After the computing industry got started, a new problem quickly emerged. How do you operate this machines and how to you program them. The development of operating systems was relatively slow compared to the advances in hardware. First system were primitive but slowly got better as demand for computing power incresed. The ideas of the Graphical User Interfaces or GUI (Gooey) go back to Doug Engelbarts Demo of the Century. However, this did not have much impact on the computer industry. One company though, Xerox, a photocopy company explored these ideas with Palo Alto Park. Steve Jobs of Apple and Bill Gates of Microsoft took notice and Apple introduced first Apple Lisa and the Macintosh. In this lecture on we look so lessons for the development of software, and see how our business theories apply. In this lecture on we look so lessons for the development of software, and see how our business theories apply. In the second part we look at where software is going, namely Artifical Intelligence. Resent developmens in AI are causing an AI boom and new AI application are coming all the time. We look at machine learning and deep learning to get an understanding of the current trends.

1. LECTURE L14
SOFTWARE AND AI

2. Software
As computers became more powerful and more common, a new
problem surfaced: software
Development of computers was a hardware problem
Software or programs did not get the same attention
Operating systems were primitive and programming
was done at a very low level

3. “[The major cause of the software crisis is] that the machines
have become several orders of magnitude more powerful!”
— Edsger Dijkstra, The Humble Programmer
Source: Software_crisis
Software Engineering was not a established field
Became known as The Software Crisis
The Software Crisis

4. Q1
What caused and what solved the
Software Crisis?

5. IBM developed OS/360 for System 360
DEC developed VMS for VAX
Unix was grew out individual efforts as response to Multix
System V, BSD, Solaris
Minix was an academic effort, Linux grew out of frustration with Minix
license
Operating Systems

6. FORTRAN
Mathematical Formula Translation System
Released in 1957
Higher level language that became
breakthrough in writing software
Created by John Backus of IBM
Came on 2.000 punched cards
Other languages followed: COBOL, Algol
Programming Languages

9. The Software Industry
First applications were non-serious
Soon business applications started to emerge
VisiCalc was the “killer-app” 20% of computer
sales was due to this program
Other business apps appeared:
Ledgers, payrolls, inventory, etc.
Disruptive technology

10. Killer Apps
Dan Bricklin and Bob Frankston
Created VisiCalc, the first spreadsheet
The spreadsheet created a new market
People bought the hardware to run the software

12. RPV
According to the RPV Theory, IBM would not be able to enter the
PC market
Their customers were asking for big and powerful machines, and
needed programs and support

13. Q2
IBM successfully entered the
PC market – according to RPV
theory this would be difficult.
How did they do this?

14. IBM PC
IBM decided to enter the PC revolution
The company was loosing market share, competition was growing
Project “Chess”
Bill Lowe was given one year to create a Personal Computer – “Acorn”
Lowe and his team – “Dirty Dozen”, went to work in Boca Raton, FL
Looked for parts outside of the company

15. The War of the OS
IBM needed an Operating System
Most popular system was Digital Research CP/M, created by Gary
Kildall
Microsoft was providing programming languages
and suggested that IBM make a deal with DR

16. Robert X. Cringely PBS documentary
The Birth of the Microsoft DOS

18. The War of the OS
IBM decided on PC-DOS from Microsoft which bought the OS from
another company
Negotiated revenue sharing with IBM
In the 80s, DOS had 90% of the OS market

19. PC-DOS
Small system
Came on a floppy

20. IBM PC
The IBM PC was introduced
12. August 1981 in New York
4.7 MHz Intel 8088, 16 kb RAM
DOS 1.0
$1.565

22. Enter the Clones

23. Enter the Clones
IBM released all the specification of the machine
Open system
This allowed new entrants to create IBM compatible machines
Compac was one of them

24. Enter the Clones
IBM controlled the market for a few years
They rationalised their product lines - deliberately restricted
performance of lower-priced models in order to prevent them from
cannibalising higher-priced models
The Compac passed them in 1986 with the Intel 386 machines
The PC market took off
IBM started to loose market share

