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Beautiful Ideas

Dr. Dario Gil Vice President Science and Solutions IBM Research

“The thing that differentiates scientists is purely an artistic ability to discern

what is a good idea, what is a beautiful idea, what is worth spending time on, and most importantly,

what is a problem that is sufficiently interesting, yet sufficiently difficult, that it hasn't yet been solved,

but the time for solving it has come now.”

-- Professor Savas Dimopoulos, Stanford University

Brazil

T.J. Watson Almaden

Austin

Ireland Zurich

Haifa

Kenya

India

China

Tokyo

Australia

IBM Research: 3,000 scientists & 12 labs

Six Nobel Laureates

Ten Medals of Technology

Five National Medals of Science

Six Turing Awards

Time

Com

pute

r “In

telli

genc

e”

Counting Machine Circa 1820

ENIAC circa 1945 Antikythera

Astronomical Computer

circa 87 BC

Abacus circa

3500 BC

Napier’s Rods circa 1600

System/360 1964

Deep Blue 1997

Calculators Watson

2011 Calculating Paradigm

Programmable Paradigm

Cognitive Paradigm

The History of Computing

Claim #1

You can not afford to ignore the learning systems trend

Trend #1: Better Machine Learning Algorithms Tom

Mitchell (CMU)

“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.”

Introduction of large scale neural networks

Introduction of large scale neural networks

650,000 neurons

5,000,000 neurons

Human Error

Ref: O. Russakovsky et al., arXiv:1409.0575v1 [cs.CV], 1 Sep 2014

Deep Learning

http://www.slideshare.net/NVIDIA/nvidia-ces-2016-press-conference

This challenge evaluates algorithms for object localization/detection and image/scene classification from images and videos at large scale.

‘What does it feel like to teach a machine?’

Jonathan Connell PhD

Trend #2: Massive Datasets (for training)

Social Media

Web Sites

Video Sharing Sites

Curated Data Sets

The Web and the Internet of Things are digitizing the world and the human experience

Trend #3: Performance and Cost of Computing

2011 2,400% improvement in performance and 90% smaller

2015

$17 billion in AI investments since 2009* Trend #4: Massive Talent & Investments Flows

*Source: Quid

1/2

“The business plans of the next 10,000

startups are easy to forecast:

Take X and add AI. This is a big deal, and

now it's here.”

Kevin Kelly (Wired)

© 2016 IBM Corporation

Trend #4: Massive Talent & Investments Flows

2/2

NYC

Cambridge, MA

Munich

Claim #2

The future of expertise will be defined by people and learning systems working collaboratively

Physical limitations

Connectivity limitations

Productivity limitations

Complexity limitations We need enhanced cognitive abilities

Enhancing human capability

“System 1” The automated you

“System 2” The reflective you

Humans incur many cognitive errors and biases

THE AUTOMATIC YOU / THE REFLECTIVE YOU

•  Fast •  Parallel •  Automatic •  Effortless •  Associative •  Slow-learning

•  Slow •  Serial •  Controlled •  Effortful •  Rule-governed •  Flexible

!  Cognitive ease " Illusions of truth !  Infers & invents causes and intentions !  Neglects ambiguity !  Is biased to believe and confirm !  Exaggerates emotional consistency !  Focuses on existing evidence and ignores

absent evidence !  Responds more strongly to losses than to gains

Cognitive Optical Errors A few “System 1” challenges

Insights from Behavioral Science

Finance Operations Marketing & Sales Medical Mergers, Acquisitions &

Divestitures Crisis and Emergency

Management Product Pricing &

Launch Discovery

Investment Decisions Project Planning Selection of Markets & Geos Diagnosis

Strategic Planning & Scenario Analysis Discovery & Diagnosis Competitive Analysis Treatment

For Institutions…

Education Large Purchases Financial Investments Medical

Selecting a college Purchasing a home Retirement investment decisions

Selecting medical plans

Financing education Purchasing a car Stock market investments

Deciding on treatment options

For the Individual…

Decisions with a high degree of cognitive complexity

Human Our expertise

Self-directed goals

Common sense

Value judgment

Machine +

All digital knowledge

Large-scale math

Pattern recognition

Statistical reasoning

23

Pushing the frontiers of IT

Source: Kurzweil 1999 – Moravec 1998

1E-5

1E-3

1E+0

1E+3

1E+6

1E+9

1E+12

$100

0 B

uys:

Com

puta

tions

per

sec

ond

1E+15

1900 1920 1940 1960

Integrated Circuit

Discrete Transistor

Vacuum Tube

Electro- Mechanical

Mechanical

2020 and Beyond 2000 1980

Carbon Nanotube

Quantum Technology

A Century of Progress A technological achievement without equal

Min

Dim

ensi

on

Fabr

icat

ed (µ

m)

Moore’s Law (1965)

