Machine Learning: Artificial Intelligence isn't just a Science Fiction topic

Raul Garreta - Tryolabs / MonkeyLearn

My Credentials● Computer Science Engineer from Udelar,

Msc in Machine Learning + NLP● Co-Founder, CTO & Product Manager at

Tryolabs.● Co-Founder at MonkeyLearn.● Professor in ML at InCo, Udelar.● Co-authored "Learning Scikit-learn:

Machine Learning in Python"

Contents● Brief intro to AI & Machine Learning (ML)● ML Applications● Cloud ML tools

What is AI?From a behavioral point of view, is an artificial agent that shows certain characteristics of intelligence like:● Reasoning● Knowledge representation● Learning● Planning● Perception

What is AI? Behavioral test = Turing Test

If I write an enough complex If-then-else structure, could it pass the test?

Random behavior?

Different fields within AIArtificial Intelligence

● General Artificial Intelligence● Expert Systems

○ Natural Language Processing○ Computer Vision○ Machine Learning○ ...

Machine LearningAlgorithms that allow computers to automatically learn to perform a task from data.

Can improve their performance over time, by adding more data.

Machine Learning DefinitionsArthur Samuel (1959): "Field of study that gives computers the ability to learn without being explicitly programmed"

Tom Mitchell (1997): "A computer program is said to learn if its performance at a task T, as measured by a performance P, improves with experience E"

Machine Learning Algorithms

● Learn to associate a particular input (set of features) to a particular output (class, number or group of instances)

● That is the process of training a ML model.● And use the learned model to predict the

outcome on new instances

Inputs: InstancesUsually we have instances of data that represent objects: documents, images, users, etc.And can be represented by a set of features:● A document is represented by a set of words.● An image is represented by a set of pixels.● A user can be represented by the age, level of

education, gender, interests, etc.

Machine Learning ProblemsClassification: assign a label (class) to a set of items.

Regression: assign a number (evaluation) to a set of items

Clustering: group items into clusters according to a similarity measure

Type of Machine Learning Algorithms

Decision TreesLinear Models

Type of Machine Learning Algorithms

Probabilistic / Statistical Models

Neural Networks / Deep Learning

Important Concepts in MLBesides the Machine Learning…● Data gathering / importation● Data preprocessing● Feature extraction● Feature selection● Performance evaluation (testing)

ApplicationsNatural Language Processing

Text Mining Speech to Text

Applications: Computer Vision

Face Recognition OCR

ApplicationsData Mining / Predictive Analytics

Recommendation Engines Medicine

ApplicationsIntelligent Agents

Robotics Game Players

Why use Machine Learning?● Solve problems that manually would be extremely

difficult or impossible.● Make predictions.● Automatically process huge amounts of information and

sources: big data.● Intelligent apps => improve UX => improve conversion

rates => $$$● Great companies use it...

● Avoid to deploy and maintain the full stack.● Be cross platform.● Not all programming languages have ML

tools.● ML requires huge amounts of computer

power.● Just solve it: good, fast, easy.

Why use a Cloud Saas ML platform?

As with other problems (eg: payments, communications) is a trend to go SaaS.

Machine Learning Platforms

Machine Learning

Microsoft Azure ML● http://azure.microsoft.com/en-

us/services/machine-learning/● Launched preview version on June 2014.● Cloud based ML platform to build predictive

numerical applications.● Technologies used in Xbox and Bing.

Machine Learning

Microsoft Azure ML● Easy to scale, Azure infrastructure.● Users can build custom R modules.● GUI and APIs.● More oriented to Data Scientists.● Pricing: pay as you go.

Machine Learning

MonkeyLearn● http://monkeylearn.com/● Launched private alpha on April 2014● Cloud based, focused on Text Mining:

extract and classify information from text.

MonkeyLearn● Easy to use.● Pre-trained modules for different

applications.● GUI and APIs.● More oriented to developers.● Pricing: freemium, pay as you go.

Conclusions● Machine Learning can allow

us to make intelligent apps.● It's a trendy topic…● New ML platforms are

emerging, allowing any developer to incorporate ML technologies.