25. PC Compatible Machines Ruled
Early 80s IBM PC became the standard
hardware
MS-DOS became the industry standard OS
Command Line Interface – CLI
Text User Interfaces – TUI

26. Key Trend
Focus in on hardware, the
software is good-enough

27. Adoption Life Cycle
Still in the early stages –
technology is the focus

28. “The best way to predict the future is to invent it.”
- Alan Key
“The Demo” of the Century in 1968

29. The Demo
1968

30. The Demo in 1968
Doug Engelbart at the Augmentation
Research Centre in Melno Park
Demonstrated the future of computing

32. Features
A pointing device – the Mouse
Hypertext, graphical user interface
Dynamic file linking
Shared-screen collaboration involving
two persons at different sites
communicating over a network with
audio and video interface

33. Lesson
Visualise the Future and show
people, and they will build it

34. Xerox Parc

35. Xerox Parc
Alto Computer 1972
Xerox created a lab in 1970
Palo Alto Research Park – PARC
PARC was a place for visionaries
The Alto computer system had
Graphical User Interface – GUI
and a mouse as an input
Desktop metaphor with Files and folders

38. Then Steve came on a visit

40. Graphical User Interfaces – GUI
Steve Jobs visited Xerox PARC 1979
Negotiated at deal with Xerox
They showed him:
Object Oriented Programming
Computer networks
Graphical User Interface
Apple started to work on this vision
The Pirate Years

41. RPV Theory
Xerox had just build the
OS of the future but they
did nothing with it

43. Graphical User Interfaces – GUI
Desktop metaphor
Point,
Click,
Drag
Files, folders
Icons
Windows, scroll bars
Menus
Graphical fonts Clipboard, cut and paste, undo
Point, activate, select

44. Apple Lisa
First commercial computer with a
GUI
Introduced in January 1983
Cost $9.995
Motorola 68000 CPU at a 5 MHz
clock rate and had 1MB RAM
Featured cooperative (non-
preemptive)
multi-tasking and virtual memory

45. Apple Lisa
First commercial computer
with a GUI
Introduced in January 1983
Cost $9.995
Impact:
Business failure
Too expensive
Too slow

46. Adjacent Possible
Technology wasn’t there yet

47. Macintosh
In 1984, Apple launched Macintosh
Cost $1.995
Graphical User Interface
This set the standard for
Operating Systems
Specification:
128 KB of RAM
Screen was a 9-inch,
512x342 pixel monochrome display

49. Macintosh
Acceptance was slow
The Mac was underpowered
The GUI required memory and power
Writing Software was difficult
Gained popularity in education and with
graphical designers – desktop publishers
Not so popular in the traditional business sector
Microsoft provided applications (office apps)

50. Others Join the Game
Microsoft launched Windows 1.01 in 1985
Gates and Microsoft believed Graphical User Interfaces
were the future
Regarded Front-end to DOS
Other players
IBM TopView, DR GEM
Impact
Software companies ignored Windows
The business sector was not ready

51. Windows 3.0
Windows finally became usable
Released May 1990
Better use of memory
Multitasking
Used the 286 and 386 hardware better
Support for CD-ROM
Solitaire
Impact:
First GUI used by the
PC market
The end of DOS, finally

52. Windows 95

54. KEY TREND
Computers become
consumer devices

56. Windows 95
Microsoft turned to consumers
Windows 95 was targeted at the consumer market
Support for the Internet
Internet Explorer
Friendlier user interfaces
Impact
Released with great fanfare
Came to dominate the OS market
The OS become more important than the
hardware

58. Operating System for Consumers

60. Operating Systems Today
Ubuntu
Mac OS X
Windows
More choices, less important

61. Operating Systems Today
iOS
Android

62. Lessons
▪ Shift from hardware to software
▪ None of the minicomputer makers became a
significant factor in the desktop personal
computer market
▪ The PC was disruptive technology
▪ The minicomputer users were not buying PCs –
yet
▪ This created a new set of entrants: Apple, Tandy,
Commodore, and IBM

