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AI Interview Questions

AI is all the hype these days. And for the right reasons.

It's smart. It's efficient. Most importantly, it can save us time and money in the long run.

That's why global AI marketing is growing at an impressive rate. In fact, it was worth €300 billion in 2021 and is forecasted to grow even more quickly.

Keeping this in mind, it's understandable why you'd want to get your foot in an AI-related career. But before you do that, you should be familiar with common AI interview questions hiring managers ask.

That's what we'll cover in this guide. Whether you're a newcomer to AI or want to advance your career, these questions will help you prepare for your next big job interview.

Kinds of Questions to Expect In AI Job Interviews

When you walk into an interview, you expect them to ask two primary types of questions; personal and professional. The same applies to AI interviews.

But since AI is a relatively new field, many people aren't aware of the professional questions an interviewer may ask. Let's discuss the type of AI interview questions you may have to answer.

General AI Questions

These questions will test your knowledge of artificial intelligence. Suppose the company works with Tensorflow. Or, they're currently working on a relevant project for which they want to hire you.

The interviewers may ask you about Tensorflow. Similarly, they'll test your machine learning knowledge if they want you to work with machine learning specialists.

Past AI Experience

If you're applying for a beginner's position, some interviewers might not ask questions about past experience. Others might want to know if you have done any personal projects or internships in the field.

Meanwhile, intermediate or expert-level jobs will require past AI experience. The interviewers will ask you where you worked before this, which position you held, what kind of projects you did, and which skills you used/gained.

AI Interview Questions for Beginners

If you're applying for your first job in artificial intelligence, congratulations and good luck! While we can't guarantee a position, we can help you better prepare for the interview.

Here are some basic questions you should know the answers to.

1. What Is Artificial Intelligence?

Artificial intelligence is a machine-based simulation of human intelligence. AI technology gives machines, particularly computer systems, the ability to perform human-like tasks.

Some examples of AI are natural language processing, chatbot customer service, speech recognition, and recommendation engine.

2. What Are Some Common Applications of Artificial Intelligence?

When answering this question, you should allude to the type of company you're applying to. Suppose you're applying at a healthcare tech company. You can discuss AI's application in healthcare in your answer.

Some possibilities for AI applications include image recognition, data processing, reporting, automation, content distribution, and predictive maintenance.

3. Is There a Difference Between AI and Machine Learning? If Yes, What Is It?

AI originated before machine learning in the 1950s. It represents machine-simulated intelligence. Meanwhile, machine learning involves programming the machines to make decisions.

While artificial intelligence is a subset of data science, machine learning is a subset of AI. Artificial intelligence experts build machines that can think and work like humans. Machine learning specialists create machines that use data to make decisions and solve problems.

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4. Can You Give An Example of AI Around Us?

One of the most basic examples of AI is in our phones. Whether it's Microsoft's Cortana, Apple's Siri, or Google Assistant, they're all examples of AI. We can talk to them, tell them to do things for us, and even ask them questions.

AI is all around us in our homes too. Smart cameras, kitchen appliances, AI cleaners, smart thermostats, and smart plugs are all good examples. Oh, and how could we forget Amazon's Alexa?

You can also talk about AI in the interviewer's office. For example, mention smart email categorisation that their company might use to segment customers. Voice-to-text features and smart personal assistants are all examples of AI.

5. In Which Areas Can We Use Artificial Intelligence?

Artificial intelligence has uses in a wide range of industries. At the lower end, AI can improve our lifestyle through smart home appliances, personalised shopping, voice assistants, and fraud protection.

But if you talk about its advanced uses, it facilitates medical image analysis, predictive analysis, natural language processing, and personalised learning. It's so powerful that companies like Tesla are using it to make self-driving cars.

6. What Are Intelligent Agents in AI?

Intelligent agents are entities with sensors that detect what's happening. These entities then use actuators to complete a task or follow a command.

An example of an intelligent agent is Amazon's Alexa. It uses voice sensors to hear your command when you tell it to add something to your shopping cart. Then, it uses its actuators to follow your instructions.

The same happens when you tell your smart TV to switch to Amazon Prime from Netflix. Or, ask Siri to set an alarm for the morning.

