Economic suffering from jobs lost to automation and the COVID-19 pandemic accelerate the need for multiple income streams from family members and businesses. The earlier post, "Rebirth of Self Worth," suggests ways children even can help generate income by adding lemonade and hot dog stands and entertainment to family yard sales. The following examples show how some businesses find new market segments eager to try their products.
Hard to mow hilly parks overgrown with invasive buckthorn bushes and honeysuckle inspired the formation of the HaakHagen Goat Grazing farm run by a couple of friends in Wisconsin. Their 88 agile goats, rented out to private landowners and government land, also help preserve prairies by nibbling invasive species and shrubs that block the sun needed by shorter native plants. The goats are gentler on the land than heavy mowing machinery, what they leave behind eliminates the need for some fertilizer and adults and children find the goats fun to watch.
Renting out RVs during the pandemic has become a new business catering to both vacationing families and virtual employees looking for office space while sheltering at home. When pleasure and business travelers return to the skies, they are likely to receive airline-branded Honeywell Safety Packs containing single-use gloves, hand wipes and face masks. Trendwatching.com reports Honeywell offers airline crews reusable packs of safety glasses and face masks with interchangeable filters.
What is obvious from these three businesses is the way they each seized opportunities to serve multiple market segments. Clothing manufacturers now have an opportunity to produce double-duty items for home and business wear. Educational suppliers can think in terms of the home and school markets. Online retailers might gain multiple incomes from pop-up holiday shops, and more and more similar ideas will create new jobs and economic growth.
Showing posts with label automation. Show all posts
Showing posts with label automation. Show all posts
Friday, June 12, 2020
Monday, October 21, 2019
Cost Out A Better Future
Democratic candidate for U.S. President, Andrew Yang, proposes sending every person over 18 years of age a government check for $1000 each month. Why? Human beings need food, shelter, and clothing every month. Yet, automation is expected to eliminate more and more of the ways people now earn the funds needed to provide these necessities, while corporations accumulate greater wealth by replacing employees with machines.
Already, Yang notes, big tech companies, such as Facebook, Amazon, and Google, grow rich by packaging and selling the private information millions and billions of individuals supply free of charge.
Technological changes require changes in other sectors of human life. When work requires manipulating information on a computer, the physical strength of men is unnecessary. Childcare is still necessary, but how it is provided and by whom need not be confined to unpaid natural mothers.
Not only childcare, but $1000-a-month paychecks also would provide compensation for those whose time creates an enjoyable community life: the shoppers and clerks who get to know each other, community leaders who organize groups and boycotts to solve problems, gardeners who share crops with neighbors and plant flowers to beautify walking paths, visitors who watch sports with the homebound, families who attend religious services together, friends and relatives who celebrate birthdays with homemade cakes, cards, and presents, and those who write "Thank you" notes.
Those dissatisfied with $1000 a month will innovate. Designs can be sleeker. Drugs can cure more. Experts can develop more effective teaching methods. Constitutions, standards, and collective agreements can harness, not only the temporary impulses of a mob, but also the independent actions of robots and AI. Dives can go deeper. Spacecraft can go farther. Games can be more fun.
$1000 a month is a small price to pay for a better world.
Already, Yang notes, big tech companies, such as Facebook, Amazon, and Google, grow rich by packaging and selling the private information millions and billions of individuals supply free of charge.
Technological changes require changes in other sectors of human life. When work requires manipulating information on a computer, the physical strength of men is unnecessary. Childcare is still necessary, but how it is provided and by whom need not be confined to unpaid natural mothers.
Not only childcare, but $1000-a-month paychecks also would provide compensation for those whose time creates an enjoyable community life: the shoppers and clerks who get to know each other, community leaders who organize groups and boycotts to solve problems, gardeners who share crops with neighbors and plant flowers to beautify walking paths, visitors who watch sports with the homebound, families who attend religious services together, friends and relatives who celebrate birthdays with homemade cakes, cards, and presents, and those who write "Thank you" notes.
