LECTURE L14SOFTWARE AND AI

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

“[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

Q1

What caused and what solved the Software Crisis?

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

FORTRANMathematical 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

The Software IndustryFirst 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

Killer AppsDan Bricklin and Bob Frankston Created VisiCalc, the first spreadsheet The spreadsheet created a new market

People bought the hardware to run the software

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

Q2

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

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

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 languagesand suggested that IBM make a deal with DR

Robert X. Cringely PBS documentary

The Birth of the Microsoft DOS

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

PC-DOS

Small system Came on a floppy

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

Enter the Clones

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

Enter the ClonesIBM 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

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

Key Trend

Focus in on hardware, the software is good-enough

Adoption Life Cycle

Still in the early stages – technology is the focus

“The best way to predict the future is to invent it.” - Alan Key

“The Demo” of the Century in 1968

The Demo 1968

The Demo in 1968Doug Engelbart at the Augmentation Research Centre in Melno Park

Demonstrated the future of computing

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

Lesson

Visualise the Future and show people, and they will build it

Xerox Parc

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

Then Steve came on a visit

Graphical User Interfaces – GUI

Steve Jobs visited Xerox PARC 1979 Negotiated at deal with Xerox

They showed him: Object Oriented ProgrammingComputer networksGraphical User Interface

Apple started to work on this vision The Pirate Years

RPV TheoryXerox had just build theOS of the future but theydid nothing with it

Graphical User Interfaces – GUIDesktop metaphor

Point, Click, Drag

Files,foldersIcons

Windows,scrollbars

Menus

Graphicalfonts Clipboard,cutandpaste,undo

Point,activate,select

Apple LisaFirst 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

Apple Lisa

First commercial computer with a GUI

Introduced in January 1983 Cost $9.995

Impact: Business failure Too expensive Too slow

Adjacent Possible

Technology wasn’t there yet

MacintoshIn 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

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)

Others Join the GameMicrosoft 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

Windows 3.0Windows 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

Windows 95

KEY TREND

Computers become consumer devices

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

Operating System for Consumers

Operating Systems Today

Ubuntu

Mac OS X

Windows

More choices, less important

Operating Systems Today

iOS

Android

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

▪ 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

▪ 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

▪ 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

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

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

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

Q3

What is the future of Personal Computers and Operating Systems?

1975 1980 1985 1990 1995 2000 2005

HardwareeraPC,Mac

SoftwareOSeraWindows,Office,MacOS

InternetHardwareConnects

IBMPC Microsoft

Apple

2010

SoftwareweberaWeb2.0,Social

2015

Internetofthings

PC Evolution

Any important technology will eventually disappear

Interaction is changing to natural interaction

Computers are changing shape and becoming

invisible

Wearables, flyable, drivable, scannable…

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

Artificial Intelligence

“AI is the new electricity” — Andrew Ng

Eric Robot “The Gomshall Robot” 1928

• http://cyberneticzoo.com/robots/1928-eric-robot-capt-richards-english/

FIRST COMPUTERS WERE CALLEDGIANT BRAINS

The 1956 Dartmouth Workshop on Artificial Intelligence

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”

GREAT OPTIMISM - OR WHAT?

AI WINTER TAKES OVER…

AI WINTER TAKES OVER… AGAIN AND AGAIN

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

Many years pass

Deep Blue vs. Garry Kasparov, 1997

IBM Watson plays Jeopardy, 2011

Something is happening…

DeepMind AlphaGo vs

Lee Sedol 2016

Move 37

Machine Learning

Symbolic AIThe 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

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

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

experience

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

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

Deep Learning

Subset of machine learning

Based on neurons and sinapses

Multiple hidden layers

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

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

Types of AI Today

Cognitive Systems Neural Networks and Deep learning  Generic Algorithms Artificial General Intelligence (AGI)

Cognitive SystemsCognitive 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.

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.

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.

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

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.

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

AI will have huge impact on the world

I just created a song

Human intelligence

Artificial intelligence We are here

Inte

lligen

ce

Time

Is this the path to Machine Intelligence?

Next

L15 Augmented and Virtual Reality