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Space Wars: The Private Sector Strikes Back



[Infographic] Space Wars: The Private Sector Strikes Back

Space Wars: The Private Sector Strikes Back

Government agencies such as NASA have been the brains behind space exploration for decades with much success. Such agencies put the first man in space, landed on the moon, and built the first reusable space vessel.

However, since the private venture behind SpaceShipOne won the $10 million Ansari X Prize in 2004 for its series of manned flights, a new age of space exploration has begun.

In fact, many companies in the private sector have started to achieve great milestones. Our infographic today, created by Visual Capitalist, documents the companies in space that are now vying for space supremacy.

The Old Guard

Rooted in the lucrative government space contracts of the past, the Old Guard consists primarily of defense and aerospace behemoths such as Boeing, Lockheed Martin, and Orbital Sciences.

To put things in perspective, United Launch Alliance (ULA), a joint venture between Boeing and Lockheed formed in 2006, charges the US Air Force a $1 billion retainer just to be “ready” to launch a satellite into space. To actually launch a satellite is another $380 million more.

Seeing this stagnant situation as a business opportunity were tech billionaires such as Jeff Bezos, Elon Musk, and Richard Branson, who have helped form The New Guard.

The New Guard

The price tag that is advertised for a launch of the Falcon 9 with SpaceX right now is $57 million. Compared to ULA prices above, this is a meaningful step in market disruption. It’s only the beginning.

Companies like Planetary Resources and Deep Space Industries plan to harvest asteroids in space for water and metals such as PGMs (platinum group metals). Richard Branson’s Virgin Galactic plans to bring super rich tourists to experience space in coming years for $250,000 a pop.

However, space isn’t for the faint of heart. Recent accidents in 2014 have made clear the elements of human and financial risk that space flight brings. In October, Orbital Sciences’ unmanned Antares rocket exploded over Virginia. A few days later, Virgin’s manned test flight of the SpaceShipTwo ended in calamity with one death and one serious injury to test pilots.

These challenging realities are part of embarking to new frontiers. So far, such incidents and risks have not deterred companies from their endeavours yet. Which companies will succeed in their quests in this new industry?

The truth is out there.

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Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.



A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

PeriodEraCompute Doubling
1950–2010Pre-Deep Learning18–24 months
2010–2016Deep Learning5–7 months
2016–2022Large-scale models11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

Perceptron Mark I1957–58695,000
Neocognitron1980228 million
NetTalk198781 billion
TD-Gammon199218 trillion
NPLM20031.1 petaFLOPs
AlexNet2012470 petaFLOPs
AlphaGo20161.9 million petaFLOPs
GPT-32020314 million petaFLOPs
Minerva20222.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.

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