Today’s digital world is flooded with more data than ever. The cliché example for big data is “It’s like finding a needle in a haystack,” but digital world problems are on a scale that makes this analogy seem silly. After all, there are many simple solutions to find a needle in a haystack that are readily available. For instance, one could use a magnet or dump the haystack into a pool of water and watch the needle sink. Big data problems are much harder, they are more like finding one specific needle in a giant stack of similar needles. There are no simple solutions to big data problems.
There are many ways to tackle big data problems. We've all heard the buzzwords: Deep Analytics, Data Visualization, Machine Learning, Artificial Intelligence, Neural Networks, Deep Space Force Calculus (The last one isn't real, but who knows what the future holds?) Significant business investments are being made into products that claim to use these technologies. Before businesses invest, it's important to understand the capabilities and limitations of the underlying technology.
Consider artificial intelligence, any business thought leader will tell you it's the wave of the future. But how can you figure out which functions within your business can actually be transformed by AI? What are the quality limitations? How can you evaluate which business service companies are using AI effectively while others could be selling hyped up linear algebra? The best way to know if an AI product is right for your business is by asking the right questions.
Artificial intelligence, is like human intelligence. There is wide variation in types, skill-sets, and ability. To understand the capabilities of a product, first segment AI products into supervised or unsupervised.
Unsupervised AI Key points:
Unsupervised AI allows the AI to form its own conclusions based on the data set. Machine brains are highly effective at finding common features and sorting based on a categorical filter.
Unsupervised AI is great for analyzing large quantities of categorical data, in the image above unsupervised AI could easily sort by other filters like eye color, clothing, species etc. It can even figure out different filters that it's programmers don't explicitly state like "male ducks with no shoes."
Unsupervised learning is helpful for people trying to make sense of massive data sets. Unsupervised learning's best use cases are when people are flexible on the outcome they would like at the end of their workflow. It can provide insights that humans might not anticipate. For instance, based on the analysis of over 1.6 million transactions on iseecars.com the best used cars to buy are orange cars. Exactly why orange cars maintain their value better than other colored vehicles is unclear, but the AI's statistical argument based on the provided data set is valid.
Supervised AI Key points:
Supervised AI uses human expected outcomes for a given workflow to shape how the AI produces the optimal solution. Supervised AI is taught how to make decisions by the humans managing the workflow. Think of a teacher teaching a student the correct answers to questions in a classroom.
Over time, the student will begin to recognize the correct answers on their own. If for some reason the correct answer changes, the teacher can go back and reteach the student the new correct answer. This feature makes supervised AI more customizable than unsupervised AI. With the right data set, supervised AI can actually become better at predicting right answers than its teacher.
What if you could customize your own AI? An AI that mimics your skills, instincts, and experience then applies them to the tasks that you don’t want to do.
What tasks would you choose to automate through AI? What are the business risks associated with using supervised vs unsupervised AI? Imagine a world where you could spend more time and energy on the parts of your job that you love. This potential world is the promise of AI in the workplace.