ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES
Ceiling Lights
ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES
ARTIFICIAL INTELLIGENCE WHAT IT SEES AND CREATES
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.
To Jerry, Nolan and Alicia for adding to the challenge. And Shiquan for the insightful AI information.
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 INTR O DU C TI ON
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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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
C H A P TE R NA ME
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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
I NTR O DUC TI O N
“ THE ART OF CREATING MACHINES THAT PERFORM FUNCTIONS THAT REQUIRE INTELLIGENCE WHEN PERFORMED BY PEOPLE.” Kur-zweil, 1990
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WHAT IS AI?
We have now explained why AI is exciting, but we
Definitions of artificial intelligence according to eight
have not said what it is. We could just say, ``Well, it
recent textbooks are shown in the table below. These
has to do with smart programs, so let’s get on and
definitions vary along two main dimensions. The ones
write some.’’ But the history of science shows that it
on top are concerned with thought processes and
is helpful to aim at the right goals. Early alchemists,
reasoning, whereas the ones on the bottom address
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
wrong foot. Only when the aim changed, to that of
ones on the right measure against an ideal concept of
finding explicit theories that gave accurate predic-
intelligence, which we will call rationality. A system is
tions of the terrestrial world, in the same way that
rational if it does the right thing.
early astronomy predicted the apparent motions of the stars and planets, could the scientific method emerge and productive science take place.
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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 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 that we often make mistakes; we are not all chess grandmasters even though we may know all the rules of chess; and unfortunately, not everyone gets an A on the exam. Some systematic errors in human reasoning are cataloged by Kahneman et al..) A human-centered approach must be an empirical science, involving hypothesis and experimental confirmation. A rationalist approach involves a combination of mathematics and engineering people in each group sometimes cast aspersions on work done in the other groups, but the truth is that each direction has yielded valuable insights.
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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E ARL Y HISTORY
Al-Jazari, designed the first PROGRAMMABLE HUMANOID ROBOT, a boat carrying four mechanical musicians powered by water flow.
1206
Pascal created the first MECHANICAL DIGITAL CALCULATING MACHINE.
1456
Invention of printing using MOVEABLE TYPE. Gutenberg Bible printed
1642
H I S TO R Y
Joseph-Marie Jacquard invented the Jacquard loom, the first PROGRAMMABLE MACHINE.
1769
Kempelen’s phony mechanical chess player, THE TURK
1801
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M O D ERN HISTORY
W
ith early twentieth century inventions in electronics and the post–World War II rise
of modern computers in Alan Turing’s laboratory in Manchester, the Moore School at Penn, Howard Aiken’s laboratory, the IBM and Bell Laboratories, and others, possibilities have given over to demonstrations. As a result of their awesome calculating power, computers in the 1940s were frequently referred to as “giant brains.” Although robots have always been part of the public’s perception of the intelligent computers, early robotics efforts had more to do with mechanical engineering than with intelligent control. Recently, robots have become very powerful vehicles for testing our ideas about intelligent behavior. But AI is not just about robots. It is also about understanding the nature of intelligent thought and action using computers as experimental devices. In the decades after the 1960s the demonstrations have become more impressive. and our ability to understand their mechanisms has grown. Considerable progress has been achieved in understanding common modes of reasoning that are not strictly 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.
H I S TO R Y
There is still much to be learned. Knowledge representation and inference remain the two major categories of issues that need to be addressed, as they were in the early demonstrations. Ongoing research on learning, reasoning with diagrams, and integration of diverse methods and systems will likely drive the next generation of demonstrations. 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. The issues our critics raise must be taken seriously. These include job displacement, failures of autonomous machines, loss of privacy, and the issue we started with: the place of humans in the universe. On the other hand we do not want to give up the benefits that AI can bring, including less drudgery in workplace, safer manufacturing and travel, increased security, and smarter decisions to preserve a habitable planet. The fantasy of intelligent machines still lives even as we accumulate evidence of the complexity of intelligence. It lives in part because we are dreamers. The evidence from working programs and limited successes points not only to what we don’t know but also to some of the methods and mechanisms we can use to create artificial intelligence for real.
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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
Window Screen Shadow
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.
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ARTIF IC I A L I N T E L L I G E N T C E
The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, numerous startups and internet
1 0 HOTTEST AI TECHNOLOGIES
giants racing to acquire them. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
Machine Learning Platforms: Providing algorithms,
Speech Recognition: Transcribe and transform human
APIs, development and training toolkits, data, as well
speech into format useful for computer applications.
as computing power to design, train, and deploy
Currently used in interactive voice response systems
models into applications, processors, and other
and mobile applications. Sample vendors: NICE,
machines. It is currently used in a wide range of
Nuance Communications, OpenText, Verint Systems.
enterprise applications, mostly `involving prediction
Virtual Agents: “The current darling of the media,� says Forrester (I believe they refer to my evolving
or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
relationships with Alexa), from simple chatbots to
AI-optimized Hardware: Graphics processing units
advanced systems that can network with humans.
(GPU) and appliances specifically designed and
Currently used in customer service and support and
architected to efficiently run AI-oriented computa-
as a smart home manager. Sample vendors: Amazon,
tional jobs. Currently primarily making a difference
Apple, Artificial Solutions, Assist AI, Creative Virtual,
in deep learning applications. Sample vendors:
Google, IBM, IPsoft, Microsoft, Satisfi.
Alluviate, Cray, Google, IBM, Intel, Nvidia.
