Crazy dreams help us make sense of our memories

A new theory suggests that dreams' illogical logic has an important purpose.

Credit: Paul Fleet/Adobe Stock
  • If consolidating memories as we sleep is like machine learning, maybe dreams keep our "algorithms" on track.
  • Machine learning is optimized by the injection of a certain amount of nonsense data.
  • Maybe dreams are just weird enough to do the same for us as we sleep.
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    Objects in lucid dreams are perceived as real, study discovers

    It's all about smooth pursuit.

    Man inside an ice caver under the Vatnajokull glacier, Vatnajokull National Park, East Iceland, Iceland. Photo by Marco Bottigelli / Getty Images
    • While lucid dreaming, we use the same eye movement patterns as when we observe physical actions.
    • However, we use different eye patterns when we imagine movement.
    • Researchers believe this might help add to our understanding of consciousness.
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    Practicing Skills In Your Sleep Can Be as Effective as Physical Training

    Just imagining movement fires the same neurons as if we were actually moving. A new study shows we can wake our sleeping mind to practice motor skills in our dreams.

     

    Lucid dreaming. (Image: Shutterstock)

    Mental training is arguably as important as physical fitness. That argument is gaining strength as a growing body of literature unravels the once-mysterious connections between consciousness and movement. We know that the murky domain of subconscious and autonomic actions greatly influences our waking lives. Now we’re learning how to train our unconscious selves for the benefit of our daily actions.

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    What Do a Robot's Dreams Look Like? Google Found Out

    They may look odd, but it’s all part of Google’s plan to solve a huge issue in machine learning: recognizing objects in images.

    Google's artificial neural networks produce some trippy images thanks to its Deep Dream program (photo credit: Michael Tyka/Google)

    When Google asked its neural network to dream, the machine begin to generating some pretty wild images. They may look odd, but it’s all part of Google’s plan to solve a huge issue in machine learning: recognizing objects in images.

    To be clear, Google’s software engineers didn’t ask a computer to dream, but they did ask its neural network to alter the images based on an original photo they fed into it, by applying layers. This was all part of their Deep Dream program.
     
    The purpose was to make it better at finding patterns, which computers are none too good at. So, engineers started by “teaching” the neural network to recognize certain objects by giving it 1.2 million images, complete with object classifications the computer could understand.

    These classifications allowed Google’s AI to learn to detect the different qualities of certain objects in an image, like a dog and a fork. But Google’s engineers wanted to go one step further, which is where Deep Dream comes in, which allowed the neural network to add those hallucinogenic qualities to images

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