Artificial Intelligence, What It Sees and Creates

ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES

ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES

ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES

To Jerry, Nolan and Alicia for adding to the challenge.

And Shiquan for the insightful AI information.

Book design copyright Š 2017 by Lili Fang. All rights reserved.

Lili Fang lilifdesign@gmail.com Published by Lili Fang for GR 330 Typography 3: Complex Hierarchy Class Instructed by William Culpepper Semester: Fall 2017 Academy of Art University, San Francisco, CA. No portion of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means electronic, mechanical, photocopying, recording, or otherwise without the express written permission of the publisher.

Typeset in Moonhouse & Myriad All information design has been reinterpreted and redesigned by Lili Fang.

Photographs by Lili Fang.

Yayoi Kusama Infinity Mirrors Seattle Art Museum

CONTENTS

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 WHAT IS AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2. HISTORY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3. NOW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4. VISUAL COGNITION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 NEURAL NETWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 GAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 STYLE TRANSFER. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 DEEP DREAM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 FACE ID. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5. FUTURE DEVELOPMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 INDEX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

1 INTRODU CTION

H

umankind has given itself the scientific name

Another reason to study AI is that these constructed

homo sapiens — man the wise — because our

intelligent entities are interesting and useful in their

mental capacities are so important to our everyday lives

own right. AI has produced many significant and

and our sense of self. The field of artificial intelligence,

impressive products even at this early stage in its

or AI, attempts to understand intelligent entities. Thus,

development. Although no one can predict the

one reason to study it is to learn more about ourselves.

future in detail, it is clear that computers with a

But unlike philosophy and psychology, which are also

human-level intelligence or better would have

concerned with intelligence, AI strives to build intel-

a huge impact on our everyday lives and on the

ligent entities and understand them.

future course of civilization.

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“ THE STUDY OF HOW TO MAKE COMPUTERS DO THINGS AT WHICH, AT THE MOMENT, PEOPLE ARE BETTER.” Rich and Knight, 1991 AI addresses one of the ultimate puzzles. How is it

AI is one of the newest disciplines. It was formally

possible for a slow, tiny brain, whether biological

initiated in 1956, when the name was coined,

or electronic, to perceive, understand, predict, and

although at that point work had been under way

manipulate a world far larger and more complicated

for about five years. Along with modern genetics, it

than itself?

is regularly cited as the ``field I would most like to

How do we go about making something with those

be in’’ by scientists in other disciplines. A student in

properties? These are hard questions, but unlike the

physics might reasonably feel that all the good ideas

search for faster-than-light travel or an antigravity

have already been taken by Galileo, Newton, Einstein,

device, the researcher in AI has solid evidence that the

and the rest, and that it takes many years of study

quest is possible. All the researcher has to do is look in

before one can contribute new ideas. AI, on the other

the mirror to see an example of an intelligent system.

hand, still has openings for a full-time Einstein.

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CHAPTER N AM E

The study of intelligence is also one of the oldest disciplines. For over 2000 years, philosophers have tried to understand how seeing, learning, remembering, and reasoning could, or should, be done. The advent of usable computers in the early 1950s turned the learned but armchair speculation concerning these mental faculties into a real experimental and theoretical discipline.

Many felt that the new idea ``Electronic Super-Brains’’ had unlimited potential for intelligence. ``Faster Than Einstein’’ was a typical headline. But as well as providing a vehicle for creating artificially intelligent entities, the computer provides a tool for testing theories of intelligence, and many theories failed to withstand the test—a case of ``out of the armchair, into the fire.’’

AI has turned out to be more difficult than many at first imagined, and modern ideas are much richer, more subtle, and more interesting as a result.

