Definition:
“State-of-the-Art” (SOTA) means the best and most advanced technology available right now.
Example:
- Old AI: Could recognize simple handwritten digits (like MNIST dataset).
- SOTA AI: Can generate entire images, write essays, compose music, or drive cars.
Goal
To understand what today’s most advanced AI systems are, how they differ from earlier AI, and how they are used in the real world.
AI didn’t appear suddenly — it evolved in stages.
Each era made AI smarter and more human-like.
1️⃣ 1950s–1980s — The Rule-Based Era (Symbolic AI)
Example Models: Expert systems
Type: Symbolic AI
Idea: Humans gave rules → AI followed them.
In the early days, computers couldn’t learn by themselves.
So, people gave them rules like —
IF temperature > 100, THEN turn off the machine.
Example:
- A medical AI might have rules like
“If fever + cough → diagnose flu.” - But if the rule wasn’t written, AI couldn’t handle it.
Keyword: AI that “thinks with logic,” not by learning.
2️⃣ 1990s–2010s — The Machine Learning Era
Example Models: Decision Trees, SVM, Naive Bayes
Type: Learning from data
“Then scientists realized — instead of writing hundreds of rules,
why not let computers learn from examples?”
Example:
- Give a computer thousands of cat and dog pictures,
and it learns the patterns by itself.
Uses: Predict stock trends, recognize handwriting, spam detection.
Keyword: AI learns patterns from data.
3️⃣ 2012–2020 — The Deep Learning Era (Neural Networks)
Example Models: CNNs (for images), RNNs (for sequences)
Type: Neural Networks
“Deep Learning is inspired by how our brain works —
it uses layers of ‘neurons’ to understand things.”
Example:
- CNNs can identify faces in photos (used in Facebook tagging).
- RNNs can understand speech or text (used in Siri or Google Translate).
Main idea: AI can now see 👀, listen 👂, and read 🧾 like humans.
Keyword: AI with multiple “brain layers.”
4️⃣ 2020–Now — Generative & Multimodal AI Era
Example Models: GPT (ChatGPT), DALL·E, Gemini
Type: Transformers, Large Language Models (LLMs)
“Now, AI doesn’t just recognize things —
it can create new things and understand many types of input together.”
Example:
- ChatGPT → writes essays, code, poems
- DALL·E → draws pictures from text
- Gemini → understands text + images + voice
Main idea: AI can now think, talk, draw, and create like humans.
Keyword: AI that can generate and understand across modes.
How AI models evolved
| Era | Example Model | Type | What it could do |
| 1950s–1980s | Rule-based systems | Symbolic AI | “If-Then” logic (like early expert systems) |
| 1990s–2010s | Machine Learning models | Learning from data | Predict trends, recognize speech/images |
| 2012–2020 | Deep Learning (CNNs, RNNs) | Neural Networks | Vision, speech, text tasks |
| 2020–Now | Generative & Multimodal models | Transformers, LLMs | Understand + generate text, images, video |
Rules ➜ Learning ➜ Deep Learning ➜ Generative AI
1950s 2000s 2010s 2020s
Each step made AI more powerful and closer to human intelligence.
| Era | Key Idea | What AI Could Do |
| 1950s–1980s | Follow rules | Solve logic-based problems |
| 1990s–2010s | Learn from data | Predict, classify, recognize |
| 2012–2020 | Deep learning | Understand images, speech, text |
| 2020–Now | Generative AI | Create text, art, video, code |
Examples of State-of-the-Art Models (2020–2025)
Different AI models specialize in different senses —
some understand text, some see images, some hear speech,
and some can do everything together!
Now that we know how AI learns, let’s see what today’s smartest AI models —
the ‘State-of-the-Art’ — can actually do.
These are the same types of models used in ChatGPT, Alexa, and even self-driving cars!
1️⃣ Language Models (They Understand and Talk Like Us)
Examples: ChatGPT, Google Gemini, Claude
What they do: Read, write, and understand text.
These are the models that can chat with you — like ChatGPT.
You type a question, and they answer in natural language.
Example use:
- Writing emails or essays
- Translating languages
- Explaining code or concepts
Analogy: “They’re like super-smart text assistants that understand language.”
2️⃣ Vision Models (They See and Recognize Things)
Examples: CLIP, Gemini Vision, SAM (Segment Anything Model)
What they do: Understand what’s in images or videos.
“Vision models are like the eyes of AI.
They can look at a picture and tell what’s inside — like a cat, tree, or car.”
Example use:
- Face recognition (in phones or cameras)
- Medical image diagnosis (X-rays, MRI scans)
- Self-driving cars detecting road signs
Analogy: “They help AI see the world.”
