Technology
Computational Design: The Future of How We Make Things is Tech-Driven
Future Design is Computational
Design is always changing, and never stagnant.
In the late 20th century, it was the emergence of Design Thinking that upended how architects, engineers, and industrial design organizations made decisions about how to make new things.
Now, the rapid pace of technological advancement has brought forth a new design methodology that will again forever alter the course of design history. Computational design, which takes advantage of mass computing power, machine learning, and large amounts of data, is changing the fundamental role of humans in the design process.
Designing With Billions of Data Points
Today’s infographic comes to us from Schneider Electric, and it looks at how the future of design will be driven by data and processing power.
While computational design is still a term with no real consensus, attempts to define it do have overlap:
Parameter setting
Algorithmic, “rules-based” code can be applied as constraints to test a wide variety of computer-driven designs
3d modelling and visualization tools
Complex 3d models can allow designers to test and create simulations for new ideas
Processing power
Using vast amounts of computational power and automation to make designs not before possible
Designing with data
Applying big data and powerful algorithms to create new designs
Generative design
By creating, testing, and analyzing thousands of design permutations, this approach mimics mother nature’s evolutionary path to design
While designers traditionally rely on intuition and experience to solve design problems, computational design is a new design methodology that can literally produce hundreds or thousands of design permutations to find the absolute best solution to a problem.
The Shifting Roles of Humans and Computers
Throughout history, humans have shaped the world with design.
But now that artificial intelligence is superior in taking on specific roles within the design process, humans will move towards shaping the things that shape the world.
Designers will be relinquishing control to technology, so that humans can do what they do best.
In other words, in the future, designers will work less on designing – and instead will supervise, mentor, and set the parameters for computational designs. Human designers would also interact with a broader group of stakeholders as additional inputs and the frequency of interactions increase.
A New Design Landscape
Disruption to traditional design methods brings more questions than answers:
- How will this change the value chain for design companies and professionals?
- Will AI-enabled computational design tools take the “craft” out of design?
- If automated design “assets” become commercial commodities, will that create new product and revenue channels for businesses?
- Who will own and manage all of this data, and does this create new roles and opportunities for companies?
As we give machines more design autonomy, it will be interesting to see how this literally changes the shape and design of objects that make up the real world.
Technology
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.

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.
-
Maps1 week ago
The Incredible Historical Map That Changed Cartography
-
Markets2 weeks ago
Charted: Six Red Flags Pointing to China’s Economy Slowing Down
-
VC+7 days ago
What’s New on VC+ in September
-
Globalization4 weeks ago
Visualizing the BRICS Expansion in 4 Charts
-
Markets2 weeks ago
The 25 Best Stocks by Shareholder Wealth Creation (1926-2022)
-
Business4 days ago
Ranked: The 20 Best Franchises to Open in the U.S.
-
Technology4 weeks ago
Nvidia vs. AMD vs. Intel: Comparing AI Chip Sales
-
Money2 weeks ago
How Much Does it Take to Be Wealthy in America?