full transcript

From the Ted Talk by Rodney Brooks: Why we will rely on robots

Unscramble the Blue Letters

Well, Arthur C. cakrle, a famous science fiction writer from the 1950s, said that, "We overestimate technology in the short term, and we underestimate it in the long term." And I think that's some of the fear that we see about jobs drppiseiaang from artificial intelligence and robots. That we're overestimating the technology in the short term. But I am worried whether we're going to get the tnghcelooy we need in the long term. Because the demographics are really going to lvaee us with lots of jobs that need doing and that we, our society, is going to have to be built on the shoulders of steel of robots in the future. So I'm scared we won't have enough robots. But fear of losing jobs to technology has been around for a long time. Back in 1957, there was a Spencer Tracy, Katharine Hepburn movie. So you know how it ended up, Spencer Tracy brought a cptmouer, a mainframe computer of 1957, in to help the librarians. The librarians in the company would do things like answer for the executives, "What are the names of Santa's reindeer?" And they would look that up. And this mfmrianae computer was going to help them with that job. Well of course a mainframe computer in 1957 wasn't much use for that job. The lirabarnis were arifad their jobs were going to disappear. But that's not what happened in fact. The number of jobs for librarians increased for a long time after 1957. It wasn't until the Internet came into play, the web came into play and search engines came into play that the need for librarians went down. And I think everyone from 1957 toaltly underestimated the level of technology we would all carry around in our hands and in our pockets today. And we can just ask: "What are the names of Santa's reindeer?" and be told instantly — or anything else we want to ask. By the way, the wages for librarians went up faster than the wages for other jobs in the U.S. over that same time period, because librarians became partners of computers. Computers became tools, and they got more tools that they could use and become more effeticve during that time. Same thing hnaepped in offices. Back in the old days, pleope used spreadsheets. Spreadsheets were spread sheets of paper, and they calculated by hand. But here was an isrtntneeig thing that came along. With the revolution around 1980 of P.C.'s, the spreadsheet programs were tuned for office workers, not to replace office wkorres, but it rstepceed office workers as being capable of being programmers. So office workers became peormragrms of spreadsheets. It increased their capabilities. They no longer had to do the mundane computations, but they could do something much more. Now taody, we're starting to see rotbos in our lives. On the left there is the PackBot from iRobot. When soldiers came across rddoisae bombs in Iraq and Afghanistan, instead of putting on a bomb suit and going out and poking with a stick, as they used to do up until about 2002, they now send the robot out. So the robot takes over the dangerous jobs. On the right are some TUGs from a cpnaomy called Aethon in prgtstbuih. These are in hundreds of hospitals across the U.S. And they take the dirty sheets down to the lrdunay. They take the dirty dehiss back to the kitchen. They bring the medicines up from the pharmacy. And it feers up the nurses and the nurse's aides from doing that mundane work of just mechanically pushing stuff around to spend more time with patients. In fact, robots have become sort of ubiquitous in our levis in many ways. But I think when it comes to factory robots, people are sort of afraid, because factory robots are dangerous to be around. In oderr to program them, you have to understand six-dimensional vectors and quaternions. And ordinary people can't interact with them. And I think it's the sort of technology that's gone wrong. It's displaced the worker from the technology. And I think we really have to look at technologies that ordinary workers can interact with. And so I want to tell you today about Baxter, which we've been talking about. And Baxter, I see, as a way — a first wave of robot that ordinary people can ianertct with in an industrial setting. So Baxter is up here. This is Chris Harbert from Rethink robiocts. We've got a coenyvor there. And if the lighting isn't too extreme — Ah, ah! There it is. It's picked up the object off the conveyor. It's going to come bring it over here and put it down. And then it'll go back, raceh for another object. The interesting thing is beaxtr has some bisac cmoomn sense. By the way, what's going on with the eyes? The eyes are on the screen there. The eyes look ahead where the robot's going to move. So a person that's interacting with the rboot understands where it's going to reach and isn't surprised by its miotnos. Here Chris took the object out of its