Understanding machine learning today can seem daunting, and technology seems to be advancing at ever-increasing speeds. So what is machine learning? Well, it used to be that you had to program a computer to do certain things. However, due to the advances in digitization and cheap computing power, data scientists can feed computers large amounts of data and enable them to learn from the experience.
From Amazon’s product recommendations and Facebook’s ability to spot faces to Google’s self-driving car, more and more companies are turning to machine learning to improve processes and create new, better ways of doing business.
What is the Difference Between Machine Learning and Artificial Intelligence?
A common misconception people have about machine learning, and artificial intelligence is thinking that the two concepts are interchangeable. The truth of the matter is that machine learning is just one aspect of the broader notion of artificial intelligence.
Think of it this way; machine learning is like training a dog to do a trick, whereas artificial intelligence is teaching the dog to reason.
Artificial intelligence refers to the simulation of human intelligence by machines. It is cognition, coupled with reason and action, and encompasses all of its subsets. Thankfully this is still some ways off.
Machine learning, on the other hand, is a form of AI where a computer analyzes bits of data to teach itself how to perform a task. It’s pattern recognition.
For example, when Siri recognizes your voice, it does so by having a computer convert a sound you make into text. The computer program that does this had to “listen” to a lot of voices and accents before it was able to deduce that the spoken word for “pizza” means pizza.
Google didn’t program its car to drive. It learned by driving millions of miles and observing the vehicles, and road markers around it.
Similarly, IBM didn’t program its computers to play chess; it fed them decades of publicly available chess games and coupled those with the rules for chess. Then IBM’s engineers wrote a program that would have the network learn the rules and strategies by analyzing the data and choosing an optimal outcome for each chess scenario.
Machine Learning is getting less expensive.
While IBM roughly spent 1.8 billion on developing Watson, today’s enterprise machine learning costs will be much cheaper.
Microsoft’s Azure Machine Learning, for example, is relatively inexpensive and should cost a few thousand per month for a machine learning cluster during your product development cycle and then scale up/down on an as-needed basis.
Most of the R&D work around the computing infrastructure is already on the market, and open-source software tools are readily available for engineers to utilize.
Tangible benefits of Machine Learning Implementation
If executed with foresight and strategy, machine learning can bring considerable benefits to the business as a whole, and to me, it should be a cornerstone of any digital transformation plan. To make the abstract seem relatable, here are some tangible examples of machine learning implementation.
The design of digital products often includes developing interactive interfaces that allow users to make choices. Take Netflix. When you select your preferences and up or down vote movies on the platform, Netflix uses your data to enhance your user experience by serving you content that is relevant to you. The hypothesis is that you’ll watch more, and have a better user experience because of it, keeping you subscribed.
Here, a machine-learning algorithm predicts what a user wants to see by combining their data, with that of others, and eliminates the need for a complicated interface.
Knowing your customers is a must in exceptional product design, and machine learning can help. By correlating KPIs to user actions within a digital product, your product designers and engineers can create machine learning models that help your business understand its customers. Think of it as doing ongoing primary marker research en masse. Because machine learning excels at collecting and analyzing data, it allows us to create better and more acute customer experiences.
Here’s an example: you’ve designed a new fitness bracelet and leaked a video of how it looks and works. A machine learning algorithm can monitor social media chatter, examine a target audience’s response to the product, and provide possible answers on how to augment the product based on feedback. You then launch the fitness bracelet, sales are good. Still, you’re seeing that certain users consistently use a feature that is two menu levels down; the machine learning algorithm can move that feature up a menu to optimize those users’ experiences with the platform.
Machine learning makes it possible to detect anomalies in the system and address them before they affect the entire design process. Instead of having to wait for hours to fix the problem, ML can anticipate breakdown and streamline maintenance.
Computers are great at repetitive tasks, but historically you had to “train them” to do a specific job. Say get from point a to point b in a straight line, but if something were blocking it, the computer would not be able to get past the blockage, unless it was programmed to do so.
What Does It Take to Get Started?
What is the desired outcome? Start with the desired result; this is the problem that you want machine learning to solve for you. With that, product managers and data scientists can formulate a plan with engineering to get a minimum viable machine learning application deployed. Utilizing data collected augment and update the machine learning application as needed to satisfy your business goals.
What to expect?
If implemented correctly, machine learning should create across the board improvements and will be the case irrespective of your position as an executive, a VP, or a Director. Minor changes will need to happen operationally; however, some people will undoubtedly be affected when implementing machine learning into your organization. It’s essential to manage these people’s expectations and communicate that their responsibilities will merely be changing.
For example, when implementing a machine learning chatbot. It will change the way customers interact with your customer service personnel. While some reps may become redundant, the machine learning chat will require copywriting skills. Ideally, people will adapt. Make sure you provide your team with the right tools to make the change swift and smooth.
Should you want to embark on this internally, here are the main steps worth considering, but integrating it within existing processes without a clear vision and long-term plan in place will make the effort fall short.
Define your objectives and ideal end state;
Analyze possible solutions;
Develop a long-term strategy (product roadmap);
Acquire the necessary expertise to ensure proper implementation;
Execute until you have a minimum viable product and augment.
Budget for ongoing improvement.
In this day and age, knowledge means power, and machine learning could help you achieve a competitive advantage. By collecting and analyzing tens of millions of data bits, it can give us a glance into why we’re behaving the way we do and why do we make individual decisions, that’s its beauty.
Why not give machine learning a spin and see what it can do for yourself. This post was edited using Grammarly, an AI system that combines machine learning with a variety of natural language processing approaches. It scores your writing style, helps improve the way you write, and even checks online for plagiarism.