Big Data May Be About To Take On Precognition

Shadow is an alarm clock. A rather clever one mind you, it wakes you ever so gently with an increasing tone and vibration, but, at its most basic level, Shadow is an alarm clock. But Hunter Lee Soik’s little Shadow app intends to do so much more.

Shadow wants to put you in the best frame of mind for recording and understanding your dreams. The app’s waking protocol, the slow increase in tone and vibration, is deigned to gently pull you from slumber, coaxing you through the hypnopompic state, the state between awake and asleep, thus increasing your chances of remembering your dreams. Once awake it prompts you to tell it your dreams, either verbally, which is then transcribed to text, or in written form. This information is then uploaded to a database where it is added to the dreams of potentially millions of people from around the world, creating the largest dream database ever known. The app will also keep track of your sleeping patterns.

It is estimated that we spend as much as five years of our lives dreaming. It is also estimated that the average person forgets 95% of their dreams. That’s 4.75 years worth of information about yourself that your subconscious has thrown at you, and you never even knew it existed. Access to this information could, and I stress could here because there is some disagreement on this point,  could help you better understand your general state of mind, better inform your decisions, and possibly improve your relationships. And the more you use the app, the better it understands you as an individual. On a deeper level, Shadow is hoping to use the dream data you input to become part of a much bigger picture. Apps and devices like Fitbit and Nike Plus already allow us to keep detailed notes on our physiological self, heart rate, calories burned, and how much sleep we get each night, but we have little understanding of how it connects to the bigger picture of self. Shadow and understanding your own dreams could help bridge that gap. Analyzing the dream data and comparing it to data gathered from other devices could lead to a better understanding of how the body works as a system. You can choose to keep your data private but even when it is shared to the cloud, all of your personally identifying markers have been stripped from it. The idea is to create a database of the collective unconscious of the world. A database that can be studied and analyzed giving us both a better understanding of ourselves as individuals and perhaps the world as a whole.

There has been increasing interest in the past ten years in a better understanding of dreaming. This year, a Japanese team was able to decode dream traits from brain activity during sleep, and researchers have linked dream content with learning, emotional processing, and creative insight. Scientists understand for the most part how we dream, but not why. We typically spend one to two hours a night in REM sleep, when most dreaming occurs. That adds up to the five years of dreaming during the average lifetime. One possible answer to the why of dreaming is that dreaming relates to mood. Studies have shown that the types of dreams someone has after getting a divorce can predict whether they will later need antidepressants, and following 9/11 dreams across the U.S. showed increased similarity to post-traumatic-stress-disorder nightmares. Shadow is designed to capture those dream trends on a broad scale. The end goal is to see patterns in the data and use that to improve and inform the self.

But…

There could be an additional benefit here. While Shadow’s database is still in beta and contains only about 9000 users, as the user base grows, and as the database grows, there will be an opportunity. It has been suggested that there is an uptick in a certain kind of dreaming prior to certain, typically catastrophic, global events. Call it precognitive dreaming. This is a fascinating idea that has been around for centuries but reporting can be problematic as most of these dreams are reported after the fact and recollection of such dreams can no doubt be skewed by the emotions involved in a post crisis situation. A database of this (potential) size and scope could hold the key to accurate reporting as any precognitive dreams would exists inside the database before the events. The database could be scoured after the fact for any catastrophic or precognitive references that might exceed random chance.

To be clear, this is not the goal of this database. Shadow is meant to be a tool that will accomplish and inform many things. For example one of Shadow’s primary goals is to create a dream database that will help determine norms. How often does the average person have nightmares versus someone with depression? Do children dream differently from adults? How do dreams change after a trauma? And if dream change precedes medical issues such as depression, could dreams be used to diagnose problems before they strike? But, with the dreams of millions of people at their fingertips, the opportunity to at least look for precognition is there.

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