Artificial Intelligence: A Journey to Deep Space

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In this sponsored post, Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies, explores how recent advancements in Artificial Intelligence, especially deep learning, are set to make an impact in the field of astronomy and astrophysics. 

Ramnath Sai Sagar, Mellanox Technologies

Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies

Since the dawn of the space age, unmanned spacecraft have flown blind, with little to no ability to make autonomous decisions based on their environment. That, however, changed in the early 2000s, when NASA started working on leveraging Artificial Intelligence (AI) and laying the foundation that would help Astronauts and Astronomers to work more efficiency in Space.  In fact, just last month, NASA’s Jet Propulsion Laboratory published how AI will govern the behavior of space probes.

Recent advancements in Artificial Intelligence, especially Deep Learning (a subfield in AI), are set to make a deeper impact in the field of astronomy and astrophysics. From navigating the unknown terrain of Mars, to analyzing petabytes of data generated from Square Kilometer Array, to finding Earth-like planets in our messy galaxy, AI is already revolutionizing our lives here on earth by building smarter and more autonomous cars, helping us find solutions to climate change, revolutionizing healthcare and much more. Mellanox is proud to be working closely with the leading companies and research organizations to make advancements in the field of Artificial Intelligence and Astronomy.

AI: The Next Industrial Revolution

Coined in 1956 by Dartmouth Assistant Professor John McCarthy, AI existed before the “Race to Space” but could only deliver rudimentary displays of intelligence in specific context. Progress was limited due to the complexities of algorithms needed to tackle various real-world issues. Many were above the ability of a mere human to execute. This however, changed in the past decade mainly due to two reasons:

  1. Storing Unstructured Data More Efficiently: Around 90 percent of data generated today are unstructured, including free-form documents, images, audio and video recordings. Traditionally, it hasn’t been possible for computers to efficiently store and process these data. However, the advancements in Hadoop and NoSQL databases, in concert with the underlying storage technologies (Software-Defined Storage, Object Storage, etc.), have enabled storing and processing petabytes of unstructured data in a far more cost effective way.
  2. Processing Data Faster: It takes massive amount of computing resource to train a sophisticated AI model – training that can take weeks to months. The advancements in the underlying hardware, including faster compute (GPUs, FPGAs etc.), faster storage (SSDs/NVMe, NVMe-over-Fabrics, etc.) and faster networks at speeds of up to 100Gb/s, has helped reduce the training time to just a week. Further, using Remote Direct Memory Access (RDMA), an industry networking standard that Mellanox has pioneered, helps to reduce days and to mere hours. (All popular AI frameworks such as Tensorflow, Caffe, Torch and Microsoft CTNK all support RDMA).

Due to this, AI now presents one of the most exciting and potentially transformative opportunities for the mankind. In fact, in some quarters it is being heralded as the next industrial revolution:

“The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first.” – Sundar Pichai, CEO of Google, October 2016

 AI for the Messy Galaxy

While humanity has made great strides in exploring the observable universe, we need to rely on intelligent robots to explore where we cannot humanly go. This is because our galaxy, the Milky Way, is one messy place, filled with cosmic dust from stars, comets, and more; concealing the very things scientists want to study. That said, there are three major challenges in leveraging AI in the future of space exploration. Firstly, the probes will have to be able to learn about and adapt to unknown environments including responding to thick layers of gas in a planet’s atmosphere, extreme temperatures or unplanned for fluctuations in gravity.

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Secondly, when a probe falls outside the communication range, would have to figure out when and how to return the data collected during the time the signal was lost. Finally, given the vast distances in space, it could take several generations before the probe reaches its destination and therefore, will need to be flexible enough to adapt to any new discoveries and innovations we make here on earth. The solution to these problems will require training AI models on petabytes of data captured using supercomputers.

The benefits of using AI to control space-exploring robots are already being realized by missions that are currently underway. For example, Opportunity, the Mars Exploration Rover, which was launched back in 2003, has an AI driving system called Autonav that allows it to explore the surface of Mars. In addition, Autonomous Exploration for Gathering Increased Science (AEGIS) has been used by the NASA Mars rover, Curiosity, since May in order to select which aspects of Mars are particularly interesting and subsequently take photos of.

artificial intelligence

Image Captured by AEGIS Enabled Curiosity’s ChemCam.

But Mars is by no means the final destination and the exploration of more challenging destinations will require even more advanced AI. For example, exploring the subsurface ocean of the Jovian moon Europa in the hope of finding alien life, will require bypassing a thick (~10km) ice crust. Controlling this exploration would be severely limited without advanced autonomy.

 Artificial Intelligence Needs Intelligent Network

Since the early age of Mellanox, we have been working closely with NASA and many research labs help solve the challenges of scientific computing, whether it’s the aerodynamic simulation of the Jet Propulsion Engine or monitoring the universe in unprecedented detail. In addition, over the last few years, Mellanox has also enabled the pioneers in the field of AI including Baidu for their advancements in autonomous cars and Yahoo for image recognition. The applications of autonomous driving and object recognition go far beyond the limits of Earth and Mellanox is proud to be working closely with several research organizations and companies and helping them achieve technological breakthroughs in the field of astronomy and astrophysics.

Forty-eight years ago, Neil Armstrong said “That’s one small step for man, one giant leap for mankind”, when he became the first human to set the foot on the surface of the moon. The next giant leap for mankind will come from the small step of a robot, powered by AI and Mellanox.

Ramnath Sai Sagar is Marketing Manager at Mellanox Technologies. This post originally ran as part of Mellanox Technologies’ Interconnected Planet blog series.