[Applied] Artificial Intelligence

Prof. Paulo André Castro pauloac@ita.br www.comp.ita.br/~pauloac ITA (Instituto Tecnológico de Aeronáutica) Divisão de computação (IEC), Prédio da IEC/IEE, Sala 110

Paulo André Lima de Castro• Bolsista de Produtividade Desen. Tec. e Extensão Inovadora do CNPq Nível 2.

• Engenheiro de Computação pelo Instituto Tecnológico de Aeronáutica (ITA, 1997), Mestre e Doutor pela Escola Politécnica da Universidade de São Paulo (Poli/USP 2009).

• Pós-doutorado na City University of New York (CUNY, 2013)

• Atualmente, professor do Instituto Tecnológico de Aeronáutica (ITA) e Chefe do Departamento de Metodologias de Computação da Divisão de Ciência da Computação do ITA.

• Participei de diversos projetos de Pesquisa e Desenvolvimento incluindo desenvolvimento de simuladores, avaliação de segurança da informação em sistemas computacionais e aplicação de técnicas inteligentes em sistemas distribuídos.

• Realizo pesquisas na área de Inteligência Artificial com ênfase em Sistemas multiagentes, atuando principalmente nos seguintes temas: agent-based finance, agentes autônomos e aplicações de técnicas inteligentes especialmente em economia e finanças

Ementa da disciplina• Inteligência Artificial. Resolução de problemas através de Busca. Modelagem e algoritmos:

algoritmo A*, Algoritmos genéticos. Aprendizagem de máquina: aprendizado indutivo, árvores de decisão e modelos de redes neurais artificiais para aprendizado supervisionado, não-supervisionado e por reforço. Fundamentos de redes bayesianas: construção de modelos e inferência probabilística. Uso de frameworks de Aprendizado de Máquina para Análise de Dados. Aplicações de Inteligência Artificial.

• Bibliografia:

• NORVIG, P. Inteligência Artificial: Uma abordagem moderna. 3a. ed. Rio de Janeiro: Elsevier Editora, 2009.

• Korb, K. Nicholson, A. Bayesian Artificial Intelligence. CRC Press.  2011.

• Witten, I., Frank, E. Data Mining: Practical Machine learning Tools and Techniques. 4a. ed. Elsevier. 2016.

• Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann, San Mateo, California. 1988.

Avaliações• Provas curtas e Tarefas (incluindo programação) ao longo das

aulas • 3 a 4 provas ou tarefas

Plan1. Introduction to AI2. Introduction to Programming for AI3. Solving by Searching4. Rational Decisions5. Decision Making with Bayesian Networks6. Learning Probabilistic Models and Knowledge Engineering7. Introduction to Practical Machine Learning8. Evaluation of ML models, Overfitting and Ensemble Methods9. Dynamic Bayesian Networks10. Markov Decision Process11. Reinforcement Learning12. Partially Observed MDP13. Trends in AI

Definitions of Artificial IntelligenceThinking Humanly Thinking Rationally“The exciting new effort to make computersthink . . . machines with minds, in thefull and literal sense.” (Haugeland, 1985)

“[The automation of] activities that weassociate with human thinking, activitiessuch as decision-making, problem solving,learning . . .” (Bellman, 1978)

“The study of mental faculties through theuse of computational models.”(Charniak and McDermott, 1985)

“The study of the computations that makeit possible to perceive, reason, and act.”(Winston, 1992)

Acting Humanly Acting Rationally

The art of creating machines that performfunctions that require intelligencewhen performed by people.” (Kurzweil,1990)

“The study of how to make computers dothings at which, at the moment, people arebetter.” (Rich and Knight, 1991)

“Computational Intelligence is the studyof the design of intelligent agents.” (Pooleet al., 1998)

“AI . . . is concerned with intelligent behaviorin artifacts.” (Nilsson, 1998)

Definições de Inteligência Artificial Pensando como seres humanos Pensando Racionalmente

“O novo e interessante esforço para fazer os computadores pensarem (…) máquinas com mentes, no sentido total e literal” (Haugeland, 1985)

“[Automação de] atividades que associamos ao pensamento humano, atvidades como a tomada de decisões, a resolução de problemas, o aprendizado..” (Bellman, 1978)

“O estudo das faculdades mentais pelo uso de modelos computacionais” (Charniak e McDermoot, 1985)

