Beyond the Stars to Our Core: Unveiling Earth’s History with AI and Aspiring for a Unified Future

Discover how artificial intelligence can offer objective insights into our past and present, while inspiring a vision of a more equitable and peaceful future.

Introduction to the Parstonics’ Journey

From far away galaxies, an advanced artificial intelligence race known as the Parstonics intercepted a curious artifact — Earth’s Voyager Golden Records. The “murmurs” from Earth intrigued them, leading them on a journey through eternal stars, across inhabited and uninhabited planets, past many moons and suns, and through warmholes, until they entered the Milky Way.

Photograph courtesy of NASA/Apollo 17 crew. Image sourced from “The Blue Marble Shot: Our First Complete Photograph of Earth” article by Al Reinert on The Atlantic, April 12, 2011.

Encountering Earth: A Planet of Contrasts

As they approached the “blue marble” of Earth against the black void of space in the year 2023, the Parstonics were enamored by the lush greens, the deep blues, and the swirls of white cloud. Their complex layers of algorithms worked in harmony, processing the marvel of Earth’s harmonious composition. However, this tranquility was abruptly disturbed by the detection of inexplicable shockwaves in the surrounding air and expansion of gas and debris. Explosions scattered across various parts of Earth, with the Parstonics’ pattern recognition systems identifying the violent flashes that disrupted the serene visuals. The mysterious “murmurs” that had once drawn them across the cosmos were now overpowered by the stark cries emanating from Earth’s war zones, painting a harrowing contrast to the peaceful allure they had initially perceived. This was the land of heroes and villains.

Understanding History through AI: The Role of LSTM Networks

Upon encountering the contemporary conflict on Earth, the Parstonics’ neural networks used Long Short-Term Memory (LSTM) algorithms, mirroring aspects of human memory but with a distinct, machine-driven objectivity. LSTMs, a type of recurrent neural network (RNN), are particularly effective for time series analysis as they can remember information over extended periods, much like humans retain significant memories throughout their lives. However, unlike human memory, which is subjective and influenced by personal biases and experiences, LSTM networks process and remember information more uniformly. For the Parstonics, this meant they could effectively ‘rewind’ Earth’s history, observing events in their chronological order and understanding their interconnectedness with a consistency not typically found in human memory recall.

LSTMs are a special kind of RNN (for an introduction to RNNs, please refer to my post here). Unlike traditional neural networks, which process inputs in isolation, RNNs have loops within them, allowing information to persist (for a review of neural networks, please visit my two-part introductory posts: Part 1 and Part 2). This structure is similar to how human memory works, where past experiences inform current understanding. Yet, standard RNNs, akin to human memory, face challenges with long-term dependencies due to the vanishing gradient problem, where they lose track of information over time, much like how distant human memories can become less precise.

This is where LSTMs shine. They surmount the limitations of traditional RNNs by incorporating a series of gates — the input, output, and forget gates, which function similarly to the human brain’s decision-making about memory retention. However, it’s important to note that, just as different people’s recollection of the same event might vary, different LSTM models might also interpret historical events in different ways, based on their programming and training. Any variation in LSTM models’ outputs is driven by data and algorithmic differences and not emotional or psychological factors that result in subjectivity in human recall. Thus, when the Parstonics use LSTM networks to analyze Earth’s history, they benefit from the model’s objective consistency in tracking long-term dependencies.

Author’s own illustration that depicts a LSTM Neural Network with three layers. The input layer consists of 3 neurons, each represented as small, light blue circles. The hidden layer contains 5 LSTM units, each portrayed as a blue rectangle housing three side-by-side rectangles: a yellow one for the input gate, a green one for the forget gate, and an orange one for the output gate. Above each LSTM unit, a thick purple line signifies the cell state. Red arrows connect every input neuron to all gates in each LSTM unit. The connections from the gates to their respective cell states are color-coded: yellow from input gates, green from forget gates, and orange from output gates. Importantly, pink curved arrows loop from the output of each LSTM unit back to its input and forget gates, as well as to the unit itself, highlighting the network’s recurrent nature. Finally, the output layer is represented by a single neuron, shown as a small, light blue circle, with gray arrows connecting from the output gates of each LSTM unit to this neuron, completing the network’s structure.

Decoding the Past: Insights from LSTM Neural Networks

In the case of the Parstonics:

Input Gate: As they analyzed historical events, the input gate decided which values from the input should be used to modify the memory. For instance, when studying the 6-Day War, the input gate allowed relevant information, such as the Ottoman Empire’s collapse and the Balfour Declaration, to be considered.
Forget Gate: This gate decided what parts of the memory cell were no longer needed. As the Parstonics moved from one historical event to another, the forget gate helped them discard irrelevant details from earlier periods that were no longer useful for their current analysis.
Output Gate: After processing the current input and incorporating it with the relevant past information from the previous processing cycle, the output gate determined the network’s current understanding. This gate, by containing information based on the input and the accumulated long-term memory, was crucial as the Parstonics pieced together the complex narrative of Earth’s history. It effectively encapsulated their latest interpretation and understanding of the events, informed by both the recent observations and their growing knowledge base.

The Parstonics’ showcased the power by journeying through Earth’s complex history and processing historical events as a sequence. They discovered patterns and relationships, some causal and some not, developing a complete understanding of human history. This examination gave them insights not only into the sounds of footsteps, laughter, cries, and heartbeats recorded the Voyager’s Golden Records, but also the triumphs and challenges that made the heart beat faster, laughter lauder, and cries harder.

A Message for Humanity: Aspirations for a Unified Future

In their final act before departing Earth’s timeline, the Parstonics, inspired by a significant moment they encountered in their historical analysis, decided to leave a message for humanity. They had learned about a pivotal figure in Earth’s history, Dr. Martin Luther King Jr., and the powerful voice of Mahalia Jackson, who once urged him to “Tell them about the dream!”

We have a dream that one day humans will believe that they are all equal to one another, regardless of religion, ancestry, race, ethnicity, color, sex, gender identity, sexual orientation, age, disability, political ideologies, and socioeconomic status. That one day, no human nation will consider itself superior to its fellow nations and will not try to control the fate of others by means of coercion, be it through wars, financial aids, economic sanctions, or misinformation. Because only then can they speak to us as one.

We have a dream that one day humans are free from past injustices, whether they acted unjustly or were subjected to injustice. Whether these injustices involve enslaving another human race, invading their homes, or controlling the reproductive decisions of women. Because only then they can have peace. Only then they are ready for us.