The Temptations Of Artificial Intelligence Technology And The Price Of Admission Posted on : May 29 - 2020

If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models.

As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.

Many industry watchers agree that AI/ML solutions, when used to good effect, can equip your organization with a significant competitive advantage. And that makes it tempting to dive right in and start implementing these technologies without first gaining a comprehensive understanding of how they work. Accessibility to myriad options is not a barrier; almost every technology vendor now offers AI/ML services. If anything, we are often inundated with choices in this domain.

But how do we know we're making the right choices and using these services to good effect? This is where a genuine, comprehensive understanding of technology becomes critically important.

For many of us, the world of AI/ML is a relatively uncharted terrain. What is artificial intelligence in modern computing? What is machine learning? The answers to these fundamental questions are the keys to unlocking the true potential of AI/ML as business solutions.

Understanding AI/ML And Its Price Of Admission

Current machine learning is a statistical process that employs a model/algorithm to explain a set of data and predict future outcomes. Many of these are "big data" algorithms that analyze huge quantities of data to generate predictions that are as accurate as possible. Once we understand this, we start to see what is required to effectively use ML as a business solution.

Simply put, we need data. We need a lot of it, and we need it to be high quality. Poor data quality is the biggest impediment to successfully adopting and deploying AI/ML solutions, and insufficient quantities of data can be a major hindrance as well. View More