Those dissatisfied with $1000 a month will innovate. Designs can be sleeker. Drugs can cure more. Experts can develop more effective teaching methods. Constitutions, standards, and collective agreements can harness, not only the temporary impulses of a mob, but also the independent actions of robots and AI. Dives can go deeper. Spacecraft can go farther. Games can be more fun.
$1000 a month is a small price to pay for a better world.
Tuesday, July 24, 2018
How to React When You've Been Wronged
Colombia's new President, Ivan Duque, will come to office facing a population that suffered hundreds of thousands killed by rebels who now are allowed to hold public office under the terms of a 2016 peace accord. Instead, many of his wronged constituents want retribution for crimes against their families.
In The Monarchy of Fear, Martha C. Nussbaum writes about Rev. Martin Luther King Jr. and the valid anger he faced as a leader of once-enslaved African-Americas in the United States. She also sees anger growing among those whose standard of living is threatened by automation and outsourcing of jobs, while others thrive from globalization.
When President Obama was asked to deliver the Nelson Mandela Annual Lecture in South Africa this year, he too reflected on the way globalization and technology can benefit the rich and powerful while facilitating inequality. But he reminded his audience about how Mandela responded: 1) to his over 20 years of captivity under an apartheid structure that defined the artificial domination of whites over blacks by studying the thinking of his enemies, and 2) to his election as President of South Africa by abiding by the constitutional limit of his presidential term and by not favoring any group.
Obama acknowledged, IT IS HARD to engage with people who look different and hold different views from you. But you have to keep teaching that idea of engaging with different people to ourselves and our children, he said.
Each of us has to hold hard, as Nelson Mandela did, even while he was in prison, to the firm belief that being a human entitles each of us to a human inheritance. All people are created equal and endowed by their creator with certain inalienable rights, Obama reminded his South African audience. What flows from that firm belief in the equality of rich and poor, woman and man, young and old, and every other human difference is Mandela's conclusion: "It's not justice if now you're on top, so I'm going to do the same thing that those folks were doing to me, and now I'm going to do it to you."
Nussbaum expresses the same idea. Saying something is wrong and should never happen again is valuable, but deciding to fix it by making the doer suffer is not helpful. Put another way, an African-American, speaking on a panel at a forum, observed it is more productive to go forward with an attitude based on the Civil Rights movement than an attitude derived from slavery.
Once you concentrate on your own value as a human being and that of all other humans and vow not to repeat past failures, there's hope for a better future.
In The Monarchy of Fear, Martha C. Nussbaum writes about Rev. Martin Luther King Jr. and the valid anger he faced as a leader of once-enslaved African-Americas in the United States. She also sees anger growing among those whose standard of living is threatened by automation and outsourcing of jobs, while others thrive from globalization.
When President Obama was asked to deliver the Nelson Mandela Annual Lecture in South Africa this year, he too reflected on the way globalization and technology can benefit the rich and powerful while facilitating inequality. But he reminded his audience about how Mandela responded: 1) to his over 20 years of captivity under an apartheid structure that defined the artificial domination of whites over blacks by studying the thinking of his enemies, and 2) to his election as President of South Africa by abiding by the constitutional limit of his presidential term and by not favoring any group.
Obama acknowledged, IT IS HARD to engage with people who look different and hold different views from you. But you have to keep teaching that idea of engaging with different people to ourselves and our children, he said.
Each of us has to hold hard, as Nelson Mandela did, even while he was in prison, to the firm belief that being a human entitles each of us to a human inheritance. All people are created equal and endowed by their creator with certain inalienable rights, Obama reminded his South African audience. What flows from that firm belief in the equality of rich and poor, woman and man, young and old, and every other human difference is Mandela's conclusion: "It's not justice if now you're on top, so I'm going to do the same thing that those folks were doing to me, and now I'm going to do it to you."
Nussbaum expresses the same idea. Saying something is wrong and should never happen again is valuable, but deciding to fix it by making the doer suffer is not helpful. Put another way, an African-American, speaking on a panel at a forum, observed it is more productive to go forward with an attitude based on the Civil Rights movement than an attitude derived from slavery.
