Think of Dreams
Since dreams have been around just as long as civilization, I would say we have been fascinated by it. From the old oracles of voices to Sigmund Freud’s psychoanalytic theory we always assume that dreams reveal something other than just our fears, desires and maybe future— maybe even all possible ones. And in this day of artificial intelligence, dreams are fast ceasing to be poetry and enigma; they are turning into digital data. Dream researchers and tech developers are building something that few would have dreamed of just a decade ago: machines we can dream with — about and once recorded.
Picture waking up to relive your dreams as you would a video. Is that now sound like science fiction? In our next life, due to AI and neurotechnology, maybe.
The Science of Sleep and Dreaming
At the heart of the science that allows for machine decoding of our dreams is our understanding of the biological and neurological processes that underlie dreaming.
Sleep is structured in this order. These are the cycle of sleep: light-n apt- sleep, we are going to have long deep sleep and RAPID-EYE-MOVEMENT (REM).
The most vivid dreams occur during REM sleep because the activity of your brain is closely related to that of an awake state. During this phase, specific regions in the brain (for instance vision centers and limbic system) have an elevated function leading to vivid, emotional dream adventures. With electroencephalograms (EEGs) and functional MRI (fMRI), scientists can sketch the patterns for a basis to someday create neural mind to read technology.
We know that if we dream certain parts of the brain light up, but understanding of the specific content, what you look at/hear/sense is a fiendish puzzle.
We hope that this still brief explanation of how dreams are deciphered help you all great night of sleep!
The Role of AI
But that is the part where artificial intelligence shines. AI is great at finding patterns in large amounts of data, and many times, patterns that are just barely under human detection. In dreams, researchers provide AI algorithms with brain data gathered during REM sleep, pairing them with recognized stimuli (visuals or words read aloud). As time progresses, the AI uncovers commonalities in particular neurons and particular sensory inputs.
Optimized to… Getting the machine to “guess” what type of images or narrative the dreamer is dreaming (types of evoked brain electrical/metabolic activity).
Deep neural networks and other machine learning models are being trialled to artificially ‘reverse engineer’ visual experience from fMRI (Ma et al., 2017).
Nevertheless, Japanese scientists taught the AI to produce a blurry but still comprehensible image of what subjects saw under free-living while freely awake — using blurry subjects and solid subjects pictures as examples. The same method is now being applied to REM sleep dynamics.
Early Results – Re-creation of Dreaming Vision
A former breakthrough paper by research at Kyoto used AI on brain images to access slimmed down pictures from dream data.
It was hardly clear, but pixels (or something of that sort that looked like the dreamer later described it as). This is how the AI-generated image from someone dreaming of a cat might be all hazy silhouette of small animal with pointy ears.
UC Berkeley also attempted a similar feat by training AI to extract movie trailers from scanner data of subjects. The machine could put together rough scenes from what visual information they had. Still, not entirely flawless — but it was a significant milestone towards mind-to-picture translation.
These advancements are really cool, and somewhat frightening. It might do the same if AI can copy what we see when awake when it needs to sleep.
Tech Undercarriage of Brain-Computer Interfaces (BCI)
Real-time access to brain activity relies on Brain-Computer Interfaces (BCI) [5]. They are the systems that allow you to communicate directly from one brain to another device. Therefore most BCs are non-invasive EEG headset and the advanced forms, such as Elon Musk´s Neuralink, will involve implanting electrodes directly into brain for faster more detailed data transfer.
BCIs are already sort of medical, like they allow paralyzed people to move robotic limbs. And when allied with AI dream interpretation, BCIs are the route to reading our thoughts.
Though we do not have BCIs in high-resolution dream-space yet, the gap keeps shrinking: neural signal fidelity is improving and AI learning models.
Reddit Comments and Cross-Posted on AnonIBBo
Of course, power begets power. The machine in your dream is one of your inescapable ethical questions right there.
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Privacy Invasion Dream Patrol: Our dreams are pretty darn private experiences we have. If a machine could intercept them, then who owns that data?
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Consent Issue: So could you collect sleep data without explicit permission from the person (dream)?
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What happens when employers/gov ask for further context?
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Mental Risks: Watching dreams play out, like a movie straight to your subconscious can be traumatizing or disturbing, especially when they contain memories of trauma or unpleasant fantasies.
Ethicists want there to be NO opt-in dream-recording technology, with transparent limits of propriety and regulation. Dream decoding could be a tool to manipulate or watch, surveil (legal persecution), if you will — but only if the information in there is accurate.
Pros: Therapy, Creativity and Insight of Self
Given the dangers, dream-recording technology may produce deep rewards:
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Mental Health: Psychologists can listen to dream recordings and help diagnose post-traumatic stress disorder (PTSD), anxiety, depression.
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Lucid Dream Mentor: In real-time with AI in the loop lucid dopers can stabilise or aim to prepare their dreams.
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Artistic Siren Calls: Artists, writers and filmmakers could connect with their subconscious on a level never before possible.
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Detection of Early Brain Disorder: Sleep is one of first phases to show a neurological disease such as, Alzheimer or Parkinson (neurologist can distinguish sleep echo).
