From backpropagation to federated learning

Welcome to your weekly deep dive into Artificial Intelligence, where we simplify the sophisticated. This week we cover:

1. Backpropagation

2. Knowledge Representation

3. Structured Prediction

4. Decision Intelligence

5. Federated Learning, and

6. Inference Engine

AI has become a buzzword in today's tech-driven world, but understanding the different terms and technologies can be challenging. RSe Global, a leader in AI solutions, is here to simplify these concepts for you.

Backpropagation

Backpropagation is a key idea in artificial intelligence (AI) and machine learning. It's a method used to train neural networks, which are computer systems designed to mimic how the human brain works. Backpropagation helps these networks learn by adjusting their internal settings, called weights and biases, to make better predictions.

Imagine a neural network as a web of interconnected points (neurons). Each neuron receives information, processes it, and sends out a result. These results then become inputs for the next layer of neurons, creating a series of steps.

When training a neural network, we give it some data and tell it what the correct answers should be. The network makes predictions and we measure the error, which is the difference between its prediction and the correct answer. Backpropagation is the process of sending this error back through the network to adjust the settings, improving its accuracy over time. This step-by-step improvement is known as gradient descent.

Real-World Example: Ever noticed the feedback option at the end of ChatGPT answers? When users mark a response as bad, this feedback acts like an error signal. Similar to backpropagation, this error signal helps the developers understand where the AI's predictions (or responses) are incorrect. They use this feedback to retrain the model, adjusting it to improve future performance. Over time, this process helps the AI system generate more accurate and helpful responses.

Knowledge Representation

Knowledge representation is about how AI systems store, organise, and use information. It's crucial for helping machines think, learn, and make decisions like humans do.

For AI to solve problems and understand the world, it needs a way to handle complex information. One method is using symbols and rules, where information is represented as symbols and logical rules connect them. This allows AI to make logical deductions.

Another method is using networks or graphs, where concepts are nodes connected by lines that show relationships. For example, in a medical database, "heart disease" might be connected to "symptoms" like "chest pain" and "treatment" like "medication." This helps AI systems understand and reason about the information they process.

Real-World Example: Ever used Siri, Alexa or Hey Google? These virtual assistants use knowledge representation to understand your questions and fetch relevant information. By organising knowledge in a structured format, they can interpret your commands and provide accurate, conversational responses, making interactions feel natural and intuitive.

Structured Prediction

Structured prediction is a technique in AI that focuses on predicting complex results rather than single answers. It’s like solving a puzzle where each piece depends on the others.

In simple machine learning tasks, you might predict a single outcome, like whether an email is spam or not. But real-world problems often need more detailed predictions. For example, understanding a sentence involves predicting the role of each word and how they fit together.

Structured prediction uses methods that capture these relationships. Techniques like graphical models and recurrent neural networks help in making predictions that respect the overall structure of the data.

Real-World Example: Think about how your phone's keyboard predicts the next word you're going to type. This is structured prediction at work. By considering the context and relationship between words, the keyboard can suggest the next word, making typing faster and more efficient.

Decision Intelligence

Decision intelligence combines AI, decision theory, and behavioral science to help people make better decisions. It uses data and smart algorithms to analyse information, consider uncertainties, and factor in human preferences.

In our data-rich world, making good decisions can be tough. Decision intelligence tools use AI to sift through data and provide insights, making the decision-making process easier.

These tools also consider human elements like emotions and values, recognising that decisions aren’t just about numbers. By understanding human behavior, decision intelligence systems can offer personalised advice that aligns with individual goals and preferences.

Example: Imagine you're planning your investments. Decision intelligence tools analyse historical data, market trends, and your personal preferences to offer tailored investment advice. They combine AI insights with an understanding of your financial goals, helping you make smarter investment decisions.

Federated Learning

Federated learning is a new way of training AI models that protects privacy. Instead of sending all data to a central place, it trains models locally on individual devices and then shares only the updates.

Traditional AI training gathers all data in one spot, which can raise privacy concerns, especially with sensitive information. Federated learning allows data to stay on devices while only sharing necessary updates with a central server. This method ensures privacy and security.

Real-World Example: Consider how health apps work on your phone. Federated learning allows these apps to improve their models by learning from data on your device, without the data ever leaving your phone. This way, they can get better at providing health insights while keeping your personal information private and secure.

Inference Engine

An inference engine is a critical part of many AI systems that helps them reason and make decisions. It processes information and generates intelligent responses based on the knowledge it has.

Inference engines can use logical reasoning, applying set rules to draw conclusions, or probabilistic reasoning, which deals with uncertainty by using probabilities to make decisions.

Real-World Example: Ever chatted with a customer service bot that actually seemed to understand what you needed (instead of driving you up the wall)? That's thanks to an inference engine. These engines process your questions, reason through the information, and provide accurate, contextually relevant responses, making the interaction surprisingly smooth and helpful.

I hope you found this newsletter insightful in breaking down the basics of Artificial Intelligence. Subscribe to our newsletter to receive next week's issue, where we will dive deeper into more advanced AI topics.

If you have any questions or topics you'd like us to cover, feel free to reach out. I’d love to hear your thoughts and feedback.

Best regards,

Adam

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