AI Developers Interview Questions

You reading this AI developer interview questions page since you plan to hire artificial intelligence developers. Expect to invest considerable time and effort in this since artificial intelligence developers are in great demand. 

The soaring demand for artificial intelligence and machine learning developers shouldn’t surprise you since an organization as authoritative as Bank of America estimates that artificial intelligence could contribute $15.7 trillion to the global economy by 2030. Cutting-edge research in this branch of computer science is in progress, and we won’t be surprised if artificial intelligence and machine learning become even more valuable. No wonder then that technology giants are investing heavily in artificial intelligence, e.g.:

• Google has an exclusive artificial intelligence startup program.
• Microsoft is regularly expanding artificial intelligence investments. 

A comprehensive set of artificial intelligence interview questions and answers could go a long way toward helping you with hiring, and that’s what we offer below:

Junior Artificial Intelligence (AI) Interview Questions

Assess the skills of junior machine learning and artificial intelligence developers using the following questions:

1. I have unlabelled data for my machine learning project. Tell me a few machine learning algorithms I can use in this situation.

Since you have unlabelled data, you need to use unsupervised learning machine learning algorithms. Depending on your requirements, use one of the following machine learning algorithms:

• K-means clustering;
• K-nearest neighbors (KNN);
• Neural networks;
• Apriori algorithm;
• Independent component analysis;
• Singular value decomposition;
• Principal component analysis;
• Anomaly detection.

2. I have a dataset containing emails, where spam emails and non-spam emails are identified. Suggest a few machine learning algorithms I can use.

You have labeled data, therefore, you can use supervised learning machine learning algorithms. Use classification algorithms, which are useful for spam filtering. The following are a few machine learning algorithms supporting supervised learning and classification:

• Decision trees;
• Random forest;
• Support vector machines;
• Logistic regression.

3. I want to develop a recommendation engine for my business. I want it to be very advanced, therefore, I don’t want to use weak AI. Which algorithms can I use?

You need to use weak AI only since that’s the only type of artificial intelligence existing now, and the reasons are as follows:

• All applications of artificial intelligence are examples of weak AI, which include recommendation engines. 
• Weak artificial intelligence can perform tasks earlier perceived as tasks that typically require human intelligence. 
• Weak artificial intelligence doesn’t necessarily have to be weak! For example, a few advanced artificial intelligence chatbots, categorized as “weak artificial intelligence”, can pass the famous Turing test, which was meant for strong AI.
• All the key capabilities of artificial intelligence currently use weak artificial intelligence. Weak artificial intelligence can include reactive machines or limited-memory machines. Even Siri and Alexa, commonly perceived as intelligent machines, use weak artificial intelligence. 
• There are predictions about strong AI, which are artificial intelligence systems that will surpass human intelligence. Some say that these artificial intelligence machines will outperform human beings. However, such artificial intelligence machines aren’t there yet. 

You can use any of the following machine learning algorithms for developing a recommendation engine:

• Clustering models;
• User-based k-nearest neighbors;
• Bayesian networks;
• Matrix factorization.

4. I have a natural language processing project, however, I have not decided which programming language to use yet. Which programming library should I use?

Since you have not decided which programming language to use on your natural language processing project, you can use CoreNLP. This natural language processing library supports both Java and Python.

5. Which computer vision library supports multiple popular programming languages while providing the most features?

OpenCV is one of the most comprehensive computer vision programming libraries. It supports C++, C, Python, and Java, all of which are prominent programming languages for artificial intelligence branches like computer vision.

6. Which speech recognition library in Python is the best?

Python has several speech recognition tools like SpeechRecognition, Librosa, and PyAudio. All of them are good, however, Speech Recognition is the best-known among them. This speech recognition Python library is very easy to use.

