Artificial Intelligence for Developers - OOP Munich

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Artificial Intelligence for Developers
Boaz Ziniman, Technical Evangelist – Amazon Web Service
@ziniman
boaz.ziniman.aws
ziniman
OOP 2019

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon.com,1995

Our deep experience with AI/ML differentiates our approach
Product
recommendation
engine
Robot-enabled
fulfillment
centers
New
product
categories
Amazon has invested in AI/ML since our inception, and we
share our knowledge and capabilities with our customers
20181995
Natural language
processing-supported
contact centers
ML-driven supply
chain and
capacity planning
Checkout-free
shopping
using deep learning

Machine Learning On AWS Today

Our mission
Put Machine Learning in the hands of
every developer and data scientist

Application
Services
Platform
Services
Frameworks
&
Infrastructure
API-driven services: Vision & Language Services, Conversational Chatbots
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.
AWS ML Stack

Application Services

Amazon Rekognition
Deep Learning-based image analysis service

Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation
Text in image
Image analysis service

Object & Scene Detection

Facial Analysis

Smiling?
Facial Analysis
(Deep) Learning from a Masterpiece
http://bit.ly/MonaLisaAI

Crowd Detection – up to 100 faces

Facial Search

Explicit Nudity
Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive
Female Swimwear or Underwear
Male Swimwear or Underwear
Revealing Clothes
Image Moderation

Celebrity Recognition

Text in Image

DEMO
https://github.com/ziniman/aws-rekognition-demo

Rekognition API example
boaz: ~/ aws rekognition detect-labels
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}’
{
"Labels": [
{
"Confidence": 99.14048767089844,
"Name": "Human"
},
{
"Confidence": 99.1404800415039,
"Name": "People"
},
{
"Confidence": 99.14048767089844,
"Name": "Person"
}……

Rekognition API example
boaz: ~/ aws rekognition detect-faces
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}'
--attributes "ALL”
{
"FaceDetails": [
{
....
"Gender": {
"Confidence": 99.9211654663086,
"Value": "Male"
},
"AgeRange": {
"High": 52,
"Low": 35
},
....

Face search - media and entertainment
A u t o m a t i n g f o o t a g e
t a g g i n g w i t h A m a z o n
R e k o g n i t i o n
Indexed 99,000 people
Saves ~9,000 hours a year in labor

Marinus Analytics
Marinus Analytics provides law enforcement
with tools founded in artificial intelligence.
Traffic Jam, is a suite of tools for use by law
enforcement agencies on sex trafficking
investigations.
Before using Amazon Rekognition, their only
recourse was manual processing; this was time-
intensive or not possible.
Now, investigators are able to take effective
action by searching through millions of records
in seconds to find victims.
http://www.marinusanalytics.com/articles/2017/10
/17/amazon-rekognition-helps-marinus-analytics-
fight-human-trafficking

Amazon Rekognition Video
Deep Learning-based video analysis service

Video Analysis

Amazon Polly
Deep Learning-based text-to-speech service

“Today in Seattle, WA
it’s 11°F”
Amazon Polly: Text In, Life-like Speech Out
58 voices across 28 languages
“Today in Seattle Washington
it’s 11 degrees Fahrenheit”

Amazon Polly: Text In, Life-like Speech Out
“Today in Mumbai,
India it’s 32°C”
“Today in Mumbai, India it’s 32
degrees Celcius”
58 voices across 28 languages

A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
• “Today in Seattle, WA, it’s 11°F”

2. Intelligible and Easy to Understand

3. Add Semantic Meaning to Text

https://www.w3.org/TR/speech-synthesis/
<speak>
The spelling of my name is
<prosody rate='x-slow'>
<say-as interpret-as="characters">Boaz</say-as>
</prosody>
</speak>

3. Add Semantic Meaning to Text
• “Richard’s number is 2122341237“
<say-as interpret-as="telephone">

Voice Modification: Vocal Tract Length
<speak>
This is Brian without any voice modifications.
<amazon:effect vocal-tract-length="+15%"> Imagine now that I got bigger… </amazon:effect>
<amazon:effect vocal-tract-length="+25%"> Suppose that I got even bigger still… </amazon:effect>
Now let's go back and hear the effect when I go in the opposite direction.
<amazon:effect vocal-tract-length="-15%"> Can you tell that I'm getting smaller? </amazon:effect>
<amazon:effect vocal-tract-length="-25%"> Now I'm even smaller than before. </amazon:effect>
</speak>

