Get Started with Artificial Intelligence
Learning Objectives
After completing this unit, you’ll be able to:
- Explain the importance of understanding fundamental concepts of artificial intelligence.
- Identify the challenges that make defining artificial intelligence difficult.
- Describe the types of tasks artificial intelligence can perform.
Time to Get Fluent in AI
Artificial intelligence (AI) has been a dream of many storytellers and sci-fi fans for years. But most people hadn’t given AI much serious thought because it was always something that might happen far into the future. Well, researchers and computer scientists haven’t been waiting for tomorrow to arrive, they’ve been working hard to make the dream of AI into a reality. In fact, as you know, we’re already well into the Age of AI.
[AI-generated images using DreamStudio at stability.ai. The first uses the prompt, “A closeup of a person sitting at a typewriter, drawn in the style of fun 2D vector artwork.” The second uses the prompt, “The scene is in a university classroom, there’s a blackboard in the background with a sketch of a neural network. In the foreground is a college student typing on computer, drawn in the style of fun 2D vector artwork.”]
For many, AI is already a part of our work, school, and personal lives, and that part will likely grow as AI capabilities advance. For us to have meaningful conversations about AI, we need a shared vocabulary and a solid foundation of core concepts to build upon. As it stands, if you ask 10 people to define artificial intelligence, you’re likely to get 10 different answers based on how it impacts their lives. In this badge we explore some of AI’s current capabilities and uses.
The Difficulty of Defining AI
The first step in defining AI is to recognize that our current notion of AI might be distorted. A steady diet of science fiction books and movies where AI is seen as a nefarious entity bent on conquering the world hasn’t helped.
Science fiction isn’t the only thing that’s complicated our view of AI. Generally speaking, we humans tend to think highly of ourselves; the benchmark by which everything else is measured. So when we speak of artificial intelligence, we can’t help but to compare it to our own intelligence and our abilities to learn and act on what we know. The problem is that humans aren’t the only intelligent beings out there. Animals, from crows to octopuses, use tools and problem solving to perform complex tasks. Even slime molds can solve mazes if given enough time.
As we’ve begun to appreciate the wide spectrum of intelligence in the animal kingdom, we’ve also started to recognize the great diversity in our own human intelligence. Maybe you know someone who’s fantastic at public speaking but can’t do math to save their life. Or someone who can always tell when you’re feeling a little anxious, but would trip over a soccer ball at the first opportunity. The point is that our intelligence is expressed in many specialized forms. We need to think of artificial intelligence in the same way. There are specific kinds of AI that are good at specific kinds of tasks. So let’s define artificial intelligence by taking a close look at what AI can do today.
Main Types of AI Capabilities
Right now there’s no singular AI that’s good at everything. That idea, known as general AI, is still somewhere in the future. Instead, over the years we’ve developed specialized AI systems that are designed to perform specific tasks. The kinds of tasks they do generally fall into one of a few broader categories.
Language Processing
On November 30, 2022, Merriam-Webster’s word of the day was quiddity. Those who learned that word got a little better at what might be the most important skill of all: communication. On that same day, the world was introduced to ChatGPT, an artificial intelligence that demonstrated its own communication skills. GPT stands for Generative Pre-trained Transformer, and since ChatGPT’s release many other GPT’s have followed, some specializing in specific types of language processing like journaling, coding, or financial analysis. GPTs are built to interpret everyday language and act on it in a meaningful way, like answering questions, writing stories or papers, summarizing information, or performing complex calculations. This is known in the industry as natural language processing, or just NLP.
NLP relies on an understanding of how words are used together for AI to extract the intention behind the words. For example, you might want to translate a document from English to German. Or maybe you want a short summary of a long, scientific paper. AI can do that too.
NLP is shaking up how we do work across nearly every kind of business. For example, AI Agents can interpret natural language and are equipped with reasoning skills are quickly replacing chatbots and copilots in customer support settings. Natural language prompts are often used to generate code to save companies countless hours of software or app development. Sales reps use NLP to request account summaries, generate Sales emails, or even create drafts of presentations for customers.
NLP is one of the fastest growing areas of generative AI, a subcategory of AI that takes words and turns them into unique images, sounds, code, and of course other words. NLP and Generative AI are such disruptive technologies that we’ve written whole badges on the topics of Natural Language Processing Basics and Generative AI Basics. Check those out when you’re done here.
Numeric Predictions
Have you looked at a weather forecast recently? Predicting rain or shine helps you decide if you should grab an umbrella. Although we’ve made weather predictions for thousands of years, AI models can do it better than any previous method.
