Artificial Intelligence: Problem-Solving Search Strategies

Have you ever wondered how Google Maps finds the fastest route for you? Or how a video game character knows where to go next?

A key component of AI drives these capabilities. When AI needs to solve a problem, such as finding the best way to get from one place to another, it searches through different possibilities. This process is known as a Search Strategy in AI.

Types of Search Strategies in AI

AI search strategies are divided into two main types: Uninformed Search and Informed Search.

★ Uninformed Search: This involves exploring options systematically without prior knowledge or hints.

For example, imagine you’re in a maze and systematically explore every path without knowing which one leads to the exit.

Example:

1. Breadth-First Search (BFS): Explores all possible paths level by level.

2. Depth-First Search (DFS): Follows a single path all the way to the end before backtracking.

Uninformed search methods are straightforward but can be time-consuming.

★ Informed Search:
Here, AI applies strategies using some hints or knowledge about the best possible path.

For example, Google Maps uses traffic data and distance estimates to calculate the shortest or fastest route.

Example:

1. A* Search: Uses distance traveled and an estimated cost to the goal to find the most efficient route.

Pathfinding in AI

One of the most common applications of searching is "Pathfinding".

Imagine a video game where your character needs to travel from the office to home.

• If AI uses an uninformed method, it explores routes systematically without prioritizing the best option.
• If AI uses an informed method, it leverages distance and time estimates to select the fastest route.

Real-Life Applications

Search strategies underpin everyday tools like navigation apps, online search engines and video game mechanics.

Whenever you use a navigation app, play a video game or search for something online, AI's search algorithms are at work, making sure you get the best results efficiently!

Reference Book: Forthcoming book titled "Artificial Intelligence: Echoes of a Silent Intellect", which will be published on Amazon soon.

Guidelines of Generative AI

★ Topics: In a structured order; from the most important to less critical topics!

1. GANs: DCGAN, StyleGAN, BigGAN.

2. Diffusion Models: DDPMs, Stable Diffusion.

2.1. Applications: Image Synthesis, Video Generation, Audio Generation.

3. Transformers: GPT, DALL-E, T5.

4. Text-to-Image Models: MidJourney, OpenAI's DALL-E.

5. VAEs: Applications in Image Generation & Compression.

6. Fine-Tuning & Custom Training: Domain-specific adaptations of pre-trained models.

7. Audio & Music Generation: WaveNet, Jukebox, Riffusion.

8. 3D & Video Generation: NeRF for 3D Modeling, GAN-based Video generation & editing Tools.

9. Ethics in Generative AI: Biases, Copyright, Safety Concerns.

10. Applications of Generative AI: Gaming, Content Creation, Drug Discovery, Digital Twins for Simulations.

★ Math for Gen AI

1. Linear Algebra: Matrices, Vectors, Eigenvalues, Eigenvectors.

2. Optimization: Gradient Descent, Convex Optimization, Backpropagation.

3. Probability & Statistics: Probability Distributions, Bayesian Inference, Hypothesis Testing.

Guidelines of Agentic AI

Agentic AI mainly operates based on Four Foundational Pillars: Memory, Planning, Decision-Making and Autonomous Execution.

★ How to Get Started with Agentic AI?

1. Reinforcement Learning (RL)

2. Large Language Models (LLMs): Start with Hugging Face and OpenAI documentation.

3. Autonomous Systems & Multi-Agent AI: Start with the book "Multi-Agent Systems" and explore open-source Autonomous AI projects.

4. Memory & Planning Systems: Start with open-source projects like LangChain and AutoGPT.

Essential Topics of Agentic AI

Most Important and Practical Aspects:

1. Core Algorithms in Agentic AI

• RL: DQN, Policy Gradient Methods (PPO, A3C), Actor-Critic Models.

• MAS: Collaboration and Competition between agents.

2. Applications of Agentic AI

• Autonomous Vehicles: Self-driving cars and drones (Tesla, Waymo).

• Robotics: Robots for industrial automation (e.g., Boston Dynamics).

• Gaming AI: Adaptive NPC behavior (e.g., AlphaGo, AlphaStar).

3. Planning & Decision Making: MDPs, MCTS.

4. Human-Agent Interaction: Natural Language Communication (Alexa, Siri), Emotional Intelligence (but still emerging).

5. Ethical Concerns: Safety in autonomous systems, Bias in AI decision-making.

6. Tools to Start With

• OpenAI Gym: RL simulation.

• Unity ML-Agents: Game-based AI training.

• PyBullet: Robotics simulation.

★ Math for Agentic AI

• Linear Algebra: For state representation (matrices, vectors).

• Probability: For Markov processes and decision-making.

• Optimization: Gradient descent and policy optimization.

Quantum Computing

Quantum computing is currently a hot topic in the tech world.

Physicist Richard Feynman first proposed that by harnessing the principles of quantum mechanics, we could construct computers that operate exponentially faster than classical ones. In 1994, a mathematician named Peter Shor developed "Shor's Algorithm" which allows a quantum computer to factor large numbers very quickly, potentially breaking today’s encryption systems. Research in this field has been ongoing ever since.

★ What is Quantum Computing? How Does Quantum Work?

The computers we use are classical computers. They operate using "bits" which are either 0 or 1. However, quantum computers work with "qubits". Qubits can hold multiple possibilities at once. For example, 50 qubits together can process over 2^50 possibilities (1.125 quadrillion). These qubits can be both 0 and 1 at the same time, a property called "Superposition."

1. Superposition: A qubit can be both 0 and 1 simultaneously. This allows a quantum computer to calculate many possibilities at once.

2. Entanglement: When two qubits are entangled or linked to each other, a change in one instantly changes the other, no matter the distance between them (this increases the computer’s speed even more). Einstein famously described this phenomenon as "Spooky Action at a Distance."

3. Interference: Interference uses quantum waves to identify the correct solution while eliminating incorrect possibilities.

With these three principles, quantum computers can quickly solve complex problems that are impossible for classical computers. For instance, 100 qubits can process 2^100 possibilities which is more than the number of particles in the universe!

★ How Are Quantum Computers Built? What Challenges Are Faced?

Building a quantum computer is extremely difficult and costs hundreds of millions of dollars. Qubits can be electrons, photons or superconducting circuits. They must operate at temperatures near absolute zero (-273°C). Moreover, qubits are highly sensitive; keeping them stable is tough and even slight movement, noise or heat can cause errors. That’s why they’re kept in special chambers. Additionally, quantum computing requires new types of algorithms which require time to develop and master.

★ Current Situation (Up to 2025):

1. Google announced "Quantum Supremacy" in 2019. Their Sycamore processor with 53 qubits, completed a task in 200 seconds that would take the world’s fastest supercomputer 10,000 years. By 2024, their Willow Chip has become even more advanced.

2. IBM’s Heron processor has 133 qubits and they’re providing researchers with cloud access through their "Quantum Center." Companies like IBM and Microsoft have brought quantum computing to the cloud, allowing ordinary people to try it, speeding up research.

3. China has set new records in quantum computing with their Jiuzhang 2.0, which uses photons.