In today’s rapidly evolving technological landscape, machine learning (ML) is more than just a buzzword; it’s a transformative tool that has been and will continue to become integral to all industries. Recently, I had the incredible opportunity to spend three enlightening days in an ML course with some brilliant engineers.
Here’s what I learned.
Machine Learning is a Tool.
ML is not a product feature but a tool used to deliver a feature. It can read, predict, recommend, create, and even seem to see and listen. At its core, it’s a model that processes inputs and outputs, facilitating specific features or capabilities. It is the small, strategic use of ML that brings the wow factor. So offerings or products are not ‘ML’; they are the code bases we have become accustomed to using, with some ML used strategically to allow the system to do really smart things.
It Does Not Have to Be Intimidating.
The concepts of neural networks and mathematical models in ML might seem as intimidating as trying to decipher math without numbers (I’m a marketer, calculus isn’t my thing). The reality is that once we get past the initial confusion and realize that machine learning isn’t some mystical wizardry, the true magic is revealed. It’s just another tool in a developer’s kit, and by breaking it down into smaller, more manageable components, the path forward becomes clear and attainable.
AI, ML, and Deep Learning – What the heck is the difference?
AI is the overarching field that encompasses various forms of intelligent technology. ML is a subset of AI focused on enabling machines to learn from data. Deep learning is a further specialization within ML, utilizing neural networks to achieve complex tasks. It’s important to note that these models require regular maintenance and updates, as they can become less effective over time.
AI automates tasks, not jobs.
Even the most advanced models rely primarily on human input. Should we all fear for our careers? I don’t think so. With the right approach, we can enhance our professional lives without fear of being replaced by machines. ML doesn’t discern fact from fiction and often produces hallucinations, making up false information. Using our professional experience, subject matter expertise, and judgment layered with the efficiencies that ML brings is what creates the value.
As we non-technical folks evaluate how we begin to adapt and leverage the wondrous new opportunities that ML makes possible, I will start by looking for tasks and processes that meet these criteria:
- They rely on data
- They drive business value
- They have an element of repetitiveness
- They make predictions
It’s then up to me to determine the outcome I hope to achieve and how machine learning can help me get there.
Embracing the Change
A technological shift as impactful as what we see going on now in the world of AI creates various responses, from excitement to fear. The lack of practical education and the rapid pace of change can be overwhelming. Yet, the potential for growth and improvement remains immense.
My journey into the world of ML continues to be an eye-opening experience. It is showing me that even a small but strategic implementation of ML can have an exponential impact on a project or an organization. Far from being a threat, AI and ML are tools we can harness to become more efficient and innovative in our professional lives. The future is here, and I’m excited to be part of an organization ready to help guide our clients into this new frontier.
Let’s not be paralyzed by fear or overwhelmed by change; instead, let’s embrace the possibilities that AI and ML bring to the table. Whether through a small pilot project or a full-scale review, the opportunity to grow and thrive is within our grasp. The time to start is now.