Image courtesy of Jeremykemp at English Wikipedia / CC BY-SA
In Everyday AI Problems, my colleague Jordan Thayer said the following:
When someone asks me “What can AI do for me?”, I often suspect the answer is “Not much” because it’s the wrong question. If someone asks me “Is there a way to make this free text machinable?” or “Are there better techniques for scheduling these work orders?” the answers are “Yes!” and “Almost certainly.” Are the techniques that solve those problems AI? Absolutely.
Is AI worth the hype? Absolutely. Jordan believes this so strongly that he pursued a doctorate in it and dedicated his entire professional career to it. I’m a bit late to the party, but I agree with him and am trying to make up for lost time! That said, AI isn’t a panacea; just because something is worthy of the hype doesn’t mean that individual deployments can’t fail because they’re a misapplication or just a bad idea overall. In the following, let’s discuss:
Artificial Intelligence is getting a computer to do anything that would otherwise require a human. Common applications include:
While the machines are trying to imitate human capabilities, the way they do that is often quite alien. Consider asking an AI and a person each to tell you whether or not a tweet expressed a positive or negative sentiment about a topic. The person would read the tweet, understand its meaning, and give you an answer.
In stark contrast, many AI approaches to sentiment analysis won’t even try to understand the text as a whole. Instead, they look at the distribution of words in the tweet, the length of those words, and the amount of punctuation used. This statistical analysis allows the AI to reliably predict the sentiment of the text without needing to produce a deeper understanding.
It’s important to keep the difference in approach in mind. While AI can do things that we typically think of as requiring a human touch, they do so by different means. Our tendency to anthropomorphize things works against our intuition here. An application of AI doesn’t think in the conventional sense. Its outputs are the result of common processes in mathematics, computer science, and engineering being rigorously applied to some problem of interest.
AI comes up in situations you might not think of, or in ways differently than you might imagine. For example, AI happens a lot in industrial settings from a controls perspective: to solve a large constraint problem to ensure that we don’t put an oil refinery in a dangerous configuration. This drone application complements it: use drones to view hard-to-reach elements of an industrial facility to reduce the cost of manual inspection.
If your project fits within these guidelines, it’s a good candidate for AI:
Some problems that you encounter every day fit those criteria, and are often solved using artificial intelligence:
AI is not the right tool for every task. It is important to remember that artificial intelligence will not replace human intelligence.
This reality means that we may never see Robby the Robot or R2-D2 or Data. They are relegated to the realm of science fiction with flying cars. AI is often silently in the background, playing a large role in many industries that directly impact our everyday lives. Here are some absolutely game-changing examples:
Cultivating crops with higher yields that are more resilient to the elements and pests is a problem whose solution affects all of us - after all, everyone needs to eat! Here, AI is being used to reduce the human effort required for phenotyping.
Phenotyping is the study of plant characteristics, like the size of the fruits they produce, under various conditions. Large-scale phenotyping used to be time and labor intensive - a human made the observations by hand. Scientists developed machine vision algorithms to do it for us.
Further, the AI planning community has been using automation of smart greenhouses as a benchmark domain for a decade. The logistics problems that ‘crop up’ here are moving plants (or equipment) to:
Medicine has advanced dramatically in the last century thanks in no small part to advances in medical imaging. Medical imaging provides information that is critical to determining what is going on with an individual so that the proper course of action can be determined.
Medical imaging is an excellent domain for deploying machine vision algorithms. In particular, deep learning has been remarkably successful in reducing the time between a first consultation and a diagnosis and increases the number of patients that can be treated by each doctor. This is also a great example of how AI works best with human collaboration.
It’s easy to get excited about AI. There is so much progress made in so many fields which all show the promise of AI. Although we must be realistic about the scope of AI, we can still get excited about its prospects.
We must be unafraid to fail. In the end, all AI deployments are an experiment, because we can’t know upfront how well a technique will work for a set of problems that we’ve never seen before. Failing, understanding, and iterating is a huge part of developing AI solutions.