The document discusses artificial intelligence, machine learning, and deep learning. It provides definitions and brief histories of AI, machine learning, and deep learning. It also discusses key technologies like Amazon Rekognition, Amazon SageMaker, GPU instances on AWS, and the AWS Academy program for education. Trends in AI like computer vision, natural language processing, robotics, and personalized recommendations are presented through examples. The document emphasizes that AWS provides a broad set of AI and machine learning services and capabilities.

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Visão Geral de Inteligência Artificial
Samir Araújo
AI Solutions Architect @ AWS

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Tendências

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• Fundação de IA
• Apps Inteligentes & Analytics
• IoT Inteligente
• Cloud na borda
• Gêmeos Digitais
• Experiencias Imersivas
• Plataforma coversacionais
• Blockchain
• Eventos
• Confiança contínua de risco adaptável
10 Tendências estratégicas para 2018 — Gartner

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• Fundação de IA
• Apps Inteligentes & Analytics
• IoT Inteligente
• Cloud na borda
• Gêmeos Digitais
• Experiencias Imersivas
• Plataforma coversacionais
• Blockchain
• Eventos
• Confiança contínua de risco adaptável
10 Tendências estratégicas para 2018 — Gartner
Envolve IA

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Artificial Intelligence, Machine
Learning, Deep Learning, MLOps
#buzZZzzzWords…
?!

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Artificial Intelligence
“the science and engineering of making intelligent machines”
(John McCarthy - ~1950s)

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Artificial Intelligence
Weak AI Strong AI (AGI)
I like artificial intelligence
Lubię sztuczną inteligencję
Imgs. Copyrighted by: TriStar pictures; Warner Bros; 20th Cetury Fox; NBC Universal; Metro-Goldwin-Mayer

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Machine Learning
“is a field of computer science that gives computers the ability to
learn without being explicitly programmed”
(Arthur Samuel - 1959)

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ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
DEEP
LEARNING

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Deep Learning
“is a subarea of machine learning that uses deep neural
networks (multilayer perceptron) to model complex
problems”

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Deep Learning
Sentiment Analysis
Object detection Autonomous Cars
Facial Recognition
Forecasting
Speech to Text/Text to Speech
Translation
Playing Video Games
Text classification
Image Segmentation
Classification
Fraud detection
Recommendation systems

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Image Segmentation
Source: https://github.com/msracver/FCIS

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Artificial Intelligence - Summary
Source: http://www.legalexecutiveinstitute.com/artificial-intelligence-in-law-the-state-of-play-2016-part-1/

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Deep Learning
A few steps back in time
UniversalPictures.com

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The first (binary) perceptron was
born in ~1940
Warren McCulloch and Walter Pitts (neuroscientists)
"A Logical Calculus of Ideas Immanent in Nervous Activity” – 1943
 It couldn't solve neither XOR nor NXOR
Source: http://wwwold.ece.utep.edu/research/webfuzzy/docs/kk-thesis/kk-thesis-html/node12.html
Linear Threshold Function

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First Neural Network Machine –
Minsky 1951
“A randomly connected network of approximately 40 Hebb
synapses”
(Minsky & Edmonds 1951)
”Neurons wire together if they fire together"
(Löwel, S. and Singer, W)
Linear Neuron

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Neurobiology – Frank Rosenblatt 1957
Source: http://cs231n.github.io/neural-networks-1/

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The dark age of Neural Networks begins…
”Perceptrons? ..this approach to AI was a dead end… ”
”…they could not, for instance, learn the simple boolean
function XOR because it is not linearly separable”
(Minsky 1969)
 Society of Mind (frames theory, not a connectionist view)
 Neural Network dark age or AI Winter started after that,
resulting in freeze to funding and publications

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Canadian ’Mafia’ fought the dark age of NN 1969-1986
Geoffrey Hinton
’Godfather’ of
DeepLearning
Yoshua Bengio
Yan LeCun

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Cases

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Autonomous cars
Object detection

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Autonomous cars
Centimeter-accurate
positioning
Real time per pixel
image segmentation

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Person Redaction
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Person Redaction

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Text to speech

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Sport Analytics

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Robot Lab - Dev center - Controller
NAO ROMEOPEPPER

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SLAM – Robot IoT
Source: https://www.youtube.com/watch?v=ynAjzHpw1XU

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Frictionless Commerce

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Personalized Recommendation/Ranking

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Medical Imaging

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How to achieve my AI goals?

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Isso é ML? Visions

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Technology Process
People
Data Scientists
Data Engineers
Software Engineers
Polly, Rekognition, etc
SageMaker
Automation
CRISP
MLOps + DevOps
Ecosystem
Business
Challenge

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CRISP-DM
• Cross Industry Standard Process for
Data Mining
• Current de facto process for doing
Data Science
• Highlights the cyclical and iterative
natures of Data Science
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

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Data Collection Data Cleansing Analytics Machine
Learning
What is ML/Ops?
MLOps AnalyticsOps
AIOps
DataOps
AnalyticsOps
MLOps

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AI at AWS
ML, Lex, Polly, Rekognition, Rekognition
Video, SageMaker, Comprehend, Transcribe,
Translate, DeepLens, MXNet, etc

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IA @AWS
Application
Services
Platform
Services
Frameworks
&
Infrastructure
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow
AWS Deep Learning AMI (Ubuntu & Amazon Linux – Cuda 8 & 9)
GPU
(P2 & P3)
MobileCPU
IoT
(Greengrass)
Amazon Machine
Learning
Mechanical TurkSpark & EMR
Vision:
Rekognition
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex
Translate
Comprehend
Gluon
SageMaker
DeepLens

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Amazon Rekognition

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Amazon SageMaker
Development pipeline

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GPU @AWS

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GPU?

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Instances P2 & P3
Name GPUs vCPUs RAM (GiB)
Network
Bandwidth
p2.xlarge 1 4 61 High
p2.8xlarge 8 32 488 10 Gbps
p2.16xlarge 16 64 732 25 Gbps
p3.2xlarge 1 8 61 10 Gbps
p3.8xlarge 4 32 244 10 Gbps
p3.16xlarge 8 64 488
25 Gbps
P2
Nvidia Tesla K80
Peering by GPUDirect
70 TeraFlops
P3
Nvidia Tesla V100
Peering by NVLink
1 PetaFlops
14x better than P2

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Amazon Deep Lens
4MP camera with MJPEG
H.264 encoding at 1080p resolution
CPU: Intel Atom® Processor
GPU: Gen9 graphics
106 GFLOPS performance
8GB RAM
16GB memory
Expandable storage via microSD card
Dual band Wi-Fi
2 USB ports
Micro HDMI
Audio out
Ubuntu OS 16.04 LTS

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Amazon S3 Amazon Glacier AWS Glue Data Lake
Amazon Redshift +
Spectrum
EMR AthenaKinesis Analytics
Platform
&
Frameworks
Services
Amazon
Rekognition
Lex
Polly Translate
Transcribe Comprehend
Snowball
AWS is the center of gravity for ML
AWS IoT
Snowmobile
DBS Migration
AWS Greengrass
ML
Edge
MTurk
Kinesis
Amazon SageMakerGPU

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AWS Academy
Ready to Teach
Institutions and educators
receive a complete curriculum,
designed to be easily integrated
into existing education
programs.
Maintained by AWS
Curriculum content is
created and updated by
AWS, saving educators
time so they can focus on
their students.
Instructor Accreditation
Member educators receive
complimentary AWS training
and discounts for AWS
Certification as part of
instructor accreditation.

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AWS Academy
https://aws.amazon.com/education/awsacademy/

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Obrigado.