A good prediction can help you answer all sorts of questions. Is this customer likely to renew their subscription? Are you at risk for a medical condition? Will there be high demand on the power grid this evening? Which sneaker style will be the most popular this season?
Often AI predictions take the form of a value between 0 (not going to happen) to 1 (definitely going to happen). Numeric predictions include more than just percent values, they can predict any numeric value, such as dollars. Maybe your business wants to predict next quarter’s sales, or figure out the optimal pricing for your latest service: Widget+. And as a consumer you’re probably already affected by these kinds of numeric predictions, even more than you realize. Just imagine a trip overseas: the airline tickets, hotel room, ridesharing, and travelers insurance are all likely to be priced by AI to perfectly balance supply and demand.
[AI-generated image using DreamStudio at stability.ai with the prompt, “A closeup of a friendly robot driving a taxi, in the style of flat 2D line art.”]
Classifications
Is a hot dog a sandwich? This question has led to countless hours of friendly philosophical debate about how we categorize things. But in the real world, the stakes can be much higher. What is this plant? Is it edible or poisonous? Is that email legitimate or a phishing attempt? Classification is often the first step in taking some kind of action, making it an incredibly valuable skill.
So it isn’t surprising that computer scientists have worked hard to create AI that’s good at classifying data. Identifying plants and phishing emails is only the tip of the iceberg. Financial institutions need to flag fraudulent transactions. Medical professionals must diagnose illnesses. Social media platforms want to identify toxic comments. All of these are examples of classification problems. AI can effectively make the first pass at classifying, and then the professionals can take it from there.
Often, AI classifiers can do the job just as well, or better, than humans. That said, most classifiers are only good at one, narrow task. So the AI that’s great at detecting phishing emails would be lousy at identifying pictures of actual fish.
Robotic Navigation
Some AIs excel at navigating a changing environment, and that includes actual navigation in the case of autonomous (hands-free) driving. AI-powered cars are already quite capable of keeping centered in a lane and following at a safe distance on the highway. They adapt to city traffic patterns, curves in the road, gusts of wind from semi trucks, and sudden stops.
AI that can adapt to changing environmental conditions have all sorts of real-world applications. For example, businesses need to produce and deliver products to their customers every day. Lots of market conditions play a role in how quickly that gets done: materials availability, manufacturing capacity, existing inventory, transportation costs, even real-time traffic. AI can optimize the supply chain even while conditions are changing.
And let’s not forget robots! Even the modest robot floor sweeper can avoid stairs and chairs. On a bigger scale, many assembly lines are fitted with robots that become faster and more efficient over time. Those same robots can adjust for changes to the production method without costly reprogramming. And researchers are creating rescue robots that can traverse disaster areas, such as a collapsed building. A robot-caterpillar that can squeeze through tiny cracks could deliver aid and hope to those trapped inside.
AI Models and Neural Networks
No conversation about AI is complete without mentioning AI models and neural networks. AI models are like super smart computer programs that learn from examples. For instance, imagine you take up birding as a hobby. The more actual birds, pictures of birds, bird sounds, bird names, locations and habits of birds you’re exposed to, the more accurately you begin to recognize various birds and where you might see them. Similar to your new hobby, an AI model learns patterns, and makes decisions and predictions by analyzing large amounts of data. Once trained, it can perform tasks based on both what it’s learned and continues to learn.
Neural networks are important tools for training AI models. Neural networks are a mix of nodes, layers, weights, biases, and a bunch of math. Together they mimic our own organic neural networks. Each neural network is carefully tuned for a specific task. Maybe it’s great at predicting rain, maybe it categorizes those birds, or maybe it keeps your car centered in the lane on the highway. Whatever the task, neural networks are a big part of what makes AI seem magical. And now you know a little bit about how the trick is done.
Originally conceived to allow machines to problem solve like humans, neural networks allow AI to identify complex relationships between input data and output classifications. In other words, they let computers learn what variables and values matter to people when trying to accomplish a goal. This is essential in AI technology, because it’s the foundation of connecting human needs – faster work, fewer errors, or an easier day–to data-driven solutions like agents that respond to natural language prompts. Neural networks are what allow complex AI models to know, for example, that a customer is trying to reset a password, even if the customer doesn’t use a specific key word or phrase during the course of an interaction.
In Summary
Artificial intelligence is the ability for a computer to perform skills typically associated with human intuition, inference, and reasoning. Many of these skills fall into broad categories like numeric predictions and language processing that are making their way into our lives through AI that supports our business use cases, educational needs, and industrial purposes.