Artificial intelligence and the Singularity - History, Trends and Reality Check

Artificial Intelligence

and the Singularity

piero scaruffi

www.scaruffi.com

October 2014

"The person who says it cannot be done should not

interrupt the person doing it" (Chinese proverb)

Table of Contents

• The future of mind

– Knowledge-based systems

– Connectionist systems

– ...and their cultural background

• The future of body

– Robots

– Implants

– Synthetic Biology

2

• The Singularity?

– Reality Check

– Accelerating Progress?

– Non-human Intelligence

– Human Intelligence

– A Critique of the Turing Test

4

A Brief History of Logic

George Boole's "The Laws Of Thought" (1854): the

laws of logic “are” the laws of thought

Propositional logic and predicate logic: true/false!

5


Axiomatization of Thought:

Gottlob Frege's "Foundations of Arithmetic"

(1884)

Giuseppe Peano's "Arithmetices Principia

Nova Methodo Exposita" (1889)

Bertrand Russell's "Principia Mathematica"

(1903)


• David Hilbert (1928)

– Entscheidungsproblem problem: the

mechanical procedure for proving

mathematical theorems

– An algorithm, not a formula

– Mathematics = blind manipulation of

symbols

– Formal system = a set of axioms and a

set of inference rules

1912 –1954

Alan Turing

8

The Cultural Context

• 1910-1950 Everything changed:

– Everyday life

– The foundations of science

– The concept of art

– The geopolitical order

9

• Electricity

• Regriferator

• Automobile

• Airlane

• Telegraph

• Telephone

• Phonograph

• Camera

• Cinema

• Radio

• Typewriter

• Calculator

• Skyscraper

• Plastic

The 1910s

10

You are a

formula Everything

is relative

You are and

you are not

You are just

a reflex You are a

probability

Everything is

uncertain

Everything is

moving away

from you

11

The emancipation of the dissonance

History is a

nightmare from

which I am trying

to awake.

12

There will always

be something you

cannot prove

Your mind

creates reality

Truth is an

opinion

Life and machines

obey the same

laws of nature

Everything is

information

Everything

comes from just

one point

Mind is a

symbol

processor

13

Cultural Context

• Bottom line:

– Nonconformism

– Anxiety

– Noise

– Freedom

14

Cultural Context

• World War II (1939-45)

• The Holocaust

• Hiroshima

• Disintegration of the British Empire

• Rise of the USA and Soviet Union

15

Alan Turing

• Hilbert’s challenge (1928): an algorithm

capable of solving all the mathematical

problems

• Turing Machine (1936): a machine whose

behavior is determined by a sequence of

symbols and whose behavior determines the

sequence of symbols

• A universal Turing machine (UTM) is a

Turing machine that can simulate an arbitrary

Turing machine

Alan Turing

• Alan Turing (1936)

– Universal Turing Machine: a Turing

machine that is able to simulate any other

Turing machine

– The universal Turing machine reads the

description of the specific Turing

machine to be simulated

Turing Machine

17

Alan Turing

(BTW, the halting problem is undecidable, i.e. Hilbert’s

Entscheidungsproblem is impossible)

Alan Turing

• Turing machines in nature: the ribosome,

which translates RNA into proteins – Genetic alphabet: nucleotides ("bases"): A, C, G, U

– The bases are combined in groups of 3 to form "codons“

– RNA is composed of a string of nucleotides ("bases") according to

certain rules

– There are special carrier molecules ("tRNA") that are attached to

specific aminoacids (proteins)

– The start codon encodes the aminoacid Methionine

– A codon is matched with a specific tRNA

– The new aminoacid is attached to the protein

– The tape then advances 3 bases to the next codon, and the process

repeats

– The protein keeps growing

– When the “stop” codon is encountered, the ribosome dissociates

from the mRNA

19

Alan Turing

• World War II:

– Breaking the Enigma code (Bombe)

– Turing worked at Bletchley Park where the

Colossus was built but it was not a universal

Turing machine (not general purpose)

Replica of the Bombe

20

The Turing Century

• Can you name any achievement of the last

50 years (from the Moon landing to animal

cloning) that would have happened even

without programmable computers?

21 21

Electronic Brains

• 1941: Konrad Zuse's Z3 programmable

electromechanical computer, the first Turing-

complete machine

• 1943: Tommy Flowers and others build the

Colossus, the world's first programmable digital

electronic computer

22 22

Electronic Brains

• 1944: Howard Aiken of IBM unveils the first computer programmed by punched paper tape, the electromechanical Harvard Mark I

• 1945: John Von Neumann designs a computer that holds its own instructions, the "stored-program architecture"

23

Electronic Brains

1945: John Von Neumann's computer architecture

Control unit:

•reads an instruction from memory

•interprets/executes the instruction

•signals the other components what to do

•Separation of instructions and data (although

both are sequences of 0s and 1s)

•Sequential processing

24 24

Electronic Brains

1946: The first non-military computer, ENIAC, or

"Electronic Numerical Integrator and Computer",

is unveiled, built by John Mauchly and Presper

Eckert at the University of Pennsylvania

25 25

Electronic Brains

Computation

26 26

Electronic Brains

• Apr 1949: The Manchester Mark 1, the first

stored-program electronic computer

• May 1949: Cambridge's EDSAC, the second


• Aug 1949: Philadelphia's EDVAC, the third


• 1950: The Pilot ACE computer

27 27

Electronic Brains

• May 1950: The first stored-program electronic computer to be deployed in the USA, the SEAC, and the first to use semiconductors instead of vacuum tubes

• Feb 1951: The Ferranti Mark 1, the first commercial computer, an evolution of the EDSAC

• 1952: A Univac 1 correctly predicts that Eisenhower would win the elections

28 28

Electronic Brains

(Computer History Museum, Mountain View)

