What do you do if you want to acquire AI skills while working in a different field?

Acquire AI skills by pursuing online courses, certifications, or workshops tailored to your interests and schedule. Seek mentorship, join AI communities, and collaborate on projects to gain practical experience. Leverage company resources or negotiate training opportunities. Stay curious, dedicated, and proactive in learning. With determination and focus, transitioning to AI from a different field is very achievable.

I dedicate time after work or on weekends to learn AI through online courses. I find ways to integrate AI aspects into my current job to gain practical experience. I join AI interest groups or meetups to network and learn from professionals in the field.

Learning takes many forms, and it largely depends on the individual. In addition, exploring topics outside your core, e.g., AI, AI, ML, Generative AI, etc., is pretty new. Research and explore online communities to get feedback and reviews to select the right online course. Connect with professionals and ask questions. Remember, AI is still a new category; gather as many facts as possible to determine your best learning approach. If an online course is a path forward, connect with people who have taken the course, inquire about the level, format, instructor, outcomes, subsequent courses and ask a version of NPS individuals who have taken the course, "Will you recommend this AI course for beginners?"

Online learning platforms like Coursera, Udacity, and edX offer a plethora of AI courses tailored to various skill levels. Utilize these platforms to access lectures, assignments, and hands-on projects at your own pace, allowing you to integrate learning into your busy schedule.

AI is a broad field and before learning AI and where you can start, need to do a self-assessment to evaluate your current skillset, interests, and career goals to identify which AI skills you want to acquire and how they align with your background and aspirations. Then, familiarize yourself with the various domains, applications, and technologies within the field of AI. Explore online resources, courses, books, and tutorials to gain a foundational understanding of AI concepts, algorithms, and tools.

Community engagement speeds up learning new AI skills. Surround yourself by following key figures and organizations within the AI industry to keep up with ongoing topics. Engage actively with their content and don’t shy away from discussions. Look into AI-focused groups on LinkedIn and connect with other professionals. Consider sharing your own experiences and insights through posts or articles, like this! It’s a great way to demonstrate your expertise and attract connections that are genuinely interested in AI.

Join AI-focused communities on platforms like LinkedIn, Reddit, or specialized forums. Engage in discussions, seek advice, and participate in collaborative projects. Networking with professionals in the field can provide valuable insights, mentorship, and opportunities for collaboration or job referrals.

Engaging with the AI community offers unparalleled opportunities for learning and growth, especially when transitioning from another field. Platforms such as GitHub for project collaboration, AI-focused forums on Reddit, and professional groups on LinkedIn are invaluable resources. Participating in discussions, sharing your projects, and networking with AI professionals can provide both inspiration and practical insights. Additionally, joining AI hackathons, whether virtual or in-person, can challenge your skills and expand your understanding. This community engagement not only accelerates your learning curve but also opens up networking opportunities that might lead to collaborative projects or job offers.

Working on practical AI projects helps understand theoretical knowledge in tangible skills. Start with simple projects, like building a basic chatbot or implementing a ml model with scikit-learn, and gradually move to more complex challenges (check Kaggle for projects and challenges). Open-source projects provide a platform to contribute and learn from real-world applications. Participating in hackathons or competitions (also on Kaggle), can offer experience and the chance to apply AI solutions to diverse problems.

If you are looking to acquire user-level Generative AI skills, it is pretty straightforward. Start with your Use Case. Is it creating content, automating tasks, generating code, or something else? There are tools available to generate text (ChatGPT), image (DALL-E or MS Designer), or (GitHub Copilot). Knowing what tools are available and what they can do is crucial. Take basic tutorials to get started and then practice with your use cases. For example, use ChatGPT to write an Insta post, or rewrite an email to your customer. Soon you will experience its capabilities. Now take a short course in prompt engineering to help you get the most from the GenAI tool. Did you ever learn to drive a car? Fearsome until practice makes you perfect.

AI will touch every field, every role in some way. It could be as a consumer of the information, a user of a system or creator/developer building the capabilities. Practical projects and experimenting, testing is an excellent way to explore and apply tools that are out there and train the models. Most companies are engaged at some level of PoC or pilot as they move up the maturity curve. The emphasis is quickly shifting from explore to trial and adoption. Examples for individuals are generating outlines, track spending, budget, research assistant. For businesses examples include customer service chatbots, digital application extensions (eg MS Office), inventory management, note taker. The idea is to find a practical project and test.

