Things to Do in L.A. Tap to enable a layout that focuses on the article. An attendee plays the Monster Hunter Wilds video game on the Sony PlayStation 5 Pro console during the Tokyo Game Show 2024 at Makuhari Messe in 2024 in Chiba, Japan. This is read by an automated voice. Please report any issues or inconsistencies here . With the start of the new year, Californians will pay a new fee every time they buy a product with a nonremovable battery -- whether it's a power tool, a PlayStation or even a singing greeting card.

Current disposal facilities for coarse-grained waste perform manual sorting of materials with heavy machinery. Large quantities of recyclable materials are lost to coarse waste, so more effective sorting processes must be developed to recover them. Two key aspects to automate the sorting process are object detection with material classification in mixed piles of waste, and autonomous control of hydraulic machinery. Because most objects in those accumulations of waste are damaged or destroyed, object detection alone is not feasible in the majority of cases. To address these challenges, we propose a classification of materials with multispectral images of ultraviolet (UV), visual (VIS), near infrared (NIR), and short-wave infrared (SWIR) spectrums. Solution for autonomous control of hydraulic heavy machines for sorting of bulky waste is being investigated using cost-effective cameras and artificial intelligence-based controllers.

At the Boulder County Recycling Center in Colorado, two team members spend all day pulling items from a conveyor belt covered in junk collected from the area's bins. One plucks out juice cartons and plastic bottles that can be reprocessed, while the other searches for contaminants in the stream of paper products headed to a fiber mill. They are Sorty McSortface and Sir Sorts-a-Lot, AI-powered robots that each resemble a supercharged mechanical arm from an arcade claw machine. Developed by the tech start-up Amp Robotics, McSortface and Sorts-a-Lot's appendages dart down with the speed of long-beaked cranes picking fish out of the water, suctioning up items they've been trained to recognize. Yes, even recycling has gotten tangled up in the AI revolution. Amp Robotics has its tech in nearly 80 facilities across the U.S., according to a company spokesperson, and in recent years, AI-powered sorting from companies such as Bulk Handling Systems and MachineX has popped up in other recycling plants.

AMP Robotics uses automation and artificial intelligence (AI) to sort materials within waste streams. We asked CEO Matanya Horowitz how this works, how data can be utilised and the ways automated sorting could impact the packaging industry. AMP Robotics developed an AI platform (AMP Neuron) to distinguish recyclable materials from waste. How did you come up with the idea? Ever since I was a child I've been interested in robotics and the origins of intelligence.

One robot's skinny leg, which relies on computer vision to detect recyclables, plucks a hunk of blue plastic off a conveyor belt, while the other's grabs a piece of an old water bottle. The machine then places those bits into sorting bins using a vacuum gripper. For the nation's 600-plus recycling facilities, which process some 67 million tons of waste, these leggy robots from AMP Robotics are one answer to the current bottlenecks facing the industry. Even before Covid-19 struck, AMP Robotics was starting to gain traction. But as boxes from home deliveries piled up at recycling centers and hiring--already a tough proposition--got even tougher as workers feared getting ill, AMP's business boomed.

Humans have been building machines to separate waste into different streams of different value requiring differing processes for decades. Until recently, we were mostly failing to do it well enough to be worth the investment. Instead, millions of people globally manually sort trash, sometimes with developed country workplace safety standards, sometimes living in developing country trash fields and scraping a living out of them. In London in the 1850s, when the population was roughly 3 million, a thousand rag and bone men plied their trade, greasy bags over their shoulders or slung on rough carts, picking through the detritus of the city to find enough items of value to allow them to pay for their lodging and food. In 1988, the World Bank estimated that 1-2% of the global population made most or all of its living picking through waste.

Humans have enlisted nearly 100 AI-powered robots in North American to come to the rescue for something humans are terrible at: recycling. Even when we try to do it right, we're often making things worse; About one out of every four of the things people throw into the recycling bin aren't recyclable at all. All those misplaced greasy pizza boxes (not recyclable) and clamshell containers tossed in with the plastics, have imperiled an industry that was never really that effective in the first place. Only a small fraction of the over 2.1 billion tons of the garbage the world produces each year gets recycled -- about 16%. And even that small sliver has gotten smaller over the past year.

Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states. To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change. Our evaluation shows that our system, ProComp, significantly outperforms two strong reading comprehension (RC) baselines. Our contributions are two-fold: the Semantic Lexicon rulebase itself, and a demonstration of how a simulation-based approach to machine reading can outperform RC methods that rely on surface cues alone. Since this work was performed, we have developed neural systems that outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the Semantic Lexicon remains a novel and potentially useful resource, and its integration with neural systems remains a currently unexplored opportunity for further improvements in machine reading about processes.