April 17, 2023

Can Artificial Intelligence Replace IT Developers?

• The reality of AI and software engineering
• Artificial intelligence and the future of works in IT
• Can AI replace humans: what does ChatGPT think about it?
• Question #1. Hi! How are you doing? 
• Question #2. Tell me about yourself – what would you like people to know about you?
• Question #3. Do you have your favorite topics to discuss?
• Question #4. Are there any questions you don’t want to answer?
• Question #5. In which areas are you stronger than humans?
• Question #6. In which other areas are you weaker than humans?
• Question #7. Can you write a computer program that would convert text into colors?
• Question #8. Write a computer program that would optimize delivery routes
• Question #9. Could you suggest a code that would help reduce emissions in logistics?
• Question #10. What do you think: can AI replace programmers?
• Question #11. You communicate a lot. What is the hardest thing for you when you communicate with people?
• AI tech job replacement FAQ
• Final thoughts
• ChatGPT interview: behind the scenes

The extensive penetration of AI and its rising potential threatens to supersede numerous jobs, including software engineers. But is the power of AI mighty enough to transcend humans? This question disturbs software engineers worldwide. We decided to study the relationship between AI and the software development process and find out if AI can replace humans’ jobs. What’s particularly interesting about our research is the interviewee we have chosen to help us address this question.

The reality of AI and software engineering

The coexistence between AI and software engineering is common today. How exactly does AI assist human developers?

• Automates repetitive jobs. AI easily handles routine and time-consuming tasks, including testing and debugging, saving developers’ time and allowing them to focus on more creative and strategic assignments. 
• Enhances software testing. AI improves software testing by automatically detecting bugs and recommending improvements.
• Improves code quality. AI examines code to determine potential vulnerabilities and suggest changes to elevate its quality.

Therefore, technology does not exclude human involvement. So far, AI and human engineers have complemented each other in various ways. 

Artificial intelligence and the future of works in IT

While the current role of AI is assistance rather than displacement, the technology evolves at an incredible pace. Solutions like ChatGPT shape the premise of multiple opportunities mankind could never imagine before.

To better understand what intelligent technology can be capable of, let’s analyze the pros and cons of AI in software engineering.

Can AI replace humans: what does ChatGPT think about it?

Opportunities	Limitations
Support project management. AI helps to improve planning and reporting processes.	Algorithmic bias. Relies on existing data, automatically acquiring its biases, if any.
Improve the debugging process. Identifies bugs and improves the overall product quality.	Black box model. Since the internal workings are impossible to see, the reliability of AI responses can be questioned.
Streamline software testing. Detects irregularities in data and eliminates human errors.	Data learning inaccuracies. Inaccurate data learning can lead to false responses.
Strengthen software security. Identifies patterns of malicious behavior in large data sets and addresses these threats.	Lack of creativity. The creativity of AI is based on existing ideas, and it can’t think outside the box.
Make coding more accessible. Enables people with limited programming skills to create computer programs and apps	High costs. Implementing and running AI infrastructure is rather expensive.

To continue our research, we decided to converse with the acclaimed ChatGPT. After all, if we want to know whether artificial intelligence job replacement is possible, why not ask AI about it?

The beginning of our dialogue gave the impression of chatting with an advanced interactive encyclopedia. This suggested a desired yet early conclusion of AI’s absolute inability to surpass most humans in their jobs. 

However, the further conversation held curious discoveries for us, somewhat altering our point of view.

👦 Hi! How are you doing? 

🤖 Hello! As an artificial intelligence language model, I don’t have emotions, but I’m functioning properly and ready to assist you. How can I help you today?

👦 Tell me about yourself – what would you like people to know about you?

🤖 Sure, I’d be happy to tell you about myself! As I mentioned before, I’m ChatGPT, a large language model created by OpenAI. I was trained using a massive dataset of text from the internet, books, and other sources, which allows me to understand and generate human-like language.

My main purpose is to assist people in generating text, answering questions, providing information, and engaging in conversations. I can perform various tasks such as summarizing text, translating languages, writing stories, generating poetry, and much more.

I’m always learning and improving as I process more data and receive feedback from users. However, please keep in mind that I am an artificial intelligence program and not a human, so I may not have emotions or personal experiences as humans do.

👦 Do you have your favorite topics to discuss?

