版權說明:本文檔由用戶提供并上傳,收益歸屬內容提供方,若內容存在侵權,請進行舉報或認領
文檔簡介
1Artificial
Intelligence
and
DeepLearningSargur
N.
Sriharisrihari@TopicsArtificial
Intelligence
HistoryAI paradigm
shiftsDeep
LearningDisruption
caused
by
AISoftware
developmentSocietalIndia
and
AI2History
of
AI3Greek
MythologyPygmalion
of
CyprusSculpts
marble
Galatea
that
came
to
life
(falls in
love)GBS’
Pygmalion:
Higgins
teaches
Eliza
to
speak
Queen’s
EnglishEliza
is
first
AI
program(Weizenbaum,
MIT)Indian
MythologyGanesa
(Buddhipriya)Parvati
forms
sandalwood
Ganesa,
whose
head
istransplanted
from
an
elephantToday
AI
is
ubiquitousAutomate
routine
laborSearchUnderstand
speechSIRI,
AlexaAutonomous
Vehicles4Handwriting
as
“Fruit
Fly”
of
AINYU,
Toronto,
Montreal,
Baidu5Technology
Ages
(50-year
spans)1771
Industrial
RevolutionArkwright’s
mill
in
Cramford1826
Age
of
Steam
and
Railways“Rocket
steam”
engine
for
Manchester
railway1875
Age
of
Steel,
Electricity
&
Heavy
Eng.Carnegie
Bessemer
steel
plant
in
Pittsburgh1908
Age
of
Oil,
Automobile,
Mass
ProductionFirst
model
T
comes
out
in
Detroit1971
Age
of
Information
&
TelecommunicationsIntel
microprocessor
announced
in
Santa
Clara2017
Age
of
Artificial
IntelligenceMachines
and
people
connectedData
is
new
oil,goldDisruptions
due
to
AI742%
of
US
jobs
at
riskover
two
decades.AI
will
outperform
humans
in*:Translating
languages
2024Driving
a
truck
2027Working
in
retail
2031Working
as
a
surgeon
2053Outperform
humans
in
all:
50%
chance
in
45
yearsAutomating
all
human
jobs
120
years*NIPS/ICML
SurveyInternational
(Rich
and
mid-income):14%
of
jobs
in
32
countries
have
70%
riskFor
32%
of
jobs,
that
probability
is
60%Thus
210m
jobs
at
risk
in
those
countries
alone.Rich-countries
less
at
risk
than
mid-income
statesNational
AI
Initiatives8“Artificial
Intelligence
is
poised
to
disrupt
the
world”NITI
AayogAI
ParadoxHard
problems
for
people
are
easy
for
AIEasy
problems
are
hard
for
AI–
Narrow
IntelligenceGeneral
IntelligencePeople
easy
tasks:9What
tasks
require
intelligence?Reasoning–
Puzzles,
JudgmentsPlanningAction
sequencesLearningImprove
with
dataNatural
languageIntegrating
skillsAbilities
to
sense,
act10Everyday
life
needs
knowledgeKnowledge
is
intuitive
and
subjectiveKey
challenge
of
AI
is
how
to
get
this
informalknowledge
into
a
computerKnowledge-based
ApproachHard-code
knowledge
in
a
formal
languageComputer
can
reason
about
statements
in
theselanguages
using
inference
rules11Knowledge-Based
AIInputHand-designedprogramOutputRule-based
SystemDisadvantage:
Unwieldy
processTime
of
human
expertsPeople
struggle
to
formalize
rules
with
enough
complexity
to
describe
the
worldThe
Machine
LearningapproachDifficulties
of
hard-coded
approach
suggests:–
Allow
computers
to
learn
from
experienceDetermine
what feature
representations
to
useMap
the
features
to
outputsDecide
whether
is
spam13Two
paradigms
in
AIInputHand-designedprogramOutputInputHand-designedFeaturesMapping
fromfeaturesOutputRule-based
SystemClassic
MachinelearningShaded
boxes
indicate
components
that
can
learn
from
dataDesigning
right
set
of
featuresSimple
Machine
Learning
depends
heavily
onrepresentation
of
given
dataFor
detecting
a
car
in
photographs–
Tire
shape
difficult
in
terms
of
pixel
values–
Shadows,
glare,
occlusion15Representation
LearningSolution:
use
ML
to
not
only
learn
mapping
fromrepresentation
to
output
but
representation
itselfBetter
results
than
hand-coded
representationsAllows
AI
systems
to
rapidly
adapt
to
new
tasksDesigning
features
can
take
great
human
effortCan
take
decades
for
a
community
of
researchersDoes
not
need
programmer
to
have
deepknowledge
of
the
problem
domain16Deep
LearningUnderstand
the
world
as
hierarchy
of
conceptsHow
these
concepts
are
built
on
top
of
each
other
isdeep,
with
many
layersWeights
learnt
by
gradient
descentxtxt
1
xt
f
(xt
)UnsupervisedRepresentationLearningAutoencoderEncoder:Converts
input
into
arepresentationDecoder:Converts
representationback
to
input18New
designs
from
representationDeep
StyleLanguage
Processed
as
NumbersTraining
DataWord-to-vecOne-hot
vector
mappedto
vector
of
300Word
embeddingSimilar
words
are
closetogether19InputAdditional
layersof
more
abstractfeaturesMapping
fromfeaturesOutputSimple
featuresThree
Paradigms
of
AIInputHand-designedprogramOutputInputHand-designedFeaturesMapping
fromfeaturesOutputRule-based
SystemClassic
MachinelearningInputFeaturesMapping
fromfeaturesOutputRepresentation
LearningDeep
LearningShaded
boxes
indicate
components
that
can
learn
from
dataDisruption
in
Software
DevelopmentDeep
Learning
is
not
just
another
toolIt
is
a
fundamental
shift
in
how
software
is
writtenSoftware
1.0
(Classical
“stack”)It
is
code
we
writee.g.,
LAMP(Linux,
Apache,
MySQL,
Python/Perl)Software
2.0
(Code
written
by
Optimizer)It
is
in
a
user
unfriendly
languageThere
are
millions
of
weightsNo
human
involved
in
coding21Software
1.0
and
2.0:
FizzbuzzPrint
i=
1
to
100,
except:if
divisible
by
3
fizz,if
divisible
by
5
buzz,if
divisible
by
both
3
and
5
fizzbuzzTwo
approaches:– Software
1.0:
C++ Software
2.0:
Python/Tensorflow122def
model(X,
w_h,
w_o):h
=
tf.nn.relu(tf.matmul(X,
w_h))return
tf.matmul(h,
w_o)Program
Space:
Software
1.0
vs
2.0*A.
