Index

A

  1. a posteriori probability, 143
  2. a priori probability, 143
  3. access control, machine learning for, 130
  4. accidents, role of boredom in, 88
  5. acting humanly/rationally, 12, 13
  6. activation functions, 159
  7. Active Park Assist, 207
  8. actuators, 191, 218
  9. Adams, Douglas (author)
    1. The Hitchhiker's Guide to the Galaxy, 37
  10. adaptive help, using AI for, 263
  11. adaptive learning, using AI for, 263
  12. adversarial games, 44–46
  13. AGI (Artificial General Intelligence), 74
  14. AI. See artificial intelligence (AI)
  15. AI effect, 40
  16. AI gizmo, 235
  17. AI winters, 16–17, 230–234, 279
  18. AI-based errors, 78–79
  19. AI-specific languages, 113
  20. Alexa, 65, 83, 267, 273
  21. algorithms
    1. about, 25–27, 39
    2. defined, 40
    3. divide-and-conquer, 151
    4. drone, 203–204
    5. evolutionary, 138
    6. learning machine, 49–53
    7. role of, 40–49
  22. Alien (film), 97, 243
  23. alpha-beta pruning, 46
  24. alphabets, 110, 111
  25. AlphaGo, 45, 53
  26. Altman, Sam (researcher), 177
  27. Amara, Roy Charles (scientist), 234
  28. Amazon Prime Air, 200
  29. Amazon's Alexa, 65, 83, 267, 273
  30. AN (Artificial Intuition), 277
  31. analogizers, 19
  32. analogy, systems that learn by, 138
  33. analysis
    1. see also data analysis, 294
    2. AI for, 18
    3. as a benefit of machine learning, 129
    4. interpretation vs., 288–289
    5. medical needs, 101–102
  34. Anderson, Chris (editor-in-chief), 125
  35. animal protection, machine learning for, 130
  36. anthropomorphization, unrealistic expectations from, 230
  37. Apache Spark (website), 123
  38. Apple Watch, 93
  39. Application Specific Integrated Circuit (ASIC), 63, 64
  40. architecture, 56
  41. Ark I (website), 259
  42. art, imitating, 171–177
  43. Arterys, 103
  44. Arthrobot, 102
  45. artifact, 175–176
  46. Artificial General Intelligence (AGI), 74
  47. artificial intelligence (AI). See also specific topics
    1. about, 7–8
    2. categorizing, 12–14
    3. history of, 14–17
    4. hype about, 18–19
    5. limits of, 226–229
    6. uses for, 17–18
  48. Artificial Intuition (AN), 277
  49. artistry, 275
  50. ASIC (Application Specific Integrated Circuit), 63, 64
  51. Asimov, Isaac (author), 74, 183–184
  52. “Asking the Right Questions About AI,” 287
  53. Atlas robot, 186
  54. Atomwise, 107
  55. augmentation
    1. of communication, 115
    2. deep learning as, 157
    3. of human senses, 118
  56. automata, 182
  57. automated data collection, 30
  58. automatic corrections, 74–76
  59. automation. See also process automation
    1. AI for, 18
    2. as a benefit of machine learning, 129
    3. developing levels of, 85–86
    4. language translation, 111–112
    5. performing tasks using, 105–108
    6. relying solely on, 87
  60. autonomous vehicle. See self-driving car (SD car)
  61. autonomy
    1. drones and, 202–203
    2. levels of, 209–210
  62. avoiding safety issues, 88–89

B

  1. backpropagation, 137, 161
  2. backward chaining, 51
  3. Banko (researcher), 39
  4. batch learning, 165
  5. Bay Labs (website), 103
  6. Bayes, Thomas (Reverend), 144–146
  7. Bayes' theorem, 19, 144–146
  8. Bayesian inference, 138
  9. Bayesian Networks, 139, 148
  10. behavior planning, 216
  11. belief, separating fact from, as a mental activity, 8
  12. Bengio, Toshua (scientist), 140, 167, 170
  13. Betamax, 63
  14. Bezos, Jeff (CEO), 253
  15. BFS (breadth-first search), 44
  16. bias, 35–36, 96, 137
  17. big data, 22
  18. biometric data, 25
  19. bipedal robot, 186
  20. Blackberry, 57
  21. blind search, 44
  22. Blue Origin (website), 253
  23. bodily-kinesthetic intelligence, 10
  24. body language, 110, 113–114
  25. Bombe machine, 59
  26. boredom, developing solutions for, 82–85
  27. Boston Dynamics, 186
  28. Bottou, Leon (scientist), 167
  29. brain imaging, 12
  30. branch node, 42
  31. branching, 41–44
  32. breadth-first search (BFS), 44
  33. Brill (researcher), 39
  34. Brockman, Greg (researcher), 177
  35. Brooks, Rodney (scientist), 218
  36. Burgess, E. (author)
    1. The Martian Probe, 248
  37. Business Case, 282
  38. Butterfly Network, 107

