No trabalho de inteligência artificial, no âmbito da disciplina de área de projecto, eu, Daniel Filipe Mirra Carneiro, aluno nº5, da turma C do 12º ano, tive como tarefas a pesquisa e realização da história da inteligência artificial e a pesquisa e elaboração de um texto sobre a inteligência artificial e a ética. No inicio deste primeiro período, as minhas ambições na disciplina de área de projecto não eram muito elevadas. Eu tinha como objectivos apenas completar as tarefas definidas para mim. As minhas tarefas eram elaborar a história da inteligência artificial e fazer um texto relacionando ética e IA. Dentro do tempo estipulado, eu cumpri esses objectivos. Na história da inteligência artificial procurei tratar os dados recolhidos e colocá-los por datas de modo que ficasse por ordem cronológica os acontecimentos (mais marcantes na história da IA). Na elaboração do texto sobre a ética e a IA, eu fiz uma pequena introdução e defini o que era a ética. Depois desenvolvi o tema da ética e a inteligência artificial (evolução tecnológica), concluindo ao fazer uma síntese do que falei no desenvolvimento e apresentando algumas soluções. No segundo período espero estar ao mesmo nível, ou subir mais um pouco no meu rendimento, excedendo as minhas expectativas.

História

A inteligência artificial pode remontar à antiguidade clássica porque era um sonho antigo dessas civilizações, mas esse sonho só pode ser realizado recentemente com o desenvolvimento do primeiro computador electrónico em 1941, e assim, finalmente a tecnologia tornou-se capaz de criar máquinas inteligentes. No séc. XIX, surge a figura de Alan Turing mas só em 1956 é que a “Inteligência Artificial” começa a ser reconhecida.

Nesta parte do trabalho vamos elaborar uma lista por datas sobre a história da inteligência artificial:

ü 1943 - 1956: A gestação.

1943: Primeiro trabalho - Modelo artificial de neurónios (Warren McCulloch e Walter Pitts)

1949: Algoritmo para modificar os pesos das ligações entre os neurónios (Donald Hebb)

ü Início dos anos 50: Programas de xadrez para computador (Claude Shannon 1950 e Alan Turing 1953)

1951: Primeira rede neuronal em computador (Marvin Minsky e Dean Edmonds)

ü 1952 - 1969: Período de muito entusiasmo e grandes expectativas (muitos avanços com sucesso).

No fim de 1955: Logic Theorist (LT) - programa que era capaz de provar teoremas. (Allen Newell e Herbert Simon);

1956: Conferência Dartmouth (10 participantes), organizada por John McCarthy, que é considerado o pai da inteligência artificia. Sete anos após a conferência, a inteligência artificial começou a ganhar ímpeto. Embora esse campo estivesse ainda indefinido, as ideias formadas na conferência foram reexaminadas e foram substituídas. Para isso foram criados centros para pesquisa como por exemplo o MIT e foram em busca de novos desafios, como por exemplo criar que possam aprender por eles mesmos.

Em 1957 foi desenvolvido General Problem Solver (GSP), que imitava o homem na forma como resolvia os problemas. Dentro das classes de puzzles limitados que resolvia, chegou-se à conclusão que a forma em como dividia um objectivo em sub-objectivos e possíveis acções era similar à forma em como o homem o fazia (Newell e Simon).

1958: John McCarthy no Lab Memo No.1 do MIT define a linguagem de programação Lisp (List Processing) que se transformou na linguagem dominante da IA. O Lisp é a segunda linguagem de programação mais antiga ainda em uso. A linguagem Fortran é um ano mais antiga.

Ainda em 1958 McCarthy publicou um artigo intitulado “Programs with common sense”, onde descrevia um programa hipotético designado por “Advice taker”, o qual pode ser visto como o primeiro sistema completo da IA. Este artigo não perdeu a sua relevância ao fim de mais de 40 anos.

Em 1959,a IBM começa a produzir alguns programas de AI, entre os quais o Geometry Theorem Prover.

Durante o ano 1952 e 1969 Arthur Samuel desenvolveu um programa capaz de jogar damas ao nível de um jogador de torneio (o programa jogava melhor do que o seu autor).

ü 1966 - 1974: Uma dose de realidade.

