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Unlocking AI 101: Powering the Future with Artificial Intelligence

Why don't artificial intelligences ever get lost?

Because they always follow the algorithm!

Welcome to our exploration of artificial intelligence and its transformative power. This post delves into foundational AI concepts, neural network training, major AI use cases, and dealing with AI hallucinations.


Understanding AI begins with key terminology:

  1. Artificial Intelligence (AI) simulates human intelligence to perform tasks like visual perception, speech recognition, and decision-making.

  2. Machine Learning (ML) is a subset of AI that uses algorithms to learn from and make predictions based on data.

  3. Neural Networks (NN) are algorithms inspired by the human brain, designed to recognize patterns through interconnected layers of nodes (neurons).


As previously mentioned, AI is able to train a neural network (NN). Let’s imagine now how this can be described in real life. Imagine an NN as a team of highly skilled chefs in a kitchen.

The input layer is like receiving a basket of raw ingredients.

Each ingredient (data point) is passed to the hidden layers, where the chefs (neurons) work together, each with a specific task, adjusting the seasoning (weights) and adding secret spices (biases).

As they pass the dish along, they taste and tweak it using special cooking techniques (activation functions) like grilling or sautéing. The final dish is then presented (output layer), and the head chef compares it to a master recipe (loss function) to see how close it is to perfection.

If it's not quite right, they go back and adjust their techniques, reworking the seasoning and spices through a process of trial and error (backpropagation and gradient descent).

This cycle repeats (iterative learning), refining the dish until it matches the master recipe.

Finally, the dish is tested by food critics (validation data) to ensure it meets the highest standards before being served to the guests (real-world application).

Through this collaborative and iterative process, the neural network learns to transform raw data into accurate predictions, just as chefs turn ingredients into a gourmet meal!

Major AI Use Cases

Dealing with AI Hallucinations

A challenge with AI, especially large language models (LLMs), is "hallucinations" where AI generates false information. To counter this, always verify AI outputs against reliable sources. Prompting AI to self-check can also help identify and correct inaccuracies. You can learn about prompt engineering in our next blog.

Stay tuned!


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