C

  1. caching, 60
  2. Caloris Basin, 242
  3. cameras, 220
  4. Čapek, Karel (author), 182–183
  5. CareSkore, 106
  6. CAT (Computerized Axial Tomography), 12
  7. Catanzaro, Bryan (researcher), 62
  8. categorizing artificial intelligence (AI), 12–14
  9. changing perspective, 293
  10. channels, 168
  11. character recognition, 167–168
  12. chatbot, 172–174
  13. cheat sheet (website), 3
  14. checkers, 46
  15. child node, 42
  16. children, teaching, 272
  17. Chinese Room argument, 72–73
  18. ChoiceMap app, 118
  19. Clark, Arthur C. (author)
    1. 2001: A Space Odyssey, 252
  20. classification
    1. image, 168
    2. problems with, 133
  21. cleansing, 123
  22. CloudMedX, 106
  23. CNN (Convolutional Neural Networks), 164
  24. CNTK (Computational Network Toolkit), 140
  25. cognitive radio, 248
  26. commercial drones, 200–202
  27. commission, as a mistruth about data, 33–34
  28. common-sense reasoning, 233
  29. communication
    1. augmenting, 115
    2. developing methods of, 110–114
    3. enhancing, 247–248
  30. complex analysis
    1. AI for, 18
    2. as a benefit of machine learning, 129
  31. Computational Network Toolkit (CNTK), 140
  32. computer applications
    1. about, 69–70
    2. AI-based errors, 78–79
    3. automatic corrections, 74–76
    4. common types of, 70–73
    5. Friendly Artificial Intelligence (FAI), 73–74
    6. making suggestions, 76–78
    7. uses in, 69–79
  33. Computerized Axial Tomography (CAT), 12
  34. “Computing Machinery and Intelligence” (Turing), 59
  35. computing system, 19–20
  36. conditional automation, as a level of autonomy in self-driving cars, 209
  37. conditional probability, 143–144
  38. conflict resolution, 51
  39. connectionists, 19
  40. connections, creating, 114–115
  41. consequences, of AI, 290
  42. controlling robots, 193
  43. Convolutional Neural Networks (CNN), 164
  44. convolutions, 168–169
  45. cores (GPU), 62
  46. corrections
    1. automatic, 74–76
    2. types of, 74–75
  47. cost effective, in CREEP acronym, 235
  48. “Cramming More Components Onto Integrated Circuits” (Moore), 23
  49. creating
    1. alphabets, 111
    2. cities in hostile environments, 257–261
    3. communication methods, 110–114
    4. connections, 114–115
    5. industrial solutions, 282–283
    6. intuitive decisions, 276–277
    7. levels of automation, 85–86
    8. moon-based resources, 260–261
    9. new things, 274–276
    10. relationships, 293
    11. resources, 284
    12. robots, 191–193
    13. safe environments, 88–89
    14. solutions, 234–237
    15. solutions for boredom, 82–85
    16. space habitats, 259–260
    17. specialized processing environments, 62–63
    18. specialized sensors, 64–65
    19. structures in space, 252–254
    20. suggestions, 76–78
    21. technology environments, 283–284
  50. creative intelligence, 10
  51. creativity, as a limit of AI, 226–227
  52. CREEP acronym, 235
  53. crime, investigating, 276
  54. critic, 244
  55. Cross, C.A. (author)
    1. The Martian Probe, 248
  56. crystals, 247
  57. customer service
    1. AI for, 18
    2. as a benefit of machine learning, 129
  58. cyborgs, 118

D

  1. da Vinci Surgical System, 104, 261
  2. D'Andrea, Raffaello (engineer), 202
  3. Dango (website), 111
  4. dark energy, 243
  5. dark space, 243
  6. DARPA (Defense Advanced Research Projects Agency), 62–63, 186, 208
  7. Dartmouth College, 15, 231
  8. data
    1. about, 21
    2. algorithms, 25–27
    3. automated collection of, 30
    4. big, 22
    5. biometric, 25
    6. devising new from old, 290–291
    7. examples of, 25
    8. human input of, 28–29
    9. learning from, 132–134
    10. limits of acquisition of, 37–38
    11. manicuring, 30–33
    12. misalignments in, 32
    13. missing, 31
    14. mistruths about, 33–37
    15. Moore's law, 23–24
    16. reliability of, 28–29
    17. role of, 21–38
    18. security of, 28
    19. separating, 32–33
    20. sources of, 27–28
    21. splitting, 150–152
    22. structured, 22
    23. types of, 22
    24. unstructured, 22
    25. using everywhere, 24–25
    26. using successfully, 27–30
    27. value of, 125
  9. data analysis
    1. about, 121
    2. defined, 39
    3. defining, 122–125
    4. importance of, 124
    5. learning from data, 132–134
    6. machine learning, 126–132
  10. data deficiencies, as a limit of AI, 228–229
  11. data manipulation, 30–33
  12. Data Never Sleeps (website), 37
  13. data record, 31
  14. data spectrum, 117–118
  15. database tables, 22
  16. decision trees, 139, 150–154
  17. deduction, 150
  18. Deep Genomics, 106
  19. deep learning
    1. about, 17, 155
    2. as augmentation, 157
    3. detecting edges and shapes, 167–171
    4. imitating art and life, 171–177
    5. mimicking learning brains, 159–161
    6. shaping neural networks, 156–159
    7. using, 161–167
  20. Deep Space Mining (website), 244
  21. Defense Advanced Research Projects Agency (DARPA), 62–63, 186, 208
  22. deficient technology, unrealistic expections from, 230
  23. deliveries, robots for, 189
  24. dendrites, 156
  25. depth-first search (DFS), 44
  26. detection, image, 168
  27. detection system, 215
  28. developmental issues, solving, 273–274
  29. DFS (depth-first search), 44
  30. discovering, as an AI failure, 290–292
  31. discriminator, 175–176
  32. display adapter, 61
  33. divide-and-conquer algorithm, 151
  34. Domingos, Peter (scientist), 19, 138, 140
  35. Doppler effect, 221
  36. driver assistance, as a level of autonomy in self-driving cars, 209
  37. drones
    1. about, 185, 190, 195
    2. for mining, 245
    3. state of the art, 196–199
    4. uses for, 199–206
  38. drop-down list boxes, 29
  39. Duke University TIKAD drone, 197, 199
  40. Dutch perspective glasses, 240
  41. Dvorak, John (author), 235
  42. dynamic foot, 101

