AI & Computer Science

AI & Computer Science

Introduction to computer science using AI for beginners.

Computer Science is a field of study that covers the theoretical and practical aspects of managing and processing data, information, and knowledge using computing devices. It includes topics such as programming, algorithms, data structures, computer systems, software engineering, artificial intelligence, machine learning, and cybersecurity.

Why CS?

The field of Computer Science is valuable because it plays a vital role in many areas of our lives. Some examples are:

  1. Advancing Technology: Computer Science research and innovation have led to the development of new technologies like smartphones, virtual assistants, and self-driving cars.

  2. Improving Efficiency: Computer Science is used in businesses and organizations to automate and optimize processes, improving efficiency, and saving time and resources.

  3. Big Data Analytics: Computer Science develops the tools and techniques necessary for processing, managing, and analyzing large sets of complex data, which is used in fields such as healthcare, finance, and social media.

  4. Cybersecurity: Computer Science plays a critical role in the design and implementation of secure systems, which is becoming increasingly important in today’s digital era.

  5. Research and Development: Computer Science is a constantly evolving field, which makes it a valuable area of study that offers many opportunities for research and development.

In summary, Computer Science is a valuable field of study that offers many innovative and practical solutions in various sectors of industry and society. Its applications are vast, and it continues to have a profound impact on our world today.

Impact on Jobs

While AI democratization is helping small entrepreneurs, definitely hurts beginners and interns that try to enter tech. Companies are not looking for you, and demand now much more from new employees. However, mentors and teachers are demotivated so is going to be harder to find free mentorship.


AI democratization has both positive and negative impacts on beginners and interns in tech. On the one hand, AI can be used to automate tasks that were previously done by humans, freeing up time and resources for beginners and interns to focus on more creative and strategic work. Additionally, AI can be used to provide beginners and interns with access to data and insights that were previously unavailable, helping them to learn and grow more quickly.

On the other hand, AI can also make it more difficult for beginners and interns to find jobs in tech. As AI becomes more sophisticated, it is able to automate more and more tasks, which means that there are fewer jobs available for humans. Additionally, AI can be used to assess candidates for jobs, which can make it more difficult for beginners and interns to stand out from the competition.

The demotivation of mentors and teachers is also a significant challenge for beginners and interns in tech. Mentors and teachers play a vital role in helping beginners and interns to learn and grow, and their demotivation can make it more difficult for beginners and interns to get the support they need.

A new hope

Despite these challenges, there are still several things that beginners and interns can do to succeed in tech. First, it is important to stay up-to-date on the latest trends in AI and to learn how to use AI to your advantage. Second, it is important to network with other people in tech and to build relationships with mentors and teachers. Finally, it is important to be persistent and never give up on your dreams.

Here are some additional tips for beginners and interns in tech:

  • Start small. Don't try to learn everything about AI all at once. Instead, focus on learning the basics and then build on your knowledge over time.

  • Find a mentor. A mentor can provide you with guidance and support as you learn about AI.

  • Get involved in the community. There are many online and offline communities where you can connect with other people who are interested in AI.

  • Don't be afraid to ask for help. If you're stuck, don't be afraid to ask for help from your mentor, friends, or family.

With hard work and dedication, beginners and interns can overcome the challenges of AI democratization and succeed in tech.

Startup prompts

AI can be a very helpful tool for learning computer science, but it's important to note that it won't replace the need for studying and practicing on your own. That said, here are some examples of prompts you can use to learn computer science using AI:

  1. "Recommend resources to learn data structures and algorithms"

  2. "What are the best programming languages for web development?"

  3. "Create a study plan for learning machine learning"

  4. "What are the top computer science books for beginners?"

  5. "Suggest a project idea to practice object-oriented programming"

Using natural language processing (NLP), AI can analyze your prompt and provide you with a response that can help guide your learning. For example, you can use chatbots like Rasa, Dialogflow, or to get personalized recommendations for resources, courses, and books that match your skill level and interests.

AI can also help you practice coding by providing instant feedback on your code and identifying errors or syntax issues. Platforms like LeetCode, HackerRank, and CodeSignal use machine learning to offer personalized coding challenges and feedback on the performance of coding exercises.

Additionally, virtual assistants like Siri, Alexa, and Google Assistant can help you find answers to specific questions related to computer science, such as definitions of technical terms or explanations of coding concepts.