7. Can You Name Some Platforms for AI Development?

Be smart when answering this question. Most companies mention the platforms they use in their job description. For example, they may have mentioned they're looking for a Microsoft Azure specialist.

When naming the platforms, discuss the software the company uses too. Here are some examples:

  • Amazon AI services

  • IBM Watson

  • H20

  • PredictionIO

  • Polyaxon

  • TensorFlow

  • Google AI Services

  • Amazon SageMaker

8. Which Programming Languages Are Commonly Used for AI? Which Ones Are You Proficient In?

Some common programming languages used in AI include Python, MATLAB, C++, Prolog, and Java. Mention which languages you've worked with before and your proficiency level.

9. What Are the Types of AI Based on Functionalities?

AI is categorised based on its functionality and strength. You can mention both types or ask the interviewer which type they want to know about. These are the AI types based on capabilities.

  • Reactive Machines: In AI, reactive machines are task-specific and have no memory. Simply put, when you give this machine an input, it provides the same output every time. Netflix recommendations are a good example of reactive machines. The machine studies your previous watch history and interests to provide you with recommendations accordingly.

  • Limited Memory AI: These machines have the ability to learn. So they can 'absorb' data and make better decisions over time. In some ways, they're similar to the human mind and its neurons. An example of limited memory AI is self-driving cars. These vehicles study the road condition, analyse it, and make decisions for speed, route, and direction.

  • Theory of Mind AI: The psychological concept of, Theory of Mind says that people have emotions, feelings, and thoughts. Theory of Mind AI refers to futuristic machines that may possess or understand their users' feelings and emotions.

  • Self-Aware AI: These AI machines have human-like reactions and consciousness. They may be able to take self-driven actions. An example of self-aware AI is a robotic arm made by experts at Columbia University. The 'arm' did not have any knowledge of what it was or what it could do. But after a day of working with itself, the robot created a self-simulation, indicating that it learned how it functions from scratch.

10. What Are the Types of AI Based on Strength?

There are three types of AI machines based on their strength.

  • Artificial Narrow Intelligence: These are general-purpose machines, such as Siri. They do tasks as commanded or advised.

  • Strong AI: These machines have the same level of intelligence as humans. For example, Pillo is a robot that can answer your health-related questions.

  • Artificial Superhuman Intelligence: These machines will be able to do everything humans can. In fact, they will be able to do more. Alpha 2 is the first humanoid robot to speak Spanish and even do yoga.

Deep learning means using multi-layered networks for sophisticated data processing. It allows the software to train itself to perform human-like tasks like image and speech recognition.

It's closely related to AI since it teaches computer systems how to process data like humans. Deep learning models recognise all types of patterns, including those in sounds, text, and pictures.

An example of deep learning and AI working in tandem is self-driving cars. Deep learning enables these vehicles to process data like traffic signs and road safety manuals. So, when these cars are on the road, they can read a stop sign like a human driver. They can also differentiate between objects on the road, such as a lamppost and a person.

12. What Are Some Subsets of AI?

There are many domains of AI based on their individual use cases. Some of them include neural networks, deep learning, machine learning, expert systems, fuzzy logic, and robotics. Speech recognition is a common AI domain we use when we tell our phone to search for something on Google.

Online recommendation systems, such as those used by Google and Netflix, study user behaviour. For example, Netflix's recommendation system studies the content you watch on the platform. Google's system identifies the keywords and links you commonly click on.

These recommendation systems then leverage user behaviour to create a recommendation list. How they relate to AI is that they are AI-driven.

They use AI algorithms to analyse data. For example, natural language processing helps these systems understand text through pattern identification. Plus, they use deep learning to interpret collected data.

14. What Are Neural Networks in AI?

A neural network is an AI method that teaches computers how to process data the way a human brain does. It's a machine learning process that involves neurons or interconnected nodes in the same way as they are in a human brain.

These networks allow machines to improve consistently. For instance, machines can identify when they make a mistake and avoid doing the same in the future. They can also solve complex problems, like recognising faces and interpreting long documents.

There are several types of neural networks in AI. Three examples are Recurrent Neural Networks, Convolutional Neural Networks, and Artificial Neural Networks.

