This article was initially published on AIQOM’s blog: https://aiqom.ai/en/blogs/5-Tips-Every-Beginner-AI-Entrepreneur-Must-Know
According to Stanford HAI’s 2022 AI Index, private investment in AI reached $93.5 billion — twice the total private investment in 2020. This represents the most significant year-over-year rise since 2014 (when investment between 2013 to 2014 more than doubled). Furthermore, the number of patents filed in 2021 rose to 30x greater than in 2015, marking a compound annual growth rate of 76.9%.
These recent findings prove that Jeff Bezos was right when he described the current era as a “renaissance” and “golden age” of AI. Samer Obeidat, CEO of Stallion AI, extensively discusses the AI Startup world in his book, “The AI Citizen”.
Solving problems is the core of your heart and soul as an entrepreneur. It is every entrepreneur’s drive to create solutions that tackle the most pressing challenges in the world today. With the aid of powerful AI tools like deep learning, the modern AI entrepreneur can solve these challenges in dynamic and potent ways.
However, despite the unique advantages offered by machine learning models and algorithms, an AI entrepreneur still requires a purposeful AI strategy. Here are 5 tips every budding AI entrepreneur must know to build successful world-changing solutions:
- Data is king: Every AI model thrives on data. Still, data should not be gathered for its own sake. Clear and logical decisions and considerations must inform its acquisition. Chief among these considerations is the question — “What is the problem I am trying to solve?” Nailing down an exact answer to this question will guide your data strategy which must be drafted from the very first day. You will need to decide how to source for data and consider critical issues like data diversity and data bias to avoid creating models that work for only a particular race or gender. You also need to adopt a plan for data cleaning and data processing.
- The user is supreme: A common mistake that many AI entrepreneurs make is to get lost in the technical details/beauty of the model that they forget the user they’re building for. Keep in mind that there is a human end-user whose opinion of your product will depend on their…