Década de 70: Sistemas com uma base de conhecimento

(knowledge base systems), que ao contrário dos métodos fracos (métodos que usam pouca informação acerca do domínio do problema e mecanismos gerais de procura) os sistemas que dispõem de uma base de conhecimento podem resolver problemas mais complexos, por exemplo:

·        DENDRAL - Análise de compostos orgânicos para determinar a sua estrutura molecular.

·        MYCIN – Sistema pericial (expert system) capaz de diagnosticar infecções no sangue (dispunha de mais de 450 regras). Este sistema tinha um desempenho tão bom como o de alguns médicos especialistas e melhor do que o de médicos ainda com pouca experiência.

Durante os anos 70 alguns métodos foram desenvolvidos e testados para a AI. Durante este período novas teorias foram criadas como por exemplo a PROLOGUE language (programação em lógica).

ü Década de 80: IA transforma-se numa industria

Em 1981o Japão lança o projecto “Quinta geração”, um plano para construir em 10 anos computadores inteligentes. As instruções dos processadores eram instruções em PROLOG. Estes sistemas deveriam ser capazes de fazer milhões de inferências por segundo.

Uma das ambições do projecto era a compreensão da linguagem natural.

Este projecto veio revitalizar a IA em todo o mundo.

1982: Surge o primeiro sistema pericial a ser comercializado, o R1. O programa ajudava a configurar encomendas de computadores. Em 1986 estimou-se que a Digital tinha poupado cerca de 40 milhões de dólares graças ao R1.

Em 1986 retomou-se o uso das redes neuronais artificias.

ü Anos 90 e século XXI

1991: Sistemas de IA utilizados com sucesso na guerra do Golfo.

1991: Um sistema pericial analisa um caso médico, chega a um diagnóstico e é capaz de explicar porque chegou a esse diagnóstico, expondo os factores que mais o influenciaram.

Em 1993 é criado um sistema capaz de conduzir um carro numa auto-estrada a cerca de 90 Km/h. O sistema usa câmaras de vídeo, radar e lasers, para se aperceber do que o rodeia.

Também em1993 é desenvolvido um sistema detecta colisões na rua, chamando automaticamente o 112.

1994: Um sistema de reserva de viagens é capaz de entender frases como “quero ir de Boston para São Francisco”. O sistema percebe mal uma em cada 10 palavras, mas é capaz de recuperar, porque compreende a forma em como as frases são compostas.

1997: O “Deep Blue” (programa concebido para jogar xadrez) vence Kasparov.

No ano de 2000 começam a surgir brinquedos inteligentes.

(2001: Computador comunica ao nível de uma criança com 15 meses.)

http://www.dennisgorelik.com/ai/overview.htm

Universal Music International | ISBN-13: 9780262201759 | English | PDF | 1347 Pages | Size: 5.35 MB

Introduction
Hundreds of programming languages are in use today—scripting languages for Internet commerce, user interface programming tools, spreadsheet macros, page format specification languages, and many others. Designing a programming language is a metaprogramming activity that bears certain similarities to programming in a regular language, with clarity and simplicity even more important than in ordinary programming. This comprehensive text uses a simple and concise framework to teach key ideas in programming language design and implementation. The book’s unique approach is based on a family of syntactically simple pedagogical languages that allow students to explore programming language concepts systematically. It takes as its premise and starting point the idea that when language behaviors become incredibly complex, the description of the behaviors must be incredibly simple.

Table of Contents

Preface xix
Acknowledgments xxi
I Foundations 1
1 Introduction 3
2 Syntax 19
3 Operational Semantics 45
4 Denotational Semantics 113
5 Fixed Points 163
II Dynamic Semantics 205
6 FL: A Functional Language 207
7 Naming 307
8 State 383
9 Control 443
10 Data 539
III Static Semantics 615
11 Simple Types 617
12 Polymorphism and Higher-order Types 701
13 Type Reconstruction 769
14 Abstract Types 839
15 Modules 889
16 Effects Describe Program Behavior 943
IV Pragmatics 1003
17 Compilation 1005
18 Garbage Collection 1119
A A Metalanguage 1147
B Our Pedagogical Languages 1197
References 1199
Index 1227

*_____________________________________________________

A Strong AI versus a Weak AI, definitions and ideas

Published July 18, 2006 Artificial Intelligence , Object Oriented Design , Ontologies , Semantic Web , Technological Singularity , Web 2.0

For those who aren’t as well versed as some of the AI researchers, there is a fundamental difference between Strong AIs and where current AI research is. If you aren’t aware, expert systems are used commonly in software design. Expert systems are a part of Weak AIs. For the moment, Türing type AIs are out of reach. Türing type AIs are like the ones represented in popular speculative fiction.

