Tag Archives: Robots

Washington State University study: Hand- versus machine-harvested juice and cider apples: A comparison of phenolic profiles

1 Sep

James Thorne wrote in the Geek Wire article, Apple-picking robots gear up for U.S. debut in Washington state:

Next fall, as you browse the produce section at your local grocery store, pay close attention to the apples. You might be witnessing American history.
For the first time, some of the apples sold in the U.S. will be picked by a robot rather than human hands. That’s thanks to agricultural automation startup Abundant Robotics, the maker of apple harvesting machines that will partake in Washington state’s next harvest.
“This will be the first season that we’re actually ready to harvest commercially,” said Abundant CEO Dan Steere. “It’s incredibly exciting.”
Abundant’s picker has more in common with a really smart Hoover vacuum than a human hand. The robot moves down rows of orchards and uses artificial intelligence with a dash of LIDAR to search for ripe apples. Once spotted, a robotic arm with a vacuum gently sucks the apples from the tree into a bin.
The achievement is owed to advances not only in machine learning and robotics but also in agriculture. The architecture of apple trees has evolved over the decades, and it’s now common to grow them on trellises like you would tomatoes or cucumbers. Modern apple trees are also smaller, derived from dwarf varietals that yield more per acre and produce fruit more quickly after being planted.
These horticultural leaps have allowed farmers to double their apple yields. They’ve also made the job of picking easier for humans and, now, for robots.
Karen Lewis, a tree fruit specialist at Washington State University who has worked with Abundant and other robotics startups, said that apple trees have reached a “sweet spot” for robotic harvesting. Orchards are now sufficiently uniform and predictable for machines to reliably pick fruit, and canopies are narrow enough for sunlight, the human eye and vision systems to penetrate.
Tech companies that are successful in agriculture, she said, are the ones that listen to what farmers need. “We’re not going to let technology be the driver here. Horticulture needs to be the driver.” https://www.geekwire.com/2019/apple-picking-robots-gear-u-s-debut-washington-state/

There are at least two issues regarding mechanical harvesting. The first is whether mechanical harvesting damages crops or results is lesser quality of the final product quality. The second is whether employment in agriculture will decline.

Science Daily reported in Hand- versus machine-harvested juice and cider apples: A comparison of phenolic profiles:

A study out of Washington State University sought to determine if there is a measurable impact of harvest method on the phenolic profile of ‘Brown Snout’ juice and cider to better inform equipment adoption.
Travis Alexander, Thomas Collins, and Carol Miles also evaluated whether different extraction methods would yield differing output in either quantity or quality of ‘Brown Snout’ apple juice and cider. Their comprehensive findings are illustrated in their article, “Comparison of the Phenolic Profiles of Juice and Cider Derived from Machine- and Hand-Harvested ‘Brown Snout’ Specialty Cider Apples in Northwest Washington” as found in the open-access journal HortTechnology, published by the American Society for Horticultural Science.
Phenolics are secondary metabolites that have attracted increasing interest in science and industry in recent years due to their beneficial health effects, primarily for their antioxidant properties. They have been proven to act as reducing agents to free radicals. Phenolics contribute significantly to the sensory profile of fermented cider, especially in those made from cider apple fruit. “Phenolics can impact the pressing of fruit, the clarification of juice, the maturation of cider, and final cider quality, including the attributes of aroma, color, taste, and mouthfeel. And so, we wanted to determine if there was a change in phenolics due to harvest method” stated Collins….’
To carry out their research, Miles said they planted a block of ‘Brown Snout’ apple trees on a low trellis system so that trees were a suitable size to fit the over-the-row small fruit harvester. Each of the eight main plots consisted of an average of nine trees. When the fruit was fully ripe, harvesting was divided equally between hand harvesting by four relatively unskilled agricultural workers and machine harvest by an over-the-row small fruit harvester. When application of the two harvest methods was complete, equal qualities of ‘Brown Snout’ apples were randomly selected from each yield supply for further evaluation.
The selected fruit were pressed separately and fermented and allowed to mature for 5 months before final assessments were conducted. At that time, the researchers determined that harvest method and duration of storage were nonsignificant for all parameters measured on juice and cider samples.
Over-the-row machine harvesting resulted in a final product of similar quality at reduced labor costs, and thus shows potential for increasing the commercial sustainability of cider apple operations.
https://www.sciencedaily.com/releases/2019/08/190830162305.htm

Citation:

Hand- versus machine-harvested juice and cider apples: A comparison of phenolic profiles
Machine-harvested apples offer cost-effective option for growers and cider makers
Date: August 30, 2019
Source: American Society for Horticultural Science
Summary:
Study conducted to determine if there is a measurable impact of harvest method on the phenolic profile of ‘Brown Snout’ juice and cider to better inform equipment adoption. Over-the-row machine harvesting resulted in a final product of similar quality at reduced labor costs, and thus shows potential for increasing the commercial sustainability of cider apple operations.

