Big Data, Inteligência Artificial, Machine Learning e o que Hollywood não vai te contar

Big Data, Inteligência Artificial, Machine Learning e o que Hollywood não vai te contar

@garucosta, spread the word!

https://soundcloud.com/gdg-casts/7-go-go-go-golang

https://soundcloud.com/gdg-casts/7-go-go-go-golang





I'm sorry, Dave, I'm afraid I can't do that

Saindo do Cinema

"I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. So we need to be very careful with artificial intelligence.

I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. With artificial intelligence we’re summoning the demon. You know those stories where there’s the guy with the pentagram, and the holy water, and he’s like — Yeah, he’s sure he can control the demon? Doesn’t work out."

"I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. So we need to be very careful with artificial intelligence.

I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. With artificial intelligence we’re summoning the demon. You know those stories where there’s the guy with the pentagram, and the holy water, and he’s like — Yeah, he’s sure he can control the demon? Doesn’t work out."

"I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned."

"I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned."

"The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”

"The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”


Inteligência Artificial

John McCarthy (1955)The science and engineering of making intelligent machines.

Strong AI vs Weak AI

Strong AI

Weak AI

Representação do Conhecimento e Raciocínio Raciocínio/Dedução/Inferência Aprendizagem (Machine learning) Planejamento (Automated planning and scheduling) Comunicação (Natural language processing) Percepção (Computer vision, Speech recognition) Movimento e Manipulação (Robotics)

Representação do Conhecimento e Raciocínio Raciocínio/Dedução/Inferência Aprendizagem (Machine learning) Planejamento (Automated planning and scheduling) Comunicação (Natural language processing) Percepção (Computer vision, Speech recognition) Movimento e Manipulação (Robotics)

Machine Learning

O que é?

Arthur Samuel (1959)The field of study that gives computers the ability to learn without being explicitly programmed.

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Tom Mitchell (1997)

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Tom Mitchell (1997)

EA experiência de jogar vários Jogos da Dama.

TA tarefa de jogar Dama.

PA probabilidade de que o programa vencerá o proximo jogo.

Data

Data

Programa

ComputadorData

Programa

ComputadorData

ProgramaOutput

ComputadorData

ProgramaOutput

Data

ComputadorData

ProgramaOutput

Data

Output

Computador

Machine Learning

Data

ProgramaOutput

Data

Output

Computador

Machine Learning

Data

ProgramaOutput

Data

OutputPrograma (Modelo)

Programa (Modelo)

Machine Learning

Data

ProgramaOutput

Data

OutputPrograma (Modelo)

Principais conceitos?

Nº(Quartos) Nº(Banheiros) Andar Preço (Mil)

3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200


3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200

Instância


3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200

Instância

Feature


3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200

Dataset


3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200

Dataset

Training Dataset


3 2 1 460

2 1 1 340

5 3 2 900

2 1 1 350

1 1 1 200

Dataset

Training Dataset

Testing Dataset

Treinamento, Modelo e Teste

Dados de Treino

Dados de Treino

ML Alg

Dados de Treino

ML Alg

Modelo

Dados de Treino

ML Alg

Modelo Dados de Teste

Dados de Treino

ML Alg


35%

Dados de Treino

ML Alg


35%

Dados de Treino

Dados de Treino

ML Alg


35%

Dados de Treino

ML Alg

Dados de Treino

ML Alg


35%

Dados de Treino

ML Alg

Modelo

Dados de Treino

ML Alg


35%

Dados de Treino

ML Alg


Dados de Treino

ML Alg


35%

Dados de Treino

ML Alg


85%

George Box (1978)All models are wrong but some are useful

Tipos de aprendizado

Supervised Learning

Não Não Sim Não

Sim Sim Não Sim

Sim Não Sim Não

Não Sim Não Sim

Unsupervised Learning

Semi-supervised Learning

Não Sim Não

Sim Não

Sim Sim

Sim

Reinforcement Learning

E o que mais?

Detecção de Spam Detecção de Fraude de Cartão de Crédito Reconhecimento de Digital Reconhecimento de Voz Detecção Facial Recomendação de Produtos Diagnóstico Médico Segmentação de Cliente Detecção de Formato

Netflix

75% da audiência do Netflix vem do Algoritmo de Recomendação

Learning to Rank

Big Data

A cada minuto:

Usuários do Facebook curtem 1.166.667 posts

Usuários do Twitter enviam 347.222 tweets

Usuários do Vine assistem 1.041.666 vídeos

Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone

Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone

Big Data é um termo meio místico

mas…

3 V's

VolumeUm enorme quantidade de dados

VelocidadeOs dados precisam ser processados rapidamente

VariedadeDados surgem de todos os lugares (mobile, sensores, Internet das coisas)

+2 V's

VeracidadeNem todo dado deve ser usando

ValorExtrair informações, insights, etc.

Como lidar com isso?

Apache Hadoop

Hadoop Distributed File System (HDFS)

YARN

MapReduce

Apache Spark

Apache Mesos

Cases

Netflix

House of Cards

eHarmony

Learning to Rank

http://www.eharmony.com/engineering/data-science-of-love/

http://www.eharmony.com/engineering/data-science-of-love/

Bitly

Por que eu deveria me importar?

W. Edwards Deming In God we trust, all others must bring data.

Você precisa entender o ambiente no qual está vivendo

Jeff HammerbacherThe best minds of my generation are thinking about how to make people click ads, … That sucks.

Vlw, flw

Bruno Henrique@garucosta github.com/brunohenrique pt.slideshare.net/garuhenr [email protected]

mailto:[email protected]

Date post:	24-Jan-2017
Category:	Technology
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Big Data, Inteligência Artificial, Machine Learning e o que Hollywood não vai te contar

Technology