date: 'Friday, August 2 2024' title: 'What Is AI and How Does It Work?' description: "A deep dive into artificial intelligence (AI), how it works, and how it's being used." image: 'ai-pros_if4v8e' author: 'Brian T. Sullivan' editor: 'Lindsey Woldt'

category: 'learn'

What Is AI and How Does It Work?

Written by Brian T. Sullivan • Edited by Lindsey Woldt

The movie A.I. was directed by Steven Spielberg and released in 2001. While I admit to not having seen it (though I swear I'll watch it eventually), I feel fairly confident in guessing that information about that movie is probably not why you clicked on this article. Instead, you've maybe been hearing people talk about things like AI or Machine Learning, and you're wondering what on Earth they even are.

After all, it seems these days that a lot of people have something to say about artificial intelligence. Some people think it's going to solve all of the world's problems, others think it's going to spell the doom of humanity, and a lot of people don't quite know what's going on but are stressed all the same.

While I don't want to tell you exactly what to think about AI here, I'll do my best to help make sure that you know a bit more about it.

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What Is AI Used For?

Artificial intelligence (AI for short) is software designed for computers to emulate one or more aspects of human intelligence, usually as it pertains to problem solving or processing "noisy" data from the real world.

Most of the things you do on your computer or phone probably don't make use of AI—you don't need AI to text your friend or to check the weather forecast. You might be using AI on some level, however, if you tell Google Assistant or Siri to text your friend that you'll be there soon.

Other examples include, meteorologists using AI to process a bunch of current weather data and predict what the weather will be like tomorrow.

AI is used so that computers can work adaptively and intelligently in complicated situations. This means that AI allows computers to learn, solve problems, make decisions, and communicate with humans accurately and naturally—both as input from humans and output to humans.

When Was AI Invented?

"Classical" AI

There have been many different approaches to implementing artificial intelligence over the decades since AI research started in the 1950s. That was when Alan Turing introduced the "Turing Test" as a concept and suggested that it could be plausible for a machine to emulate human intelligence.

Early efforts largely focused on hard-coding algorithms for a computer to complete specific, complex tasks. This is how a lot of chess programs have been written.

While programs designed this way can do many impressive things, this approach has limitations. After all, people are essential for the machine to understand the concepts of a given problem and program them accurately for this kind of AI to work.

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How Does AI Work?

Training Massive Amounts of Information

These days, when you hear people talking about AI, they're probably referring to the subset of AI known as machine learning. Machine learning (ML) was conceptualized in the 1950s, the same time as the "classical" AI approach. It has taken off since the early 2010s. However, thanks to the innovations in processing power (especially with GPUs) that made it possible to train systems on enormous amounts of data.

After all, training on data is the quintessential component of how machine learning processes information and operates. Instead of hard-coding all of the facts and logic for a problem into a program, ML systems are presented with massive amounts of information and learn from it to produce appropriate outputs.

Neural Networks and Deep Learning

The main way that machine learning is accomplished is through some kind of neural network. This is also sometimes referred to as deep learning. While neural networks were originally named on the premise that they were analogous to how neurons work in the brain, later developments in neuroscience suggest that this is not the case.

Even so, the name has stuck, and that's what we're rolling with. A neural network is a collection of different layers of interconnected "neurons" or "nodes." Each node receives input from a previous layer, does some kind of calculation, and produces an output to the next layer.

Zooming out, the process for deep learning is that you give input to a neural network. The network then crunches numbers on that input and produces an output on the other end.

During the training stage, the network gets feedback on all outputs to say whether they are right or wrong. The given feedback allows the network to adapt, and try again. This process repeats until the outputs have reached a certain level of acceptability or a certain number of training cycles have been completed.

Probability-Based Outputs

Unlike "classical" AI, which focuses on programming computers to handle complex problems with explicit, logical procedures, machine learning works on probabilities. After training, a neural network produces outputs based on the probability of being what you want, based on the given input. This is often a major concern with artificial intelligence since an answer that is probably correct can be quite different from the correct answer.

Neural Networks in Problem-Solving

At the same time, though, neural networks are being used in efforts to solve large, complicated AI problems that would be very difficult for people to manually program. We have begun to see the fruits of these efforts with things like facial recognition software, improved natural language processing, and generative AI models for text and images.

How Is AI Being Used?

Recommendations Algorithms and Autonomous Agents

Many AI tools we interact with these days are voice interfaces and recommendation algorithms found in search engines and social media. Both involve determining what a user is asking for and selecting what option or task will satisfy the user's request.