hand, and Baxter didn't go and try to put it down; it went back and realized it had to get another one. It's got a little bit of basic common snsee, goes and picks the objects. And Baxter's safe to interact with. You wouldn't want to do this with a crunert industrial robot. But with Baxter it doesn't hurt. It feels the force, understands that Chris is there and doesn't push through him and hurt him. But I think the most interesting thing about Baxter is the user interface. And so Chris is going to come and grab the other arm now. And when he grabs an arm, it goes into zero-force gravity-compensated mode and graphics come up on the screen. You can see some icons on the left of the screen there for what was about its right arm. He's going to put something in its hand, he's going to bring it over here, press a button and let go of that thing in the hand. And the robot figures out, ah, he must mean I want to put stuff down. It puts a little icon there. He comes over here, and he gets the fingers to grasp together, and the robot infers, ah, you want an object for me to pick up. That puts the geren icon there. He's going to map out an area of where the robot should pick up the oejcbt from. It just moves it around, and the robot freigus out that was an area search. He didn't have to select that from a menu. And now he's going to go off and train the vuaisl appaeanrce of that object while we cnontuie talking. So as we continue here, I want to tell you about what this is like in factories. These robots we're shipping every day. They go to faercoits around the country. This is Mildred. Mildred's a factory worker in Connecticut. She's wrkoed on the line for over 20 years. One hour after she saw her first industrial robot, she had programmed it to do some tasks in the frctoay. She decided she really liked robots. And it was doing the simple repetitive tasks that she had had to do beforehand. Now she's got the robot doing it. When we first went out to talk to people in factories about how we could get robots to interact with them better, one of the qouisents we asked them was, "Do you want your children to work in a factory?" The universal answer was "No, I want a better job than that for my children." And as a result of that, mderlid is very typical of today's factory workers in the U.S. They're older, and they're getting older and older. There aren't many young people coming into factory work. And as their tkass become more onerous on them, we need to give them tools that they can cltoorblaae with, so that they can be part of the solution, so that they can continue to work and we can continue to prudoce in the U.S. And so our vision is that Mildred who's the line worker becomes Mildred the robot trainer. She ltfis her game, like the office workers of the 1980s lifted their game of what they could do. We're not giving them tools that they have to go and study for yreas and years in order to use. They're tools that they can just learn how to operate in a few minutes. There's two great forces that are both volitional but inevitable. That's climate change and demographics. Demographics is really going to change our world. This is the percentage of adults who are working age. And it's gone down slightly over the last 40 years. But over the next 40 years, it's going to change dramatically, even in China. The percentage of adults who are wnrikog age dorps dramatically. And turned up the other way, the people who are rmrtieenet age goes up very, very fast, as the baby boomers get to retirement age. That means there will be more people with fewer social security dollars competing for services. But more than that, as we get older we get more frail and we can't do all the tasks we used to do. If we look at the statistics on the ages of caregivers, before our eyes those caregivers are getting older and older. That's happening statistically right now. And as the number of people who are older, above retirement age and getting older, as they increase, there will be less people to take care of them. And I think we're really going to have to have robots to help us. And I don't mean robots in terms of cpanonoims. I mean robots doing the things that we normally do for ourselves but get harder as we get odelr. Getting the groceries in from the car, up the srtais, into the kitchen. Or even, as we get very much older, driving our cars to go visit people. And I think robotics gives people a chance to have dignity as they get older by having cootrnl of the robotic solution. So they don't have to rely on people that are getting scarcer to help them. And so I really think that we're going to be spending more time with robots like Baxter and working with robots like Baxter in our daily lives. And that we will — Here, Baxter, it's good. And that we will all come to rely on robots over the next 40 years as part of our everyday lives. Thanks very much. (apalupse)