E

  1. earthquakes, 264
  2. ECG (electrocardiogram), 95
  3. edges, detecting, 167–171
  4. Edison, Thomas (inventor), 274
  5. effective, in CREEP acronym, 235
  6. effectors, 191
  7. efficiency, machine
    1. AI for, 18
    2. as a benefit of machine learning, 130
  8. efficiency, of humans, 261–263
  9. efficient, in CREEP acronym, 235
  10. Einstein, Albert (scientist), 59
  11. elder care, robots for, 189
  12. electrocardiogram (ECG), 95
  13. elements, finding new, 247
  14. ELIZA, 173, 232
  15. emojis, 111
  16. emoticons, 111
  17. empathy, 96, 292–294
  18. Encyclopaedia Britannica, 242
  19. end-to-end learning, 166–167
  20. end-to-end solution, 214
  21. Engineering 360, 283
  22. Engineering and Physical Sciences Research Council (EPSRC), 184
  23. Enigma code, 55
  24. Enlitic, 103
  25. entertainment, robots for, 189
  26. entropy, 152
  27. environments
    1. creating safe, 88–89
    2. flexibility of, 234
    3. interacting with the, 65–66
    4. predicting, 216
  28. Eon Flux (film), 101
  29. EPSCR (Engineering and Physical Sciences Research Council), 184
  30. errors, AI-based, 78–79
  31. European Extremely Large Telescope, 241
  32. Evans, Benedict (blogger), 212
  33. evolutionaries, 19
  34. evolutionary algorithms, 138
  35. Ex Machina (film), 18
  36. exoplanets, 239–240
  37. exoskeletons, 97–98
  38. expert systems, 16, 50–52
  39. exploration, space, 248–252
  40. expressions, repositories of, 25
  41. exteroceptive sensors, 219
  42. extraplanetary resources, mining, 285–286
  43. extrapolation, 274, 290
  44. eye-gaze systems, 100

F

  1. FAA (Federal Aviation Administration), 205
  2. faces, repositories of, 25
  3. fact, separating from fiction, 277
  4. FAI (Friendly Artificial Intelligence), 73–74
  5. failure(s)
    1. of AI, 287–294
    2. single points of, 57
  6. feature creation, 167
  7. feature detector, 170
  8. Federal Aviation Administration (FAA), 205
  9. feed-forward input, 159–160
  10. fiction, separating fact from, 277
  11. Field Programmable Gate Arrays (FPGAs), 64
  12. fields, 31
  13. filter, 170
  14. finding
    1. elements, 247
    2. potential problem sources, 265–266
  15. fine-tuning, 165
  16. first-order logic, 51
  17. flat-file transformation, 122
  18. flight, 275–276
  19. Ford, Henry (founder of Ford Motor Company), 85, 211
  20. forms, 29
  21. forward chaining, 51
  22. Foster, John Stuart, Jr. (nuclear physicist), 196
  23. FPGAs (Field Programmable Gate Arrays), 64
  24. frame of reference, as a mistruth about data, 36–37
  25. fraud detection
    1. AI for, 17
    2. as a benefit of machine learning, 129
  26. Friendly Artificial Intelligence (FAI), 73–74
  27. full automation, as a level of autonomy in self-driving cars, 210
  28. future, predicting the, 106
  29. fuzzy logic, 52

G

  1. Galilei, Galileo (scientist), 125, 240
  2. GALILEO, 215
  3. games, using for therapy, 95–97
  4. GAN (Generative Adversarial Networks), 171–177
  5. Gardner, Howard (psychologist), 9
  6. Generative Adversarial Networks (GAN), 171–177
  7. generative-based models, 173
  8. geo-fencing, 206
  9. gizmo, 235
  10. GLONASS, 215
  11. GNMT (Google Neural Machine Translation) system, 112
  12. Go game, 53, 157
  13. Goddard, Robert H. (rocket pioneer), 248
  14. Golem, 182
  15. Goodfello, Iam (researcher), 175–176
  16. Google, 26–27
  17. Google Brain Project (website), 62
  18. Google DeepMind, 53, 105, 134
  19. Google Neural Machine Translation (GNMT) system, 112
  20. Google Smart Reply, 174
  21. Google Translate, 111–112
  22. Google's AI (website), 111
  23. Google's Allo (website), 111
  24. Google's Home, 83
  25. Google's MobileNets, 200
  26. Google's Tensor Processing Unit (TPU), 63, 64
  27. Google's TensorFlow (website), 140, 166
  28. GPS, 215
  29. Graham, Bette Nesmith (inventor), 275
  30. graph, 42–44
  31. Graphcore, 64
  32. Graphics Processing Units (GPUs), 59–62, 162
  33. graphs, 146–150
  34. groups, suggestions based on, 77

H

  1. habitats, terraforming vs., 261
  2. Hadoop (website), 123
  3. Haffner, Patrick (scientist), 167
  4. HAL9000, 140
  5. hardware
    1. augmentation of, 100
    2. common, 233
    3. increasing capabilities of, 63–64
    4. standard, 56–58
  6. Harvard Architecture, 58
  7. harvesting water, 245
  8. Hauppauge 4860, 61
  9. Hawking, Stephen (physicist), 204
  10. hazardous waste, 254
  11. Her (film), 18, 273
  12. Herr, Hugh (scientist), 101, 280
  13. heuristic strategy, 44
  14. heuristics, 46–49
  15. Hidden Figures (film), 243
  16. hidden information, locating, using AI for, 263
  17. high automation, as a level of autonomy in self-driving cars, 210
  18. hill-climbing optimization, 47–48
  19. Hinton, Geoffrey (scientist), 140, 161, 162, 170
  20. Hintze, Arend (professor), 14
  21. hiring, using AI for, 262
  22. history
    1. of AI, 14–17
    2. self-driving car (SD car), 208
  23. The Hitchhiker's Guide to the Galaxy (Adams), 37
  24. Hopfield, John (scientist), 233
  25. Hubble telescope, 240
  26. human behavior, 289
  27. human foot, active, 280–281
  28. human interaction
    1. about, 109–110
    2. developing communication methods, 110–114
    3. exchanging ideas, 114–116
    4. human sensory perception, 117–118
    5. multimedia, 116–117
    6. performing, 272–274
  29. human occupations
    1. about, 255–256
    2. creating cities in hostile environments, 257–261
    3. efficiency of humans, 261–263
    4. fixing planetary scale problems, 263–268
    5. space, 256–257
    6. that remain safe, 271–277
  30. human processes, rational processes vs., 13
  31. human senses, 118
  32. human sensory perception, 117–118
  33. human/AI collaboration, 236
  34. humanly
    1. acting, 12
    2. thinking, 12–13
  35. humanoids, 186–188
  36. humans
    1. as a data source, 27, 28–29
    2. making more capable, 95–98
    3. when they do it better, 236
  37. human-specific interactions, 280–281
  38. Humbly, Clive (mathematician), 122
  39. Hummingbird update (Google), 27
  40. hypothesis space, 128

