Technology
Charted: The Exponential Growth in AI Computation
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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.
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.
Period | Era | Compute Doubling |
---|---|---|
1950–2010 | Pre-Deep Learning | 18–24 months |
2010–2016 | Deep Learning | 5–7 months |
2016–2022 | Large-scale models | 11 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.
AI | Year | FLOPs |
---|---|---|
Theseus | 1950 | 40 |
Perceptron Mark I | 1957–58 | 695,000 |
Neocognitron | 1980 | 228 million |
NetTalk | 1987 | 81 billion |
TD-Gammon | 1992 | 18 trillion |
NPLM | 2003 | 1.1 petaFLOPs |
AlexNet | 2012 | 470 petaFLOPs |
AlphaGo | 2016 | 1.9 million petaFLOPs |
GPT-3 | 2020 | 314 million petaFLOPs |
Minerva | 2022 | 2.7 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.
This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.
Technology
Ranked: Most Popular Gaming Genres by Generation
Adventure was the most popular gaming genres for Millennials, Gen Z, and Gen Alpha. Which genres were popular for Boomers and Gen X?
Most Played Gaming Genres by Generation
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
From high-fantasy adventures like Elden Ring to viral mobile hits like Candy Crush, gaming is a universally-beloved hobby enjoyed by people of all walks of life.
This visualization shows the three most popular gaming genres for each generation: Baby Boomers (1945-1964), Gen X (1965-1980), Millennials (1981-1994), Gen Z (1995-2009), and Gen Alpha (2010 and later).
The figures come from Newzoo’s Global Gamer Study 2024, which surveyed over 73,000 people across 36 markets. The percentages represents the share of respondents who played the genre on any device in the past six months.
Younger Generations Prefer Adventure and Action
Adventure games such as Skyrim, The Last of Us, and Baldur’s Gate 3 were the most popular among the three younger generations, while puzzle games like Candy Crush were the most popular for the two oldest generations.
Generation | Top genre | Share of respondents | Second-most popular genre | Share of respondents | Third-most popular genre | Share of respondents |
---|---|---|---|---|---|---|
Gen Alpha | Adventure | 54% | Fighting | 44% | Racing | 42% |
Gen Z | Adventure | 45% | Fighting | 38% | Shooter | 38% |
Millennials | Adventure | 46% | Puzzle | 37% | Shooter | 35% |
Gen X | Puzzle | 46% | Adventure | 33% | Strategy | 27% |
Baby Boomers | Puzzle | 50% | Arcade | 25% | Casino | 21% |
Among Gen Z, Minecraft, Fortnite, and Call of Duty games were some of the most-played adventure titles. While the youngest generation, Alpha, shares Gen Z’s interest in adventure and action games, racing games beat out shooters for Generation Alpha’s third-most played kind of game genre.
Generally, younger generations tend to be more engaged in video games.
Around 94% of Gen Alpha and 90% of Gen Z consider themselves gaming enthusiasts, which includes playing, watching, or otherwise engaging with video games, compared to just 67% of Gen X and 47% of Baby Boomers.
Overall, console games still make up the majority of the gaming industry’s market share at 57%, compared to PC games making up 43%.
Learn More on the Voronoi App
To learn more about the video game industry, check out this graphic that visualizes console launch prices, adjusted for inflation.
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