Glass Roof, Seattle

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IN TRODU CTION

AI currently encompasses a huge variety of subfields, from general-purpose areas such as perception and logical reasoning, to specific tasks such as playing chess, proving mathematical theorems, writing poetry, and diagnosing diseases. Often, scientists in other fields move gradually into artificial intelligence, where they find the tools and vocabulary to systematize and automate the intellectual tasks on which they have been working all their lives. Similarly, workers in AI can choose to apply their methods to any area of human intellectual endeavor. In this sense, it is truly a universal field.

Computer Recognizes Shapes

“ THE ART OF CREATING MACHINES THAT PERFORM FUNCTIONS THAT REQUIRE INTELLIGENCE WHEN PERFORMED BY PEOPLE.�

Kur-zweil, 1990

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This gives us four possible goals to pursue in artificial intelligence: Systems that think like humans.

Systems that think rationally. Systems that act like humans. Systems that act rationally.

Historically, all four approaches have been followed.

As one might expect, a tension exists between the approaches centered around humans and approaches

WHAT IS AI?

centered around rationality. (We should point out that by distinguishing between human and rational behavior, we are not suggesting that humans are necessarily ``irrational’’ in the sense of ``emotionally unstable’’ or ``insane.’’ One merely need note

We have now explained why AI is exciting, but we

Definitions of artificial intelligence according to eight

that we often make mistakes; we are not all chess

have not said what it is. We could just say, ``Well, it

recent textbooks are shown in the table below. These

grandmasters even though we may know all the rules

has to do with smart programs, so let’s get on and

definitions vary along two main dimensions. The ones

of chess; and unfortunately, not everyone gets an

write some.’’ But the history of science shows that it

on top are concerned with thought processes and

A on the exam. Some systematic errors in human

is helpful to aim at the right goals. Early alchemists,

reasoning, whereas the ones on the bottom address

reasoning are cataloged by Kahneman et al..)

looking for a potion for eternal life and a method

behavior. Also, the definitions on the left measure

to turn lead into gold, were probably off on the

success in terms of human performance, whereas the

science, involving hypothesis and experimental

wrong foot. Only when the aim changed, to that of

ones on the right measure against an ideal concept of

confirmation. A rationalist approach involves a com-

finding explicit theories that gave accurate predic-

intelligence, which we will call rationality. A system is

bination of mathematics and engineering people

tions of the terrestrial world, in the same way that

rational if it does the right thing.

in each group sometimes cast aspersions on work

A human-centered approach must be an empirical

early astronomy predicted the apparent motions of

done in the other groups, but the truth is that each

the stars and planets, could the scientific method

direction has yielded valuable insights.

emerge and productive science take place.

Floor lightings, Seattle

Yayoi Kusama Infinity Mirrors Seattle Art Museum

2 HISTORY

T

he intellectual roots of AI, and the concept of

The modern history of AI begins with the develop-

intelligent machines, may be found in Greek

ment of stored-program electronic computers. After

mythology. Intelligent artifacts appear in literature

modern computers became available, following

since then, with real (and fraudulent) mechanical

World War II, it has become possible to create programs

devices actually demonstrated to behave with some

that perform difficult intellectual tasks. From these

degree of intelligence. Greek myths of Hephaestus,

programs, general tools are constructed which have

the blacksmith who manufactured mechanical servants,

applications in a wide variety of everday problems.

and the bronze man Talos incorporate the idea of

Some of these computational milestones are listed

intelligent robots. Many other myths in antiquity

below under “Modern History.”

involve human-like artifacts. Some of these conceptual achievements are listed below under “Early History.”

Garage, Seattle

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HISTORY

E ARL Y HISTORY

Al-Jazari, designed the first PROGRAMMABLE HUMANOID ROBOT, a boat carrying four mechanical musicians powered by water flow.

1206

Joseph-Marie Jacquard invented the Jacquard loom, the first PROGRAMMABLE MACHINE.

Pascal created the first MECHANICAL DIGITAL CALCULATING MACHINE.