Semiconductors in Production 100 nm

7nm technology

A unique period in history

© 2016 IBM Corporation

Nanoscopes developed by IBM

Atomic manipulation with the scanning tunneling microscope (STM), single Xe adatoms; D. M. Eigler and E. K. Schweizer, Nature (1990) Olympicene radical (C19H11) imaged with the atomic force microscope (AFM), to scale; L. Gross et al. Science (2009); B. Schuler et al. Phys. Rev. Lett. (2013)

© 2016 IBM Corporation

Physical Analysis: Pushing the Limits of Measurement First complete Measurement of Atomic Structures: atoms, bonds, charge distribution, bond order

Bond Order

A081023.112303.dat Ch: 3 Biasvoltage: 0.16950V Current: 1.1E-10A Temperature: 4.71737 [K]

0 5 10 15 20 250

2

4

6

8

10

12

140

0.1

0.2

0.3

0.4

0.5

AFM

Atomic Positions and Chemical Bonds

Charge Distribution within Molecular Switch Reaction Intermediates (Arynes) Science 337, 1326 (2012) Nature Chemistry 7, 623–628 (2015) Nature Nanotech. 7, 227 (2012)

Science 325, 1110 (2009)

© 2016 IBM Corporation

Molecular identification

First application of AFM for molecular structure identification

A091218.170556.dat C

h: 3 Biasvoltage: -0.15010V Current: 1.2E-12A

Temperature: 4.79412 [K]

02

46

810

1214

1618

024681012141618

0 0.2

0.4

0.6

0.8

1

A091218.170556.dat C

h: 3 Biasvoltage: -0.15010V Current: 1.2E-12A

Temperature: 4.79412 [K]

02

46

810

1214

1618

024681012141618

0 0.2

0.4

0.6

0.8

1

Mariana Trench Challenger Deep (-10911 m)

Kaiko – lost in 2003

Leo Gross et al. Nature Chemistry (2010)

© 2016 IBM Corporation April TK, 2016 | dgil@us.ibm.com | Copyright 2016 IBM Corporation

Bruno Schuler et al., “Reversible Bergman cyclization by atomic manipulation”, Nature Chemistry 8, 220–224 (25 Jan. 2016) doi:10.1038/nchem.2438

Reversible Bergman cyclization by atomic manipulation

31

The$Quantum$Fron.er$$

$$

ROLF LANDAUER

$$

CHARLES BENNETT

ORIGINS OF QUANTUM INFORMATION SCIENCE “Is there a fundamental limit to the energy efficiency of computation?”

“Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d

better make it quantum mechanical, and by golly, it’s a wonderful problem, because it

doesn’t look so easy.” $

/Richard$P.$Feynman$

NATURE ISN’T CLASSICAL, DAMMIT, AND IF YOU WANT TO MAKE A SIMULATION OF NATURE, YOU’D BETTER MAKE IT QUANTUM MECHANICAL, AND BY GOLLY, IT’S A WONDERFUL PROBLEM, BECAUSE IT DOESN’T LOOK SO EASY.”

RICHARD P. FEYNMAN

“$

Photo: Bomazi

0

1

QUBIT

‘1’

‘0’

‘0’ + ‘1’

BITS

Shor’s'algorithm'(1994)'

Exponential speed-up: A task taking 2100 seconds (1025 days) on a

classical computer might take 100 seconds on a quantum computer

The problem of multiplication vs factoring

937 x 947 = N (easy)

887339 = p x q (harder)

Modulus (1024 bits): de b7 26 43 a6 99 85 cd 38 a7 15 09 b9 cf 0f c9

c3 55 8c 88 ee 8c 8d 28 27 24 4b 2a 5e a0 d8 16 fa 61 18 4b cf 6d 60 80 d3 35 40 32 72 c0 8f 12 d8 e5 4e 8f b9 b2 f6 d9 15 5e 5a 86 31 a3 ba 86 aa 6b c8 d9 71 8c cc cd 27 13 1e 9d 42 5d 38 f6 a7 ac ef fa 62 f3 18 81 d4 24 46 7f 01 77 7c c6

2a 89 14 99 bb 98 39 1d a8 19 fb 39 00 44 7d 1b 94 6a 78 2d 69 ad c0 7a 2c fa d0 da 20 12 98 d3

Public key example:

= p × q

(just short of impossible)

One'of'the'following'must'be'true*:'

–  Strong$Church/Turing**$thesis$is$false$

–  Factoring$is$easy$

–  Quantum$mechanics$is$wrong$

* Scott Aaronson, PhD thesis, UC Berkeley

Shor’s algorithm jumpstarted the interest in quantum computing

** Church-Turing thesis: anything that can be simulated efficiently can be simulated efficiently on existing digital computers

Shor’s algorithm

best classical algorithm (number field sieve)

