Category Archives: Robotics

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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For those who like to find winning investments this free list of growing companies with recent insider purchasing, could be just the ticket.

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

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

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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

Quadintel published a new report on theLab RoboticsMarket. The research report consists of thorough information about demand, growth, opportunities, challenges, and restraints. In addition, it delivers an in-depth analysis of the structure and possibility of global and regional industries.

Global Lab Robotics Market is valued approximately at USD $$ Billion in 2021 and is anticipated to grow with a healthy growth rate of more than % over the forecast period 2022-2030.

Lab Robotics are the robots used in laboratories to perform multiple tasks like capping clipping labeling and dispensing. They help in managing workflow. Accuracy of the robots while performing tasks in the laboratories, high productivity with minimal wastage has driven the Lab Robotics Market.

Request To Download Sample of This Strategic Report: https://www.quadintel.com/request-sample/lab-robotics-market/QI037

For Instance: A study carried out in the Tokyo evaluated that up to 80% of task in life science industry can benefit vastly with high productivity rate by using lab robotics Also, increasing number of widespread evaluation studies and development of industry and task specific lab robots is most likely to boost the overall growth of the Global Lab Robotics Market. However, lack of utility guidelines, high installation cost and lack flexibility during multiple tasks can obstruct the markets expansion over the projection period of 2022-2028.

The key regions considered for the GlobalLab Robotics Marketstudy includes Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is the leading region across the world. Advancement in robotic technology and presence of trained professionals to control the robots is driving the market growth in the North America. Whereas, Asia Pacific is also anticipated to exhibit highest growth rate over the forecast period 2022-2028. The market is expected to grow during the projected period, due to rising automation in the laboratories.

COVID-19 Impact Analysis

The pandemic of COVID-19 has also reduced the availability of and demand for non-COVID-19-related medical treatment. A wide range of treatments, including emergency care for acute diseases, routine check-ups, and recommended cancer screenings, are being postponed or avoided by patients. Undiagnosed illnesses and a failure to intervene early will have serious long-term health consequences. COVID-19 has accelerated a variety of existing and emerging healthcare trends, including changing consumer attitudes and habits, the convergence of life science and health care, rapid advances in digital health technologies, and new talent and care delivery models, to name a few.

Request a Sample PDF copy of the report @https://www.quadintel.com/request-sample/lab-robotics-market/QI037

Major market player included in this report are:

Ab Controls

Aurora Biomed

Peak Analysis and Automation

Thermo Fisher Scientific

Yaskawa Electric

Tecan Group

Chemspeed Technologies Als Automated Lab Solutions

Hudson Robotics

Universal Robots

St Robotics

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the Application within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By End-User:

Pharmaceutical Industry

Life Science Industry

Biopharmaceutical Industry

Research Laboratories

Clinical Laboratories

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By Region:

North America

U.S.

Canada

Europe

UK

Germany

France

Spain

Italy

ROE

Asia Pacific

China

India

Japan

Australia

South Korea

RoAPAC

Latin America

Brazil

Mexico

Rest of the World

Furthermore, years considered for the study are as follows:

Historical year 2018, 2019, 2020

Base year 2021

Forecast period 2022 to 2028

Target Audience of the Global Lab Robotics Market in Market Study:

Key Consulting Companies & Advisors

Large, medium-sized, and small enterprises

Venture capitalists

Value-Added Resellers (VARs)

Third-party knowledge providers

Investment bankers

Investors

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Table of Contents:

Factors Influencing

The global market is forecast to witness a rapid growth, owing to increasing demand for technological advancements from end-users. Moreover, increasing investments in research and development activities, launches, partnerships, and other strategic initiatives will benefit the market. Furthermore, the growing focus of authorities towards increasing urbanization and industrialization is forecast to drive the market growth.

What aspects regarding the regional analysis Market are included in this report?

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Lab Robotics Market 2022 Projections and Future Opportunities Recorded for the Period 2030 Designer Women - Designer Women

Machine automation has been rising rapidly since the 1960s. And Artificial Intelligence has increased the ways that robotics can enhance our lives. In recent years, even the most typically human interactions are being mechanized.

Take going to a restaurant for example. Robots are now being used as waiters and cleaning staff, but theres one restaurant in Paris thats using a different recipe for automation.

Since the Covid crisis, the hotel and restaurant industry has been experiencing an unprecedented global employment crisis. In France, it has lost nearly 10% of its workers. So, could robots be a solution to this shortage?

