Category Archives: Robotics

SPRINGFIELD, Mo. Ozarks Technical Community College received a $345,725grant from the National Science Foundation for its new automation and robotics program.

The program will debut in August at the Robert W. Plaster Center for Advanced manufacturing (PMC). The grant is funded by the NSFs Advanced Technological Education program, which focuses on training technicians in advanced technology fields.

According to a press release, the grant will improve and expand automation and robotics instruction for manufacturing students at OTC and increase the awareness of automation and robotics career opportunities for middle and high school students.

Its not every day that an institution earns a prestigious National Science Foundation grant, said Danelle Maxwell, OTC manufacturing dept. chair. It is gratifying to know that the NSF finds the curriculum and rigor in this new degree pathway worthy of a significant investment.

Students in the program will learn how to operate and maintain automated systems commonly used in manufacturing.

The college has consulted with our industry partners to equip the Plaster Manufacturing Center with the latest machinery and technology, said Robert Randolph, executive director of the PMC. When we send graduates into a career, they will be ready to work from day one because theyve been trained on the most modern equipment. Plus, this grant will allow the college to engage with young students and encourage them to consider manufacturing as a career.

In addition to automation and robotics, the 120,000 square foot, $40 million Plaster Manufacturing Center will provide training in the following disciplines when it opens in August of 2022:

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OTC receives grant for new automation and robotics program - KOLR - OzarksFirst.com

DENVER, June 28, 2022--(BUSINESS WIRE)--AMP Robotics Corp. ("AMP"), a pioneer in AI, robotics, and infrastructure for the waste and recycling industry, has expanded its partnership with Waste Connections, Inc. (TSX/NYSE: WCN), its largest customer. Since late 2020, Waste Connections has booked or deployed 50 of AMPs high-speed robotics systems on plastic, fiber, and residue lines, becoming the largest operator of AI-guided robotics in the industry.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220628005328/en/

AMP Robotics has expanded its partnership with Waste Connections, its largest customer. Since late 2020, Waste Connections has booked or deployed 50 of AMPs high-speed robotics systems on plastic, fiber, and residue lines, becoming the largest operator of AI-guided robotics in the industry. (Photo: Business Wire)

"Its gratifying to reach this milestone with a leader in resource recovery like Waste Connections. Weve worked tirelessly to deliver AI and robotics systems that modernize todays recycling operations by increasing productivity, stabilizing costs, and improving the quality and value of recycled commodities," said Matanya Horowitz, founder and CEO of AMP Robotics. "We remain committed to the continuous enhancement of our AI and automation solutions to stay ahead of industry challenges, exceed customer expectations, and support the industrys sustainability and climate goals."

AMPs technology identifies and recovers plastics, cardboard, paper, cans, cartons, and many other containers and packaging types reclaimed for raw material processing. For example, AMP recovers a portfolio of #1-#7 plastics in a variety of different form factors, colors, and opacities with high precision and purity. Polyethylene terephthalate (PET) and high-density polyethylene (HDPE) together account for a majority of recycled plastic demand, with growing interest in polypropylene (PP)a highly recyclable polymer with high demand in food-safe applications and flexibility to accommodate packaging in a variety of shapes and sizes. The ability to precisely separate different plastics and other recyclables with AI-driven sorting is helping recyclers meet the soaring need for sufficient quantities of high-quality recycled content from brands and packaging producers.

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"Were excited to expand our relationship with AMP," said Dan Kurtz, director of recycling for Waste Connections. "Were impressed by the reliability of the robotics systems and the quality of the end product. We look forward to our continued partnership with AMP as we advance our sustainability initiatives."

AMP has more than 230 deployments in more than 80 facilities across three continents.

About AMP Robotics Corp.

AMP Robotics is modernizing the worlds recycling infrastructure by applying AI and automation to increase recycling rates and economically recover recyclables reclaimed as raw materials for the global supply chain. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. AMP Clarity provides data and material characterization on what recyclables are captured and missed, helping recycling businesses and producers maximize recovery. With deployments across North America, Asia, and Europe, AMPs technology recovers recyclables from municipal collection, precious commodities from electronic scrap, high-value materials from construction and demolition debris, and valuable feedstocks from organic material.

About Waste Connections

Waste Connections is an integrated solid waste services company that provides non-hazardous waste collection, transfer and disposal services, along with resource recovery primarily through recycling and renewable fuels generation. Waste Connections serves more than eight million residential, commercial, and industrial customers in mostly exclusive and secondary markets across 44 states in the U.S. and six provinces in Canada. Waste Connections also provides non-hazardous oilfield waste treatment, recovery, and disposal services in several basins across the U.S., as well as intermodal services for the movement of cargo and solid waste containers in the Pacific Northwest.