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Mid-level Artificial Intelligence interview questions

If you want to hire mid-level artificial intelligence developers, then use the following interview questions:

1. What helps deep learning systems to act like a human brain?

Deep learning systems are computer systems that act like a human brain since they use neural network algorithms. A series of neural network algorithms enables a deep learning system to act as if it utilizes human intelligence. 

A process known as “forward propagation” helps deep learning networks to navigate nodes that are connected to each other. The forward propagation process enables the deep learning networks to improve their outputs, which are predictions. 

Yet another process called “back propagation” computes the errors in the predictions made by “forward propagation”. The back propagation process uses different algorithms for this, e.g., gradient descent. Together, Forward and backward propagation help in making predictions and correcting them.

2. What is the “Markov property”?

Let’s first understand a few key terms required to explain Markov’s decision process, for understanding “Markov property”. These terms are as follows:

• Agent: This is the software being trained using reinforcement learning, one of the machine learning techniques. 
• Environment: The agent deals with its environment, however, the agent can’t control the environment. An agent only controls its actions.
• State: It refers to the present situation of the agent. For example, let’s assume the current state is “S_a”, and the nest state is “S_b”. We are concerned about whether the next step will be a particular state “S_x”.
• Action: The agent makes a decision at the current time step, and that’s called the “action”.
• Policy: The “policy” refers to the “thoughts” of the agent behind taking an action. In reality, the probability distribution of a set of actions is the “policy”. 

Now, the “Markov property” mandates that the current state of an agent must only depend on the state immediately before. No other earlier state can have any impact on the current state. 

Mathematically, the “Markov property” requires that the probability of the next state (“S_b”) being a particular state (“S_x”) depends only on the current state (“S_a”).

3. I need to minimize the cost function in my machine learning model. My training data set is large, and it has many features. Time and computational resources aren’t constraints in my artificial intelligence project. Which machine learning algorithm do you recommend?

You should use batch gradient descent. This machine learning algorithm does very well as an optimization algorithm. Batch gradient descent can scale very well with the number of features, which is high in your training data set. While batch gradient descent can take time and plenty of computational resources, your artificial intelligence project doesn’t have these constraints.   

4. I am developing an expert system that will use an inference engine. Suggest key strategies to optimize its speed and memory consumption.

Consider the following key strategies to optimize the inference engine of your expert system:

• You need to make the pipeline for input data processing pipeline efficient by streamlining it.
• Try to keep the machine learning model as simple as you can, which also improves the model’s performance.
• Review the code and remove inefficiencies as much as you can.
• Explore hardware acceleration solutions and use a suitable one.

5. I plan to develop a driving time estimator for a specific geography. I have data containing driving speed at different weather conditions, various degrees of traffic congestion, different festivals, etc. Between supervised and unsupervised learning, which approach do you suggest?

You should use supervised learning. A key machine learning-related decision-making factor is whether you have labeled or unlabeled data. In this case, you have labeled data. It can be hard to get such training data, and you might have incurred significant costs to obtain/derive it. You should now take advantage of it and use supervised learning since it uses labeled data.

6. Which machine learning algorithm do you suggest for my image recognition project and why?

You should use “Convolutional Neural Network” (CNN). The following factors make CNN a good choice for image recognition:

• Unlike a traditional neural network, a CNN might have nodes in a layer that are not connected to nodes in the next layer. This provides better efficiency, which is important in an image recognition project.
• Layers in a CNN have fewer parameters than layers in a regular neural network. CNNs offer more efficiency on image recognition projects due to this.
• CNNs have pooling layers that decrease the dimensionality of data, therefore, they process data involving images efficiently.
• The “parameter sharing” technique used in CNNs considerably reduces the parameters for image recognition, which makes CNNs efficient.

