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Top Artificial Intelligence (AI) Interview Questions & Answers for 2024

Artificial Intelligence stands at the forefront of technological innovation, representing the pinnacle of human ingenuity melded with machine intelligence. Its importance reverberates across industries, revolutionising the way we live, work, and interact with technology. The global artificial intelligence market soared to an unprecedented value of USD 150.2 billion in 2023, forecasting an astonishing compound annual growth rate (CAGR) of 36.8% from 2023 to 2030. This meteoric rise in AI’s market value is a testament to its indispensable role in modern society.

 

As industries scramble to adopt AI’s potential, the demand for skilled professionals in this domain skyrockets. It’s not just a career path; it’s a gateway to shaping the future. If seeking employment in this dynamic field, arming oneself with answers to the most frequently asked Artificial Intelligence interview questions becomes important.

 

These AI interview questions can serve as your passport to unlocking the myriad opportunities nestled within this ever-evolving landscape.

 

Table of Contents:

 

 

Artificial Intelligence Interview Questions for Freshers

 

1. What is Artificial Intelligence?

 

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. It involves creating algorithms or systems that enable machines to simulate human-like thinking, reasoning, learning, problem-solving, perception, and decision-making capabilities.

 

AI encompasses various subfields, such as machine learning, which focuses on creating algorithms that allow systems to learn and improve from data without explicit programming, and natural language processing, which involves enabling computers to understand, interpret, and generate human language.

 

2. What is the future of Artificial Intelligence?

The future of AI seems to promise significant advancements in machine learning and natural language processing. These advancements are set to pave the way for the development of highly intricate and independent AI systems. These systems are likely to find application in diverse fields like autonomous vehicles, personalised assistants, and sophisticated robotics.

 

Furthermore, AI’s impact is anticipated to be substantial in critical sectors such as healthcare, finance, and manufacturing. Nevertheless, as AI progresses and becomes more ingrained in our lives, it’s crucial to contemplate the ethical and societal impacts of this technology. Responsible development and usage of AI should be prioritised to ensure its ethical deployment in society.

 

3. How do you evaluate the performance of a machine-learning model?

 

Evaluating the performance of a machine learning model is crucial to understanding how well it generalises to new, unseen data. Here are some common methods used to evaluate model performance:

  • Train-Test Split
  • Cross-Validation
  • Metrics
  • Confusion Matrix
  • ROC Curves and Precision-Recall Curves
  • Bias-Variance Tradeoff Analysis
  • Domain-Specific Metrics

The choice of evaluation method depends on the nature of the problem, the available data, and the specific goals of the machine learning task. It’s essential to select the appropriate evaluation strategy to ensure a comprehensive understanding of the model’s performance.

 

4. What are the limitations or ethical considerations of AI?

 

AI presents immense potential, but it’s essential to acknowledge its limitations and ethical considerations. Some key limitations and ethical concerns include:

 

  • Bias and Fairness
  • Transparency and Explainability
  • Privacy Concern
  • Job Displacement
  • Security Risks
  • Autonomy and Accountability
  • Ethical Decision-making Dilemma

Addressing these limitations and ethical concerns requires a multi-disciplinary approach involving technologists, ethicists, policymakers, and society at large. Regulations, ethical guidelines, transparency in AI development, and ongoing ethical assessments are vital to ensure AI systems are developed and deployed responsibly for the benefit of society.

 

Artificial Intelligence Interview Questions for Experienced

 

1. What is Q-Learning?


Q-learning is a fundamental technique in reinforcement learning, a type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a specific goal. Specifically, Q-Learning is used to enable an agent to learn an optimal action-selection strategy in a Markov decision process (MDP).

 

2. What do you understand by the term ‘overfitting’ in machine learning?

 

Overfitting’ is a common problem in machine learning where a model learns to perform extremely well on the training data but fails to generalise its performance to new, unseen data. It occurs when a model learns not just the underlying patterns in the data but also the noise and random fluctuations present in the training dataset.

 

Key characteristics of overfitting:

 

  • High Accuracy on Training Data
  • Poor Performance on Test Data

 

Causes of overfitting:

 

  • Complex Models
  • Insufficient Data
  • Lack of Regularization
  • Incorrect Feature Selection
  • Ways to mitigate overfitting
  • Cross-Validation
  • Simpler Models
  • Regularisation Techniques
  • Feature Selection

By understanding and addressing overfitting, machine learning practitioners can create models that generalise well to new data, ensuring their reliability and usefulness in real-world applications.

 

3. Explain the concept of reinforcement learning with an example.

 

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent learns to achieve a goal by receiving feedback in the form of rewards or punishments based on their actions. The goal is to maximise cumulative reward over time.

