L14 Software and AI
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The document discusses the evolution of software and operating systems, highlighting the challenges faced during the 'software crisis' as hardware capabilities increased without corresponding software advancements. It details the development of significant technologies, including IBM's entry into the PC market, the rise of graphical user interfaces, and the growth of AI, showcasing key milestones and influential figures in the tech industry. Finally, it forecasts the future trajectory of personal computing and AI, emphasizing the shift towards machine learning and cognitive systems.
![“[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](https://image.slidesharecdn.com/l14softwareandai-170224131014/85/L14-Software-and-AI-3-320.jpg)
L14 Software and AI
- 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
- 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
- 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