Based in Tennessee, the boatbuilder is making waves with its artisan process. Breakthroughs, discoveries, and DIY tips sent every weekday. Wakeboarding and wakesurfing have seen a sharp rise in popularity over the last 20 to 30 years. They're slightly different sports, though both start with a tow rope pulled by a powerboat. When wakeboarding, the rider keeps hold of the rope, while wakesurfing allows the watersports enthusiast to surf the waves made by the boat, hands-free.

Industries can rethink how work gets done, raising the bar for productivity and workplace safety. Across industries, companies are feeling the squeeze from labor shortages, rising costs and nonstop pressure to boost efficiency. Robots are quickly becoming real-life solutions, and their promise has never felt more relevant. With factories and warehouses scrambling to fill essential roles, the search for fresh ideas is heating up. That's where AEON comes in.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I once witnessed a friend going through a severe midlife crisis. Basically overnight, this formerly serious and well-adjusted middle-aged man dumped his wife for a much younger girlfriend, got a face tattoo, and built a full-sized halfpipe in his house. Soon, we were barraged with music recommendations (all stuff he'd listened to in high school and college) and life updates laden with "hip" "slang" ("Despite the age gap, my situationship with Triniteigh is lowkey lit"). It was a transparent--and, from a certain perspective, even sympathetic--response to a universal anxiety: He'd seen that the good times were over, and that only decline lay ahead. But, like all nostalgists, he didn't realize that you can't ever truly go back; you can only go backward. The United States, under President Donald Trump, seems to be undergoing a similar midlife crisis, as this reactionary administration attempts to brute-force the country back to a golden age that many people are realizing either didn't exist in the first place or has been permanently lost to the mists of time and modernization.

The basic machine for grinding a steel ball bearing has been the same since around 1900, but manufacturers have been steadily automating everything around it. Today, the process is driven by a conveyor belt, and, for the most part, it's automatic. The most urgent task for humans is to figure out when things are going wrong--and even that could soon be handed over to AI. The Schaeffler factory in Hamburg starts with steel wire that is cut and pressed into rough balls. Those balls are hardened in a series of furnaces, and then put through three increasingly precise grinders until they are spherical to within a tenth of a micron.

ACM Fellow Daniela Rus has been dreaming of robots since she was a child, imagining mechanical shoes to help her jump higher. As director of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), Rus has done pioneering work in modular robots, soft robotics, novel neural networks, and more. Her talk on the future of robotics and AI was featured at a recent TED conference, and this year she released a pair of books for the general public, including The Mind's Mirror: Risk and Reward in the Age of AI. Throughout her career, Rus has maintained a dual focus on improving both the bodies and the brains of intelligent machines. This traces back to her Ph.D. thesis, when she discovered the algorithms she'd developed for dexterous manipulation were too advanced for the robotic hands of the day.

Abandon all hope, ye who merge spreadsheet cells! Last week, at its annual I/O conference, Google spent hours detailing how large language models would help the knowledge workers of the world unload their busywork onto a legion of eager, capable neural networks. The company will soon introduce AI functions into programs such as Gmail, Google Sheets, and Google Slides that will allow users to type simple commands and receive complex outputs: entire email compositions, for example, or auto-generated tables. The future that Google is promising feels familiar--it's all about heightened convenience and one-click efficiency--and I hate it. Workplace AI feels like the purest distillation of a corrosive ideology that demands frictionless productivity from workers: The easier our labor becomes, the more of it we can do, and the more of it we'll be expected to do.

And latency almost always matters when it comes to running artificial intelligence/machine learning (AI/ML) workloads. Great AI requires a lot of data, and it demands it immediately." That's both the blessing and the curse in any sector – industrial and manufacturing are prominent examples, but the principle applies widely across businesses – that generates tons of machine data outside of their centralized clouds or data centers and wants to feed it to an ML model or other form of automation for any number of purposes. Whether you're working with IoT data on a factory floor, or medical diagnostic data in a healthcare facility – or one of many other scenarios where AI/ML use cases are rolling out – you probably can't do so optimally if you're trying to send everything (or close to it) on a round-trip from the edge to the cloud and back again. In fact, if you're dealing with huge volumes of data, your trip might never get off the ground. "I've seen situations in manufacturing facilities ...

Digital-industrial innovation can now prove its mettle. For the last ten years, digital-industrial innovation has been seen as the poor, unglamorous cousin of pure digital innovation. It arrived late to the party. Its applications were developed on factory floors, in what was looked down upon as "old" economy. General Electric GE, which pioneered what it called the Industrial Internet, found that attracting software talent was one of its biggest difficulties.

With the rise of digitization, we're gathering more and more data that, if used to its full potential, will help businesses counter uncertainty and make business outcomes more predictable. Nowadays, companies face countless challenges -- inflation, supply chain delays, natural disasters, and global pandemics. The most valuable part of AI is its ability to take in huge amounts of data and calculate every possible outcome, then make recommendations based on a variety of parameters. It can also offer solutions to lessen these problems without the need for human interference. Combined with a fully integrated end-to-end ERP system, AI can be a critical factor in streamlining business processes.

Training AI models can be challenging when using crowdsourced or publicly available datasets. The most useful neural networks are ones that replace expert knowledge to solve real problems. One example would be a visual inspection model for the factory floor. Human performance comes into tiers: general or expert. An expert is someone who has spent a minimum amount of time or has received practical training in the field.