Artificial intelligence, or AI, is one of those terms you hear what is artificial intelligence all the time these days. But if you asked the average person on the street to explain what is artificial intelligence, could they?

Let’s find out together in this straightforward guide to understanding artificial intelligence (AI). I’ll explain complex concepts in simple terms, like I’m talking to my 16-year-old cousin. My goal is to make this topic approachable and even fun.

So buckle up! By the end, you’ll be able to confidently answer the question: what is artificial intelligence?

A Lighthearted Introduction

Before we dive in, I want to share a funny story about me and technology. I’m famous among my friends for being the worst at programming my DVR. One time I accidentally set it to record reruns of Seinfeld for an entire year! And I still have no idea how I pulled that off.

I tell this story because I was once totally clueless about technology topics like AI. I found them intimidating and confusing. But over time, as I read and learned, things became more straightforward. My hope is that by the end of this article, concepts that once seemed complex and technical will feel simple and intuitive to you too.

So if I can get a handle on this stuff, anyone can! Now let’s get started…

Defining the Term of what is artificial intelligence

What is artificial intelligence refers to computer systems or machines that are designed to perform tasks that would otherwise require human intelligence. Key words here are “artificial” and “intelligence.”

Let’s break it down:

• Artificial Means man-made, or non-natural. Essentially anything that is not biologically human.
• Intelligence Refers to the ability to learn and understand things, and then use that knowledge to solve problems.

So put those two words together, and what is artificial intelligence? Man-made non-biological systems that show characteristics of human intelligence – things like learning, reasoning, perceiving, problem-solving and even creativity.

Two Types of AI

There are generally considered to be two types of what is artificial intelligence:

1. Narrow or Weak AI: This type focuses on single narrow tasks, such as playing chess, driving a car, or translating languages. Siri is an example of narrow AI.
2. General or Strong AI: This refers to machines that can understand or learn any intellectual task that a human being can. In science fiction, think Terminator or the robots in Westworld. No examples of true strong AI exist yet.

For now, all forms of AI we have remain narrow. Even IBM’s Watson supercomputer, which crushed Jeopardy champions, is weak AI. But scientists are working towards developing strong AI, even general intelligence surpassing human capabilities. Exciting times ahead!

Now that we’ve defined what is artificial intelligence on a basic level, let’s go over some common applications. This part gets really fun!

Common AI Applications

What is artificial intelligence has exploded in capability and adoption over the last decade. Once mostly in the realm of science fiction and academia, AI now plays a growing role in our everyday lives.

Let’s look at some common real-world AI applications:

Smart Assistants

AI powers popular virtual assistants like Siri, Alexa and Google Assistant to understand voice requests and respond appropriately. Ask Siri to set a timer or play a song and watch AI in action!

These assistants keep getting smarter too. Google Duplex can now make haircut appointments over the phone, mimicking human speech patterns and conversations. Crazy but true!

Product Recommendations

Have you ever shopped online and seen product suggestions “You might also like…”? That’s AI combing previous purchase data to identify your tastes and recommend similar items. Pretty handy!

And as you keep shopping, the AI adjusts recommendations further to better reflect your preferences. It’s like having a personal shopper bot.

Self-Driving Cars

Cars that drive themselves were once pure fantasy. But AI and machine learning have turned sci-fi into reality. Waymo cars have already driven over 20 million miles autonomously on public roads. And every mile makes them smarter!

AI is the brains behind self-driving tech, enabling real-time navigation, object recognition and obstacle avoidance. With AI in the driver’s seat, the future of transportation is self-driving. Vroom vroom!

Language Translation

Ever tried using Google Translate or Microsoft Translator to convert a paragraph into another language? I have, and I’m consistently stunned how technology can translate full sentences in seconds.

AI and deep learning enable apps to analyze vocabulary, grammar and language structure to translate text more accurately than ever. It’s an invaluable tool for a connected, global marketplace.

The list goes on. Nearly every industry is finding ways to incorporate AI and machine learning to drive efficiency and uncover insights. And this is just the beginning! Exponential tech improvements suggest AI will play an enormous role in coming years.

Now that you have a sense of what is artificial intelligence and does, you may be wondering: how exactly does AI work? Let’s explore next…

How AI Actually Works

Earlier I defined AI as what is artificial intelligence demonstrated by machines. But…can machines really be intelligent? After all, a computer is just hardware and software given instructions by humans programmers. Where does intelligence come from?

