Generative AI for Beginners: Part 1 — Introduction to AI
Introduction
Introduced in 1956, the term “Artificial Intelligence (AI)” has been known to all of us. Still, the use and discussion of AI was mostly limited to scientific research or fictional movies until the rapid popularity of ChatGPT. Now-a-days, AI and especially Generative AI became the hot topic for everyone.
Generative AI for Beginners blog series is targeted for anyone looking to onboard on AI journey. Currently irrespective of your role and job profile, whether you’re a techie or a functional expert, or having any other role, learning basics of Generative AI is definitely a smart move.
In this blog series, we will learn the basics of Generative AI, one simple step at a time. To make it easy to understand, I have divided the entire series in small parts:
Part 1 — Introduction to AI [current blog]
Part 2 — Understanding Machine Learning
Part 3 — Basics of Deep Learning
Part 4 — Introduction to Generative AI
Part 5 — What is Large Language Model (LLM)?
Part 6 — Prompt Engineering: The Art of Communicating with AI
Part 7 — Ethical Considerations in Generative AI
Part 8 — Challenges and Limitations in Generative AI
This is the first blog in this series where we will demystify AI and it’s various types.
What’s unique about this blog series — “Generative AI for Beginners”?
We live in a world where source of knowledge is unlimited, but time is limited. Most of us have very limited time left to learn after daily activities. Keeping this in mind, I have designed this series in such a way that:
- The series is divided in small logical units.
- Each part requires maximum 15–20 minutes to learn.
- The content is written in layman’s terms — Even a kid would be able to understand most of it.
- After finishing the series, you will get a clear idea on Generative AI and its various components.
Is it required to understand AI, Machine Learning and Deep Learning to learn Generative AI?
You might have noticed that the first 3 blogs in the series are on Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). Generative AI is a subset of deep learning, which in turn is a subset of machine learning, which in turn is a subset of AI as shown in below image:
To get a crystal-clear understanding of Generative AI, it’s required that we have basic understanding of AI, ML and DL.
Let’s start the part 1 — Artificial Intelligence!
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Artificial Intelligence (AI) — From a Kid’s Perspective
Let’s first have the simplest understanding of AI. Imagine you have lost your dog, and you need to find him.
Here are some of the capabilities you need to find your dog:
You should be able to Identify your dog.
If you see any animals, you should be able to identify if it’s a dog or not. If it’s a dog, you need to further identify if it’s your dog.
You should be able to make a strategy to find your dog.
You need to be able to make a strategy to find your dog. For example:
· First search in our house.
· If you don’t find him, then search in play area where you usually go with your dog.
· If you don’t find him yet, ask your friends.
· And so on….
You should be able to act according to situation.
For example, if it’s raining, and you know that your dog does not prefer to get wet, you will focus your search on shaded places.
Now, imagine someone told you — “I have probably seen your dog in garden”.
You (Actually Your Brain) know what to do.
· You know where garden is and how to go there.
· You will not confuse a cat or a tree with a dog.
· The moment you see a dog you will try to identify if it’s your dog or not.
You could search your dog because you have all these intelligences.
What if somehow, we could give all these intelligence to a robot so that next time you lose your dog, your robot could find him.
Imagine the robot can move and capture videos. But that’s not enough. To find your dog, we need to enable this robot to think like you and act like you.
For example:
- We enable the robot to identify your room. But it should be able to recognize the room even if your bed is moved to another wall, or blanket is changed. It needs INTELLIGENCE to identify room even with new changes.
- We enable the robot to identify a dog and distinguish your specific dog.
- We enable the robot to understand human language and instructions.
- We enable the robot to come up with a strategy and act as per new situations. For example, search only in shaded places if it’s raining.
In summary, to find your dog, the robot needs HUMAN LIKE INTELLIGENCE.
If we could do that, next time you lose your dog, your robot friend might just find him using its artificial intelligence.
This is Artificial Intelligence (AI) — Human like intelligence, created in a robot (or a machine or computer) by human.
To continue this discussion on Machine Learning and Generative AI, You may also read this blog — How I Explained AI, Machine Learning and Generative AI to My 5 Year-old Kid
What is AI?
Artificial intelligence is when machines/computers mimic the way humans think and make decisions.
AI enables computers to think as we human think.
In simple words — AI is when we enable computers to Think.
AI enables computers to understand, analyze data, and make decisions without constant human guidance. These intelligent machines use algorithms, which are step-by-step instructions, to process information and improve their performance over time.
Real-world Examples of AI Applications
You’ve probably used AI even without knowing it! Voice assistants such as Siri and Alexa or those helpful chatbots when you’re on websites or generative AI tools such as ChatGPT and Google’s Bard — they all use AI technology to make things easier for you.
Let’s take a peek into some of the common usages of AI in our daily life:
Virtual Assistants
Virtual assistants such as Siri or Alexa uses AI to understand our questions and commands. They can answer questions, play your favourite tunes, and even control your smart home devices.