I

  1. I, Robot (film), 204
  2. IA (Intelligence Augmentation), 118
  3. IBM's WatsonPaths, 105
  4. IC (Integrated Circuit), 23–24, 243
  5. icons, explained, 3
  6. ideas
    1. exchanging, 114–116
    2. original, as a limit of AI, 227
  7. image classification, 168
  8. image detection, 168
  9. image segmentation, 168
  10. ImageNet, 170
  11. images, as unstructured data, 22
  12. imagination, as a limit of AI, 227
  13. The Imitation Game (film), 55, 59
  14. implementing
    1. new senses, 291–292
    2. portable patient monitoring, 92–95
  15. increasing hardware capabilities, 63–64
  16. induction, 150
  17. “Induction of Decision Trees” (Quinlan), 147, 152–154
  18. industrial settings, process automation in, 85–87
  19. industrial solutions, developing, 282–283
  20. industrial utilization, 81
  21. industrializing space, 253–254
  22. Industry 4.0, 188
  23. inference engine, 51
  24. infomercial, 235–236
  25. information, robots for, 189
  26. informed strategy, 44
  27. inspecting, 123
  28. Integrated Circuit (IC), 23–24, 243
  29. intelligence, 8–11
  30. Intelligence Augmentation (IA), 118
  31. Intelligence Processing Unit (IPU), 64
  32. interacting, with the environment, 65–66. See also human interaction
  33. interlingua, 112
  34. International Space Station (website), 239, 282
  35. Internet, 24
  36. Internet of Things (IoT), 25
  37. interpersonal intelligence, 10
  38. interpolate, 274
  39. interpreting, analyzing vs., 288–289
  40. intrapersonal intelligence, 11
  41. introspection, 12
  42. intuitive decisions, 276–277
  43. inventing, 274–275
  44. investigating crime, 276
  45. IoT (Internet of Things), 25
  46. IPU (Intelligence Processing Unit), 64
  47. iRobot's PackBot, 190

J

  1. J3016 standard, 209
  2. JAWS (Job Access With Speech), 100
  3. Jintronix add-on, 97
  4. job, 85–86
  5. Job Access With Speech (JAWS), 100

K

  1. Kálmán, Rudolf E. (engineer), 221–222
  2. Kalman filter, 219, 221–222
  3. Keep It Simple, Stupid (KISS) principle, 237
  4. Kepler 90, 242
  5. kernel, 170
  6. KISS (Keep It Simple, Stupid) principle, 237
  7. knowledge, prior, 143
  8. knowledge base, 51
  9. K'Watch, 93

L

  1. Lambda Cold Dark Matter (LCDM) model, 242
  2. lander, 250
  3. language, 110, 111–112, 113
  4. The Language Instinct: How the Mind Creates Language (Pinker), 218
  5. laser rangefinder (LIDAR), 48
  6. latency, 60, 193
  7. Lauritzen, Steffen L. (author)
    1. “Local computations with probabilities on graphical structures and their application to expert systems,” 149–150
  8. lawn mowing, robots for, 189
  9. LCDM (Lambda Cold Dark Matter) model, 242
  10. leaf node, 42
  11. leaps of faith, 293–294
  12. learning. See also deep learning; machine learning
    1. batch, 165
    2. end-to-end, 166–167
    3. as a mental activity, 8
    4. need for, 234
    5. online, 165
    6. reinforcement, 134
    7. roads to, 136–140
    8. supervised, 133
    9. transfer, 165
    10. tribes of, 19
    11. unsupervised, 134
  13. learning machine
    1. about, 49–50
    2. expert systems, 50–52
    3. Go game, 53
    4. machine learning, 52–53
  14. LeCun, Yann (scientist), 140, 162, 164, 167, 170, 175
  15. LeNet5, 167, 170
  16. Li, Fei-Fei (professor), 170
  17. lidar, 220
  18. LIDAR (laser rangefinder), 48
  19. life, imitating, 171–177
  20. Lifeboat Foundation (website), 259
  21. limited memory, 14
  22. linguistic intelligence, 11
  23. LinkedIn, 115
  24. Lippershey, Hans (eyeglass maker), 240
  25. List Processing (LisP), 16, 50
  26. “Local computations with probabilities on graphical structures and their application to expert systems” (Lauritzen and Spiegelhalter), 149–150
  27. local search, 46–49
  28. localization, 215
  29. locomotion capability, 191
  30. logical programming, 233
  31. logical-mathematical intelligence, 11, 15
  32. low-dimensional embedding, 192
  33. Luna 9, 248
  34. Lunar XPRIZE (website), 251

