from inception to the goal state.
Here are some of the search algorithms

  • Breadth-first search
  • Depth-first search
  • Bidirectional search
  • Uniform cost search

As if we see the powers that are exploiting the power of computer system, the curiosity of human lead him to wonder, “Can a machine think and behave like humans do?” Thus, AI was started with the intention of creating similar intelligence in machines. Also, that we find and regard high in humans.

Following are some areas where AI has a great impact:

  • Autonomous Transportation
  • Education-system powered by AI.
  • Healthcare
  • Predictive Policing
  • Space Exploration
  • Entertainment, etc.

a. Availability
Due to mass production of software, expert systems are easily available.
b. Less Production Cost
As production cost of an expert system is reasonable. Thus, it makes them affordable.
c. Speed
Generally, expert systems offer great speed. Also, reduce the amount of work that an individual puts in.
d. Less Error Rate
Generally, an error rate of the expert system is low in comparison to human errors.
e. Reduced danger
They can be used in any risky environments where humans cannot work with.
f. Permanence
The knowledge will last long indefinitely.
g. Multiple expertise
Generally, it can be designed to have knowledge of many experts.
h. Explanation
They are capable of explaining in detail the reasoning that led to a conclusion.

Basically, it performs sorting in increasing the cost of the path to a node. Also, always expands the least cost node. Although, it is identical to Breadth-First search if each transition has the same cost. It explores paths in the increasing order of cost.

AI-powered tools are applied in various spheres of the economy, including:
Natural Language Processing

  • Chatbots
  • Sentiment analysis
  • Sales prediction
  • Self-driving cars
  • Facial expression recognition
  • Image tagging

An expert system mainly contains three components:

  • User Interface: It enables a user to interact or communicate with the expert system to find the solution for a problem.
  • Inference Engine: It is called the main processing unit or brain of the expert system. It applies different inference rules to the knowledge base to draw a conclusion from it. The system extracts the information from the KB with the help of an inference engine.
  • Knowledge Base: The knowledge base is a type of storage area that stores the domain-specific and high-quality knowledge.

Like I said that AI is everywhere and currently has a deep impact on our surroundings, we can see AI touch in the below listed things

Basically, four parts are shown in this-
a. Fuzzification Module
We use this module to transform the system inputs. As this is a crisp number. Also, helps in splitting the input signal into various five steps.
LP
x is Large Positive
MP
x is Medium Positive
S
x is Small
MN
x is Medium Negative
LN
x is Large Negative
b. Knowledge Base
In this, we have to store it in IF-THEN rules that were provided by experts.
c. Inference Engine
Generally, it helps in simulating the human reasoning process. That is by making fuzzy inference on the inputs and IF-THEN rules.
d. Defuzzification Module
In this module, we have to transform fuzzy set into a crisp value. That set was obtained by an inference engine.
Although, the membership functions always work on a same concept i.e fuzzy sets of variables.

Fuzzy logic is a method of encoding human learning for AI. It imitates the decision making process of humans through IF-THEN instances and the digital values of YES and NO. It is based on degrees of truth. Dr. Lotfi Zadeh of the University of California at Berkeley was the first person to put forth the idea of fuzzy logic.

The state of the process in HMM’s model is described by a ‘Single Discrete Random Variable’.

Below are the top five programming languages that are widely used for the development of Artificial Intelligence:

  • Python
  • Java
  • Lisp
  • R
  • Prolog
    Among the above five languages, Python is the most used language for AI development due to its simplicity and availability of lots of libraries, such as Numpy, Pandas, etc.

Artificial Intelligence is used by one another after the company for its benefits. Also, it’s fact that artificial intelligence is reached in our day-to-day life. Moreover, with a breakneck speed. On the basis of this information, arises a new question: Is it possible that artificial Intelligence outperforms human performance? If yes, then is it happens and how much does it take? Only when Artificial Intelligence is able to do a job better than humans.

Following are the best AI software platforms:

  • Tensor Flow
  • Azure Machine Learning
  • Ayasdi
  • Playment
  • Salesforce Einstein
  • Cloud Machine Learning

To Create Expert Systems it is the type of system in which the system exhibit intelligent behavior, and advice its users. b. To Implement Human Intelligence in Machines It is the way of creating the systems that understand, think, learn, and behave like humans.

