What do you do if you're a professional considering a career switch to AI?

I switched from DWH to AI about 5 years ago. In my experience, Data & Mathematics are the two sides of a coin called AI. You don't need to understand every mathematical equation but you need to understand the distribution of data, any patterns emerging, the visualization every algorithm offers. People from any background can work in the field of AI as long as they possess these basic skills. It is not a fancy job, as marketed, it needs tenacity to perceive data from every angle possible to achieve the cool results, IF any.

That depends on the role you envision for yourself within AI. AI isn’t an industry in itself; rather, it’s a tool used to address problems across various industries. If one possesses deep expertise in a specific domain and understand its unique challenges, they could leverage AI to craft solutions tailored to those problems.

Contemplating an AI career switch? Try these. 1. Self-assess your skills and identify strengths (math, coding) and areas for improvement. 2.Research areas like machine learning or computer vision and see your own flavour and likings 3. Start with online AI courses. Providers like the Advanced Computing For Executives (ACE), NUS School of Computing offer flexible AI learning options. 4.Build a portfolio and showcase your skills through personal projects. 5.Network with AI Professionals. Connect with mentors and build your industry knowledge.

Start with trying to find out what is that you are really interested in? What aspects of AI are most appealing to you? Machine learning, computer vision, natural language processing? Understanding your interests will guide you towards a specific niche within the vast field of AI. Then Assess your existing skillset, Do you have programming experience (Python is a popular choice)? Are you strong in data analysis or problem-solving? Identify transferable skills that can be leveraged in your AI career. I know instead of a response I am writing more questions but this kind of deep thinking and open dialogue is needed.

If you are a professional aiming to transition to AI, here are essential steps to follow: 1) Choose a specific AI role matching your skills like data scientist, AI ethicist. 2) Gain in-depth knowledge about the selected role, required skills, and industry trends. 3) Assess your transferable skills and technical competencies. 4) Enrol in short-term AI courses to upskill. 5) Undertake practical projects to enhance your experience and build a strong portfolio. By following these steps diligently, you can smoothly shift your career towards AI.

Remember that AI existed long before 2022 - there are many fundamental aspects to AI that require learning in order to master the totality of the technology. Start from first principles and learn the math behind common algorithms, understand conceptually what each algorithm is trying to do, then progress to more in-depth projects and courses

Be prepared to invest time and effort in learning new skills. There are numerous online courses, bootcamps, and even university programs available to equip you with the knowledge and technical expertise required for an AI role. Start by familiarizing yourself with the fundamentals of AI. Online resources, books, and introductory courses can provide a solid foundation.

Embarking on a career switch to AI involves a strategic approach to learning: - Begin with online courses and workshops to understand AI fundamentals, including algorithms and neural networks. - Dive into resources such as books and scholarly articles to deepen your understanding of AI principles and their applications. - Practice by engaging in hands-on projects or challenges that allow you to apply AI concepts in real-world scenarios. - Join AI-related forums and networking groups to connect with professionals and gain insights into the industry. - Consider certifications or formal education programs to validate your skills and enhance your employability in the AI field.

Yes!!! Dive into the basics of AI by enrolling in online courses, attending workshops, or reading books and articles on AI principles and technologies. Focus on understanding how the tools work and how you can best use them. Gaining a solid grasp of AI fundamentals is essential for building a strong foundation in the field.

Depending on your background and existing knowledge, you may need to acquire additional education and training in AI-related topics. Consider enrolling in online courses, bootcamps, workshops, or pursuing a formal degree program in AI, machine learning, or data science. Develop proficiency in key AI skills and technologies, including programming languages (e.g., Python, R), machine learning frameworks (e.g., TensorFlow, PyTorch), statistical analysis, data manipulation, and data visualization. Practice coding and work on AI projects to gain hands-on experience.

Just do it. It sounds a bit esoteric at first, but there are plenty of simple interfaces that you can play around with. Rather than trying "hey do what I do for a job and show me how clever you are" and being unimpressed, try using it for things you are not familiar with, and see whether it helps to learn or do things more easily than how you would have approached it in the absence of AI. Experience will come with time, and simply having a go.

“If someone offers you an amazing opportunity but you are not sure you can do it, say yes - then learn how to do it later” quote Richard Branson

The field of AI is constantly evolving. Be prepared to continuously learn and update your skills to stay relevant. Freelance and volunteer work can be best. While technical skills are essential, don't neglect the importance of soft skills like communication, teamwork, and critical thinking.

