Building AI: Layers of Innovation that Shaped the Past, Present, and Future - Part 4

Omed Habib

November 9, 2023

Darkweb v2.0 public release is here

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What has changed in our latest release?

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All new features available for all public channel users

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Coding collaboration with over 200 users at once

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Real-time code save every 0.1 seconds

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Part four of our five-part series dives into the heart of AI's evolution: the development of sophisticated algorithms. This segment outlines the transition from the early, simpler algorithms to the complex deep learning, reinforcement learning (RL), and Generative Adversarial Networks (GANs) that form the backbone of modern AI. We begin by likening early AI algorithms to basic recipes, efficient for simple tasks but inadequate for the complex demands of modern data and tasks. The advent of deep learning, with its ability to autonomously identify data features, marked a significant leap, enabling the wonders of voice recognition, facial recognition, and more. The piece also highlights the transformative role of RL algorithms in decision-making AI applications and the innovative GANs that have revolutionized content creation. Additionally, we acknowledge the pioneers like Geoffrey Hinton, Yann LeCun, Yoshua Bengio, Richard Sutton, Andrew Barto, and Ian Goodfellow, whose contributions have been instrumental in these advancements. This chapter paints a vivid picture of how these algorithms, once impractical due to limitations in data and processing power, are now driving breakthroughs in AI, reshaping the landscape of technology and innovation.

Layer 4: Algorithms: The New Wave of Intelligence

The Problem

Imagine having all the right ingredients to bake a delicious cake but needing the precise recipe to combine them. That was the state of AI algorithms in the early days. Yes, algorithms are like recipes in coding, but modern AI's sheer complexity and requirements need more refined and sophisticated instructions.

The history of machine learning algorithms began with simple linear regressions and decision trees. They worked for basic predictions, but the rapidly growing datasets and more complex tasks needed something more powerful.

Deep Learning Algorithms

Deep learning is a subset of machine learning that uses neural networks with many layers (hence the 'deep'). These algorithms can automatically find the features to look for in the data, which was traditionally done manually.

Why is Deep Learning Amazing? Deep learning can handle massive amounts of data and automatically learn from it. It's the technology behind voice assistants, facial recognition, and many other marvels of modern AI.

Who Created Deep Learning Algorithms? Although deep learning's roots go back to the 1950s, key figures like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio are credited with advancing deep learning in the late 20th and early 21st centuries.

Reinforcement Learning (RL) Algorithms

RL is a type of machine learning where an agent learns to make decisions by interacting with an environment. It's like training a dog; the agent receives rewards or penalties for its actions.

Why is RL Amazing? It's behind the AI that defeated world champions in games like Go and Poker. AlphaGo, created by DeepMind, used RL to beat the human Go champion.

Who Created RL Algorithms? RL has been developed over several decades, with contributions from computer scientists like Richard Sutton and Andrew Barto.

Generative Adversarial Networks (GANs)

GANs consist of two neural networks – one to generate fake data and another to detect it. They're like a forger and a detective in constant battle.

Why are GANs Amazing? They can create realistic images, sounds and even write text. Artists and designers are using them for creative projects.

Who Created GANs? Ian Goodfellow and his colleagues introduced GANs in 2014.

Machine Learning's Role

Machine learning (ML) is the umbrella term for these algorithms. It's a field within AI that teaches computers to learn from data and make decisions or predictions. Deep learning, RL, and GANs are all part of ML.

What's Changed?

These algorithms weren't necessarily impossible before, but they were impractical. The lack of processing power and data meant training them would be too long or expensive. With the advances in hardware and the availability of big data, these algorithms have become feasible and led to AI breakthroughs.

Fun Fact

The game of Go was considered a grand challenge for AI due to its complexity. DeepMind's AlphaGo defeated a world-champion Go player in 2016, marking a turning point. What's fascinating is that there are more possible board configurations in Go than atoms in the universe!


  1. 1957: Frank Rosenblatt introduces the Perceptron, an early form of neural network.
  2. 1986: Introduction of backpropagation, a key training algorithm for neural networks.
  3. 1998: Yann LeCun develops LeNet-5, an early convolutional neural network.
  4. 2013: DeepMind's DQN combines deep learning with reinforcement learning.
  5. 2014: Ian Goodfellow and colleagues introduce Generative Adversarial Networks (GANs). End Date: Ongoing (as new algorithms and techniques continue to be developed)