Once you concentrate on your own value as a human being and that of all other humans and vow not to repeat past failures, there's hope for a better future.
Friday, July 21, 2017
AI Only Provides Opportunities for Rich People. Really?
"He fixes radios by thinking!"
The book, Surely You're Joking, Mr. Feynman! recounts this answer a man gave someone who saw the Nobel Prize winning physicist walking back and forth, when he was supposed to be fixing a radio. The book also tells how Feynman learned trigonometry by reading a book he checked out from the library, when he was eleven or twelve.
I was reminded of these items when I read a July 7, 2017 article (theverge.com) by James Vincent. He cites studies that conclude people from working class and poorer backgrounds lack: 1) the ability to retrain for AI and robotic automation, and 2) the "soft skills" of communication, confidence, motivation, and resilience. Job losses and inequality will increase as artificial intelligence eliminates the administrative positions that have traditionally enabled these employees without higher educations to move up the corporate ladder.
Yet, I remember the way the movie Hidden Figures showed a woman who made a contribution to the early US space program learned computer language from a library book, and I began to question the inevitability of this prognosis.
In another example, a young Muslim woman I know, who doesn't come from a family of means, taught herself to sew by watching YouTube videos. She spent her last year of high school writing the essays and organizing the portfolio she needed to gain admission to the Fashion Institute of Technology.
During the summer, colleges and universities offer scholarships to programs in a wide variety of fields. During the school year, they sponsor debating, math, computer, chess, and other competitions open to all. And every school is beefing up the STEM courses in science, technology, engineering, and mathematics that prepare students to land positions in fields that have no pay gaps for those from different socio-economic backgrounds.
The rich cannot corner the market on walking and thinking.
The book, Surely You're Joking, Mr. Feynman! recounts this answer a man gave someone who saw the Nobel Prize winning physicist walking back and forth, when he was supposed to be fixing a radio. The book also tells how Feynman learned trigonometry by reading a book he checked out from the library, when he was eleven or twelve.
I was reminded of these items when I read a July 7, 2017 article (theverge.com) by James Vincent. He cites studies that conclude people from working class and poorer backgrounds lack: 1) the ability to retrain for AI and robotic automation, and 2) the "soft skills" of communication, confidence, motivation, and resilience. Job losses and inequality will increase as artificial intelligence eliminates the administrative positions that have traditionally enabled these employees without higher educations to move up the corporate ladder.
Yet, I remember the way the movie Hidden Figures showed a woman who made a contribution to the early US space program learned computer language from a library book, and I began to question the inevitability of this prognosis.
In another example, a young Muslim woman I know, who doesn't come from a family of means, taught herself to sew by watching YouTube videos. She spent her last year of high school writing the essays and organizing the portfolio she needed to gain admission to the Fashion Institute of Technology.
During the summer, colleges and universities offer scholarships to programs in a wide variety of fields. During the school year, they sponsor debating, math, computer, chess, and other competitions open to all. And every school is beefing up the STEM courses in science, technology, engineering, and mathematics that prepare students to land positions in fields that have no pay gaps for those from different socio-economic backgrounds.
The rich cannot corner the market on walking and thinking.
Sunday, June 25, 2017
Blind Trust in AI Is a Mistake
For better or worse, combining algorithms with images collected by drones, satellites, and video feeds from other monitors enhances aerial intelligence in a variety of fields.
Overhead movie and TV shots already provide a different perspective, just as viewing the Earth or a rocket launch from a space craft or satellite does. These new perspectives offer advantages besides entertainment value and a chance to study the dwindling ice cap at the North Pole.
Seen from above, data about landscapes has various applications. The famous Texas Gulf Sulphur Company case involving insider trading began with aerial geophysical surveys in eastern Canada. When pilots in planes scanning the ground saw the needles in their instruments going wild, they could pinpoint the possible location of electrically conductive sulphide deposits containing zinc and copper along with sulphur.