But how far are they, really?

From Wikipedia’s entry on Strong AIs.

In the philosophy of artificial intelligence, strong AI is the supposition that some forms of artificial intelligence can truly reason and solve problems; strong AI supposes that it is possible for machines to become sapient, or self-aware, but may or may not exhibit human-like thought processes. The term strong AI was originally coined by John Searle, who writes:

“according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind”

The term “artificial intelligence” would equate to the same concept as what we call “strong AI” based on the literal meanings of “artificial” and “intelligence“. However, initial research into artificial intelligence was focused on narrow fields such as pattern recognition and automated scheduling, in hopes that they would eventually allow for an understanding of true intelligence. The term “artificial intelligence” thus came to encompass these narrower fields (”weak AI”) as well as the idea of strong AI.

Weak AI

In contrast to strong AI, weak AI refers to the use of software to study or accomplish specific problem solving or reasoning tasks that do not encompass (or in some cases, are completely outside of) the full range of human cognitive abilities. An example of weak AI software would be a chess program such as Deep Blue. Unlike strong AI, a weak AI does not achieve self-awareness or demonstrate a wide range of human-level cognitive abilities, and is merely an (arguably) intelligent, more specific problem-solver.

Some argue that weak AI programs cannot be called “intelligent” because they cannot really think. In response to claims that weak AI software such as Deep Blue are not really thinking, Drew McDermott wrote:

“Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn’t really fly because it doesn’t flap its wings.”

He argued that Deep Blue does possess intelligence and is simply lacking breadth of intelligence.

Others note that Deep Blue is merely a powerful, heuristic search tree, stating that claims of it “thinking” about chess are similar to claims of single cells “thinking” about protein synthesis; both are unaware of anything at all, and both merely follow a program which has been encoded within them. Many among these critics are proponents of Weak AI, claiming that machines can never be truly intelligent, while other, Strong AI proponents simply state that true self-awareness and thought as we know it may require a specific kind of “program” designed to observe and take into account the processes of one’s own brain. Many evolutionary psychologists point out that humans may have developed just such a program especially strongly for the purpose of social interaction or perhaps even deception, two behaviours at which humans are vastly superior to other animals.

General artificial intelligence

General artificial intelligence research aims to create AI that can replicate humans intelligence completely, often called an Artificial General Intelligence (AGI) to distinguish from less ambitious AI projects. As yet, researchers have devoted little attention to AGI, many claiming intelligence is too complex to be completely replicated. Some small groups of computer scientists are doing some AGI research, however. Organizations pursuing AGI include the Adaptive AI, Artificial General Intelligence Research Institute (AGIRI), CCortex, CodeSimian, Novamente LLC and the Singularity Institute for Artificial Intelligence. One recent addition is Numenta, a project based on the theories of Jeff Hawkins, the creator of the Palm Pilot. While Numenta takes a computational approach to general intelligence, Hawkins is also the founder of the RedWood Neuroscience Institute, which explores conscious thought from a biological perspective.

By most measures, demonstrated progress towards strong AI has been limited. No system can pass a full Turing test for unlimited amounts of time, although some AI systems do now fool some people at least initially (see the Loebner prize winners). Few active AI researchers are prepared to publicly predict whether, or when, such systems will be developed, perhaps due to the failure of bold, unfulfilled predictions for AI research progress in past years. There is also the problem of the AI effect, where any achievement by a machine tends to be deprecated as a sign of true intelligence.

Philosophy of strong AI and consciousness

John Searle and most others involved in this debate address whether a machine that works solely through the transformation of encoded data could be a mind, not the wider issue of monism versus dualism (i.e., whether a machine of any type, including biological machines, could contain a mind).

Searle states in his Chinese room argument that information processors carry encoded data which describe other things. The encoded data itself is meaningless without a cross reference to the things it describes. This leads Searle to point out that there is no meaning or understanding in an information processor itself. As a result Searle claims that even a machine that passed the Turing test would not necessarily be conscious in the human sense.