7. Which risks should I be aware of when using the Q-learning algorithm?

The Q-learning algorithm can be of great help when planning the machine learning process for many business requirements across many fields. However, this model-free machine learning algorithm can expose you to the following risks:

• It might offer lower stability.
• The Q-learning algorithm might overestimate the Q-values.
• You might not get the convergence that you need.
• This algorithm might need plenty of memory. The convergence might be slow.
• Hyperparameter settings might impact this algorithm considerably.
• This algorithm might display a high degree of sensitivity to initial conditions.
• There might not be enough exploration, and you might need to plan for more exploration strategies.

8. Tell me how I can effectively execute my Natural Language Understanding (NLU) project.

You should do the following for a successful NLU project:

• You need to define entities and intents clearly for the natural language understanding model to understand human language well.
• Collect representative training data from a diverse range of sources.
• Diligently perform the data pre-processing and cleaning tasks, which is important for every artificial intelligence project.
• You should have sufficient training data for both entity and intent, and it’s important to strike a balance here.
• Take advantage of pre-trained language models to save training time and effort.
• Analyze your business objectives carefully before choosing a machine learning algorithm.
• Even if the model is performing well with past data, regularly re-test using newly available data points. 
• Keep the NLU model up-to-date and accurate by updating and re-training it.

9. I have many vast data sets containing diverse knowledge bases. Many of these data sets have complex data structures, and there is no clear distribution of data. What kind of machine learning models should I use, and what risks should I look out for?

You should use non-parametric models instead of parametric models. Data sets with complex data structures and no known distribution of data might not work very well with parametric models. You currently need flexibility, which is offered by non-parametric models.

Depending on your artificial intelligence project requirements, you can consider one of the following options to create non-parametric models:

• Kernel density estimation (KDE): You can use Seaborn, a Python data visualization library.
• Decision trees: Scikit-learn, a popular Python library can help you to create decision trees.
• K-nearest neighbors (KNN): You can use Scikit-learn to implement the KNN algorithm too.

Watch out for the following risks:

• Non-parametric models might consume a lot of computational resources. 
• The knowledge representation that you create from the knowledge bases using such models might be hard to interpret. 
• Overfitting of data might happen.

10. I have a computer vision project. What kind of neural network should I use? Also, how do I indicate which variables are more important?

You should use a Feedforward neural network since they are commonly used in computer vision projects. Also known as multi-layer perceptrons (MLPs), feedforward neural networks have multiple layers. They have one input layer, potentially multiple hidden layers, and an output layer. 

After the determination of the input layer, you should assign weight to different variables. Variables with larger weights indicate more importance.

Senior Artificial Intelligence Interview Questions

Evaluate the skills of senior machine learning and artificial intelligence developers using the following questions: 

1. Explain how the following code example can help with my neural network.

Code example:

“

def tanh_function(m):

  return (np.exp(m) – np.exp(-m)) / (np.exp(m) + np.exp(-m))

“

Answer:

You are using the tanh() function of Python by utilizing the NumPy library. 

Mathematically, the function will look like this:

“

tanh m = {(e raised to the power of m) – (e raised to the power of negative m)}/ {(e raised to the power of m) + (e raised to the power of negative m)}

“

The value will come in between (-1) and (+1), therefore, the mean will be 0 or very close to it. This helps with the hidden layer of neural networks. That’s because the tanh function brings the mean near 0, which centers the data. The neural network can now learn from the next layer relatively easily.

2. Why the following code snippet could be useful for my deep learning models?

Code snippet:

“

def relu(a):

            return max(0.0, a)

“

Answer:

You are using the Rectified Linear Unit (ReLU) in Python. This function will return the value of “a” if “a” is positive. You will get a 0 if “a” is negative though. 

ReLU is a well-known activation function for deep learning models due to the following reasons:

• Since it interprets only the positive part of the argument, it doesn’t suffer from the problem of vanishing gradient. That makes it a useful activation function for deep neural networks.
• ReLU adds the property of non-linearity to a deep learning model.   

3. I have the following 2 data sets where the second one has the names of cities misspelled. How can I join them based on names of cities using Python?

