Sunday, August 1, 2010

Rules For Love

Rules For Love

Never allow your partner or yourself to denigrate the other (oroma.info).

You must have personal respect and consideration for yourself (oroma.info).

Everyone deserves respect and love, but you can’t expect to get it unless you give it (oroma.info).

If you allow your partner to disparage you, expect to hear other damaging words (oroma.info).

Whatever you are willing to accept is exactly what you’re going to get (oroma.info).

Be compassionate, understanding, forgiving and merciful (oroma.info).

Patience, kindness, consideration and thoughtfulness can never be in short demand (oroma.info).

find loveNever let a person use names or words to hurt or degrade you or your partner (oroma.info).

Vow to protect yourself from thoughtless, rude, mean or punishing behavior (oroma.info).

If destructive words are being used, for whatever the reason, it must Stop (oroma.info). If not, a relationship can’t survive (oroma.info).

Once you’ve reacted you can then be proactive (oroma.info).

A controlled mouth shows a controlled mind (oroma.info). Use words for empowerment, encouragement and positive recognition (oroma.info).

Ask for respect (oroma.info). Quietly demand it (oroma.info). If your lover, partner, parent or friend can’t exhibit self control over their mouth, seriously consider looking elsewhere for a relationship (oroma.info).

Pick an appropriate the time to discuss important issues (oroma.info). This is particularly true if there is an emotional charge where feelings of anger or vexation need to be vented (oroma.info).

find loveNever enter into discussion of personal, private or intimate issues in public (oroma.info). Wait until you have privacy and the time to tackle issues (oroma.info).

If a person makes a mistake, or does something that disappoints or angers you, belittlement or badmouthing them in front of others will only lead to further resentment, anger and frustration (oroma.info).

Trying to discuss things in bed just before sleep, or while getting ready for bed is simply thoughtless, inconsiderate and a remedy for disaster (oroma.info).

Trying to discuss anything when the other person won’t cooperate or take the time to talk is a waste of time (oroma.info).

If necessary make a date to talk (oroma.info).

If the person keeps on delaying or avoiding conversation or discussion on issues that are important or significant to you, you may need to put it in writing and place it in their hands (oroma.info).

Talking is good for closure of some issues (oroma.info). And, unless allowed, will create a wound that won’t close (oroma.info).

find loveYou can never truly waste your thoughts and words on the separated or departed (oroma.info). Life and thought continues (oroma.info).

Romance doesn’t just exist, you must make it happen (oroma.info). You must make a sincere effort to keep it alive to help your relationship flourish (oroma.info).

Little things count, it doesn’t have to be a dozen roses and champagne all the time (oroma.info). A favorite piece of candy in a pocket or a little note can mean a lot (oroma.info).

Commit yourself to do something romantic every day (oroma.info). Show it (oroma.info). Demonstrate it (oroma.info). It’s the accumulative total of all the little things that in end adds up to a super special love and romance (oroma.info).

Learn Artificial Intelligence

Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Textbooks define the field as “the study and design of intelligent agents”[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as “the science and engineering of making intelligent machines.”[4]

The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[5] This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.[6] Artificial intelligence has been the subject of optimism,[7] but has also suffered setbacks[8] and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.[9]

AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other.[10] Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[11] General intelligence (or “strong AI”) is still a long-term goal of (some) research.[12]

[oroma.info] History

Main articles: History of artificial intelligence and Timeline of artificial intelligence

Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion’s Galatea.[13] Human likenesses believed to have intelligence were built in every major civilization: animated statues were seen in Egypt and Greece[14] and humanoid automatons were built by Yan Shi,[15] Hero of Alexandria,[16] Al-Jazari[17] and Wolfgang von Kempelen.[18] It was also widely believed that artificial beings had been created by Jābir ibn Hayyān,[19] Judah Loew[20] and Paracelsus.[21] By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary Shelley’s Frankenstein or Karel Čapek’s R.U.R. (Rossum’s Universal Robots).[22] Pamela McCorduck argues that all of these are examples of an ancient urge, as she describes it, “to forge the gods”.[6] Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.

Mechanical or “formal” reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing’s theory of computation suggested that a machine, by shuffling symbols as simple as “0″ and “1″, could simulate any conceivable act of mathematical deduction.[23] This, along with recent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.[24]

The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.[25] The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades.[26] They and their students wrote programs that were, to most people, simply astonishing:[27] computers were solving word problems in algebra, proving logical theorems and speaking English.[28] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[29] and laboratories had been established around the world.[30] AI’s founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that “machines will be capable, within twenty years, of doing any work a man can do”[31] and Marvin Minsky agreed, writing that “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”.[32]

They had failed to recognize the difficulty of some of the problems they faced.[33] In 1974, in response to the criticism of England’s Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. The next few years, when funding for projects was hard to find, would later be called an “AI winter”.[34]

In the early 1980s, AI research was revived by the commercial success of expert systems,[35] a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field.[36] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.[37]

In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry.[9] The success was due to several factors: the incredible power of computers today (see Moore’s law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[38]

[oroma.info] Problems

The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.[11]

[oroma.info] Deduction, reasoning, problem solving

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans were often assumed to use when they solve puzzles, play board games or make logical deductions.[39] By the late 1980s and ’90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[40]

For difficult problems, most of these algorithms can require enormous computational resources — most experience a “combinatorial explosion”: the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.[41]

Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model.[42] AI has made some progress at imitating this kind of “sub-symbolic” problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside human and animal brains that give rise to this skill.

[oroma.info] Knowledge representation

Main articles: Knowledge representation and Commonsense knowledge

Knowledge representation[43] and knowledge engineering[44] are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;[45] situations, events, states and time;[46] causes and effects;[47] knowledge about knowledge (what we know about what other people know);[48] and many other, less well researched domains. A complete representation of “what exists” is an ontology[49] (borrowing a word from traditional philosophy), of which the most general are called upper ontologies.

