Professionals have been interviewing in various top companies for Artificial Intelligence job interview. In that process, not only they get an opportunity to interact with many great minds, but also had a peek with a sense of what people really look for when interviewing someone. I believe that if I'd had this knowledge before, I could have avoided many mistakes and have prepared in a much better manner, which is what the motivation behind this post is, to be able to help someone bag their dream place of work. So these questions and answers will gives you an idea that what questions are asked by interviewer.
Following are the questions which are asked in interviews on Artificial Intelligence:
1. What is the difference between Strong Artificial Intelligence & Weak Artificial Intelligence?
Narrow application, scope is very limited
Good at specific tasks
Uses supervised and unsupervised learning
Eg. Siri, Alexa
Widely applied, scope is vast
Incredible human- level intelligence
Uses clustering and association to process data.
Ex. Advanced Robotics
2. What is Artificial Intelligence?
AI is a field of computer science wherein the cognitive functions of human brain is 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.
3. List some applications of AI.
Natural language processing
Facial expression recognition
4. List the programming languages in AI.
5. What is Tower of Hanoi?
Tower of Hanoi is a mathematical puzzle which shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using decision tree and Breath first search algorithm(BFS) we can solve Tower of Hanoi using AI.
6. What is Turing test?
The Turing test is a method to test the machine's ability to match the human level intelligence. A machine is used to challenge the human intelligence that when it passes the test, it is considered as intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing about people to mimic a human.
7. What is an expert system & characteristics of expert system?
An artificial intelligence program that has expert-level knowledge about a specific area and knows how to utilize its information to react appropriately. These systems have expertise to substitute a human expert. Their characteristics include:
Adequate response time
8. List the advantages of Expert system.
Ability to reason
Unbiased in nature
9. What is A* algorithm search method?
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 the various points called as the nodes.
10. What is Breadth-First Search Algorithm?
Start with the root node, then proceed through neighboring nodes. Further, moves towards next level of nodes. Till the arrangement is found, produces one tree at any given moment. As this pursuit can be executed utilizing FIFO(First in First Out) data structure. This strategy gives the shortest path to the solution.
11. What is Depth-First Search Algorithm?
Depth first search is based on LIFO (Last In First Out). A recursion is implemented with LIFO stack data structure. Thus, the nodes were different order than in BFS. The path is stored in each iteration from root to leaf node in linear with space requirement.
12. What is Bidirectional Search Algorithm?
The search begins forward from the beginning state and in reverse from objective state. The search meets to identify a common state. The initial state way is linked with the objective state in reverse way. Each search is done just up to half of the aggregate way.
13. What is Iterative Deepening Depth-First Search Algorithm?
The repetitive search process of level 1, level 2 happens in this search. The search process continues till the solution is found. Nodes are generated till a single node is created. Stack of nodes are saved. The search ends once the solution is found.
14. What is Uniform Cost Search Algorithm?
The uniform cost search performs sorting in increasing cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has same cost. It investigates ways in the expanding order of cost.
15. How Game theory and AI related?
AI system uses game theory for enhancement, it requires more than one participant which narrows the field quite a bit. The two fundamental roles:
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. Ex. Auctions.
16. Explain Alpha-beta pruning?
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 â??n' depth, prunes entire subtrees and leaves.
17. What is fuzzy logic?
Fuzzy logic is the 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.
18. List the application of Fuzzy logic.
Facial pattern recognition
Air conditioners, washing machines, vacuum cleaners
Antiskid braking systems, transmission systems
Control of subway systems and unmanned helicopters
Weather forecasting systems
Project risk assessment
Medical diagnosis and treatment plans
19. What is Partial order planning?
A problem has to be solved in a sequential approach to attain some goal, the partial-order plan specifies all actions that need to be taken, but specifies an ordering of the actions only when required.
20. What is FOPL?
First-order Predicate logic is 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.
21. What is Tensorflow?
TensorFlow is an open source machine learning library. It is fast, flexible and a low-level toolkit for doing complex algorithm and offers the users customizability to build experimental learning architectures and to work on them to produce desired outputs.
22. List the different Algorithm techniques in Machine Learning?
Learning to Learn
23. What is Deep learning?
It is a subset of machine learning which is used to create an artificial multi-layer neural network. They have self-learning capability based on previous instances and provides high accuracy.
24. Differentiate between supervised, unsupervised and reinforcement learning?
25. Name few Machine Learning algorithms?
Support vector machines
Naive Bayes etc.
26. What is Naive Bayes?
Naive Bayes machine learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes independent and equal contribution to the outcome
27. Differentiate parametric and non-parametric models?
28. What is Perceptron in Machine Learning?
Perceptron is an algorithm which is able to simulate the ability of the brain to understand and discard, it is for supervised classification of the input into one of several possible non-binary outputs.
29. List the extraction techniques used for dimensionality reduction?
Independent Component Analysis
Principal Component Analysis
Kernel Based Principal Component Analysis
30. Is kNN different from kmeans clustering?
Minimal training model
Used in classification and regression of known data.
Exhaustive training model
Used in Population demographics, Market segmentation, Social media trends, Anomaly detection.
31. What is ensemble learning?
Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve the classification, prediction, function approximation etc of a model.
32. List the steps involved in Machine learning?
Choosing an appropriate model
Training the data set
33. What is hash table?
A hash table is a data structure that is used to produce an associative array which are mostly used for database indexing.
34. What is regularization in Machine learning?
Regularization comes to picture when the model is either overfit or underfit. It is basically used to minimize the error in the dataset. A new piece of information is fit in the data set to avoid fitting issues.
35. What are the components of relational evaluation techniques?
Ground Truth Acquisition
Cross Validation Technique
36. What is model accuracy and model performance?
Model accuracy is just a subset of model performance. Model accuracy is based on model performance of algorithm, model performance is based on the datasets we feed as input to the algorithm.
37. Define F1 score?
It is the weighted average of precision and recall. It considers both false positive and false negative into account. It is used to measure the model's performance.
38. List the applications of Machine learning?
Image, speech, face detection
Manufacturing and Inventory management
Fraud detection etc.
39. How do you select important variables in the dataset?
Lasso Regression method.
Random Forest, plot variable importance chart.
40. What is a recommendation system?
A recognition 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.