How can application developers transition to a career in artificial intelligence?

This question is actually meaningless. Application developers can develop an application as per the client's requirement. Requirement can be about a business like banking or insurance software or scientific like matrix inversion or transportation problem. Any software application delivers some intelligence that can solve a problem, whether we term it as Artificial Intelligence or not. No application developer develops same kind of application throughout his/her lifetime. Transition always happen, may be from one technology to other or one business domain to other. All application developers are pursuing a career in intelligence. 'Artificial Intelligence' is just another kind of application involving prediction using statistical techniques.

When you started writing agents to search for keywords in your log files to raise alerts, wrote listeners to read from a queue, wrote push/pull based on events you were already writing AI Bots. Things have matured enough to be able to handle more complex topics making it easier for us to be able achieve the target in a rapid time to market ratio. Almost every developer must have written a chat agent which is now a BOT, but we spent time and effort and now its relatively much quicker. Developers have always matured over time and should learn these new technology patterns for a quicker agile development cycle.

Moving forward, the application development process (DevOps / DevSecOps) along with the applications developed are going to include increasingly more AI. If you don't want to be left behind, start to understand the basics of integrating AI with applications and then move on to understanding how to incorporate it into the DevOps process.

To transition to a career in AI, you need to: Learn the fundamentals of AI, machine learning, and data science through formal or online education. Master the programming languages and frameworks used in AI development, such as Python, R, TensorFlow, PyTorch, etc123. Develop skills in data science and analytics, such as data collection, manipulation, visualization, and interpretation. Gain hands-on experience with AI projects, either through personal, academic, or professional settings.

It is very important to understand the basics. Attend some training classes that offer hands on experience with some real industry use cases. Then apply the skills learnt on simple projects in your company/industry

This should be like step 5. My best advice is start with a very general area. Do you want to work in language or images? Do you want to develop models or consume models? Start high level, learn a little bit about your choice, and then start to lean in as you learn more.

Before choosing a focus area, I would like you to try many different things. There are unlimited things you can do with AI, you can generate images, videos, music. Imagination is the limit. I want you to try everything out, and then decide what gave you the best experience and the most fun. Once you know that, see if that's relevant to your different topics of interest. If that's so, try to merge them. For example, if you find out that you really enjoy creating conversational prompts and changing the way AI behaves (giving it a specific personality), you may want to dive deeper into AI Chatbots. And, if on top of that, you are a web developer, you should definitely include AI Chatbots as a part of your offer.

Learn by picking up a use case and build a solution for it. This will boost your passion towards learning AI without any compulsion as you are solving a business problem of your choice. One example is that when I was looking at an e-commerce application case study, I would like to understand how I can build a recommendation engine where an AI based technology can be applied (querying data from users, products and purchase history) to build a model and make it available to the end users so that it will give recommendations to the end users when they buy certain products online. Once we try to implement we learn all the aspects of the technology and tools used.

To understand better how these tools and frameworks connect, a good guide would be to follow the application of MLOps end-to-end. That way, one can observe how the information flows and consider what options exist for every chain component.

As I said earlier, hands on learning is key. Highly recommend python as your base language. Get jupyter notebooks up and running on your local machine until you've completed some projects. I find it so much easier to experiment with TensorFlow, PyTorch, etc in a notebook. At some point, you'll want to train models. For that, you'll need a cloud provider such as Google cloud with GPU resources to complete larger scale training cost effectively.

Python should definitely be your go-to language. But I believe that if you want to start seeing for yourself the impact AI can have, you should start with no-code tools, like Voiceflow, Stack AI, or Botpress. Why? Because you can use them for free, test different things and just play around without spending any tokens. If, on the other hand, you go straight to building AI Chatbots/Apps with Python, you will probably need some tokens to start with (if you want to use ChatGPT API in your projects for example). And it's just harder to start this way in general. Slow steps, but move forward.

Remember that you don't need to be a developer or advance coder to leverage python (or similar tools/frameworks) to help you develop AI solutions. ChatGPT can write, spot check and optimize the Python code for you with simple and clear prompts to help get you started.

Social profiles are your friend here. For your dev projects, GitHub is a top choice. Create markup files for every project you complete to explain what you did and what you learned. For learning, LinkedIn profiles are a great way to present your work and link it to your experience. My profile has several certifications from Coursera.

Take advantage of social media. If you want, you can even document your entire journey on YouTube. You don't have to edit those videos. Also, you should share your projects on LinkedIn, Medium (or your blog), and other social media that you prefer. AI is a new field, so if you can take advantage of that right now and position yourself as an AI Expert, you will be a no-match to your competition in the future. More and more people are starting to understand the power of personal brand, and I want you to create one as well.

The industry is still changing so rapidly. A year ago, very few people knew about prompt engineering. Now there are so many different prompt methodologies it's impossible to keep up with everything. My best advice is keep learning, try to keep up, but also recognize you can't be an expert at everything. If you are overwhelmed, narrow your focus.

To be honest, AI is surprising me every day. It is constantly evolving, and you need to keep up with everything (at least everything around your area, because AI itself is a broad term). However, if you are using ChatGPT a lot for your projects, you should be the first person to know about any updates that are coming out. Sign up for newsletters, turn your notifications on, find other trustworthy people, and devour information about new things and updates. But before you do that, you should make it clear what's your area of focus, so that you won't sign up for any news not related to your area of expertise.

One of the best skills you can build is in a passion for continuous learning. What's true today, won't be true tomorrow at the rate this technology is moving. You have to be a lifelong learner to stay ahead and in the AI game.