Finding the right domain within AI can be a process of exploration and self-assessment. Here's a roadmap to help you navigate this journey:

1. **Self-Assessment**: Start by identifying your interests, strengths, and skills. Reflect on what aspects of AI excite you the most. Do you enjoy working with data, creating algorithms, building applications, or solving specific problems?

2. **Research**: Explore the various domains within AI. Some prominent ones include:

  - Machine Learning: Focuses on creating algorithms that enable computers to learn from and make predictions or decisions based on data.

  - Natural Language Processing (NLP): Involves enabling computers to understand, interpret, and generate human language.

  - Computer Vision: Deals with enabling computers to interpret and understand visual information from the real world.

  - Robotics: Integrates AI techniques to create intelligent robots capable of interacting with the physical world.

  - Reinforcement Learning: Concerns training agents to make sequences of decisions in order to achieve a goal.

3. **Market Demand**: While all domains within AI have their own demand, some may have more immediate or growing demand in the market. Currently, fields like machine learning, natural language processing, computer vision, and data science are particularly sought after.

4. **Skill Development**: Once you've identified your interests and considered market demand, focus on developing the necessary skills. This may include learning programming languages (Python is highly recommended), understanding statistics and linear algebra, and familiarizing yourself with relevant frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.

5. **Specialization**: Consider specializing within your chosen domain. For example, within machine learning, you could specialize in deep learning, reinforcement learning, or generative adversarial networks (GANs). Specialization can help you stand out in the job market and become an expert in a specific area.

6. **Interdisciplinary Knowledge**: AI intersects with various other fields such as software development, cybersecurity, blockchain, healthcare, finance, and more. Depending on your interests, you may choose to incorporate knowledge from these domains into your AI education. For example, if you're interested in AI for cybersecurity, you might study cryptography, network security, and ethical hacking alongside AI techniques.

7. **Continuous Learning**: AI is a rapidly evolving field, so it's essential to stay updated with the latest developments, research papers, and technologies. Engage in online courses, attend workshops and conferences, participate in hackathons, and join relevant communities to keep learning and growing in your chosen domain.

By following this roadmap, you can tailor your AI education to align with your interests, market demand, and long-term career goals. Remember that the journey in AI is ongoing, and staying curious and adaptable will be key to your success.

AI is just a tool, like a calculator is to mathematics. Definitely, it is revolutionizing the industry. You see, AI is merging multiple domains. There is a transit shift in paradigm. Though there is not an exact answer for which field, but a fresher can choose to be either an enabler, developer, or user of AI. Enablers connect others to AI, spread awareness, and act as middlemen for adoption. Users use it to enhance their work, business, or job, while developers are developing the AI. Then a technology comes. Let's not make a debate if it is dangerous or not; the technology, if revolutionizing, simplifies potential problems in a more cost-effective way. You can choose any domain that has ample tools to solve problems and contribute to the growth of people.