Artificial and Natural Intelligence

Artificial intelligence (AI) is the established name for the field, but the term “artificial intelligence” is a source of much confusion because artificial intelligence may be interpreted as the opposite of real intelligence.

For any phenomenon, you can distinguish real versus fake, where the fake is non-real. You can also distinguish natural versus artificial. Natural means occurring in nature and artificial means made by people.

Example 1.1.

A tsunami is a large wave in an ocean. Natural tsunamis occur from time to time and are caused by earthquakes or landslides. You could imagine an artificial tsunami that was made by people, for example, by exploding a bomb in the ocean, yet which is still a real tsunami. One could also imagine fake tsunamis: either artificial, using computer graphics, or natural, for example, a mirage that looks like a tsunami but is not one.

It is arguable that intelligence is different: you cannot have fake intelligence. If an agent behaves intelligently, it is intelligent. It is only the external behavior that defines intelligence; acting intelligently is being intelligent. Thus, artificial intelligence, if and when it is achieved, will be real intelligence created artificially.

This idea of intelligence being defined by external behavior was the motivation for a test for intelligence designed by Turing [1950], which has become known as the Turing test. The Turing test consists of an imitation game where an interrogator can ask a witness, via a text interface, any question. If the interrogator cannot distinguish the witness from a human, the witness must be intelligent. Figure 1.1 shows a possible dialog that Turing suggested. An agent that is not really intelligent could not fake intelligence for arbitrary topics.

There has been much debate about the usefulness of Turing test. Unfortunately, although it may provide a test for how to recognize intelligence, it does not provide a way to realize intelligence.

Recently Levesque [2014] suggested a new form of question, which he called a Winograd schema after the following example of Winograd [1972]:

  • The city councilmen refused the demonstrators a permit because they feared violence. Who feared violence?

  • The city councilmen refused the demonstrators a permit because they advocated violence. Who advocated violence?

These two sentences only differ in one word feared/advocated, but have the opposite answer. Answering such a question does not depend on trickery or lying, but depends on knowing something about the world that humans understand, but computers currently do not.

Winograd schemas have the property that (a) humans can easily disambiguate them and (b) there is no simple grammatical or statistical test that could disambiguate them. For example, the sentences above would not qualify if “demonstrators feared violence” was much less or more likely than “councilmen feared violence” (or similarly with advocating).

Example 1.2.

The following examples are due to Davis [2015]:

  • Steve follows Fred’s example in everything. He [admires/influences] him hugely. Who [admires/influences] whom?

  • The table won’t fit through the doorway because it is too [wide/narrow]. What is too [wide/narrow]?

  • Grace was happy to trade me her sweater for my jacket. She thinks it looks [great/dowdy] on her. What looks [great/dowdy] on Grace?

  • Bill thinks that calling attention to himself was rude [to/of] Bert. Who called attention to himself?

Each of these have their own reasons why one answer is preferred to the other. A computer that can reliably answer these questions needs to know about all of these reasons, and require the ability to do commonsense reasoning.

The obvious naturally intelligent agent is the human being. Some people might say that worms, insects, or bacteria are intelligent, but more people would say that dogs, whales, or monkeys are intelligent (see Exercise 1). One class of intelligent agents that may be more intelligent than humans is the class oforganizations. Ant colonies are a prototypical example of organizations. Each individual ant may not be very intelligent, but an ant colony can act more intelligently than any individual ant. The colony can discover food and exploit it very effectively as well as adapt to changing circumstances. Corporations can be more intelligent than individual people. Companies develop, manufacture, and distribute products where the sum of the skills required is much more than any individual could master. Modern computers, from low-level hardware to high-level software, are more complicated than any human can understand, yet they are manufactured daily by organizations of humans. Human society viewed as an agent is arguably the most intelligent agent known.

It is instructive to consider where human intelligence comes from. There are three main sources:

  • Biology
    • Humans have evolved into adaptable animals that can survive in various habitats.
  • Culture
    • Culture provides not only language, but also useful tools, useful concepts, and the wisdom that is passed from parents and teachers to children.
  • Lifelong learning
    • Humans learn throughout their life and accumulate knowledge and skills.

These sources interact in complex ways. Biological evolution has provided stages of growth that allow for different learning at different stages of life. Biology and culture have evolved together; humans can be helpless at birth presumably because of our culture of looking after infants. Culture interacts strongly with learning. A major part of lifelong learning is what people are taught by parents and teachers. Language, which is part of culture, provides distinctions in the world that are useful for learning.

When building an intelligent system, the designers have to decide which of these sources of intelligence need to be programmed in, and which can be learned. It is very unlikely we will be able to build an agent that starts with a clean slate and learns everything. Similarly, most interesting and useful intelligent agents learn to improve their behavior

 

What is Artificial Intelligence?

Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently. Let us examine each part of this definition.

An agent is something that acts in an environment; it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries.

We are interested in what an agent does; that is, how it acts. We judge an agent by its actions.

An agent acts intelligently when

  • What it does is appropriate for its circumstances and its goals, taking into account the short-term and long-term consequences of its actions
  • it is flexible to changing environments and changing goals
  • it learns from experience
  • it makes appropriate choices given its perceptual and computational limitations

computational agent is an agent whose decisions about its actions can be explained in terms of computation. That is, the decision can be broken down into primitive operations that can be implemented in a physical device. This computation can take many forms. In humans this computation is carried out in “wetware”; in computers it is carried out in “hardware.” Although there are some agents that are arguably not computational, such as the wind and rain eroding a landscape, it is an open question whether all intelligent agents are computational.

All agents are limited. No agents are omniscient or omnipotent. Agents can only observe everything about the world in very specialized domains, where “the world” is very constrained. Agents have finite memory. Agents in the real world do not have unlimited time to act.

The central scientific goal of AI is to understand the principles that make intelligent behavior possible in natural or artificial systems. This is done by

  • The analysis of natural and artificial agents
  • Formulating and testing hypotheses about what it takes to construct intelligent agents and

  • Designing, building, and experimenting with computational systems that perform tasks commonly viewed as requiring intelligence.

As part of science, researchers build empirical systems to test hypotheses or to explore the space of possible designs. These are quite distinct from applications that are built to be useful for an application domain.

The definition is not for intelligent thought alone. We are only interested in thinking intelligently insofar as it leads to more intelligent behavior. The role of thought is to affect action.

The central engineering goal of AI is the design and synthesis of useful, intelligent artifacts. We actually want to build agents that act intelligently. Such agents are useful in many applications.

Pengenalan Android

Android adalah Sistem Operasi berbasis Linux yang pada awalnya dikembangkan untuk perangkat seluler berbasis layar sentuh seperti smartphone, tablet, dan komputer. Android sendiri saat ini dikembangkan oleh Google Inc. Pada mulanya, Google Inc. membeli Android Inc., pendatang baru yang membuat peranti lunak untuk ponsel. Kemudian untuk mengembangkan Android, dibentuklah Open Handset Alliance, konsorsium dari 34 perusahaan peranti keras, peranti lunak, dan telekomunikasi, termasuk Google, HTC, Intel, Motorola, Qualcomm, T-Mobile, dan Nvidia.   Dalam perkembangannya, Android merambah ke beberapa media lain, seperti Android TV untuk televisi, Android Auto untuk mobil, dan Wear OS untuk jam pintar.

 

Android is an open source and Linux-based operating system for mobile devices such as smartphones and tablet computers. Android was developed by the Open Handset Alliance, led by Google, and other companies. This tutorial will teach you basic Android programming and will also take you through some advance concepts related to Android application development.