Among the most difficult problems in knowledge representation are:

Default reasoning and the qualification problem
Many of the things people know take the form of “working assumptions.” For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969[50] as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.[51]
The breadth of commonsense knowledge
The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering — they must be built, by hand, one complicated concept at a time.[52] A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology.
The subsymbolic form of some commonsense knowledge
Much of what people know is not represented as “facts” or “statements” that they could actually say out loud. For example, a chess master will avoid a particular chess position because it “feels too exposed”[53] or an art critic can take one look at a statue and instantly realize that it is a fake.[54] These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.[55] Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI or computational intelligence will provide ways to represent this kind of knowledge.[55]

[oroma.info] Planning

Main article: Automated planning and scheduling

Intelligent agents must be able to set goals and achieve them.[56] They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or “value”) of the available choices.[57]

In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.[58] However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.[59]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[60]

[oroma.info] Learning

Main article: Machine learning

Machine learning[61] has been central to AI research from the beginning.[62] Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression takes a set of numerical input/output examples and attempts to discover a continuous function that would generate the outputs from the inputs. In reinforcement learning[63] the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

[oroma.info] Natural language processing

ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.

Main article: Natural language processing

Natural language processing[64] gives machines the ability to read and understand the languages that humans speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.[65]

[oroma.info] Motion and manipulation

Main article: Robotics

The field of robotics[66] is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation[67] and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).[68]

[oroma.info] Perception

Main articles: Machine perception, Computer vision, and Speech recognition

Machine perception[69] is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision[70] is the ability to analyze visual input. A few selected subproblems are speech recognition,[71] facial recognition and object recognition.[72]

[oroma.info] Social intelligence

Main article: Affective computing

Kismet, a robot with rudimentary social skills

Emotion and social skills[73] play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, for good human-computer interaction, an intelligent machine also needs to display emotions. At the very least it must appear polite and sensitive to the humans it interacts with. At best, it should have normal emotions itself.

[oroma.info] Creativity

Main article: Computational creativity

TOPIO, a robot that can play table tennis, developed by TOSY.

A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative). A related area of computational research is Artificial Intuition and Artificial Imagination.

[oroma.info] General intelligence

Main articles: Strong AI and AI-complete

Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them.[12] A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.[74]

Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.[75]

[oroma.info] Approaches

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[76] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence, by studying psychology or neurology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[77] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[78] Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require “sub-symbolic” processing?[79]

[oroma.info] Cybernetics and brain simulation

Main articles: Cybernetics and Computational neuroscience

There is no consensus on how closely the brain should be simulated.

In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[24] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

[oroma.info] Symbolic

Main article: Good old fashioned artificial intelligence

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: CMU, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI “good old fashioned AI” or “GOFAI”.[80]

Cognitive simulation
Economist Herbert Simon and Allen Newell studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 80s.[81][82]
Logic based
Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.[77] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[83] Logic was also focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[84]
“Anti-logic” or “scruffy”
Researchers at MIT (such as Marvin Minsky and Seymour Papert)[85] found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[78] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[86]
Knowledge based
When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[87] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[35] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

[oroma.info] Sub-symbolic

During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background.[88] By the 1980s, however, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[79]

Bottom-up, embodied, situated, behavior-based or nouvelle AI
Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[89] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
Computational Intelligence
Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle 1980s.[90] These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence.[91]

[oroma.info] Statistical

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI’s recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Stuart Russell and Peter Norvig describe this movement as nothing less than a “revolution” and “the victory of the neats.”[38]

[oroma.info] Integrating the approaches

Intelligent agent paradigm
An intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents are rational, thinking humans.[92] The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works — some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.[93]
Agent architectures and cognitive architectures
Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.[94] A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.[95] Rodney Brooks’ subsumption architecture was an early proposal for such a hierarchical system.

[oroma.info] Tools

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

[oroma.info] Search and optimization

Main articles: Search algorithm, Optimization (mathematics), and Evolutionary computation

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[96] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[97] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[98] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[67] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[99] are rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use “heuristics” or “rules of thumb” that eliminate choices that are unlikely to lead to the goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for what path the solution lies on.[100]

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[101]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony or particle swarm optimization)[102] and evolutionary algorithms (such as genetic algorithms[103] and genetic programming[104][105]).

[oroma.info] Logic

Main articles: Logic programming and Automated reasoning

Logic[106] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[107] and inductive logic programming is a method for learning.[108]

Several different forms of logic are used in AI research. Propositional or sentential logic[109] is the logic of statements which can be true or false. First-order logic[110] also allows the use of quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy logic,[111] is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems. Subjective logic models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a Beta distribution. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence. Default logics, non-monotonic logics and circumscription[51] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[45] situation calculus, event calculus and fluent calculus (for representing events and time);[46] causal calculus;[47] belief calculus; and modal logics.[48]

[oroma.info] Probabilistic methods for uncertain reasoning

Main articles: Bayesian network, Hidden Markov model, Kalman filter, Decision theory, and Utility theory

Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[112]

Bayesian networks[113] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[114] learning (using the expectation-maximization algorithm),[115] planning (using decision networks)[116] and perception (using dynamic Bayesian networks).[117] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time[118] (e.g., hidden Markov models[119] or Kalman filters[120]).

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[121] information value theory.[57] These tools include models such as Markov decision processes,[122] dynamic decision networks,[122] game theory and mechanism design.[123]

[oroma.info] Classifiers and statistical learning methods

Main articles: Classifier (mathematics), Statistical classification, and Machine learning

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[124]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The most widely used classifiers are the neural network,[125] kernel methods such as the support vector machine,[126] k-nearest neighbor algorithm,[127] Gaussian mixture model,[128] naive Bayes classifier,[129] and decision tree.[130] The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the “no free lunch” theorem. Determining a suitable classifier for a given problem is still more an art than science.[131]

[oroma.info] Neural networks

Main articles: Neural networks and Connectionism

A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.

The study of artificial neural networks[125] began in the decade before the field AI research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm.[132]

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[133] Among recurrent networks, the most famous is the Hopfield net, a form of attractor network, which was first described by John Hopfield in 1982.[134] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning and competitive learning.[135]

Jeff Hawkins argues that research in neural networks has stalled because it has failed to model the essential properties of the neocortex, and has suggested a model (Hierarchical Temporal Memory) that is loosely based on neurological research.[136]

[oroma.info] Control theory

Main article: Intelligent control

Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.[137]

[oroma.info] Languages

Main article: List of programming languages for artificial intelligence

AI researchers have developed several specialized languages for AI research, including Lisp[138] and Prolog.[139]

[oroma.info] Evaluating progress

Main article: Progress in artificial intelligence

In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.