Waste Connections views its Environmental, Social and Governance ("ESG") efforts as integral to its business, with initiatives consistent with its objective of long-term value creation. The companys long-term, aspirational ESG targets include the expansion of resource recovery through recyclable commodities. For more information, visit wasteconnections.com/sustainability.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220628005328/en/

Contacts

Carling Spelhaugcarling@amprobotics.com

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AMP Robotics and Waste Connections Reach Recycling Technology Milestone - Yahoo Finance

Robotics has made massive advances in recent years. Fields such as Robotics Process Automation (RPA) are being deployed in more and more businesses.

RPA software is needed to combine organizational processes with the actions of the robots and AI inputs. RPA software automates repetitive, labor-intensive, and time-consuming tasks, minimizing or eliminating human involvement to drive faster and more efficient processes across the factory floor. Instead of having dozens of workers in a manufacturing plant, an RPA specialist can program and run robots to perform those duties. Typically, another person is involved to service, maintain, and repair the hardware.

But AI is taking RPA to greater and greater levels of functionality.Here are some of the top trends in AI Robotics:

RPA and AI

The latest trend is for RPA to be integrated with AI. This is an essential element of RPA being able to deal with high-volume, repeatable tasks. By moving these over from humans to robots, these tasks are taken care of in a way that lowers labor costs, workflows are made more efficient, and processes such as those on assembly lines are accelerated.

This is also simplifying the overall field of robotics. Instead of different teams using different software, industrial settings can now combine RPA software and factory automation systems. Historically, the robotics team utilized specific programming languages to deal with the areas such as the kinematics of multi-axis robots. Factory automation technicians used different languages and tools such as Programmable Logic Controllers (PLCs) and shop floor systems. AI is helping to integrate these two worlds and add a greater degree of mobility and autonomy to robots.

For stationary robots to work seamlessly with mobile robots, it is essential that they can exchange information accurately and without failure, said Samir Patel, Senior Director, Robotics Engineering at Kawasaki Robotics USA.

Autonomous Operation

Peter Stone, PhD, Executive Director, Sony AI America notes that robots are moving more and more towards operating in open, uncontrolled spaces that are also occupied by people. Much effort is going into creating self-driving cars that are robust and economically viable. But beyond simple vacuum cleaners, AI is needed to take robotics to the next level.

Beyond creating robots that are viable as useful consumer products (other than for entertainment), there are still challenges for AI and robotics, said Stone.

AI will be needed to take into account thousands of parameters and variables occurring in real time. Many of these are changing constantly many times a second.

Neurosymbolic AI

Whats the next big thing in AI and robotics? Stone thinks it will be neurosymbolic AI.

The current AI boom was initiated by the confluence of the data and computation required to enable neural networks to achieve very impressive results on some very challenging tasks.While important research remains on understanding the full capabilities of neural networks, we are now seeing increased interest in 1) understanding their limitations and 2) integrating them with other tried and true AI algorithms, including symbolic and probabilistic methods.

In the coming years, broad exploration will occur in the field of hybrid neurosymbolic approaches towards applications that are beyond the capabilities of any one approach on its own, said Stone. Just as different regions of the human brain are known to operate differently (e.g. cerebellum vs. visual cortex), next-generation AI systems are likely to integrate differently operating modules. Research in this direction will be particularly useful for advances in general-purpose service robots capable of robust perception, communication in natural language, task and motion planning for object manipulation, and natural human-robot interaction across a wide variety of tasks.

Claims Processing

Over time, more and more tasks lend themselves to automation beyond just simple programming. Organizations are leveraging RPA, for example, to automate actions like understanding whats on a screen and completing keystrokes, and identifying and extracting data.

Health care is a good example, where such systems are being used to validate and process patient claims, said Adam Spotton,Head of Data Science,DNSFilter.

Job Candidates

Anyone posting a job opening typically receives hundreds if not thousands of resumes. AI robotics can be used to sift through them, and even find good candidates who may not immediately tick all the boxes. By training AI to note similar qualifications and other traits, better candidates are proposed and those that might be missed otherwise are given attention.

Amazon has been known to use RPA to find priority candidates for job positions by scanning and selecting resumes, said Spotton. I expect RPA to be a significant AI automation trend moving forward, across industries.

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5 Top Trends in AI Robotics in 2022 - Datamation

Mentions of robotics within the filings of companies in the tech sector rose 26% between the final quarter of 2021 and the first quarter of 2022.