1456

Invention of printing using MOVEABLE TYPE. Gutenberg Bible printed

1642

1769

Kempelen’s phony mechanical chess player, THE TURK

1801

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HISTORY

M O D ERN HISTORY

There is still much to be learned. Knowledge representation and inference remain the two major

W

categories of issues that need to be addressed, as ith early twentieth century inventions in

they were in the early demonstrations. Ongoing

electronics and the post–World War II rise

research on learning, reasoning with diagrams, and

of modern computers in Alan Turing’s laboratory in

integration of diverse methods and systems will

Manchester, the Moore School at Penn, Howard

likely drive the next generation of demonstrations.

Aiken’s laboratory, the IBM and Bell Laboratories,

With our successes in AI, however, come increased

and others, possibilities have given over to demon-

responsibility to consider the societal implications of

strations. As a result of their awesome calculating

technological success and educate decision makers

power, computers in the 1940s were frequently

and the general public so they can plan for them. The

referred to as “giant brains.” Although robots have

issues our critics raise must be taken seriously. These

always been part of the public’s perception of the

include job displacement, failures of autonomous

intelligent computers, early robotics efforts had

machines, loss of privacy, and the issue we started

more to do with mechanical engineering than with

with: the place of humans in the universe. On the

intelligent control. Recently, robots have become

other hand we do not want to give up the benefits

very powerful vehicles for testing our ideas about

that AI can bring, including less drudgery in workplace,

intelligent behavior.

safer manufacturing and travel, increased security, and

But AI is not just about robots. It is also about understanding the nature of intelligent thought and

smarter decisions to preserve a habitable planet.

The fantasy of intelligent machines still lives even

action using computers as experimental devices. In

as we accumulate evidence of the complexity of

the decades after the 1960s the demonstrations have

intelligence. It lives in part because we are dreamers.

become more impressive. and our ability to under-

The evidence from working programs and limited

stand their mechanisms has grown. Considerable

successes points not only to what we don’t know

progress has been achieved in understanding

but also to some of the methods and mechanisms

common modes of reasoning that are not strictly

we can use to create artificial intelligence for real.

deductive, such as case-based reasoning, analogy, induction, reasoning under uncertainty, and default reasoning. Contemporary research on intelligent agents and autonomous vehicles, among others, shows that many methods need to be integrated in successful systems.

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“ WITH OUR SUCCESSES IN AI, HOWEVER, COME INCREASED RESPONSIBILITY TO CONSIDER THE SOCIETAL IMPLICATIONS OF TECHNOLOGICAL SUCCESS AND EDUCATE DECISION MAKERS AND THE GENERAL PUBLIC SO THEY CAN PLAN FOR THEM.” Bruce G. Buchanan

Highway Tunnel, Seattle

3 NOW

T

he market for AI technologies is flourishing.

Coined in 1955 to describe a new computer science

Beyond the hype and the heightened media

sub-discipline, “Artificial Intelligence” today includes

attention, the numerous startups and the internet

a variety of technologies and tools, some are time-

giants racing to acquire them, there is a significant

tested, others relatively new. To help make sense

increase in investment and adoption by enterprises.

of what’s hot and what’s not, Forrester published a

A Narrative Science survey found last year that 38%

TechRadar report on Artificial Intelligence recently

of enterprises are already using AI, growing to 62%

for application development professionals, a detailed

by 2018. Forrester Research predicted a greater than

analysis of 13 technologies enterprises should consider

300% increase in investment in artificial intelligence

adopting to support human decision-making.

in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

Window Screen Shadow

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The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, numerous startups and internet

N OW

1 0 HOTTEST AI TECHNOLOGIES

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Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentiment, sentence structure

giants racing to acquire them.

and meaning, and intent through statistical and

Natural Language Generation: Producing text from

machine learning methods. Currently used in fraud

computer data. Currently used in customer service,

detection and security, a wide range of automated

report generation, and summarizing business intel-

assistants, and applications for mining unstructured

ligence insights. Sample vendors: Attivio, Automated

data. Sample vendors: Basis Technology, Coveo,

Insights, Cambridge Semantics, Digital Reasoning,

Expert System, Indico, Knime, Lexalytics, Sinequa,

Lucidworks, Narrative Science, SAS, Yseop.

