What are Neural Networks?

How do they work?

Neurons and layers are the building blocks of neural networks. Neurons process incoming data by applying a "weight" to each input, indicating its importance, and passing the result to the next layer. Networks are organized in layers: data enters through the input layer, passes through the hidden layers where neurons process it, and exits at the output layer as the final result. Each layer refines the data, allowing the network to learn and improve its predictions over time.

History and inventors

Neural networks have developed over time through the efforts of key pioneers. In 1943, Warren McCulloch and Walter Pitts introduced the first mathematical model of an artificial neuron, providing the foundation for neural network theory. Later, in 1949, Donald Hebb proposed the concept of "Hebbian learning," suggesting that neural connections strengthen when neurons fire together, a principle that remains influential in modern machine learning. The 1950s saw the emergence of the perceptron, created by Frank Rosenblatt in 1958, which demonstrated how artificial neurons could perform simple binary classification tasks.

Development gained speed in the 1980s with the advent of backpropagation, a method for training multi-layer neural networks. Paul Werbos initially proposed backpropagation in 1974, but it wasn't until 1986, when Geoffrey Hinton, David Rumelhart, and Ronald Williams refined and popularized it, that researchers recognized its potential. This innovation allowed neural networks to learn more complex patterns and solve real-world problems, marking a resurgence of interest in the field. By 1989, Yann LeCun had applied backpropagation to convolutional neural networks (CNNs), making significant strides in image recognition.

The 2000s and 2010s ushered in the era of deep learning, transforming neural networks into powerful AI tools. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio pioneered techniques such as deep belief networks and CNNs, enabling breakthroughs in speech, vision, and natural language processing. In 2012, the success of AlexNet, a CNN developed by Alex Krizhevsky and Hinton, in the ImageNet competition showcased the practical power of deep learning. Innovations such as Ian Goodfellow's Generative Adversarial Networks (GANs) in 2014 and DeepMind's AlphaGo in 2016 further demonstrated the versatility of neural networks.

Learning process

Neural networks learn by adjusting weights and biases through training. Each neuron uses weights to prioritize inputs and biases to change its output, with activation functions determining when a neuron should pass information forward. During training, the network compares its predictions to the correct answers, gradually adjusting its weights and biases to minimize errors. This process, repeated over time, makes the network more accurate at recognizing patterns and predicting outcomes.

The link between neural networks, LLMs, and AGI

Neural networks are the foundational technology behind large language models (LLMs) and a stepping stone toward artificial general intelligence (AGI). Neural networks mimic the brain's structure, using interconnected layers of artificial neurons to process and learn from data. Large language models, such as GPT, are specialized applications of neural networks that typically employ transformer architectures to process and generate text that resembles human language. These models are trained on massive datasets, enabling them to perform tasks such as translation, summarization, and question answering.

While LLMs demonstrate impressive capabilities, they are examples of narrow AI — designed to excel in specific tasks — rather than AGI, which refers to machines with human-like reasoning and problem-solving abilities across diverse domains. AGI would require neural networks with advanced architectures capable of integrating reasoning, memory, and adaptability, far beyond current LLMs. Neural networks and LLMs are thus critical milestones, but achieving AGI will require overcoming significant challenges in understanding generalization, creativity, and genuine autonomy.

Types of neural networks

Neural networks come in several types, with each of them suited to performing specific tasks:

Feedforward Neural Networks (FNNs): The simplest type, where information flows in one direction — from input to output — often used for classification tasks.

Convolutional Neural Networks (CNNs): These are specialized for processing grid-like data, such as images, by recognizing patterns in local regions.

Recurrent Neural Networks (RNNs): These are designed for sequential data, such as time series or text, where past inputs inform future decisions.

Generative Adversarial Networks (GANs): These consist of two networks (a generator and a discriminator) that work together to generate new data, often used to create images or videos.

Real-world Applications of Neural Networks

Image Recognition: Neural networks power facial recognition in smartphones, object detection in self-driving cars, and medical imaging (for example, identifying tumors in X-rays and MRIs). They excel at identifying patterns and features in images.

Speech Recognition: Virtual assistants, such as Siri and Alexa, use neural networks to convert spoken language into text, understand context, and generate responses. They enable voice commands and conversation.

Natural Language Processing (NLP): Neural networks are fundamental to translation services (such as Google Translate), chatbots, and search engines, enabling them to understand and generate human language.

Business Applications: Neural networks enable businesses to detect fraud, create personalized marketing strategies, and utilize predictive analytics to forecast trends and customer behavior, ultimately enhancing decision-making and customer experiences.

Everyday Uses of Neural Networks

Google Photos: Neural networks enable Google Photos to automatically recognize and categorize images by identifying faces, objects, and scenes. It makes photo searches easier for users by automatically tagging and grouping photos.

Netflix: Netflix's recommendation engine uses neural networks to predict which shows you'll want to watch next. By analyzing your viewing habits, the engine suggests shows and movies you're likely to enjoy.

Healthcare: Neural networks aid in disease diagnosis by analyzing medical images, such as detecting early signs of cancer in MRIs and CT scans.

AlphaGo: Neural networks enabled the AI AlphaGo to master the complex game of Go, surpassing human world champions by learning strategies that outperformed human intuition.

AlphaFold: AlphaFold uses neural networks to predict protein structures, aiding scientific breakthroughs in biology and drug discovery.

AlphaChip: Designed to optimize AI hardware using neural networks, AlphaChip improves computing efficiency for complex tasks such as deep learning.

The future of neural networks

Advances in artificial general intelligence (AGI) could equip machines with a more human-like ability to comprehend and solve complex tasks. Key developments are expected in autonomous vehicles, personalized medicine, and climate science, making these networks pivotal in high-stakes fields. Additionally, new hardware, such as neuromorphic chips, will enable neural networks to run faster and more efficiently, opening the door to broader applications and more sustainable AI. As neural networks evolve, they have the potential to reshape industries and everyday technology fundamentally.

How to explore neural networks

There are plenty of free online resources available to help you learn more about how neural networks work.

Beginner-Friendly Tutorials: Google's Machine Learning Crash Course and Fast.AI's Practical Deep Learning for Coders is a two-part, easily accessible way to get better acquainted with neural networks.

Interactive Tools: You can engage with neural networks daily through platforms such as Google Search (which uses neural networks for query understanding), Netflix (for personalized recommendations), and social media (for content curation).

Further Reading: Explore neural network frameworks, such as TensorFlow and PyTorch, for hands-on experimentation and project development. These platforms offer extensive documentation and community support to aid your learning journey.