1 Date: Tue, 24 Nov 92 22:25:59 -0500
 
   2 From: ben@cs.UMD.EDU (Ben Shneiderman)
 
   3 To: bam@cs.cmu.edu, ben@cs.umd.edu, callahan@cerc.wvu.wvnet.edu,
 
   4         hopkins@bongo.garnet.cs.cmu.edu, weiser.pa@xerox.com
 
   5 Subject: Re: more pie menus!
 
   7 I couldn't resist sending you all this latest essay which is
 
   8 destined for IEEE Software...some readers expect it to generate
 
   9 some strong responses...Ben
 
  12 Beyond Intelligent Machines:
 
  13     Designing Predictable and Controllable User Interfaces
 
  16   Ben Shneiderman  November 24, 1992
 
  18        University of Maryland, College Park, MD 20742  
 
  20    Professor, Department of Computer Science,
 
  21    Head, Human-Computer Interaction Laboratory at the 
 
  22       Center for Automation Research &
 
  23    Member, Institute for Systems Research
 
  28 An important shift is occurring from the old vision of computers
 
  29 as 'intelligent' to a new vision based on predictable and controllable
 
  30 user interfaces that depend on direct manipulation of objects and actions.
 
  31 Appropriate metaphors and terminology are important since they shape
 
  32 the thoughts of researchers, designers, managers, congress-people, 
 
  33 journalists, etc.  Most of us have learned the importance of gender 
 
  34 neutral terminology and similarly I have been strongly opposed to 
 
  35 suggesting that computers are 'intelligent' or 'smart' for several 
 
  38 1) Limits to Imagination
 
  40 I think we should have much greater ambition than to make a computer 
 
  41 behave like an intelligent butler or other human agent.  Computer 
 
  42 supported cooperative work (CSCW), hypertext/hypermedia, multi-media, 
 
  43 information visualization, and virtual realities are powerful 
 
  44 technologies that enable human users to accomplish tasks that no human 
 
  45 has ever done.  If we describe computers in human terms then we
 
  46 run the risk of limiting our ambition and creativity in the design
 
  47 of future computer capabilities.
 
  50 2) Predictability and Control are Desirable
 
  52 If machines are 'intelligent' or 'adaptive' then they may become less 
 
  53 predictable and controllable.  Our usability studies show that users 
 
  54 want feelings of mastery, 
 
  55 competence, and understanding that come from a predictable and 
 
  56 controllable interface.  Most users seek a sense of 
 
  57 accomplishment at the end of the day, not the sense that this 
 
  58 'intelligent' machine magically did their job for them. 
 
  61 3) Human Responsibility
 
  63 I am concerned that if designers are successful in convincing the users 
 
  64 that computers are intelligent, then the users will have a reduced sense 
 
  65 of responsibility for failures.  The tendency to blame the machine is 
 
  66 already widespread and I think we will be on dangerous grounds if we 
 
  70 4)  Machines are not People  AND  People are not Machines
 
  72 I have a basic philosophical objection to the suggestion that machines 
 
  73 are, or can ever be, intelligent.  I know that many of my colleagues are 
 
  74 quite happy to call machines intelligent and knowledgeable, but I prefer 
 
  75 to treat and think about machines in very different ways from the way I 
 
  76 treat and think about people.
 
  79 The lessons of history
 
  81 While some productive work has been done under the banner of
 
  82 `intelligent', often those who use this term reveal how little they
 
  83 know about what users want or need.  The users's goal is not to
 
  84 interact with an 'intelligent' machine, but to create, communicate, 
 
  85 explore, plan, draw, compose, design, or learn.  Ample evidence 
 
  86 exists of the misguided directions brought by 'intelligent' machines:
 
  88   - natural language interaction seems clumsy and slow compared to 
 
  89 direct manipulation and information visualization methods that use 
 
  90 rapid, high-resolution, color displays with pointing devices.  Lotus HAL 
 
  91 is gone, AI INTELLECT hangs on but is not catching on.  There are some 
 
  92 interesting directions for tools that support human work through 
 
  93 natural language processing: aiding human translators, parsing 
 
  94 texts, and generating reports from structured databases.
 