Linguamatics, Mindbreeze,

Machine Learning Platforms: Providing algorithms,

Decision Management: Engines that insert rules and

Speech Recognition: Transcribe and transform human

APIs, development and training toolkits, data, as well

logic into AI systems and used for initial setup/training

Robotic Process Automation: Using scripts and

speech into format useful for computer applications.

as computing power to design, train, and deploy

and ongoing maintenance and tuning. A mature

other methods to automate human action to support

Currently used in interactive voice response systems

models into applications, processors, and other

technology, it is used in a wide variety of enterprise

efficient business processes. Currently used where it’s

and mobile applications. Sample vendors: NICE,

machines. It is currently used in a wide range of

applications, assisting in or performing automated

too expensive or inefficient for humans to execute a

Nuance Communications, OpenText, Verint Systems.

enterprise applications, mostly `involving prediction

decision-making. Sample vendors: Advanced Systems

task or a process. Sample vendors: Advanced Systems

or classification. Sample vendors: Amazon, Fractal

Concepts, Informatica, Maana, Pegasystems, UiPath.

Concepts, Automation Anywhere, Blue Prism, UiPath,

Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving

Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.

Deep Learning Platforms: A special type of machine

MathWorks, Peltarion, WorkFusion.

relationships with Alexa), from simple chatbots to

AI-optimized Hardware: Graphics processing units

learning consisting of artificial neural networks with

Biometrics: Enable more natural interactions between

advanced systems that can network with humans.

(GPU) and appliances specifically designed and

multiple abstraction layers. Currently primarily used

humans and machines, including but not limited to

Currently used in customer service and support and

architected to efficiently run AI-oriented computa-

in pattern recognition and classification applications

image and touch recognition, body language, and

as a smart home manager. Sample vendors: Amazon,

tional jobs. Currently primarily making a difference

supported by very large data sets. Sample vendors:

speech. Currently used primarily in market research.

Apple, Artificial Solutions, Assist AI, Creative Virtual,

in deep learning applications. Sample vendors:

Deep Instinct, Ersatz Labs, Fluid AI, MathWorks,

Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst,

Google, IBM, IPsoft, Microsoft, Satisfi.

Alluviate, Cray, Google, IBM, Intel, Nvidia.

Peltarion. Saffron Technology, Sentient Technologies.

Sensory, Synqera, Tahzoo.

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N OW

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TechRadar™: Artificial Intelligence Technologies Development Q1 ’17 Time to reach next phase 5-10 years to next phase 3-5 years to next phase 1-3 years to next phase

Decision Management AI-optimized Hardware Machine Learning Platforms Virtual Agents Text Analytics and NLP Robotic Process Automation Speech Recognition Image and Video Analysis Biometrics Semantic Technology Natural Language Generation

Add Value

Deep Learning Platforms

Business Value

Swarm Intelligence

Subtract Value

Creation Ecosystem Phase

Survival

Growth

Equilibrium

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OBSTACLES

There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles

42%

39%

No Defined Business Case

Not Clear What AI’s Usage

to AI adoption as expressed by companies with no plans of investing in AI.

42%

39%

No Defined Business Case

Not Clear What AI’s Usage

33%

29%

33% Don’t Have The Required Skills

29% Need To Modernize Data Management Platform First

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Car Headlight

4 VISUAL COGNITION

T

he field of Computer Vision was riddled with

In the early days, Computer Vision was achieved

hopes and disappointments, dreams and dis-

through techniques grafted from the machine learn-

illusionment. However, in only the past few years

ing community. Support Vector Machines (SVMs)

we’ve regained hope, with the resurgence of neural

would help us classify images, and algorithms like

networks and the remarkable improvements in

SURF and SIFT would help us search visually and

image recognition accuracy they have introduced.

recognize features in images. For some time, we’ve

There is a great bounty on this technology, as it

been able to use computers to, at least, augment

is considered the final missing piece in obtaining

humans in labeling, classifying, and searching for

true Artificial Intelligence. That being said, image

images. This wasn’t without some disappointment.

recognition has a long road ahead of itself before

The accuracy of these techniques was clearly lacking,

it can achieve human parity.

and it was obvious to any user of the technology that it was unmistakably artificial.