Polly API example
aws polly synthesize-speech
--output-format mp3 --voice-id Matthew --text-type ssml
--text '<speak>
<amazon:auto-breaths>
<prosody rate="x-slow" pitch="low">Here is my little secret.</prosody>
<amazon:breath duration="long" volume="x-loud"/>
<amazon:effect name="whispered">
<prosody rate="x-slow">
<prosody pitch="x-low">I</prosody>
killed Mufasa!
</prosody>
</amazon:effect>
</amazon:auto-breaths>
</speak>'
mufasa.mp3

“This is a new technology that can give users
more choice and better accessibility to our
content, so we wanted to create an
experiment to dive deeper into the user
experience. After a month, we’ll take what
we’ve learned about how users engage with
this feature to develop our first iteration of a
product with Amazon Polly.“
Joseph Price, Product Manager
The Washington Post
https://www.washingtonpost.com/pr/wp/201
7/06/09/the-washington-post-to-start-
experimenting-with-audio-articles-using-
amazon-polly

Amazon Transcribe
Automatic speech recognition service

“Hello, this is Allan
speaking”

Support for
telephony
audio
Timestamp
generation
Intelligent punctuation
and formatting
Recognize multiple
speakers
Custom
vocabulary
Multiple
languages

ringDNA
RingDNA is an end-to-end communications
platform for sales teams.
Hundreds of enterprise organizations use
RingDNA to increase productivity, engage in
smarter sales conversations, gain predictive
sales insights and improve their win rate.
Speech to Text
"A critical component of RingDNA’s Conversation
AI requires best of breed speech-to-text to deliver
transcriptions of every phone call. RingDNA is
excited about Amazon Transcribe since it
provides high-quality speech recognition at scale,
helping us to better transcribe every call to text"
Howard Brown, CEO & Founder, RingDNA
https://www.youtube.com/watch?v=1ZJ_f1bDdog

Amazon Translate
Neural Machine Translation Service

“Hello, what’s up? Do
you want to go see a
movie tonight?”
Amazon Translate
Natural and fluent language translation
“Hallo, was gibt's?
Möchten Sie heute Abend
einen Film sehen?"

Automatic translation
Real-time
translation
Powered by Deep
Learning
21 Language pairs
(417 translation combinations)
Language detection

Translate API example
boazz: ~$ aws translate translate-text
--text "Hello, what’s up? Do you want to go see a movie tonight?"
--source-language-code auto --target-language-code de
{
"TargetLanguageCode": "de",
"TranslatedText": "Hallo, was gibt's? Möchten Sie heute Abend einen Film
sehen?",
"SourceLanguageCode": "en"
}

import boto3
translate = boto3.client("translate")
lang_flag_pairs = [("fr", "!"), ("de", """),
("es", "#"), ("pt", "$"),
("zh", "%"), ("ar", "&"),
("ja", "'"), ("ru", "("),
("it", ")"), ("zh-TW", "*"),
(”he", "+"), ("cs", ",")]
for lang, flag in lang_flag_pairs:
print(flag)
print(translate.translate_text(
Text="Hello, World",
SourceLanguageCode="en",
TargetLanguageCode=lang
)['TranslatedText'])

!
Bonjour, Monde
"
Hallo, Welt
#
Hola, Mundo
$
Olá, Mundo
%
&
‫ﻣ‬‫ﺮ‬‫ﺣ‬‫ﺒ‬‫ﺎ‬،‫ا‬‫ﻟ‬‫ﻌ‬‫ﺎ‬‫ﻟ‬‫ﻢ‬
'
(
Привет, Мир
)
Ciao, Mondo
*
,
+
‫ש‬‫ל‬‫ו‬‫ם‬‫ע‬‫ו‬‫ל‬‫ם‬
,
Ahoj, světe.
https://github.com/ziniman/aws-translate-demo

Demo – https://translate.boaz.cloud
https://github.com/ziniman/aws-translate-demo

Hotels.com
Machine Translation
At Hotels.com, we are committed to offering all of our customers the most
relevant and up to date information about their destination. To achieve that,
we operate 90 localized websites in 41 languages. We have more than 25M
customer reviews and more are coming in every day, making a great candidate
for machine translation. Having evaluated Amazon Translate and several other
solutions, we believe that Amazon Translate presents a quick, efficient and
most importantly, accurate solution.
Matt Fryer, VP and Chief Data Science Officer, Hotels.com