Ultimately, AI can be a helpful tool for learning computer science, but it's important to remember that the most effective way to learn is to practice and apply concepts through projects and real-world experiences.


Here are some of the most valuable and in-demand subdomains of computer science for a beginner:

  • Web development: Web development is the process of creating websites. It is a broad field that includes front-end development, back-end development, and full-stack development. Front-end developers create the user interface of a website, while back-end developers create the server-side logic. Full-stack developers do both front-end and back-end development.

  • Data science: Data science is the process of extracting knowledge from data. It is a rapidly growing field that is in high demand in many industries. Data scientists use a variety of tools and techniques to analyze data, including machine learning, artificial intelligence, and statistics.

  • Cybersecurity: Cybersecurity is the practice of protecting computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. It is a critical field in today's world, as businesses and governments rely more and more on technology. Cybersecurity professionals use a variety of tools and techniques to protect systems and networks, including firewalls, intrusion detection systems, and encryption.

  • Software engineering: Software engineering is the process of designing, developing, testing, and deploying software. It is a discipline that combines computer science and engineering principles to create reliable and efficient software. Software engineers use a variety of tools and techniques to develop software, including programming languages, data structures, and algorithms.

  • Artificial intelligence: Artificial intelligence (AI) is the ability of a computer or machine to mimic human intelligence. AI is a rapidly growing field with a wide range of applications, including healthcare, transportation, and finance. AI researchers and developers use a variety of tools and techniques to create AI systems, including machine learning, natural language processing, and computer vision.

These are just a few of the many valuable and in-demand subdomains of computer science. The best subdomain for you will depend on your interests and skills. If you are not sure where to start, I recommend taking some online courses or reading some books on computer science. This will help you to learn more about the different subdomains and find one that you are interested in pursuing.


Learning Computer Science and AI can be an exciting journey. Here is a roadmap that you can follow to get started:

  1. Learn Programming Fundamentals: Start with a programming language like Python or Java to learn basic programming concepts such as loops, conditionals, data types, and functions. You can learn this through various online courses like Codecademy, Udemy, or Coursera.

  2. Learn Data Structures and Algorithms: To work on AI projects, understanding data structures and algorithms is critical. Learning about structures such as stacks, queues, linked lists, and trees, and the accompanying algorithms that operate on them is essential. You can learn this by taking online courses or books on the subject.

  3. Learn AI Fundamentals: Once you have a good understanding of programming and data structures, you can then start exploring AI fundamentals. Topics can include supervised and unsupervised learning, neural networks, gradient descent, decision trees, and clustering algorithms. You can take online courses, attend workshops or read textbooks.

  4. Practice Projects: To cement your knowledge of programming and AI, it’s essential to work on projects. Start with simple projects such as sentiment analysis, handwriting recognition, or simple chatbots. You can then build more complex projects as you advance your skills.

  5. Participate in Competitions: Competitions like Kaggle, a platform that offers datasets and challenges to data scientists, will allow you to put your skills into practice. Participating in these competitions is a great way of testing your skills while providing learning opportunities.

  6. 4. Join an AI community. This is a great way to connect with other AI learners and get help when you need it. There are many AI communities online and in your local area.

  7. Stay up-to-date on AI research. This will help you to stay ahead of the curve and to learn about the latest advances in AI. You can stay up-to-date on AI research by reading AI journals, attending AI conferences, and following AI blogs and podcasts.

In summary, mastering AI is no easy feat, but with this roadmap, a good study plan, and dedicated practice, you can achieve the goal of understanding and utilizing AI in applications.


Sure, here are some free resources you can use to become proficient in AI:

  1. Machine Learning for Coders Course: This course is designed for both developers and non-developers who want to learn machine learning. Students work with Python, TensorFlow, and Jupyter notebooks to build, train, and deploy deep learning models.

  2. Google Machine Learning Crash Course: This is a free course that teaches developers to build and train machine learning models using TensorFlow. The course includes video lessons, interactive visualizations, and hands-on exercises.

  3. Machine Learning Crash Course by Google Developers: This course covers the fundamentals of machine learning, including linear algebra, probability, and statistics. It includes interactive exercises, video lectures, and Jupyter notebooks.

  4. ML Courses by Stanford University: Stanford offers a series of free online courses on Machine learning, artificial intelligence, and Deep Learning. The courses are available on Coursera and cover the basics of machine learning to advanced concepts.