Busy Street, New York

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Take the following example of a ceramic coffee mug. Two-dimensional, planar image recognition in the form of SURF/SIFT would identify this image above as coffee mug. But, of course, this isn’t a mug. The comptuer accurately recognizes the mug, but we’re nowhere closer to understanding the image than we were before.

On the other hand, classification techniques, like SVM, can tell us this is a cup, or perhaps a coffee mug, but what have we really gained? There are a variety Coffee Mugs

of early applications for this type of technology, but the accuracy and reliability are so low, that at best, they provide a novelty. Newer deep learning techniques can identify this as a coffee mug, but then again, any 3-year old could probably tell us the same.

VISU AL COGN ITION

What happens when we break the mug?

Perhaps you’ve never seen a broken ceramic coffee mug, though I’m sure you’ve seen broken things, perhaps even broken cups. With understanding, you’ve assembled the concept of broken with what you’ve identified the object to be, knowing that it is now a broken ceramic coffee mug. We do this task effortlessly, assuming its existence in our cognitive processes, yet it highlights a tremendous difference between the current computer vision and true Visual Cognition.

Dropping a mug

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Broken Mug Pieces

The Missing Piece If we’re going to create true Artificial Intelligence in our lifetimes, we ought to start with Visual Cognition. Believe it or not, 50% of the neural tissue in the human brain is directly or indirectly related to vision and over 66% of our neural activity is recruited in visual processing, indicating that one of the most critical pieces to our understanding of the world is visual. If this is indeed so, then what happens in blind patients? That could, perhaps, be the most illuminating fact about our human visual understanding, in that patients who are completely blind build up similar cognitive processing ability despite the lack of visual input. In other studies, blind people who learned to read through Braille showed similar brain activity as those reading words from a page through their eyes, indicating a higher-level visual-like processing at the core of the cognition.

Regardless of the input mode, a significant portion of the Artificial/Human Intelligence gap lies with Visual Cognition and Understanding. The closer we get to parity on artificial visual processing and higher-level thought abstractions, the closer we will be to finally solving that missing piece of the AI puzzle.

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4.1 NEURAL NETWORK

Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected.

Most of today’s neural nets are organized into layers of nodes, and they’re “feed forward,” meaning that data moves through them in only one direction.

An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.

Neural Network Illustration

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ARTIF IC I A L I N T E L L I G E N T C E Bellevue Library, Bellevue

To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.

When a neural net is being trained, all of its weights and thresholds are initially set to random values.

Training data is fed to the bottom layer — the input

“ ONE REASON I’M SO OPTIMISTIC ABOUT AI IS THAT IMPROVEMENTS IN BASIC RESEARCH TO IMPROVE SYSTEMS ACROSS SO MANY

layer — and it passes through the succeeding layers,

DIFFERENT FIELDS, FROM

getting multiplied and added together in complex

DIAGNOSING DISEASES TO KEEP

ways, until it finally arrives, radically transformed, at the output layer. During training, the weights

US HEALTHY, TO IMPROVING SELF-

and thresholds are continually adjusted until the

DRIVING CARS TO KEEP US SAFE,

training data with the same labels consistently

AND FROM SHOWING YOU BETTER

yield similar outputs.