M

  1. machine efficiency
    1. AI for, 18
    2. as a benefit of machine learning, 130
  2. machine learning
    1. about, 17, 52–53, 126–132, 135–136
    2. benefits of, 129–130
    3. decision trees, 150–154
    4. limits of, 131–132
    5. probabilities, 140–150
    6. roads to learning, 136–140
  3. Machine Learning For Dummies (Mueller and Massaron), 123, 127, 161
  4. Magellan Telescope, 241
  5. Magnetic Resonance Imaging (MRI), 12
  6. magnetoception, 292
  7. Magnetoencephalography (MEG), 12
  8. manicuring data, 30–33
  9. manipulating capabilities, 191
  10. mapping, 128
  11. Mariner 4, 250
  12. Mars, 241, 250–251, 254, 257, 260
  13. Mars Curiosity probe, 249, 251
  14. Marsnik, 250
  15. The Martian Probe (Burgess and Cross), 248
  16. Massachusetts Institute of Technology (MIT), 213
  17. Massaron, Luca (author)
    1. Machine Learning For Dummies, 123, 127, 161
    2. Python for Data Science For Dummies, 123
  18. master algorithm, 19
  19. matrix format, 122
  20. McCorduck, Pamela (author), 40
  21. mean, 150
  22. meanings, considering, 8, 122
  23. mechanics, 183
  24. media, unrealistic expections from, 230
  25. medical devices, security and, 94
  26. medical needs
    1. about, 91–92
    2. analysis, 101–102
    3. combining robots and medical professionals, 108
    4. implementing portable patient monitoring, 92–95
    5. making humans capable, 95–98
    6. performing tasks using automation, 105–108
    7. special needs, 99–101
    8. surgical techniques, 102–105
  27. medical records, 105–106
  28. medications, 107–108, 281
  29. MEG (Magnetoencephalography), 12
  30. memorizing sequences, 171–172
  31. memory
    1. limited, 14
    2. speed of, 60
  32. metals, obtaining, 245–246
  33. microcontrollers, 58
  34. Microsoft's Brainwave (website), 64
  35. Microsoft's Tay, 79
  36. military drones, 196–197
  37. mind, theory of, 14
  38. “Minds, Brains, and Programs” (Searle), 72
  39. mining
    1. extraplanetary resources, 285–286
    2. rare-earth, 245–246
    3. space, 243–248
  40. min-max approximation, 45
  41. Minsky, Marvin (scientist), 218, 232
  42. missing data, 31
  43. MIT (Massachusetts Institute of Technology), 213
  44. Mitsuku (website), 132
  45. mobile robots, 185
  46. mobility, future of, 209–214
  47. Model Predictive Control (MPC), 217
  48. modeling, 123, 150
  49. “Modeling human boredom at work: mathematical formulations and a probabilistic framework,” 88
  50. Monet (artist), 275
  51. monitors
    1. critical wearable, 93–94
    2. movable, 94–95
    3. wearing, 92–93
  52. moon landing, 241
  53. Moon Minerology Mapper (website), 246
  54. moon-based resources, building, 260–261
  55. moonquakes, 260
  56. Moore, Gordon (cofounder of Intel and Fairchild Semiconductor), 23
  57. Moore's law, 23–24
  58. Moov monitor, 93
  59. Moravec, Hans (scientist), 218
  60. Moravec paradox, 218
  61. Mori, Masahiro (professor), 187
  62. Motiv, 93
  63. Mountain Pass mine, 246
  64. MPC (Model Predictive Control), 217
  65. MRI (Magnetic Resonance Imaging), 12
  66. Mueller, John Paul (author)
    1. Machine Learning For Dummies, 123, 127, 161
    2. Python for Data Science For Dummies, 123
  67. multiconjugate adaptive optics, 241
  68. multimedia, 116–117
  69. multithreading, 61
  70. multiverse theory, 248
  71. Musk, Elon (researcher), 177, 204
  72. MYCIN, 50–51

N

  1. Naïve Bayes, 139, 143–144
  2. NASA, 206, 259, 260
  3. Natural Language Processing (NLP), 173
  4. neural networks
    1. architecture of, 159
    2. shaping, 156–159
    3. simple, 159–160
  5. Neurala, Inc. (website), 62
  6. neurons, 156
  7. Newell, Allen (researcher), 232
  8. Ng, Andrew (researcher), 62
  9. Nilsson, Nils J. (professor), 158
  10. NLP (Natural Language Processing), 173
  11. no free lunch theorem, 136
  12. nonstarter applications
    1. about, 225
    2. AI winters, 230–234
    3. applying AI correctly, 229
    4. creating solutions, 234–237
    5. limits of AI, 226–229
    6. unrealistic expectations, 229–230
  13. nonverbal communication, 110
  14. Norvig, Peter (director of research at Google), 49
  15. NP-complete problems, 40–41
  16. nursing, 272–273
  17. NVidia, 214–215

O

  1. Obama, Barack (US President), 222
  2. oceans, building cities in, 258–259
  3. Oil & Gas Monitor, 283
  4. OK Google, 25
  5. omission, as a mistruth about data, 34
  6. Oncora Medical, 106
  7. online learning, 165
  8. OpenAI, 171, 177
  9. open-source frameworks, 166
  10. optimization, using local search and heuristics, 46
  11. Orbital ATK, 284–285
  12. Orbital Technologies, 252
  13. outcomes, predicting, 150–152
  14. Outer Space Treaty (website), 260
  15. overfitting, 131

