The document provides an overview of artificial intelligence and its applications for business. It defines AI and describes three levels of AI from narrow to super intelligence. It then discusses how algorithms like natural language processing, machine learning, deep learning and recommender systems work. The document outlines some common AI skills like speech recognition, translation and computer vision. It also discusses platforms from IBM, Google and Microsoft that can be used to build custom AI solutions.
Developer Data Modeling Mistakes: From Postgres to NoSQL
AI for Business Doc: Key Skills and Algorithms
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Artificial Intelligence
for Business
Nicola Mattina
June 2017
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In our imagination…
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Artificial Intelligence in everyday products…
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A definition of artificial intelligence
The capacity of a computer to
perform operations analogous
to learning and decision
making in humans.
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Why is AI becoming usable for business?
AlgorithmsData Computing
Power
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3 levels of Artificial Intelligence
Artificial
Narrow
Intelligence
Specialized in
one area.
Artificial
General
Intelligence
Specialized in
all area.
Artificial
Super
Intelligence
Smarter than humans
in every way.
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Artificial
Narrow
Intelligence
Specialized in
one area.
Artificial
General
Intelligence
Specialized in
all area.
Artificial
Super
Intelligence
Smarter than humans
in every way.
3 levels of Artificial Intelligence
W
ork
in
Progress
Singularity
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From Data to Skills
Data Algorithms Skill+ =
Images
Deep Neural
Networks
Image
Recognition
Text
Support Vector
Machine
Text
Classification
+ =
+ =
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Algorithms
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Families of Algorithms*
Natural Language Processing
Semantic Technologies
Machine Learning
Deep Learning
Recommender Systems
* The landscape of AI technologies is more extended. I’m naming just a few of them.
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Natural Language Processing (NLP)
http://www.moshebergman.com/study-notes/text-retrieval/week1.html
Natural Language
Processing is used to
analyze any text to extract
topics, sentiment,
meaning and ultimately to
gain knowledge.
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Semantic Technologies
Semantic technologies are based on
ontologies, a formal naming and
definition of the types, properties,
and interrelationships of the entities
that really or fundamentally exist for
a particular domain of discourse.
Person
Student Professor
Lecture
EmailName
Student # Research
Field
Lecture # Topic
IsA IsA
Attends Holds
Entity
Attribute
Relation
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Ontologies are used to extract entities and relations from texts
Nicola teaches an artificial intelligence for business
course to Mario and Giovanni on Monday.
Professor Lecture
Student
Holds
Student Attends
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Semantic Technologies we use everyday
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A definition of Machine Learning
Machine learning provides
computers with the ability to
learn without being explicitly
programmed.
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A machine can learn to solve these problems
Regression analysis is a statistical process for
estimating the relationships among variables.
Classification is a general process related to
categorization, the process in which ideas and objects
are recognized, differentiated, and understood.
Politics
Tech
Sport
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A machine can learn to solve these problems
Anomaly detection is the identification of items, events
or observations which do not conform to an expected
pattern or other items in a dataset.
Clustering is the task of grouping a set of objects in
such a way that objects in the same group (called a
cluster) are more similar (in some sense or another) to
each other than to those in other groups (clusters)
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A machine learns creating models
Data ML Algorithm
Trained ModelUnlabeled Data Prediction
Training
Prediction
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Supervised
Learning
Machines are trained through 3 strategies
Learning with a labeled
training set
Example. Email spam
detector with training set
of already labeled emails.
Discovering patterns
in unlabeled data.
Example. Cluster similar
documents based on the
text content.
Learning based on
feedback or reward.
Example. Learn to play
chess by winning or losing.
Unsupervised
Learning
Reinforcement
Learning
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A definition of Deep Learning
Deep Learning is part of the
machine learning field of
learning representations of
data. Exceptional effective at
learning patterns.
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Deep learning works by imitating the brain
It utilizes learning
algorithms that derive
meaning out of data by
using a hierarchy of
multiple layers that mimic
the neural networks of our
brain.
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Recommender Systems
A recommender system seeks to
predict the "rating" or
"preference" that a user would
give to an item.
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Technologies
and Research
These (plus other) technologies are available on the market as…
Platforms, SaaS and APIs
Services built on AI
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Technologies and Research
These (plus other) technologies are available on the market as…
Platforms, SaaS and APIs
Services built on AI
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Skills available on the market
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What are (some of) the skills of Artificial Intelligence today?