NO W
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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 and meaning, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Sinequa, Decision Management: Engines that insert rules and
Linguamatics, Mindbreeze,
logic into AI systems and used for initial setup/training
Robotic Process Automation: Using scripts and
and ongoing maintenance and tuning. A mature
other methods to automate human action to support
technology, it is used in a wide variety of enterprise
efficient business processes. Currently used where it’s
applications, assisting in or performing automated
too expensive or inefficient for humans to execute a
decision-making. Sample vendors: Advanced Systems
task or a process. Sample vendors: Advanced Systems
Concepts, Informatica, Maana, Pegasystems, UiPath.
Concepts, Automation Anywhere, Blue Prism, UiPath,
Deep Learning Platforms: A special type of machine
MathWorks, Peltarion, WorkFusion.
learning consisting of artificial neural networks with
Biometrics: Enable more natural interactions between
multiple abstraction layers. Currently primarily used
humans and machines, including but not limited to
in pattern recognition and classification applications
image and touch recognition, body language, and
supported by very large data sets. Sample vendors:
speech. Currently used primarily in market research.
Deep Instinct, Ersatz Labs, Fluid AI, MathWorks,
Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst,
Peltarion. Saffron Technology, Sentient Technologies.
Sensory, Synqera, Tahzoo.
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ARTIF IC I A L I N T E L L I G E N T C E
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 Deep Learning Platforms Swarm Intelligence
Creation Ecosystem Phase
Survival
NO W
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Add Value
Business Value Subtract Value
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 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%
NO W
42%
39%
No Defined Business Case
Not Clear What AI’s Usage
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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Coffee Mugs
VI S UA L C O G NI TI O N
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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 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.
Dropping a mug
V I S UA L C O G NI TI O N
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.
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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.
V I S UA L C O G NI TI O N
Broken Mug Pieces
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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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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
Bellevue Library, Bellevue
VI S UA L C O G NI TI O N
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
Generator
Real Data
VI S UA L C O G NI TI O N
59
Real
Discriminator
Fake
How does it work? 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.
Dancing Game Floor, Cancun, Mexico
VI S UA L C O G NI TI O N
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.
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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.
VI S UA L C O G NI TI O N
Office Building Reflection, Bellevue
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Plants through Glass Fountain
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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.
VI S UA L C O G NI TI O N
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
Art Wall at Old Town Temecula Community Theater
The importance of DeepDream does not lie in the resultant images, but in the computer’s grasp of similarity, the analogical process that lies at the heart of creativity, as well as its trained and stochastic nature, a far cry from the deterministic algorithms that form the fundament of computer science. Art historian Barbara Maria Stafford calls analogy “a metamorphic and metaphoric practice for weaving discordant particulars into a partial concordance,” the ability to understand that 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.
VI S UA L C O G NI TI O N
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But “vaguely resemble” is significant. It is this sort of analogical processing that will lead to far more impressive accomplishments in artificial intelligence: robots with agency, story writing, natural language conversation, and creative problem solving. There is no mythical secret sauce required to generate “human” creativity, just ever-increasing layers of complexity and emergent behavior. We are still a long way from that point, but DeepDream shows how far we’ve come.
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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
ARTIF IC I A L I N T E L L I G E N T C E
V I S UA L C O G NI TI O N
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).
Crosswalk, Bellevue
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Taillight
5 FUTURE
T
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
Balcony, Seattle
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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 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.
Hands
ARE THEY TAKING OUR JOBS AWAY?
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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 convince you otherwise—because she is an actress who drew on her knowledge of love.
FUTUR E
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Imagine a situation in which you informed a smart machine that you were going to pull its plug, and 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.
Bellevue Connection, Bellevue
FUTUR E
85
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.
Elevator Door
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INDEX
Affectiva 33
Deep learning 60, 64
Agnitio 33
Deep Learning Platform 33, 34
Aiken’s Laboratory 26
Discriminator 51
AI-optimized Hardware 32, 34
Ecker 56
Alan Turing 26
Face ID 66
Algorithms 39
FaceFirst 33
AlphaGo 74
Forrester Research 31, 36
Artificial intelligence 18
Gatys 56
Autonomous machines 27
Generative Adversarial Networks 51
Aylien 55
General AI 71
Barbara Maria Stafford 64
Generator 51
Basis Technology 33
Greek Mythology 23
Bell Laboratories 26
Hephaestus 23
Biometrics 33, 34
Human-centered approach 19
Blue Prism 33
Ian Goodfellow 51
Computer Vision 39
IDC 31
David Auerbach 63
Image and Video Analysis 34
Decision Management 33, 34
Image cognition 39
DeepDream 60
Infrared Emitter 66
Intelligent machines 23
Speech Recognition 32
King Crimson album 60
Style Transfer 56
Machine Learning Platforms 32, 34
Support Vector Machines 39
Mathworks 33
SURF 39
Natural Language Generation 32, 34
SVM 39
Neural Activity 44
Swarm Intelligence 41
Neural Algorithm 56
Synqera 33
Neural Network 46, 60
Tahzoo 33
Neural Representations 56
Talos 23
Neural Tissue 44
TechRadar 31, 34
NLP 33
Text Analytics 33, 34
Node 46, 48
The Moore School 26
OpenAI 51
Touch ID 69
Peltarion 33
Training Data 51
Rationality 18, 19
TrueDepth 66, 69
Robotic Process Automation 33
UiPath 22
Semantic Technology 34
Virtual Agents 32, 34
Sensory 33
Visual Cognition 40, 43
SIFT 39
Visual processing 44
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