Optimization Problem or Optimization Opportunity?

A Look at the Inherent Challenges of the Optimization Problem in Artificial Intelligence

AI has opened Pandora’s box in so many ways, but the optimization problem is one that plagues most programmers and developers leveraging AI. In a nutshell, AI has a vast number of variables and constraints to work within, and finding the more effective and efficient process can be daunting. 

We’re constantly looking for how to work within these large data sets to build solutions that don’t take forever to process. The major challenge of optimization problems is inherently in finding the most effective way to process resources, make decisions, and solve problems. It requires advanced algorithms to process various solutions and find the optimal solution. Machine learning techniques, such as artificial neural networks, have proven to be valuable tools in tackling optimization problems in AI.

Better Over Time

We’ve seen how AI can improve the process over time and learn from the initial solutions. AI can learn from data and improve performance over time – we’ve seen that time and time again. However, the optimization problem is still a daunting thing to tackle when first building out solutions or evolving solutions in new ways. It’s an underlying concern in all of our minds. 

What are Investors in AI asking about When it comes to Optimization? 

As companies and startups implement AI more and more, you’re seeing investors who are asking smarter questions. At Sentiero, we’re hyper-focused on startups using data and AI specifically, so we’re primed to think this way. However, other investors are starting to get wise about asking questions on this issue – even if they don’t always have the exact vocabulary to use terms like “optimization problem.” 

Investors ultimately want to understand a few things: 

  • How have you designed your algorithm to manage the high volume of data for efficiency? 
  • Have you evolved or redesigned your solution based on previous designs to improve optimization? 
  • What potential challenges are you facing within your specific data ecosystem? 
  • How is your solution poised to improve upon itself over time? 
  • What problems are you solving now? And what other potential problems can your solution solve in the future, given your data resources and current models? 
  • How much have you invested so far to get to where you’re at? How much is estimated to help you meet certain milestones or goals with your AI model?

The optimization problem is something we’re all dealing with in any AI-related business. It feels like there are infinite possibilities and infinite data, but that also means there are seemingly infinite ways to get to our solutions. Experience, education, and creativity are vital on any team to help move toward more efficiency in our AI solutions. Tapping into the minds within your own team, your investors, and your network can help you move the needle toward efficiency in ways you simply can’t move on your own. Together, optimization becomes less of a problem and more of an opportunity!