  • Google Cloud AI platform
  • Microsoft Azure AI platform
  • IBM Watson
  • TensorFlow
  • Infosys Nia
  • Rainbird
  • Dialogflow

a. Availability
Due to mass production of software, expert systems are easily available.
b. Less Production Cost
As production cost of an expert system is reasonable. Thus, it makes them affordable.
c. Speed
Generally, expert systems offer great speed. Also, reduce the amount of work that an individual puts in.
d. Less Error Rate
Generally, an error rate of the expert system is low in comparison to human errors.
e. Reduced danger
They can be used in any risky environments where humans cannot work with.
f. Permanence
The knowledge will last long indefinitely.
g. Multiple expertise
Generally, it can be designed to have knowledge of many experts.
h. Explanation
They are capable of explaining in detail the reasoning that led to a conclusion.

Basically, robots have their specific aim. As they manipulate the objects. For Example- by perceiving, picking, moving, modifying the physical properties of an object.
What are Robots?
Generally, robots are the artificial agents acting in the real world environment. Robotics is a branch of Artificial Intelligence. That is composed of Electrical, and Mechanical Engineering. Also, Computer Science for designing, construction, and application of robots.

To perform this search we need to follow steps. As it performs the DFS starting to level 1, starts and then executes a complete depth-first search to level 2. Moreover, we have to continue searching process till we find the solution. We have to generate nodes till single nodes are created. Also, it saves only stack of nodes. As soon as he finds a solution at depth d, the algorithm ends, The number of nodes created at depth d is bd and at depth d-1 is bd-1.

Computer vision is a field of Artificial Intelligence that is used to train the 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 detections, etc.

Like I said that AI is everywhere and currently has a deep impact on our surroundings, we can see AI touch in the below listed things

  • Smartphones
  • Smart Cars and Drones
  • Social Media Feeds
  • Media players
  • Video games and many more areas.

There are some areas of fuzzy logic applications. These are-
a. Automotive Systems

  • Automatic Gearboxes
  • Four-Wheel Steering
  • Vehicle environment control
    b. Consumer Electronic Goods
  • Hi-Fi Systems
  • Photocopiers
  • Still and Video Cameras
  • Television
    c. Domestic Goods
  • Microwave Ovens
  • Refrigerators
  • Toasters
  • Vacuum Cleaners
  • Washing Machines
    d. Environment Control
  • Air Conditioners/Dryers/Heaters
  • Humidifiers

It’s one of the critical AI interview questions, so be sure to prepare it. FOPL stands for First-Order Predicate Logic. It’s a collection of formal systems, and each statement has a subject and a predicate. A predicate can have only one subject, and it has the ability to modify the subject.

The intelligent agent can be any autonomous entity that perceives its environment through the sensors and act on it using the actuators for achieving its goal.

These Intelligent agents in AI are used in the following applications:

  • Information Access and Navigations such as Search Engine
  • Repetitive Activities
  • Domain Experts
  • Chatbots, etc.

a. Lexical ambiguity
It’s predefined at a very primitive level such as word-level.

b. Syntax Level ambiguity
Basically, in this, we can define a sentence in a parsed way in different ways.

c. Referential ambiguity
Generally, referential ambiguity says that we have to refer something using pronouns only.

Machines are predicted to be better than humans in translating languages;

Working in the retail sector, and can completely outperform humans by 2060.

As a result, MI researchers believed that AI will become better than humans in the next 40-year time frame.

To build AI smarter, companies have already acquired around 34 AI startups. These companies are reinforcing their leads in the world of Artificial Intelligence.

In every sphere of life, AI is present. We use AI to organize big data into different patterns and structures. Also, patterns help in a neural network, machine learning, and data analytics.

From 80’s to now, Artificial intelligence is now part of our everyday lives, it’s very hard to believe. Moreover, it is becoming more intelligent and accepted every day. Also, with lots of opportunities for business.

In Artificial Intelligence, you study the cognitive functions of the human brain and try to replicate them on a system (or machine). It’s a branch of computer science and has applications in many industries and areas. You can also say that Artificial Intelligence focuses on creating intelligent machines that perform functions like humans.