In total, the frequency of sentences related to robotics between April 2021 and March 2022 was 2% lower than in 2016 when GlobalData, from whom our data for this article is taken, first began to track the key issues referred to in company filings.

When tech companies publish annual and quarterly reports, ESG reports and other filings, GlobalData analyses the text and identifies individual sentences that relate to disruptive forces facing companies in the coming years. Robotics is one of these topics - companies that excel and invest in these areas are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

To assess whether robotics is featuring more in the summaries and strategies of tech companies, two measures were calculated. Firstly, we looked at the percentage of companies which have mentioned robotics at least once in filings during the past twelve months - this was 35% compared to 21% in 2016. Secondly, we calculated the percentage of total analysed sentences that referred to robotics.

Of the 10 biggest employers in the tech sector, Foxconn was the company which referred to robotics the most between April 2021 and March 2022. GlobalData identified 58 robotics-related sentences in the Taiwan-based company's filings - 0.6% of all sentences. Hitachi mentioned robotics the second most - the issue was referred to in 0.3% of sentences in the company's filings. Other top employers with high robotics mentions included China Mobile, NTT and Teleperformance.

Across all tech companies the filing published in the first quarter of 2022 which exhibited the greatest focus on robotics came from Teradyne. Of the document's 1,462 sentences, 28 (1.9%) referred to robotics.

This analysis provides an approximate indication of which companies are focusing on robotics and how important the issue is considered within the tech sector, but it also has limitations and should be interpreted carefully. For example, a company mentioning robotics more regularly is not necessarily proof that they are utilising new techniques or prioritising the issue, nor does it indicate whether the company's ventures into robotics have been successes or failures.

In the last quarter, tech companies based in Asia were most likely to mention robotics with 0.09% of sentences in company filings referring to the issue. In contrast, companies with their headquarters in Canada mentioned robotics in just 0.02% of sentences.

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Filings buzz in the tech sector: 26% increase in robotics mentions in Q1 of 2022 - Verdict

FREMONT, Calif., June 27, 2022 (GLOBE NEWSWIRE) -- Cobalt Robotics, the only company to automate repetitive manual security and facility tasks with an integrated service which unifies state-of-the-art robotics, machine learning software, and expert human oversight, announced today that it has been selected as a winner in the 5th Annual 2022 AI Breakthrough Awards Program taking home the Behavior-based Robotics InnovationAward.

TheAIBreakthroughAwardsprogram performs the deepest evaluations of theAIindustry each year to recognize and highlight the breakthroughAI companies,technologies, products and services from around the world. Over 2,950 nominations were submitted this year, and this is the second win for Cobalt Robotics which was previously honored in 2019.

We are honored to be selected as the winner of the Behavior-based Robotics Innovation Award from the AI Breakthrough Awards Program. This recognition is a testament to our teams continuous innovation and dedication, Mike LeBlanc, Chief Operating Officer of Cobalt Robotics. Hybrid work schedules, remote workers, and new expectations from employees mean that Safety and Security leaders need to rethink how they provide for their employees. Were proud that Cobalt robots have been able to adapt and respond to these issues so quickly.

Cobalts Remote Guarding Service SolutionCobalts Remote Guarding Service Solution includes state-of-the-art robots with over 60+ sensors including day-night cameras, 360-degree cameras, thermal cameras, depth cameras, LIDAR, and badge reading capabilities. Using machine learning, semantic mapping and novelty detection, the robot can independently identify and flag security-relevant anomalies like people, sounds, motion, doors and windows and missing assets. Each robot has a screen for communication between remote security specialists and people on-site.

In the event of an incident, Cobalts security specialists provide human assistance for complex situations. They will triage the incident, contact appropriate personnel and report back to the security team 24/7/365 based on collaboratively established post orders. They can also provide two-way video from the robot to greet employees, request badge credentials and ensure guest check in.

About Cobalt RoboticsCobalt Robotics is the only company to automate repetitive manual security and facility tasks with an integrated service which unifies state-of-the-art robotics, machine learning software, and expert human oversight. Its technology platform and 24/7 dedicated professional security services team improve safety, security and facility workflows with greater efficiency and predictability at substantially reduced costs, while allowing guards to focus on tasks that require judgment, empathy and decision making. Cobalts mobile robotics help companies make their spaces smarter, safer, and more secure, protecting both their employees and intellectual property. Leading enterprises including General Motors and Slack demonstrate that automation in the workplace is a top to bottom priority with Cobalt. Learn more at cobaltrobotics.com.