CONTENT IN NEWS FEED TO DELIVERING YOU MORE RELEVANT SEARCH RESULTS.” Mark Zuckerberg, 2017

VISU AL COGN ITION

4.2 GENERATIVE ADVERSARIAL NETWORKS

GANs are an interesting idea that were first introduced in 2014 by a group of researchers at the University of Montreal lead by Ian Goodfellow (now at OpenAI).

The main idea behind a GAN is to have two competing neural network models.

One takes noise as input and generates samples (and so is called the generator).

The other model, the discriminator, receives samples from both the generator and the training data, and has to be able to distinguish between the two sources.

These two networks play a continuous game, where the generator is learning to produce more and more realistic samples, and the discriminator is learning to get better and better at distinguishing generated data from real data. These two networks are trained simultaneously, and the hope is that the competition will drive the generated samples to be indistinguishable from real data.

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Random Noise Random Noise

Generator Generator

Real

Real

Fake

Fake

Discriminator Discriminator

How does it work?

Real Data Real Data

The structure of GANs are setup so that the discriminator is given data from both the generator and the true data source. The discriminator is given training samples from both real and generated data sources, and then the generator is trained on the discriminator’s response.

VISU AL COGN ITION

Generative Adversarial Networks are an interesting development, giving us a new way to do unsupervised learning. Most of the successful applications of GANs have been in the domain of computer vision, but here at Aylien we are researching ways to apply these techniques to natural language processing. If you’re working on the same idea and would like to compare notes then please get in touch.

One big open problem in this area is how best to evaluate these sorts of models. In the image domain it is quite easy to at least look at the generated samples, although this is obviously not a satisfying solution. In the text domain this is even less useful (unless perhaps your goal is to generate prose). With generative models that are based on maximum likelihood training, we can usually produce some metric based on likelihood (or some lower bound to the likelihood) of unseen test data, but that is not applicable here. Some GAN papers have produced likelihood estimates based on kernel density estimates from generated samples, but this technique seems to break down in higher dimensional spaces. Another solution is to only evaluate on some downstream task (such as classification). If you have any other suggestions then we would love to hear from you.

Dancing Game Floor, Cancun, Mexico

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Office Building Reflection, Bellevue

4.3 STYLE TRANSFER

Style transfer is the technique of recomposing images in the style of other images.

These were mostly created using Justin Johnson’s code based on the paper by Gatys, Ecker, and Bethge demonstrating a method for restyling images using convolutional neural networks. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.

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Plants through Glass Fountain

Plants

THE STYLE here basically means, the patterns, the brushstrokes, etc. It is much more elaborate than transforming color in color space and the results are even more interesting.

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4.4 DEEPDREAM

Google recently introduced a trippy program called DeepDream, which early adopters have used to generate psychedelic variations of everything. By taking these objects and subjecting them to analysis using a “neural net”— or what’s known as “deep learning”— DeepDream produces images bubbling with eyes, birds, blinding contours, and other things that look like they belong on the cover of a King Crimson album. It’s dazzling and surreal—further evidence of Salvador Dalí’s discovery that eyeballs can only improve art—but also creepy. It suggests some sort of creativity in the mind of the machine doing the processing, and some sort of dreamer at the heart of the dream.

Light Reflection on building

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“ DEEPDREAM’S SORT OF ANALOGICAL PROCESSING WILL LEAD TO FAR MORE IMPRESSIVE ACCOMPLISHMENTS IN ARTIFICIAL INTELLIGENCE.” David Auerbach

Yayoi Kusama Infinity Mirrors Seattle Art Museum

VISU AL COGN ITION

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Art Wall at Old Town Temecula Community Theater

The importance of DeepDream does not lie in the resultant images, but in the

But “vaguely resemble” is significant. It is this sort of analogical processing that

computer’s grasp of similarity, the analogical process that lies at the heart of

will lead to far more impressive accomplishments in artificial intelligence: robots

creativity, as well as its trained and stochastic nature, a far cry from the deterministic

with agency, story writing, natural language conversation, and creative problem

algorithms that form the fundament of computer science. Art historian Barbara

solving. There is no mythical secret sauce required to generate “human” creativity,

Maria Stafford calls analogy “a metamorphic and metaphoric practice for weaving

just ever-increasing layers of complexity and emergent behavior. We are still a

discordant particulars into a partial concordance,” the ability to understand that

long way from that point, but DeepDream shows how far we’ve come.

something can be like another thing without being identical to it. What DeepDream does is computationally intensive yet incredibly primitive next to the feats of the brain—in essence, it’s finding bits of a picture that vaguely resemble its archetypes.

