![[BACK]](/cvsweb/icons/back.gif){kind=icon}
Return to Beyond-Intelligent-Machines CVS log ![[TXT]](/cvsweb/icons/text.gif){kind=icon}
![[DIR]](/cvsweb/icons/dir.gif){kind=icon}
Up to [Micropolis - Activity] / micropolis / src / notes|
Return to Beyond-Intelligent-Machines CVS log | Up to [Micropolis - Activity] / micropolis / src / notes |
1.1 root 1: Date: Tue, 24 Nov 92 22:25:59 -0500
2: From: [email protected] (Ben Shneiderman)
3: To: [email protected], [email protected], [email protected],
4: [email protected], [email protected]
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.
This archive runs on limited infrastructure. Preserving old code on modern bandwidth. Automated agents are requested to crawl responsibly.