Some of those questions have actually never been asked (e.g. However, today I see that the algorithm provides data for a model that can answer many questions about memory. SuperMemo worked well enough to make this just a neat theoretical exercise. The paper suggests that the idea of Algorithm SM-17 must have been born around 1990.įrom the very early beginnings of the two component model of memory, I wanted to build an algorithm around it. These were the original names for stability and retrievability used in the years 1988-1990. Like an old fossil, the paper tells me that the idea of Algorithm SM-17 must have been born around 1990.įigure: A picture of a matrix named "new strength" with rows marked as "strength" and columns marked as "durability". While preparing materials for this article, in my archive, I found a picture of a matrix named "new strength" with rows marked as "strength" and columns marked as "durability". Today, I know neural creativity is a breakthrough tool, but I am still using it half-heartedly and not as often as its simpler alternative: subset review.Īlgorithm SM-17 was in the making for nearly a quarter of a century. However, it took me quite a while to appreciate the extent of that fact. When incremental reading came to life in 2000, only I knew it was a monumental thing. Without Janusz Murakowski, vital big data: repetition history record in SuperMemo might have been delayed by 1-2 years.
Anyek wicky windows#
Without Tomasz Kuehn, SuperMemo for Windows might have arrived 2-3 years later. Imagine the surprise! When I came up with the first spaced repetition algorithm, it took me over 2 years before I decided to recruit the first user. I plotted my first forgetting curve in 1984, I forgot about it within a few months and recalled the fact 34 years later at the time when my whole life revolves around forgetting curves. The problem with ideas is that they may often seem a fraction as attractive as they are. SuperMemo story shows that if you have an idea, you should put it to life (unless you have another better idea). We even have a nice portion of data combined with sleep logs that can now add a new dimension to the model: homeostatic readiness for learning, homeostatic fatigue, and even the circadian factor. We have all the tools, and we have a lot of data. The only limit on further progress in understanding memory is the imagination, availability of time, and the ability to pose the right questions.
Today we can almost perfectly describe memory with the toolset employed in Algorithm SM-17. Science is a bit like SuperMemo, today it seems like the reward is little, but in the long-term, the benefits can be stunning. A moral of the story is that all governments and companies should spare no resources for good science.
The uncovered truths show their power and value years after the discovery.
The problem with science and invention is that they are blind and unpredictable. In human endeavor, science is often a side effect of human curiosity while other burning projects often receive priority. It may seem amazing today, but despite its simplicity, it took three long decades of starts and stops before a good model could emerge. 3.4 Convergence of sub-stabilities for composite memory tracesĪ perfect mathematical description of long-term memory is just about a corner.3.3 Memory complexity in spaced repetition.3.2 Expected increase in memory stability.3.1 Linear increase in value of review over time.3 Conclusions derived from stability increase formula.2.3.3 Dependence of stability increase on retrievability (2018).2.3.2 Dependence of stability increase on R.2.3.1 Dependence of stability increase on S.2.3 Symbolic formula for stability increase.2 Increase in memory stability with rehearsal.1 Why a simple idea could not materialize?.
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