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4.5 FACE ID

Apple uses a combination of infrared emitter and sensor (which it calls TrueDepth) to paint 30,000 points of infrared light on and around your face. The reflection is measured, which allows it to calculate depth and angle from the camera for each dot. That lets it both create a kind of 3D fingerprint of your face that can used to compare against later, and use the same system for live tracking your facial expressions and lip movement, and other selfie special effects.

Picture of a Girl

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VISU AL COGN ITION

Security is important to all of us to protect information on our devices. Apple has done some important things to safeguard the information, the same way they did with Touch ID. Face ID uses the TrueDepth camera and machine learning for a secure authentication solution.

Face ID data - including mathematical representations of your face - is encrypted and protected with a key available only to the Secure Enclave.

The probability a random person in the population could look at your iPhone X and unlock it using Face ID is approximately 1 in 1,000,000 (versus 1 in 50,000 for Touch ID). As an additional protection, Face ID allows only five unsuccessful match attempts before a passcode is required. The statistical probability is different for twins and siblings that look like you and among children under the age of 13, because their distinct facial features may not have fully developed.

Face ID matches against depth information, which isn’t found in print or 2D digital photographs. It’s designed to protect against spoofing by masks or other techniques through the use of sophisticated anti-spoofing neural networks. Face ID is even attention-aware. It recognizes if your eyes are open and looking towards the device. This makes it difficult for someone to unlock your iPhone without your knowledge (such as when you are sleeping).

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his is clearly threatening news for loan officers.

The AI revolution is on the scale of the Industrial

The core functions of other jobs—such as

Revolution—probably larger and definitely faster.

tellers, tele-salespeople, paralegals, reporters,

But while robots may take over jobs, believe me

stock traders, research analysts, and radiologists

when I tell you there is no danger that they will take

will gradually be replaced by AI software. And as

over. These AIs run “narrow” applications that master

robotics evolve, including semi-autonomous and

a single domain each time, but remain strictly under

autonomous hardware, AI will perform the labor

human control. The necessary ingredient of dystopia

of factory workers, construction workers, drivers,

is “General AI”—AI that by itself learns common

delivery people, and many others.

sense reasoning, creativity, and planning, and that has self-awareness, feelings, and desires. This is the stuff of the singularity that the Cassandras predict.

But General AI isn’t here

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There are simply no known engineering algorithms for it. And I don’t expect to see them any time soon.

The “singularity” hypothesis extrapolates exponential

ARE THEY TAKING OUR JOBS AWAY?

growth from the recent boom, but ignores the fact that continued exponential growth requires scientific breakthroughs that are unlikely to be solved for a hundred years, if ever.

So based on these engineering realities, instead of discussing this fictional super-intelligence, we should focus on the very real “narrow” AI applications and extensions. These will proliferate quickly, leading to massive value creation and an Age of Plenty, because AI will produce fortunes, make strides to eradicate poverty and hunger, and give all of us more spare time and freedom to do what we love. But it will also usher in an Age of Confusion.

As an Oxford study postulates, AI will replace half of human jobs, and many people will become depressed as they lose their jobs and the purpose that comes with gainful employment.

It is imperative that we focus on the certainty of these serious issues, rather than talking about dystopia, singularity, or super-intelligence. Perhaps the most vexing question is: How do we create enough jobs to place these displaced workers? The answer to this question will determine whether the alternate ending to the AI story will be happy or tragic.

