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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

Work

in Pro

gress

Singularity

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From Data to Skills

Data Algorithms Skill+ =

ImagesDeep Neural

NetworksImage

Recognition

TextSupport Vector

MachineText

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 FridayI 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 ClarifaiRecognize

your products

Text Monkey LearnText

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

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