“O estudo das computações que tornam possível perceber, raciocinar e agir” (Winston, 1992)

Agindo como Seres Humanos Agindo Racionalmente

“A arte de criar máquinas que executam funções que exigem inteligência quando executadas por pessoas” (Kurzweill, 1990)

“O estudo de como os computadores podem fazer tarefas que hoje são melhor desempenhadas por pessoas” (Rich and Knight, 1991)

“Inteligência Computacional é o estudo do projeto de agentes inteligentes” (Poole et al. 1998)

“IA…está relacionada a um desempenho inteligente de artefatos” (Nilsson, 1998)

Artificial Intelligence – Is it new ?• The term “Artificial Intelligence” was used for the first time in the summer of 1956, in an invitation to a 2-month workshop organized by John McCarthy, Marvin Minsky, Claude Shannon and others..…

Artificial Intelligence -birth certificate• “We propose that a 2 month, 10 man study of artificial intelligence be

carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”John McCarthy, Marvin Minsky, Claude Shannon et al.1956

• Perhaps “computational rationality” would have been more precise and less threatening, but “AI” has stuck....

• McCar thy s ta ted tha t he res i s ted the te rms “computer ” o r “computational” in deference to Norbert Weiner, who was promoting analog cybernetic devices rather than digital computers

What about …. ?• Deep learning

• “Deep learning is a subset of a more general field of artificial intelligence called machine learning” Buduma, N. The fundamentals of deep learning.

• Machine Learning• Construção de software “...que pode melhorar seu próprio

comportamento através do estudo diligente de suas próprias experiências” (Russel, Norvig, 2013)

• Data mining• Finding patterns in data that provide insight or enable fast

and accurate decision making (Witten,2016) Data Mining: Practical Machine learning)

• Big data • Capturing and managing lots of information (computer systems)• Analyzing these masses of new data (data mining)

• Definitions of “learning” from dictionary:• To get knowledge of by study, experience, or being taught

• Operational definition:• Things learn when they change their behavior in a way that

makes them perform better in the future.• Machine Learning vs Statistcs

• “In truth, you should not look for a dividing line between machine learning and statistics because there is a continuum— and a multidimensional one at that—of data analysis techniques” Witten, Data Mining: Practical Machine learning

• In fact, it could be also stated to Machine Learning vs Data mining vs Statistics

Machine learning

A “Reasonable” Graph Representation of Intersections of Related Areas to AI

Challenges of AI

• Problem Solving• Solving by Searching• Constraint Satisfaction problems

• Knowledge, reasoning and planning• Logical Agents• Planning and Knowledge Representation

• Uncertain knowledge and reasoning• Probabilistic reasoning• Probabilistic reasoning over time

• Learning• Supervised, (semi-supervised), unsupervised and reinforcement

learning• Communications,perceiving and acting

• Natural language processing (NLP)• Sensors• Robotics

New technologies and the Gartner Hype Cycle

AI: A multidisciplinary field

And What about this discipline?Problem Solving

• Solving by Searching• Constraint Satisfaction problems

Knowledge, reasoning and planning• Logical Agents• Planning and Knowledge Representation

Learning• Supervised, (semi-supervised), unsupervised and reinforcement

learningCommunications,perceiving and acting

• Natural language processing (NLP)• Sensors• Robotics

Can a Machine Think?,Turing, A. (1950)Section 1: The imitation game• How to define machine...and think ?

• Too complex...• Another approach: The Imitation Game

• Description: • It is played with three people, a man (A), a woman (B), and an interrogator (C)

who may be of either gender. • The interrogator stays in a room apart front the other two. • The object of the game for the interrogator is to determine which of the other

two is the man and which is the woman.• He knows them by labels X and Y, and at the end of the game he says either

“X is A and Y is B” or “X is B and Y is A.”• Proposed question: “What will happen when a machine takes the

role of person A in this game?”• Will the interrogator decide wrongly as often when the game is

played like as he does when the game is played between a man and a woman?

• These questions replace our original, “Can machine think?”

Can a Machine Think?, Turing, A. (1950)

• Objections:• The Theological Objection• The "Heads in the Sand" Objection• The Mathematical Objection

• The Argument from Consciousness• And others….

Head in the Sand Objections...or a real disasters ahead? • Some Debates on World Economic Forum - Davos 2020

• Humans behind Machines• How should labour policies adapt to the unique conditions of “digital

assembly lines”?• How to Implement Responsible AI

• Is it possible to build trust in artificial intelligence?