• Goldstine and Eckert with the electronics

needed to store a single decimal digit

29 29

Electronic Brains

Computer programmers of 1951: Patsy Simmers

(holding an ENIAC board) Gail Taylor (holding an

EDVAC board), Milly Beck (holding an ORDVAC

board), Norma Stec (holding a BRLESC-I board)

30 30

Electronic Brains

1954: IBM's first “mass-produced” computer, the 650

(1,800 units sold - $200-400,000 each)

31 31

Electronic Brains

• USA/ Semiconductors

– 1947: AT&T's Bell Labs invent the transistor (William Shockley, John Bardeen, Walter Brattain)

– 1949: The USA files an antitrust lawsuit against AT&T

– 1952: AT&T's symposium on the transistor, open to everybody

– 1954: Texas Instruments introduces the first commercial transistor

– 1954: The first transistor radio (“Regency”)

32 32

Electronic Brains

• USA/ Semiconductors

– 1961: Texas Instruments introduces the first commercial integrated circuit

– Military and space applications use the integrated circuit

– 1965: Gordon Moore predicts that the processing power of computers will double every 18 months

– 1971: Intel invents the microprocessor

– Universities are irrelevant in semiconductor progress because the manufacturing process is too costly

– Universities are crucial for progress in computers

Jack Kilby’s I.C.

Intel 4004

33

Electronic Brains

The future of your brain is coming faster

than your brain can think…

34 34

Electronic Brains

Software

• 1958: Jim Backus (at IBM) invents the FORTRAN

programming language, the first machine-

independent language

• 1964: IBM introduces the first "operating system" for

computers (the OS/360)

• 1968: The Arpanet is established based on Baran’s

idea (four nodes: UCLA, Stanford Research

Institute, UCSB, University of Utah)

• 1969: the Unix operating system is born

35 35

Electronic Brains

Democratizing technology

• Antitrust policies contribute to the rapid diffusion

of intellectual property throughout the computer

and semiconductor industries

• 1956: IBM and AT&T settle antitrust suits by

licensing their technologies to competitors

• 1969: The “unbundling” of software by IBM

creates the software industry

Cybernetics

The Steam Engine

• Biggest impact on daily life since the printing press

• Inventors are ordinary people, not academics

• The automation of manufacturing begins in

Lancashire, not at a university

James Watt (1776)

Cybernetics

Norbert Wiener (1947)

• Bridge between machines and nature,

between "artificial" systems and natural

systems

• Feedback, by sending back the output as

input, helps control the proper functioning of

the machine

• A control system is realized by a loop of

action and feedback

• A control system is capable of achieving a

"goal", is capable of "purposeful" behavior

• Living organisms are control systems

38

The Turing Test

1950: Alan Turing's "Computing Machinery and Intelligence"

(the "Turing Test")

Can machines think?

39

The Turing Test

The Turing Test (1950)

• Hide a human in a room and a machine in another

room and type them questions: if you cannot find

out which one is which based on their answers,

then the machine is intelligent

40

The Turing Test

The “Turing point”: a computer can be said to be intelligent if its

answers are indistinguishable from the answers of a human

being

? ?

41


1954: Demonstration of a machine-

translation system by Leon Dostert's team

at Georgetown University and Cuthbert

Hurd's team at IBM

1956: Dartmouth conference on Artificial

Intelligence

Artificial Intelligence (1956): the discipline of

building machines that are as intelligent

as humans

42


1956: Allen Newell and Herbert Simon

demonstrate the "Logic Theorist“, the first

A.I. program, that uses “heuristics” (rules of

thumb) and proves 38 of the 52 theorems

in Whitehead’s and Russell’s “Principia

Mathematica”

1957: “General Problem Solver” (1957): a

generalization of the Logic Theorist but

now a model of human cognition

43


1957: Noam Chomsky's "Syntactic Structures"

S stands for Sentence, NP for Noun Phrase, VP for Verb Phrase, Det for Determiner,

Aux for Auxiliary (verb), N for Noun, and V for Verb stem

44


1957: Frank Rosenblatt's Perceptron, the first artificial neural network

45


1959: John McCarthy's "Programs with

Common Sense" focuses on knowledge

representation

1959: Arthur Samuel's Checkers, the world's

first self-learning program

1960: Hilary Putnam's Computational

Functionalism

1962: Joseph Engelberger deploys the

industrial robot Unimate at General Motors

46


1963 Irving John Good speculates about "ultraintelligent machines" (the "singularity")

1964: IBM's "Shoebox" for speech recognition

1965: Ed Feigenbaum's Dendral expert system: domain-specific knowledge

47


1966: Ross Quillian's semantic networks

48


1966: Joe Weizenbaum's Eliza

1967: Zuse suggests that the universe is a computation

1968: Peter Toma founds Systran to commercialize machine-translation systems

49


1969: Marvin Minsky & Samuel Papert's

"Perceptrons" kill neural networks

1969: Stanford Research Institute's Shakey the

Robot

1972: Bruce Buchanan's MYCIN

•a knowledge base

•a patient database

•a consultation/explanation program

•a knowledge acquisition program

Knowledge is organised as a series of IF THEN rules

50


1972: Terry Winograd's Shrdlu

51


1972: Hubert Dreyfus's "What Computers Can't Do"

1974: Marvin Minsky's Frame (see chapter on “Cognition”)

1975: Roger Schank's Script (see chapter on “Cognition”)

1975: John Holland's Genetic Algorithms

1976: Doug Lenat's AM

1979: Cordell Green's system for automatic programming

1979: Drew McDermott's non-monotonic logic

1979: David Marr's theory of vision

52


1980: John Searle’s "Chinese Room"

