Technology

Computational Design: The Future of How We Make Things is Tech-Driven

Published

5 years ago

September 4, 2018

Marc Doucette

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.

Related Topics:ai computational design design thinking classical design design tech big data

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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.

Creator Program

Published

4 days ago

September 18, 2023

Pallavi Rao

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.

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.