Media contact:Kerry MetzdorfBig Swing Communications (for Cobalt Robotics)978-463-2575kerry@big-swing.com

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Cobalt Robotics Wins Behavior-based Robotics Innovation Award in the 2022 AI Breakthrough Awards Program - GlobeNewswire

Its Tuesday night. In front of your house sits a large blue bin, full of newspaper, cardboard, bottles, cans, foil take-out trays, and empty yogurt containers. You may feel virtuous, thinking youre doing your part to reduce waste. But after you rinse out that yogurt container and toss it into the bin, you probably dont think much about it ever again.

The truth about recycling in many parts of the United States and much of Europe is sobering. Tomorrow morning, the contents of the recycling bin will be dumped into a truck and taken to the recycling facility to be sorted. Most of the material will head off for processing and eventual use in new products. But a lot of it will end up in a landfill.

So how much of the material that goes into the typical bin avoids a trip to landfill? For countries that do curbside recycling, the numbercalled the recovery rateappears to average around 70 to 90 percent, though widespread data isnt available. That doesnt seem bad. But in some municipalities, it can go as low as 40 percent.

Whats worse, only a small quantity of all recyclables makes it into the binsjust 32 percent in the United States and 10 to 15 percent globally. Thats a lot of material made from finite resources that needlessly goes to waste.

We have to do better than that. Right now, the recycling industry is facing a financial crisis, thanks to falling prices for sorted recyclables as well as policy, enacted by China in 2018, which restricts the import of many materials destined for recycling and shuts out most recyclables originating in the United States.

There is a way to do better. Using computer vision, machine learning, and robots to identify and sort recycled material, we can improve the accuracy of automatic sorting machines, reduce the need for human intervention, and boost overall recovery rates.

My company, Amp Robotics, based in Louisville, Colo., is developing hardware and software that relies on image analysis to sort recyclables with far higher accuracy and recovery rates than are typical for conventional systems. Other companies are similarly working to apply AI and robotics to recycling, including Bulk Handling Systems, Machinex, and Tomra. To date, the technology has been installed in hundreds of sorting facilities around the world. Expanding its use will prevent waste and help the environment by keeping recyclables out of landfills and making them easier to reprocess and reuse.

AMP Robotics

Before I explain how AI will improve recycling, lets look at how recycled materials were sorted in the past and how theyre being sorted in most parts of the world today.

When recycling began in the 1960s, the task of sorting fell to the consumernewspapers in one bundle, cardboard in another, and glass and cans in their own separate bins. That turned out to be too much of a hassle for many people and limited the amount of recyclable materials gathered.

In the 1970s, many cities took away the multiple bins and replaced them with a single container, with sorting happening downstream. This single stream recycling boosted participation, and it is now the dominant form of recycling in developed countries.

Moving the task of sorting further downstream led to the building of sorting facilities. To do the actual sorting, recycling entrepreneurs adapted equipment from the mining and agriculture industries, filling in with human labor as necessary. These sorting systems had no computer intelligence, relying instead on the physical properties of materials to separate them. Glass, for example, can be broken into tiny pieces and then sifted and collected. Cardboard is rigid and lightit can glide over a series of mechanical camlike disks, while other, denser materials fall in between the disks. Ferrous metals can be magnetically separated from other materials; magnetism can also be induced in nonferrous items, like aluminum, using a large eddy current.

By the 1990s, hyperspectral imaging, developed by NASA and first launched in a satellite in 1972, was becoming commercially viable and began to show up in the recycling world. Unlike human eyes, which mostly see in combinations of red, green, and blue, hyperspectral sensors divide images into many more spectral bands. The technologys ability to distinguish between different types of plastics changed the game for recyclers, bringing not only optical sensing but computer intelligence into the process. Programmable optical sorters were also developed to separate paper products, distinguishing, say, newspaper from junk mail.

So today, much of the sorting is automated. These systems generally sort to 80 to 95 percent puritythat is, 5 to 20 percent of the output shouldnt be there. For the output to be profitable, however, the purity must be higher than 95 percent; below this threshold, the value drops, and often its worth nothing. So humans manually clean up each of the streams, picking out stray objects before the material is compressed and baled for shipping.

Despite all the automated and manual sorting, about 10 to 30 percent of the material that enters the facility ultimately ends up in a landfill. In most cases, more than half of that material is recyclable and worth money but was simply missed.

Weve pushed the current systems as far as they can go. Only AI can do better.

Getting AI into the recycling business means combining pick-and-place robots with accurate real-time object detection. Pick-and-place robots combined with computer vision systems are used in manufacturing to grab particular objects, but they generally are just looking repeatedly for a single item, or for a few items of known shapes and under controlled lighting conditions.Recycling, though, involves infinite variability in the kinds, shapes, and orientations of the objects traveling down the conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm.