  5. edX MIT Artificial Intelligence Course: edX offers numerous free courses on Artificial Intelligence. The courses introduce basic ideas and techniques from artificial intelligence and machine learning.

  6. Intro to Deep Learning with PyTorch: This free course teaches the fundamentals of deep learning, including artificial neural networks and convolutions neural networks, and uses PyTorch.

  7. OpenAI: OpenAI has a wealth of resources, including free courses, research papers, and software tools. Their machine learning resources and AI learning resources are excellent places to start your AI journey.

  8. Kaggle: Kaggle is a platform that hosts machine learning competitions. You can work on real-world problems using real data sets and build your portfolio while learning.

  9. Mlu explain: Visual explanations of core machine learning concepts. My favorite resource good for beginners.

These resources will provide you with an excellent foundation in AI and machine learning. As you learn, take the time to tackle projects and problems that interest you to enhance your knowledge and skills. Good luck!

What is LLM?

A large language model (LLM) is a statistical method that predicts the next word in a sequence based on the words that have already been seen. LLMs are trained on massive datasets of text, and they can be used for a variety of tasks, including natural language understanding, natural language generation, question answering, and code generation.

Notorious LLM's

Some of the most notorious large language models in existence include:

  • GPT-3 (OpenAI)

  • Jurassic-1 Jumbo (Hugging Face)

  • Megatron-Turing NLG (Google AI)

  • Bloom (Google AI)

  • WuDao 2.0 (Beijing Academy of Artificial Intelligence)

  • WuDao 3.0 (Beijing Academy of Artificial Intelligence)

  • LaMDA (Google AI)

  • PaLM (Google AI)

  • Chinchilla (Google AI)

These models have been used to generate text, translate languages, write different kinds of creative content, and answer questions in an informative way. However, they have also been criticized for their potential to be used for malicious purposes, such as generating fake news or spreading misinformation.

Risks of LLM's

  • Large language models can be used to generate fake news and misinformation.

  • They can be used to create deepfakes, which are videos or audio recordings that have been manipulated to make it appear as if someone is saying or doing something they never said or did.

  • They can be used to create spam and other forms of unwanted content.

  • They can be used to target people with advertising or other forms of manipulation.

Benefits of LLM's

  • Large language models can be used to generate creative content, such as poems, stories, and scripts.

  • They can be used to translate languages.

  • They can be used to answer questions in an informative way.

  • They can be used to help people with disabilities, such as dyslexia or speech impairments.

  • They can be used to develop new technologies, such as self-driving cars and virtual assistants.

It is important to be aware of both the risks and benefits of large language models. As these models continue to develop, it is important to use them responsibly and ethically.

Have a conversation

The easiest to get started using AI Is to register for an online web interface where you can ask questions and receive back answers from the AI conversational bots.


ChatGPT is an AI-powered messaging platform designed to connect people with knowledgeable chatbots. It provides a way for users to ask questions related to various topics such as education, technology, business, health, and more, and receive quick and accurate responses from chatbots that are trained to answer those questions.

ChatGPT uses a Natural Language Processing (NLP) algorithm that enables it to understand user input and respond conversationally. The chatbots are trained on a huge amount of data and can understand different languages, dialects, and accents.

One of the unique features of ChatGPT is its ability to provide personalized responses to users based on their preferences and previous interactions. This feature allows users to engage in a more meaningful conversation with the chatbot.

Overall, ChatGPT provides an efficient and convenient way for users to get answers to their questions from knowledgeable chatbots.


Bard is a low-code conversational AI platform that enables you to quickly design, deploy and manage chatbots without the need for coding. With Bard, you can build complex conversational workflows using a drag-and-drop interface that makes it easy to design conversational scenarios and integrate them into your website, mobile app, or other platforms.

Bard offers a wide range of features, including natural language processing, sentiment analysis, entity recognition, and machine learning capabilities. These features enable you to create intelligent chatbots that can understand and respond to user inquiries in a human-like manner.

Bard is designed for non-technical users, making it easy for anyone to create chatbots, without the need for programming knowledge. The platform offers a variety of templates and pre-built components that help you get started quickly, and you can customize these templates to suit your specific needs.

Overall, Bard is a powerful conversational AI platform that provides an intuitive and straightforward way to create and deploy chatbots for your business.

Disclaim: I have created this article with ChatGPT. Some information may be out of date. If you find a mistake help me fix it. Thank you.

Don't panic. The future is bright! Learn and prosper.🖖

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