Classical Record: 230 digits

39$

0$ 1$ 1$ 0$

•  Each bit is in a definite state, 0 or 1 •  Reading a bit does not change the state •  You can copy a bit •  All of the information of a bit is stored in that bit

bit 1 bit 2 bit 3 bit 4

Classical$Informa.on$

40$

?$ ?$ ?$ ?$

Entangled$Quantum$Informa.on$

qubit 1

Correlations •  Each qubit is in a definite state

•  Can be in superposition state – |0> and |1> •  Reading a qubit can change the state •  You cannot copy a qubit state (no cloning) •  Information can be stored in correlations of qubits

•  Entanglement: non-classical correlation

qubit 2 qubit 3 qubit 4

Quantum$Informa.on$

41$

•  Superposi.on:$$each$qubit$in$2$states$simultaneously$•  Entanglement:$qubits$share$informa.on,$so$$

–  N$entangled$qubits$in$superposi.on$states$span$all$2N$states$–  N$bits$in$a$classical$machine$represent$ONLY$1$out$of$2N$states$$

•  Interference:$$–  Construc.ve$interference$enhances$correct$answer$–$ONLY$0$or$1$at$end.$–  Destruc.ve$interference$suppresses$incorrect$answers$

•  Power$grows$exponen.ally$with$number$of$qubits$–  Doubles$with$each$added$qubit$

•  Compare$to$linear$growth$of$conven.onal:$double$by$doubling$the$number$of$bits…$

Why$are$Some$Quantum$Algorithms$So$Fast?$

Why Quantum Information is Fragile!

Qubits “trap” and control state of single microwave photon or atom Energy state >> Thermal energy, RF noise OR you lose qubit state. - This is why we operate at 0.02 Kelvin… - Why cryostat (refrigerator) is well-shielded… Qubit chip

Cryostat (refrigerator)

0.02 K

0.10 K

0.30 K

Qubit Transistor Quantum System

Conventional System

Vision: A new device defines a new computational system

43

IBM Quantum Processor

A “Small” Quantum Computer

Quantum Computing on the Cloud: The Quantum Experience

Since launch: !  >30,000 users for over 100 countries !  200,000 experiments !  8 scientific publications !  >350 major media articles !  20,000 media mentions !  138 million Twitter impressions

http://www.research.ibm.com/quantum/

A$final$reflec.on$on$culture$

The Culture of Science The Culture of the Road Map The Culture of Agile

A Story of Three Cultures

DGil@US.IBM.COM | IBM Confidential | © 2016 IBM Corporation 52 25 August, 2016

The Culture of Science

53 DGil@US.IBM.COM | IBM Confidential | © 2016 IBM Corporation

Science “suggests a process of uncommon rationality, inspired observation, and near-saintly tolerance for failure. ...

“The term ‘science’ also entails people aiming high. ...

“In both theory and practice, science … is perceived as a noble endeavor.”1

The culture of science is a culture of engagement with a broad, worldwide community that seeks truths by using proven methods of

enquiry.

In industry research labs, the context of the science is the business. 1 Kevin Kelly, “The Third Culture.” Science, 13 Feb 1998. Vol. 279, Issue 5353, pp. 992-993.

Available at http://science.sciencemag.org/content/279/5353/992

25 August, 2016

Road map culture is always looking up to a vision of where to be 3, 5, and even 10 years in the future.

Experts commit to future targets and delivery dates without fully knowing how to reach them.

Road map culture is intensely team-oriented and unrelenting. A missed target means dropping out of the race.

The Culture of the Road Map

54 DGil@US.IBM.COM | IBM Confidential | © 2016 IBM Corporation 25 August, 2016

Semiconductor Technology Roadmap

55

1998

2014

Area: ~80X ↓ Power: ~20,000X ↓ Speed: ~7-10X ↑

Area: ~14.3X ↓ Power: ~4,000X ↓

Speed: ~7.5X ↑

p substrate, doping α*NA

Scaled Device

L/αxd/α

GATE

n+ source

n+ drain

WIRINGVoltage, V / α

W/αtox/α

R.H. Dennard, IEDM, 72

25 August, 2016

Agile culture practices, encourages and rewards collaboration, speed and value.

Agile is a culture of openness and sharing, where people build for reuse and consumption is measured.

Agile focuses on the rapid innovation of bite-sized pieces that are easy to deploy and consume.

Agile culture is outcome-driven with processes informed by user/value centered design and continuous feedback/learning.

Agile culture is always asking “What is the Minimum Viable Product (MVP)?”

The Culture of Agile

56 DGil@US.IBM.COM | IBM Confidential | © 2016 IBM Corporation 25 August, 2016

The Culture of the Road Map

57 25 August, 2016

The Culture of Agile

The Culture of Science

To solve the most difficult problems in business and the world, we must

incorporate the best in these three complimentary ways of working.

IBM T.J.Watson Research Center, Yorktown Heights, New York

59 DGil@US.IBM.COM | IBM Confidential | © 2016 IBM Corporation 25 August, 2016