These are great philosophical questions, and they troubled early AI researchers too. To answer them, let’s unpack how what is artificial intelligence systems fundamentally work under the hood.

“The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” – Edsger Dijkstra

What Enables AI Capability?

Two key ingredients enable what is artificial intelligence to perform human-like tasks:

1. Data

The concepts, information, examples and rules programmers feed the AI system to base its logic and decisions upon.

Think of data as the experiences and knowledge AI uses to learn. Much like a baby develops an understanding of the world through observing things and interacting with people. More on this shortly.

2. Algorithms

The step-by-step programming instructions given to the AI system so it can take in data, process it, and produce the desired output.

Algorithms enable the AI to adjust behaviors based on the patterns it identifies in data, without needing explicit human direction.

Together, data and algorithms enable machines to mimic human intelligence traits like reasoning and learning. With the right combination, AI can keep improving at tasks without total human oversight.

Now let’s explore a key algorithm powering modern AI…

Machine Learning Algorithms

The recent explosion in AI capabilities ties directly to advances in machine learning. But what is machine learning exactly?

Machine learning refers to AI systems that can learn, adjust and improve at tasks dynamically without explicit programming. Through exposure to training data, machine learning models self-tune performance based on experience over time.

Imagine you want to teach a computer to recognize cats. Using machine learning, you provide thousands of cat photos for the system to analyze. By processing these images, it learns the visual patterns specific to cats vs dogs, people, backgrounds, etc in order to recognize cats accurately.

And with more data, the model continues getting smarter and more reliable at identifying cats correctly. All without new programming from humans once initially set up.

Two widely used machine learning methods include:

Supervised learning: Providing the AI model labeled training data, such as images with the correct classifications already defined to learn from. Like cats = cats, dogs = dogs.

Unsupervised learning: Providing the AI model unlabeled data to find patterns and groupings on its own without human guidance. Like sorting a giant pile of random photos by what’s in them.

These self-tuning capabilities make machine learning a powerful AI technique for handling complex tasks with multivariate data, like image recognition and language translation.

Once an AI system has developed mastery over one task through machine learning, researchers will often publish the model for others to download and use. This helps speed breakthroughs in commercial applications. Powerful stuff!

Okay, we just covered a ton of ground on how AI and machine learning work conceptually from a technology perspective. Let’s shift gears now…

The History of AI

What is artificial intelligence may seem like a very 21st century concept given hype over modern tech like self-driving cars. But the quest to create intelligence from machinery dates back much further.

Let’s take a quick run through history to see how early imagination, math and electronics have evolved into today’s AI-enabled robots and apps:

The Early Thinkers

You can trace ideas about synthetic what is artificial intelligence to Greek mythology tales of mechanical men built by Hephaestus and myths of Golems, clay figures brought to life. Early thinkers like Rene Descartes also proposed advanced machines could simulate animals and humans. These concepts would shape modern AI.

The Birth of Computing

In the 1930s, mathematician Alan Turing conceived the idea of a universal computer capable of executing any mathematical problem. Just a decade later, Turing tested one of the first AI programs on a computer he helped design. The seeds of AI were planted!

The Dartmouth Conference

In 1956, math professor John McCarthy organized an influential and now famous conference at Dartmouth College attended by early AI pioneers like Claude Shannon. This is considered the seminal event kicking off serious AI research to achieve what is artificial intelligence.

The Rise of Expert Systems and Neural Networks

In the 1960s and 1970s, scientists developed AI programs focused on specific knowledge domains, called expert systems, as well as machine learning neural networks modeled after the human brain. While primitive by today’s standards, these established important frameworks.

AI Winters

When early hype didn’t immediately equal fully intelligent machines, research funding and interest temporarily dried up in the 1970s and again in late 80s/early 90s – hence “AI winters.” But advances often occurred nonetheless.

The Machine Learning Breakthrough

While AI development continued in fits and starts for decades, the real acceleration kicked into hyperdrive in the 2000s thanks to machine learning algorithms combined with growing data and compute power. Huge tech companies increasingly invested in AI, cementing its central role for modern software.