Journal Reference:
Travis R. Alexander, Thomas S. Collins, Carol A. Miles. Comparison of the Phenolic Profiles of Juice and Cider Derived from Machine- and Hand-harvested ‘Brown Snout’ Specialty Cider Apples in Northwest Washington. HortTechnology, 2019; 29 (4): 423 DOI: 10.21273/HORTTECH04342-19

Here is the press release from American Society for Horticultural Science:

NEWS RELEASE 30-AUG-2019
Hand- versus machine-harvested juice and cider apples: A comparison of phenolic profiles
Machine-harvested apples offer cost-effective option for growers and cider makers
AMERICAN SOCIETY FOR HORTICULTURAL SCIENCE
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MOUNT VERNON, WASHINGTON–Hand-harvested versus Machine-harvested Juice and Cider Apples: A Comparison of Phenolic Profiles
A study out of Washington State University sought to determine if there is a measurable impact of harvest method on the phenolic profile of ‘Brown Snout’ juice and cider to better inform equipment adoption.
Travis Alexander, Thomas Collins, and Carol Miles also evaluated whether different extraction methods would yield differing output in either quantity or quality of ‘Brown Snout’ apple juice and cider. Their comprehensive findings are illustrated in their article, “Comparison of the Phenolic Profiles of Juice and Cider Derived from Machine- and Hand-Harvested ‘Brown Snout’ Specialty Cider Apples in Northwest Washington” as found in the open-access journal HortTechnology, published by the American Society for Horticultural Science.
Phenolics are secondary metabolites that have attracted increasing interest in science and industry in recent years due to their beneficial health effects, primarily for their antioxidant properties. They have been proven to act as reducing agents to free radicals. Phenolics contribute significantly to the sensory profile of fermented cider, especially in those made from cider apple fruit. “Phenolics can impact the pressing of fruit, the clarification of juice, the maturation of cider, and final cider quality, including the attributes of aroma, color, taste, and mouthfeel. And so, we wanted to determine if there was a change in phenolics due to harvest method” stated Collins.
“The ‘Brown Snout’ specialty cider apple is desired by cider makers for its relatively high levels of phenolics, and over-the-row machine harvesting of ‘Brown Snout’ has been demonstrated to provide similar yield to hand harvest at a significantly lower cost” says Alexander.
To carry out their research, Miles said they planted a block of ‘Brown Snout’ apple trees on a low trellis system so that trees were a suitable size to fit the over-the-row small fruit harvester. Each of the eight main plots consisted of an average of nine trees. When the fruit was fully ripe, harvesting was divided equally between hand harvesting by four relatively unskilled agricultural workers and machine harvest by an over-the-row small fruit harvester. When application of the two harvest methods was complete, equal qualities of ‘Brown Snout’ apples were randomly selected from each yield supply for further evaluation.
The selected fruit were pressed separately and fermented and allowed to mature for 5 months before final assessments were conducted. At that time, the researchers determined that harvest method and duration of storage were nonsignificant for all parameters measured on juice and cider samples.
Over-the-row machine harvesting resulted in a final product of similar quality at reduced labor costs, and thus shows potential for increasing the commercial sustainability of cider apple operations.
###
The complete article is available on the ASHS HortTechnology electronic journal web site: https://journals.ashs.org/horttech/view/journals/horttech/29/4/article-p423.xml. DOI: https://doi.org/10.21273/HORTTECH04342-19 . Or you may contact Travis Alexander of Washington State University at travis.alexander@wsu.edu or call him at (360) 848-6120.
Founded in 1903, the American Society for Horticultural Science (ASHS) is the largest organization dedicated to advancing all facets of horticulture research, education, and application. More information at ashs.org.
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
David Meyer wrote in the Fortune article, Robots May Steal As Many As 800 Million Jobs in the Next 13 Years:
A new study by the McKinsey Global Institute estimates that between 400 million and 800 million of today’s jobs will be automated by 2030.
The research adds fresh perspective to what is becoming an increasingly concerning picture of the future employment landscape. “We’re all going to have to change and learn how to do new things over time,” institute partner Michael Chui told Bloomberg.
In the U.S., it seems it’s the middle class that has the most to fear, with office administrators and construction equipment operators among those who may lose their jobs to technology or see their wages depressed to keep them competitive with robots and automated systems…. https://fortune.com/2017/11/29/robots-automation-replace-jobs-mckinsey-report-800-million/