P

  1. padding, 170
  2. parabolic vomit comet flight, 253
  3. Paro robot, 189
  4. partial automation, as a level of autonomy in self-driving cars, 209
  5. particle accelerators, 247
  6. pathfinding algorithm, 49
  7. Pepyne, David L. (engineer), 136
  8. perceptions, 192, 218–222
  9. perceptron, 156–159
  10. performing
    1. human interaction, 272–274
    2. scientific investigation, 253
    3. space mining, 243–248
  11. periodic table, 247
  12. personal needs, addressing, 273
  13. perspective
    1. changing, 293
    2. as a mistruth about data, 34–35
  14. PET (Positron Emission Tomography), 12
  15. physical interactions, 66
  16. Picasso (artist), 275
  17. PID (Proportional-Integral-Derivative) controller, 217
  18. pilot projects, 209
  19. Pinker, Steven (author)
    1. The Language Instinct: How the Mind Creates Language, 218
  20. planets, exploring, 286
  21. planning, 41–44, 218
  22. portable patient monitoring, implementing, 92–95
  23. Positron Emission Tomography (PET), 12
  24. positronic brain, 183–184
  25. practical, in CREEP acronym, 235
  26. precision agriculture, 202
  27. predictions, 130, 150–152
  28. prefetching, 60
  29. prior knowledge, 143
  30. probabilities, 140–150, 143–144
  31. probes, 248–249
  32. procedures, making safer, 106–107
  33. process automation
    1. about, 81–82
    2. creating safe environments, 88–89
    3. developing solutions for boredom, 82–85
    4. in industrial settings, 85–87
  34. processing environment, creating specialized, 62–63
  35. processor caching, 60
  36. Project Wing, 200
  37. Prolog, 16
  38. Proportional-Integral-Derivative (PID) controller, 217
  39. proprioceptive sensors, 219
  40. prosthetics, 101, 280
  41. proximity surrounds, 203
  42. psychological testing, 12
  43. Psychometrics Centre (Cambridge University), 25
  44. Python (website), 166
  45. Python for Data Science For Dummies (Mueller and Massaron), 123
  46. PyTorch (website), 166

Q

  1. QardioCore, 95
  2. quadcopter, 197–199
  3. Quinlan, John Ross (scientist), 50, 151
    1. “Induction of Decision Trees,” 147, 152–154

R

  1. radar (RAdio Detection and Ranging), 221
  2. radiation hardened electronic components, 190
  3. RAM, specialty, 60
  4. random choice, 47
  5. rare-earth mining, 245–246
  6. rational processes, human processes vs., 13
  7. rationally acting/thinking, 13
  8. reactive machines, 14
  9. reactors, 247
  10. real-time monitoring, 276–277, 281
  11. reasoning
    1. common-sense, 233
    2. as a mental activity, 8
    3. symbolic, 137
  12. Rectified Linear Unit (ReLU), 159
  13. Recurrent Neural Networks (RNN), 171–177
  14. Recursion Pharmaceuticals, 108
  15. reference, frame of, as a mistruth about data, 36–37
  16. regression problems, 133
  17. regulatory issues, with drones, 205–206
  18. reinforcement learning, 134
  19. relationships
    1. developing, 293
    2. seeing, as a mental activity, 8
  20. reliability, of data, 28–29
  21. ReLU (Rectified Linear Unit), 159
  22. Remember icon, 3
  23. Remotely Piloted Aircraft (RPA), 196
  24. reproducible, in CREEP acronym, 235
  25. rescaling, 122
  26. resource scheduling
    1. AI for, 18
    2. as a benefit of machine learning, 129
  27. resources, developing, 284
  28. retrieval-based models, 173
  29. Rheumatic Heart Disease (RHD), 103
  30. RightWriter (website), 16
  31. Rivest, Ronald (computer scientist), 45
  32. RNN (Recurrent Neural Networks), 171–177
  33. Robot Process Automation (RPA), 86
  34. robot technologies, 282–283
  35. robotic missions, 249–251
  36. robots
    1. about, 86–87, 181–182
    2. assembling basic, 191–193
    3. combining with medical professionals, 108
    4. for mining, 245
    5. roles of, 182–191
  37. rocket fuel, 245
  38. root node, 42
  39. Rosenblatt, Frank (psychologist), 157
  40. Rosetta comet, 241
  41. Rossum's Universal Robots (play), 182
  42. rover, 250
  43. RPA (Remotely Piloted Aircraft), 196
  44. RPA (Robot Process Automation), 86
  45. Rudy, 108
  46. Rumelhart, David (psychologist), 233

S

  1. SAE International (website), 209
  2. safety issues, avoiding, 88–89
  3. safety systems
    1. AI for, 18
    2. as a benefit of machine learning, 130
  4. SAM (Script Applier Mechanism) (website), 72
  5. “Scaling to Very Very Large Corpora for Natural Language Disambiguation” (Banko and Brill), 39
  6. scheduling
    1. AI for, 18
    2. as a benefit of machine learning, 129
    3. using AI for, 262
  7. scientific investigation, performing, 253
  8. Script Applier Mechanism (SAM) (website), 72
  9. Searle, John (author)
    1. “Minds, Brains, and Programs,” 72
  10. security
    1. of data, 28
    2. medical devices and, 94
  11. segmentation, image, 168
  12. self-awareness, 14
  13. self-driving car (SD car)
    1. about, 207–208
    2. future of mobility, 209–214
    3. getting into, 214–218
    4. history of, 208
    5. uncertainty of perceptions, 218–222
  14. Senseable Cities, 123
  15. senses, implementing new, 291–292
  16. sensing hardware, 219
  17. sensors
    1. about, 191, 219–221
    2. adding specialized, 64–65
    3. as a data source, 27–28
    4. specialized, 64–65
  18. Sentrian, 93
  19. separating data, 32–33
  20. sequences, memorizing, 171–172
  21. shallow networks, 162
  22. Shannon, Claude (mathematician), 151
  23. shapes, detecting, 167–171
  24. Simon, H. A. (economist), 232
  25. simulated annealing, 47–48
  26. single points of failure, 57
  27. single-mindedness, 58
  28. singularity, 19, 292
  29. Siri, 267, 273
  30. 16 Psyche (website), 285
  31. Smart Tissue Autonomous Robot (STAR), 105
  32. smart watches, 25
  33. smell, 66
  34. societal contributions, 279–286
  35. software-based solutions, for special needs, 100
  36. solutions
    1. creating, 234–237
    2. defining potential, 266–267
    3. effects of, 267
    4. industrial, 282–283
  37. sonar, 48
  38. sound recordings, as unstructured data, 22
  39. Soyuz rocket, 252
  40. space
    1. about, 239–240
    2. building structures in, 252–254
    3. exploration, 248–252
    4. human occupations in, 256–257
    5. industrializing, 253–254
    6. mining, 243–248
    7. universe, 240–243
    8. working with AI in, 284–286
  41. space factories, 253
  42. space habitats, 259–260
  43. space stations, 284–285
  44. space vacation, 252–253
  45. SpaceX, 253, 256, 284–285
  46. special needs, 99–101, 116
  47. specialized hardware
    1. about, 55–56
    2. adding specialized sensors, 64–65
    3. creating specialized processing environments, 62–63
    4. GPUs, 59–62
    5. increasing capabilities of, 63–64
    6. methods for interacting with environment, 65–66
    7. standard hardware, 56–58
  48. specialty RAM, 60
  49. specialty robots, 190–191
  50. Spiegelhalter, David J. (author)
    1. “Local computations with probabilities on graphical structures and their application to expert systems,” 149–150
  51. splitting data, 150–152
  52. standard hardware, relying on, 56–58
  53. Stanford Machine Learning Group (website), 157
  54. Stanley car, 208
  55. STAR (Smart Tissue Autonomous Robot), 105
  56. Statistics vs. Machine Learning (blog), 140
  57. stochastic environment, 193
  58. stopping rules, 152
  59. storage, using space for, 254
  60. stride, 170
  61. structured data, 22
  62. structured prediction, 177
  63. Sudoku, 49
  64. suggestions, making, 76–78
  65. supervised learning, 133
  66. surgical techniques, 102–105
  67. Sutskever, Ilya (researcher), 177
  68. symbolic logic, 15
  69. symbolic reasoning, 137
  70. symbolists, 19
  71. sympathy, 96
  72. synesthesia, 117
  73. Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE), 62–63

