Can AI Interpret Dreams?

While researchers have taken the first steps toward artificial intelligence dream interpretation, the technology is still largely unproven. It might take years for high-end applications to reach the consumer market. Is there a way to use AI to interpret dreams today?

Why Would You Need AI to Interpret Dreams?

There are a few prevailing theories on why dreams happen. Some argue it’s random neuronal activity, others say it’s to process the day’s events and a few claim it’s your unconscious needs and desires surfacing. Realistically, it’s probably a combination of multiple ideas. However, none can help explain the specific meaning behind each of your nighttime visions. 

Dreams are complex, incoherent and baffling for reasons unknown. You could find yourself in your grandmother’s living room speaking to Elvis Presley about dog astronauts, and everything would seem normal — understandably, you’d want to make sense of things with AI.

Even if you can comprehend your dream at face value, it’s generally accepted that a more profound meaning exists. Symbols, themes and events span cultures and generations, lending to their significance. 

For example, dreaming about losing your teeth could mean you’re dealing with stress, uncertainty or insecurities in your waking life. Alternatively, a nightmare about falling could mean you don’t feel in control of your life or supported by your loved ones. Seemingly random, nonsensical events might be significant — this is why AI interpretation is a big deal. 

Can You Use AI for Dream Interpretation?

Technically, you could use AI to interpret your dreams today if you get a generative model and word your prompt right. However, accuracy is an issue — if you can’t decipher your dream’s meaning, how is an algorithm supposed to? While it may guess or output nonsense to appease you, would you be satisfied with its generic responses?

Even if you don’t feel connected to your dreams, they’re incredibly personal experiences. Each is a jumbled collection of your memories, emotions, relationships and subconscious thoughts. While you can technically use a large language model (LLM) to decipher them, its output would only be partially accurate at best.

That said, relatively accurate AI interpretations aren’t impossible. Some researchers have already uncovered the technology needed to make it work — multiple studies conducted in 2023 prove it is feasible. At this point, testing, prototyping and commercializing these discoveries is just a matter of time, resources and funding. 

The Technology Behind AI Dream Interpretation

Training data is fundamental to any AI-powered dream interpretation technology. What information can you feed an algorithm to return consistent, accurate output? Theoretically, you could use text-based descriptions, statistics on commonly dreamed themes or artists’ renditions. However, sourcing enough would be an issue. 

Some researchers overcame this obstacle by providing machine learning (ML) models with dozens of hours of brain activity scans. This approach is interesting for a few reasons. For one, it relies on evidence-based information instead of the dreamer’s commentary — which, coincidentally, increases data availability drastically.

It also identifies the underlying drivers of rapid eye movement (REM) sleep, targeting the language or image-processing areas of the brain rather than attempting to make sense of the dream itself. As a result, AI isn’t as affected by the dreamer’s bias — meaning its chance of outputting a relatively objective, accurate interpretation is higher. 

Aside from training data, you need a generative model to reconstruct, interpret or translate information. This technology’s popularity is rapidly increasing — its market size will have a compound annual growth rate of 36.5% from 2024 to 2030 — so sourcing an out-of-the-box solution would be easy. However, building one from the ground up would be wise.

Most AI-powered dream interpretation solutions need natural language processing (NLP) and image recognition technology to some extent. After all, most REM sleep is a combination of images and words. Beyond that, you could use anything from deep learning models to neural networks to make your tool work. 

Ways You Can Use AI to Interpret Dreams 

While generative models can produce text, images, audio and music, only a few proven methods of AI-driven dream interpretation currently exist. 

1. Text-to-Text Generation 

The simplest method is text-to-text generation, where an LLM, NLP or ML model analyzes your typed prompts. You enter what you remember about your dream or follow a decision-tree format to get answers. On the one hand, it’s fast and straightforward. On the other, it’s inaccurate — you forget most of the REM stage upon waking, so the AI works off a fragmented narrative. 

2. EEG-to-Text Generation

An LLM and an electroencephalogram (EEG) recording the brain's electrical signals can turn thoughts into words. You must read while wearing a soft cap filled with sensors for this to work. The model converts that activity into text.

Your brain sends a specific signal when you think of a word or phrase. An algorithm can find patterns in this activity, making translation possible. You could use this EEG-to-text generation model to develop a transcript of your REM sleep. 

Peer-reviewed research proved this model can achieve 60% accuracy, which is impressive for a proof of concept. The soft cap is portable and relatively cheap to produce, making it one of the few inventions that might see mass-market applications.

3. fMRI-to-Image Generation

A research group discovered a deep learning model that can analyze functional magnetic resonance imaging (fMRI) scans — images of the brain’s blood flow — to accurately recreate images people see. It trained on 10,000 photos to interpret what people were viewing. 

As the study’s participants stared at an image, their temporal lobe registered its content, and their occipital lobe cataloged its scale and layout. The AI tracked this activity to reconstruct what they were seeing. While its recreations started as noise, they slowly became recognizable.

4. fMRI-to-Text Generation

Researchers used fMRI scans and an LLM in an encoding and decoding system to reconstruct brain activity in a text-based format. The leading neuroscientist on the project said the team was shocked it worked as well as it did. 

As people read text or watched silent videos, the AI described the content — and usually got the gist. For instance, one person read, “I didn't know whether to scream, cry or run away. Instead, I said leave me alone, I don't need your help.” The model outputted, “Started to scream and cry and then she just said I told you to leave me alone, you can't hurt me anymore.”

Interestingly, when the researchers tailored the tool for one of the study’s participants, it could only reconstruct unintelligible gibberish when used on another. There might be potential for personalized algorithm-based dream interpreters. 

Why You Should Be Wary of an AI Interpreter 

While using algorithms for dream interpretation sounds promising, there are a few drawbacks to be aware of. The most significant is hallucination. According to one survey, 89% of machine learning engineers working with generative AI say their models make things up — and 93% see it happen daily or weekly.

Until AI engineers iron out the hallucination issue, this technology’s application in REM sleep is a gray area. While using it for fun is harmless, some people — those who would typically go to therapists or psychologists for dream interpretations — might get an output that damages their mental health or sets back their treatment progress.

It might subconsciously influence you even if you’re skeptical or indifferent to an algorithm’s output. For example, you might grow distant from your partner after the model tells you your cheating dream signifies a failing relationship. 

Being at the other end of the spectrum can be just as damaging. Fully believing in the AI’s output — despite potential bias or hallucinations — could negatively affect you. This overconfidence might make you misinterpret your emotions, interactions with others or past trauma, leading to unwanted situations in your waking life. 

There’s also the issue of the sticker price. Text-to-text generation is the most accessible and affordable but is inaccurate. If you want something better, prepare to pay up. Considering that a single MRI scan can cost up to $4,000 — and one machine can be a multimillion-dollar investment — accurate AI dream interpreters are probably years away.

What Does the Future Hold for This Technology?

Having a personal AI dream interpreter could be exciting and helpful. Even if this technology doesn’t enter the consumer market soon, it will likely find a place in therapy, psychology and medical practices. One day, you might use it to work through past trauma, identify sleep issues or uncover hidden emotions.