15. What is Automatic Programming?

Automation programming in AI refers to systems that help humans in some type of programming. These systems have four basic characteristics:

  • A specification method

  • A problem area

  • Target language

  • Operation method

An example of automatic programming is Google/MIT App Inventor. You can drag and drop components like text boxes and buttons to create functional applications without too much knowledge of manual coding.

16. What Are Different Types of Machine Learning?

There are three types of machine learning.

Supervised: In this type, machines require external supervision for learning. For example, external supervisors can give the machine a labelled dataset split into test and training sets. The machine can then extract patterns from this data. Or it may even create a model for predictive analysis.

  • Unsupervised Learning: The machine doesn't require an external supervisor in this machine learning type. The machine can train with the unlabelled dataset. These machines solve clustering and association problems.

Reinforcement Learning: In this type, the intelligent agent works with its environment to learn through feedback. These machines use the Q-Learning algorithm. They get different rewards based on their actions. If they perform an action right, they get a reward, but if they do a bad action, they get a penalty.

AI Interview Questions for Intermediate and Expert Positions

The more advanced and high-paying the position, the more difficult you can expect the questions to get. You'll also need prior experience and relevant certification for such jobs.

Here are some AI interview questions for expert-level jobs.

17. What Is Q-Learning?

Q-Learning is an off-policy and model-free algorithm that uses the current state of the intelligent agent to find the best action series. The 'Q' in Q-Learning is for quality.

Unlike model-based algorithms that use reward and transition functions to determine the optimal policy, a model-free algorithm learns the consequences of its actions without rewards. Instead, it uses its experiences to find the optimal policy.

Q-Learning is also value-based. So, it contains value functions that learn about the most valuable state and takes action accordingly.

Here are some key terms in Q-Learning:

  • State: A state is the position of an intelligent agent in an environment.

  • Reward: It is the reward the agent receives for doing something right. The negative reward is also called a penalty.

  • Action: It is everything an agent does in an environment.

  • Episode: It is the end of the stage. Here, an agent cannot take any new action. The episode arises after the agent achieves or fails at its goal.

18. How Is Strong AI Different From Weak AI?

A strong AI is an AI that has consciousness and cognitive abilities similar to humans. It can perform many humanistic functions since it has a mind of its own. Machines with strong AI are not a reality yet.

Weak AI or narrow AI has limited functionality. Although these machines can do complex tasks, they don't have human abilities. Weak AI does not have the same range of functions as strong AI since it doesn't possess a mind.

We are surrounded by weak AI today. Some examples include Google Assistant and Amazon Alexa.

19. How Can We Test a Machine's Intelligence?

In AI, it's very important to test the intelligence of a machine to determine if it is capable of doing the expected tasks. The Turing Test is the mechanism we use to determine a machine's intelligence. Alan Turing, known for his work with the first 'computers' developed this test in 1950.

The test is somewhat like a three-player interrogation. One of these interrogators is a human. They have to interrogate the other two players, one of which is also a human, while the other is a computer.

The interrogator testing the two asks questions from both parties. In this test, the computer does its best to be different from the second human. If the computer is hard to distinguish from humans, it is highly intelligent.

Here's an example. Player A is the questioner. Player B is a computer chess player, while Player C is another human. Although Player A knows one of the other players is a computer, they do not know which one.

All three players interact with each other through a screen and a keyboard. So, Player A's judgement is not clouded by speech.

Now, Player A has to play chess with two opponents and determine which is a computer and which is a human. The harder it is for Player A to find the computer, the more intelligent the machine is.

20. What Is Computer Vision?

In artificial intelligence, computer vision is the field of study that enables AI machines to interpret visual stimuli, such as images. The machines deduce meaning from these stimuli and use them to take the required action.

AI gives these machines the ability to think. Meanwhile, computer vision allows them to observe visual stimuli. In this way, computer vision works like human vision, while AI is the brain.

Modern computer vision runs on pattern recognition. AI trains computers on large amounts of visual data. For example, if we want a computer to identify blonde hair in a database of pictures, we can train it on millions of pictures of blonde-haired individuals.

The machine will find patterns common in all blonde hair pictures. After that, whenever we submit a picture of a blonde-haired individual, the machine will be able to recognise it as a blonde person.

A good example of computer vision is in the palm of your hand. If you use facial recognition to unlock your phone, you're using computer vision.