1980: Intellicorp, the first major start-up for


1982: Japan's Fifth Generation Computer

Systems project

1980s: Second A.I. bubble

53


1982: John Hopfield’s simulation of annealing

1983: Geoffrey Hinton's and Terry Sejnowski's Boltzmann

machine for unsupervised learning

1985: Judea Pearl's "Bayesian Networks"

1986: Paul Smolensky's Restricted Boltzmann machine

2006: Geoffrey Hinton's Deep Belief Networks

Rummelhart network

Neurons arranged in layers, each neuron

linked to neurons of the neighboring

layers, but no links within the same layer

Requires training with supervision

Hopfield networks

Multidirectional data flow

Total integration between input and output

data

All neurons are linked between themselves

Trained with or without supervision

54


Genealogy of Intelligent Machines

Hydraulic machines

Steam engines

Cybernetics

Neural networks

Logic

Hilbert

Turing Machine

Computers

Expert Systems

55

Artificial Intelligence 1997: IBM's "Deep Blue" chess machine beats the world's

chess champion, Garry Kasparov

2011: IBM's Watson debuts on a tv show

2014: Vladimir Veselov's and Eugene Demchenko's program Eugene Goostman passes the Turing test

56

Information-based System

Data

Base

Who is the

president of

the USA?

Where is

Rome?

OBAMA

ITALY

57

Knowledge-based System

Know

ledge

Base

Who will the

president of

the USA?

Where is

Atlantis?

X

Y

58


Artificial

Intelligence

A New Class of

Applications

A New Class of

Technologies

59


A New Class of

Applications

Expert

Tasks Heuristics Uncertainty

“Complex”

Problem Solving

The algorithm does not exist

A medical encyclopedia is not equivalent to a physician

The algorithm is too complicated

Design a cruise ship

There is an algorithm but it is “useless”

Don’t touch boiling water

The algorithm is not possible

Italy will win the next world cup

60


A New Class of

Technologies

Non-sequential

Programming

Symbolic

Processing

Knowledge

Engineering

Uncertain

Reasoning

Common Sense

Small minds are concerned with the extraordinary,

great minds with the ordinary"

(Blaise Pascal)

62

Common Sense Types of inference

Induction

Concept formation

Probabilistic reasoning

Abduction

Diagnosis/troubleshooting

Scientific theories

Analogy

Transformation

Derivation

63

Common Sense

• Plausible reasoning

– Quick, efficient response to problems when an

exact solution is not necessary

• Non- monotonic Logic

– Second thoughts: inferences are made

provisionally and can be withdrawn at any time

Common Sense

The Frame Problem

– Classical logic deducts all that is possible from all

that is available

– In the real world the amount of information that is

available is infinite

– It is not possible to represent what does “not”

change in the universe as a result of an action

("ramification problem“)

– Infinite things change, because one can go into

greater and greater detail of description

– The number of preconditions to the execution of

any action is also infinite ("qualification problem“)

Common Sense

Uncertainty

“Maybe i will go shopping”

“I almost won the game”

“This cherry is red”

“Bob is an idiot”

Probability

Probability measures "how often" an event occurs

But we interpret probability as “belief”

Glenn Shafer’s and Stuart Dempster’s “Theory of

Evidence” (1968)

Common Sense

Principle of Incompatibility (Pierre Duhem)

The certainty that a proposition is true

decreases with any increase of its

precision

The power of a vague assertion rests in its

being vague (“I am not tall”)

A very precise assertion is almost never

certain (“I am 1.71cm tall”)

Common Sense

Heuristics

• Knowledge that humans tend to share in a

natural way: rain is wet, lions are dangerous,

most politicians are crooks, carpets get

stained…

• Rules of thumbs

György Polya (1940s): “Heuretics“ - the nature,

power and behavior of heuristics: where it

comes from, how it becomes so convincing,

how it changes

68

Connectionism

A neural network is a set of interconnected neurons

(simple processing units)

Each neuron receives signals from other neurons and

sends an output to other neurons

The signals are “amplified” by the “strength” of the

connection

Connectionism

The strength of the connection changes over time

according to a feedback mechanism (output desired

minus actual output)

The net can be “trained”

Output

Correction

algorithm

Connectionism

• Distributed memory

• Non-sequential programming

• Fault-tolerance

• Recognition

• Learning

Connectionism

Where are we?

Biggest neural computer:

– Stanford and NVIDIA (2013): 11.2 billion connections

(three servers accelerated using 16 GPUs)

– Google (2012): 1.7 billion connections (on a 1,000-

server network) learn to recognize cats in YouTube

videos

• Worm’s brain:

– 1,000 neurons

– But the worm’s brain still outperforms neural nets

Human brain:

– 100 billion neurons

– 200,000 billion connections

72

The Decline of Knowledge-based Systems

• 1. Google it… – Artificial Intelligence was trying to develop

“expert systems” capable of finding a solution to every problem in a given domain, just like a human expert in that domain

– Overt assumption: Domain knowledge is the key to finding solutions

– Hidden assumption: Logical inference is the key to finding the solution

73


• Google it… – Artificial Intelligence never delivered on the

promises of “expert systems”…

– …but search engines did: there is at least one webpage somewhere that has the solution to a given problem, and it’s just a matter of finding it

– Crwdsourcing did it

74


• Google it… – Logical inference (intelligence) is irrelevant.