Neuroimaging Modalities for Decoding Dreams
Tools of modern neuroscience, with functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) especially for tracking activity within a dreaming brain.
EEG measures electrical brain activity at the level (fMRI measure changes in neural magnitude from neural correlate of blood flow). A neural correlate. These do not “see” dreams, but they make it possible for researchers to recognise in what areas of the brain activate when some cognitive, emotional and image processing happen.
So in dreams, we try to link these patterns of activity to particular visual or sensory experiences. Suppose the occipital lobe is activated in a certain pattern during REM sleep and then later, the person wakes up to say “oh dream it was a mountain.” The more data you can provide, the same data with labels assigned in multiple participants, more would be the possibility for AI to detect common associations and thus become predictive. GPT utilisent cette logique.
It’s almost like compiling a dream lexicon, but someone who writes is in brain cells and blood issuing.
Predictive Models and Training in AI
Machine learning in general is unique to reach AI that tries to understand dreams different from most other common tasks in that world. Fanciful and surreal (not to mention non-linear, dreams aren’t linear). You could dream a dream in your old bedroom and then edit that dream flying over a city made from ice cream. The results are difficultly matching the logic-based structure most AI models are built around.
Researchers are testing potential solutions through generative models: Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs) — machine learning algorithms that can produce new images off of learned data patterns using AI. These models can convert brain activity data into more-or-less rough drawls of the dream’s product and, through iteration and feedback, refine their output.
Researchers also improve the accuracy of natural language processing (NLP) to match recorded dream reports with brain activity data from spoken words. The AI is taught to convert not only image but concepts such as fear, fall or joy into neural signatures.
These are dream translators; a model that combines visual and verbal signals to approximate what someone dreamed.
Tech Titans and Their Sleep Problem
Brain decoding technologies are being funded by numerous top organizations and companies:
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Reality Labs at Facebook/Meta: Silent speech recognition and neural interfaces that were partially funded for BCI research.
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Neuralink (Elon Musk): Creating high bandwidth brain interfaces that may someday enable reading thoughts or dreams in a real-time fashion.
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Google DeepMind: Working on neuroscience-inspired artificial neural networks and memory models.
None of the companies have publicly said what they are doing, but the overlap between their aims + dream decoding is substantial.
Crack the dream-code even one of these tech giants and we may well head toward era in which the human subconscious is a new form of digital content — but this time, archivable, searchable and editable.
Can We Remote Control Our Dreams using AI?
It gets even wilder if we are able to decode dreams—it enters the realm of near sci-fi: directing them.
Lucid dreaming — the ability of dreamers to become aware that what they are experiencing is not real and we as psychologists and spiritual practitioners have been fascinated with this concept. Now devices with AI are trying to wake people into lucidness by whispering sound, light or vibration cues to catch them during REM phases.
Perhaps an AI recognizing that you are entering into the beginning of a nightmare, introducing music in your sleep environment; calming prompts via scent pulses or guided relaxation visuals. AI could also learn your dream preferences and create an entire reality for you nocturnally to explore — like Netflix but for the subconscious.
One such endeavor are companies experimenting with things like Targeted Dream Incubation (being played while the dream incubator goes off) in order to fill dreams with themes or goals. The purpose of this: probing dream stretching, recharging the creative, binding or skills in molding.
The Dreaming as a Cultural Artifact
That is, the impact of culture on that technology in an ongoing process as this technology evolves.
→ Platforms could emerge, for example, allowing people to post dreams and watch / comment them — dream-sharing.
– You get dream commerce: Influencers can monetize their subconscious content or brands trying to dream-plant products — an area where researchers have done some experiments.
The line between what is private and what can now be turned into future entertainment is dark, threatening consensual space for selfhood.
Future Forecast: More To Come in Dream Tech
If we look to the future, in about 5–15 years we might see:
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Consumer-grade recorder for dreams
Inexpensive EEG-fMRI hybrids could be designed to harvest the dreams by mimicking poorly in low-fidelity dream summaries or topographies of emotional intensity. -
AI Dream Journals
Apps that take old bedroom and translate behaviour based on sleep data into written dreams or sketches, using your own personal AI. -
Dream Mental-Health Diagnostics
Dreams of a psychologist who studies dream patterns in order to discern early signs and symptoms of illnesses such as schizophrenia, depression or neurodegeneration. -
Dream Navigation Interfaces
Tools to get you “driving” your dreams with voice commands, gestures and biofeedback. -
The Dream Ethics Council
Dream data, including how it is harvested, stored, and shared might form a need for certifiers/licensees in this field; perhaps institutions would be required to institute to regulate this.
Final Frontier of Your Inner Space: Dreams
Since the dawn of time, dreams have been portals into those deeply mysterious regions of the human psyche — spaces where logic vanishes, time warps, identity blurs. We are not just getting to the dream, but also recording it, visiting it and designing it — with an AI-powered Dream Recorder.
Such a profound rethink in what consciousness represents, and what memory consists of. For the first time in the transience of sleep, not only could its folding plan become a physical extension to waking but maybe — ironically or not — an interface between imagination and reality; a neural encoding that machines could decode.
It’s all coming together, the Hypnodrome is baked and assembled.
Will you choose to record it?
Or let it drift away with the sunrise, untouched?