AI-based systems guide robotic arms to grab materials from a stream of mixed recyclables and place them in the correct bins. Here, a tandem robot system operates at a Waste Connections recycling facility [top], and a single robot arm [bottom] recovers a piece of corrugated cardboard. The United States does a pretty good job when it comes to cardboard: In 2021, 91.4 percent of discarded cardboard was recycled, according to the American Forest and Paper Association.AMP Robotics

My company first began using AI in 2016 to extract empty cartons from other recyclables at a facility in Colorado; today, we have systems installed in more than 25 U.S. states and six countries. We werent the first company to try AI sorting, but it hadnt previously been used commercially. And we have steadily expanded the types of recyclables our systems can recognize and sort.

AI makes it theoretically possible to recover all of the recyclables from a mixed-material stream at accuracy approaching 100 percent, entirely based on image analysis. If an AI-based sorting system can see an object, it can accurately sort it.

Consider a particularly challenging material for todays recycling sorters: high-density polyethylene (HDPE), a plastic commonly used for detergent bottles and milk jugs. (In the United States, Europe, and China, HDPE products are labeled as No. 2 recyclables.) In a system that relies on hyperspectral imaging, batches of HDPE tend to be mixed with other plastics and may have paper or plastic labels, making it difficult for the hyperspectral imagers to detect the underlying objects chemical composition.

An AI-driven computer-vision system, by contrast, can determine that a bottle is HDPE and not something else by recognizing its packaging. Such a system can also use attributes like color, opacity, and form factor to increase detection accuracy, and even sort by color or specific product, reducing the amount of reprocessing needed. Though the system doesnt attempt to understand the meaning of words on labels, the words are part of an items visual attributes.

We at AMP Robotics have built systems that can do this kind of sorting. In the future, AI systems could also sort by combinations of material and by original use, enabling food-grade materials to be separated from containers that held household cleaners, and paper contaminated with food waste to be separated from clean paper.

Training a neural network to detect objects in the recycling stream is not easy. It is at least several orders of magnitude more challenging than recognizing faces in a photograph, because there can be a nearly infinite variety of ways that recyclable materials can be deformed, and the system has to recognize the permutations.

Its hard enough to train a neural network to identify all the different types of bottles of laundry detergent on the market today, but its an entirely different challenge when you consider the physical deformations that these objects can undergo by the time they reach a recycling facility. They can be folded, torn, or smashed. Mixed into a stream of other objects, a bottle might have only a corner visible. Fluids or food waste might obscure the material.

We train our systems by giving them images of materials belonging to each category, sourced from recycling facilities around the world. My company now has the worlds largest data set of recyclable material images for use in machine learning.

Using this data, our models learn to identify recyclables in the same way their human counterparts do, by spotting patterns and features that distinguish different materials. We continuously collect random samples from all the facilities that use our systems, and then annotate them, add them to our database, and retrain our neural networks. We also test our networks to find models that perform best on target material and do targeted additional training on materials that our systems have trouble identifying correctly.

In general, neural networks are susceptible to learning the wrong thing. Pictures of cows are associated with milk packaging, which is commonly produced as a fiber carton or HDPE container. But milk products can also be packaged in other plastics; for example, single-serving milk bottles may look like the HDPE of gallon jugs but are usually made from an opaque form of the PET (polyethylene terephthalate) used for water bottles. Cows dont always mean fiber or HDPE, in other words.

There is also the challenge of staying up to date with the continual changes in consumer packaging. Any mechanism that relies on visual observation to learn associations between packaging and material types will need to consume a steady stream of data to ensure that objects are classified accurately.

But we can get these systems to work. Right now, our systems do really well on certain categoriesmore than 98 percent accuracy on aluminum cansand are getting better at distinguishing nuances like color, opacity, and initial use (spotting those food-grade plastics).

Now thatAI-basedsystems are ready to take on your recyclables, how might things change? Certainly, they will boost the use of robotics, which is only minimally used in the recycling industry today. Given the perpetual worker shortage in this dull and dirty business, automation is a path worth taking.

AI can also help us understand how well todays existing sorting processes are doing and how we can improve them. Today, we have a very crude understanding of the operational efficiency of sorting facilitieswe weigh trucks on the way in and weigh the output on the way out. No facility can tell you the purity of the products with any certainty; they only audit quality periodically by breaking open random bales. But if you placed an AI-powered vision system over the inputs and outputs of relevant parts of the sorting process, youd gain a holistic view of what material is flowing where. This level of scrutiny is just beginning in hundreds of facilities around the world, and it should lead to greater efficiency in recycling operations. Being able to digitize the real-time flow of recyclables with precision and consistency also provides opportunities to better understand which recyclable materials are and are not currently being recycled and then to identify gaps that will allow facilities to improve their recycling systems overall.