 

Think not of yourself as the architect of your career but as the sculptor. Expect to have to do a lot of hard hammering and chiseling and scraping and polishing.-
B.C. Forbes

Resources:

In Praise of Short-Term Thinking
For hundreds of years, economic observers have feared that machines were making human workers obsolete. In a sense, they’ve been right. https://www.theatlantic.com/business/archive/2015/09/jobs-automation-technological-unemployment-history/403576/

Will robots and AI take your job? The economic and political consequences of automation                                               https://www.brookings.edu/blog/techtank/2018/04/18/will-robots-and-ai-take-your-job-the-economic-and-political-consequences-of-automation/

Will machines eventually take on every job?              http://www.bbc.com/future/story/20150805-will-machines-eventually-take-on-every-job

Every study we could find on what automation will do to jobs, in one chart: There are about as many opinions as there are experts. https://www.technologyreview.com/s/610005/every-study-we-could-find-on-what-automation-will-do-to-jobs-in-one-chart/

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Cornell University study: Faster robots demoralize co-workers

13 Mar

Mojtaba Arvin wrote in the Machine Learning article, The robot that became racist:

AI that learnt from the web finds white-sounding names ‘pleasant’ and …
Humans look to the power of machine learning to make better and more effective decisions.
However, it seems that some algorithms are learning more than just how to recognize patterns – they are being taught how to be as biased as the humans they learn from.
Researchers found that a widely used AI characterizes black-sounding names as ‘unpleasant’, which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web.
Researchers found that a widely used AI characterizes black-sounding names as ‘unpleasant’, which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web
Machine learning has been adopted to make a range of decisions, from approving loans to determining what kind of health insurance, reports Jordan Pearson with Motherboard.
A recent example was reported by Pro Publica in May, when an algorithm used by officials in Florida automatically rated a more seasoned white criminal as being a lower risk of committing a future crime, than a black offender with only misdemeanors on her record.
Now, researchers at Princeton University have reproduced a stockpile of documented human prejudices in an algorithm using text pulled from the internet.
HOW A ROBOT BECAME RACIST
Princeton University conducted a word associate task with the popular algorithm GloVe, an unsupervised AI that uses online text to understand human language.
The team gave the AI words like ‘flowers’ and ‘insects’ to pair with other words that the researchers defined as being ‘pleasant’ or ‘unpleasant’ like ‘family’ or ‘crash’ – which it did successfully.
Then algorithm was given a list of white-sounding names, like Emily and Matt, and black-sounding ones, such as Ebony and Jamal’, which it was prompted to do the same word association.
The AI linked the white-sounding names with ‘pleasant’ and black-sounding names as ‘unpleasant’.
Princeton’s results do not just prove datasets are polluted with prejudices and assumptions, but the algorithms currently being used for researchers are reproducing human’s worst values – racism and assumption… https://www.artificialintelligenceonline.com/19050/the-robot-that-became-racist-ai-that-learnt-from-the-web-finds-white-sounding-names-pleasant-and/

See, The robot that became racist: AI that learnt from the web finds white-sounding names ‘pleasant’ and black-sounding names ‘unpleasant’ http://www.dailymail.co.uk/sciencetech/article-3760795/The-robot-racist-AI-learnt-web-finds-white-sounding-names-pleasant-black-sounding-names-unpleasant.html

Science Daily reported in Faster robots demoralize co-workers:

It’s not whether you win or lose; it’s how hard the robot is working.
A Cornell University-led team has found that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort — and they tend to dislike the robots.
The study, “Monetary-Incentive Competition Between Humans and Robots: Experimental Results,” brought together behavioral economists and roboticists to explore, for the first time, how a robot’s performance affects humans’ behavior and reactions when they’re competing against each other simultaneously.
Their findings validated behavioral economists’ theories about loss aversion, which predicts that people won’t try as hard when their competitors are doing better, and suggests how workplaces might optimize teams of people and robots working together.
“Humans and machines already share many workplaces, sometimes working on similar or even identical tasks,” said Guy Hoffman, assistant professor in the Sibley School of Mechanical and Aerospace Engineering. Hoffman and Ori Heffetz, associate professor of economics in the Samuel Curtis Johnson Graduate School of Management, are senior authors of the study.
“Think about a cashier working side-by-side with an automatic check-out machine, or someone operating a forklift in a warehouse which also employs delivery robots driving right next to them,” Hoffman said. “While it may be tempting to design such robots for optimal productivity, engineers and managers need to take into consideration how the robots’ performance may affect the human workers’ effort and attitudes toward the robot and even toward themselves. Our research is the first that specifically sheds light on these effects….”
After each round, participants filled out a questionnaire rating the robot’s competence, their own competence and the robot’s likability. The researchers found that as the robot performed better, people rated its competence higher, its likability lower and their own competence lower.
The research was partly supported by the Israel Science Foundation. https://www.sciencedaily.com/releases/2019/03/190311173205.htm

Citation:

Faster robots demoralize co-workers
Date: March 11, 2019
Source: Cornell University
Summary:
New research finds that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort — and they tend to dislike the robots.