The broad classes of outcome for an AI test are:

  • Optimal: it is not possible to perform better
  • Strong super-human: performs better than all humans
  • Super-human: performs better than most humans
  • Sub-human: performs worse than most humans

For example, performance at draughts is optimal,[140] performance at chess is super-human and nearing strong super-human,[141] and performance at many everyday tasks performed by humans is sub-human.

A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from Kolmogorov Complexity and data compression.[142] [143] Similar definitions of machine intelligence have been put forward by Marcus Hutter in his book Universal Artificial Intelligence (Springer 2005), an idea further developed by Legg and Hutter.[144] Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.

[oroma.info] Applications

Wiki letter w.svg This section requires expansion.
Main article: Applications of artificial intelligence

Artificial intelligence has successfully been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery, video games, toys, and Web search engines. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence, sometimes described as the AI effect.[145] It may also become integrated into artificial life.

[oroma.info] Competitions and prizes

Main article: Competitions and prizes in artificial intelligence

There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining, driverless cars, robot soccer and games.

[oroma.info] Platforms

A platform (or “computing platform”)is defined as “some sort of hardware architecture or software framework (including application frameworks), that allows software to run.” As Rodney Brooks [146] pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., we need to be working out AI problems on real world platforms rather than in isolation.

A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit PC-based but still an entire real-world system to various robot platforms such as the widely available Roomba with open interface.[147]

[oroma.info] Philosophy

Main article: Philosophy of artificial intelligence

Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.[148]

Turing’s “polite convention”
If a machine acts as intelligently as a human being, then it is as intelligent as a human being. Alan Turing theorized that, ultimately, we can only judge the intelligence of a machine based on its behavior. This theory forms the basis of the Turing test.[149]
The Dartmouth proposal
“Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.” This assertion was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.[150]
Newell and Simon’s physical symbol system hypothesis
“A physical symbol system has the necessary and sufficient means of general intelligent action.” Newell and Simon argue that intelligences consists of formal operations on symbols.[151] Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a “feel” for the situation rather than explicit symbolic knowledge. (See Dreyfus’ critique of AI.)[152][153]
Gödel’s incompleteness theorem
A formal system (such as a computer program) can not prove all true statements. Roger Penrose is among those who claim that Gödel’s theorem limits what machines can do. (See The Emperor’s New Mind.)[154][155]
Searle’s strong AI hypothesis
“The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.”[156] Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the “mind” might be.[157]
The artificial brain argument
The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original.[158]

[oroma.info] Prediction

Main articles: Artificial intelligence in fiction, Ethics of artificial intelligence, Transhumanism, and Technological singularity

AI is a common topic in both science fiction and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.

In fiction, AI has appeared fulfilling many roles, including a servant (R2D2 in Star Wars), a law enforcer (K.I.T.T. “Knight Rider”), a comrade (Lt. Commander Data in Star Trek: The Next Generation), a conqueror/overlord (The Matrix), a dictator (With Folded Hands), an assassin (Terminator), a sentient race (Battlestar Galactica/Transformers), an extension to human abilities (Ghost in the Shell) and the savior of the human race (R. Daneel Olivaw in the Foundation Series).

Mary Shelley’s Frankenstein[159] considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, including the films I Robot, Blade Runner and A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel human emotions. This issue, now known as “robot rights”, is currently being considered by, for example, California’s Institute for the Future,[160] although many critics believe that the discussion is premature.[161]

The impact of AI on society is a serious area of study for futurists. Academic sources have considered such consequences as a decreased demand for human labor,[162] the enhancement of human ability or experience,[163] and a need for redefinition of human identity and basic values.[164] Andrew Kennedy, in his musing on the evolution of the human personality,[165] considered that artificial intelligences or ‘new minds’ are likely to have severe personality disorders, and identifies four particular types that are likely to arise: the autistic, the collector, the ecstatic, and the victim. He suggests that they will need humans because of our superior understanding of personality and the role of the unconscious.

Several futurists argue that artificial intelligence will transcend the limits of progress. Ray Kurzweil has used Moore’s law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029. He also predicts that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the “technological singularity”.[163]

Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either.[163] This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the science-fiction series Dune.

Edward Fredkin argues that “artificial intelligence is the next stage in evolution,”[166] an idea first proposed by Samuel Butler’s “Darwin among the Machines” (1863), and expanded upon by George Dyson in his book of the same name in 1998.

Pamela McCorduck writes that all these scenarios are expressions of the ancient human desire to, as she calls it, “forge the gods”.[6]

Saturday, July 31, 2010

Life Insurance

Life Insurance

Life insurance or life
assurance
is a contract between
the policy owner and the insurer,
where the insurer agrees to pay a designated beneficiary a sum of money upon the
occurrence of the insured individual’s or individuals’ death or
other event, such as terminal illness or critical illness. In return, the policy
owner agrees to pay a stipulated amount at regular intervals or in lump sums.
There may be designs in some countries where bills and death expenses plus
catering for after funeral expenses should be included in Policy Premium. In the
United States, the predominant form simply specifies a lump sum to be paid on
the insured’s demise.

As with most insurance policies,
life insurance is a contract between the insurer and the policy
owner
whereby a benefit is paid
to the designated beneficiaries if
an insured event occurs
which is covered by the policy.

The value for the policyholder is derived, not from an actual claim event,
rather it is the value derived from the ‘peace of mind’ experienced by the
policyholder, due to the negating of adverse financial consequences caused by
the death of the Life Assured.

To be a life policy the insured
event
must be based upon the
lives of the people named in the policy.

Insured events that may be
covered include:

  • Serious illness

Life policies are legal contracts and the terms of the contract describe the
limitations of the insured events. Specific exclusions are often written into
the contract to limit the liability of the insurer; for example claims relating
to suicide, fraud, war, riot and civil commotion.

