Artificial Intelligence Interview Questions

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Artificial Intelligence Interview Questions

Preparing to interview candidates applying for an artificial intelligence (AI) position is not for the weak of heart. Creating language models that identify, predict, and replicate human behaviors (which are among the primary purposes of current AI), requires many skills. To find a great candidate you'll not only need to assess their technical knowledge, you'll also need to look at their ability to iterate and innovate, problem-solve, and integrate solutions into real-world applications.

AI is continuing to evolve with every generation, and selecting the right person to implement AI into your systems is critical. That's why you want to be sure that candidates with both technical and soft skills are present in your new hire.

Types of Artificial Intelligence Interview Questions

As an interviewer, being prepared with a framework of interview questions on artificial intelligence can certainly help you get off on the right foot. Many companies like to categorize AI interviews into three main parts: technical, behavioral, and case study. Let’s take a more in-depth look at what you can expect from each of these categories.

Technical Interview Questions

Candidates should expect a varied range of artificial intelligence interview questions that cover both the theoretical underpinnings and practical applications of AI. These questions might cover anything from machine learning algorithms, data handling, ethical considerations in AI, and scenario-based problem-solving to gauge the candidate’s comprehensive understanding of artificial intelligence.

Behavioral Interview Questions

Behavioral questions often assess more of a candidate's soft skills than anything else - in this case, looking at their past experiences with AI, we can use behavioral interview questions on artificial intelligence to assess a candidate's past experience with AI projects, including the methods and frameworks they've used, the challenges they've faced, and the outcomes of their work. These questions help interviewers assess how candidates apply their knowledge in practical settings, their ability to learn from past experiences, and their capacity for innovation and strategic thinking in the AI domain.

Case Study Interview Questions

Lastly, with the rise of new paradigms such as generative AI, candidates should be prepared for case-study-based generative AI interview questions. These questions could focus on their usage and experience with generative models, such as GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), or other neural network architectures. The questions might also explore how candidates have leveraged generative AI for creative problem-solving, content generation, or data augmentation, showcasing their ability to harness the latest advancements in AI for a specific application.

Either way, these are complex interviews with a lot at stake. If you'd like to create your own personal AI interview guide, click this link, paste your job description into the text box, and we'll deliver a custom interview guide created by our AI-powered interview intelligence software.

Good Questions To Ask About Artificial Intelligence

There are many good questions to ask about artificial intelligence in interviews. Most will focus on a candidate's technical knowledge of the AI concepts themselves but some should also focus on a candidate's past experience with these tools, and their vision for how AI can be implemented to increase efficiency, and effectiveness, and unlock insights that may not have been possible before.

The artificial intelligence interview questions and answers in this section will follow the same 3 frameworks from above with the first set covering concepts such as deep learning, machine learning, and natural language processing.

Technical Interview Questions

  • What is the difference between supervised and unsupervised learning?
  • Can you explain how a neural network works?
  • How would you evaluate the performance of an AI model?

Behavioral Interview Questions

  • Can you give an example of a time when you had to think outside the box to solve a problem?
  • How do you handle conflicting opinions within a team?
  • Can you tell me about a project where you had to balance multiple stakeholders' needs?

Case Study Interview Questions

  • How would you use AI to improve customer service for an e-commerce company?
  • Design a recommendation system for a music streaming service.
  • How would you identify and mitigate potential biases in an AI model?

AI interview questions and answers should be well-defined, especially for interviewers who lack technical experience in AI (which if we're honest is most of us). So let's include an answer here to give you an example of the level of technical detail you're looking for.

Q: What's the difference between supervised and unsupervised learning?

A:  Supervised learning is a type of machine learning where the algorithm learns from labeled data, making predictions based on features and known outcomes. In contrast, unsupervised learning does not use labels in the training data and instead focuses on finding patterns within the data through techniques such as clustering or association rule mining. Essentially, supervised learning requires pre-defined outcomes while unsupervised learning seeks to find patterns and relationships without prior knowledge.

As you can see, the level of specificity required to assess an AI candidate's proficiency is quite high. If you don't have technical experience with AI, it's still essential to have a solid understanding of the concepts and applications so you can ask relevant and meaningful questions. Even so, artificial intelligence interview questions can be complex and challenging.

Remember that a candidate's soft skills, past experience, vision, and creativity are also vital to the success of your AI initiatives. So don't overlook a candidate's fit for your culture and team by getting lost in the technical questions. They're all important to long-term success.

Pillar's interview intelligence can make AI interviews simple. With interview insights that give real-time feedback and interview questions tailored to your job description, you can assess a candidate's technical knowledge and soft skills with ease.

AI And Machine Learning Questions

Basic interview questions on artificial intelligence wouldn't be complete without machine learning. After all, machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from data without explicit programming. Artificial intelligence and machine learning interview questions often overlap, as both fields are closely related and work together to achieve common goals.

Here are some key interview questions that blend the topics of AI and machine learning:

  • What is machine learning, and how does it relate to artificial intelligence?

A: Machine learning (ML) is a subset of artificial intelligence (AI) focusing on algorithms and models enabling computers to perform tasks without explicit programming. ML models learn from data, recognizing patterns and making decisions based on it. This learning process enhances the accuracy and efficiency of ML systems over time. In the context of AI, machine learning is crucial for various applications like speech recognition, recommendation systems, and autonomous vehicles. In essence, AI encompasses smart task execution by machines, while machine learning involves algorithms teaching machines how to perform these tasks.

  • What are the different types of machine learning?

A: The three main categories of machine learning are supervised, unsupervised, and reinforcement learning. In supervised learning, algorithms learn from labeled data to make predictions. In contrast, unsupervised learning uses unlabeled data to identify patterns and relationships within it. Reinforcement learning involves an algorithm interacting with its environment to learn through trial and error and receive rewards for desired actions.

  • Can you explain the bias-variance tradeoff in machine learning models?

A: The bias-variance tradeoff is a fundamental concept in developing accurate ML models. Bias refers to the assumptions made by an algorithm, while variance relates to the sensitivity of an algorithm to changes in data. Finding the right balance between bias and variance is crucial as high bias can result in underfitting, while high variance leads to overfitting. In general, a model with low bias and low variance performs the best.

  • How would you handle missing data in a machine-learning project?

A: There are several techniques for handling missing data, depending on the type of ML model and the amount of missing data. One approach is to remove the rows or columns with missing values, but this can reduce the size and quality of the dataset. Another option is to replace the missing values with a statistical measure like the mean or median for numerical data or mode for categorical data. Additionally, you can use machine learning techniques such as imputation to estimate and fill in missing values based on the rest of the data.

  • What is deep learning, and how does it differ from traditional machine learning?

A: Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. These networks are inspired by the structure and function of the human brain, with multiple layers of interconnected nodes that process information in a hierarchical manner. Deep learning models have shown great success in complex tasks such as image and speech recognition, often outperforming traditional machine learning algorithms. However, deep learning models require a large amount of data and computational power to train effectively.

These questions on artificial intelligence with answers barely scratch the surface of this massive and rapidly evolving field. They do however provide a solid starting point for assessing a candidate's knowledge and understanding of the key concepts of AI and machine learning.

Hopefully, this guide has helped you understand the importance of having a well-rounded approach to interviewing candidates for these highly technical positions. If you're struggling to come up with AI and machine learning questions for your next interview, check out our interview question generator or book a demo of our interview intelligence software today.