Human beings and Machine Learning

Human beings learn from experiences.

Human beings learn from experiences through a process of observation, interpretation, and internalization. When we encounter new situations or information, our brains try to make sense of them by comparing them to our existing knowledge and beliefs. This process helps us understand and remember what we've learned. 

Additionally, experiences often involve some form of feedback, which helps reinforce or adjust our understanding of the world. This feedback can come from our own reflections, interactions with others, or the consequences of our actions. 

Over time, these experiences and the lessons we draw from them shape our thoughts, behaviors, and decision-making processes.

Machine on the other hand follows instructions. Machines learn from instructions and data through a process known as machine learning. 

In machine learning, algorithms are used to analyze data and learn patterns, which are then used to make predictions or decisions. 

Here's a simplified explanation of how this process works:

1. Data Collection: Relevant data is collected, which could include examples of past experiences, images, text, or any other type of information that the machine will learn from.

2. Data Preprocessing: The data is cleaned and formatted to make it suitable for analysis. This may involve removing irrelevant information, handling missing values, and standardizing the data.

3. Model Training: The machine learning model is trained using the preprocessed data. During training, the model learns the underlying patterns in the data by adjusting its internal parameters.

4. Model Evaluation: The trained model is evaluated using a separate set of data called the validation set. This helps assess how well the model has learned and generalizes from the training data.

5. Model Deployment: Once the model is trained and evaluated, it can be deployed to make predictions or decisions on new, unseen data.

6. Feedback Loop: In some cases, the model's performance on new data is used as feedback to further improve the model. This iterative process is known as "learning from feedback" or "online learning."

To cap it all, machine learning enables machines to improve their performance on a task by learning from both instructions (in the form of the algorithm) and data.