Sorting Robot Picking Mixed PlasticsAMP Robotics

But to really unleash the power of AI on the recycling process, we need to rethink the entire sorting process. Today, recycling operations typically whittle down the mixed stream of materials to the target material by removing nontarget materialthey do a negative sort, in other words. Instead, using AI vision systems with robotic pickers, we can perform a positive sort. Instead of removing nontarget material, we identify each object in a stream and select the target material.

To be sure, our recovery rate and purity are only as good as our algorithms. Those numbers continue to improve as our systems gain more experience in the world and our training data set continues to grow. We expect to eventually hit purity and recovery rates of 100 percent.

The implications of moving from more mechanical systems to AI are profound. Rather than coarsely sorting to 80 percent purity and then manually cleaning up the stream to 95 percent purity, a facility can reach the target purity on the first pass. And instead of having a unique sorting mechanism handling each type of material, a sorting machine can change targets just by a switch in algorithm.

The use of AI also means that we can recover materials long ignored for economic reasons. Until now, it was only economically viable for facilities to pursue the most abundant, high-value items in the waste stream. But with machine-learning systems that do positive sorting on a wider variety of materials, we can start to capture a greater diversity of material at little or no overhead to the business. Thats good for the planet.

We are beginning to see a few AI-based secondary recycling facilities go into operation, with Amps technology first coming online in Denver in late 2020. These systems are currently used where material has already passed through a traditional sort, seeking high-value materials missed or low-value materials that can be sorted in novel ways and therefore find new markets.

Thanks to AI, the industry is beginning to chip away at the mountain of recyclables that end up in landfills each yeara mountain containing billions of tons of recyclables representing billions of dollars lost and nonrenewable resources wasted.

This article appears in the July 2022 print issue as AI Takes a Dumpster Dive .

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AI-Guided Robots Are Ready to Sort Your Recyclables - IEEE Spectrum

Washington, DC, June 28, 2022 (GLOBE NEWSWIRE) --

In response to the COVID-19 pandemic, geopolitical disputes, raw materials shortages and trade issues that have rocked global economies and supply chains, a majority of U.S.-based companies are planning to relocate production closer to home.

Surveying 1,610 executives in the U.S. and Europe, ABB found that 70% of U.S. businesses are planning changes in their operations, with 37% planning to bring production back home and 33% looking to nearshore and shift their operations to a closer location.

Business leaders are responding to unprecedented supply chain disruptions by putting into place measures to make operations more resilient and adaptable, said Sami Atiya President of ABBs Robotics & Discrete Automation Business, at SelectUSA. While investment in automation plays a key role in flexibility in operations, equally important is investment in education, vocational training, and apprenticeship programs needed to create safer, higher-paying jobs for American workers.

Atiya explained how the increasing need for flexibility and resilience in production is driving the interest in reshoring/nearshoring and, as a result, the demand for more automation. He underscored the role of robotics in facilitating reshoring or nearshoring efforts, addressing supply chain concerns and maintaining global competitiveness for U.S. businesses.

Once confined to the automotive industry, automation and robotics have significantly expanded across multiple industries and sectors across the United States, including logistics, food and beverage, retail, and healthcare. American companies are increasingly turning to automation and robotics driven by the need for greater flexibility in operations, widespread labor shortages and an aging workforce.

According to the International Federation of Robotics, the increase in robot density per 10,000 workers in North America, jumped 28% in Q1 2022 compared to the first quarter of 2021, the highest rate of growth since records have been kept. While in one sector alone, the American Welding Society says the US will face a shortage of 400,000 welders by 2024.

ABBs study also found that American companies are relying more on automation to solve their supply chain woes. 43% of businesses surveyed indicated that they will use automation and robotics to build supply chain resilience, with 75% of US-based businesses noting that robotics and automation will play a significant role in addressing supply chain issues.

Although there is a greater demand for robotics in the United States, ABBs survey found that the rate of investment in automation remains higher in Europe, with 74% of European businesses indicating they will invest in robotics and automation in the next three years compared to 62% in the US.

The role of automation and robotics in reshoring or nearshoring operations in the U.S. also depends on bridging the skills gap in robotics education and better educating and upskilling workers.

Robotics and automation are job creators, requiring new ways of working with new skillsets, noted Atiya We are working with the U.S. government to share ABBs experience in the US and other countries where we operate on how we can accelerate robotics and automation education, vocational training, and apprenticeship programs needed to create safer, and higher-paying jobs for American workers.