– It’s the quantity of information (not the quality of inference) that matters

– All we needed is a (digital) library big enough and computers powerful enough to search it

– What those computers don’t need is: intelligence

75


• Google it… – A person can solve any problem as long as

she is capable of searching the Web for the solution

– No other skills required beyond reading skills

– No large, expensive supercomputer required: just a (relatively dumb) smartphone

– The Web plus the search engine does what AI wanted to do: it gives an answer to every possible question that a human can answer (in fact, many more than any one person can answer)

76


• 2. Big Data

– Very soon Homo Sapiens will be producing

more data every year than in the previous

200,000 years

– Big Data shift the disadvantage to the

knowledge-based approach: too much

knowledge makes it unfeasible

76

77


• 3. Cheap computing

– Computational power per $ increased

dramatically

– Neural nets and Bayesian nets became

feasible (“deep learning”)

– A.I. based on statistical analysis

– “Best Guess AI”

• Translation

• Search

• Voice recognition

78

2010s

• Machine learning

– Statistical method yields a plausible

result but it has not learned why

– The learned skills cannot be applied to

other fields

79

2010s

• Machine learning

– Jürgen Schmidhuber (1990-…): active, unsupervised, curious, creative systems that create their own self-generated tasks to improve their understanding of the world

• an adaptive predictor or compressor or model of the growing data history as the agent is interacting with its environment

• a reinforcement learner (motivated to invent skills leading to interesting or surprising novel patterns that the predictor/compressor can learn)

80

2010s

• The personal assistant

– Siri (2011)

– GoogleNow (2012)

– Tom Mitchell’s “learning personal

assistant” at CMU (1994-…)

Apple 2011

Stanley Kubrick (1968)

“2001: A Space Odyssey”

81

2010s

• Multi-billion dollar investments in artificial intelligence and robotics in the 2010s

– Amazon (Kiva, 2012),

– Google (Industrial Robotics, Meka, Holomni, Bot & Dolly, DNNresearch, Schaft, Bost, DeepMind, 2013-14),

– IBM (Watson project),

– Microsoft (Project Adam, 2014),

– Apple (Siri, 2011),

– Facebook (DeepFace, 2013),

– Yahoo (LookFlow, 2013),

– etc 81

82

2010s

• DeepMinds machine learning

• Facebook image recognition

• Wise.io

• Saffron

• Narrative Science

• …

• Ebola fighting robot

82

83

The Future of Mind

Computers

Humans

Decision

Making

Data

Processing

1960 2014 Algorithmic Programming Artificial Intelligence

Singularity?

84

The Future of Body

84

85

Robots

86

Robots

• Stats

87

Robots

1962: Joseph Engelberger deploys the

industrial robot Unimate at General

Motors

1969: Stanford Research Institute's

Shakey the Robot

88

Robots

• Valentino Breitenberg’s “vehicles” (1984)

– Vehicle 1: a motor and a sensor

– Vehicle 2: two motors and two sensors

– Increase little by little the circuitry, and

these vehicles seem to acquire not only

new skills, but also a personality.

89

Robots 2000: Cynthia Breazeal's emotional robot, "Kismet"

2003: Hiroshi Ishiguro's Actroid, a young woman

Which is the robot?

90

Robots 2004: Mark Tilden's biomorphic robot Robosapien

2005: Honda's humanoid robot "Asimo"

Asimo over the years

Robots

Special purpose robots:

2001: NEC PaPeRo (a social robot targeting children)

2005: Toyota's Partner (designed for assistance and elderly

care applications)

2007: RobotCub Consortium aggreement, the iCub (for

research in embodied cognition)

2008: Aldebaran Robotics' Nao (for research and education)

2010: NASA's Robonaut-2 (for exploration)

92

Robots 2005: Boston Dynamics' quadruped robot "BigDog“

2008: Nexi (MIT Media Lab), a mobile-dexterous-social robot

2010: Lola Canamero's Nao, a robot that can show its

emotions

2011: Osamu Hasegawa's SOINN-based robot that learns

functions it was not programmed to do

2012: Rodney Brooks' hand programmable robot "Baxter"

93

Robots

• Stats

94

Case study: Japan

Takayama Festival of Mechanical Puppets

95

Case study: Japan

• Joruri/ puppet theater (~1650)

• “Automated mechanisms, or karakuri, were originally

separate from the puppets, used only in stage machinery or

in robot dolls that performed between acts. But the

machinery eventually found its way into the bodies of the

puppets” (Chris Bolton)

96

Case study: Japan

• Oriza Hirata’s robot theater

“I, Worker” (2008)

“Sayonara” (2010)

97

Case study: Japan

• The uncanny valley – Ernst Jentsch: “On the Psychology of the Uncanny” (1906)

– Masahiro Mori: “The Uncanny Valley” (1970)

98

Case study: Japan

• The uncanny valley

– Japanese robots tend to be female because they

look less threatening

99


• McKensey on A.I.

99

100

A Brief History of Bionics

Jose Delgado

101

A Brief History of Bionic Beings

1957: The first electrical implant in an ear (André Djourno and Charles Eyriès)

1961: William House invents the "cochlear implant", an electronic implant that sends signals from the ear directly to the auditory nerve (as opposed to hearing aids that simply amplify the sound in the ear)

1952: Jose Delgado publishes the first paper on implanting electrodes into human brains: "Permanent Implantation of Multi-lead Electrodes in the Brain"

1965 : Jose Delgado controls a bull via a remote device, injecting fear at will into the beast's brain

1969: Jose Delgado’s book "Physical Control of the Mind - Toward a Psychocivilized Society"

1969: Jose Delgado implants devices in the brain of a monkey and then sends signals in response to the brain's activity, thus creating the first bidirectional brain-machine-brain interface.

102


1997: Remotely controlled

cockroaches at Univ of Tokyo

1998: Philip Kennedy develops a brain

implant that can capture the "will"

of a paralyzed man to move an

arm (output neuroprosthetics:

getting data out of the brain into a

machine)

103


2000: William Dobelle develops an implanted vision system that allows blind people to see outlines of the scene. His patients Jens Naumann and Cheri Robertson become "bionic" celebrities.

