At the 2023 World Economic Forum’s Growth Summit, Economist Richard Baldwin said, “AI won’t take your job, but somebody who knows how to use it will.” Since hearing this quote at ClearanceJobs Connect 2024, the idea of how to incorporate AI, learn more about it, take advantage of it, and be prepared for it, has been running through my mind. How does one, when it feels both early and late in the game, learn how to be ‘AI-enabled’ in today’s world?
Top Steps to Jumpstart Your Career In AI
Starting a career in AI seems like an overwhelming, but also exciting journey. The field offers various opportunities in technical and non-technical roles. Here’s a roadmap to help you get started:
1. Identify Your Area of Interest
AI spans many industries, so finding a niche that aligns with your skills and passion is important. Some popular areas include:
- Machine Learning/Deep Learning Engineering: Focused on building AI models.
- AI Research: Involves studying and developing new AI algorithms.
- Data Science: Involves analyzing data to train AI models.
- AI Product Management: Overseeing AI products and applications.
- Ethics and Policy in AI: Ensuring responsible use of AI.
2. Develop Relevant Skills
Technical Skills:
- Programming: Python is widely used in AI. Learn libraries such as TensorFlow, PyTorch, and Scikit-learn.
- Math and Statistics: AI involves a lot of mathematics, particularly linear algebra, calculus, probability, and optimization.
- Machine Learning: Gain a solid understanding of algorithms like decision trees, neural networks, and reinforcement learning.
- Data Handling: Learn how to clean and process data, and work with large datasets.
Non-Technical Skills:
- AI Ethics: Understanding the ethical implications of AI is important in policy and communication roles.
- AI Communication: Translating complex AI concepts into language that the public or stakeholders can understand.
3. Get Formal Education
- Degrees/Certificates: Pursue a relevant degree (e.g., Computer Science, Data Science, AI) or certifications from platforms like Coursera, edX, or Udacity. Look into programs like Google’s AI courses or IBM’s AI engineering certifications.
- Bootcamps: Short-term, intensive bootcamps can give you hands-on experience and fast-track your learning.
4. Build a Portfolio
For technical roles, building a portfolio of projects is essential. Some ideas include:
- Create a simple machine learning model (e.g., image classifier, sentiment analysis).
- Contribute to open-source AI projects on GitHub.
- Share your projects on platforms like Kaggle or GitHub to demonstrate your skills.
5. Leverage Networking
- AI Conferences and Meetups: Attend AI/ML conferences in person or online to meet people in the industry. Examples include NeurIPS, ICML, or local AI meetups.
- Join AI Communities: Online forums like Reddit’s r/MachineLearning, LinkedIn AI groups, and Slack channels are great places to learn and connect.
- Leverage Existing Experience: Your experience could be valuable in AI companies looking for roles you already have experience in.
6. Stay Updated
AI is a rapidly evolving field, so it’s crucial to stay informed about the latest trends, breakthroughs, and tools. Follow AI news, blogs, and research papers, and subscribe to newsletters such as OpenAI’s updates, Google AI’s blog, and others.
The New Cyber in Tech
AI feels like the new cyber industry, even though they overlap. There is a fear of being too far behind to be considered knowledgeable, but it is still early in development when people do not really know how to define those with the skills to use AI.
Regardless of the roles that you pursue, learn how to use AI tools, learn how to explain your skills with those tools, and you will be competitive in the ever-developing AI job market.