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Love is what will always differentiate us from AI. Narrow AI has no self awareness, emotions, or a “heart.” Narrow AI has no sense of beauty, fun, or humor. It doesn’t even have self-consciousness or feelingz . Can you imagine the ecstasy that comes from beating a world champion? AlphaGo bested the globe’s best player, but took no pleasure in the game, felt no happiness from winning, and had no desire to hug a loved one after its victory.

Despite what science fiction movies may portray, I can tell you responsibly that AI programs cannot love. Scarlett Johansson may have been able to

Imagine a situation in which you informed a smart

convince you otherwise—because she is an actress

machine that you were going to pull its plug, and

who drew on her knowledge of love.

then changed your mind and gave it a reprieve. The machine would not change its outlook on life or vow to spend more time with its fellow machines. It would not grow, as I did, or serve others more generously.

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So, this is the alternate ending to the narrative of AI dystopia. An ending in which AI performs the bulk of repetitive jobs, but the gap is filled by opportunities that require our humanity.

Can I guarantee that scientists in the future will never make the breakthroughs that will lead to the kind of general-intelligence computer capabilities that might truly threaten us? Not absolutely.

But I think that the real danger is not that such a scenario will happen, but that we won’t embrace the option to double down on humanity while also using AI to improve our lives. This decision is ultimately up to us: Whatever we choose may become a self-fulfilling prophecy. If we choose a world in which we are fully replaceable by machines, whether it happens or not, we are surrendering our humanity and our pursuit for meaning.

If everyone capitulates, our humanity will come to an end.

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Such a capitulation is not only premature, but also irresponsible to our legacy, our ancestors, and our maker. On the other hand, if we choose to pursue our humanity, and even if the improbable happen and machines truly replace us, we can then capitulate knowing that we did the responsible thing, and that we had fun doing it. We will have no regrets over how we lived.

I do not think the day will ever come, unless we foolishly make it happen ourselves. Let us choose to let machines be machines, and let humans be humans. Let us choose to use our machines, and love one another.

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INDEX

Affectiva 33

Deep learning 60, 64

Intelligent machines 23

Speech Recognition 32

Agnitio 33

Deep Learning Platform 33, 34

King Crimson album 60

Style Transfer 56

Aiken’s Laboratory 26

Discriminator 51

Machine Learning Platforms 32, 34

Support Vector Machines 39

AI-optimized Hardware 32, 34

Ecker 56

Mathworks 33

SURF 39

Alan Turing 26

Face ID 66

Natural Language Generation 32, 34

SVM 39

Algorithms 39

FaceFirst 33

Neural Activity 44

Swarm Intelligence 41

AlphaGo 74

Forrester Research 31, 36

Neural Algorithm 56

Synqera 33

Artificial intelligence 18

Gatys 56

Neural Network 46, 60

Tahzoo 33

Autonomous machines 27

Generative Adversarial Networks 51

Neural Representations 56

Talos 23

Aylien 55

General AI 71

Neural Tissue 44

TechRadar 31, 34

Barbara Maria Stafford 64

Generator 51

NLP 33

Text Analytics 33, 34

Basis Technology 33

Greek Mythology 23

Node 46, 48

The Moore School 26

Bell Laboratories 26

Hephaestus 23

OpenAI 51

Touch ID 69

Biometrics 33, 34

Human-centered approach 19

Peltarion 33

Training Data 51

Blue Prism 33

Ian Goodfellow 51

Rationality 18, 19

TrueDepth 66, 69

Computer Vision 39

IDC 31

Robotic Process Automation 33

UiPath 22

David Auerbach 63

Image and Video Analysis 34

Semantic Technology 34

Virtual Agents 32, 34

Decision Management 33, 34

Image cognition 39

Sensory 33

Visual Cognition 40, 43

DeepDream 60

Infrared Emitter 66

SIFT 39

Visual processing 44

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