Imagine if a robot replaces the chef? In a Paris restaurant, this future is very much in the present. A robot named Pazzi prepares and serves up to 80 pizzas per hour without any human intervention.

The process is simple and transparent: the customer orders at an automatic kiosk before the robot's articulated arms flatten the dough, spread the sauce, add the ingredients - all of them organic - and place the pizza in the oven. The pick-up is done with a QR code.

"The biggest technical difficulty was mastering organic products," saysSebastien Roverso, Co-Founder of Pazzi. "Dough is a living substance, there are yeasts. The product has characteristics that aren't constant. Sauces can have varying thicknesses. So it was extremely complex to develop a technology capable of managing any type of ingredient, while respecting the products."

Robots can perform cooking tasks much more easily, more quickly and to a higher standard than a team of kitchen workers," says Philippe Goldman, Ceo of Pazzi Robotics

"That's the advantage of robotics, which already help in numerous sectors. There is no reason why they shouldn't be used in the fast-food sector."

Although this technology does come at a cost of around 300,000, the founders say using robots will allow chefs to spend more on quality ingredients.

At the current pace, up to 85 million jobs could be displaced by 2025.

But the organization also believes that automation will create more jobs than it removes. Predicting that a staggering 97 million new roles will be needed over the same timeframe.

And as companies race to stay ahead, global spending on robotics is set to hit $242 billion dollars as soon as next year.

There are firms focused on figuring out how robots and humans can work together rather than simply taking our jobs. One company at the forefront of automation is ABB. Marc Segura is the President of the Robotics Division at ABB.

He points to two factors pushing the world towards faster robotization.

"E-commerce has been one," he says. "The other one has been 'co-bots', 'co-bots' and their drive into making robotics accessible, but for a wider number of companies, notably small and medium-sized enterprises. Because what we need to lower are two things. One is the total cost of ownership and the total cost of integration and that calls for simpler, leaner robots that don't need a lot of ancillary equipment to perform certain tasks. So, lean robots with ease of use, robots affordable and easy to deploy. This is really happening. This will help many other companies adopt robotics and benefit from the profits they bring."

So, whether you work in a shop, factory or office - having affordable co-bots as co-workers could become the norm. For the millions of us who may feel nervous about losing our jobs to robots one day, its not all gloom and doom - there are experts who believe that most of us humans will still be relevant for some time to come.

Nahia Orduna is the author of the book Your Digital Reinvention.

"I'm convinced that robots won't steal our jobs," she says. "85 million jobs will be displaced by a shift of labour between the machines and the humans. But 97 million jobs are going to appear. And these are the new jobs like the jobs of tomorrow. Let me share with you my favourite restaurant here. I live in Germany in the middle of Munich. It's an Italian restaurant. Every time I go, I'm greeted by the waiters. Maybe the pizza is done by a robot in the kitchen. But the Italian waiter makes me feel like I'm on holiday in Italy and that will not disappear. So again, they are like all this human interaction is not at risk to be automated because it's all about our human experience."

So trusting robots and algorithms to complete tasks that dont require real people means human skills can be better used in other ways.

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Should we be worried about the robotics revolution? - Euronews

The rest of the startup universe may be struggling to bring in funds, but its still a good time to get a robotic raise. Agtech is high on that list. The median age of farmers is 55 years old in the United States, and finding human help is increasingly more difficult of late.

FarmWise has been at this for a while, deploying its autonomous weeding robotics at farms in California and Arizona for the last few years. The Central Californian company says its robots have logged 15,000 commercial hours on vegetable farms, all told, capturing some 450 million scans of crops for its database.

Today the company announced a $45 million raise, led by Fall Line Capital and Middleland Capital. GV, Taylor Farms, Calibrate Ventures, Playground Global, SVG Ventures and Wilbur Ellis also got in on the Series B, which brings FarmWises total equity raise to $65 million, to date.

The funding will go toward accelerating the firms R&D and rollout of its existing product.

We started FarmWise with the conviction that farmers should be supplied with cost-effective, sustainable solutions to feed a growing world, and artificial intelligence is the ideal technology to make this a reality, co-founder and CEO, Sebastien Boyer said in a release. With rising costs in the agricultural industry, were continuing to expand our technology to work with many more farmers.

The round also finds Fall Line Capital co-founder and managing director, Clay Mitchell, joining its board.

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Agtech robotics firm FarmWise just raised another $45 million - TechCrunch