When Argentina invaded Britain's Falkland Islands in April, 1982, it's been reported the only map the defenders possessed showed perfect picnic spots. Planes took to the air to locate the landing spot that enabled British troops to declare victory at Port Stanley in June, 1982.
Nowadays, the aim is to write algorithms that look for certain activities among millions of images. A robber can program an algorithm to tell a drone's camera to identify where delivery trucks leave packages. An algorithm can call attention to a large group of people and cars arriving at a North Korean missile testing site. Then, an analyst can figure out why, because, to date, artificial intelligence (AI) does not explain how and why it reaches a conclusion.
Since artificial intelligence's algorithms operate in their own "black boxes," humans are unable to evaluate the process used to arrive at conclusions. Humans cannot replicate AI processes independently. And if an algorithm makes a mistake, AI provides no clues to the reasoning that went astray.
In other words, robots without supervision can take actions based on conclusions dictated by faulty algorithms. An early attempt to treat patients based on a "machine model" provides a good example. Doctors treating pneumonia patients who also have asthma admit them to the hospital immediately, but the machine readout said to send them home. The "machine" saw pneumonia/asthma patients in the hospital recovered quickly and decided they had no reason to be admitted in the first place. The "machine" did not have the information that their rapid recovery occurred, because they were admitted to the hospital's intensive care unit.
Google's top artificial intelligence expert, John Giannandrea, speaking at a conference on the relationship between humans and AI, emphasized the effect of bias in algorithms. Not only does it affect the news and ads social media allows us to see, but he also echoed the idea that AI bias can determine the kind of medical treatment a person receives and, based on AI's predictions about the likelihood of a convict committing future offenses, it can affect a judge's decision regarding parole.
Joy Buolamwini's Algorithmic Justice League found facial-analysis software was prone to making mistakes recognizing the female gender, especially of darker-skinned women. AI is developed by and often tested primarily on light-skinned men, but recognition technology, for example, is promoted for hiring, policing, and military applications involving diverse populations. Since facial recognition screening fails to provide clear identifications of some populations, it also has the potential to be used to identify non-white suspects and to discriminate against hiring non-white employees.
When humans know they are dealing with imperfect information, whether they are playing poker, treating cancer, choosing a stock, catching a criminal, or waging war, how can they have confidence in authorizing and repeating a "black box" solution that requires blind trust? Who would take moral and legal responsibility for a mistake. The human who authorized action based on AI, wrote the algorithm, or determined the data base the algorithm used to determine its conclusion? And then there is the question of the moral and legal responsibility for a robot that malfunctions while it is carrying out the "right" conclusion.
Research is trying to determine what elements are necessary to help AI reach the best conclusions. Statistics can't always be trusted. Numbers that show terrorists are Muslims or repeat criminals are African Americans do nothing to suggest how an individual Muslim or African American should be screened or treated. AI research is further complicated by findings that also suggest the mind/intellect and will that control moral values and actions are separate from the physical brain that controls other human activities and diseases such as epilepsy and Parkinson's.
Automated solutions require new safeguards: to defend against hacking that alters information, to eliminate bias, to verify accuracy by checking multiple sources, and to determine accountability and responsibility for actions.
Overhead movie and TV shots already provide a different perspective, just as viewing the Earth or a rocket launch from a space craft or satellite does. These new perspectives offer advantages besides entertainment value and a chance to study the dwindling ice cap at the North Pole.
Seen from above, data about landscapes has various applications. The famous Texas Gulf Sulphur Company case involving insider trading began with aerial geophysical surveys in eastern Canada. When pilots in planes scanning the ground saw the needles in their instruments going wild, they could pinpoint the possible location of electrically conductive sulphide deposits containing zinc and copper along with sulphur.
When Argentina invaded Britain's Falkland Islands in April, 1982, it's been reported the only map the defenders possessed showed perfect picnic spots. Planes took to the air to locate the landing spot that enabled British troops to declare victory at Port Stanley in June, 1982.
Nowadays, the aim is to write algorithms that look for certain activities among millions of images. A robber can program an algorithm to tell a drone's camera to identify where delivery trucks leave packages. An algorithm can call attention to a large group of people and cars arriving at a North Korean missile testing site. Then, an analyst can figure out why, because, to date, artificial intelligence (AI) does not explain how and why it reaches a conclusion.