T

  1. tabu search, 47–48
  2. target function, 128
  3. tasking, 58
  4. tastes, 65
  5. teaching children, 272
  6. Technical Stuff icon, 3
  7. technology environments, 283–284
  8. Tensor Processing Unit (TPU), 63, 64
  9. TensorFlow (website), 140, 166
  10. Terminator (film), 113, 204
  11. terraforming, habitats vs., 261
  12. Tesco's fidelity card program, 122
  13. tetrachromats, 65
  14. theory of mind, 14
  15. therapy, using games for, 95–97
  16. thinking
    1. humanly, 12–13
    2. outside the box, 276
    3. rationally, 13
  17. third-world countries, 103
  18. Thirty-Meter Telescope, 241
  19. 3-D printing, 282
  20. 3-D technology, 187
  21. 3Scan, 107
  22. Thrun, Sebastian (scientist), 208
  23. Tip icon, 3
  24. tower jumping, 241
  25. TPU (Tensor Processing Unit), 63, 64
  26. training, 127
  27. trajectory planning, 217
  28. transfer learning, 165
  29. transforming, 122
  30. translation invariance, 167
  31. TravelTips4Life, 242
  32. traversing graphs, 44
  33. trends, defining, 115–116
  34. trolley problem, 185, 213
  35. trust, 293
  36. truths, grasping, as a mental activity, 8
  37. Turbine, 107
  38. Turing, Alan (computer scientist), 55, 59, 172
  39. Turing Test, 12, 173
  40. twiddle, 47–48
  41. 2001: A Space Odyssey (Clark), 252

U

  1. UACV (unmanned aerial combat vehicles), 196
  2. UAS (unmanned aircraft system), 196
  3. UAV (unmanned aerial vehicles), 196
  4. ultrasonic sensors, 221
  5. uncanny valley, 187
  6. undefined problems, unrealistic expections from, 230
  7. understanding
    1. as an AI failure, 288–290
    2. as a mental activity, 8
  8. underwater cities, 258
  9. Unicode emoji chart, 111
  10. Unimate, 183
  11. uninformed strategy, 44
  12. universe, 240–243
  13. unmanned aerial combat vehicles (UACV), 196
  14. Unmanned Aerial Systems Traffic Management (UTM), 206
  15. unmanned aerial vehicles (UAV), 196
  16. unmanned aircraft system (UAS), 196
  17. unstructured data, 22
  18. unsupervised learning, 134
  19. updates (website), 4
  20. UTM (Unmanned Aerial Systems Traffic Management), 206

V

  1. value network, 53
  2. vanishing gradient, 161–162
  3. VBA (Visual Basic for Applications), 86
  4. Versace, Massimiliano (CEO), 62
  5. VHS, 63
  6. videos, as unstructured data, 22
  7. Vischeck (website), 100
  8. Visual Basic for Applications (VBA), 86
  9. visual-spatial intelligence, 9
  10. volcanic eruptions, 265
  11. von Neumann, John (mathematician), 56, 59
  12. Von Neumann architecture, 55–56
  13. Von Neumann bottleneck, 56, 57, 60–61
  14. Voyager 1, 242
  15. VP-Expert (website), 16