Open Cloze

Well, Arthur C. ______, a famous science fiction writer from the 1950s, said that, "We overestimate technology in the short term, and we underestimate it in the long term." And I think that's some of the fear that we see about jobs ____________ from artificial intelligence and robots. That we're overestimating the technology in the short term. But I am worried whether we're going to get the __________ we need in the long term. Because the demographics are really going to _____ us with lots of jobs that need doing and that we, our society, is going to have to be built on the shoulders of steel of robots in the future. So I'm scared we won't have enough robots. But fear of losing jobs to technology has been around for a long time. Back in 1957, there was a Spencer Tracy, Katharine Hepburn movie. So you know how it ended up, Spencer Tracy brought a ________, a mainframe computer of 1957, in to help the librarians. The librarians in the company would do things like answer for the executives, "What are the names of Santa's reindeer?" And they would look that up. And this _________ computer was going to help them with that job. Well of course a mainframe computer in 1957 wasn't much use for that job. The __________ were ______ their jobs were going to disappear. But that's not what happened in fact. The number of jobs for librarians increased for a long time after 1957. It wasn't until the Internet came into play, the web came into play and search engines came into play that the need for librarians went down. And I think everyone from 1957 _______ underestimated the level of technology we would all carry around in our hands and in our pockets today. And we can just ask: "What are the names of Santa's reindeer?" and be told instantly — or anything else we want to ask. By the way, the wages for librarians went up faster than the wages for other jobs in the U.S. over that same time period, because librarians became partners of computers. Computers became tools, and they got more tools that they could use and become more _________ during that time. Same thing ________ in offices. Back in the old days, ______ used spreadsheets. Spreadsheets were spread sheets of paper, and they calculated by hand. But here was an ___________ thing that came along. With the revolution around 1980 of P.C.'s, the spreadsheet programs were tuned for office workers, not to replace office _______, but it _________ office workers as being capable of being programmers. So office workers became ___________ of spreadsheets. It increased their capabilities. They no longer had to do the mundane computations, but they could do something much more. Now _____, we're starting to see ______ in our lives. On the left there is the PackBot from iRobot. When soldiers came across ________ bombs in Iraq and Afghanistan, instead of putting on a bomb suit and going out and poking with a stick, as they used to do up until about 2002, they now send the robot out. So the robot takes over the dangerous jobs. On the right are some TUGs from a _______ called Aethon in __________. These are in hundreds of hospitals across the U.S. And they take the dirty sheets down to the _______. They take the dirty ______ back to the kitchen. They bring the medicines up from the pharmacy. And it _____ up the nurses and the nurse's aides from doing that mundane work of just mechanically pushing stuff around to spend more time with patients. In fact, robots have become sort of ubiquitous in our _____ in many ways. But I think when it comes to factory robots, people are sort of afraid, because factory robots are dangerous to be around. In _____ to program them, you have to understand six-dimensional vectors and quaternions. And ordinary people can't interact with them. And I think it's the sort of technology that's gone wrong. It's displaced the worker from the technology. And I think we really have to look at technologies that ordinary workers can interact with. And so I want to tell you today about Baxter, which we've been talking about. And Baxter, I see, as a way — a first wave of robot that ordinary people can ________ with in an industrial setting. So Baxter is up here. This is Chris Harbert from Rethink ________. We've got a ________ there. And if the lighting isn't too extreme — Ah, ah! There it is. It's picked up the object off the conveyor. It's going to come bring it over here and put it down. And then it'll go back, _____ for another object. The interesting thing is ______ has some _____ ______ sense. By the way, what's going on with the eyes? The eyes are on the screen there. The eyes look ahead where the robot's going to move. So a person that's interacting with the _____ understands where it's going to reach and isn't surprised by its _______. Here Chris took the object out of its hand, and Baxter didn't go and try to put it