AI isn't really limited to a particular industry or field. In 2024, it's clearly a General Purpose Technology. So the simplest thing you can do is start using AI for nearly all tasks that theoretically allow it. That way, you'll learn very fast what AI is capable of, and learn to communicate with it effectively. You'll get a clear view on what you'll want to automate in your job. From there, you can start learning some basic prompt engineering that is applicable to your specific tasks.

Enroll in certificate programs, diplomas, or degrees (it can be online) if you're seeking a structured path. Many universities and colleges offer specialized programs in AI and ML, there's many options in edX. Some programs often include capstone projects or internships for hands-on experience. Part-time or online degree programs are an option if you want to study without pausing your career.

Design your environment to support your learning journey. Two actionable steps: 1. Join communities 2. Go to relevant in person events

Major corporations like Microsoft and Google provide numerous AI certifications. You can earn these certifications by passing specified exams. I have personally acquired several Microsoft certifications in different solution areas, including AI, and I highly recommend them to anyone seeking to augment their technical and professional abilities and gain credentials in the pertinent fields.

A cross-disciplinary approach to AI is highly beneficial. Seek opportunities to apply AI within your current field (automating tasks, analyzing data, or enhancing decision-making processes). This approach positions you as a valuable bridge between AI and your field of expertise. For example, AI models for sustainable city development.

Surround yourself, but don't overwhelm yourself with AI. Remember to have fun when learning. Don't just use AI for professional applications, use it in your leisure time. You might learn something that can be applied to a professional task. Or be productive and find creative ways to integrate process automation into your daily life.

We are fortunate to live in a time where an abundance of resources are readily available online, enabling us to broaden our knowledge and refine our skills. Stay curious and continue learning about topics that pique your interest. Don't be overwhelmed by the surplus of information; instead, select two or three subjects that intrigue you and delve deeper into them.

The field you're in is irrelevant when it comes to acquiring new AI skills. It's never been easier to learn about AI. I find podcasts to be the easiest way to stay up-to-date on news, and I find a mix of in-person networking and LinkedIn to be the best way to engage with other AI professionals.

To acquire AI skills while working in a different field, the key is to stay curious and hands-on. Start by exploring user-friendly AI tools like ChatGPT, Claude or Gemini. Begin with simple tasks such as summarizing texts, drafting emails, or creating communication materials. This 'test and learn' approach allows you to creatively explore AI's capabilities and limitations. As you grow more comfortable, assess how AI can enhance efficiency in your daily tasks. This method of practical exploration is effective across any field, helping you seamlessly integrate AI into your workflow.

If you're looking to develop AI skills while working in a different field, there are plenty of free resources to help you start. Platforms like Coursera and edX allow you to audit courses from top universities at no cost. For a more practical approach, Fast.ai offers hands-on learning in deep learning, and MIT OpenCourseWare provides free access to course materials from MIT. Google AI Education and Kaggle Learn deliver targeted courses on TensorFlow, machine learning principles, and "micro-courses" in data science. For comprehensive AI education, consider Stanford Online and DeepLearning.ai by Andrew Ng, which are renowned for their depth. Additionally, GitHub offers an extensive range of projects, code examples, and tutorials.

If you're looking to develop AI skills while working in a different field, there are plenty of free resources to help you start. Platforms like Coursera and edX allow you to audit courses from top universities at no cost. For a more practical approach, Fast.ai offers hands-on learning in deep learning, and MIT OpenCourseWare provides free access to course materials from MIT. Google AI Education and Kaggle Learn deliver targeted courses on TensorFlow, machine learning principles, and "micro-courses" in data science. For comprehensive AI education, consider Stanford Online and DeepLearning.ai by Andrew Ng, which are renowned for their depth. Additionally, GitHub offers an extensive range of projects, code examples, and tutorials.

Do not let the prospect of AI intimidate you, particularly regarding concerns that it may potentially supplant certain roles in the future. Instead, embrace it and strive to comprehend its workings. I firmly believe that AI will not render anyone obsolete, provided we commit to understanding and effectively utilizing it.