Data set 1:

City Population

New York 10,000,000

San Francisco 9,000,000

Washington DC 8,000,000

Los Angeles 9,500,000

Chicago 8,500,000

Date set 2:

City Number of restaurants

Ney Work 25,000

Sac Nranfisco 22,000

Wanhingtos DC 21,000

Log Anseles 23, 000

Chigaco 24,000

Answer:

Standard methods will not be able to identify that the cities are just misspelled in the second data set. These will not conclude that “Sac Nranfisco” is actually San Francisco. 

You need to use fuzzy logic. Fuzzy logic is used widely in artificial intelligence branches like machine learning, natural language processing, etc. To be more precise, you need to use fuzzy string matching.

Python offers an open-source library named “TheFuzz” for fuzzy string matching. You can easily integrate this library with Pandas, a popular Python library used often on artificial intelligence projects. 

Subsequently, use a Python lambda function utilizing the “process.extractOne()” method on the city name column, and use “fuzz.partial_ratio”. TheFuzz library then converts the misspelled city names, subsequently, you can join using the city names.

4. Using Python, I am developing an artificial intelligence software product to detect articles claimed to be written by human beings but are actually created by artificial intelligence tools. At a high level, suggest how I can regularly improve this product so that it catches artificial intelligence-written articles progressively harder to detect.

You should create Generative Adversarial Networks (GANs). GANs are deep neural networks. 

They include a “generator” network, which produces fake content. The other network in a GAN is a “discriminator”, which detects fake content from the original. GANs use the “Game Theory” to make the generator create even more sophisticated fake content and the discriminator to spot them.  

You can create GANs using Python relatively easily. Utilize well-known open-source Python artificial intelligence and machine learning libraries like NumPy and Keras, furthermore, use other standard technologies to create the user interface.   

5. I plan to develop a cursive handwriting recognition system powered by artificial intelligence. Suggest which artificial intelligence and machine learning techniques I should use.

You should use statistical pattern recognition (SPR). It’s a form of the pattern recognition technique. SPR uses statistical models to do the job that the human reasoning faculty does in identifying handwriting. You can easily implement SPR using a popular programming language like Python, where you can use one of the following prominent machine learning algorithms:

• Vector machines;
• Neural networks;
• Linear discriminants;
• K-nearest neighbors;
• Bayesian Methods.

6. State the result of the below Python code and provide reasons.

Code example:

“

import numpy as np

def sigmoid(x):

    return 1 / (1 + np.exp(-a))

input_a = 2

output_a = sigmoid(input_a)

print(“The answer is:” output_a)

7. Write the Python code for calculating the Euclidean distance between points (3,4) and (6,8), and state the distance.

The Python code is as follows:

“

import numpy as np

def distance_euclidean(a1, a2):

  a1, a2=np.array(a1), np.array(a2)

  return np.sqrt(np.sum((a1 – a2)**2))

1stpoint = (3,4)

2ndpoint = (6,8)

the_distance = distance_euclidean(1stpoint, 2ndpoint)

print(“the distance between the 2 points is” the_distance) 

“

The distance is 5 units. 

Answer:

The result of the above code example will be 0.881.

You are using the Sigmoid function with a variable “a”, whose value is 2. 

The formula of the Sigmoid function is s(a) = 1/{(1+(e raised to the power of minus a)}, where “e” is Euler’s number. The approximate value of “e” is 2.71. By performing the required calculations, we get the answer as 0.881. 

Hiring Expert Artificial Intelligence/Machine Learning Developers?

You can certainly use the above artificial intelligence questions to interview developers, however, the interview process will take time. On the other hand, we at DevTeam.Space already have dedicated artificial intelligence and machine learning developers with relevant expertise. 

Our world-class development processes help our developers considerably. You always get supportable and maintainable code from us. Our artificial intelligence-powered agile processes help you remain on top of the project. We also offer complementary support from a dedicated tech account manager.

Contact DevTeam.Space to know more about how we can help you.