21. What Types of Agents Are Used in Artificial Intelligence?

There are five main types of agents in artificial intelligence.

  • Simple Reflex Agents: These are simple agents that function on the current percept rather than historical percepts. In this case, the agent only performs a function or takes an action when a condition is met. For example, the facial recognition system in your phone unlocks the device when it sees your face.

  • Model-Based Agents: A model-based agent uses the condition-action rule. It has two factors; internal state and model. While the model explains the agent's surroundings, the internal state relies on perceptual history. So, the agent can read its environment partially and use the model related to it.

  • Goal-Based Agent: Goal-based agents make decisions based on their goals. Their actions are meant to achieve the said goals. The agent considers different situations through searching and planning. In doing so, it considers possible actions it can take to achieve its goals. Then, it chooses the best course of action and changes its behaviour when needed.

  • Utility-Based Agents: A utility-based agent doesn't only try to reach the goal. It takes the easiest, cheapest, and safest route to get to its goals.

  • Learning Agents: These are the smartest intelligence agents since they can learn from past experiences.

22. What Are Bayesian Networks?

Bayesian networks are graphical models representing variables with their dependencies. These networks help create AI systems because the network uses portability to find the best course of action to reach a goal or result.

Bayesian networks are used in unsupervised and supervised AI learning. So, they can also take and use data without any manual intervention. They can perform various tasks, such as gaining insights, making decisions, detecting anomalies, and reasoning.

For instance, in healthcare, you can use a Bayesian network to determine if there's a strong correlation between symptoms and disease. The network contains nodes that represent variables. Meanwhile, the edges connect these nodes and determine how strong the relationship between them is.

Modern AI uses Bayesian networks for fraud detection, computer vision, voice recognition, natural language processing, and other systems.

23. What Is An Expert System?

An expert system is an AI program with expert-level knowledge about a particular area. These systems can replace human experts. They have the following characteristics:

  • Adequate response time

  • Reliability

  • High-quality performance

  • Comprehension

24. What Are the Advantages of an Expert System?

An expert system has many advantages. For one, it is an economical way to get expert knowledge quickly.

Expert systems can also provide information that a human expert may not be able to offer due to their limited understanding of a subject or field. The most important advantage of an expert reason is that it is unbiased.

While human experts may be biassed due to their affiliation or preference for a certain outcome, expert systems lack this shortcoming. They can also reason, which helps them respond accurately to queries.

25. Can You Explain How Reinforcement Learning Works?

A reinforcement learning system typically has two components; environment and agent.

The environment is the agent's surroundings, while the agent is the reinforcement learning algorithm. In this system, the environment sends the agent a state. The agent then responds appropriately to this state based on its environment.

When the agent responds, the environment relays the next state and the reward - positive or negative. The agent responds to this state and updates its knowledge using the reward the environment has sent.

For example, if it's a positive reward, the agent will regard its previous response as good or helpful. On the contrary, if it receives a negative environmental reward, it will update its knowledge by considering its previous response as bad or unwanted.

26. What Is Reward Maximisation in Reinforcement Learning?

In reinforcement learning, you have to train the agent in a way that takes the course of action that gives it the highest reward. Ideally, the agent should always maximise its reward. But this is not always the case.

So, every time an agent performs a function, the environment rewards or penalises it accordingly. Over time, the agent learns which courses of action give it the highest rewards. So, it tries to perfect them.

27. What is Markov's Decision Making?

The Markov decision process is a decision-making approach based on a mathematical framework. MDP machines evaluate their decision based on the system's current environment and state.

The decision-making process relies on four types of variables. These include discrete, continuous, infinite, and finite.

MDP models are common in two AI sub-areas; reinforcement learning and probabilistic planning. In the former, appications learn to make decisions based on the feedback they get from their environment. Meanwhile, the latter is a discipline in which machines use known models to reach an agent's objectives.

28. Can You Explain Markov's Decision Making With an Example?

Let's take the example of a hungry deer in a zoo or a wildlife sanctuary. It finds a place where it sees a cauliflower and a mushroom. The mushroom is on its left, and the cauliflower is on its right.

While the cauliflower provides the deer with the required nutrition, it's also close to a thorny bush. So, whenever the deer eats the cauliflower, it also gets stuck in the bush or may even be injured.