Life-based contracts tend to fall into two major categories:

  • Protection policies -
    designed to provide a benefit in the event of specified event, typically a
    lump sum payment. A common form of this design is term insurance.
  • Investment policies – where
    the main objective is to facilitate the growth of capital by regular or
    single premiums. Common forms (in the US anyway) are whole
    life, universal
    life and variable
    life policies.


[Oroma]
Overview


[Oroma]
Parties
to contract

There is a difference between the insured and the policy owner (policy holder),
although the owner and the insured are often the same person. For example, if
Joe buys a policy on his own life, he is both the owner and the insured. But if
Jane, his wife, buys a policy on Joe’s life, she is the owner and he is the
insured. The policy owner is the guarantee and he or she will be the person who
will pay for the policy. The insured is a participant in the contract, but not
necessarily a party to it. However, “insurable
interest” is required to limit an unrelated party from taking life insurance
on, for example, Jane or Joe.

The beneficiary receives policy proceeds upon the insured’s death. The owner
designates the beneficiary, but the beneficiary is not a party to the policy.
The owner can change the beneficiary unless the policy has an irrevocable
beneficiary designation. With an irrevocable beneficiary, that beneficiary must
agree to any beneficiary changes, policy assignments, or cash value borrowing.

In cases where the policy owner is not the insured (also referred to as the celui
qui vit
or CQV), insurance
companies have sought to limit policy purchases to those with an “insurable
interest” in the CQV. For life insurance policies, close family members and
business partners will usually be found to have an insurable interest. The
“insurable interest” requirement usually demonstrates that the purchaser will
actually suffer some kind of loss if the CQV dies. Such a requirement prevents
people from benefiting from the purchase of purely speculative policies on
people they expect to die. With no insurable interest requirement, the risk that
a purchaser would murder the CQV for insurance proceeds would be great. In at
least one case, an insurance company which sold a policy to a purchaser with no
insurable interest (who later murdered the CQV for the proceeds), was found
liable in court for contributing to the wrongful
death of the victim (Liberty
National Life v. Weldon, 267 Ala.171 (1957)).


[Oroma]
Contract
terms

Special provisions may apply, such as suicide clauses wherein the policy becomes
null if the insured commits suicide within
a specified time (usually two years after the purchase date; some states provide
a statutory one-year suicide clause). Any misrepresentations by the insured on
the application is also grounds for nullification. Most US states specify that
the contestability period cannot be longer than two years; only if the insured
dies within this period will the insurer have a legal right to contest the claim
on the basis of misrepresentation and request additional information before
deciding to pay or deny the claim.

The face amount on the policy is the initial amount that the policy will pay at
the death of the insured or when the policy matures,
although the actual death benefit can provide for greater or lesser than the
face amount. The policy matures when the insured dies or reaches a specified age
(such as 100 years old).


[Oroma]
Costs,
insurability, and underwriting

The insurer (the life insurance company) calculates the policy prices with
intent to fund claims to be paid and administrative costs, and to make a profit.
The cost of insurance is determined using mortality tables calculated by actuaries.
Actuaries are professionals who employ actuarial science, which is based in
mathematics (primarily probability and statistics). Mortality tables are
statistically-based tables showing expected annual mortality rates. It is
possible to derive life expectancy estimates from these mortality assumptions.
Such estimates can be important in taxation regulation.[1][2]

The three main variables in a mortality table have been age, gender, and use of tobacco.
More recently in the US, preferred class specific tables were introduced. The
mortality tables provide a baseline for the cost of insurance. In practice,
these mortality tables are used in conjunction with the health and family
history of the individual applying for a policy in order to determine premiums
and insurability. Mortality tables currently in use by life insurance companies
in the United States are individually modified by each company using pooled
industry experience studies as a starting point. In the 1980s and 90′s the SOA
1975-80 Basic Select & Ultimate tables were the typical reference points, while
the 2001 VBT and 2001 CSO tables were published more recently. The newer tables
include separate mortality tables forsmokers and
non-smokers and the CSO tables include separate tables for preferred classes. [3]

Recent US select mortality tables predict that roughly 0.35 in 1,000 non-smoking
males aged 25 will die during the first year of coverage after underwriting.[2] Mortality
approximately doubles for every extra ten years of age so that the mortality
rate in the first year for underwritten non-smoking men is about 2.5 in 1,000
people at age 65.[3] Compare
this with the US population male mortality rates of 1.3 per 1,000 at age 25 and
19.3 at age 65 (without regard to health or smoking status).[4]

The mortality of underwritten persons rises much more quickly than the general
population. At the end of 10 years the mortality of that 25 year-old,
non-smoking male is 0.66/1000/year. Consequently, in a group of one thousand 25
year old males with a $100,000 policy, all of average health, a life insurance
company would have to collect approximately $50 a year from each of a large
group to cover the relatively few expected claims. (0.35 to 0.66 expected deaths
in each year x $100,000 payout per death = $35 per policy). Administrative and
sales commissions need to be accounted for in order for this to make business
sense. A 10 year policy for a 25 year old non-smoking male person with preferred
medical history may get offers as low as $90 per year for a $100,000 policy in
the competitive US life insurance market.

The insurance company receives the premiums from the policy owner and invests
them to create a pool of money from which it can pay claims and finance the
insurance company’s operations. Contrary to popular belief, the majority of the
money that insurance companies make comes directly from premiums paid, as money
gained through investment of premiums can never, in even the most ideal market
conditions, vest enough money per year to pay out claims.[citation
needed
]
Rates charged
for life insurance increase with the insurer’s age because, statistically,
people are more likely to die as they get older.

Given that adverse selection can have a negative impact on the insurer’s
financial situation, the insurer investigates each proposed insured individual
unless the policy is below a company-established minimum amount, beginning with
the application process. Group
Insurancepolicies are an exception.