63. ▪ In the late 1980s the performance of PCs met the
needs of minicomputer users
▪ This severely wounded minicomputer makers –
many of them failed
▪ At same time IBM succeeded in entering the PC
market – how?
▪ It created an autonomous organization in Florida –
far away from it’s New York headquarters
▪ They created the PC market
▪ Then headquarters took control and lost control to
the Clones
Lessons

64. ▪ Xerox mangement did not enter the computer
market
▪ PARC members tried to show management – but
they “just didn’t get it”
▪ Xerox is in the copying documents business –
their customers were not asking for computer
systems
▪ Visionary Computers did not fit their resources,
processes and values
– RPV theory
Lessons

65. ▪ Doug Englebart envisioned the future of
computers
▪ Xerox PARC built the visionary computer – but
did not pursue it
▪ Early enthusiast like Ed Roberts of MITS and
others did not get rich of computers and
software
▪ Visionaries like Dan Bricklin and Bob Frankston
invented VisiCalc – did not make much money
Lessons

66. Lessons
▪ Bill Gates saw the potential of software and started
Microsoft
▪ Took the opportunity with MITS
▪ Focused on software
▪ Gary Kildall invented the C/PM system but Microsoft
bought similar OS and succeeded
▪ Wrote software for Apple and later Macintosh
▪ You don’t have to have superior products to win
▪ You don’t have to invent technology – just use it

67. Lessons
▪ Apple and Steve Jobs saw the potential of computers
and then GUIs
▪ GUI were slow to appear
▪ Infrastructure product - needs software and users
▪ Stretched the hardware at the time
▪ Disruptive with new market – consumers
▪ Apple Lisa failed – lacking in performance
▪ The Macintosh started slowly and found some niche
market in Desktop Publishing and schools

68. Lessons
▪ Windows 95 was marketed to the consumer
▪ First mass market of Operating Systems
– The Internet helped
▪ Today we have three major Operating Systems
– Linux (Unix based)
– MacOS (Unix based)
– Windows

69. Q3
What is the future of Personal Computers and
Operating Systems?

70. 1975 1980 1985 1990 1995 2000 2005
Hardware era
PC, Mac
Software OS era
Windows, Office, MacOS
Internet
Hardware Connects
IBM PC Microsoft
Apple
2010
Software web era
Web 2.0, Social
2015
Internet of things
PC Evolution

71. Any important technology will eventually disappear

72. Interaction is changing to
natural interaction

73. Computers are changing
shape and becoming
invisible

74. Wearables, flyable, drivable, scannable…

75. The Network is the Computer
The Internet cloud
More programs and data is stored on network
servers
The Personal Computer becomes one of the form
factors to access the network
Examples
Amazon API
Google Apps
Facework Platform API

76. Artificial Intelligence
“AI is the new electricity”
— Andrew Ng

77. Eric Robot “The Gomshall Robot”
1928
• http://cyberneticzoo.com/robots/1928-eric-robot-capt-richards-english/

78. FIRST COMPUTERS WERE CALLED
GIANT BRAINS

79. The 1956 Dartmouth Workshop
on Artificial Intelligence

80. John McCarthy Marvin Minsky Claude Shannon Nathaniel Rochester
“…solve kinds of problems now reserved for
humans…if a carefully selected group of scientists
work on it together for a summer”

81. GREAT OPTIMISM - OR WHAT?

82. AI WINTER TAKES OVER…

83. AI WINTER TAKES OVER… AGAIN AND AGAIN

84. It proved to be difficult to create truly intelligent software
Anything that worked was regarded as software, like
search alorighms but not intelligence
For decades AI research went through springs and
winters
Thus, ironically, AI has been very successful but at the
same time failed