2002: John Chapin debuts the "roborats", rats whose brains are fed electrical signals via a remote computer to guide their movements

104


2002: Miguel Nicolelis makes a monkey's brain control a robot's arm via an implanted microchip

2005: Cathy Hutchinson, a paralyzed woman, receives a brain implant from John Donoghue's team that allows her to operate a robotic arm (output neuroprosthetics)

2004: Theodore Berger demonstrates a hippocampal prosthesis that can provide the long-term-memory function lost by a damaged hippocampus

105


The age of two-way neural transmission…

2006: The Defense Advanced Research Projects Agency (Darpa) asks scientists to submit "innovative proposals to develop technology to create insect-cyborgs

2013: Miguel Nicolelis makes two rats communicate by capturing the "thoughts" of one rat's brain and sending them to the other rat's brain over the Internet

106

A Brief History of Bionics The age of two-way neural

transmission…

2013: Rajesh Rao and Andrea Stocco devise a way to send a brain signal from Rao's brain to Stocco's hand over the Internet, i.e. Rao makes Stocco's hand move, the first time that a human controls the body part of another human

2014: An amputee, Dennis Aabo, receives an artificial hand from Silvestro Micera's team capable of sending electrical signals to the nervous system so as to create the touch sensation

107


Neuro-engineering?

(http://targetedindividualscanada.com) (http://its-interesting.com)

108 108

Biotech

Genetics

1944: Oswald Avery discovers that genes are made of DNA

1953: Francis Crick and James Watson discover the double helix of the DNA

1961: Jacob and Monod discover gene regulation

1961: Jacob and Brenner discover messenger RNA

1961: Marshall Nirenberg cracks the genetic code (translation of four-letter genetic code into twenty-letter language of proteins)

109 109

Biotech

Genetics

1973: Stanley Cohen and Herbert Boyer create the first recombinant DNA organism

1976: Genentech, the first major biotech company

1977: Frederick Sanger publishes the first full DNA genome of a living being

1990: William French Anderson performs the first procedure of gene therapy

1992: Calgene creates the "Flavr Savr" tomato, the first genetically-engineered food to be sold in stores

110

Biotech

1997: Ian Wilmut clones the first mammal, the sheep Dolly

2003: The Human Genome Project is completed

2006: Personal genomics (23andMe, Syapse, Genophen)

2010: Craig Venter and Hamilton Smith reprogram a bacterium's DNA

2010: Cheap printers for living beings (OpenPCR, Cambrian Genomics)

2012: Markus Covert simulates an entire living organism in software (Mycoplasma Genitalium)

2012: Crisp/Cas9 technique for genome editing

Biotech

The price of DNA sequencing your genome has dropped 99% since 2003 ($3,000 in 2013)

111

Moore's law vs Cost per genome

Biotech

112

Biohacking

• “Biology is technology” (Rob Carlson)

• A community of worldwide hobbyists

• Public-domain databases of genetic parts

• MIT Registry of Standard Biological Parts (2003)

• iGEM Jamboree and BioBricks (2004)

• BioCurious (2010)

113

Biohacking

• Biocurious (Sunnyvale) - -20C Freezer

– PCR Machines

– qPCR

– Balance

– Autoclave

– Micropipettes, single and multi-channel

– Fluorescent Microscope

– Microcentrifuges

– Protein Purification System

– Vortexers

– Ultrasonic Bath

– CO2 Incubator

114

Biohacking

• iGEM = International Genetically Engineered Machine

• “Open source” biotech

• Global grassroots synthetic-biology revolution

• Student bioengineers from all over the world create new life forms and race them every year at the iGEM Jamboree in Boston (since 2004)

• 2014: 2,500 competitors from 32 countries

• Repository of 20,000 biological parts (biobricks)

• They create mostly microbes (e.g., organisms detecting and eliminating water pollutants)

115

Drew Endy

(Stanford), iGEM

and BioBricks

Foundation

Biohacking

• PCR printers (identify a piece of DNA and make copies of it)

• OpenPCR (cheap Polymerase Chain Reaction printer)

• Cambrian Genomics: a laser printer for living beings

116

Biohacking

• Autodesk’s Project Cyborg: design tools for biohackers (quote: “Project Cyborg is a cloud-based meta-platform of design tools for programming matter across domains and scales”)

117

118

The Future of Body

Machines

Humans

Mind Uploading

Flesh and Bones

2014

Singularity?

Bio re-engineering

Neural Implants

119

The Future of Body

• Meditation:

– The longest living bodies on the planet

have no brain: bacteria and trees.

120

The Singularity?

120

Ray Kurzweil at the

Singularity University

121

The Singularity?

Ray Kurzweil’s predictions

• Infinite life extension: “Medical technology will be more than a thousand times more advanced than it is today… every new year of research guaranteeing at least one more year of life expectancy”* (2022)

• Precise computer simulations of all regions of the human brain (2027)

• Small computers will have the same processing power as human brains (2029)

• 2030s: Mind uploading - humans become software-based

• 2045: The Singularity 121 * recently postponed to 2040

122

The Singularity?

2014: Deep Knowledge Ventures (Hong Kong)

appoints an algorithm to its board of directors

122

123

The Singularity?

• The Apocalypse has happened many times – Book of Revelation (1st c AD)

– …

– Year 1,000

– …

– Nostradamus (16th century)

– …

– Pierre Teilhard de Chardin’s Omega Point (1950)

– Dorothy Martin/Marion Keech’s planet Clarion (1954)

– Nuclear holocaust (1950s-80s)

– Year 2,000

– Harold Camping’s Biblical calculations (2011)

– End of the Mayan calendar (2012)

123

Albrecht Dürer: The

Four Horsemen of the

Apocalypse (1498)

124

The Singularity?

Five arguments against the Singularity

1. Reality Check

2. Accelerating Progress?

3. Non-human Intelligence

4. Human Intelligence

5. A Critique of the Turing Test

124

125

Reality Check

Why the Singularity is not Coming any

Time Soon & other Meditations on the

Post-Human Condition and the Future

of Intelligence

126

Reality Check

• The curse of Moore’s law

– The motivation to come up with creative ideas in A.I. was due to slow, big and expensive machines.

