As a researcher of prebuilt AI products, I spend most of my free time learning and tinkering around with new things.
Prebuilt AI products are ready-to-use software, tools, or solutions that have AI capabilities built into them. It's like walking into a store and buying something right off the shelf. Then there are custom AI solutions that are built or customized for specific purposes--often requiring developers, engineers, or data scientists to build them.
I enjoy researching prebuilt AI products because they make AI accessible to companies that may not have the budget for advanced solutions. With the right prebuilt AI tools and a well-thought-out implementation strategy, any-given company or organization can achieve things it once thought were impossible.
What's the Role of an AI Researcher, You Ask?
The work of researchers can vary across fields. In my case, I focus on understanding available prebuilt tools for companies, learning how they function, deploying them in specific company contexts, and discovering new or alternative ways to maximize their use. This is especially important because companies often don't have the time or expertise to experiment with and test these products beyond their intended use. As a result, they sometimes unnecessarily increase their budgets to incorporate new AI components when what they already have on hand could do the job effectively.
That said, if you're new to researching prebuilt AI products, having a well-defined approach can save you a lot of time and effort. I have three recommendations to get you started:
I recommend first gaining an understanding of the big players offering prebuilt AI products. These are companies like Google Inc., Microsoft, Oracle, Meta, IBM, Amazon Web Services, and many others.
By understanding the prebuilt AI products developed by these companies, one can gain insights into the experiences of both individual users and businesses. This knowledge can shed light on the benefits and challenges faced by users and businesses when implementing and using these AI solutions.
Second, I recommend leading the launch of an AI product from end to end. This can be a freelance or passion project, and doesn't have to be robust or complex.
In my opinion, the biggest benefit of having hands-on experience is gaining the ability to articulate the potential usability and impact of an AI product to stakeholders within a specific business context. Additionally, you'll also gain the know-how when it comes to incorporating feedback throughout the product development process.
Lastly, I recommend immersing yourself in a community where complex innovation is happening. Open-source communities are great environments for this as they often host meetings where contributors share their progress and contributions.
Joining an innovation-focused community offers two main benefits. First, you can keep up with the latest trends and innovations. Second, you can compare your own experiences with the fast-paced changes happening in your community. This lets you gauge upcoming trends against your knowledge of AI products and their components.
In closing, I hope this blog post has given you new insights into the world of researching prebuilt AI products. AI in any capacity is a big part of today's world. It'll be that way for years to come.