Some philosophers hold that if Weak AI is possible then Strong AI must also be possible. Daniel C. Dennett argues in Consciousness Explained that if there is no magic spark or soul, then Man is just a machine, and he asks why the Man-machine should have a privileged position over all other possible machines when it comes to intelligence or ‘mind’. In the same work, he proposes his Multiple Drafts Model of consciousness. Simon Blackburn in his introduction to philosophy, Think, points out that you might appear intelligent but there is no way of telling if that intelligence is real (i.e., a ‘mind’). However, if the discussion is limited to strong AI rather than artificial consciousness it may be possible to identify features of human minds that do not occur in information processing computers.

Many strong AI proponents believe the mind is subject to the Church-Turing thesis. This belief is seen by some as counter-intuitive and even problematic, because an information processor can be constructed out of balls and wood. Although such a device would be very slow and failure-prone, it could do anything that a modern computer can do. If the mind is Turing-compatible, it implies that, at least in principle, a device made of rolling balls and wooden channels can contain a conscious mind.

Roger Penrose attacked the applicability of the Church-Turing thesis directly by drawing attention to the halting problem in which certain types of computation cannot be performed by information systems yet are alleged to be performed by human minds.

Ultimately the truth of Strong AI depends upon whether information processing machines can include all the properties of minds such as consciousness. However, Weak AI is independent of the Strong AI problem and there can be no doubt that many of the features of modern computers such as multiplication or database searching might have been considered ‘intelligent’ only a century ago.

Methods of production

Computer simulating human brain model

This is seen by many as the quickest means of achieving strong AI, as it doesn’t require complete understanding. It would require three things:

• Hardware. An extremely powerful computer would be required for such a model. Futurist Ray Kurzweil estimates 1 million MIPS. If Moore’s law continues, this will be available for £1000 by 2020.
• Software. This is usually considered the hard part. Firstly it relies on the assumption that the human mind is the central nervous system and is governed by physical laws.
• Understanding. Finally, it requires sufficient understanding thereof to be able to model it mathematically. This could be done either by understanding the central nervous system, or by mapping and copying it. Neuroimaging technologies are improving rapidly, and Kurzweil predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.

Once such a model is built, it will be easily altered and thus open to trial and error experimentation. This is likely to lead to huge advances in understanding, allowing the model’s intelligence to be improved/motivations altered.

Current research in the area is using one of the fastest supercomputer architectures in the world, namely the Blue Gene platform created by IBM to simulate a single Neocortical Column consisting of approximately 60,000 neurons and 5km of interconnecting synapses. More information can be found here.

The eventual goal of the project is to use supercomputers to simulate an entire brain.

Prospective applications

This section may contain original research or unverified claims.
Please help Wikipedia by adding references. See the talk page for details.

Seed AI/technological singularity

A strong AI which performs recursive improvement would increase in intelligence indefinitely and exponentially, starting on the human level. The vastly superhuman intelligence thus produced would be capable of developing technology at a far faster rate than human scientists. Arguably it would be impossible for humans of relatively minuscule intelligence to predict what it would come up with - thus the term singularity.

Assuming that the functional human model approach is taken, some modifications will need to be made before this can occur.

The most significant being alterations to its motivations. Evolutionary psychology holds that humans are entirely motivated by an intricate set up of, ‘desire for anticipation of pleasure’ and ‘desire for anticipation of pain avoidance’ developed by natural selection. From this, it is posited, stems all human desires.

With an understanding of the model, all the desires of the model could be removed and new ones added - recursive self improvement being necessary for a technological singularity. Arguably the most important thing is to equip the Seed AI with only the desire to serve mankind - implicit in this is self improvement. For this reason the Singularity Institute for Artificial Intelligence was set up.

Note - if evolutionary psychology is wrong, we will be able to find out from the model.

The Arts

A strong AI may be able to produce original works of music, art, literature and philosophy. It should be noted however, that a strong AI is not a necessary requirement for the creation of novel works of art. There have already been weak AI painting programs created that have been able to manipulate a paintbrush through external hardware in order to paint original, non-random and interesting pieces of art. AAron is one example of such software. More information can be found here: http://www.stanford.edu/group/SHR/4-2/text/cohen.html

Cognitive Robotics

Cognitive Robotics involves applying various fields of Artificial Intelligence to Robotics. Strong AI in particular would be a great asset to this field.