– Brute force (100s of supercomputers running in parallel) can find solutions using fairly dumb techniques

– Actually, you can find the answer to most questions by simply using a search engine: no need to think, no need for intelligence

127

Reality Check

• Recognizing a cat is something that any mouse

can do (it took 16,000 computers working in

parallel)

• Voice recognition and handwriting recognition

still fail most of the time, especially in

everyday interactions

128

Reality Check

• IBM's Watson does not understand the question

(it is fed in digital format)

• IBM’s "Deep Blue" beat a chess master but was

given unfair advantages

• “What Curiosity (robot) has done in 200 days a

human field researcher could do in an easy

afternoon" (NASA planetary scientist Chris

McKay, 2013)

129

Reality Check

• Machine translation in 2013 (random sentences

from my website translated by Google):

– "Graham Nash the content of which led nasal

harmony“

– "On that album historian who gave the blues

revival“

– "Started with a pompous hype on wave of

hippie phenomenon"

130

Reality Check

• A remote-controlled toy is NOT a step

toward superhuman intelligence

• Human-looking automata that mimic

human behavior have been built since

ancient times

• A human being is NOT a toy (yet)

131

Reality Check

• The brain of the roundworm (a few

hundred neurons connected by a few

thousand synapses) is still smarter than

the smartest neural network ever built.

132

Reality Check

• An easy science

– Artificial Intelligence is not subjected to the

same scrutiny as other sciences

– Its success stories are largely unproven

133

Reality Check

• 60 years later it is not machines that learned

to understand human language but humans

who got used to speak like machines in order

to be understood by automated customer

support (and mostly not even speak it but

simply press keys)

134

Reality Check

• What “automation” really means…

– The jobs that have been automated are

repetitive and trivial.

– And in most cases the automation of those

jobs has required the user/customer to accept

a lower (not higher) quality of service.

– The more automation around you, the more

you (you) are forced to behave like a machine

to interact with machines

135

Reality Check

• Intelligent Behavior from Structured

Environments

136

Reality Check

• Structuring the Environment

– We structure the chaos of nature because it

makes it easier to survive and thrive in it

– The more we structure the environment, the

easier for extremely dumb people and

machines to survive and thrive in it.

– It is easy to build a machine that has to operate

in a highly structured environment

– What really "does it" is not the machine: it's the

structured environment

137

Reality Check

• Semantics

– It is not intelligent to talk about intelligent

machines: whatever they do is not what we do,

and, therefore, is neither "intelligent" nor "stupid"

(attributes invented to define human behavior)

– We apply to machines many words invented for

humans simply because we don't have a

vocabulary for the states of machines

138

Reality Check

• Semantics

– Memory is reconstructive

– Data storage is not “memory”

– Exponentially increasing data storage does not

mean better memory

– What is “computer speed”?

– Who is faster at picking a cherry from a tree,

the fastest computer in the world or you?

139

Reality Check

• Where A.I. is truly successful…

– Most machine intelligence is being employed to couple real-time customization and machine learning in order to understand who you are and tailor situations in real time that will prompt you to buy some products (custom advertising)

– "The best minds of my generation are thinking about how to make people click ads" (former Facebook research scientist Jeff Hammerbacher in 2012)

– So far A.I. has not created better doctors or engineers, but better traveling salesmen

140

Artificial General Intelligence

• Task-specific vs General-purpose

Intelligence

• Originally, A.I. was looking for general-

purpose intelligence

• Today’s A.I. is looking for task-specific

intelligence (recognizing a cat, driving a car)

141


• How to simulate an average human (not just one

human task) - the “logic theorist” solution (1960s):

create a system that can perform reasoning on

knowledge and infer the correct behavior for any

situation

• How to simulate an average human (not just one

human task) - the brute force solution (2000s):

create one specific program/robot for each of the

millions of possible situations, and then millions of

their variants

142


• The Multiplication of Appliances and Artificial Intelligence by Enumeration – We have machines that dispense money

(ATMs), machines that wash clothes (washing machines), machines that control the temperature of a room (thermostats), and machines that control the speed of a car (cruise controls).

– We can build machines for all the other tasks and then collectively call them “equal” to humans

143


• The enumeration problem: which human functions qualify as "intelligent"? – There are very human functions that people

don't normally associate with "intelligence". They just happen to be things that human bodies do.

– Do we really want machines that fall asleep or urinate?

– We swing arms when we walk, but we don't consider "swinging arms while walking" a necessary feature of intelligent beings.

144

Tips for better A.I.

1. IBM's Watson of 2013 consumes 85,000 Watts

compared with the human brain's 20 Watts.

2. The brain is an analog device, not digital

3. What we need: a machine that has only a limited

knowledge of all the chess games ever played and

is allowed to run only so many logical steps before

making a move and can still consistently beat the

world champion of chess.

4. Memory is not storage

145

Tips for better A.I.

• What conditions may foster a breakthrough:

it is not the abundance of a resource (such

as computing power or information) that

triggers a major paradigm shift but the

scarcity of a resource.

146 146

Accelerating progress?

• One century ago, within a relatively short period of time, the world adopted:

– the car,

– the airplane,

– the telephone,

– the radio

– the record

– cinema

• while at the same time the visual arts went through

– Impressionism,

– Cubism

– Expressionism

147 147


• while at the same time science came up with

– Quantum Mechanics

– Relativity

• while at the same time the office was revolutionized by

– cash registers,

– adding machines,

– typewriters

• while at the same time the home was revolutionized by

– dishwasher,

– refrigerator,

– air conditioning

148 148


• while at the same time cities adopted high-rise

buildings

149 149


• There were only 5 radio stations in 1921 but already 525 in 1923

• The USA produced 11,200 cars in 1903, but already 1.5 million in 1916

• By 1917 a whopping 40% of households had a telephone in the USA up from 5% in 1900.