85. Many years pass

86. Deep Blue vs. Garry Kasparov, 1997

87. IBM Watson plays Jeopardy, 2011

88. Something is happening…

91. DeepMind AlphaGo
vs
Lee Sedol
2016

92. Move 37

93. Machine Learning

94. Symbolic AI
The earliest way to that researcher approached AI was
manipulation of symbols
This is called "good old fashioned AI" or “GOFAI"
The theory was that human intelligence could be achieved by
high-level symbolic or human-readable representations of
problems
Many search algorithms grew out of symbolic AI
Out of this grew cognitive systems and expert systems

95. Expert Systems
Expert systems are systems that contain rules and facts
By answering series of questions users are lead to the conclusion
based on the facts and the rules
Expert system require knowledge or data about a narrow specific
field

96. Machine Learning is a study of computer
algorithms that improve automatically through
experience

97. Machine Learning
The general term for systems that can be trained to learn is
machine learning
One way to use machine learning is by simulating learning in the
brain
This is what is called neural networks
It is important to understand that learning systems are not
programmed in the task they perform, they are feed data and
trained

98. Machine Learning
A computer program is said to learn from experience E with
respect to some class of tasks T and performance measure P, if its
performance at tasks in T, as measured by P, improves with
experience E

99. Deep Learning
Subset of machine
learning
Based on neurons and
sinapses
Multiple hidden layers

101. Machine Learning
Breakthroughs in computer performance (GPUs), algorithms, cloud
computing and big data, has finally created an environment where
neural networks - systems that learn have become a reality
The ideas of learning systems came very early but failed to become
practical

103. Fraud detection
Web search results
Real-time ads on web pages and mobile devices
Text-based sentiment analysis
Credit scoring and next-best offers
Prediction of equipment failures
New pricing models
Network intrusion detection
Pattern and image recognition
Email spam filtering
Application

112. Types of AI Today
Cognitive Systems
Neural Networks and Deep learning
Generic Algorithms
Artificial General Intelligence (AGI)

113. Cognitive Systems
Cognitive systems are knowledge based systems
They are fed with information and can observe and learn
They work on sort of act – learn – loop
These systems are human architected as they need to be feed
with lots of information.
IBM Watson is an example of cognitive systems.

114. Neural Networks and Deep learning
Old technique developed in the 1950s and 60s
Today, with new unprecedented scale both of data and computing
power these systems they have new properties that allow them to
solve problems previously very difficult, like image labelling
These neural networks work like this: the network is made up of
neurons and connectors. They have input layer, hidden layers of
neurons and connectors, and output layer.

115. Generic Algorithms
Work similar to biology’s natural selection or survival of the fittest
To begin with the algorithms are given a task and to solve it they
will try random solutions
The outcomes of these are then evaluated by a fitness function
and some of the outcomes will perform better than others. The
better ones are upgraded and the worse are downgraded and this
is then repeated.

116. Artificial General Intelligence (AGI)
What AGI is about is unsupervised learning which is one of the
hardest problems in AI
The other types of AI use labelled data and a fitness function
It can be instructed on how to improve
In general, AGI has shown little progress, except for some isolated
cases like game cases

117. State of AI Today
We are in an AI Boom
Google, IBM and other tech giants started to develop more
solutions, such as pattern recognition, interpretation of medical
images, visualizing, recognizing objects in images, controlling
cars and robots to name few.

118. Google has TensorFlow, an Open Source Software Library for
Machine Intelligence
Machine Learning Platform
Now platforms are becoming available
Amazon has Amazon Machine Learning
Microsoft is providing machine learning as part of Cortana Analytics
Suite - Microsoft Azure Machine Learning
Facebook has FBLearner Flow

121. AI will have huge impact on the world

123. I just created a song

126. Human intelligence
Artificial intelligence We are here
Intelligence
Time
Is this the path to Machine Intelligence?

127. Next
L15 Augmented and Virtual Reality