Comparison of computers to the human brain

Parallelism vs speed

The brain gets its power from performing many parallel operations, a computer from performing operations very quickly.

The human brain has roughly 100 billion neurons operating simultaneously, connected by roughly 100 trillion synapses. Although estimates of the brain’s processing power put it at around 10^14 neuron updates per second, it is expected that the first unoptimized simulations of a human brain will require a computer capable of 10^18 FLOPS. By comparison a general purpose CPU (circa 2006) operates at a few GFLOPS.

However, a neuron is estimated to spike 200 times per second (this giving an upper limit on the number of operations). Signals between them are transmitted at a maximum speed of 150 meters per second. A modern 2GHz processor operates at 2 billion cycles per second or 10,000,000 times faster than a human neuron and signals in electronic computers travel at roughly the speed of light (300 000 kilometres per second). Even so, the limited number of transistors and their functional properties mean that they cannot replicate human brain functions.

If nanotechnology allowed the construction of devices of similar size and parallelism to the brain running at the speed of a modern computer, then a human model within would experience time as if it were occurring more slowly than it really was (relative to how humans experience time). Thus, an artificial brain could experience the elapsing of 1 minute as actually taking much longer, perhaps as if it were several hours. However, since the perception of how long something takes is different from the actual duration of the time period, how the artificial brain perceives the time period would depend entirely on the calculations and specific type of cognition during that time period.

The relationship between HLAI and the human brain

The data structure of a human brain and something like a calculator are totally different. On one hand a calculator can process thousands of equations each second but the human brain processes only 1 equation per second. This doesn’t mean that the calculator is more superior than a human brain. It just means that the brain is a different form of computer that processes information differently. The human brain is a very powerful computer that can learn from past experiences and understand common sense knowledge which is something current computers can’t do.

The human brain consists of 10 billion neurons and 60 trillion connections. The data are stored in the neurons in terms of encapsulation and commonality. Although the brain has only 10 billion neurons it is able to store almost 8,000 trillion data because of the connections that each neuron has with other neurons. The data are also global in nature and each neuron will have associations with other neurons. All of the neurons and their connections are either strengthened or forgotten. The neurons get strengthened by a process of chemical electricity that makes their connections with other neurons stronger (or weaker).

When an object is recognized like an image or a sound, electricity is run through that neuron and its connections. This is how psychologists can understand what parts of the brain does what functions — by using a computer to analyze the electrical activities in the brain. Since there are many sensations coming into our brain each second, there isn’t just one area the brain is active but activity will run in multiple areas of the brain at the same time.

I did some observation of how the brain sends electricity throughout the neurons and came to the conclusion that we can actually simulate this activity in a software. First the brain locates an object (let’s call this object the target object). In this case an object could be anything — it can be an image of a car or a sound of a dog barking. Once the brain locates the target object in memory it runs electricity throughout all of the connections associated with that object. This will strengthen not only the target object that has been located but it will bring all the other objects (call these element objects) closer to the target object.

When the AI locates the three visual objects: A, B, C in memory it will run electricity through these nodes and all of its connections.

The mind has a fixed timeline. Only one element object can be activated at a given time in this timeline. This is how we prevent too much information from being processed and allow the AI to focus on the things that it senses from the 5 senses.

This finding is important because we know that the target object that the brain has located has to be strengthened. This is done by applying chemical electricity through that located target object. The only question I had was: “why did the electricity propagate throughout all of its connections too?”. Would that not strengthen all the element objects around the target object too?

The reason why the brain had to propagate electricity throughout all of the target object’s connections is because that is how the conscious is presented. The conscious is the voice in your head that speaks to you. It also gives you information about a situation, or help you solve a problem, or tell you definition of words. All the element objects from all the target objects will compete with one another to activate in the mind (the mind can only take in a limited amount of information). When that information is activated in the mind a lesser amount of electricity will be applied to that information and its connections. This is how the mind travels from one subject matter to the next.

The brain modifies information by constantly applying chemical electricity throughout all the target objects coming in from the 5 senses. The electricity strengthens not only that target object but it strengthens all the element objects that have association with the target object. This form of storing, retrieving, and modifying information in a network is what allows the host to have human-level intelligence.