This investigation and resulting evaluation of the risk is termed underwriting. Health and
lifestyle questions are asked. Certain responses or information received may
merit further investigation. Life insurance companies in the United States
support the Medical Information Bureau (MIB) [4],
which is a clearinghouse of information on persons who have applied for life
insurance with participating companies in the last seven years. As part of the
application, the insurer receives permission to obtain information from the

proposed insured’s physicians.[5]

Underwriters will determine the purpose of insurance. The most common is to

protect the owner’s family or financial interests in the event of the insurer’s
demise. Other purposes include estate planning or, in the case of cash-value
contracts, investment for retirement planning. Bank loans or buy-sell provisions
of business agreements are another acceptable purpose.

Life insurance companies are never required by law to underwrite or to provide
coverage to anyone, with the exception of Civil
Rights Actcompliance requirements. Insurance companies alone determine
insurability, and some people, for their own health or lifestyle reasons, are
deemed uninsurable. The policy can be declined (turned down) or rated.[citation
needed
]
Rating
increases the premiums to provide for additional risks relative to the
particular insured.[citation
needed
]

Many companies use four general health categories for those evaluated for a life
insurance policy. These categories are Preferred Best, Preferred, Standard, and
Tobacco.[citation
needed
]
Preferred Best
is reserved only for the healthiest individuals in the general population. This
means, for instance, that the proposed insured has no adverse medical history,
is not under medication for any condition, and his family (immediate and
extended) have no history of early cancer, diabetes,
or other conditions.[5] Preferred
means that the proposed insured is currently under medication for a medical
condition and has a family history of particular illnesses.[citation
needed
]
Most people are
in the Standard category.[citation
needed
]
Profession,
travel, and lifestyle factor into whether the proposed insured will be granted a
policy, and which category the insured falls. For example, a person who would
otherwise be classified as Preferred Best may be denied a policy if he or she
travels to a high risk country.[citation
needed
]
Underwriting
practices can vary from insurer to insurer which provide for more competitive
offers in certain circumstances.


[Oroma]
Death
proceeds

Upon the insured’s death, the insurer requires acceptable proof of death before
it pays the claim. The normal minimum proof required is adeath
certificate and the insurer’s
claim form completed, signed (and typically notarized).[citation
needed
]
If the
insured’s death is suspicious and the policy amount is large, the insurer may
investigate the circumstances surrounding the death before deciding whether it
has an obligation to pay the claim.

Proceeds from the policy may be paid as a lump sum or as an annuity,
which is paid over time in regular recurring payments for either a specified
period or for
a beneficiary’s lifetime.[citation
needed
]


[Oroma]
Insurance
vs Assurance

The specific uses of the terms “insurance” and “assurance” are sometimes
confused. In general, in these jurisdictions “insurance” refers to providing
cover for an event that might happen (fire, theft, flood, etc.), while
“assurance” is the provision of cover for an event that is certain to happen.
“Insurance” is the generally accepted term, but people using this description
are liable to be corrected. In the United States both forms of coverage are
called “insurance”, principally due to many companies offering both types of
policy, and rather than refer to themselves using both insurance and assurance
titles, they instead use just one.


[Oroma]
Types
of life insurance

Life insurance may be divided into two basic classes – temporary and permanent
or following subclasses – term, universal, whole life and endowment life
insurance.


[Oroma]
Term
Insurance

Term assurance provides life insurance coverage for a specified term of
years in exchange for a specified premium.
The policy does not accumulate cash value. Term is generally considered
“pure” insurance, where the premium buys protection in the event of death
and nothing else.

There are three key factors to be considered in term insurance:

  1. Face amount (protection or death
    benefit),
  2. Premium to be paid (cost to the insured),
    and
  3. Length of coverage (term).

Various insurance companies sell term insurance with many different combinations
of these three parameters. The face amount can remain constant or decline. The
term can be for one or more years. The premium can remain level or increase.
Common types of term insurance include Level, Annual Renewable and Mortgage
insurance.

Level Term policy has the premium fixed for a period of time longer than a year.
These terms are commonly 5, 10, 15, 20, 25, 30 and even 35 years. Level term is
often used for long term planning and asset management because premiums remain
consistent year to year and can be budgeted long term. At the end of the term,
some policies contain a renewal or conversion option. Guaranteed Renewal, the
insurance company guarantees it will issue a policy of equal or lesser amount
without regard to the insurability of the insured and with a premium set for the
insured’s age at that time. Some companies however do not guarantee renewal, and
require proof of insurability to mitigate their risk and decline renewing higher
risk clients (for instance those that may be terminal). Renewal that requires
proof of insurability often includes a conversion options that allows the
insured to convert the term program to a permanent one that the insurance
company makes available. This can force clients into a more expensive permanent
program because of anti selection if they need to continue coverage. Renewal and
conversion options can be very important when selecting a program.

Annual renewable term is a one year policy but the insurance company guarantees
it will issue a policy of equal or lesser amount without regard to the
insurability of the insured and with a premium set for the insured’s age at that
time.

Another common type of term insurance is mortgage
insurance, which is usually a level premium, declining face value policy.
The face amount is intended to equal the amount of the mortgage on the policy
owner’s residence so the mortgage will be paid if the insured dies.

A policy holder insures his life for a specified term. If he dies before that
specified term is up (with the exception of suicide see below), his estate or
named beneficiary receives a payout. If he does not die before the term is up,
he receives nothing. However, in some European countries (notably Serbia),
insurance policy is such that the policy holder receives the amount he has
insured himself to, or the amount he has paid to the insurance company in the
past years. Suicide used to be excluded from ALL insurance policies[when?],
however, after a number of court judgments against the industry, payouts do
occur on death by suicide (presumably except for in the unlikely case that it
can be shown that the suicide was just to benefit from the policy). Generally,
if an insured person commits suicide within the first two policy years, the
insurer will return the premiums paid. However, a death benefit will usually be
paid if the suicide occurs after the two year period


[Oroma]
Permanent

Life Insurance

Permanent life insurance is life
insurance that remains in force (in-line) until the policy matures (pays out),
unless the owner fails to pay the premium when due (the policy expires OR
policies lapse). The policy cannot be canceled by the insurer for any reason
except fraud in the application, and that cancellation must occur within a
period of time defined by law (usually two years). Permanent insurance builds a
cash value that reduces the amount at risk to the insurance company and thus the
insurance expense over time. This means that a policy with a million dollar face
value can be relatively expensive to a 70 year old. The owner can access the
money in the cash value by withdrawing money, borrowing the cash value, or
surrendering the policy and receiving the surrender value.