Polly
def speak(text_string, voice="Joanna"):
try:
# Request speech synthesis
response = polly.synthesize_speech(Text=text_string,
TextType="text", OutputFormat="pcm", VoiceId=voice)
except (BotoCoreError, ClientError) as error:
# The service returned an error, exit gracefully
print(error)
exit(-1)
# Access the audio stream from the response
if "AudioStream" in response:
stream = pya.open(format=pya.get_format_from_width(width=2), channels=1, rate=16000,
output=True)
stream.write(response['AudioStream'].read())
sleep(1)
stream.stop_stream()
stream.close()
else:
# The response didn't contain audio data, return False
print("Could not stream audio")
return(False)

Rekognition
def reko_detect_labels(image_bytes):
response = reko.detect_labels(
Image={
'Bytes': image_bytes
},
MaxLabels=8,
MinConfidence=60
)
return response
def reko_detect_faces(image_bytes):
response = reko.detect_faces(
Image={
'Bytes': image_bytes
},
Attributes=['ALL']
)
return response

Amazon Comprehend
Natural Language Processing

Fully managed natural language processing
Discover valuable insights from text
Entities
Key Phrases
Language
Sentiment

Topic modeling
STORM
WORLD SERIES
AUSTRALIASTOCK MARKET
WASHINGTON
HEALTH
CRISIS
MACHINE
LEARNING
LIBRARY OF
NEWS ARTICLES *

Run Amazon Comprehend on S3 Bucket
import boto3
import json
s3 = boto3.resource('s3’)
bucket_name = ‘my_bucket’
region_name = ‘us-east-1’
bucket = s3.Bucket(bucket_name)
comprehend = boto3.client(service_name='comprehend', region_name=region)
for obj in bucket.objects.all():
body = obj.get()['Body'].read()
text = body
sentiment_response = comprehend.detect_sentiment(Text=text, LanguageCode='en’)
print(json.dumps(sentiment_response, sort_keys=True, indent=4))

Demo
https://github.com/ziniman/aws-comprehend-demo

Amazon Comprehend Medical
Protect patient
information
Lower medical document
processing costs
K E Y F E AT U R E S
Extract medical data
quickly and accurately
Medical
Conditions
Anatomy
Entities
PHI
Identification
Medication and
Dosage
Extraction
No ML experience
required
NEW
Extract text and data from virtually any document

Amazon Lex
Conversational Interfaces

Intents
A particular goal that the
user wants to achieve
Utterances
Spoken or typed phrases
that invoke your intent
Slots
Data the user must provide to fulfill the
intent
Prompts
Questions that ask the user to input
data
Fulfillment
The business logic required to fulfill the
user’s intent
BookHotel

New Services

Amazon Personalize
Context-aware
Recommendations
Automated
machine learning
Bring existing algorithms
from Amazon SageMaker
Continuous
learning
Data is kept
private and
encrypted
NEW
Improve customer experiences with personalization and recommendations

Amazon Forecast
Consider
multiple
time-series
at once
Automatic
machine
learning
Visualize
forecasts &
import results into
business apps
Evaluate model
accuracy
Privacy &
encryption
Bring existing
algorithms from
Amazon
SageMaker
NEW
Improve forecasting accuracy by up to 50% at 1/10th the cost
Schedule
forecasts
and training

Amazon Textract
Optical Character
Recognition
(OCR)
Key-value pair
detection
Adjustable
confidence
thresholds
Table
detection
Bounding box
coordinates
No ML experience
required
NEW
Extract text and data from virtually any document

FRAMEWORKS AND INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES
Amazon
Rekognition
Democratization of AI
Amazon
Rekognition Video
Amazon SageMaker AWS DeepLens Amazon DeepRacer
Deep Learning AMI
Amazon EMR
Amazon Polly
Amazon Transcribe
Amazon Lex
Amazon Translate
Amazon Comprehend
Amazon Personalize
Amazon Forecast
Amazon Textract

Thank You!
Give me feedback
http://bit.ly/2QZddrm
Boaz Ziniman - Technical Evangelist
Amazon Web Service
@ziniman
boaz.ziniman.aws
ziniman

Artificial Intelligence for Developers - OOP Munich

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