This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters influencing the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are critical for the production outcome. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.

This year, veteran video game developers Garry Kitchen and David Crane released a new game for the Atari 2600 – despite the fact that the console was discontinued some 30 years ago. Companies such as Limited Run Games and Strictly Limited Games are manufacturing brand new cartridges, and sometimes never-before-released games, for consoles that predate the smartphone. "The market's not remotely dead for these consoles," says Josh Fairhurst, head of North Carolina-based Limited Run. Prices for retro games have gone through the roof in recent years, as evidenced by a recent slew of record-breaking auction bids for classic titles, including the sale of a mint copy of Super Mario 64 for $1.5m (£1.1m). The supply of old games is limited, and demand is increasing: not just from older people who want to collect games they remember from their youth, but also from those who weren't even born when the Sega Mega Drive was cutting-edge.

Earlier this year, a nearly perfect copy of Super Mario Bros. for the NES sold for $660,000. Now, a mere three months later, The Legend of Zelda has shattered that record. On Friday, a rare, early production version of the NES classic sold for $870,000 at auction. Outside of a single sealed copy from its original manufacturing run, it's believed the game that sold this week is one of the earliest known sealed copies of The Legend of Zelda in existence. According to Heritage Auctions, the cartridge sold on Friday comes from the game's "NES-R" production run.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. This definitely counts as a high score. While "Super Mario Brothers" is arguably the most famous game for the original Nintendo Entertainment System, "The Legend of Zelda" was also very popular. Many people who grew up in the '80s probably remember owning the bright gold cartridge.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimise manufacturing operations to the shop floor level. Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

A rare "Celebration" Apple-1 computer built by Steve Wozniak and Steve Jobs in 1976 sold for 815,000 this week, according to MacRumors, after a bid for 1.2 million was pulled at the last minute. This particular board was not part of a known production run, and was never sold to the public. The Apple-1 is the very first Apple computer, the first personal computer marketed to consumers, and the first Apple product in history. To build it, Steve Jobs sold his only means of getting around, a Volkswagen Microbus, and Steve Wozniak sold his HP-65 calculator. The auction, held on CharityBuzz, was for the computer and a variety of accessories.