Since AI is becoming the new “buzz” in the tech world, it is important to note its different aspects of it. There are, especially, two different kinds of AI programs: Applied AI programs and the Theoretical AI programs, and the difference between them is the subject of discussion here.
What Are the Key Differences Between Applied AI and Theoretical AI Programs?
| Aspects | Applied AI Programs | Theoretical Programs |
|---|---|---|
| Fundamentals | Uses existing tools to solve real-world problems. | Focuses on inventing new concepts and algorithms. |
| Focus | Practical results and deployment. | Mathematical frameworks and inventing new rules. |
| Skills Needed | Software Engineering, Data Preparation, and Cloud AI APIs. | Advanced Calculus, Linear Algebra, and Complex Statistics. |
| Real-World Examples | Using IBM Watson Health to detect diseases in medical scans or building a chatbot for a retail website. | Designing artificial neural networks that mimic the human brain and researching concepts such as Artificial General Intelligence (AGI). |
Applied AI and Theoretical AI Program: Curriculum Differences
| Feature | Applied AI | Theoretical AI |
|---|---|---|
| Main Goal | Building products and automating business workflows. | Discovering new mathematical and computational concepts. |
| Core Skills | Prompt engineering using APIs, data preparation, and workflow deployment. | Linear Algebra, Calculus, Probability Theory, and Logic. |
| Common Tools | Python libraries like Pandas, TensorFlow, and OpenAI APIs. | Math frameworks, research papers, and low-level code frameworks. |
| Typical Project | Creating a customer service chatbot for an online shop. | Designing a new, faster neural network architecture. |
What Skills Do Students Develop in Applied AI Programs and Theoretical AI Programs?
These are the skills developed in Applied AI:
1. Technical Skills
- Machine Learning and Deep Learning
- Prompt Engineering
- Natural Language Processing (NLP)
- Computer Vision
- AI Too Integration
2. Practical & Business Skills
- Data Analysis & Preprocessing
- Workflow Automation
- Critical Thinking
- Ethical Awareness
- Communication
These are the skills developed in Theoretical AI:
1. Relative Machines
- Logic & Optimization
- Pattern Matching
2. Limited Memory
- Predictive Modeling
- Natural Language Processing
- Computer Vision
3. Theory of Mind
- Emotion Recognition
- Empathy-Driven Response
- Intent Prediction
4. Self-Awareness
- Independent Thought
What Career Opportunities Are Available After Applied AI and Theoretical AI Programs?
These are the job opportunities available in Applied AI:
- Machine Learning Engineer
- AI Product Manager
- Data Scientist
- AI Developer/Software Engineer
- Prompt Engineer
The average salary of an Applied AI professional in India ranges from INR 6 LPA to INR 15 LPA. For senior professionals, it can range from INR 20 LPA to INR 55 LPA.
These are the job opportunities available in Theoretical AI:
- AI Research Scientist
- Algorithm and Data Theorist
- AI Ethics Specialist
Which Program Is Suitable for Different Career Goals?
Both these AI programs has its own advantages, career opportunities, as well as limitations. Each will be suitable for an individual who is more inclined, interested in, and passionate about whichever AI field it is.
If you are more interested in building things, using AI to apply to your project, and seeing it function every day in real time, then Applied AI is your field. It will help you envision a product or system where the AI can be utilized. Whereas if you are more interested in research and guiding the development and rise of AI in general, then Theoretical AI is your field of choice.
Those who pursue Theoretical AI are less invested in extracting the benefits from it and more interested in finding out its full potential. They are excited about figuring out the limitations of AI and seeing how much of its effect can be expanded beyond known territory. Applied AI enthusiasts and professionals are invested in putting the tool to best use, often to their financial benefit.
Key Takeaways
The key takeaways from this article are:
- The differences between Applied AI and Theoretical AI are specific, and they need to be explored.
- Just knowing you want to build your life in AI is not enough. You need to know the various types of fields that exist within it, and how they differ from one another.
- Between these two programs, each is suitable for a specific kind of person. Know the differences, the advantages, and the limitations of each before pursuing it.
