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How Much Data is Generated Each Day?

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How Much Data is Generated Each Day?

How Much Data is Generated Each Day?

View the full-size version of the infographic by clicking here

You’ve probably heard of kilobytes, megabytes, gigabytes, or even terabytes.

These data units are common everyday amounts that the average person may run into. Units this size may be big enough to quantify the amount of data sent in an email attachment, or the data stored on a hard drive, for example.

In the coming years, however, these common units will begin to seem more quaint – that’s because the entire digital universe is expected to reach 44 zettabytes by 2020.

If this number is correct, it will mean there are 40 times more bytes than there are stars in the observable universe.

A Crash Course in Data

Today’s infographic comes to us from Raconteur, and it gives us a picture of this new data reality.

Before we get to how much data is created each day – both now, and in the future – it’s worth getting acquainted with how data scales in terms of units.

AbbreviationUnitValueSize (in bytes)
bbit0 or 11/8 of a byte
Bbytes8 bits1 byte
KBkilobytes1,000 bytes1,000 bytes
MBmegabyte1,000² bytes1,000,000 bytes
GBgigabyte1,000³ bytes1,000,000,000 bytes
TBterabyte1,000⁴ bytes1,000,000,000,000 bytes
PBpetabyte1,000⁵ bytes1,000,000,000,000,000 bytes
EBexabyte1,000⁶ bytes1,000,000,000,000,000,000 bytes
ZBzettabyte1,000⁷ bytes1,000,000,000,000,000,000,000 bytes
YByottabyte1,000⁸ bytes1,000,000,000,000,000,000,000,000 bytes

There’s no doubt that data literacy will only become more important in the future, so make sure you know your zettabytes from your yottabytes!

A Day of Data

How much data is generated in a day – and what could this look like as we enter an even more data-driven future?

Here are some key daily statistics highlighted in the infographic:

  • 500 million tweets are sent
  • 294 billion emails are sent
  • 4 petabytes of data are created on Facebook
  • 4 terabytes of data are created from each connected car
  • 65 billion messages are sent on WhatsApp
  • 5 billion searches are made

By 2025, it’s estimated that 463 exabytes of data will be created each day globally – that’s the equivalent of 212,765,957 DVDs per day!

If you think the above information is fascinating, see what happens in an internet minute.

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AI

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.

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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.

AIYearFLOPs
Theseus195040
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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