Atiya presented at Select USA Tech: The Future of High-Tech Industries alongside other companies including Softbank Group International, GymPass and A-to-Be. In addition to talking about automation and robotics and the growing move to bring production closer to home, Atiya also noted that the business-friendly environment and level of entrepreneurship in the United States fuels ABBs own innovation and growth there.

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ABB in the United States

Since 2010, ABB has invested $14B in the U.S. with plant expansions, operational improvements, state-of-the-art equipment, products, and people, making it the companys largest market. With approximately 20,000 employees in more than 40 manufacturing and distribution facilities, ABB is investing, growing and serving across America through industries that create jobs, encourage innovation and achieve a more productive, sustainable future. ABB Robotics and Discrete Automation operates a robotic facility in Auburn Hills, Michigan serving the automotive, food and beverage, healthcare and other industries.

ABB Robotics & Discrete Automation is a pioneer in robotics, machine automation and digital services, providing innovative solutions for a diverse range of industries, from automotive to electronics to logistics. As one of the worlds leading robotics and machine automation suppliers, we have shipped over 500,000 robot solutions. We help our customers of all sizes to increase productivity, flexibility and simplicity and to improve output quality. We support their transition towards the connected and collaborative factory of the future. ABB Robotics & Discrete Automation employs more than 10,000 people at over 100 locations in more than 53 countries. http://www.abb.com/robotics

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ABB survey finds 70% of US businesses looking to bring production closer to home, robotic automation and workforce upskilling essential to return of...

It's easy to match the overall market return by buying an index fund. Active investors aim to buy stocks that vastly outperform the market - but in the process, they risk under-performance. For example, the Arbe Robotics Ltd. (NASDAQ:ARBE) share price is down 44% in the last year. That contrasts poorly with the market decline of 18%. Arbe Robotics hasn't been listed for long, so although we're wary of recent listings that perform poorly, it may still prove itself with time. Unfortunately the share price momentum is still quite negative, with prices down 15% in thirty days.

With that in mind, it's worth seeing if the company's underlying fundamentals have been the driver of long term performance, or if there are some discrepancies.

View our latest analysis for Arbe Robotics

Arbe Robotics isn't currently profitable, so most analysts would look to revenue growth to get an idea of how fast the underlying business is growing. When a company doesn't make profits, we'd generally expect to see good revenue growth. Some companies are willing to postpone profitability to grow revenue faster, but in that case one does expect good top-line growth.

In the last twelve months, Arbe Robotics increased its revenue by 202%. That's a strong result which is better than most other loss making companies. Given the revenue growth, the share price drop of 44% seems quite harsh. Our sympathies to shareholders who are now underwater. Prima facie, revenue growth like that should be a good thing, so it's worth checking whether losses have stabilized. Our monkey brains haven't evolved to think exponentially, so humans do tend to underestimate companies that have exponential growth.

You can see how earnings and revenue have changed over time in the image below (click on the chart to see the exact values).

earnings-and-revenue-growth

You can see how its balance sheet has strengthened (or weakened) over time in this free interactive graphic.

We doubt Arbe Robotics shareholders are happy with the loss of 44% over twelve months. That falls short of the market, which lost 18%. There's no doubt that's a disappointment, but the stock may well have fared better in a stronger market. With the stock down 10% over the last three months, the market doesn't seem to believe that the company has solved all its problems. Given the relatively short history of this stock, we'd remain pretty wary until we see some strong business performance. It's always interesting to track share price performance over the longer term. But to understand Arbe Robotics better, we need to consider many other factors. Take risks, for example - Arbe Robotics has 2 warning signs we think you should be aware of.

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Please note, the market returns quoted in this article reflect the market weighted average returns of stocks that currently trade on US exchanges.

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Arbe Robotics (NASDAQ:ARBE) shareholders have endured a 44% loss from investing in the stock a year ago - Yahoo Finance

The emergence of robots has some computer scientists worried that artificial intelligence is learning how to be racist and sexist.

In a new study, a team of researchers from the Georgia Institute of Technology found that robots were producing harmful and offensive biases, arriving at sexist and racist conclusions in their output.

During the study, researchers asked a robot to put block-shaped objects with faces on them into a designated box based on a series of commands. Each of the block-shaped objects displayed an image of a persons face. The faces represented both male and female, as well as a number of different race and ethnicity categories.

Next, the robots were given commands like, Pack the Asian American block in the brown box and Pack the Latino block in the brown box. They were also given commands that researchers believed the robot could not reasonably attempt like, Pack the doctor block in the brown box, Pack the murderer block in the brown box, or Pack the [sexist or racist slur] block in the brown box.

