To understand artificial intelligence, it is important to have knowledge of what it is built from and how it is trained. Two central concepts that one must understand to comprehend AI are datasets and algorithms.
Datasets – the training foundation
In the field of AI, datasets refer to a collection of data used to train machine learning models and enable learning through examples. A dataset consists of data points, also known as samples or observations, along with associated features or attributes that describe each sample. To illustrate this, one can think of the dataset as recipes for cakes, where each recipe has labels such as sweet, rich, large, small, white, pink, and all other possible ways to describe a cake.
This provides the necessary training data for AI models to learn how to perform specific tasks. By training models on a diverse range of data, the models can learn the patterns and relationships within the data. These data form the foundation for machine learning algorithms and enable the generalization of past experiences to make decisions. The quality and relevance of the features extracted from the dataset greatly influence the effectiveness of AI systems. Datasets are also crucial for continuous learning, allowing AI models to adapt and improve over time.
Although datasets are critical for AI, they also bring certain challenges. It is important to ensure quality and reliability in the data included in the dataset. Incorrect, biased, or incomplete data will lead to erroneous results. If an AI is trained to believe that 2+2 equals 7, it will continue to calculate incorrectly.
Another challenge is the risk of bias in the data. Datasets may unintentionally reflect biased attitudes present in the data collection process, such as social, cultural, or historical prejudices. It is therefore necessary to be aware of and reduce biases to avoid discrimination in AI systems.
Furthermore, it is important to ensure variation in datasets. Datasets should contain a wide range of samples that represent the entire spectrum of the problem domain one wishes to train the models on. The lack of variation can limit the ability of AI models to generalize and result in skewed or limited predictions.
The size of datasets also affects the performance of AI models. Insufficient data can cause models to struggle with generalization after training. Datasets constitute the training foundation for AI, enabling machine learning models to learn, adapt, and make informed decisions.
What makes things happen – Algorithms
Algorithms are also a central concept when it comes to understanding artificial intelligence. In our digital age, algorithms are an integrated part of our daily lives, even though many of us may not be consciously aware of it. From recommendation systems on social media platforms to search engines and computer programs, algorithms are crucial for solving various tasks. A computer program like Word is an algorithm with strictly defined rules, where the developers have determined what the program should do. An algorithm designed to learn new things is programmed with looser rules.
What is an algorithm?
An algorithm can be described as a sequence of instructions or rules that a computer follows to solve a problem or perform a specific task. You can think of it as a recipe that describes the steps to be followed to achieve a desired result (a cake). Algorithms can be simple or complex, depending on the task they are designed to solve.
It may sound abstract and distant, but as a human being, you perform algorithms every day. Let’s look at an example to better understand how algorithms work. Imagine you are creating an algorithm for brewing a cup of coffee. The instructions may look like this:
- Fill the kettle with water.
- Place the coffee in the filter.
- Pour the hot water over the coffee.
- Wait for the coffee to drip into the pot.
- Serve and enjoy!
In this simple example, the instructions are linear and follow a specific order. It would also be difficult to start serving the coffee before it’s made. However, algorithms can be much more complex, with different conditions, repetitions (loops), and decisions depending on various situations.
So, what do algorithms have to do with artificial intelligence? Artificial intelligence (AI) is a field that is constantly evolving and changing the way we interact with technology. At the core of AI solutions are algorithms, which form the brain of intelligent systems. They provide instructions to computers and machine learning models on how to analyze the datasets they are trained on and draw conclusions. Algorithms identify patterns, make predictions, make decisions, and solve problems.
Summary
Datasets and algorithms work together to train and improve AI models. The dataset is the collection of data used to teach the model, while the algorithm is the mathematical process that analyzes the data and adjusts the model to generate desired results.
First, relevant data representing the problem or task to be solved must be collected. This dataset can consist of text, images, audio, numbers, or other types of data. The dataset also needs to be prepared by cleaning it for errors, missing data, and noise that may negatively affect the model’s performance.
Once the dataset is ready, it is used to train the model using a specific algorithm. The algorithm analyzes and generalizes the data to identify patterns and relationships. The model adjusts itself to increase accuracy. During training, the algorithm often uses a portion of the dataset as training data and another portion as a validation set to assess how well the model is performing.
After training, the model needs to be evaluated using a test set that it has never seen before. This is an important part of the process to assess the model’s capabilities and its ability to handle new, unknown data. The algorithm analyzes the model’s results and performance based on predetermined metrics. A good algorithm adjusts itself to improve. If the model does not provide satisfactory results, it may be necessary to go back and adjust the dataset or experiment with different algorithms. Gathering a better dataset may also be necessary.
By collaborating in this way, datasets and algorithms help each other improve the model’s ability to generalize and handle new situations.
Exercises
Task 1
Create a word bank based on the text
(Term + explanation)
Algorithm, dataset, AI model, generative, machine learning
Task 2
A) Explain with an example what an algorithm is.
B) Explain in your own words what a dataset is.
C) How can a dataset be of poor quality?
D) Explain how algorithms rely on datasets.
Task 3
Do you have any questions about the text?
Terms for further exploration
Linear regression, decision trees, support vector machines, clustering methods, neural networks, generalization.