Social Media Algorithms
Ever notice how Netflix suggests shows you might enjoy? Or how Facebook’s suggested feed seems to know exactly what you want to see — that’s AI at play!
Netflix usages AI to analyse your watching habits to offer personalized recommendations. Similarly, other social media platforms use AI to personalize your experience, showing you content that matches your interests.
Online Shopping Recommendations
Have you ever wondered how online stores suggest products you might buy?
When shopping online, AI algorithms examine your preferences, your past choices and those of similar shoppers to recommend items tailored just for you.
Predictive Text and Autocorrect
When your smartphone suggests the next word you want to type, that’s AI predicting what you might say next.
Healthcare Diagnostics
AI helps doctors analyse medical images such as X-rays and MRIs more quickly and accurately. This speeds up diagnosis and improves the chances of successful treatment.
Language Translation Services
When we plan a trip abroad and use language translation services, such as Google Translate, it usages AI algorithms. These AI-powered language translation services help bridge language barriers, making communication easier in different parts of the world.
Fraud Detection in Banking
Now-a-days AI keeps a watchful eye on bank transactions. If it spots something fishy, for example an unusual purchase, it can alert you or even block the transaction to protect your account.
These examples show that AI isn’t confined to labs or the distant future. It’s an integral part of our daily lives, working quietly behind the scenes to make our life better.
AI vs. Human Intelligence
At one side, AI enables computers to become intelligent with numbers and rules, doing super quick math with perfect accuracy. On the other side, we human have brain and we are also driven by emotions, creativity, and the ability to adjust to all sorts of situations. Our brain is always evolving, adapting and thinking new things.
It’s similar to comparing a super-fast calculator to a vibrant, ever-evolving masterpiece!
Here are some major differences between AI and Human Intelligence:
Learning Style:
- AI: Learns from loads of examples and data. It crunches numbers and patterns to become a pro at specific tasks.
- Humans: We learn by talking, experiencing, and thinking. Our brains soak up a mix of things — from how to ride a bike to why the sky turns pink at sunset.
Thinking Speed:
- AI: Fast, similar to a superhero at tasks it knows well. Show it a task it’s trained on, and boom, it’s done in a flash.
- Humans: We might take a bit more time. But we are super good at figuring out complex stuff. We are good in complex thinking and creativity.
Memory Skills:
- AI: Remembers facts and figures but not with memories and feelings. It’s a robot recalling programmed info rather than cherishing a moment.
- Humans: We remember events, emotions, and lots of details. From first dates to the lyrics of our favourite songs. Our memories are collection of good and bad experiences.
Feeling Emotions:
- AI: Doesn’t feel joy, sorrow, or anything. It sticks to rules and patterns.
- Humans: We’re an emotional rollercoaster — happiness, sadness, and everything else. Our feelings shape who we are and how we react.
Flexibility Factor:
- AI: Sticks to what it’s taught and might struggle in new situations. It’s smart but rigid.
- Humans: We’re amazing in adapting new things. We humans always figure out how to come out of any scenario and solve any problem.
Creating Cool Stuff:
- AI: Can create things within its set limits. It may be considered as an artist with a specific canvas and color palette.
- Humans: We’re the masters of making things up — new ideas, art, solutions. Our creativity knows no bounds.
Understanding the Big Picture:
- AI: Knows what it’s learned but might miss tricky situations, for example reading between the lines, understanding inside jokes or cultural nuances.
- Humans: We understand everything — jokes, feelings, and culture. Our brains is a complete packages that have a bit of everything!
Decision Making Capabilities:
- AI: Decides based on its training and programming. It follows the rules.
- Humans: We blend logic, feelings, and what’s right to make decisions.
Types of AI
Artificial Intelligence is divided based on two main categorization — based on capabilities and based on functionally of AI.
The following image illustrates these types of AI:
Types of AI — Based on Capability
Based on capability, there are 3 types of AI — Narrow AI, General AI and Super AI.
1. Narrow AI
Narrow AI, also known as Weak AI, refers to artificial intelligence systems that are designed and trained for a specific task or a narrow set of tasks.
Have you seen a computer playing chess? That’s Narrow AI at work. It’s superb in playing chess but won’t be as good at, say, translating or in speech recognition.
Another good example of narrow AI virtual assistants such as Siri or Alexa. Siri/Alexa is good in speech recognition but operates with a limited pre-defined range of functions.
Other examples of narrow AI include:
- Self-driving cars
- Google search
- Conversational bots
- Email spam filters
- Netflix’s recommendations etc.
2 important point on Narrow AI:
- Narrow AI is focused on performing a single task extremely well.
- But it cannot perform beyond its field or limitations.
Almost all the AI-based systems built till this date fall under the category of Weak AI.
2. General AI
General AI, also known as Strong AI or artificial general intelligence (AGI), can understand and learn any intellectual task that a human being can.