Some popular ways to evaluate the performance of the ML model are:

Confusion Matrix: It is N*N table with different sets of value that is used to determine the performance of the classification model in machine learning.
F1 score: It is the harmonic mean of precision and recall, which is used as one of the best metrics to evaluate the ML model.
Gain and lift charts: Gain & Lift charts are used to determine the rank ordering of the probabilities.
AUC-ROC curve: The AUC-ROC is another performance metric. The ROC is the plot between the sensitivity.
Gini Coefficient: It is used in the classification problems, also known as the Gini Index. It determines the inequality between the values of variables. The high value of the Gini represents a good model.
Root mean squared error: It is one of the most popular metrics used for the evaluation of the regression model. It works by assuming that errors are unbiased and have a normal distribution.
Cross-Validation: It is another popular technique for evaluating the performance of the machine learning model. In this, the models are trained on subsets of the input data and evaluated on the complementary subset of the data.

Basically, artificial intelligence relates to following disciplines such as –

  • Computer Science
  • Biology
  • Psychology
  • Linguistics
  • Mathematics and
  • Engineering

The most popular domains in AI are:

  • Machine Learning
  • Neural Networks
  • Robotics
  • Expert Systems
  • Fuzzy Logic Systems
  • Natural Language Processing

To construct a robot we need following parts−

a. Power Supply
Generally, robots are powered by batteries, solar power, hydraulic.
b. Actuators
Basically, we use this to convert energy into movement.
c. Electric motors (AC/DC)
Generally, we need this for the rotational movement.
d. Pneumatic Air Muscles
Basically, we can say that they contract almost 40% when the air is sucked in them.

e. Muscle Wires
Although, we have noticed that it contract by 5% when an electric current is passed through them.
f. Piezo Motors and Ultrasonic Motors
Basically, we use it for industrial robots.

g. Sensors
Generally, we use it in task environment as it provides information of real-time knowledge.

There can be multiple long paths with the cost ≤ C*.

Uniform Cost search must explore them all.

Minimax algorithm is a backtracking algorithm used for decision making in game theory. This algorithm provides the optimal moves for a player by assuming that another player is also playing optimally.

This algorithm is based on two players, one is called MAX, and the other is called the MIN.

Following terminologies that are used in the Minimax Algorithm:

  • Game tree: A tree structure with all possible moves.
  • Initial State: The initial state of the board.
  • Terminal State: Position of the board where the game finishes.
  • Utility Function: The function that assigns a numeric value for the outcome of the game.
  • Generally, in this system, we can take imprecise, distorted, noisy input information.
  • Also, these logics are easy to construct and understand.
  • Basically, it’s solution to complex problems. Such as medicine.
  • Also, we can relate math in concept within fuzzy logic. Also, these concepts are very simple.
  • Due to the flexibility of fuzzy logic, we can add and delete rules in FLS system.

Game Theory is a specialized branch of mathematics that deals with opposing players trying to achieve a particular set of goals. It’s about choosing from a group of rational choices when you have multiple agents. Experts use this algorithm in AI when they have various agents in a problem.

a. A finger on the pulse Maybe the time is going on it’s not right for your business to harness the value of AI. Although, doesn’t mean you should stop keeping up like others are using AI. Only reading IT journal trade is a good place to start. Rather start focusing on how businesses are leveraging AI.

b. Piggyback on the innovators To implement AI, there are so many resources present from an industry that will help you. For example Google has developed a machine learning system, TensorFlow. That has released as open source software.

c. Brainstorm potential uses with your team If you want, a team must be engaged in encouraging in the areas of business, AI could be deployed. Data-heavy, inefficient are processes that are likely benefit. Moreover, find where these exist.

d. Start small and focus on creating real value It’s not mandatory to move forward for the sake only. Rather, it’s necessary to focus on objectives and start finding the best solution for it. Moreover, mean finding the specific process to run AI pilot. Also, see how it goes, learn and build from there.

e. Prepare the ground Before, to maximize the value of AI, its good to ensure your current process. I.e working in a best possible way.