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This week in an article published in the journal Nature, researchers at Google detailed how they used AI to design the next generation of tensor processing units (TPU), enterprise application-specific integrated circuits optimized for AI workloads. While the work hasn’t been new – Google has refined the technique for most of the years – it has provided the clearest illustration to date of the potential of AI in hardware design. Previous experiences did not produce commercially viable products, only prototypes. But the Nature The article suggests that AI can at the very least augment human designers to speed up the brainstorming process.

Beyond chips, companies like Oqton, based in the United States and Belgium, are applying AI to design areas, including additive manufacturing. from Oqton The platform automates CNC, metal and polymer 3D printing and hybrid additive and subtractive workflows, such as the creation of castable jewelry wax. It suggests a range of optimizations and fixes informed by AI inspection algorithms, as well as pre-analyzes of part geometry and real-time calibration. For example, Oqton can automatically adjust geometries to achieve parts within the required tolerances, simulating the effects of heat treatment such as warping, shrinkage and stress reduction on titanium, cobalt, chromium, zirconia and other materials.

While still in the research stage, MIT’s Computing and Artificial Intelligence Lab has developed an AI-powered tool called LaserFactory which can print fully functional robots and drones. LaserFactory is based on a three-ingredient recipe that allows users to create structural geometry, print traces, and assemble electronic components such as sensors, circuits, and actuators. As the researchers behind LaserFactory note in an article describing their work, it could in theory be used for tasks such as delivery or search and rescue.

At Renault, engineers rely on AI-based software created by Siemens software for digital industries to automate the design of automated manual transmission (AMT) systems in cars. The AMT, which behaves like an automatic transmission but allows drivers to electronically change gears using a push button, can take up to a year of trial and error to design, develop and validate in depth. But Siemens’ tool allows Renault engineers to drag, drop and connect icons to graphically create an AMT model. The software predicts the behavior and performance of AMT components and makes the necessary improvements early in the development cycle.

Even Nutella is leveraging 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, in collaboration with advertising agency Ogilvy & Mather Italia, the company splashed 7 million unique designs on “Nutella Unica” jars all over Italy, which sold out within a month.

Change of philosophy

People might perceive these apps as taking the agency away from human designers, but the co-authors of a recent Harvard Business School study work document argue that AI actually enables designers to overcome limitations of the past – in scale and scope to learning.

“In the context of AI factories, solutions can even be more user-centric, more creative and continually updated through learning iterations that span the entire lifecycle of a product. Yet we have found that AI is profoundly changing the practice of design, ”write the co-authors. “Problem-solving tasks, traditionally performed by designers, are now automated in learning loops that operate without volume and speed limitations. These loops think in a radically different way of a designer: they tackle complex problems through very simple tasks, iterated exponentially.

In a recent blog post, user experience designer Miklos Philips echoed the conclusions of the contributors to the Harvard Business Review article, noting that designers working with AI can create prototypes quickly and inexpensively because of the increased efficiency it offers. The power of AI will be in how quickly it can analyze large amounts of data and suggest design adjustments, he says, so that a designer can select and approve the adjustments based on the data and create the designs. the most effective to test quickly.

In any case, the return on investment of AI-assisted design tools is potentially substantial. According to a 2020 PricewaterhouseCoopers investigation, companies in the manufacturing sector expect efficiency gains over the next five years as a result of digital transformations, including the adoption of AI and machine learning. Unsurprisingly, 76% of people surveyed in a Google Cloud report published this week said they have turned to “disruptive technologies” like AI, data analytics and the cloud, especially to help overcome the challenges brought on by the pandemic.

Considering the business value, AI-based design is likely to stay and grow. This is generally good news not only for designers, but also for businesses and consumers who should reap the benefits of automation in the creation of physical products.

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