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This week in a paper published in the journal Nature, researchers at Google detailed how they used AI to design the next generation of tensor processing units (TPU), the company’s application-specific integrated circuits optimized for AI workloads. While the work wasn’t novel — Google’s been refining the technique for the better part of years — it gave the clearest illustration yet of AI’s potential in hardware design. Previous experiments didn’t yield commercially viable products, only prototypes. But the Nature paper suggests AI can at the very least augment human designers to accelerate the brainstorming process.
Beyond chips, companies like U.S.- and Belgium-based Oqton are applying AI to design domains including additive manufacturing. Oqton’s platform automates CNC, metal, and polymer 3D printing and hybrid additive and subtractive workflows, like creating castable jewelry wax. It suggests a range of optimizations and fixes informed by AI inspection algorithms, as well as by pre-analyses of part geometry and real-time calibration. For example, Oqton can automatically adjust geometries to get parts within required tolerances, simulating heat treatment effects like warpage, shrinkage, and stress relief on titanium, cobalt, chrome, zirconia, and other materials.
While it’s still in the research stages, MIT’s Computer Science and Artificial Intelligence Laboratory developed an AI-powered tool called LaserFactory that can print fully functional robots and drones. LaserFactory leverages a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors, circuits, and actuators. As the researchers behind LaserFactory note in a paper describing their work, it could in theory be used for jobs like delivery or search-and-rescue.
At Renault, engineers are leveraging AI-powered software created by Siemens Digital Industries Software to automate the design of automated manual transmission (AMT) systems in cars. AMT, which behaves like an automatic transmission but allows drivers to shift gears electronically using a push-button, can take up to a year of trial and error to ideate, develop, and thoroughly validate. But Siemen’s tool enables Renault engineers to drag, drop, and connect icons to graphically create a model of an AMT. The software predicts the behavior and performance of the AMT’s components and makes any necessary refinements early in the development cycle.
Even Nutella is tapping AI for physical products, using the technology to pull from a database of dozens of patterns and colors to create different versions of its packaging. In 2017, working with advertising agency Ogilvy & Mather Italia, the company splashed over 7 million unique designs on “Nutella Unica” jars throughout Italy, which sold out in a month.
People might perceive these applications as taking agency away from human designers, but the coauthors of a recent Harvard Business School working paper argue that AI actually enables designers to overcome past limitations — from scale and scope to learning.
“In the context of AI factories, solutions may even be more user-centered, more creative, and continuously updated through learning iterations that span the entire life cycle of a product. Yet, we found that AI profoundly changes the practice of design,” the coauthors write. “Problem solving tasks, traditionally carried on by designers, are now automated into learning loops that operate without limitations of volume and speed. These loops think in a radically different way than a designer: they address complex problems through very simple tasks, iterated exponentially.”
In a recent blog post, user experience designer Miklos Philips echoed the findings of the Harvard Business Review paper contributors, noting that designers working with AI can create prototypes quickly and more cheaply due to the increased efficiency it offers. AI’s power will lie in the speed in which it can analyze vast amounts of data and suggest design adjustments, he says, so that a designer can cherry-pick and approve adjustments based on data and create the most effective designs to test expediently.
In any case, the ROI of AI-assisted design tools is potentially substantial. According to a 2020 PricewaterhouseCoopers survey, companies in manufacturing expect efficiency gains over the next five years attributable to digital transformations, including the adoption of AI and machine learning. Perhaps unsurprisingly, 76% of respondents to a Google Cloud report published this week said they’ve turned to “disruptive technologies” like AI, data analytics, and the cloud, particularly to help navigate challenges brought on by the pandemic.
Given the business value, AI-powered design is likely here to stay — and to grow. That’s generally good news not only for designers, but for the enterprises and consumers that stand to reap the benefits of automation across physical product creation.
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