September 16, 2022
Editorial - Perceiving memory decay improves AI performance , by Dr. Howard Rankin
AI Science - #6 Memory decay subjectivity and objectivity, by Michael Hentschel
AI Logic - IntualityAI simulates short and long-term memory loss, by Grant Renier
Memory Decay – Perceiving this human bias helps AI to better predict data trends
“I consider that a man’s brain originally is like a little empty attic, and you have to stock it with such furniture as you choose. A fool takes in all the lumber of every sort that he comes across, so that the knowledge which might be useful to him gets crowded out, or at best is jumbled up with a lot of other things, so that he has a difficulty in laying his hands upon it.” -Sherlock Holmes
Sherlock Holmes is often quoted as referring to the “brain attic”, and specifically his warning not to fill the attic with too much “irrelevant stuff”. He notoriously limited his interests so that his attic would not become overcrowded with unnecessary information that had outlived its usefulness. If he were around today, Sherlock would clearly see the parallel between cognition and the need for extra RAM and cloud storage in our technological devices.
Computers are still largely designed to work the way we do: to prioritize the importance of data by trying to remember only what is remarkable or valuable. And most often, distant events are not remembered accurately or at all. Unlike computers that only forget what they are told, humans accidentally AND preferentially forget, and along the axis of time, memory decays. Memory Decay is a critical concept both for analysis, prediction and the aging brain. The key question is, “at what point does historical data become irrelevant?” Moreover, not just irrelevant but unnecessary or even detrimental.
In the human brain, the rubric seems to be that if you haven’t “used” the memory for a while, it gets eroded if not discarded. What was the name of your chemistry teacher in third grade, fifty years ago? It’s okay, if not beneficial, for you not to use up valuable storage space in your brain remembering this irrelevant detail. Save some space for more important data like your wife’s birthday.
History can be overrated. Some historic data might be useful, but some of it might be misleading as well as energy and space consuming. So what if the hottest day ever recorded in London in July was in 1951? How relevant is that for today’s analysis of Britain’s current climate?
The greater the past data available, the more there is to forget. Unstructured data is not even easily searched, let alone interpreted. Missing data means that rational decisions may be rendered impossible, but where decisions must nevertheless be made, short-cuts like intuition and simplification allow imperfect conclusions.
Another problem with historical data is that the context changes over time, and what happened long ago may have absolutely no relevance to today’s environment and questions. One way of managing this problem is to have a decay of historic data so that it becomes less and less relevant and doesn’t overly influence today’s analysis and prediction. Information needs to be weighted by its relevance, and much information quickly loses its relevance. This is why Memory Decay is a critical bias to include when IntualityAI analyzes large data sets.
by Dr Howard Rankin
Data Selectively Modulated by the Human Brain
Human Behavior is as complex as we can imagine. Behavior has been deeply studied as a vast complex domain of decisions that are made rationally and irrationally. Casual observers might say humans tend ONLY to be able to decide irrationally, while computers are ONLY able to decide rationally. Reality results from the mix of all these decisions. Anything touched or thought about by humans involves irrationality, which is to say, everything.
Memory Decay science is both subjective and objective, based on the brain firing, functioning or not functioning, literally at will, with natural ageing impacts, and human/natural memory prioritization preferences. The totality of irrational memory impacts yields varied decisions and a significantly altered reality.
If we want to better predict future reality, our computer needs to understand human irrationality. If we want to use an effective AI, we must humanize its inherent computer-only rationality. We call this “Intuitive Rationality.”
How We Do It: IntualityAI first analyzes all available big data for its patterns, including recognition of repetitive patterns where humans act irrationally (12 human biases of which Memory Decay is only one), where IntualityAI can therefore enhance typical linear data progression calculations. Adjustment factors are used where human behavior has in the past veered from purely rational expectations, and future expectations are thereby adjusted to obtain a more real-world prediction.
by Michael Hentschel
In the research paper, "Short-Term Memory to Long-Term Memory Transition . . . ", by Ting, Sung-Hyun, Wei, et al, they summarize:
". . . forgetfulness is not always a disadvantage since it releases memory storage for more important or more frequently accessed pieces of information and is thought to be necessary for individuals to adapt to new environments. Eventually, only memories that are of significance are transformed from short-term memory into long-term memory through repeated stimulation."
The memory decay bias is an important function in Intuitive Rationality prediction-making. The researchers' chart shows how short and long-term memory decays over 20 events. Four decades of millions of events from many independent IntualityAI applications produces very similar curves, by mathematically simulating this process. In has eliminated the need for continuous regression analysis of large bases of historical data, a standard method used in current AI applications. We humans don't do that. We basically update our current cognitive 'state' with the next microsecond event, mostly subconsciously without recalling history. IntualityAI is doing the same for exceptional prediction results.
A humanized AI enabled to recognize human perceptions and behaviors
to reflect a more intuitive rationality
for more successful predictive analysis
Simulated Perceptions of Human Behavior
Memory Decay Bias
Fast and Frugal Bias
Risk Aversion Bias
Hot Hand Bias
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