Over time, the deer will learn to associate the cauliflower with injury and discomfort. So, it will head towards the mushroom on its left.

In this example of Markov's decision-making, the environment is the zoo. Meanwhile, the agent is the deer. The zoo or environment reveals the surroundings of the agent or the deer.

Whenever the agent performs a function, a situation arises, such as him getting injured. In artificial intelligence, these situations are called states.

The agent also gets rewards based on his function. For instance, when he eats mushrooms, he gets nutrition. When he eats cauliflower, he gets nutrition but also receives a penalty - the injury.

Over time, it learns that eating the mushroom is optimal for this situation or state.

29. What Is Generality?

In artificial intelligence, generality refers to how well a model adapts to new data drawn from the same domain. The ability allows a model to learn something once and then apply it to all tasks in the domain.

30. What Is a Parametric Model?

A parametric model has a fixed number of parameters for building a model in machine learning. Examples of parametric models are logical regression and Naive Bayes. These models are best used when there's a variation in the distribution of each group.

31. What Is a Non-Parametric Model?

A non-parametric model has flexibility in the number of parameters used for building the model. It can perform well in many conditions but does best when the spread of each group is equal. An example of a non-parametric model is KNN.

32. How Does Facebook's Face Recognition Model Work?

Facebook's DeepFace tool relies on deep learning algorithms to verify faces. The feature gives you photo tag suggestions when you upload a picture. Here's how DeepFace works:

  • The system scans your image and makes a three-dimensional model. It rotates the picture into different angles.

  • Then, a neural network model comes into play. DeepFace determines the similarities between the picture you've uploaded and other images of the person in the photo. It studies features like nose shape, eye colour, distance between eyes, etc.

  • A human face has 68 facial points. The DeepFace system checks for all of them.

  • After it has mapped the person's face, it searches for similar information.

  • When it finds the same data, it gives you suggestions on who to tag.

33. Can You Tell Five Ways to Determine the Efficiency of a Machine Learning Model?

There are many ways to evaluate a machine learning model's performance. Here are five of them.

  • F1 Score: The F1 score tests the efficiency of a machine-learning model by combining recall and precision. It measures a model's prediction accuracy by determining its true positives, false negatives, and false positives. A higher F1 score indicates good model performance.

  • AUC-ROC Curve: The AUC-ROC curve represents a model's performance graphically, showing a correlation between positive and negative classes. It measures how many times a model gives false positives compared to positives.

  • Confusion Matrix: The confusion matrix measures how accurate a machine learning model is by determining the number of times it has predicted true positives and false negatives correctly. It also measures true negatives and false positives.

  • Gini Coefficient: The Gini coefficient or Gini Index determines inequality between variables' values. A higher Gini value means the model is good.

  • Root Mean Squared Error: This method measures how far a model's predictions are from true values. The smaller this difference, the better the model is.

34. What Is TensorFlow?

TensorFlow is a Google-made open-source maths library most machine learning applications use. AI experts use TensorFlow to train and deploy their models in the cloud.

35. How Can You Use AI in Fraud Detection?

AI can use rule-based algorithms to detect fraud. Simply put, it recognises the pattern of fraudulent transactions and blocks them upon encountering them.

Here's how it works:

The developer collects data through web scraping or a survey in the first step. If you want to create a transaction fraud detection model, you'll collect transaction details, shopping habits, personal details, and so on.

After you've collected heaps of data, you'll remove the redundant and irrelevant information. It helps remove inconsistencies from the data, which may result in poor efficiency.

Now, you determine the relationship between different variables. For example, there's a strong relationship between a consumer's age and their transaction size. Some machine learning algorithms you can use for this step are logistic regression and decision trees.

Finally, build the AI model using these machine learning algorithms and train it to study the correlation between predictor variables.

36. What Do Deep Learning Frameworks Do?

Think of a deep learning framework as a building block for neural network implementation. These frameworks help you design, train, and validate these networks.

Some examples of deep learning frameworks are Keras, PyTorch, and TensorFlow. Keras is a Python-based open-source neural network library. It allows developers to experiment with deep neural networks.

TensorFlow is another library for neural networks. But it works for dataflow programming. Meanwhile, PyTorch is used for natural language processing.