• The Wright brothers flew the first plane in 1903: during World War I (1915-18) more than 200,000 planes were built

150 150


• On the other hand today:

– 44 years after the Moon landing we still haven't

sent a human being to any planet

– The only supersonic plane (the Concorde) has

been retired

– We still drive cars, fly on planes, talk in

phones, use the same kitchen appliances

151 151


• We chronically underestimate progress in

previous centuries because most of us are

ignorant about those eras.

152

A Comparative History of Accelerating

Progress

• On April 3, 1988 the Los Angeles Times

Magazine ran a piece titled "L.A. 2013“

– two robots per family (including cooking

and washing)

– Intelligent kitchen appliances widespread

– self-driving cars widespread

153

A Comparative History of Accelerating

Progress

• Today there is a lot of change

• But change is not necessarily progress

• It is mostly fashion created by marketing

and/or planned obsolescence (progress

for whom?)

154

What would Turing say today?

What took you

guys so long???

155


• Why did it take you so long?

– The Hubble telescope transmits 0.1 terabytes of data

a week, about one million times more data than the

Palomar telescope of 1936

– In 1940 the highest point ever reached by an aviator

was 10 kms. In 1969 Neil Armstrong traveled 380

million kms up in the sky, i.e. 38 million times

higher.

– In 60 years the speed of computers has increased

“only” ten thousand times

156


• Hardware: other than miniaturization, what

has really changed?

– It still runs on electricity

– It still uses binary logic

– It is still a Turing machine (e.g., wildly

different in nature and structure from a

human brain)

157


• Software: other than having 12 million programmers work on thousands of programs (instead of the six who programmed the ENIAC), what has really changed?

– It is still written in an artificial language that is difficult to understand

– It is still full of bugs

– It still changes all the time

– It is still sequential processing (e.g., wildly different in nature and structure from a human brain)

158


And I’m

supposed to

be impressed?

159

Non-human Intelligence

• Super-human intelligence has been around for a

long time: many animals have powers we don't

have

160


• Bats can avoid objects in absolute darkness at impressive speeds

• Migratory animals can navigate vast territories

• Birds are equipped with a sixth sense for the Earth's magnetic field

• Some animals have the ability to camouflage

• The best color vision is in birds, fish and insects

• Many animals have night vision

• Animals can see, sniff and hear things that we cannot

161


• And don't underestimate the brain of an insect

either: how many people can fly and land upside

down on a ceiling?

162


• We already built machines that can do things that

are impossible for humans:

– Telescopes and microscopes can see things that

humans cannot see

– We cannot do what light bulbs do

– We cannot touch the groove of a rotating vinyl

record and produce the sound of an entire

philharmonic orchestra

163

Super-human Machine Intelligence

• The medieval clock could already do

something that no human can

possibly do: keeping time

• That’s why we have to ask “What

time is it?”

164


• What is the difference between non-

human intelligence (which is already here

and has always existed) and super-human

intelligence?

165

Super-human intelligence

• Possible: Colin McGinn’s cognitive closure

(there are things we will never understand)

• Impossible: David Deutsch’s endless

explanation (we are as intelligent as it gets)

166

Dangers of machine intelligence

• Who's Responsible for a Machine's Action?

• We believe machines more than we believe

humans

• Should there be speed limits for machines?

• We are criminalizing Common Sense

• You Are a Budget

• The dangers of clouding - Wikipedia as a

force for evil

167

Dangers of machine intelligence

• The biggest danger of all: decelerating

human intelligence

168

The Turing Point

• The Turing Test was asking “when can machines be

said to be as intelligent as humans?”

• This “Turing point” can be achieved by

1. Making machines smarter, or

2. Making humans dumber

HOMO MACHINE

IQ

HOMO MACHINE

IQ 1. 2.

169

What can machines do now that they

could not do 50 years ago?

• They are faster, cheaper, can store larger

amounts of information and can use

telecommunication lines

170

What can humans do now that they could

not do 50 years ago?

• Use the new machines

• On the other hand, they are not capable of doing a lot of things that they were capable of doing 50 years ago from arithmetic to finding a place not to mention attention span and social skills (and some of these skills may be vital for survival)

• Survival skills are higher in low-tech societies (this has been true for a while)

• General knowledge (history, geography, math) is higher in low-tech societies (coming soon)

171

The Post-Turing Thesis

• If machines are not getting

much smarter while humans

are getting dumber…

• … then eventually we will

have machines that are

smarter than humans

• The Turing Point (the

Singularity?) is coming HOMO MACHINE

IQ

172

A Tool is not a Skill

• In a sense, technology is about giving people

the tools to become dumber and still continue

to perform

• People make tools that make people

obsolete, redundant and dumb

173

Decelerating Human Intelligence

• The success of many high-tech projects

depends not on making smarter technology

but on making dumber users

• Users must change behavior in order to make

a new device or application appear more

useful than it is.

174

Turning People into Machines

• “They” increasingly expect us to behave like machines in order to interact efficiently with machines: we have to speak a “machine language” to phone customer support, automatic teller machines, gas pumps, etc.

• In most phone and web transactions the first question you are asked is a number (account #, frequent flyer#…) and you are talking to a machine

• Rules and regulations (driving a car, eating at restaurants, crossing a street) increasingly turn us into machines that must follow simple sequential steps in order to get what we need

175

Turning People into Machines

• Rules to hike in the *wilderness* (there is even a rule

for peeing)

176

Decelerating Human Intelligence

• Is it possible that humans have moved a

lot closer towards machines than

machines have moved towards humans?