• Convert Speech to Text
• Recognize a Speaker
• Classify Text
• Analyze Sentiments
• Moderate Contents
• Translate Languages
• Understand Commands
• Extract information
• Manage knowledge
• Recognize things in images
• Recognize things in videos
• Recommend things
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Convert Speech to Text
https://cloud.google.com/speech/ https://trint.com
SaaS: From €11.00 to €16.20/hourAPI: From $1.44/hour
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Recognize a Speaker
Microsoft Speaker Recognition API
https://azure.microsoft.com/en-us/services/cognitive-services/speaker-recognition/
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Classify Text
IBM Natural Language Classifier
https://www.ibm.com/watson/developercloud/nl-classifier.html
Classifiers groups texts in categories
based on similarities.
IE, a classifier can predict that these 3
phrases are similar and belong to the
same category:
• Is there a parking at the airport?
• Where can i park at the airport?
• I need to park a car at the airport
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Text Classification Examples
Visit Monkey Learn to test text
classification used for:
• Language Detection
• Topic Detection
• Sentiment Analysis
Note how unpredictable is the
outcome when you don’t know
how the algorithm was trained.
Monkey Learn
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IBM Watson Tone Analyzer
https://www.ibm.com/watson/developercloud/tone-analyzer.html
Analyze Sentiments
Google Cloud Natural Language API
https://cloud.google.com/natural-language/
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Moderate Contents
Microsoft provides a machine-
assisted moderation of text and
images, augmented with human
review tools.
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Translate Languages
IBM Watson Language Translator
https://www.ibm.com/watson/developercloud/language-translator.html
Google Cloud Translation API
https://cloud.google.com/translate/
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Understand Commands
Alexa, tell plan my trip next Friday
I need a vacation
I’d like to take a trip
wake word launch invocation name utterance slot value
PlanMyTripIntent {value: 2017-12-20}
slot valueintent
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Virtual Assistants understand Commands
Amazon Echo for Alexa
https://www.amazon.com/Amazon-Echo-And-Alexa-Devices/b?ie=UTF8&node=9818047011
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Capital One Virtual Assistant for Alexa
Alexa, ask Capital One, how much
did I spend last weekend?
Between December 9th and
December 11th, you spent a total of
$90.25 on your Venture Card.
Alexa, ask Capital One, how much
did I spend at Starbucks last month?
Between November 1st and
November 30th, you spent a total of
$43.00 at Starbucks on your
Quicksilver Card.
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Amazon Voice Shopping
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Manage (simple) Conversations
IBM Watson Conversation
https://www.ibm.com/watson/developercloud/conversation.html
api.ai
https://api.ai
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IBM Watson Conversation
IBM has a very
powerful tool to
create
conversations
to create virtual
assistants and
chatbots.
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Chatbots
https://botlist.co Poncho
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Extract information from unstructured texts: Google
Google Natural Language API:
• extracts entities (people, places, events and much
more);
• understands sentiment;
• analyzes syntax and parse intents.
Google Cloud Jobs API delivers relevant job results
understanding the relationships between job content,
skills, seniority, location and many other signals.
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Extract information from unstructured texts: IBM
IBM Watson Natural Language Understanding:
• extracts entities;
• understands sentiment;
• analyzes syntax and parse intents.
IBM Watson Natural Language Understanding can be
paired with IBM Watson Knowledge Studio to use
custom ontologies.
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Managing knowledge is not an easy task
The team creates a type system that
defines entity types and relation types
for the information of interest to the
application that will use the model.
A group of two or more human
annotators annotates a set of source
documents. Any inconsistencies in
annotation are resolved, and one set of
optimally annotated documents is built,
which forms the ground truth.
Watson Knowledge Studio uses the
ground truth to train a model.
The trained model is used to find
entities, relations, and coreferences in
new, never-seen-before documents.
Source: https://www.ibm.com/watson/developercloud/doc/wks/index.html
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Recognize things in images
Google Cloud Vision API
https://cloud.google.com/vision/
Typical features of a vision API:
• label objects
• detect explicit contents
• detect logos
• detect landmarks
• detect faces
• transcribe text (OCR)
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Clearifai offers many pre-trained models
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DocParser is specialized in extracting information from pdf files
DocParser uses OCR (Optical Character
Recognition) to extract data from
scanned documents.
https://docparser.com
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Recognize things in videos
Google Cloud Video Intelligence
https://cloud.google.com/video-intelligence/
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Stabilize videos
Microsoft Video API
https://azure.microsoft.com/en-us/services/cognitive-services/video-api/
Intelligent video processing
produces stable video output,
detects motion, creates intelligent
thumbnails, and detects and
tracks faces.
