AI 101: Must-Know Fundamentals to Getting Started in Artificial Intelligence
Chatbots, self-driving cars, face recognition – AI is turning sci-fi tropes into everyday tech. But between the hype and heavy-duty math equations, AI foundations often get lost in translation. Let’s decode this mysterious field back to the ABCs so anyone can start speaking the language of thinking machines!
Our latest guide gently unravels the complex threads of AI, making them easy to grasp.
Artificial Intelligence has become an integral part of our modern world. From voice-activated virtual assistants to self-driving cars, AI is revolutionizing the way we live and work. However, understanding the key concepts and principles behind AI can be a daunting task for beginners. Let’s learn the ABC’s of AI and dip our toes into this fascinating and groundbreaking world!
Simplified AI Concepts
In this guide, we break down the complex jargon and technical terms associated with AI into bite-sized insights. You may have asked yourself these questions:
What exactly…is AI?
How does it ACTUALLY…Work?
Can someone explain to me just the basics of AI?
Fear not, weary traveler, for we will face EXACTLY those obvious, yet nefarious questions and we’ll even do it without spitting out even a single line of code.
Artificial Intelligence is a sub-branch of Computer Science that aims to create “intelligent machines” that can think like humans to help automate processes and human tasks.
AI systems use a combination of algorithms and machine learning to simulate human intelligence. The goal of AI is to create machines that can think, act, and respond like humans.
Algorithms are like recipes: fixed, step-by-step instructions to complete a task. A chocolate chip cookie recipe contains the algorithms to produce cookies – add wet ingredients, mix, scoop dough, bake, etc. AI algorithms also provide defined procedures for accomplishing goals like predicting housing prices or identifying objects in images. In fact, AI Algorithms helped generate many of the images you’ll see in this post, like the one below. Algorithms are where creativity meets problem solving!
Deciphering algorithms is a fundamental part of understanding AI, since they govern the processes that actually run the AI, just like the quality of a recipe determines the quality of that delicious cookie. Is anyone else getting hungry?
Machine learning models are a bit similar to toddlers learning skills from experience. A toddler isn’t explicitly programmed how to walk or talk! They experiment, fall down frequently, but gradually improve by trying again based on experience. Machine Learning models also work through data to find patterns, all the while making mistakes and improving over time. With enough quality data and training, they master any number of a variety of skills like generating captions for images or detecting fraud in bank statements.
“Machine learning (ML) is at the heart of AI.”
Computers can now learn from data (information) and improve their performance (at completing human-like tasks or achieving human-like intelligence) over time. There are three different types of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning, each of which have various applications in different fields, from healthcare to finance.
Neural Networks are another essential component of AI that have recently exploded in popularity. They are actually inspired by the neural pathways of the human brain and how they can be trained to recognize patterns and make predictions.
Like detectives, they pass signals between computational nodes (traffic lights) to identify clues in unstructured data. A facial recognition network may analyze pixel patterns, looking at various visual features to determine a portion of the image represents a pair of eyes, nose or a mouth. Each layer of nodes helps to detect different clues, which are then pulled together to get the final “understanding” of the overall subject (e.g. the overall face in the case of visual recognition)
Deep learning takes this neural network metaphor deeper by adding more hidden layers. While a shallow network may recognize simple shapes, a deep network can see a face among diverse backgrounds. More layers reveal more complex feature patterns, like understanding context in language translation.
Real-world Applications
AI is not just a buzzword (well at least not all the time); it’s a reality that is shaping our everyday experiences. In this guide, we explore the real-world applications of AI, showcasing how it is transforming various industries and sectors.
You Already Use AI Every Day
AI powers many familiar digital experiences through algorithms applied to data:
Chatbots use natural language processing to understand conversational text/voice inputs.
Recommendation engines analyze your preferences to curate personalized suggestions.
Autonomous cars rely on computer vision to navigate safely based on visual perception of the environment.
Fraud detection flags abnormal spending patterns by learning legitimate user behavior.
Let’s jump into some more detail about some of the cutting-edge tech in the A.I. world to help wrap your head around valuable and ubiquitous applications of the A.I. field!
Virtual Assistants
VIRTUAL ASSISTANTS
Imagine having a virtual assistant that can understand your voice commands and provide smart responses.
AI-powered virtual assistants, like Siri and Alexa, are becoming increasingly popular, making our lives easier and more convenient.
The technology behind these virtual assistants is their capacity to use “natural language processing” and “machine learning” to understand and respond to human speech.
AI in Health Care
HEALTHCARE
AI is also revolutionizing the world of healthcare in the most vulnerable parts of our human experience.
AI-assisted Surgery enables data-driven decision-making via decision support systems and cognitive robotic assistance.
AI-powered drug discovery to find new possibilities for treatments for conditions where there is no viable option for patients.
AI in Finance
FINANCE
In the realm of finance, AI is making waves with its ability to analyze vast amounts of data and make predictions. AI in finance is modernizing the entire industry by streamlining traditionally manual banking processes and unlocking deeper insights from generated data to help dictate how and where investments are made. AI is being used in finance in a variety of ways, including:
Investing
Lending
Fraud detection
Risk analysis for insurance
Customer service (using chatbots)
The market value of AI in finance was estimated to be $9.45 billion in 2021 and is expected to grow 16.5 percent by 2030. Here are 31 ways where AI is being used in Finance!
AI In Retail
ONLINE SHOPPING
Online shopping has also been revolutionized by AI!
Personalized product recommendations: Online retailers like Amazon and Netflix use AI to recommend products and content to customers based on their past purchases and viewing history. This helps customers find products they may be interested in and helps retailers increase sales and customer satisfaction.
The Future of AI is Bright and Urgent
AI will continue rapidly evolving. The impact of Artificial Intelligence (AI) on the job market is significant and multifaceted. By 2025, AI is expected to create 133 million new jobs worldwide, while eliminating 75 million, resulting in a net gain of 58 million jobs. This underscores the transformation in the job landscape, where certain jobs are displaced, while new roles requiring different skills emerge.
With conscience and competence, emerging generations can direct this powerful tech toward equitable ends. But we must dramatically accelerate and democratize access to AI skill-building to realize that vision. Its full potential hinges on developing talent with both technical competence and ethical conscience to steer progress. AI 101 or “AI Fundamentals” crash courses can help democratize access to hands-on training beyond elite grad programs.
There has never been a better time to learn the fundamentals of A.I. for transforming careers!