37. What is Natural Language Processing?

NLP is a machine learning technology that enables computers to understand and interpret human language. There are two primary components of an NLP system.

First, it maps the language input into representations. Second, it analyses aspects of this language.

When generating natural language, an AI system does the following:

  • Planning the text

  • Planning the sentences

  • Realising the text

38. How Does NLP Differ From Text Mining?

Text minimin merely extracts insights from any type of data - structured or unstructured. Meanwhile, NLP 'understands' this data.

You can use text processing languages, such as statistical models, to mine text. But NLP requires advanced machine learning models.

When you use text mining, you'll only get word patterns and frequency. On the other hand, NLP's outcomes include grammatical structure and semantic meaning of your text.

39. What Are Hyperparameters?

Hyperparameters are variables used to define a network's structure. An example is the learning rate. These variables show how you trained a network.

40. Which Algorithms Are Used to Optimise Hyperparameters?

We can optimise hyperparameters using the following algorithms:

  • Grid Search: It uses two hyperparameter sets; the number of layers and the learning rate. The algorithm employs these two sets to train the network for every possible combination.

  • Bayesian Optimisation: In this type, the algorithm automates the model-tuning process. It is an ideal approach for complex, expensive, and noisy objective functions. Using this optimisation method, you can find the best hyperparameters in machine learning models.

  • Random Search: It evaluates network sets based on probability distribution. For instance, it selects 100 parameters randomly to check them instead of going through 100,000 samples.

41. What Is Overfitting? How Do You Overcome It?

Overfitting is a situation in machine learning in which the algorithm begins to over-analyse data. So, the system receives more noise than helpful information. You can prevent overfitting through the following:

  • Cross-validation

  • Feature removal

  • Ensemble models

  • Early stopping

The method you choose to overcome overfitting will depend on the model and its operation.

42. How Does KNN Differ From K-Means Clustering?

Both K-Nearest Neighbors (KNN) and K-Means Clustering are standard machine learning algorithms. However, they differ in their purpose and implementation.

K-means is an unsupervised clustering algorithm. It is an exhaustive training model used in social media trend monitoring, population demographics, and anomaly detection.

Meanwhile, KNN involves supervised classification algorithms. It is commonly used in classifying and regressing known data.

43. What Is FOPL?

FOPL, or First Order Predicate Logic, is a way to represent knowledge in AI. The language develops object-based information and explains the relationships between these objects.

It contains one set of each:

  • Constant symbols

  • Variables

  • Predicate symbols

  • Function symbols

In addition, it contains a logical connective, which combines the constant, predicate, and function systems. FOPL follows some principles, including uniqueness, completeness, and consistency.

Due to these rules, the system can form a logical structure that it uses to understand data.

Scenario-Based AI Interview Questions

When you go for an interview, the hiring managers may also ask you scenario-based AI questions. These questions will help them check your cognitive abilities. Here are some questions to expect.

44. Suppose You're a New Business Owner. How Can You Use AI to Improve Your Everyday Processes?

A business owner can use AI in many ways to improve customer experience and increase revenue generation. Here are some methods.

  • Sales Forecasting: The business can use AI to determine consumer demands. For example, AI can study market trends and consumer sentiment. It can then forecast the sales for the season or year.

  • Consumer Insights: AI can also give businesses insights into consumer preferences and interests. An AI model can determine which consumer segments can spend more than others. You can then focus on marketing to this segment excessively.

  • Personalised User Experience: Consumers love getting customised messages and marketing material. AI can help a business craft personalised suggestions for each customer segment.

  • Customer Support: AI-powered chatbots can eliminate the need for human customer support acts. They can also provide instant responses to customers in need.

  • Process Automation: A new business owner can automate many company processes using AI. For example, an automated tool can send welcome emails to new customers.

  • AI Translators: Businesses that need to market to global consumers can use AI translators for marketing and customer support.

  • Smart Email Categorisation: Businesses can use AI to categorise emails and prioritise important messages automatically.

45. How Can Your Business Use Collaborative Filtering to Get Customers to Spend More?

The best way to understand collaborative filtering is to use Amazon's example. When you shop for something on Amazon, the website shows you a 'Customers who bought this also bought' section.

In this section, you can see that customers who maybe bought a printer also bought paper and cartridges. By showing you these recommendations, Amazon gets you to spend more.