W

  1. War Games (film), 204
  2. War Operation Plan Response (WPOR), 204
  3. WarGames (film), 44
  4. Warning icon, 3
  5. water, harvesting, 245
  6. waveguides, 284
  7. WDV (Wearable Defibrillator Vest), 94
  8. Wearable Defibrillator Vest (WDV), 94
  9. websites
    1. Active Park Assist, 207
    2. AI winter, 279
    3. AI-based errors, 78, 79
    4. Alexa, 65
    5. Alien (film), 243
    6. Amazon Prime Air, 200
    7. Apache Spark, 123
    8. Apple Watch, 93
    9. Ark I, 259
    10. Arterys, 103
    11. Arthrobot, 102
    12. Artificial Intuition (AN), 277
    13. “Asking the Right Questions About AI,” 287
    14. Atomwise, 107
    15. backpropagation, 161
    16. Bay Labs, 103
    17. Blue Origin, 253
    18. boredom, 85
    19. Business Case, 282
    20. Butterfly Network, 107
    21. Caloris Basin, 242
    22. CareSkore, 106
    23. cheat sheet, 3
    24. Chinese Room argument, 72
    25. ChoiceMap app, 118
    26. classification types, 14
    27. CloudMedX, 106
    28. Computational Network Toolkit (CNTK), 140
    29. “Computing Machinery and Intelligence” (Turing), 59
    30. convolutions, 169
    31. cyborgs, 118
    32. da Vinci Surgical System, 104
    33. Dango, 111
    34. DARPA Grand Challenge, 208
    35. Data Never Sleeps, 37
    36. Deep Genomics, 106
    37. Deep Space Mining, 244
    38. Domingos, Peter (scientist), 19, 138
    39. earthquakes, 264
    40. Edison, Thomas (inventor), 274
    41. ELIZA, 173
    42. emojis, 111
    43. emoticons, 111
    44. Engineering 360, 283
    45. Enigma code, 55
    46. Enlitic, 103
    47. Ex Machina (film), 18
    48. exoplanets, 239
    49. exoskeletons, 97
    50. eye-gaze systems, 100
    51. Federal Aviation Administration (FAA), 205
    52. first-order logic, 51
    53. Google Brain Project, 62
    54. Google neural network playground, 160
    55. Google's AI, 111
    56. Google's Allo, 111
    57. Google's DeepMind, 53, 105
    58. Google's Tensor Processing Unit (TPU), 63, 64
    59. Google's TensorFlow, 140, 166
    60. GPU cores, 62
    61. Graham, Bette Nesmith (inventor), 275
    62. Hadoop, 123
    63. Harvard Architecture, 58
    64. Hauppauge 4860, 61
    65. Her (film), 18
    66. Hidden Figures (film), 243
    67. Hubble telescope, 240
    68. IBM's WatsonPaths, 105
    69. ImageNet, 170
    70. The Imitation Game (film), 55
    71. Intelligence Augmentation (IA), 118
    72. Intelligence Processing Unit (IPU), 64
    73. interlingua, 112
    74. International Space Station, 239, 282
    75. J3016 standard, 209
    76. Jintronix add-on, 97
    77. Job Access With Speech (JAWS), 100
    78. Kalmanfilter, 219
    79. K'Watch, 93
    80. leaps of faith, 294
    81. LeCun, Yann (scientist), 164, 167
    82. Lifeboat Foundation, 259
    83. LinkedIn, 115
    84. Lunar XPRIZE, 251
    85. Mars, 250–251, 257
    86. Mars Curiosity probe, 249
    87. Massachusetts Institute of Technology (MIT), 213
    88. medical device security, 94
    89. medications, 281
    90. memory, 60
    91. Microsoft's Brainwave, 64
    92. “Minds, Brains, and Programs” (Searle), 72
    93. Mitsuku, 132
    94. “Modeling human boredom at work: mathematical formulations and a probabilistic framework,” 88
    95. Monet (artist), 275
    96. Moon Minerology Mapper, 246
    97. Moov monitor, 93
    98. Motiv, 93
    99. multiconjugate adaptive optics, 241
    100. multiverse theory, 248
    101. NASA, 206
    102. Neurala, Inc., 62
    103. neuronal structure, 156
    104. no free lunch theorem, 136
    105. NVidia, 214
    106. ocean cities, 258
    107. Oil & Gas Monitor, 283
    108. Oncora Medical, 106
    109. OpenAI, 171
    110. Orbital ATK, 284–285
    111. Outer Space Treaty, 260
    112. periodic table, 247
    113. Picasso (artist), 275
    114. Project Wing, 200
    115. Python, 166
    116. PyTorch, 166
    117. QardioCore, 95
    118. real-time monitoring, 281
    119. Recursion Pharmaceuticals, 108
    120. RightWriter, 16
    121. Robot Process Automation (RPA), 86
    122. Rudy, 108
    123. SAE International, 209
    124. “Scaling to Very Very Large Corpora for Natural Language Disambiguation” (Banko and Brill), 39
    125. Script Applier Mechanism (SAM), 72
    126. self-driving car (SD car), 208
    127. Senseable Cities, 123
    128. Sentrian, 93
    129. singularity, 292
    130. 16 Psyche, 285
    131. Smart Tissue Autonomous Robot (STAR), 105
    132. space factories, 253
    133. SpaceX, 253, 256, 284–285
    134. Stanford Machine Learning Group, 157
    135. Statistics vs. Machine Learning (blog), 140
    136. Sudoku, 49
    137. synesthesia, 117
    138. Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE), 63
    139. telescopes, 240
    140. Tensor Processing Unit (TPU), 63, 64
    141. TensorFlow, 140, 166
    142. Terminator (film), 113
    143. tetrachromats, 65
    144. 3Scan, 107
    145. TravelTips4Life, 242
    146. Turbine, 107
    147. Turing Test, 12
    148. Unicode emoji chart, 111
    149. Unimate, 183
    150. updates, 4
    151. Vischeck, 100
    152. volcanic eruptions, 265
    153. Von Neumann architecture, 55, 56
    154. VP-Expert, 16
    155. waveguides, 284
    156. Wearable Defibrillator Vest (WDV), 94
    157. Whole Biome, 107
    158. Who's On First?, 36
    159. Wright brothers, 275
    160. YouTube, 110
    161. Zephyr Health, 106
    162. Zero Gravity, 253
  10. weights, 137, 160–161
  11. Weizenbaum, Joseph (scientist), 173, 232
  12. Welchman, Gordon (mathematician), 59
  13. Weller, Deutsche (author), 197
  14. Whiteout, 275
  15. Whole Biome, 107
  16. Who's On First? (website), 36
  17. Wissner-Gross, Alexander (research scientist), 126
  18. WOPR (War Operation Plan Response), 204
  19. Wozniak, Steve (Apple cofounder), 204
  20. Wright brothers, 275–276

X

  1. Xbox Kinect, 97

Y

  1. Yo-Chi Ho (mathematician), 136
  2. YOLO, 200
  3. YouTube (website), 110

Z

  1. Zephyr Health, 106
  2. Zero Gravity, 253