# The Secret to Never Forgetting Anything

### Spaced Repetition Will Make You Smarter Than Einstein (Probably)

Spaced repetition is a technique for improving memory retention and recall by spacing out the intervals at which information is reviewed or studied. The idea behind spaced repetition is that by reviewing information at increasingly longer intervals, your brain is more likely to remember it in the long term. Spaced repetition is based on the forgetting curve which is a concept that was developed by German psychologist Hermann Ebbinghaus in the late 19th century.

The basic principle of spaced repetition is that information is more effectively retained when it is revisited at the optimal time. Instead of cramming information into your brain all at once, you can review it in smaller, more manageable chunks over a longer period of time. This allows you to remember the information more easily and for a longer period of time.

Spaced repetition can be applied to a variety of learning activities, including studying for exams, learning new languages, and memorizing important facts and figures. There are several software tools available that use spaced repetition algorithms to help you remember information more effectively, such as Anki and SuperMemo.

### The Forgetting Curve

The forgetting curve shows that we tend to forget information very quickly after learning it, but the rate of forgetting slows down over time. By reviewing information just before we forget it, we can reinforce it in our memory and prevent it from being lost.

The forgetting curve typically looks like a steep decline in memory retention soon after learning, followed by a more gradual decline over time. The exact shape of the curve can vary depending on a number of factors, such as the complexity of the information being learned, the individual's prior knowledge of the topic, and the amount of reinforcement the information receives.

By reviewing information just before we're about to forget it, we can reinforce it in our memory and make it more likely that we'll remember it in the long term. The exact timing of spaced repetition intervals will vary depending on the individual and the specific information being learned, but in general, the intervals should be longer for information that is more easily remembered, and shorter for information that is more difficult to remember.

### Spaced Repetition Algorithms

**SuperMemo algorithm**: This is one of the oldest and most well-known spaced repetition algorithms. It calculates the optimal time to review information based on how well you've remembered it in the past. The algorithm uses a formula that takes into account how long you've known the information, how often you've reviewed it, and how difficult it is to remember.**SM-2 algorithm:**This is a modified version of the SuperMemo algorithm that is commonly used in flashcard software, such as Anki. The algorithm adjusts the intervals based on how difficult the information is to remember, and it takes into account how long it has been since you last reviewed the information.**Leitner system**: This is a simple algorithm that is based on the use of a series of boxes, typically used with flashcards. Information starts in Box 1, and as you review it correctly, it moves to Box 2, then Box 3, and so on. If you get a flashcard wrong, it moves back to Box 1. The algorithm uses the boxes to determine how often to review the information.**Mnemosyne algorithm**: This algorithm is similar to the SuperMemo algorithm, but it includes a number of additional features, such as the ability to adjust the intervals based on how confident you are in your answer.

### SuperMemo Algorithm Explained

The SuperMemo algorithm is was one of the earliest algorithms developed specifically for spaced repetition learning, and it has been used extensively in research on memory and learning.

The SuperMemo algorithm has also been incorporated into a number of popular spaced repetition software tools, including Anki and Mnemosyne. These tools use modified versions of the SuperMemo algorithm to optimize the timing of spaced repetition intervals based on your individual learning needs.

Here's an example of how the algorithm might work:

Let's say you're trying to memorize a list of vocabulary words for your Spanish class. You learn the words for the first time on Monday, and you get them all correct.

According to the SuperMemo algorithm, you should review the words again the next day, Tuesday. If you get them all correct again, you should review them again on Thursday. If you get them all correct again on Thursday, you should review them again the following Monday.

Each time you review the words and get them all correct, the interval between reviews gets longer. If you make a mistake, the algorithm will adjust the interval accordingly, so you review the word more frequently.

To calculate the optimal review interval, the SuperMemo algorithm uses a formula that takes into account how long you've known the information, how often you've reviewed it, and how difficult it is to remember. The formula looks like this:

`Interval = Previous interval * Difficulty factor`

The difficulty factor is a number between 0 and 1 that represents how difficult the information is to remember. The more difficult the information, the smaller the difficulty factor.

For example, let's say the interval between your first and second review is 1 day, and the difficulty factor for the vocabulary words is 0.8. To calculate the interval between your second and third review, you would multiply the previous interval (1 day) by the difficulty factor (0.8):

Interval = 1 day * 0.8 = 0.8 days (rounded up to 1 day)

So in this case, you would review the vocabulary words again on Thursday, which is 1 day after your second review.

### The Future of Spaced Repetition

The following are Neural Network based spaced repetition algorithms that have been developed in the last few years:

**SRS Transformer:**This is a neural network-based algorithm that uses a transformer architecture to optimize spaced repetition intervals. The algorithm takes into account factors such as the difficulty of the material and the learner's performance history to determine the optimal review interval.**Deep Spaced Repetition:**This is another neural network-based algorithm that uses deep learning to optimize spaced repetition intervals. The algorithm uses a combination of convolutional neural networks and recurrent neural networks to model the forgetting curve and predict the optimal review time for each item.**Neural-SR:**This is a neural network-based algorithm that uses a deep neural network to model the relationship between spaced repetition intervals and long-term memory retention. The algorithm uses reinforcement learning to determine the optimal review interval for each item based on the learner's performance history.

These neural network-based algorithms are designed to improve upon traditional spaced repetition algorithms by taking into account more complex factors such as the learner's individual characteristics and performance history. By using machine learning techniques to optimize spaced repetition intervals, these algorithms can provide more personalized and effective learning experiences. However, they are also more complex and computationally intensive than traditional algorithms, and may require more data to train effectively.

### Conclusion

Spaced repetition has been shown to be effective for a wide range of learning activities, including studying for exams, learning new languages, and memorizing important facts and figures. There are several software tools available that use spaced repetition algorithms to help individuals remember information more effectively. By incorporating spaced repetition into your learning process you can improve your ability to remember and recall information and it can help make you a super learner.