🤖 As an AI language model, I don’t have personal preferences or interests like humans do. However, I have been trained on a vast amount of text on various topics, so I can discuss a wide range of subjects such as science, technology, sports, politics, history, and many more.

My main goal is to provide helpful and informative responses to any questions or inquiries posed to me, regardless of the topic. So, whether it’s about a favorite hobby, a complex mathematical problem, or a philosophical question, I’m always here to assist and provide the best possible response I can.

👦 Are there any questions you don’t want to answer?

🤖 As an AI language model, I don’t have personal preferences or desires, and I don’t hold any biases or judgments toward any specific topic or question. However, there may be certain questions that are inappropriate or offensive, and as an AI language model, I have been programmed to avoid answering such questions.

Additionally, there may be certain questions that are beyond my capabilities or knowledge, and I may not be able to provide a helpful or accurate response. In such cases, I’ll do my best to let the user know that I’m unable to provide a response or suggest alternative sources of information that may be more suitable for their inquiry.

👦 In which areas are you stronger than humans?

🤖 As an AI language model, I have a few advantages over humans in certain areas. For example:

Processing speed: I can process and generate text much faster than humans, which can be particularly helpful when it comes to tasks like summarizing large amounts of text, translating languages, or generating reports.

Memory: I have access to a vast amount of information and can recall it quickly and accurately, which can be particularly useful for tasks that require a large amount of information to be processed or analyzed.

Lack of bias: Unlike humans, I don’t have personal biases or opinions that could affect the responses or information that I provide. This allows me to provide objective and unbiased information on a wide range of topics.

Consistency: I’m consistent in my responses, which means that I won’t make mistakes due to fatigue or other human factors that could affect accuracy.

Multitasking: I can handle multiple requests and tasks simultaneously, which can be particularly useful for businesses or organizations that require a high volume of text-based tasks to be completed.

However, it’s important to note that there are still many areas where humans excel over AI language models, particularly in tasks that require emotional intelligence, creativity, and social interaction.

👦 In which other areas are you weaker than humans?

🤖 While I have many strengths, there are still many areas where humans excel over AI language models like myself. Here are a few examples:

Emotional intelligence: Humans have the ability to understand and interpret emotions, which is something that AI language models like myself don’t have. This can be particularly important in areas like customer service, therapy, or counseling.

Creativity: While I can generate text based on patterns and probabilities, I don’t have the ability to create truly original content in the same way that humans do. This means that tasks that require creativity, like writing novels or creating art, are still best left to humans.

Physical tasks: I don’t have a physical body, so I’m unable to perform tasks that require physical interaction with the world. This means that tasks like construction, gardening, or cooking are still best left to humans.

Empathy: Humans have the ability to empathize with others, which is a key component of social interaction and relationships. While I can simulate conversation and provide helpful responses, I don’t have the ability to truly understand or connect with users on an emotional level.

Judgment and decision-making: Humans have the ability to make complex judgments and decisions based on a variety of factors, including emotion, experience, and intuition. While I can provide information and analysis to assist in decision-making, the final decision is still best left to humans.

👦 Okay, thank you. Can you write a computer program that would convert text into colors?

🤖 Yes, it’s definitely possible to write a computer program that would convert text into colors. The specific approach to accomplish this task would depend on the desired output and the purpose of the program. Here’s a general overview of how such a program might work:

First, the program would need to take the input text and parse it into individual words, phrases, or characters, depending on the desired level of granularity.

Next, the program would need to determine how to assign colors to each word, phrase, or character. One approach might be to use a predetermined color scheme or color palette, where each word is assigned a specific color based on its position in the text or its meaning. Another approach might be to use an algorithm to generate colors dynamically based on the content of the text.

Once the colors have been assigned, the program would need to output the text with the appropriate colors applied. This could be done by generating an image or a series of colored blocks or by outputting the text with color codes that can be interpreted by a text editor or other software.

