%20(1).png)
AI is everywhere these days, from the autocorrect feature on your phone to Netflix’s show recommendations - but how does it actually work? Today we’ll break down the basics of how AI works, how it gets smarter, ways you might have seen it in your day to day life, and what the future holds.
Key Takeaways:
- AI works by learning patterns from data.
- Machine learning is the basis behind modern AI.
- Neural networks, inspired by the brain, are a key part of AI.
- AI is already part of everyday life.
- AI isn't magic - it has limitations and can make mistakes.
What Exactly Is Artificial Intelligence?
Artificial Intelligence (AI) is a broad term for a computer system that can perform tasks that normally requires human intelligence, whether it’s understanding language, recognizing images, making decisions, or solving problems.
The term artificial intelligence was coined in 1956 by a researcher named John McCarthy during a workshop at Dartmouth College, but even though the idea has been around for decades, AI remained pretty basic for a long time. It’s only in recent years that it’s advanced enough to become a household term.
When we talk about AI today, we usually mean machines or software that can learn from experience and adjust to new inputs. There are different kinds of AI, but a common distinction is between narrow AI and general AI:
- Narrow AI is good at a specific task, like translating speech or playing chess. These AI bots are great at doing their speciality, but can’t do anything outside their defined jobs.
- General AI is an AI that could understand or learn any intellectual task that a human can, the kind of AI you see in a sci-fi movie. We haven't achieved general AI, and it remains a theoretical goal for the future.
As it stands, today’s current AI is extremely good in one area, clueless in others, so it won’t start aiming for global domination any time soon.
Data and Algorithms
Two key areas of AI are data and algorithms. The examples that AI learns from come in the form of data, which includes numbers, text, pictures, audio clips, and any other form of information we provide.
Algorithms are like a recipe or set of instructions that tell the computer how to interpret and learn from the provided data, allowing the system to adjust itself based on the data it receives.
The quality and quantity of data are crucial - if the AI trains on bad or biased data it will learn the wrong lessons, which is why AI can still make mistakes or promote harmful behaviors.
How AI Gets Smarter
While in its early days AI was programmed with a bunch of rules, the latest changes in modern AI mean that we don’t need to provide explicit rules for everything but instead we can let the AI learn by example - machine learning in a nutshell.
One part of machine learning is the neural network, inspired by the structure of the brain which has billions of interconnected neurons. An artificial neural network has layers of nodes (neurons) that each perform a simple calculation. The network layers are typically organized so that the first layer takes the raw input data, intermediate layers create progressively more abstract features, and the final layer outputs a prediction or decision.
AI In The Everyday
AI might sound very theoretical and complex, but chances are if you’re using a computer to read this article you’ve come across AI at least several times in the last week.
One huge example is voice assistants and chatbots - so if you’ve ever asked Siri about the weather, you’ve used AI. Netflix and Spotify also use AI to recommend shows and curate playlists based on your listening habits, and Gmail uses AI to filter out spam from your inbox. Your social media feed is also curated thanks to AI, showing you things you’ll likely engage with based on past interactions.
A more recent example is self-driving cars, which use cameras, lidar, radar, and other sensors to perceive its surroundings and process the data to decide how to proceed. AI is also transforming healthcare, where it helps doctors analyze X-rays and streamline patient workflows. For a deeper dive, check out our guide on AI in clinic practice management.
Limitations Of AI
With all the hype around AI, it's easy to overestimate what it can do, but it’s important to remember that AI is not a magic brain or a sentient being and it does have significant limitations, the foremost being a lack of true understanding. AI can simulate intelligence, but it doesn’t truly understand meaning the way we do. It’s also dependent on the data it was trained on, so if the data is biased, the AI will reflect that and can’t adapt otherwise. On the same note, AI can be extremely confident in providing the wrong answer, which can be catastrophic in the wrong contexts, like for self-driving cars.
Because of this, one of the biggest challenges of AI is using it responsibly, which means being transparent on the data it’s been trained on, double checking its answers to ensure that the information is in fact correct, and not using AI to do work where errors could cost lives.
What Does The Future Hold?
When I think about the future of AI, I’m excited albeit a little cautious. On one hand, AI has advanced at a pace I never imagined (if you told me ten years ago that I'd have a realistic conversation with AI on my phone or that an AI could beat world champions at complex games like Go, I'd have been skeptical).
We can also expect AI to be used more widely across various industries like in healthcare, where AI can assist doctors by quickly scanning X-rays or MRIs faster and perhaps even more precisely in some cases. We’ve also been making progress when it comes to generalizing AI's capabilities. Right now, if you want an AI to do something new, you often have to train a model from scratch or at least fine-tune an existing one on new data but researchers are looking at ways for AI to learn more like humans - picking up one task and then using that knowledge to help learn another. There's also interest in making AI that requires far less data to learn a task.
Will we ever achieve that sci-fi dream/nightmare of a human level intelligent AI? Some experts believe it's only a matter of decades before we create an AI that is as intelligent as a human across the board but others (myself included) think there's something fundamentally different about how humans think that current computers might never replicate.
One thing is for certain, AI is here to stay, and it's going to become a bigger part of our lives. Just as electricity or the internet were once astonishing new inventions that eventually faded into the background of everyday life, AI might do the same.
That said, I think it's important for everyone to have a basic understanding of how AI works, because when you know that an AI is basically learning from patterns in data, you can appreciate its achievements without being scared by its potential, and you can also approach it critically (which you should).
Ultimately, AI is a tool, and like any other tool it’s only as good as its handler.