down; it went back and realized it had to get another one. It's got a little bit of basic common _____, goes and picks the objects. And Baxter's safe to interact with. You wouldn't want to do this with a _______ industrial robot. But with Baxter it doesn't hurt. It feels the force, understands that Chris is there and doesn't push through him and hurt him. But I think the most interesting thing about Baxter is the user interface. And so Chris is going to come and grab the other arm now. And when he grabs an arm, it goes into zero-force gravity-compensated mode and graphics come up on the screen. You can see some icons on the left of the screen there for what was about its right arm. He's going to put something in its hand, he's going to bring it over here, press a button and let go of that thing in the hand. And the robot figures out, ah, he must mean I want to put stuff down. It puts a little icon there. He comes over here, and he gets the fingers to grasp together, and the robot infers, ah, you want an object for me to pick up. That puts the _____ icon there. He's going to map out an area of where the robot should pick up the ______ from. It just moves it around, and the robot _______ out that was an area search. He didn't have to select that from a menu. And now he's going to go off and train the ______ __________ of that object while we ________ talking. So as we continue here, I want to tell you about what this is like in factories. These robots we're shipping every day. They go to _________ around the country. This is Mildred. Mildred's a factory worker in Connecticut. She's ______ on the line for over 20 years. One hour after she saw her first industrial robot, she had programmed it to do some tasks in the _______. She decided she really liked robots. And it was doing the simple repetitive tasks that she had had to do beforehand. Now she's got the robot doing it. When we first went out to talk to people in factories about how we could get robots to interact with them better, one of the _________ we asked them was, "Do you want your children to work in a factory?" The universal answer was "No, I want a better job than that for my children." And as a result of that, _______ is very typical of today's factory workers in the U.S. They're older, and they're getting older and older. There aren't many young people coming into factory work. And as their _____ become more onerous on them, we need to give them tools that they can ___________ with, so that they can be part of the solution, so that they can continue to work and we can continue to _______ in the U.S. And so our vision is that Mildred who's the line worker becomes Mildred the robot trainer. She _____ her game, like the office workers of the 1980s lifted their game of what they could do. We're not giving them tools that they have to go and study for _____ and years in order to use. They're tools that they can just learn how to operate in a few minutes. There's two great forces that are both volitional but inevitable. That's climate change and demographics. Demographics is really going to change our world. This is the percentage of adults who are working age. And it's gone down slightly over the last 40 years. But over the next 40 years, it's going to change dramatically, even in China. The percentage of adults who are _______ age _____ dramatically. And turned up the other way, the people who are __________ age goes up very, very fast, as the baby boomers get to retirement age. That means there will be more people with fewer social security dollars competing for services. But more than that, as we get older we get more frail and we can't do all the tasks we used to do. If we look at the statistics on the ages of caregivers, before our eyes those caregivers are getting older and older. That's happening statistically right now. And as the number of people who are older, above retirement age and getting older, as they increase, there will be less people to take care of them. And I think we're really going to have to have robots to help us. And I don't mean robots in terms of __________. I mean robots doing the things that we normally do for ourselves but get harder as we get _____. Getting the groceries in from the car, up the ______, into the kitchen. Or even, as we get very much older, driving our cars to go visit people. And I think robotics gives people a chance to have dignity as they get older by having _______ of the robotic solution. So they don't have to rely on people that are getting scarcer to help them. And so I really think that we're going to be spending more time with robots like Baxter and working with robots like Baxter in our daily lives. And that we will — Here, Baxter, it's good. And that we will all come to rely on robots over the next 40 years as part of our everyday lives. Thanks very much. (________)