 

Here’s an example to illustrate this:

 

Imagine teaching a dog new tricks. When the dog performs a desired action, like sitting or fetching a ball, you reward it with a treat. In this scenario:

 

  • The dog is the agent.
  • The tricks it learns are its actions.
  • The treat is the reward.
  • You are the environment providing feedback to the dog.

Initially, the dog doesn’t know what actions will earn the treat. It might try various actions, and when it accidentally sits, you give it a treat. The dog begins associating sitting with the reward. Over time, it learns to associate other actions, like rolling over or fetching, with rewards through this trial-and-error process.

 

Similarly, in reinforcement learning, an agent explores its environment, taking actions to maximise its total reward. It learns from the consequences of its actions, receiving rewards or penalties, and adjusts its strategy to achieve its objective more effectively over time. The agent’s goal is to figure out the best actions to take in different situations to maximise its cumulative reward.

 

4. How would you explain the concept of natural language processing (NLP) to someone new to AI?

 

Natural Language Processing, or NLP, is like teaching computers to understand and work with human language. Just as we use language to communicate with each other, NLP allows computers to understand, interpret, and respond to human language in a way that’s similar to how we do.

 

Think about how we talk to our phones or computers using voice commands or how we type questions into search engines. NLP is what enables these devices to understand what we’re saying or typing and respond accordingly.

 

It involves teaching machines to recognise the words we use, understand their meanings, and even grasp the context or emotions behind them. NLP helps in various ways, from translation tools that convert languages to chatbots that hold conversations like a human.

 

Artificial Intelligence Scenario-Based Question

 

1. You’re designing an AI-powered recommendation system for an e-commerce platform. Users complain about irrelevant product suggestions. What steps would you take to improve the recommendations?


I would enhance the recommendation algorithm by incorporating collaborative filtering techniques, analysing user behaviour more deeply, and integrating feedback loops to personalise recommendations based on user preferences and purchase history.

 

2. While developing a machine learning model for predicting stock prices, you notice inconsistent predictions with high volatility. How would you address this issue?


I’d explore using more sophisticated models like recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) to capture temporal dependencies in stock data. Additionally, feature engineering and including more relevant external data like market sentiment could help improve the model’s accuracy.

 

3. You’re implementing an image recognition system for a self-driving car. How would you ensure the system can accurately identify and differentiate between various objects on the road?


I’d employ a deep convolutional neural network (CNN) architecture trained on a diverse dataset that includes various road objects, pedestrians, vehicles, and different environmental conditions. Additionally, continual retraining on new data and augmentation techniques would help maintain accuracy.

 

4. Your natural language processing (NLP) model for sentiment analysis is misclassifying certain nuanced sentiments. How could you refine the model to handle these subtleties?


I would consider leveraging more advanced models like transformers (e.g., BERT) fine-tuned on a specialised dataset that captures nuanced sentiments. Additionally, ensemble methods or incorporating lexicons specific to nuanced sentiments could improve the model’s performance. These scenarios address common challenges across different domains of AI, offering approaches and solutions to tackle them effectively.

 

Long Story Short

 

Artificial Intelligence stands as the landmark of technological advancement, promising transformative innovation across industries. As its demand escalates, the need for skilled professionals steepens. The Accelerator Program in Artificial Intelligence and Machine Learning at Hero Vired emerges as a light for aspiring AI enthusiasts, offering comprehensive education and hands-on experience to navigate this dynamic field. With a curriculum designed by industry experts and a focus on practical skills, Hero Vired equips learners to excel in the AI landscape of tomorrow. Embrace the future of technology with Hero Vired’s AI course today and shape the innovations of tomorrow! Enrol now to unfold on your AI journey!

 

FAQ's

Go through the following tips to ace your Artificial Intelligence interview:

  • Familiarise yourself with the interview platform

  • Set your interview desk up for success

  • Dress to impress

  •  Prep yourself like a traditional in-person interview

  •  Practice active listening

  • Pay attention to your non-verbal cues

  • Reference relevant keywords

  • Emphasise your soft skills

  • Manage your time

  • Check your digital presence

  • What are the Common Uses and Applications of AI? 

  • What are Intelligent Agents, and How are They Used in AI?

  • What is Tensorflow, and What is It Used For?

  • What is Machine Learning, and How Does It Relate to AI?

  • What are Neural Networks, and How Do They Relate to AI?

Several AI roles require a bachelor's degree or higher, yet some entry-level positions accept an associate degree or equivalent expertise. Typically, AI experts pursue degrees in computer science, mathematics, or related fields during their undergraduate studies.
Mastering programming is the foundational skill for aspiring AI engineers. Proficiency in languages like Python, R, Java, and C++ is crucial for building and deploying AI models.

In top-tier roles, AI engineers may command salaries reaching 50 lakhs, with an average annual income exceeding $100,000 for the profession.

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