While a lot of this utilizes the traditional approaches to AI, they have also begun to incorporate more deep learning models in recent years, to improve performance. This is also the case with technology such as navigation for autonomous robots—ranging from a Roomba vacuum cleaner to a self-driving car or the robot dog, Spot, by Boston Dynamics.

Speech Recognition and Natural Language Processing

Speech recognition and natural language processing (NLP) can also be useful tools for transcription and translation. Transcribing, captioning, or translating are all within the capabilities of AI, and these systems can help people—especially those small and independent creators—make their work more available and accessible to others.

These tools do not yet consistently perform as well as a real person. There is a lot of time, skill, and cost involved in doing this work by hand; so these AI tools can be a valuable option for people with limited resources.

So of course, the current AI buzz contains generative AI (genAI).

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Generative AI and Chatbots

These are tools that usually make use of deep learning models to produce something based on an input. The generated result is either a text or an image, but it can also be audio, video, or runnable code. Many text-based genAIs serve as chatbots, such as ChatGPT or Character.AI, and writing assistants, like QuillBot and Grammarly.

Two of the most popular image A.I. generators are DALL-E1 and Midjourney. Various other tools for generating these images (often called "AI Art") frequently integrate one of those models into their system. Again, while these sorts of tools have existed for quite some time, they have exploded in popularity and performance as deep learning systems have improved.

Artificial Intelligence Pros

Convenience and Usage of AI in Daily Life

As you've seen, AI has many commonplace uses that, while not 100% perfect, do not necessarily even feel like artificial intelligence when we use them.

I know that I, personally, really appreciate using a voice assistant to tell my phone to play a certain song while I'm driving or to start a timer for 15 minutes when I'm in the kitchen and my hands are messy.

AI in Helping Society

On a larger scale, AI cannot only do certain tasks that are tedious or difficult for humans, but they can also help people.

AI software helps search & rescue robots navigate dangerous terrain. Not only that, but AI can give those robots the ability to interact with humans in a natural, comforting way to help them deal with a stressful or traumatic situation.

Similarly, there is ongoing research into using deep learning to process actual neural activity in the brain as input to drive prosthetic limbs or give a voice to people who have lost the ability to speak.

Artificial Intelligence Cons

AI Energy Consumption

While artificial intelligence offers many benefits, it still has its problems. A major issue with AI is the amount of electricity it requires to run.

The International Energy Agency released a report in early 2024 that indicated electricity consumption by the "Data Center, AI, and Cryptocurrency" sector could double by 2026. It's said that this could be equivalent to the current electricity consumption of the entire country of Japan.

Training Data for AI

Another worry to be concerned about is the relationship AI has with the information it's being provided.

Neural networks require a lot of training data to get to the point where their outputs are decent. Thus, there have been various observations that the training data utilized may infringe upon people's privacy and intellectual property.

AI Misinformation

On the output end, there are similar concerns about neural networks producing materials (including text, images, or deepfake video and audio) that could be used to spread misinformation. There are also concerns that AI tools will put people out of work, including areas that typically require a uniquely human touch, like art, literature, and even software development.

Societal Concerns

Some of these concerns have more to do with how people and companies use AI than with AI itself. That does not negate the concerns people and society have, and these issues are major areas of debate that you will probably encounter regarding AI if you haven't already.

So, What Should You Think About AI?

Honestly, that's up to you.

Confusing and Potentially Dangerous, but Extremely Useful

The fact is that there is a lot of AI that we have been using for years (sometimes even decades) without even thinking about it. It is useful and helpful in many ways, and it frees us up to do more fun and interesting things.

There are also newer instances of AI, especially AI driven by deep learning neural networks, that are already proving extremely useful in some ways and dangerous and confusing in other ways. The current buzz is because these A.I. tools are a new addition to technology, and we haven't figured out where they fit in our lives.

Let It Do the Grunt Work

As an artist myself, I admit to having played with several genAI tools for images. I got bored with them quickly, and I don't use genAI in any art I produce.

That being said, tedious, non-creative tasks in my art process are things that I would gladly have a machine do for me. Unfortunately, I don't know of any current A.I. tools capable of performing those tedious and non-creative tasks how I'd want them done, so I'll continue doing them by hand and wish I was doing the more creative parts.

In Conclusion

Artificial intelligence is a big, complex topic. It can feel scary, especially when you don't know what it is or how it operates. At the same time, it can do some neat things.

AI can also drastically improve some people's lives.

It is not perfect, and there are real environmental and ethical concerns related to the creation and use of AI tools. Bearing that in mind, it is still pretty cool to see what can be accomplished just by running electricity through some circuits on silicon chips.

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