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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! | |
6 | ||
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 | |
10 | ||
11 | ||
12 | Beyond Intelligent Machines: | |
13 | Designing Predictable and Controllable User Interfaces | |
14 | ||
15 | ||
16 | Ben Shneiderman November 24, 1992 | |
17 | ||
18 | University of Maryland, College Park, MD 20742 | |
19 | ||
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 | |
24 | ||
25 | ||
26 | Who's in control? | |
27 | ||
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 | |
36 | reasons: | |
37 | ||
38 | 1) Limits to Imagination | |
39 | ||
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. | |
48 | ||
49 | ||
50 | 2) Predictability and Control are Desirable | |
51 | ||
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. | |
59 | ||
60 | ||
61 | 3) Human Responsibility | |
62 | ||
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 | |
67 | encourage this trend. | |
68 | ||
69 | ||
70 | 4) Machines are not People AND People are not Machines | |
71 | ||
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. | |
77 | ||
78 | ||
79 | The lessons of history | |
80 | ||
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: | |
87 | ||
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. | |
95 | ||
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. | |
104 | ||
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 | |
112 | deal with dynamic | |
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. | |
116 | ||
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. | |
122 | ||
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. | |
127 | ||
128 | ||
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.' | |
137 | ||
138 | ||
139 | An alternate vision | |
140 | ||
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). | |
147 | ||
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 | |
156 | formatted. | |
157 | ||
158 | ||
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. | |
178 | ||
179 | ||
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). | |
194 | ||
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. | |
203 | ||
204 | ||
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). | |
213 | ||
214 | ||
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. | |
222 | ||
223 | ||
224 | A scientific approach to user interface research | |
225 | ||
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. | |
233 | ||
234 | ||
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. |