Collaborative filtering is the process by which the website shows you these recommendations. In this method, the system analyses the purchase history of the website's customers. The system may do this using the following:

  • Item-Based Filtering: Let's take printers as an example. The system analyses the purchase history of all consumers who bought the printer. It then makes recommendations based on the other items they bought off the website.

  • User-Based Filtering: In this type, the system analyses the purchase history of consumers who bought similar items. In our example, it may be cartridges. It then recommends products based on the things the customers who bought the cartridges purchased.

  • Matrix Functioning: The system studies the user-item interaction to generalise customer preferences. Then, it uses this data to give recommendations.

Besides these methods, an AI system may also use deep learning techniques. For example, it may learn data patterns and predict which items the customers may be interested in.

46. Let's Say You Want to Incorporate a Chatbot Into Your Business. How Would You Do That?

A chatbot simulates human conversations. So, it can answer questions and provide information the customers need. The way you'd use a chatbot will depend on your business.

For example, a basic chatbot can answer frequently asked questions. Suppose you're a tree removal company. You can have a basic chatbot on your website's home screen.

When someone visits your website, they will see the chatbot. They can ask the chatbot common queries like pricing, method, and how to book a consultation.

Meanwhile, advanced chatbots can be even more personalised. For example, a healthcare chatbot may help patients understand their medical records. Such a chatbot can also book an appointment for a patient.

When incorporating chatbots into your business, it's essential to keep them simple. While you may be an AI expert, the people using the chatbot won't be. So, it should be intuitive and easy to work with.

47. Suppose You're Working With Marketers and Designers On a Project. How Would You Explain AI to Them In Simple Words?

AI is basically a machine's ability to do something humans can do, but faster and with fewer errors. Let's take your phone's Siri as an example. You tell Siri to call your mom.

Before you had Siri, it was something you did on your own. Now, Siri is your AI assistant. You tell it to call your mom. It calls your mom.

Now, let's take it to a higher level. You're creating a video streaming service, and you want to extend people's watch time.

If your platform has a million users, there's no way for you to keep an eye on every single one of your users manually unless you're willing to hire a million people for this task.

So, what do you do instead? You use AI.

The AI algorithm automatically analyses what people are watching. Let's say Viewer A has been watching a lot of K-dramas lately. The AI system in your streaming service will recommend Korean shows to them, giving them a reason to stay on your platform.

In this way, we can teach AI to do all sorts of things previously humans did. Why does this work? Because AI is faster. It is also cheaper than hiring thousands of people to do menial tasks.


If you're planning to enter the AI field or want to score a higher-paying job, a certification can help. The more specialised knowledge you have, the better you will be for a position.

Speaking of specialised knowledge, the Professional Academy Diploma in Artificial Intelligence for Business from UCD Academy can equip you with the knowledge you need to ace any job screening. The course starts with an introduction to AI.

It then goes deeper into AI applications and the technology associated with them. Some examples of modules include cognitive engagement technology, industry development, digital transformation, and process automation technology.

By the end of the course, you will know how to use AI for an organisation's success - just what you need to set yourself apart from other job candidates. While you're at it, why not enrol in some other business courses to enhance your skills? Find your desired UCD Academy courses here.

Frequently Asked Questions

How Do I Pass an AI Job Interview?

Consider an AI job interview just like any other interview. Practise your verbal and non-verbal cues. Prepare answers to common questions beforehand. Read the job description a few times before going in for the interview. Incorporate these keywords in your responses to make the interviews see you're fit for the job.

How Do I Get My First Job in AI?

If you're applying to a specific niche, enrol in a relevant course or diploma to hone your knowledge. Search for jobs on LinkedIn or online job boards. Apply to as many jobs as possible and be confident in your interviews.

Is It Hard to Get a Job in AI?

The AI job market is flourishing right now. But if you lack the skills employers seek, you'll find it hard to land a job. The key is to ensure you have the skills the job description mentions. Previous work experience will strengthen your case.

Which Skills Are Required For an AI Job?

Besides AI skills, you must be good at machine learning, programming, and database modelling. You must also have people skills, such as communication, teamwork, cognitive abilities, and problem-solving skills. AI jobs can often be stressful. So, you must do well under stress, especially with tight deadlines.