Artificial Intelligence Interview Questions & Answers- Part 5

Answer:

• Gates (including forget, memory, update, and read gates)
• Tanh activation function (producing values between -1 and 1)
• Sigmoid activation function (producing values between 0 and 1)

Answer:

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

Answer:

An autoencoder is a neural network type that learns to encode and decode data, typically aiming to reconstruct the original input. Some applications of autoencoders include:

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

Answer:

Components of GAN:

• Generator
• Discriminator

Deployment steps:

• Train the model
• Validate and finalize the model
• Save the model
• Load the saved model for future predictions

Answer:

A TensorFlow cluster consists of multiple “tasks” that collaborate in executing a distributed TensorFlow graph. Each task is linked with a TensorFlow server, which contains a “master” used for creating sessions and a “worker” responsible for executing graph operations. A cluster can be divided into one or more “jobs,” with each job containing one or more tasks.

Answer:

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

Answer:

Intermediate tensors are tensors that are not direct inputs or outputs of the Session.run() call but are involved in the computation between the inputs and outputs. They will be freed at or before the end of the call.

Sessions can own resources, such as tf.Variable, tf.QueueBase, and tf.ReaderBase, which consume memory. When the session is closed by invoking tf.Session.close(), these resources and the associated memory are released.

Answer:

When the tf.Variable.initializer operation for a variable is run in a session for the first time, its lifespan begins. It is destroyed when the tf.Session.close operation is executed.

Answer:

Yes, logical inference can be solved in propositional logic by utilizing concepts such as logical equivalence, process satisfaction, and validation checking.

Answer:

There are several algorithms employed for hyperparameter optimization, but three widely used ones are:

• Bayesian optimization
• Grid search
• Random search

Answer:

The stages of AI learning include the following:

1. Artificial General Intelligence (AGI): Also known as Strong AI, AGI refers to machines that possess human-like thinking and decision-making abilities, raising concerns about their impact on human existence.
2. Artificial Normal Intelligence (ANI): Also known as Weak AI, ANI refers to AI systems that can perform specific tasks or activities but lack the broader cognitive abilities of humans.
3. Artificial Super Intelligence (ASI): ASI represents AI systems that surpass human capabilities and can perform any task that a human can. Examples include advanced humanoid robots like Alpha 2.

Answer:

Markov’s Decision Processes (MDPs) are a mathematical framework used to address sequential decision problems under uncertainty and in the field of reinforcement learning. The objective of MDPs is to determine the optimal policy that maximizes the cumulative reward gained by an agent.

Answer:

The key elements of a Markov Decision Process (MDP) are as follows:

1. A set of finite states (S)
2. A set of finite actions (A)
3. Rewards
4. Policy (Pa)

In an MDP, an agent takes actions (A) to transition from an initial state to an end state, receiving rewards along the way. The agent’s sequence of actions can be defined as a policy (Pa).

Answer:

K-means clustering is an unsupervised learning algorithm widely used to identify clusters within a dataset. It aims to group data objects based on similarity without any prior labeled data. The algorithm iteratively assigns data points to clusters and updates the cluster centroids until convergence. Once the clustering is complete, new data points can be assigned to the most appropriate cluster.

Answer:

K-means clustering finds applications in various domains, including:

1. Astronomy: Identifying star clusters or galaxies.
2. Search engines: Grouping similar web pages or search results.
3. Computer vision: Image segmentation and object recognition.
4. Customer profiling: Segmenting customers based on their purchasing behavior.
5. Market segmentation: Identifying distinct customer segments based on demographic or behavioral data.

Answer:

The heuristic approach is often the best choice for game-playing problems. It involves employing intelligent guesswork to make decisions. For example, in chess matches between humans and computers, brute force computation is combined with heuristics to evaluate hundreds of thousands of positions.

Answer:

Breadth-first search and best-first search are two search strategies used in AI:

• Breadth-first search explores nodes at the current depth level before moving to the next level, ensuring a complete exploration of each depth level.
• Best-first search selects the most promising node for expansion based on an evaluation function, typically considering heuristics. It does not guarantee complete exploration of depth levels but focuses on the most promising path.

Answer:

In “Artificial Intelligence,” frames and scripts are knowledge representation structures:

• Frames are a variant of semantic networks and organize non-procedural knowledge in an expert system. They divide knowledge into substructures and represent stereotyped situations.
• Scripts are similar to frames but emphasize the ordered values that fill the slots. They are used in natural language understanding systems to organize a knowledge base around situational understanding.

Answer:

In top-down inductive learning methods, three types of literals are available:

1. Predicates: Used to express relationships or properties.
2. Equality and Inequality: Used to compare values or variables.
3. Arithmetic Literals: Used to perform mathematical operations or comparisons.

Answer:

Various software platforms for AI development include:

• Amazon AI services
• TensorFlow
• Google AI services
• Microsoft Azure AI platform
• Infosys Nia
• IBM Watson
• H2O
• Polyaxon
• PredictionI