Karpathy,
Tesla23Software
1.0By
writing
each
line
of
code,
programmer
identifies
a
point
inprogram
space
with
some
desirable
behaviorSoftware
2.0Restrict
search
space
to
continuous
subset
of
program
spaceSearch
is
made
efficient
by
using
stochastic
gradient
descentBenefits
of
Software
2.0Computationally
homogeneousSandwich
of
two
operations:
matrix
multiply,
RELUSimple
to
bake
into
siliconSmall
instruction
setConstant
run
timeEvery
iteration
of
forward
pass
has
same
FLOPSConstant
memory
useHighly
portable:
sequence
of
matrix
multiplies
is
easierVery
agileC++
is
hard
to
speed-up,
instead
remove
half
of
channelsCan
meld
into
optimal
wholeSoftware
often
has
modules,
can
jointly
optimize–
It
is
better
than
you24Limitations
of
Software
2.0stackAfter
training
we
have
large
networks
that
workvery
well,
but
hard
to
tell
how90%
accurate
model
we
understand99%
accurate
model
we
don’tCan
fail
unintuitively
(Adversarial
examples)Correct
steering
With
darker
imagey
=“panda”
y
=“gibbon”with
58%
with
99%confidence
confidenceHow
Deep
Learning
disrupts
CSSoftware
DeveloperPresent:
Write
and
maintain
layers
of
tangled
codeFuture:
A
teacher–
curate
training
data
&
analyze
resultsMathematicsPresent: Logic,
Discrete
mathematicsFuture:
Probability
Theory,
Calculus,
Linear
AlgebraProgramming
EnvironmentsPresent:
C++,
JavaFuture:
Tensorflow/Pytorch/Gluon/…–
In
ten
years
most
software
jobs
won’t
involve
programmingHardwarePresent:
CPUsFuture:
GPUs26The
transitionConventional
software
can
be
replaced
with
adeep
learning
solution
with
improvement,
e.g.*,Upgrading
search
rankingData
center
energy
usageLanguage
translationSolving
G
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯系上傳者。文件的所有權益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網頁內容里面會有圖紙預覽,若沒有圖紙預覽就沒有圖紙。
- 4. 未經權益所有人同意不得將文件中的內容挪作商業(yè)或盈利用途。
- 5. 人人文庫網僅提供信息存儲空間,僅對用戶上傳內容的表現方式做保護處理,對用戶上傳分享的文檔內容本身不做任何修改或編輯,并不能對任何下載內容負責。
- 6. 下載文件中如有侵權或不適當內容,請與我們聯系,我們立即糾正。
- 7. 本站不保證下載資源的準確性、安全性和完整性, 同時也不承擔用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。
最新文檔
- 交通事故賠償金協議書七篇
- 鮑恩病病因介紹
- 勞務派遣書面協議書七篇
- 《數據資產入表合規(guī)規(guī)范指南》(征求意見稿)
- (參考)雕刻工藝品投資項目可行性研究報告
- 2023年天津市南開區(qū)高考語文二模試卷
- 《廉政公署專題》課件
- 電工培訓課件之跌落熔絲的操作
- 《廣告創(chuàng)意文案設計》課件
- 內蒙古呼倫貝爾市阿榮旗2023-2024學年七年級上學期期末考試數學試卷(含答案)
- 2024秋期國家開放大學《公共政策概論》一平臺在線形考(形考任務1至4)試題及答案
- 《2024版 CSCO非小細胞肺癌診療指南》解讀
- GB 44497-2024智能網聯汽車自動駕駛數據記錄系統(tǒng)
- 家具售后合同協議書
- 空氣動力學數值方法:有限體積法(FVM):離散化技術與數值通量
- 下肢靜脈曲張的靜脈內射頻消融術
- 北師大版七上冊數學期末沖刺復習
- 物流管理專業(yè)培養(yǎng)專題方案調研綜合報告樣本
- 小學語文整本書閱讀《夏洛的網》導讀課公開課一等獎創(chuàng)新教學設計
- 建筑鋼結構質量通病及防治措施
- 骨科中醫(yī)護理方案總結與優(yōu)化(2篇)
評論
0/150
提交評論