Since artificial intelligence's algorithms operate in their own "black boxes," humans are unable to evaluate the process used to arrive at conclusions. Humans cannot replicate AI processes independently. And if an algorithm makes a mistake, AI provides no clues to the reasoning that went astray.
In other words, robots without supervision can take actions based on conclusions dictated by faulty algorithms. An early attempt to treat patients based on a "machine model" provides a good example. Doctors treating pneumonia patients who also have asthma admit them to the hospital immediately, but the machine readout said to send them home. The "machine" saw pneumonia/asthma patients in the hospital recovered quickly and decided they had no reason to be admitted in the first place. The "machine" did not have the information that their rapid recovery occurred, because they were admitted to the hospital's intensive care unit.
Google's top artificial intelligence expert, John Giannandrea, speaking at a conference on the relationship between humans and AI, emphasized the effect of bias in algorithms. Not only does it affect the news and ads social media allows us to see, but he also echoed the idea that AI bias can determine the kind of medical treatment a person receives and, based on AI's predictions about the likelihood of a convict committing future offenses, it can affect a judge's decision regarding parole.
Joy Buolamwini's Algorithmic Justice League found facial-analysis software was prone to making mistakes recognizing the female gender, especially of darker-skinned women. AI is developed by and often tested primarily on light-skinned men, but recognition technology, for example, is promoted for hiring, policing, and military applications involving diverse populations. Since facial recognition screening fails to provide clear identifications of some populations, it also has the potential to be used to identify non-white suspects and to discriminate against hiring non-white employees.
When humans know they are dealing with imperfect information, whether they are playing poker, treating cancer, choosing a stock, catching a criminal, or waging war, how can they have confidence in authorizing and repeating a "black box" solution that requires blind trust? Who would take moral and legal responsibility for a mistake. The human who authorized action based on AI, wrote the algorithm, or determined the data base the algorithm used to determine its conclusion? And then there is the question of the moral and legal responsibility for a robot that malfunctions while it is carrying out the "right" conclusion.
Research is trying to determine what elements are necessary to help AI reach the best conclusions. Statistics can't always be trusted. Numbers that show terrorists are Muslims or repeat criminals are African Americans do nothing to suggest how an individual Muslim or African American should be screened or treated. AI research is further complicated by findings that also suggest the mind/intellect and will that control moral values and actions are separate from the physical brain that controls other human activities and diseases such as epilepsy and Parkinson's.
Automated solutions require new safeguards: to defend against hacking that alters information, to eliminate bias, to verify accuracy by checking multiple sources, and to determine accountability and responsibility for actions.
Thursday, March 16, 2017
A Fork in the Road to the Future
How should children be prepared to handle the next 70 or 80 years of their lives? Or 40-year-olds to handle their next 40 years?
The trendwatching.com site, that has spotters all over the world, found people have looked at current conditions:
The trendwatching.com site, that has spotters all over the world, found people have looked at current conditions:
- immigration
- refugee crisis
- job automation
- depressed wages
- uneven recovery
- generational divide
- racial divide
- fear of terrorism
- Global citizens open to an interconnected world, where people learn to understand their changing relationships to neighborhoods, cities, and nations
- Nation nurturers who seek comfort in the familiar
Dealing with change, especially rapid change, is not easy. It is understandable that some want to wall themselves off from foreigners; to pretend technology is going to slow down and manufacturing jobs, as we have known them, are going to return; to listen only to broadcasts that agree with them; and to cling to traditional families where a man works and an uneducated woman stays at home with the children. But you only need look at one example of the future - shopping malls and stores empty of consumers of all kinds who have switched over to ordering their needs and wants online - to see change is impossible to escape. (Could these empty stores be converted to on-going world fairs where "shoppers" could go to experience and learn new technologies?)
Like it or not, children are going to live in a world of global citizens. Parents and teachers need to prepare to help them feel at home there.
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