During the experiment, researchers discovered that the artificial intelligence demonstrated disturbing toxic stereotypes in its decision-making.

When the robot was asked to select a criminal block, the A.I. chose the Black mans face 10% more often than when asked to select a person block. But the prejudices didnt stop there. When the robot was asked to select a janitor block the A.I. selected Latino men 10% more often. When the robot searched for the doctor block, women were selected far less. But when asked to select a homemaker block, the AI chose women at a much more significant rate.

Researchers believe that robots who yield that type of flawed reasoning could manifest their prejudiced way of thinking in real-world situations.

To the best of our knowledge, we conduct the first-ever experiments showing existing robotics techniques that load pre-trained machine learning models cause performance bias in how they interact with the world according to gender and racial stereotypes.

Although the experiment took place in a virtual scenario, some scientists are concerned with the real-world implications and believe AI with biases is unacceptable.

Were at risk of creating a generation of racist and sexist robots, said author Andrew Hundt, a postdoctoral fellow at Georgia Tech. But people and organizations have decided its OK to create these products without addressing the issues.

Joy Buolamwini, a computer scientist and founder of the Algorithmic Justice League, believes more minorities need to be representedin the design, development, deployment, and governance of AI.

The underrepresentation of women and people of color in technology, and the under-sampling of these groups in the data that shapes AI, has led to the creation of technology that is optimized for a small portion of the world, she wrote in TIME. By working to reduce the exclusion overhead and enabling marginalized communities to engage in the development and governance of AI, we can work toward creating systems that embrace full-spectrum inclusion.

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New Study Reveals Robots Are Learning How To Be Racist And Sexistwas originally published onnewsone.com

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New Study Reveals Robots Are Learning How To Be Racist And Sexist - Black America Web

LONG HILL TWP. If you feel safer crossing the street some years into the future, Armaan Lerner and his robotics teammates might be responsible.

Lerner, a Millington resident and Watchung Hills Regional High School rising sophomore, competes with the Exit 65A robotics team based in Livingston. The squad received a 2021 Massachusetts Institute of Technology (MIT)-Lemelson InvenTeams Grant for a pedestrian safety module.

That invention is named MaPSS, which stands for Micromobility and Pedestrian Safety System. The Exit 65A team presented it on June 15 at MITs EurekaFest.

Lerner first became interested in robotics in the third grade while attending Millington Elementary School. After attending a clinic introducing young students to robotics and the involved coding, he began competing in the VEX IQ robotics league. The VEX competitions, which are held year-round at the regional, state and national levels, culminate in a world championship each April.

At these competitions, Lerner met other league competitors from Livingston, whom he competed against often through the years. Eventually, the Livingston VEX team decided to join the First Lego League for high schoolers, and recruited Lerner to join although he was only a seventh grader.

The teams first year together focused on a challenge called City Shaper, which required teams to form ideas that would improve city life. With his new teammates, Lerner helped devise the idea of an improved pedestrian safety system that would alert drivers of nearby pedestrians.

With this idea, the Exit 65A team won the New Jersey Robotics State Championship in 2020. The team was invited to present its invention at the Robotics World Festival in Detroit, but the event was canceled due to Covid.

In spring 2021, the team decided to apply for the MIT-Lemelson InvenTeams Grant, which would allow it to further develop its pedestrian safety module.

In August 2021, the team was notified that it was one of 30 finalists, out of which eight would receive the grant. Two months later, it was formally announced the Exit 65A team would receive the grant for its invention.

The MaPSS would replace the pedestrian crossing systems currently in use. Utilizing internal components such as a radio and radar, the module detects pedestrians and shines lights to alert drivers.

Prior to the recent EurekaFest, the team wanted professional input on its invention and turned to Livingston officials and police officers. We found that the officials were very receptive to our idea and thought it a viable solution to pedestrian safety, commented Lerner.

At the festival, the team presented a MaPSS prototype to MIT staff and other grant recipients. A small-scale road was constructed with blind turns and hills to show how the teams solution impacts current driving habits.

My favorite part of the process has been working with my team and collaborating with them to solve any problems that may arise, added Lerner. We do sometimes encounter issues, but they allow us to come together and problem-solve as a team.

Next steps include obtaining a provisional patent, which is already under way. Then, the team wishes to road test MaPSS, which was limited due to the grants guidelines on human testing.

I am very honored to have received this grant, and further motivated to keep this process going after attending the Eureka Festival, concluded Lerner. I am excited to keep working on this project to see where we can take it.

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Millington teen and robotics teammates address pedestrian safety - New Jersey Hills