Undertake AI projects, either independently or through online platforms like Kaggle or GitHub, to demonstrate your skills and expertise. Build a portfolio showcasing your projects, code samples, and outcomes to potential employers or clients. Engage with professionals in the AI community through networking events, meetups, conferences, online forums, and social media platforms like LinkedIn. Connect with AI professionals, researchers, recruiters, and potential mentors to gain insights, advice, and career opportunities.

Apply your new knowledge by working on practical AI projects. This could involve personal projects, contributions to open-source AI projects, or internships. Practical experience is crucial for reinforcing your learning and demonstrating your skills to potential employers. It also helps you build a portfolio that showcases your ability to apply AI solutions to real-world problems.

Connect with people working in the field. Attend industry events, join online communities, or reach out directly to professionals on platforms like LinkedIn. Learn about their career paths and the skills they find most valuable. Explore the various job opportunities within AI. There are roles for data scientists, machine learning engineers, AI product managers, and many others. Research the specific skills and qualifications required for your target role.

Consider applying for internships, entry-level positions, or apprenticeships in AI-related roles to gain practical experience and industry exposure. Look for opportunities to work with AI startups, research labs, consulting firms, or established companies with AI initiatives.

In a career switch to AI, networking widely is key. Attend industry conferences, seminars, and meetups to connect with AI professionals. Join online forums, social media groups, and professional networks like LinkedIn to engage with peers and experts. Participate in discussions, share insights, and ask questions to deepen your understanding of AI. Offer to collaborate on projects or volunteer your skills to gain visibility. Building a broad network can lead to mentorship opportunities, collaborations, and job prospects. It's about who you know as much as what you know in the AI field.

Customize your resume and cover letter to highlight your relevant skills, experiences, and achievements related to AI. Emphasize any transferable skills, projects, or certifications that demonstrate your commitment to transitioning into the AI field. Practice common interview questions and technical assessments related to AI, machine learning, and data science. Be prepared to discuss your background, skills, project experiences, and your motivation for transitioning into AI.

When pivoting to an AI career, updating your resume is vital. Highlight transferable skills and any relevant AI or tech experience. Tailor your resume to AI roles by emphasizing projects or roles that required technical skills, problem-solving, and analytical abilities. Include any AI-related courses, certifications, or self-taught skills. If you've worked on AI projects, even as a hobby or volunteer, detail these experiences. Use industry-specific keywords that AI hiring managers look for. Lastly, ensure your resume is clear, concise, and formatted to pass through Applicant Tracking Systems commonly used by tech companies. A well-crafted resume can open doors to opportunities in the AI field.

Transitioning to a new career takes time and effort. Be persistent in your learning and job search, and don't get discouraged by setbacks.

Embrace a mindset of lifelong learning by staying curious and actively seeking out new AI developments to remain competitive in the field. Engage with industry thought leaders through blogs, podcasts, and social media to gain insights and stay informed about cutting-edge AI technologies. Subscribe to reputable AI publications like MIT Technology Review or Towards Data Science to stay updated on the latest trends and research in the field. Branch out into different areas of AI, such as computer vision or natural language processing, to broaden your skill set and increase your career opportunities. Real Story: A finance rep transitioned to AI by enrolling in online courses, attending workshops, leading to a successful career switch.

Keep calm and research. This is an ever evolving field with many scientists discovering new approaches to old problems everyday. The more you are updated on the latest in your area of interest (viz. NLP, Computer Vision, Gen AI, Deep Learning, Traditional ML), the better it'll be for your growth.

The tools keep getting better / more intuitive every day. It is worth trying a range of them, and continuing to have a go with alternatives for different use cases. It doesn't hurt to think about getting under the hood as to "how" these things work, either. Even if you don't have a computer science background, the manner in which these tools have come together are indeed impressive, but their limitations (as well as their potential) will become clearer the more that this can be understood.

AI is moving at such a fast pace that if you aren't working with it on a daily basis you need to schedule time virtually every day for research, experimentation, and training.

"Getting into AI" can mean many things, but a key area is applied AI. This is about understanding how to use AI in practice, which requires knowledge of how organizations operate and what AI can potentially do to help. This field is wide open for anyone eager to learn and make a significant impact.

Switch if you want to. Also, know why you want to use AI. More importantly what value will it bring. Just not for sake of experimenting AI and never productionize. Onr more thing yo remember, for 1+1 to solve you dont need AI.

Career switches can be a big deal. With the way AI is developing, I think many roles (current and future) will incorporate elements of AI. So it’s not a binary “switch to a career in AI”, but a spectrum of “how can I leverage AI to enhance the roles or skills that I’m currently good at/enjoy” -> “how can I lead AI initiatives” -> “how do build a career in AI”.