177

The Silicon Valley Paradigm

• “They” increasingly expect us to study lengthy

manuals and to guess how a machine works

rather than design machines that do what we

want the way we like it

• A study by the Technical University of

Eindhoven found that half of the returned

electronic devices are not malfunctioning: the

consumer just couldn't figure out how to use

them

178

The Singularity

• The Turing Test may become a self-

fulfilling prophecy: as we (claim to) build

“smarter” machines, we may make dumber

people.

• Eventually there will be an army of greater-

than-human intelligence

179

The Future is not You

• The combination of smartphones and websites offers a glimpse of a day when one will not need to know anything because it will be possible to find everything in a second anywhere at any time by using just one omnipowerful tool.

• An individual will only need to be good at operating that one tool. That tool will be able to access an almost infinite library of knowledge and… intelligence.

180

The Difference: You vs It

• Human minds are better than machines at

– Improvisation

– Imagination

– (in a word: "creative improvisation")

• Human minds can manage dangerous and

unpredictable situations

• Human minds can be “irrational”

181


• Modern society organizes our lives to remove

danger and unpredictability.

• Modern society empowers us with tools that

eliminate the need for improvisation and

imagination

• Modern society dislikes (and sometimes outlaws)

irrationality

182


• We build

– Redundancy

– Backups

– Distributed systems

• to make sure that machines can do their job 24/7

in any conditions.

• We do not build anything to make sure that minds

can still do their job of creative improvisation

183

A Critique of the Turing Test

(while we’re still intelligent)

184

The Turing Test

The “Turing point”: a computer can be said to be intelligent if its

answers are indistinguishable from the answers of a human

being

? ?

185

The Turing Test

The fundamental critique to the Turing Test

• The computer (a Turing machine) cannot (qualitatively)

do what the human brain does because the brain

– does parallel processing rather than sequential

processing

– uses pattern matching rather than binary logic

– is a connectionist network rather than a Turing

machine

186

The Turing Test

The Turing Test

• John Searle’s Chinese room (1980)

– Whatever a computer is computing, the computer

does not "know" that it is computing it

– A computer does not know what it is doing,

therefore “that” is not what it is doing

– Objection: The room + the machine “knows”

187

The Turing Test

The Turing Test

• Hubert Dreyfus (1972):

– Experience vs knowledge

– Meaning is contextual

– Novice to expert

– Minds do not use a theory about the everyday world

– Know-how vs know that

• Terry Winograd

– Intelligent systems act, don't think.

– People are “thrown” in the real world

188

The Turing Test

The Turing Test

• Rodney Brooks (1986)

– Situated reasoning

– Intelligence cannot be separated from the body.

– Intelligence is not only a process of the brain, it is

embodied in the physical world

– Cognition is grounded in the physical interactions

with the world

– There is no need for a central representation of the

world

– Objection: Brooks’ robots can’t do math

189

The Turing Test

The Turing Test

• John Randolph Lucas (1961) & Roger Penrose

– Goedel’s limit: Every formal system

(>Arithmetic) contains a statement that cannot

be proved

– Some logical operations are not computable,

nonetheless the human mind can treat them (at

least to prove that they are not computable)

– The human mind is superior to a computing

machine

190

The Turing Test

The Turing Test

• John Randolph Lucas (1961) & Roger Penrose

– Objection: a computer can observe the failure of

“another” computer’s formal system

– Goedel’s theorem is about the limitation of the

human mind: a machine that escapes Goedel’s

theorem can exist and can be discovered by

humans, but not built by humans

191

The Turing Test

• What is measured: intelligence, cognition, brain, mind, or consciousness?

• What is measured: one machine, ..., all machines?

• What is intelligence? What is a brain? What is a mind? What is life?

• Who is the observer? Who is the judge?

• What is the instrument (instrument = observer)?

• What if a human fails the Turing test?

The Turing Test

192

The Turing Test

• Someone has hidden a person in a room and a

computer in the other room.

• We are allowed to ask any questions.

• The person and the computer reply in their own way.

• If we cannot tell which one is the person and which

one is the computer, then the computer has become

intelligent.

193

Who is Testing

• Someone has to determine whether the answers to

her questions come from a human or a machine

• Who is the judge who decides if the Turing Test

succeeds? What instrument does this test use?

• A human? A machine?

• How “intelligent” is the judge?

194

Who is Testing

• Can a mentally retarded person judge the test?

• Can somebody under the influence of drugs

perform it?

• …a priest, an attorney, an Australian aborigine, a

farmer, a librarian, a physician, an economist...?

• …the most intelligent human?

• The result of the test can vary wildly depending

on who is the judge

195

Who are we Testing?

• If a machine fails the test (i.e. the judge thinks the

machine is a machine), then Turing concludes that

the machine is not intelligent

• What does Turing conclude if a human fails the test

(if the judge thinks that the human is a machine)?

That humans are not intelligent?

196

What are we Testing?

• The Turing Test is about behavior

• The Turing test measures how good a machine

is at answering questions, nothing more.

• “Can a machine be built that will fool a human

being into believing it is another human being?”

is not identical to “Can a machine think?”

• If we answer “yes” to the first question, we don’t

necessarily answer “yes” to the second.

197

The Turing Point

• The Turing Test asks when can we say that a

machine has become as intelligent as humans.

• The Turing Test is about humans as much as it is

about the machine because it can be equivalently

be formulated as: when can we say that humans

have become less intelligent than a machine?

• The Turing Test cannot be abstracted from a

sociological context. Whenever one separates

sociology and technology, one misses the point.

198

The ultimate Turing Test

• Build a machine that reproduces my brain,

neuron by neuron, synapses by synapses

• Will that machine behave exactly like me?

• If yes, is that machine “me”?

The Turing Test

199

The End (for now)

“A man provided with paper, pencil, and

rubber (and subject to strict discipline) is in

effect a universal machine”

(Alan Turing, 1948)

Education

Artificial intelligence and the Singularity - History, Trends and Reality Check