Solution

  1. dishes
  2. motions
  3. librarians
  4. laundry
  5. lives
  6. roadside
  7. factories
  8. worked
  9. drops
  10. retirement
  11. companions
  12. company
  13. continue
  14. applause
  15. clarke
  16. basic
  17. order
  18. collaborate
  19. respected
  20. interesting
  21. visual
  22. effective
  23. figures
  24. programmers
  25. current
  26. computer
  27. frees
  28. baxter
  29. leave
  30. sense
  31. robot
  32. stairs
  33. robotics
  34. totally
  35. mainframe
  36. older
  37. object
  38. reach
  39. conveyor
  40. afraid
  41. workers
  42. green
  43. technology
  44. disappearing
  45. questions
  46. people
  47. today
  48. factory
  49. lifts
  50. control
  51. years
  52. tasks
  53. working
  54. robots
  55. happened
  56. pittsburgh
  57. appearance
  58. mildred
  59. interact
  60. common
  61. produce

Original Text

Well, Arthur C. Clarke, a famous science fiction writer from the 1950s, said that, "We overestimate technology in the short term, and we underestimate it in the long term." And I think that's some of the fear that we see about jobs disappearing from artificial intelligence and robots. That we're overestimating the technology in the short term. But I am worried whether we're going to get the technology we need in the long term. Because the demographics are really going to leave us with lots of jobs that need doing and that we, our society, is going to have to be built on the shoulders of steel of robots in the future. So I'm scared we won't have enough robots. But fear of losing jobs to technology has been around for a long time. Back in 1957, there was a Spencer Tracy, Katharine Hepburn movie. So you know how it ended up, Spencer Tracy brought a computer, a mainframe computer of 1957, in to help the librarians. The librarians in the company would do things like answer for the executives, "What are the names of Santa's reindeer?" And they would look that up. And this mainframe computer was going to help them with that job. Well of course a mainframe computer in 1957 wasn't much use for that job. The librarians were afraid their jobs were going to disappear. But that's not what happened in fact. The number of jobs for librarians increased for a long time after 1957. It wasn't until the Internet came into play, the web came into play and search engines came into play that the need for librarians went down. And I think everyone from 1957 totally underestimated the level of technology we would all carry around in our hands and in our pockets today. And we can just ask: "What are the names of Santa's reindeer?" and be told instantly — or anything else we want to ask. By the way, the wages for librarians went up faster than the wages for other jobs in the U.S. over that same time period, because librarians became partners of computers. Computers became tools, and they got more tools that they could use and become more effective during that time. Same thing happened in offices. Back in the old days, people used spreadsheets. Spreadsheets were spread sheets of paper, and they calculated by hand. But here was an interesting thing that came along. With the revolution around 1980 of P.C.'s, the spreadsheet programs were tuned for office workers, not to replace office workers, but it respected office workers as being capable of being programmers. So office workers became programmers of spreadsheets. It increased their capabilities. They no longer had to do the mundane computations, but they could do something much more. Now today, we're starting to see robots in our lives. On the left there is the PackBot from iRobot. When soldiers came across roadside bombs in Iraq and Afghanistan, instead of putting on a bomb suit and going out and poking with a stick, as they used to do up until about 2002, they now send the robot out. So the robot takes over the dangerous jobs. On the right are some TUGs from a company called Aethon in Pittsburgh. These are in hundreds of hospitals across the U.S. And they take the dirty sheets down to the laundry. They take the dirty dishes back to the kitchen. They bring the medicines up from the pharmacy. And it frees up the nurses and the nurse's aides from doing that mundane work of just mechanically pushing stuff around to spend more time with patients. In fact, robots have become sort of ubiquitous in our lives in many ways. But I think when it comes to factory robots, people are sort of afraid, because factory robots are dangerous to be around. In order to program them, you have to understand six-dimensional vectors and quaternions. And ordinary people can't interact with them. And I think it's the sort of technology that's gone wrong. It's displaced the worker from the technology. And I think we really have to look at technologies that ordinary workers can interact with. And so I want to tell you today about Baxter, which we've been talking about. And Baxter, I see, as a way — a first wave of robot that ordinary people can interact with in an industrial setting. So Baxter is up here. This is Chris Harbert from Rethink Robotics. We've got a conveyor there. And if the lighting isn't too extreme — Ah, ah! There it is. It's picked up the object off the conveyor. It's going to come bring it over here and put it down. And then