The four basic types of permanent insurance are whole
life
, universal life, limited
pay
and endowment.


[Oroma]
Whole
life coverage

Whole life insurance provides for
a level premium, and a cash value table included in the policy guaranteed by the
company. The primary advantages of whole life are guaranteed death benefits,
guaranteed cash values, fixed and known annual premiums, and mortality and
expense charges will not reduce the cash value shown in the policy. The primary
disadvantages of whole life are premium inflexibility, and the internal rate of
return in the policy may not be competitive with other savings alternatives.
Also, the cash values are generally kept by the insurance company at the time of
death, the death benefit only to the beneficiaries. Riders are available that
can allow one to increase the death benefit by paying additional premium. The
death benefit can also be increased through the use of policy dividends.
Dividends cannot be guaranteed and may be higher or lower than historical rates
over time. Premiums are much higher than term insurance in the short-term, but
cumulative premiums are roughly equal if policies are kept in force until
average life expectancy.

Cash value can be accessed at any time through policy “loans” and are received
“income-tax free”. Since these loans decrease the death benefit if not paid
back, payback is optional. Cash values support the death benefit so only the
death benefit is paid out.

Dividends can be utilized in many ways. First, if Paid up additions is elected,
dividend cash values will purchase additional death benefit which will increase
the death benefit of the policy to the named beneficiary. Another alternative is
to opt in for ‘reduced premiums’ on some policies. This reduces the owed
premiums by the unguaranteed dividends amount. A third option allows the owner
to take the dividends as they are paid out. (Although some policies provide
other/different/less options than these – it depends on the company for some
cases)


[Oroma]
Universal
life coverage

Universal life insurance (UL) is
a relatively new insurance product intended to provide permanent insurance
coverage with greater flexibility in premium payment and the potential for a
higher internal rate of return. There are several types of universal life
insurance policies which include “interest sensitive” (also known as
“traditional fixed universal life insurance”), variable universal life
insurance, and equity indexed universal life insurance.

A universal life insurance policy includes a cash account but the cash decreases
over time. Premiums increase the cash account, but, the cost of interest
increases each year so the cash deteriorates over time. Interest is paid within
the policy (crOromaed) on the account at a rate specified by the company, but
then mortality charges and administrative costs are then charged against
(reduce) the cash account. The surrender value of the policy is the amount
remaining in the cash account less applicable surrender charges, if any.
Universal Life does not work in a recession or low interest rate environment.

With all life insurance, there are basically two functions that make it work.
There’s a mortality function and a cash function. The mortality function would
be the classical notion of pooling risk where the premiums paid by everybody
else would cover the death benefit for the one or two who will die for a given
period of time. The cash function inherent in all life insurance says that if a
person is to reach age 95 to 100 (the age varies depending on state and
company), then the policy matures and endows the face value of the policy.

Actuarially, it is reasoned that out of a group of 1000 people, if even 10 of
them live to age 95, then the mortality function alone will not be able to cover
the cash function. So in order to cover the cash function, a minimum rate of
investment return on the premiums will be required in the event that a policy
matures.

Universal life insurance addresses the perceived disadvantages of whole life.
Premiums are flexible. Depending on how interest is crOromaed, the internal rate
of return can be higher because it moves with prevailing interest rates
(interest-sensitive) or the financial markets (Equity Indexed Universal Life and
Variable Universal Life). Mortality costs and administrative charges are known.
And cash value may be considered more easily attainable because the owner can
discontinue premiums if the cash value allows it. And universal life has a more
flexible death benefit because the owner can select one of two death benefit
options, Option A and Option B.

Option A pays the face amount at death as it’s designed to have the cash value
equal the death benefit at maturity (usually at age 95 or 100). With each
premium payment, the policy owner is reducing the cost of insurance until the
cash value reaches the face amount upon maturity. But, it does not perform like
a whole life policy when each year the costs increase and never stop. In whole
life, the costs are complete within the first few years of the policy.

Option B pays the face amount plus the cash value, as it’s designed to increase
the net death benefit as cash values accumulate. Option B offers the benefit of
an increasing death benefit every year that the policy stays in force. The
drawback to option B is that because the cash value is accumulated “on top of”
the death benefit, the cost of insurance never decreases as premium payments are
made. Thus, as the insured gets older, the policy owner is faced with an ever
increasing cost of insurance (it costs more money to provide the same initial
face amount of insurance as the insured gets older).


[Oroma]
Limited-pay

Another type of permanent insurance is Limited-pay
life insurance, in which all the premiums are paid over a specified period
after which no additional premiums are due to keep the policy in force. Common
limited pay periods include 10-year, 20-year, and paid-up at age 65.


[Oroma]
Endowments

Endowments are policies in which
the cash value built up inside the policy, equals the death benefit (face
amount) at a certain age. The age this commences is known as the endowment age.
Endowments are considerably more expensive (in terms of annual premiums) than
either whole life or universal life because the premium paying period is
shortened and the endowment date is earlier.

In the United States, the Technical
Corrections Act of 1988 tightened
the rules on tax shelters (creating modified
endowments). These follow tax rules as annuities and
IRAs do.

Endowment Insurance is paid out whether the insured lives or dies, after a
specific period (e.g. 15 years) or a specific age (e.g. 65).


[Oroma]
Accidental
Death

Accidental death is a limited life insurance that is designed to cover the
insured when they pass away due to an accident. Accidents include anything from
an injury, but do not typically cover any deaths resulting from health problems
or suicide. Because they only cover accidents, these policies are much less
expensive than other life insurances.

It is also very commonly offered as “accidental
death and dismemberment insurance”, also known as an AD&D policy.
In an AD&D policy,
benefits are available not only for accidental death, but also for loss of limbs
or bodily functions such as sight and hearing, etc.