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SaaS Version: Microsoft Video Indexer
Microsoft has packaged all its
video APIs in a SaaS called Video
Indexer.
You can upload a video and get:
• audio transcript
• face detection and indexing
• scene detection
• sentiment analysis
• content moderation
• text translation
• …
https://vi.microsoft.com
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Recommend things
Recombee
https://www.recombee.com
Microsoft Recommendations API
https://azure.microsoft.com/en-us/services/cognitive-services/recommendations/
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IBM Watson vs. Google vs. Microsoft
IBM Watson Google Microsoft
Speech • Speech to Text
• Text to Speech
• Cloud Speech API • Custom Speech Service
• Bing Speech API
• Speaker Recognition API
Language • Natural Language Classifier
• Natural Language
Understanding
• Personality Insights
• Language Translator
• Conversation
• Tone Analyzer
• Cloud Natural Language API
• Cloud Jobs API
• Cloud Translation API
• Language Understanding
Intelligent Service Web
Language Model API
• Translator Text API
• Bing Spell Check API
• Text Analytics API
• Linguistic Analysis API
• Translator Speech API
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IBM Watson vs. Google vs. Microsoft
IBM Watson Google Microsoft
Vision • Visual Recognition • Cloud Vision API
• Cloud Video Intelligence API
• Computer Vision API
• Face API
• Content Moderator
• Emotion API
• Video API
• Custom Vision Service
• Video Indexer
Knowledge
& Search
• Discovery
• Discovery News
• Knowledge Studio
• Recommendations API
• Academic Knowledge API
• Knowledge Exploration
Service
• Entity Linking Intelligence
Service API
• Custom Decision Service
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Build your own Skills
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If out-of-the-box solutions are not enough…
Train a model
with your data
Hire a
Data Scientist
Develop and train
a new algorithm
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Train a model with your data
Your
Data
Existing
API
Your
Skill+ =
Images Clarifai
Recognize
your products
Text Monkey Learn
Text
Classification
+ =
+ =
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Start from scratch using a ML Engine
Google Machine Learning Engine
https://cloud.google.com/ml-engine/
Amazon AWS AI
https://aws.amazon.com/amazon-ai/
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Add AI to your processes
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Blueprint
Design
• Map the process
• Find bottlenecks
and opportunities
• Scout for the most
appropriate skill
Deploy
• Do you need a data
scientist?
• Train/Develop the
skill
• Integrate
• Deploy
Learn
• Learn from real
usage
• Repeat the process
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Architecture
AI technologies evolve.
Be prepared to switch from one API to another.
Company Systems
Integration
Solution
AI API
AI API
AI API
CRM
Marketing
Automation
SFA
Customer
Care
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Sources
To make this presentation, I consulted many sources. Among these, the most relevant are:
• Sam Wouters, Demystifying Artificial Intelligence
• Luke Masuch, Deep Learning - The Past, Present and Future of Artificial Intelligence
• Nathan Pacer, Venture Scanner - AI Report Q1 2017
It is released under a Creative Commons BY SA NC excepct for the contents that are extracted from the
presentations listed above. Those contents are property of their authors.
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Nicola Mattina
Polymath, husband, and father of two girls. I'm
passionate about digital innovation. I design products
and business models. Over the past twenty years, I
worked mainly as a consultant helping complex
organizations to understand and embrace digital
transformation.
In 2013, I co-founded and invested in Stamplay, a low-
code development platform to make it easy to connect
APIs to support business processes.
http://blog.nicolamattina.it
https://www.linkedin.com/in/nicolamattina
https://www.facebook.com/nicolamattina
ciao@nicolamattina.it
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AI for Business Learning Experience
This presentation is part of a workshop that will help
you understand artificial intelligence tools and how they
can be employed across your organization.
Lectures and activities are customized considering the
background of the participants to highlight the use of
artificial intelligence in a specific industry and in three
different areas: product development, customer care,
business operations.
Workshop structure
• 120’ lectures
• 2 activities to apply the concepts
• 1 practical toolkit
If you want to engage us, just write me an email to
ciao@nicolamattina.it.
I will be happy to talk to you to understand your needs
and design a unique learning experience for your
organization.
4 steps
• Interviews to customize the workshop
• Proposal
• One-Day full immersion workshop
• Report and 1-2-1 follow up with participants