it'll go back, reach for another object. The interesting thing is Baxter has some basic common sense. By the way, what's going on with the eyes? The eyes are on the screen there. The eyes look ahead where the robot's going to move. So a person that's interacting with the robot understands where it's going to reach and isn't surprised by its motions. Here Chris took the object out of its hand, and Baxter didn't go and try to put it down; it went back and realized it had to get another one. It's got a little bit of basic common sense, goes and picks the objects. And Baxter's safe to interact with. You wouldn't want to do this with a current industrial robot. But with Baxter it doesn't hurt. It feels the force, understands that Chris is there and doesn't push through him and hurt him. But I think the most interesting thing about Baxter is the user interface. And so Chris is going to come and grab the other arm now. And when he grabs an arm, it goes into zero-force gravity-compensated mode and graphics come up on the screen. You can see some icons on the left of the screen there for what was about its right arm. He's going to put something in its hand, he's going to bring it over here, press a button and let go of that thing in the hand. And the robot figures out, ah, he must mean I want to put stuff down. It puts a little icon there. He comes over here, and he gets the fingers to grasp together, and the robot infers, ah, you want an object for me to pick up. That puts the green icon there. He's going to map out an area of where the robot should pick up the object from. It just moves it around, and the robot figures out that was an area search. He didn't have to select that from a menu. And now he's going to go off and train the visual appearance of that object while we continue talking. So as we continue here, I want to tell you about what this is like in factories. These robots we're shipping every day. They go to factories around the country. This is Mildred. Mildred's a factory worker in Connecticut. She's worked on the line for over 20 years. One hour after she saw her first industrial robot, she had programmed it to do some tasks in the factory. She decided she really liked robots. And it was doing the simple repetitive tasks that she had had to do beforehand. Now she's got the robot doing it. When we first went out to talk to people in factories about how we could get robots to interact with them better, one of the questions we asked them was, "Do you want your children to work in a factory?" The universal answer was "No, I want a better job than that for my children." And as a result of that, Mildred is very typical of today's factory workers in the U.S. They're older, and they're getting older and older. There aren't many young people coming into factory work. And as their tasks become more onerous on them, we need to give them tools that they can collaborate with, so that they can be part of the solution, so that they can continue to work and we can continue to produce in the U.S. And so our vision is that Mildred who's the line worker becomes Mildred the robot trainer. She lifts her game, like the office workers of the 1980s lifted their game of what they could do. We're not giving them tools that they have to go and study for years and years in order to use. They're tools that they can just learn how to operate in a few minutes. There's two great forces that are both volitional but inevitable. That's climate change and demographics. Demographics is really going to change our world. This is the percentage of adults who are working age. And it's gone down slightly over the last 40 years. But over the next 40 years, it's going to change dramatically, even in China. The percentage of adults who are working age drops dramatically. And turned up the other way, the people who are retirement age goes up very, very fast, as the baby boomers get to retirement age. That means there will be more people with fewer social security dollars competing for services. But more than that, as we get older we get more frail and we can't do all the tasks we used to do. If we look at the statistics on the ages of caregivers, before our eyes those caregivers are getting older and older. That's happening statistically right now. And as the number of people who are older, above retirement age and getting older, as they increase, there will be less people to take care of them. And I think we're really going to have to have robots to help us. And I don't mean robots in terms of companions. I mean robots doing the things that we normally do for ourselves but get harder as we get older. Getting the groceries in from the car, up the stairs, into the kitchen. Or even, as we get very much older, driving our cars to go visit people. And I think robotics gives people a chance to have dignity as they get older by having control of the robotic solution. So they don't have to rely on people that are getting scarcer to help them. And so I really think that we're going to be spending more time with robots like Baxter and working with robots like Baxter in our daily lives. And that we will — Here, Baxter, it's good. And that we will all come to rely on robots over the next 40 years as part of our everyday lives. Thanks very much. (Applause)