Learning from childhood to adulthood and how the pathways become more complex

When the machine is at its early stages of life, it will have to build its pathways from simple data then as it gets older and there are more data in memory it will organize the pathways into complex intelligence. Just like how we humans have to learn to walk, to talk, to move, to eat, these machines have to go through life the same way. Lets illustrate the gradual forming of simple data into intelligent data by outlining a series of stages.

1. innate reflexes

2. trained to do things

3. sequential events

4. sentence commands

5. give robot option commands

6. practice makes perfect

7. copy other peoples behavior

1. innate reflexes

In this stage the robot will learn all the different objects that are in the environment from the 5 senses. Things like cat, dog, table, chair, red, blue, car, house, I, her, him, loud, soft etc. are learned and stored in memory. The 3-dimensional floater of all the objects will be created. Then the robot will start to move its arms and legs from innate built in reflexes. Movement of the arms, the legs, movement of the mouth, and controlling the vocal cords are the things that the robot must learn first. These experiences must be stored in memory in an organized way. Curiosity will be the factor that steers the robot into doing things that it never did before. Things like new objects it never learned before will have top priority over old objects it learned. New sensations will be more focused on then old sensations. By the time the robot learns most of the objects around him its memory banks will be filled with data and things around the robot will be more familiar. Meaning of the objects will also be established.

2. Trained to do things

This part is where a teacher will guide the robot into doing things that are appropriate and to force the robot to learn things that it supposed to know. Things like walking, and grabbing object, and throwing things around must be learned. The guide is used so that the robot will learn important things that it can use to control the environment. A thing like walking is important because you want to get from one destination to another. Writing using a pencil is important because it must learn to write letters. Things like walking and writing and speaking must be learned by a guide because we cant preprogram the robot to learn these things.

Although the guide isnt something we want to store in memory, the point is that the more we guide it the stronger the desired created pathway will be. When it is strong enough it can be used by itself and the guide pathway will be forgotten. The robot will find a way to use the desired created pathway to accomplish a goal. Walking for example, if the robot knows that walking will get it from one destination to the next, then when it sees food, it will use the walking path to go from its current location to the food. Reward is also playing a part in this learning process.

Also, during this process simple sequential consequences will be understood. Things like what is the consequence of dropping a ball, where should the ball be when you drop it, and solid objects and soft objects have different properties.

3. Sequential events

In this stage the robot begins to learn how objects interact with one another. When two objects hit each other both objects suffer, when the robot fall down its painful, when it grabs a solid object it has the same shape, but if it grabs a soft object it bends its shape. So, sequential events will be learned. The consequences of the robots actions in comparison to the environment will also be learned. By learning all these things the individual data in memory will turn more complex and long. The robot will be able to piece together the outcome of an event just by looking at its past. Another thing to remember is that curiosity is the key to new pathways. The more unique the event is the more the robot wants to learn it. The old events it learned many times will be ignored because it learned it already, but the new sensations will guide it to learn new things. Think of curiosity as a form of pleasure and old sensation as pain. Since this robot does things in terms of pleasure it will look for new data from the environment. At this stage things like lying and magic cant be distinguished yet. The robot will not be able to lie yet and if it sees a man flying in the sky or walking on water the robot will think it is real.

4. Sentence commands

This part will require the robot to know basic grammar like the names of most objects that are around the environment. These basic grammar must be thought to the robot and understood by the robot. The rules program will do the rest by assigning the meaning for the grammar. Even hidden objects must be understood like jump, run, walk, loud, soft, etc. Once a basic language is established we can combine sequential events with grammar and force the robot to do things by using words as the tool. An example would be if you said sit and the robot sits. When you say: pick up the book the robot will pick up the book. When you say: read the first paragraph and the robot reads the first paragraph of the book. These are commands that you give to the robot to indicate what you want it to do. There is no deception, or lying involved in the command process. Its simply someone giving a command and the robot taking the action. The robot may not understand what you said and make a mistake, but having a voice in the head that tells the robot to do things hasnt been created yet.

5. Giving the robot option commands

This part is an extension of the last stage. Instead of saying a word and letting the robot do things we can add trees to the command pathways and let the robot decide what it wants to do. This is very affective because trees combined with commands allow the robot to use if statements to accomplish a goal.

