Here’s How We Could Begin Decoding an Alien Message Using Math


In 1974, the Arecibo radio telescope broadcasted a notable 1,679-bit message into the cosmos. But if extraterrestrials transmitted a similar sequence to us, how might we initiate its decoding? A fresh mathematical technique offers a potential solution.

For those deciphering the Arecibo transmission – an illustration showcasing a human figure, the structure of DNA, our solar system, and the telescope itself, among other data – the primary step would be realizing it’s an image.

This particular image measures 23 pixels in width and spans 73 pixels in height. The transmission process involved the radio dish toggling between two distinct frequencies, symbolizing zeros and ones. Arranging these bits in any way other than 23 pixels in each row would render the image unintelligible.

A comparable dilemma arises if we receive extraterrestrial communication. How would we discern its dimensional structure and scale? Embedded within the Arecibo transmission is a hint: 23 and 73 are both prime numbers.

It’s a concept that another intelligent species might understand, provided they find prime numbers intriguing. But as Brian McConnell, a software expert from Notion Labs in San Francisco and writer of The Alien Communication Handbook, suggests, extraterrestrial communications could vary in structure and dimensions.

Such a message could be a multidimensional database where every item might represent multiple values or nested lists. Messages resembling physics simulations might contain metrics for each spacetime coordinate.

The innovative decoding technique, designed by Hector Zenil, a digital scholar from the University of Cambridge and founder of Oxford Immune Algorithmics, and his team, evaluates incoming bit sequences by examining every feasible dimension combination.

For instance, a 100-bit sequence could be configured as 1×100 or 10×10 (two-dimensional), 4x5x5 (three-dimensional), 2x2x5x5 (four-dimensional), and so forth.

Subsequently, the method inspects the organization of each potential arrangement. To assess localized order, it segments the message.

Within each segment, it cross-references a vast library of tiny pre-programmed algorithms to see how many yield a matching segment. The more matches, the higher the localized order score. By averaging these scores, an aggregate score for local order emerges.

Simultaneously, the team evaluates the global order of each arrangement by determining how much a compression tool can reduce its size without loss — in mathematical terms, regular structures compress better than random ones.

Combining these scores provides an indication of the most probable original configuration. This technique was put to the test on an enlarged version of the Arecibo message, now with a width of 138 pixels. In a specific test, bit sequences were transformed into images up to 200 pixels wide, a subset of conceivable configurations.

When plotted with image width versus probability score, distinct peaks appeared, with 138 being the most pronounced. This method also proved efficient when deciphering other bit-encoded transmissions, such as diverse images, an audio clip, and a 3D MRI image.

Moreover, the technique can account for noise disruptions that may occur as messages journey across space. Even with a quarter of the bits being altered, the original 23-pixel width of the Arecibo message was distinguishable.

Douglas Vakoch, a SETI investigator and leader of METI International, which explores potential communication with extraterrestrial beings, highlighted that this new technique allows prime numbers to serve a secondary role in message interpretation. As he articulated in an email, rather than solely guiding format discovery, they could be used to verify accurate decoding.

However, detecting and structuring a message is just part of the challenge. Determining its meaning remains. Could a pattern represent an extraterrestrial entity, a vessel, a mathematical formula, or just an anomaly? Zenil believes the method could also benefit earthly tasks, such as decoding cellular signals.

He has already applied similar tactics to pinpoint key elements in gene regulatory networks. These strategies, aimed at assembling algorithmic components to clarify or foresee data, could pave the way to achieving more advanced artificial intelligence.

By not relying solely on human interpretations, it broadens the horizon to uncover intelligence forms that diverge from our own understanding.