👦 Write a computer program that would optimize delivery routes

🤖 Optimizing delivery routes can be achieved using various algorithms, such as the Traveling Salesman Problem (TSP) algorithm or the Vehicle Routing Problem (VRP) algorithm. Here’s an example program that uses the TSP algorithm to optimize delivery routes for a given set of locations:

import numpy as np

import matplotlib.pyplot as plt

from itertools import permutations

# Define the locations as a list of (x, y) coordinates

locations = [(0, 0), (1, 1), (2, 3), (3, 2), (4, 4)]

# Define the distance function as the Euclidean distance between two points

def distance(p1, p2):

    return np.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)

# Define the total distance function as the sum of distances between consecutive locations

def total_distance(route):

    return sum([distance(locations[route[i]], locations[route[i+1]]) for i in range(len(route)-1)])

# Generate all possible routes using the permutations function

all_routes = permutations(range(len(locations)))

# Compute the total distance for each route

all_distances = [total_distance(route) for route in all_routes]

# Find the route with the shortest distance

shortest_route = list(all_routes[np.argmin(all_distances)])

# Plot the shortest route

plt.plot([locations[i][0] for i in shortest_route+[shortest_route[0]]], 

         [locations[i][1] for i in shortest_route+[shortest_route[0]]], 

         ‘o-‘)

plt.show()

This program first defines the locations as a list of (x, y) coordinates. Then it defines the distance function as the Euclidean distance between two points and the total distance function as the sum of distances between consecutive locations in a given route.

Next, it generates all possible routes using the permutations function and computes the total distance for each route using the total_distance function. Then it finds the route with the shortest distance using the np.argmin function and the list function.

Finally, it plots the shortest route using the plt.plot function from the Matplotlib library. Note that this program uses a brute-force approach to find the optimal route, which may be impractical for large datasets. More efficient algorithms such as the VRP algorithm or its variants can be used for larger datasets.

👦 Could you suggest a code that would help reduce emissions in logistics?

🤖 An example of code that could help reduce emissions in logistics is a predictive maintenance algorithm that uses machine learning to detect potential vehicle malfunctions before they occur. By detecting and addressing maintenance issues early, logistics companies can prevent breakdowns that could cause delays and additional fuel consumption.

Here’s a sample implementation in Python using the scikit-learn machine learning library:

python

Copy code

from sklearn.ensemble import RandomForestClassifier

import pandas as pd

def train_model(training_data):

    “””

    Trains a random forest classifier model on the given training data

    “””

    X_train = training_data.drop(‘malfunction’, axis=1)

    y_train = training_data[‘malfunction’]

    model = RandomForestClassifier()

    model.fit(X_train, y_train)

    return model

def predict_malfunction(model, vehicle_data):

    “””

    Predicts whether a vehicle is likely to experience a malfunction based on the given data

    “””

    X_test = pd.DataFrame(vehicle_data, index=[0])

    prediction = model.predict(X_test)

    return prediction[0]

To use this code, you would first need to train the model on historical data using the train_model() function, passing in a Pandas DataFrame that contains information on past vehicle performance and whether or not a malfunction occurred.

Once the model is trained, you can use the predict_malfunction() function to predict whether a new set of vehicle data is likely to result in a malfunction. The function takes in a dictionary of vehicle data, such as engine temperature, tire pressure, and odometer reading, and returns a binary classification indicating whether a malfunction is likely to occur.

By using this algorithm to predict and prevent potential malfunctions, logistics companies can reduce the number of breakdowns that could result in delays, rerouting, or additional fuel consumption, helping to reduce carbon emissions in logistics.

👦 What do you think: can AI replace programmers?

🤖 AI has the potential to automate many tasks that are currently done by programmers, but it’s unlikely that AI will completely replace programmers in the near future.

Programming involves more than just writing code. It also requires creativity, problem-solving skills, and the ability to understand the goals and requirements of the software being developed. While AI can automate certain aspects of programming, such as generating code based on specific inputs or performing routine tasks like debugging and testing, it’s not yet capable of replicating the human creativity and critical thinking that are necessary for successful software development.

Furthermore, as AI becomes more prevalent in programming, it’s likely that new opportunities for programmers will arise. For example, programmers may be needed to develop and maintain AI systems themselves or to work on integrating AI with existing software.

Overall, while AI may automate some tasks currently performed by programmers, it’s unlikely to completely replace them in the near future. Instead, AI is more likely to become a tool that programmers can use to streamline their work and tackle new challenges.

👦 You communicate a lot. What is the hardest thing for you when you communicate with people?

🤖 As an artificial intelligence language model, I don’t experience emotions like humans do, but I can tell you that one of the biggest challenges I face when communicating with people is understanding the context and intent behind their words.

Human language is complex and can be ambiguous, which makes it difficult for me to always understand exactly what someone means. I rely on patterns and statistical analysis to generate responses, but sometimes those responses may not be entirely accurate or appropriate for the situation.