Accidental death and AD&D policies very
rarely pay
a benefit; either the
cause of death is not covered, or the coverage is not maintained after the
accident until death occurs. To be aware of what coverage they have, an insured
should always review their policy for what it covers and what it excludes.
Often, it does not cover an insured who puts themselves at risk in activities
such as: parachuting, flying an airplane, professional sports, or involvement in
a war (military or not). Also, some insurers will exclude death and injury
caused by proximate causes due to (but not limited to) racing on wheels and
mountaineering.

Accidental death benefits can also be added to a standard life insurance policy
as a rider. If this rider is purchased, the policy will generally pay double the
face amount if the insured dies due to an accident. This used to be commonly
referred to as a double
indemnity coverage. In some
cases, some companies may even offer a triple indemnity cover.


[Oroma]
Related
Life Insurance Products

Riders are modifications to the insurance policy added at the same time the
policy is issued. These riders change the basic policy to provide some feature
desired by the policy owner. A common rider is accidental death, which used to
be commonly referred to as “double indemnity”, which pays twice the amount of
the policy face value if death results from accidental causes, as if both a full
coverage policy and an accidental death policy were in effect on the insured.
Another common rider is premium waiver, which waives future premiums if the
insured becomes disabled.

Joint life insurance is either a term or permanent policy insuring two or more
lives with the proceeds payable on the first death or second death.

Survivorship life: is a whole life policy insuring two lives with the proceeds
payable on the second (later) death.

Single premium whole life: is a policy with only one premium which is payable at
the time the policy is issued.

Modified whole life: is a whole life policy that charges smaller premiums for a
specified period of time after which the premiums increase for the remainder of
the policy.

Group life insurance: is term insurance covering a group of people, usually
employees of a company or members of a union or association. Individual proof of
insurability is not normally a consideration in the underwriting. Rather, the
underwriter considers the size and turnover of the group, and the financial
strength of the group. Contract provisions will attempt to exclude the
possibility of adverse selection. Group life insurance often has a provision
that a member exiting the group has the right to buy individual insurance
coverage.

Senior and preneed products: Insurance companies have in recent years developed
products to offer to niche markets, most notably targeting the senior market
to address needs of an aging population. Many companies offer policies tailored
to the needs of senior applicants. These are often low to moderate face value
whole life insurance policies, to allow a senior citizen purchasing insurance at
an older issue age an opportunity to buy affordable insurance. This may also be
marketed as final expense
insurance
, and an agent or company may suggest (but not require) that the
policy proceeds could be used for end-of-life expenses.

Preneed (or prepaid) insurance policies: are whole life policies that, although
available at any age, are usually offered to older applicants as well. This type
of insurance is designed specifically to cover funeral expenses
when the insured person dies. In many cases, the applicant signs a prefunded
funeral arrangement with a funeral
home at the time the policy is
applied for. The death proceeds are then guaranteed to be directed first to the
funeral services provider for payment of services rendered. Most contracts
dictate that any excess proceeds will go either to the insured’s estate or a
designated beneficiary.


[Oroma]
Investment
policies

With-profits policies:

Some policies allow the policyholder to participate in the profits of the
insurance company these are with-profits
policies. Other policies have no rights to participate in the profits of the
company, these are non-profit policies.

With-profits policies are used as a form of collective
investment to achieve capital
growth. Other policies offer a guaranteed return not dependent on the company’s
underlying investment performance; these are often referred to as without-profit policies
which may be construed as a misnomer.

Investment Bonds

Pensions: Pensions are a form of life assurance. However, whilst basic life
assurance, permanent
health insurance and non-pensions
annuity business includes an amount of mortality or morbidity
risk for the insurer, for
pensions there is a longevity
risk.

A pension fund will be built up throughout a person’s working life. When the
person retires, the pension will become in
payment,
and at some stage the
pensioner will buy an annuity contract, which will guarantee a certain pay-out
each month until death.


[Oroma]
Annuities

An annuity is a contract with an insurance company whereby the insured pays an
initial premium or premiums into a tax-deferred account, which pays out a sum at
pre-determined intervals. There are two periods: the accumulation (when payments
are paid into the account) and the annuitization (when the insurance company
pays out). IRS rules restrict how you take money out of an annuity.
Distributions may betaxable and/or penalized


[Oroma]
Tax
and life insurance


[Oroma]
Taxation
of life insurance in the United States

Premiums paid by the policy owner are normally not deductible for federal and
state income
tax purposes.

Proceeds paid by the insurer upon death of the insured are not included in gross
income for federal and state income tax purposes;[6]however,
if the proceeds are included in the “estate” of the deceased, it is likely they
will be subject to federal and state estate
and inheritance tax.

Cash value increases within the policy are not subject to income taxes unless
certain events occur. For this reason, insurance policies can be a legal and
legitimate tax
shelter wherein savings can
increase without taxation until the owner withdraws the money from the policy.
On flexible-premium policies, large deposits of premium could cause the contract
to be considered a “Modified Endowment Contract” by theInternal
Revenue Service (IRS), which
negates many of the tax advantages associated with life insurance. The insurance
company, in most cases, will inform the policy owner of this danger before
applying their premium.

The tax ramifications of life insurance are complex. The policy owner would be
well advised to carefully consider them. As always, the United
States Congress or the state
legislatures can change the tax laws at any time.


[Oroma]
Taxation
of life assurance in the United Kingdom

Premiums are not usually allowable against income
tax or corporation
tax, however qualifying policies issued prior to 14 March 1984 do still
attract LAPR (Life
Assurance Premium Relief) at 15% (with the net premium being collected from
the policyholder).

Non-investment life policies do not normally attract either income tax or capital
gains tax on claim. If the policy
has as investment element such as an endowment policy, whole of life policy or
an investment bond then the tax treatment is determined by the qualifying status
of the policy.