Journal Reference:
Alap Kshirsagar, Bnaya Dreyfuss, Guy Ishai, Ori Heffetz, Guy Hoffman. Monetary-Incentive Competition Between Humans and Robots: Experimental Results. In Proc. of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI’19), IEEE, 2019 (forthcoming); [link]

Here is the press release from Cornell University:

PUBLIC RELEASE: 11-MAR-2019

Faster robots demoralize co-workers

CORNELL UNIVERSITY

ITHACA, N.Y. – It’s not whether you win or lose; it’s how hard the robot is working.
A Cornell University-led team has found that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort – and they tend to dislike the robots.
The study, “Monetary-Incentive Competition Between Humans and Robots: Experimental Results,” brought together behavioral economists and roboticists to explore, for the first time, how a robot’s performance affects humans’ behavior and reactions when they’re competing against each other simultaneously.
Their findings validated behavioral economists’ theories about loss aversion, which predicts that people won’t try as hard when their competitors are doing better, and suggests how workplaces might optimize teams of people and robots working together.
“Humans and machines already share many workplaces, sometimes working on similar or even identical tasks,” said Guy Hoffman, assistant professor in the Sibley School of Mechanical and Aerospace Engineering. Hoffman and Ori Heffetz, associate professor of economics in the Samuel Curtis Johnson Graduate School of Management, are senior authors of the study.
“Think about a cashier working side-by-side with an automatic check-out machine, or someone operating a forklift in a warehouse which also employs delivery robots driving right next to them,” Hoffman said. “While it may be tempting to design such robots for optimal productivity, engineers and managers need to take into consideration how the robots’ performance may affect the human workers’ effort and attitudes toward the robot and even toward themselves. Our research is the first that specifically sheds light on these effects.”
Alap Kshirsagar, a doctoral student in mechanical engineering, is the paper’s first author. In the study, humans competed against a robot in a tedious task – counting the number of times the letter G appears in a string of characters, and then placing a block in the bin corresponding to the number of occurrences. The person’s chance of winning each round was determined by a lottery based on the difference between the human’s and robot’s scores: If their scores were the same, the human had a 50 percent chance of winning the prize, and that likelihood rose or fell depending which participant was doing better.
To make sure competitors were aware of the stakes, the screen indicated their chance of winning at each moment.
After each round, participants filled out a questionnaire rating the robot’s competence, their own competence and the robot’s likability. The researchers found that as the robot performed better, people rated its competence higher, its likability lower and their own competence lower.
###
The research was partly supported by the Israel Science Foundation.
Cornell University has dedicated television and audio studios available for media interviews supporting full HD, ISDN and web-based platforms.
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Evan Selinger and Woodrow Hartzog wrote about robots in The dangers of trusting robots.

According to Selinger and Hartzog:

We also need to think long and hard about how information is being stored and shared when it comes to robots that can record our every move. Some recording devices may have been designed for entertainment but can easily be adapted for more nefarious purposes. Take Nixie, the wearable camera that can fly off your wrist at a moment’s notice and take aerial shots around you. It doesn’t take much imagination to see how such technology could be abused.
Most people guard their secrets in the presence of a recording device. But what happens once we get used to a robot around the house, answering our every beck and call? We may be at risk of letting our guard down, treating them as extended members of the family. If the technology around us is able to record and process speech, images and movement – never mind eavesdrop on our juiciest secrets – what will happen to that information? Where will it be stored, who will have access? If our internet history is anything to go by, these details could be worth their weight in gold to advertising companies. If we grow accustomed to having trusted robots integrated into our daily lives, our words and deeds could easily become overly-exposed…. http://www.bbc.com/future/story/20150812-how-to-tell-a-good-robot-from-the-bad

We have to prove that digital manufacturing is inclusive. Then, the true narrative will emerge: Welcome, robots. You’ll help us. But humans are still our future.
Joe Kaeser

Resources:

Artificial Intelligence Will Redesign Healthcare                             https://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare

9 Ways Artificial Intelligence is Affecting the Medical Field https://www.healthcentral.com/slideshow/8-ways-artificial-intelligence-is-affecting-the-medical-field#slide=2

Where information leads to Hope. © Dr. Wilda.com

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