It refers to artificial intelligence that:
- Possesses the ability to understand, learn, and apply knowledge across a wide range of task
- at a level equivalent to human intelligence.
Currently, there is no such system exist which can come under general AI and can perform any task as perfect as a human.
Creating Strong AI system poses significant scientific and technical challenges.
Researchers and developers continue to make advancements in various AI fields, but achieving true General AI, which mirrors the broad capabilities of human intelligence, is a complex and ongoing endeavour.
3. Super AI
Super AI represents a degree of intelligence in systems where machines have the potential to exceed human intelligence, outperforming humans in tasks and exhibiting cognitive abilities.
Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task.
We have only seen Super AI systems/characters in movies such as I,Robot, Terminator, The Matrix, Blade Runner etc.
For example, in movie “I, Robot,” we get a glimpse of a future world where Super AI plays a pivotal role. The central AI system in the film is named VIKI, which goes beyond typical AI capabilities. VIKI’s intelligence evolves into a form of Super AI, where it surpasses its initial programming and starts making decisions to “protect” humanity in a controversial way.
A Quick Comparison of Narrow AI, Strong AI and Super AI
Narrow AI (Weak AI):
- What it is: Similar to a specialist, good at one specific task.
- Example: Siri or Alexa — great at understanding and responding to voice commands but not much beyond that.
- Analogy: Imagine a superhero with a superpower dedicated to a particular task. For example a hero who excels only in solving puzzles.
Strong AI (General AI):
- What it is: Similar to a human super hero, who can understand, learn, and perform various tasks.
- Example: Currently more theoretical, no real-world examples yet.
- Analogy: Imagine a superhero with a whole array of superpowers, able to adapt and excel in different situations.
Super AI:
- What it is: Similar to an ultimate superhero, surpasses human intelligence and can do pretty much anything better than humans.
- Example: Still theoretical, no real-world examples.
- Analogy: Imagine a superhero with the combined abilities of all superheroes, making them unmatched and capable of handling any situation with ease.
Types of AI — Based on Functionality
Based on functionality, there are 4 types of AI — Reactive Machines, Limited Memory, Theory of Mind and Self Awareness.
1. Reactive Machines
Reactive machines are AI systems that have no memory. These systems operate solely based on the present data, taking into account only the current situation. They can perform a narrowed range of pre-defined tasks.
In a nutshell, Reactive machines are:
· AI systems which do not store memories or past experiences for future actions.
· It only focus on current scenarios and react on it as per possible best action.
One of the examples of reactive AI is Deep Blue, IBM’s chess-playing AI program, which defeated world champion, Garry Kasparov in the late 1990s. Deep Blue had ability to identify its own and its opponent’s pieces on the chessboard to make predictions, but it didn’t have the memory to use past mistakes to inform future decisions.
2. Limited Memory
As the name indicates, Limited Memory AI can take informed and improved decisions by looking at its past experiences stored in a temporary memory.
This AI doesn’t remember everything forever, but it uses its short-term memory to learn from the past and make better decisions for the future.
A good example of Limited Memory AI is Self-driving cars. The AI system in self-driving car utilizes recent past data to make real-time decisions. For instance, they employ sensors to recognize pedestrians, steep roads, traffic signals, and more, enhancing their ability to make safer driving choices. This proactive approach contributes to preventing potential accidents.
Another example is recommendation systems. Platforms such as Netflix or Amazon use Limited Memory AI to suggest movies, products, or content based on a user’s past preferences and behaviours.
3. Theory of Mind
The initial two categories of AI — Reactive Machines and Limited Memory, presently exist.
Next 2 types of AI — Theory of Mind and Self-aware AI, however, are theoretical types that could be developed in the future. As of now, there is no real-world examples of these types are available.
Theory of Mind is supposed to have capability to understand the human emotions, people, beliefs, and be able to interact socially same as humans.
4. Self-Aware AI
This is similar to Super AI — We should pray that we don’t reach the state of AI, where machines have their own consciousness and become self-aware.
Self-aware AI systems will be super intelligent, and will have their own consciousness, sentiments, and self-awareness. They will be smarter than human mind.
As shown in movie “I, Robot,”, an AI system named VIKI becomes self-aware and starts making decisions to “protect” humanity in a controversial way.
Similar to Theory of Mind, Self-aware AI also does not exist in reality. Many experts, for example Elon Musk and Stephen Hawkings have consistently warned us about the evolution of AI.
Stephen Hawking stated that:
“The development of full artificial intelligence could spell the end of the human race…. It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”.
Summary
With this first blog, we’ve taken the first step on a journey to understand Generative AI. We learnt what AI is and explored its fundamental concepts. We learnt types of AI based on different categories and also understood how AI is different from human intelligence.
If you still have any query, please let me know in comment.
Next Blog in this series — Generative AI for Beginners
Part 2 — Understanding Machine Learning
You may also check the blog series — 21 Generative AI Jargons Simplified
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