3️⃣ Speech Models (They Listen and Talk)
Examples: Whisper, Siri, Alexa
What they do: Convert speech ↔ text and understand voices.
“Speech models help AI hear and talk.
When you say ‘Hey Siri!’ or ‘Alexa, play music,’ these models recognize your words.”
Example use:
- Voice assistants
- Subtitles in YouTube videos
- Voice typing on phones
Analogy: “They give AI ears and a mouth.”
4️⃣ Multimodal Models (They Understand Everything Together)
Examples: GPT-4 (with images), Gemini
What they do: Understand text + images + sound + video together.
These are all-rounder models — they can look, listen, and read at the same time.
For example, you can upload an image and ask, ‘What’s happening here?’ and it answers.
Example use:
- Reading diagrams or charts
- Describing photos
- Combining voice, video, and text understanding
Analogy: They’re like humans — they can see, hear, and read all together.
5️⃣ Generative Models (They Create New Things)
Examples: DALL·E
What they do: Create new images, designs, and art from text.
Generative models are like digital artists.
You tell them, ‘Draw a cat flying a spaceship,’ and they actually make that image!”
Example use:
- Art and design
- Marketing and animation
- Education (visualizing concepts)
Analogy: They make AI creative.
6️⃣ Reinforcement Learning Models (They Learn by Trying)
Examples: AlphaGo
What they do: Learn by trial and error, just like humans learning a skill.
These models learn by doing things again and again —
like how you learn to ride a bicycle by falling and improving each time.
Example use:
- Game-playing AIs (Chess, Go, video games)
- Robotics
- Autonomous driving
Analogy: They make AI learn from experience.
| Model Type | Example | Function / Use |
| Language Models | ChatGPT, Google Gemini, Claude | Understand and generate text — like your AI assistant |
| Vision Models | OpenAI CLIP, Google DeepMind’s Gemini Vision, SAM (Segment Anything Model) | Understand images and objects |
| Speech Models | Whisper, Siri, Alexa | Convert speech ↔ text, voice control |
| Multimodal Models | GPT-4, Gemini, LLaVA | Handle text + image + sound + video together |
| Generative Models | DALL·E, Midjourney, Stable Diffusion | Create images, art, and designs |
| Reinforcement Learning Models | AlphaGo, DeepMind’s MuZero | Learn by trial and error — play games, control robots |
So, just like we humans have senses — seeing, hearing, speaking, and thinking —
AI also has models for each sense.
Together, these make today’s AI systems powerful and useful in our daily lives.
4. How they work
You don’t need equations — just the idea:
- They are trained on large data (text, images, speech).
- They use neural networks — layers that mimic how the brain learns patterns.
- They “learn” to make predictions or generate outputs.
Example:
ChatGPT learned language patterns from billions of sentences.
When you ask it something, it predicts the most meaningful next word.
State-of-the-Art AI Models are today’s most powerful systems that combine large-scale data, neural networks, and computing power to perform tasks like understanding, generating, and reasoning — often better than humans in specific areas.
Example
What is DALL·E?
DALL·E (pronounced “dolly”) is an AI model created by OpenAI that can generate original images from text descriptions.
It combines “language understanding” (like ChatGPT) with “image generation” (like an artist).
The name “DALL·E” is inspired by:
- Salvador Dalí — the famous surrealist painter 🎨
- WALL·E — the cute Pixar robot 🤖
What DALL·E Can Do
| Task | Example | What Happens |
| 🖼️ Create images from text | “A cat sitting on Mars wearing a space helmet” | DALL·E draws a realistic or cartoon-style image of that! |
| 🎭 Combine unusual ideas | “An elephant playing violin in the rain” | It blends concepts creatively. |
| 🎨 Change image styles | “The Taj Mahal in Van Gogh painting style” | It mimics famous art styles. |
| 🧑💻 Edit existing images | You upload a photo and say, “Add a rainbow in the sky.” | It edits only that part while keeping the rest realistic. |
| 🏠 Design / visualize concepts | “Modern living room with natural lighting and wooden furniture” | It can create interior designs or architecture ideas. |
| 👗 Fashion and product design | “A futuristic smartwatch with transparent display” | Generates new product concepts. |
How It Works
DALL·E is built on a transformer neural network, similar to ChatGPT, but:
- Instead of predicting words, it predicts image pixels.
- It learned from millions of image–caption pairs.
- It understands the meaning of words and how they should look visually.
- Example:
- If you type “a red apple on a blue plate,”
DALL·E understands what “apple,” “red,” and “plate” look like —
and creates a matching image!