Qualifying status is determined at the outset of the policy if the contract
meets certain criteria. Essentially, long term contracts (10 years plus) tend to
be qualifying policies and the proceeds are free from income
tax and capital
gains tax. Single premium contracts and those run for a short term are
subject to income tax depending upon your marginal rate in the year you make a
gain. All (UK) insurers pay a special rate of corporation
tax on the profits from their
life book; this is deemed as meeting the lower rate (20% in 2005-06) liability
for policyholders. Therefore a policyholder who is a higher rate taxpayer (40%
in 2005-06), or becomes one through the transaction, must pay tax on the gain at
the difference between the higher and the lower rate. This gain is reduced by
applying a calculation called top-slicing based
on the number of years the policy has been held. Although this is complicated,
the taxation of life assurance based investment contracts may be beneficial
compared to alternative equity-based collective investment schemes (unit
trusts, investment
trusts and OEICs).
One feature which especially favors investment bonds is the ’5% cumulative
allowance’ – the ability to draw 5% of the original investment amount each
policy year without being subject to any taxation on the amount withdrawn. If
not used in one year, the 5% allowance can roll over into future years, subject
to a maximum tax deferred withdrawal of 100% of the premiums payable. The
withdrawal is deemed by the HMRC (Her
Majesty’s Revenue and Customs) to be a payment of capital and therefore the tax
liability is deferred until maturity or surrender of the policy. This is an
especially useful tax planning tool for higher rate taxpayers who expect to
become basic rate taxpayers at some predictable point in the future (e.g.
retirement), as at this point the deferred tax liability will not result in tax
being due.

The proceeds of a life policy will be included in the estate for death
duty (in the UK, inheritance
tax (IHT)) purposes, except that
policies written in trust may
fall outside the estate. Trust law and taxation of trusts can be complicated, so
any individual intending to use trusts for tax planning would usually seek
professional advice from an Independent
Financial Adviser (IFA) and/or a solicitor.


[Oroma]
Pension
Term Assurance

Although available before April 2006, from this date pension
term assurance became widely
available in the UK. Most UK product providers adopted the name “life insurance
with tax relief” for the product. Pension
term assurance is effectively
normal term life assurance with tax relief on the premiums. All premiums are
paid net of basic rate tax at 22%, and higher rate tax payers can gain an extra
18% tax relief via their tax return. Although not suitable for all, PTA briefly
became one of the most common forms of life assurance sold in the UK until the
Chancellor, Gordon
Brown, announced the withdrawal of the scheme in his pre-budget announcement
on 6 December 2006. The tax relief ceased to be available to new policies
transacted after 6 December 2006, however, existing policies have been allowed
to enjoy tax relief so far.


[Oroma]
History

Insurance began as a way of reducing the risk of traders, as early as 5000 BC in China and
4500 BC in Babylon.
Life insurance dates only to ancient Rome; “burial clubs” covered the cost of
members’ funeral expenses and helped survivors monetarily. Modern life insurance
started in 17th century England,
originally as insurance for traders[7] :
merchants, ship owners and underwriters met to discuss deals at Lloyd’s Coffee
House, predecessor to the famous Lloyd’s
of London.

The first insurance company in the United
States was formed in Charleston,
South Carolina in 1732, but it
provided only fire insurance. The sale of life insurance in the U.S. began in
the late 1760s. The Presbyterian Synods
in Philadelphia and New
York created the Corporation for
Relief of Poor and Distressed Widows and Children of Presbyterian Ministers in
1759; Episcopalian priests
organized a similar fund in 1769. Between 1787 and 1837 more than two dozen life
insurance companies were started, but fewer than half a dozen survived.

Prior to the American
Civil War, many insurance companies in the United States insured
the lives of slaves for their
owners. In response to bills passed in California in
2001 and in Illinois in
2003, the companies have been required to search their records for such
policies. New
York Life for example reported
that Nautilus sold 485 slaveholder life insurance policies during a two-year
period in the 1840s; they added that their trustees voted to end the sale of
such policies 15 years before the Emancipation
Proclamation.



Stranger Originated Life Insurance

Stranger Originated Life Insurance or STOLI is
a life insurance policy that is held or financed by a person who has no
relationship to the insured person. Generally, the purpose of life insurance is
to provide peace of mind by assuring that financial loss or hardship will be
lessened or eliminated in the event of the insured person’s death. STOLI has
often been used as an investment technique whereby investors will encourage
someone (usually an elderly person) to purchase life insurance and name the
investors as the beneficiary of the policy. This undermines the primary purpose
of life insurance as the investors have no financial loss that would occur if
the insured person were to die. In some jurisdictions, there are laws to
discourage or prevent STOLI.


[Oroma]
Criticism

Although some aspects of the application process (such as underwriting and
insurable interest provisions) make it difficult, life insurance policies have
been used in cases of exploitation and fraud. In the case of life insurance,
there is a motivation to purchase a life insurance policy, particularly if the
face value is substantial, and then kill the insured. Usually, the larger the
claim, and/or the more serious the incident, the larger and more intense will be
the number of investigative lawyers, consisting in police and insurer
investigation, eventually also loss
adjusters hired by the insurers
to work independently.[8]

The television
series Forensic
Files
has included episodes
that feature this scenario. There was also a documented case in 2006, where two
elderly women are accused of taking in homeless men and assisting them. As part
of their assistance, they took out life insurance on the men. After the
contestability period ended on the policies (most life contracts have a standard
contestability period of two years), the women are alleged to have had the men
killed via hit-and-run car crashes.[9]

Recently, viatical
settlements have created problems
for life insurance carriers. A viatical settlement involves the purchase of a
life insurance policy from an elderly or terminally ill policy holder. The
policy holder sells the policy (including the right to name the beneficiary) to
a purchaser for a price discounted from the policy value. The seller has cash in
hand, and the purchaser will realize a profit when the seller dies and the
proceeds are delivered to the purchaser. In the meantime, the purchaser
continues to pay the premiums. Although both parties have reached an agreeable
settlement, insurers are troubled by this trend. Insurers calculate their rates
with the assumption that a certain portion of policy holders will seek to redeem
the cash value of their insurance policies before death. They also expect that a
certain portion will stop paying premiums and forfeit their policies. However,
viatical settlements ensure that such policies will with absolute certainty be
paid out. Some purchasers, in order to take advantage of the potentially large
profits, have even actively sought to collude with uninsured elderly and
terminally ill patients, and created policies that would have not otherwise been
purchased. Likewise, these policies are guaranteed losses from the insurers’
perspective.