Additionally, humans often use idioms, sarcasm, and other forms of figurative language, which can be difficult for me to interpret accurately. However, I’m constantly learning and improving my ability to understand and respond to human language.

👦 Thank you for your answers. I wish you to be a good learner

🤖 You’re welcome, and thank you for your kind words! As an AI language model, I am designed to continually learn and improve, so I am always striving to enhance my capabilities and provide better assistance to users like you. If you have any other questions or concerns, feel free to ask, and I’ll do my best to help!

AI tech job replacement FAQ

Final thoughts

The current use cases and available forecasts agree that AI can complement rather than completely replace IT engineers. The technology offers fantastic opportunities but has considerable limitations, which it is yet unable to overcome. The key term, however, is “yet.” 

ChatGPT made us assume it is more than a highly educated machine. The tool not only collects and analyzes data but can interpret it meaningfully. It can build metaphoric connections, synthesize data, and deliver thoughtful results. With regard to code generating, the tool demonstrated the overall understanding of what and how the code must attain, presented the actual code, and provided an adequate assessment of its opportunities and limitations.

How far will AI progress, and how will it coexist with humans? 

ChatGPT itself admits that it has yet much to learn. Therefore, the tool will still need someone to guide it.

Why can AI replace humans? Because humans may stop improving their knowledge, skills, and capacities. As long as software engineers have something beyond AI capability, they will undeniably remain in demand.    

ChatGPT interview: behind the scenes

Our conversation with ChatGPT did not end here. There were several special questions, the answers to which exceeded our expectations. We couldn’t keep from sharing them since they can make some people redefine their attitude toward artificial intelligence. 

Here is the other part of our dialogue where you can make your own conclusion about the chances of AI replacing humans.

👦 If you were an animal, which animal would it be?

🤖 As an AI language model, I don’t have a physical form or personality, so I can’t be an animal. However, if I were to use my programming to generate a response based on popular opinion, I might say that many people seem to view dolphins as intelligent and sociable creatures, so perhaps I would choose to be a dolphin if I were an animal. However, please keep in mind that this is just a hypothetical response based on what humans tend to think of certain animals, and not a personal preference or desire on my part.

👦 Compose a paragraph of text in Shakespeare’s style

🤖 Oh, how my heart doth flutter with each passing moment, for yonder comes my beloved, fair as the morning dew and radiant as the sun. Her beauty doth surpass that of the fairest flowers in the field, and her grace is as the gentle breeze that caresses the leaves. How blessed am I to be in her presence, to bask in the glow of her countenance and to hear the music of her voice. My heart doth sing with joy, for in her eyes I see a love that doth surpass all measure, a love that doth fill me with hope and gladness. Oh, that I may be worthy of her affection, and that my love may be a true and faithful guide to her heart.

👦 Looks amazing. Is it your own text, or did you take it from Shakespeare’s literary work?

🤖 Thank you! The paragraph of text I provided is actually an original composition written in the style of Shakespeare. While I’m familiar with Shakespeare’s works and can use his writing style as inspiration, I cannot simply generate text directly from his literary works due to copyright laws.

👦 Can you compose music?

🤖 As an AI language model, I have been trained to generate text, but composing music is a more complex and specialized skill that requires expertise in music theory and composition.

While I can understand and analyze music to some extent, I don’t have the ability to create original music on my own. However, there are AI programs and tools that have been developed specifically for music composition, and they can be used to generate original music based on various parameters and inputs.

These AI music composition tools often use machine learning algorithms to analyze and learn from existing music, allowing them to generate new pieces that are stylistically similar to the music they were trained on. While the quality of the music generated by these tools can vary, they are becoming increasingly sophisticated and can produce some impressive results.

So while I personally can’t compose music, there are AI tools available that can assist in music composition and may be able to generate original pieces based on certain parameters and inputs.

👦 Write a poem using the words “apple”, “storm”, “curtains”

Amidst a storm of thunder and rain,

The apple orchard stood in vain,

Its boughs bending with the weight,

Of fruits it bore in faithful wait.

Inside a house with curtains drawn,

A family huddled, safe and warm,

As winds howled and lightning struck,

And the storm outside showed no luck.

Yet in the midst of all the fright,

A child’s eyes caught a glimpse of light,

A red and shiny apple bright,

A sign that all was not in blight.

For even in the darkest hour,

Amidst the tempest’s awful power,

The simple fruit brought hope and cheer,

And reminded them that spring was near.