  96   - speech I/O in talking cars and vending machines is gone.
 
  97 Voice recognition is fine for handicapped users plus special situations, 
 
  98 but doesn't seem to be viable in general office, home, or school 
 
  99 settings.  Our recent studies suggest that speech I/O has a greater
 
 100 interference with short term and working memory than hand-eye 
 
 101 coordination for mouse menu selection.  Voice store and forward,
 
 102 phone-based information retrieval, and voice annotation have great 
 
 103 potential but these are not the 'intelligent' applications.
 
 105   - adaptive interfaces are unstable and unpredictable, often leading 
 
 106 users to worry about what will change next.  I see only modest chances 
 
 107 for success in user modeling to recognize the level of expertise and
 
 108 revise the interface accordingly - can anyone point to successful
 
 109 studies or commercial products?  By contrast, user controlled 
 
 110 adaptation through control panels, cruise control for cars, and 
 
 111 remote controls for TV are success stories.  While algorithms to 
 
 113 issues in network or disk space management are needed, the task domain 
 
 114 and user interface issues of the application program
 
 115 should generally be under direct user control.
 
 117   - Intelligent CAI (Computer Assisted Instruction) only prolonged the 
 
 118 time (compared to traditional CAI) until the users felt they were the 
 
 119 victims of the machine.  Newer variations such as Intelligent Tutoring 
 
 120 Systems are giving way to Interactive Learning Environments where 
 
 121 students are in control and actively creating or exploring.
 
 123   - intelligent talking robots with five-fingered hands and human facial 
 
 124 features (quaint fantasy that did well in Hollywood but not in Detroit 
 
 125 or elsewhere) are mostly gone in favor of flexible manufacturing systems 
 
 126 that enable supervisors to specify behavior with predictable results.
 
 129 It seems that some designers continue to ignore this historical pattern 
 
 130 and still dream of creating 'intelligent' or 'smart' machines.  It is an 
 
 131 ancient and primitive fantasy, and its seems most new technologies must 
 
 132 pass through this child-like animistic phase.  Lewis Mumford identified 
 
 133 this pattern (Technics and Civilization, 1934) when he wrote about the 
 
 134 Obstacle of Animism: 'the most ineffective kind of machine is the 
 
 135 realistic mechanical imitation of a man or another animal...for 
 
 136 thousands of years animism has stood in the way of...development.'   
 
 141 My point in this essay is not merely to counter a popular design 
 
 142 philosophy, but to offer a new vision that is more in harmony with what 
 
 143 users want.  I believe that the future will be filled with powerful, but 
 
 144 predictable and controllable computers that genuinely serve human needs  
 
 145 (Designing the User Interface: Strategies for Effective Human-Computer
 
 146 Interaction, Second Edition, Addison-Wesley Publ. Co., Reading, MA, 1992).
 
 148 In this vision of predictable and controllable (PC) computing, 
 
 149 the promising strategies are rapid,
 
 150 visual, animated, colorful, high resolution interfaces built on
 
 151 meaningful control panels, appropriate preference boxes,
 
 152 user-selectable toolbars, rapid menu selection, easy to create macros,
 
 153 and comprehensible shortcuts.  These enable me to specify rapidly, 
 
 154 accurately, and confidently how I want my email filtered, what documents 
 
 155 I want retrieved and in what order, and how my documents will be 
 
 159 Our Human-Computer Interaction Laboratory has applied these principles 
 
 160 to information visualization methods that give users X-ray vision to see 
 
 161 through their mountains of data.  Treemaps enable users to see (and 
 
 162 hear) 2-3000 nodes of hierarchically structured information by utilizing 
 
 163 every pixel on the display.  Each node is represented by a rectangle 
 
 164 whose location preserves the logical tree structure and whose area is 
 
 165 proportional to one of its attributes.  Color represents a second 
 
 166 attribute and sound a third (B. Johnson & D. Turo,  Improving the 
 
 167 Visualization of Hierarchies with Treemaps: Design Issues and 
 
 168 Experimentation, Proc. IEEE Visualization '92).  Treemaps have been 
 
 169 applied to Macintosh directory browsing (Figure 1), in which area could 
 
 170 be set to file size, color to application type, and sound to file age 
 
 171 (our TreeViz application is available from the University of Maryland's 
 
 172 Office of Technology Liaison, (301) 405-4210).  When users first try 
 
 173 TreeViz they usually discover duplicate or misplaced files, redundant 
 
 174 and chaotic directories, and many useless files or applications.  Other 
 
 175 applications include: stock market portfolio management, sales data, 
 
 176 voting patterns, sports (48 statistics on 459 NBA players, in 27 teams, 
 
 177 in four leagues), etc.
 