Frequently Occurring Word Combinations

ngrams of length 2

collocation frequency
mainframe computer 3
office workers 3
retirement age 3
long time 2
ordinary people 2
basic common 2
robot figures 2
working age 2

Important Words

  1. adults
  2. aethon
  3. afghanistan
  4. afraid
  5. age
  6. ages
  7. ah
  8. aides
  9. answer
  10. appearance
  11. applause
  12. area
  13. arm
  14. arthur
  15. artificial
  16. asked
  17. baby
  18. basic
  19. baxter
  20. bit
  21. bomb
  22. bombs
  23. boomers
  24. bring
  25. brought
  26. built
  27. button
  28. calculated
  29. called
  30. capabilities
  31. capable
  32. car
  33. care
  34. caregivers
  35. carry
  36. cars
  37. chance
  38. change
  39. children
  40. china
  41. chris
  42. clarke
  43. climate
  44. collaborate
  45. coming
  46. common
  47. companions
  48. company
  49. competing
  50. computations
  51. computer
  52. computers
  53. connecticut
  54. continue
  55. control
  56. conveyor
  57. country
  58. current
  59. daily
  60. dangerous
  61. day
  62. days
  63. decided
  64. demographics
  65. dignity
  66. dirty
  67. disappear
  68. disappearing
  69. dishes
  70. displaced
  71. dollars
  72. dramatically
  73. driving
  74. drops
  75. effective
  76. ended
  77. engines
  78. everyday
  79. executives
  80. extreme
  81. eyes
  82. fact
  83. factories
  84. factory
  85. famous
  86. fast
  87. faster
  88. fear
  89. feels
  90. fiction
  91. figures
  92. fingers
  93. force
  94. forces
  95. frail
  96. frees
  97. future
  98. game
  99. give
  100. giving
  101. good
  102. grab
  103. grabs
  104. graphics
  105. grasp
  106. great
  107. green
  108. groceries
  109. hand
  110. hands
  111. happened
  112. happening
  113. harbert
  114. harder
  115. hepburn
  116. hospitals
  117. hour
  118. hundreds
  119. hurt
  120. icon
  121. icons
  122. increase
  123. increased
  124. industrial
  125. inevitable
  126. infers
  127. instantly
  128. intelligence
  129. interact
  130. interacting
  131. interesting
  132. interface
  133. internet
  134. iraq
  135. irobot
  136. job
  137. jobs
  138. katharine
  139. kitchen
  140. laundry
  141. learn
  142. leave
  143. left
  144. level
  145. librarians
  146. lifted
  147. lifts
  148. lighting
  149. line
  150. lives
  151. long
  152. longer
  153. losing
  154. lots
  155. mainframe
  156. map
  157. means
  158. mechanically
  159. medicines
  160. menu
  161. mildred
  162. minutes
  163. mode
  164. motions
  165. move
  166. moves
  167. movie
  168. mundane
  169. names
  170. number
  171. nurses
  172. object
  173. objects
  174. office
  175. offices
  176. older
  177. onerous
  178. operate
  179. order
  180. ordinary
  181. overestimate
  182. overestimating
  183. packbot
  184. paper
  185. part
  186. partners
  187. patients
  188. people
  189. percentage
  190. period
  191. person
  192. pharmacy
  193. pick
  194. picked
  195. picks
  196. pittsburgh
  197. play
  198. pockets
  199. poking
  200. press
  201. produce
  202. program
  203. programmed
  204. programmers
  205. programs
  206. push
  207. pushing
  208. put
  209. puts
  210. putting
  211. quaternions
  212. questions
  213. reach
  214. realized
  215. reindeer
  216. rely
  217. repetitive
  218. replace
  219. respected
  220. result
  221. rethink
  222. retirement
  223. revolution
  224. roadside
  225. robot
  226. robotic
  227. robotics
  228. robots
  229. safe
  230. scarcer
  231. scared
  232. science
  233. screen
  234. search
  235. security
  236. select
  237. send
  238. sense
  239. services
  240. setting
  241. sheets
  242. shipping
  243. short
  244. shoulders
  245. simple
  246. slightly
  247. social
  248. society
  249. soldiers
  250. solution
  251. sort
  252. spencer
  253. spend
  254. spending
  255. spread
  256. spreadsheet
  257. spreadsheets
  258. stairs
  259. starting
  260. statistically
  261. statistics
  262. steel
  263. stick
  264. study
  265. stuff
  266. suit
  267. surprised
  268. takes
  269. talk
  270. talking
  271. tasks
  272. technologies
  273. technology
  274. term
  275. terms
  276. time
  277. today
  278. told
  279. tools
  280. totally
  281. tracy
  282. train
  283. trainer
  284. tugs
  285. tuned
  286. turned
  287. typical
  288. ubiquitous
  289. underestimate
  290. underestimated
  291. understand
  292. understands
  293. universal
  294. user
  295. vectors
  296. vision
  297. visit
  298. visual
  299. volitional
  300. wages
  301. wave
  302. ways
  303. web
  304. work
  305. worked
  306. worker
  307. workers
  308. working
  309. world
  310. worried
  311. writer
  312. wrong
  313. years
  314. young