And this history brings us to today with AI powering more and more of our devices, services and decisions across industries. But despite massive progress made, we likely still have a long ways to go before realizing the full vision of artificial general what is artificial intelligence envisioned by early AI pioneers.

Exciting times ahead! With more data to learn from than ever before and breakthroughs happening regularly, AI promises to become even more integrated into our lives in coming years.

AI Industry Trends and Stats

Beyond cool AI applications in consumer tech like Siri or Netflix, what is artificial intelligence is transforming major industries daily. Let’s look at two important trends underway tied to enterprise adoption:

AI Investment is Exploding

• Venture capital funding for AI startups has increased 14X since 2000, signaling tremendous confidence in future impact across sectors
• 80% of IT leaders say AI is a strategic priority for their business today
• Global spend on AI is forecasted to grow from ~$50B in 2020 to $110B by 2024 as more functions leverage intelligent automation

AI Hiring is White Hot

• There has been a massive talent migration into machine learning natural language engineering roles recently. Hiring demand increased over 74% from 2020 to 2021, driving up salaries.
• Companies urgently seek software engineers with AI and data science skills. LinkedIn lists “AI Specialist” as their top emerging job for 2022.
• Universities neural network are racing to meet exploding enrollment demand for machine learning degree programs.

The expert consensus seems to be that AI adoption and impact across industries are still just getting started. We’ll likely continue seeing it transform digital experiences and unlock business value this decade through smarter automation and insights.

Current AI Limitations

As positively as I’ve portrayed AI so far, it’s important I address the technology’s current limitations too. Truth is, despite news-grabbing demos, modern what is artificial intelligence remains quite narrow and unreliable in some respects.

Let’s ground ourselves in reality by looking at a few ways AI tends to fall short:

Narrow Capabilities: While AI can beat humans at specialized tasks like chess or math, our overall natural what is artificial intelligence remains vastly superior. Without explicit programming, AI lacks general world knowledge we intuitively leverage daily.

Brittle Understanding: Unlike human learning, machine learning models often miss context or form biases from badly trained data that lead to errors. They do not adaptly handle exceptions and nuances.

Black Box Operation: Experts don’t fully understand why AIs make every choice, holding back diagnosing failures. And opaque logic presents challenges for managing potential ethical issues.

Data Dependency: AI advancement completely hinges on algorithms processing quality datasets. Without enough relevant training data to learn from, machine learning stalls.

Specialist Oversight: To function optimally, AI requires regular input from engineers and researchers for maintaining and updating models to fix issues. The tech does not fully run or improve itself…yet!

Now, progress is steadily being made improving how AI systems overcome these limitations through better data practices, adversarial testing, explainability tools and more transparent modeling techniques. But for now, it remains an imperfect technology requiring extensive human tuning and vigilance.

In pop culture, we may see AI depicted as flawless autonomous super-intelligences, like Tony Stark’s Jarvis managing his life. But the reality stands far from that fiction. As consumers and professionals, we must approach real-world AI understanding both its genuine potential for progress AND shortcomings needing further work.

So in summary, where do we currently stand on AI capability? Read on for the big takeaways…

Key Takeaways and Summary

Congratulations, you made it to the end of my beginner’s guide explaining what is artificial intelligence! Let’s recap the key takeaways:

What is AI?

• AI refers to what is artificial intelligence demonstrated by computers and machines.
• Specific applications displaying traits like learning, problem-solving, perception and prediction.
• Current systems show narrow what is artificial intelligence geared for large amounts of specific tasks.

How Does AI Work?

• AI functionality derives from data combined with algorithms.
• Machine learning algorithms enable AI systems to learn from data without explicit programming.
• Models self-improve at tasks by processing computer science more relevant training data over time.

Why Does AI Matter?

• What is artificial intelligence drives many technologies we rely on daily, like online search engines and content recommenders.
• AI adoption in business and industry is accelerating to increase efficiency, insights and automation.

What’s Next for AI?

• Researchers aim to enhance machine learning model robustness and generalizability to support more expansive use cases.
• Developing greater AI explainability and transparency to build trust while managing ethics concerns.
• Progress toward artificial general what is artificial intelligence exceeding human capacities in any domain.

And that’s what is artificial intelligence at 50,000 feet! I aimed to provide an accessible overview for what often stands as a complex subject. Let me know if you have any other AI-related questions in the comments!