 180 Dynamic queries allow rapid adjustment of query parameters and immediate 
 
 181 display of updated result sets.  These animations enable users to 
 
 182 develop intuitions, discover patterns, spot trends, find exceptions, and 
 
 183 see anomalies.  The Dynamic HomeFinder prototype (Figure 2) allows users 
 
 184 to adjust the cost, number of bedrooms, location, etc. and see points of 
 
 185 light come and go on a map to indicate a matching home.  Users execute
 
 186 up to 100 queries/second (rather than one query per 100 seconds)
 
 187 producing a revealing animated view of where high or low priced homes
 
 188 are found, and there are no syntax errors.  Clicking on a point of
 
 189 light brings up a description or image (videotape available, or for 
 
 190 an empirical comparison with a natural language system, see 
 
 191 Williamson, C. and Shneiderman, B., The Dynamic HomeFinder: Evaluating 
 
 192 dynamic queries in a real-estate information exploration system, 1992 
 
 193 ACM SIGIR Proceedings).  
 
 195 Dynamic queries are very effective when a visual environment such as a
 
 196 map, calendar, or schematic diagram are available, but they can be
 
 197 easily applied with standard text file output (Figure 3).  Dynamic 
 
 198 queries exemplify the future of interaction; You don't need to
 
 199 describe your goals, negotiate with an intelligent agent, and wait for
 
 200 a response, you Just Do It!   Furthermore, dynamically seeing the 
 
 201 results enables you to explore and rapidly reformulate your goals in 
 
 202 an engaging videogame-like manner.  
 
 205 Open problems in information visualization include screen organization, 
 
 206 widget design, algorithms for rapid search and display, use of color
 
 207 and sound, and strategies to accommodate human perceptual skills.  
 
 208 We also see promise in expanding macro makers into the graphical 
 
 209 environment with visual triggers based on controlled replay of 
 
 210 desired actions - the 
 
 211 general idea is Programming in the User Interface (PITUI) to 
 
 212 Do-What-I-Did (DWID).
 
 215 I want to encourage the exploration of new metaphors and visions of how 
 
 216 computers can empower people by presenting information, allowing rapid 
 
 217 selection, supporting personally specified automation, and providing 
 
 218 relevant feedback.  Metaphors related to controlling tools or machines 
 
 219 such as driving, steering, flying, directing, conducting, piloting, 
 
 220 or operating seem more generative of effective and acceptable 
 
 221 interfaces, than 'intelligent' machines.
 
 224 A scientific approach to user interface research
 
 226 Whether you agree with the design philosophy in this essay, and
 
 227 especially if you disagree, I hope that you will add to our scientific
 
 228 knowledge by conducting well-designed empirical studies of learning
 
 229 time, measuring performance time for appropriate tasks, recording error
 
 230 rates, evaluating human retention of interface features, and assessing
 
 231 subjective satisfaction.  There's much work to be done to make
 
 232 computing accessible, effective, and enjoyable.
 
 235 Acknowledgements:  This essay was prompted by the discussion between 
 
 236 Mark Weiser and Bill Hefley, stimulated by lively email and personal 
 
 237 discussions with Paul Resnick, Tom Malone, and Christopher Fry at MIT, 
 
 238 and refined by comments from Catherine Plaisant, Rick Chimera, Brian 
 
 239 Johnson, David Turo, Richard Huddleston, and Richard Potter at the 
 
 240 Human-Computer Interaction Lab at Univ. of Maryland.  I appreciate Bill 
 
 241 Curtis's support for this vision.  Thanks to all.