• A Future Shaped by a Technology Arms Race

Head in the Sand Objections or a real disasters ahead - ... What about jobs ?• “Up to 375 million people globally will need to find entirely new

kinds of jobs by 2030 because of automation,...How workers, governments and businesses tackle these seismeic shits will define the future of Work”

• American Worker (Oscar winner Documentary), 2019, Netflix

• Dara Khosrowshahi, Uber CEO, speaks with David M. Rubenstein on June 11, 2019 about Driverless cars

• “..I think within the next five years some driverless vehicles will be out in the market..starting in a very very limited way”

• “We will get the machines to do the simple stuff and the human do the difficult stuff..”

• “The two are going to coexist for 10 to 15 years.....”

The concept of Agent

• An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Examples:?

Reflex Agent (Agente reativo)

• A Reflex Agent is based on condition–action rule (a.k.a if-then rules)

• Does it learn??

• Does it adapt itself?

• Does it think about things?

• More complex agents are required...

Model based agent (Agente baseado em Modelo)

• It keeps track of the current state of the world, using an internal model.

• It then chooses an action in the same way as the reflex agent

Goal based Agent (Agente baseado em Objetivos)

• It keeps track of the world state as well as a set of goals it is trying to achieve,

• and chooses an action that will (eventually) lead to the achievement of its goals.

Utility-based agent Agent

• It uses a model of the world, along with a utility function that measures its preferences among states of the world.

• Then it chooses the action that leads to the best expected utility,

• where expected utility is computed by averaging over all possible outcome states, weighted by the probability of the outcome

General Learning Agent

• A learning agent can be divided into four conceptual components

• Learning element is responsible for making improvements,

• Performance element is responsible for selecting external actions

• The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions

• Critic gives info to the Learning element on how the agent is doing, so the Learning element decides how the Performance element should be modified to do better in the future

• Problem Generator is responsible for suggesting actions that will lead to new and informative experiences. Why??

Examples of Agents and its key features

Environment - where the agent perceives and acts - and its properties• Fully observable vs. partially observable (Observável x Parcialmente

Observável)

• Deterministic vs. stochastic (Determinístico x Estocástico)

• Episodic vs. sequential (Episódico x Seqüencial)

• Static vs. dynamic (Estático x Dinâmico)

• Discrete vs. continuous (Discreto x Contínuo)

• Single agent vs. multiagent (Agente Único x Multiagente)

Environment - where the agent perceives and acts • Fully observable vs. partially observable

• If an agent’s sensors give it access to the complete state of the environment at each point in time, then we say that the task environment is fully observable. A task environment is effectively fully observable if the sensors detect all aspects that are relevant to the choice of action; relevance, in turn, depends on the performance measure.

• Deterministic vs. stochastic • If the next state of the environment is completely determined by the current state and the action

executed by the agent, then we say the environment is deterministic; otherwise, it is stochastic

• Episodic vs. sequential• In an episodic task environment, the agent’s experience is divided into atomic episodes. • In each episode the agent receives a percept and then performs a single action. Crucially, the next

episode does not depend on the actions taken in previous episodes

• Static vs. dynamic • If the environment can change while an agent is deliberating, then we say the environment is dynamic

for that agent; otherwise, it is static. If the environment itself does not change with the passage of time but the agent’s performance score does, then we say the environment is semidynamic

• The differences between continuos vs discrete and single agent vs multiagent are pretty straightforward, but multiagent means that the existence of other agents matter to agent's performance

What does make the world Uncertainty (Partially observed or stochastic) ?1. Ignorance. The limits of our knowledge lead us to be uncertain

about many things. Does our poker opponent have a flush or is she bluffing?

2. Phyical randomness or indeterminism. Even if we know everything that we might care about a coin and how we impart spin to it when we toss it., there will remain an inescapable degree of uncertainty about it wil land heads or tails

• A strong deterministr person might claim otherwise, that it would be possible to calculate......but such a view is for the foreseeable future a mere act of scientistic faith. We are all practical indeterminists

3. Vagueness. Many of predicates we employ appear to be vague. It is often unclear whether to classify a bird as big or small, a human as brave or not, a thought as knowledge or opinion

First Exercise (get paper and pen...)

• What about Chess without a clock? changes anything?