Artificial Intelligence Is The Key To Crack The Mysteries Of The Universe, Here’s Why!

Original article was published by Bharath K on Artificial Intelligence on Medium


How can Artificial Intelligence help overcome this?

With advancements in technologies of artificial intelligence in the fields of data science, exploratory data analysis, and computer vision, we can achieve results beyond our imagination.

Since we started the conversation with telescopes, AI is the best solution to combat unclear images at longer distances. If you wondering how exactly this works then you would be surprised to know that artificial intelligence especially the field of computer vision and deep neural networks deals absolutely fantastic with images.

Thanks to the development on these grounds we have a way of creating many clear visuals that can recognize how these blur re-images can be reconstructed to create more copies. Finally, we can determine the true positive rate and the false positive rate on these exceptionally effective images produced by these neural networks.

As described in this research paper, applying this tech to the hunt for gravitational lenses was surprisingly straightforward. First, the scientists made a dataset to train the neural network with, which meant generating 6 million fake images showing what gravitational lenses do and do not look like. Then, they turned the neural network loose on the data, leaving it to slowly identify patterns. A bit of fine-tuning later, and they had a program that recognized gravitational lenses in the blink of an eye.

Computer simulations to interpret and digitally engineer a clear picture to represent the billions of entities in our Universe has been a conceptual and theoretical philosophy experimented by scientists but to no avail. Artificial Intelligence has changed this spectrum evidently. The credit for this accomplishment goes to the researchers who developed the deep neural network architecture called the Deep Density Displacement Model (D³M).

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According to this research paper, the Deep Density Displacement Model (D³M) learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Their extensive analysis demonstrates that D³M outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. The Deep Density Displacement Model (D³M) is also able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters.

This model constructed was a shocking surprise to astrophysicists and even the creators of the particular design. It produced a precise and accurate response way beyond the imagination of the developers. The simulations produced by the D³M were exceptionally accurate and even made a 3-D simulation of the entire Universe leaving the entire team of developers amazed.

The advancements in Artificial Intelligence is not limited to just image segmentation using telescopes or simulations of the entire Universe. Astronauts have a tough time surviving in space, traveling to the moon, and other space expeditions. However, there is a solution to even the problems that arise due to the complications of these space ventures. The use of Artificial Intelligence Rovers and Robotic equipment.

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The advanced modern AI robotic rovers can be used to replace the role of astronomers in outer space. The Mars rover is one such example of a modern-day rover. The benefit of attaching a highly advanced artificial intelligence system with satellites, robotic rovers, and spaceships engrosses our chances of discoveries far beyond human comprehension.

Intelligent data transmission software onboard Mars rovers remove human scheduling errors, which can otherwise cause valuable data to be lost. This increases the useful data that comes from our planetary neighbor. The same technology could also be used in long-term missions that will explore the Solar System, meaning that they will require minimal oversight from human controllers on Earth.

Robots with artificial intelligence technology as a whole encompass so much more. The use of artificial intelligence for space applications nowadays is also widespread, ranging from robots that go where no human can go to autonomous spacecraft and swarm intelligence. But also the way satellite images are analyzed, the management of mega-constellations, and even finding extra-solar planets have been made easier by employing artificial intelligence.

GIF From ESA

The final topic of discussion that we will uncover in this article is the major new development in the field of artificial intelligence, which could perhaps be the most significant and exceptional discovery. This concept could enlighten us about the paramount structures, designs, and capabilities of the Universe. Welcome the new AI called the “Dark Emulator.”

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The one concept which has bamboozled and puzzled scientists for over generations is the theory behind dark matter. Not only the secrets to the entire structure of the Universe can be unveiled, but also hypothesis and complex distinctions of modern physics concepts could potentially be solved with a detailed study and breakthrough of dark matter or dark energy. The Dark Emulator AI can be the best possible tool to solve the problems of astrophysicists. According to the lead author Nishimichi —

“We built an extraordinarily large database using a supercomputer, which took us three years to finish, but now we can recreate it on a laptop in a matter of seconds. I feel like there is great potential in data science. Using this result, I hope we can work our way toward uncovering the greatest mystery of modern physics, which is to uncover what dark energy is. I also think this method we’ve developed will be useful in other fields such as natural sciences or social sciences.”
— Nishimichi.

The Dark Emulator learns from the existing data and creates multiple virtual universes and keeps learning from these repeatedly. Upon further testing the resulting tool with real-life surveys, it was able to successfully predict the weak gravitational lensing effects in the Hyper Suprime-Cam survey, along with the three-dimensional galaxy distribution patterns recorded in the Sloan Digital Sky Survey to within 2 to 3% accuracy in a matter of seconds. In comparison, running simulations individually through a supercomputer without the AI would take several days.