So, the tree decides what the robot will do. If a teacher gives the command then the robot will listen, if its a friend that gives the command the robot wont listen. There are also innate likes and dislikes the robot will have and there are commands out there that tap into that kind of thing. For example, if the robot was given this command: pick the food you like to eat. Within the robots memory there are powerpoints that determine an objects worth. PM will tap into that and pick the one with the most powerpoints. Commands like: pick the color you like, eat the food you like, play with the toy you like, buy the present you want, wear the clothes you love, and so forth will all depend on the robot. These likes and dislikes can also be a learned thing.

6. Practice makes perfect

Now, lets get on with a more complex way the pathways can be formed. When we practice something like riding a bike, we are actually creating new pathways to ride the bike. Practicing will help the robot to decide the best newly created pathway to pick to accomplish a goal. We can build a pathway in memory that will treat practicing something as a command.

This simple example shows that by using English we can guide the robot to do infinite amounts of tasks. The above example is a practice pathway. It uses a command that will tell the robot to do something until a desired outcome is present. If it doesnt accomplish the goal then it will repeat itself until the task is completed. At the same time this is happening more trees can be added to this practice pathway like, if you practiced for 7 times and you still didnt accomplish a goal then quite. Or when you are hungry and you dont have the strength to shoot then stop practicing. The existing pathways will add, strengthen, or minus trees from it as the robot learns more. Instead of following commands there are other factors to consider before you take action to accomplish the commands. The robot will do the things that a society will consider appropriate at the time. If a society says it should lie in order to not do the task then thats what the robot will do. If a society says the command isnt appropriate in this type of situation then the robot will not follow it. If the robot finds the command dangerous and it can really damage itself, then it will not carry out the command. This is where the inner voice that is the core of the consciousness is built. The consciousness is the average of the things thought to the robot by society.

7. Copy other peoples behavior

This part is a very powerful tool used to learn things. We can go ahead and train a tree that will allow the robot to copy certain things from what it sees. Things that it sees on TV will be learned and copied by the robot. Copying will allow the robot to learn the most appropriate things to do in a society. When it is in a situation it will do things in terms of what society as a whole did. The way it dresses, the way it behaves in school, the things that it likes/dislikes, how to dress, how to take care of itself, how to get money, how to get food to survive, what to say to certain people, how to make friends, how to get good grades in school, and finding answers to questions. All these things are pathways that were learned by copying other people in our environment.

This part will require not only trees but also relations to past data and innate instructions of the robot. Pattern matching will find these hidden things and put them in the pathways. Something as complex as copying people require that you understand the relationship between the robot and other objects. If other people move their hand, you will copy them by moving your hand. You would need to know that your hand is one object and it belongs to you as an individual and that the other person you try to copy has a hand too and they are an individual too. Also, you have to understand when to copy them. If a copy is one second after you see the person do the action, then one second is the time it takes to copy their action.

From all these pathways we can build on each other and make even more complex thinking such as representing a hierarchy system. Things like parent-child relationships, who is the grandfather of the family, or what does having a brother really mean, will be represented by complex thinking. When people say thats your father, there are lots of complex things we need to know before we can understand that kind of thing. Complex things such as: where do humans come from?, or parents are supposed to take care of their kids or everyone has one female parent and a male parent or the male parent is the father and the female parent is the mother. It is a very complicated intelligent system when it comes to representing a family tree and in order to understand it we must first learn the simple things.

Robotics

The big question is: how are the reflexes and innate traits built into the robot? The answer to the question isnt perfect but we can observe the human body and how it functions and simulate this behavior on the robot. Things such as the taste of a food should yield the same taste for both the human and the robot. Pain and the degree of pain for certain senses should be the same. The attractiveness or the ugliness of objects should be the same too. The field of robotics has already accomplished a lot in terms of making robotic parts similar to human parts. Im sure there are a lot more improvements that can be made to advance the science of robotics in the near future.

Remember, we arent trying to duplicate human behavior exactly, we are trying to create something similar. As long as the traits that humans and robots have are similar they can learn to understand each other. The dissimilar traits between robots and humans can be understood by complex intelligence.

Other topics:

1. Learning language and common sense knowledge
2. AI two-player games using the universal artificial intelligence program

3. Autonomous cars — how the AI will drive in the city

4. Image processing — robot vision

Objects
Objects can be anything. It can be sound, it can be vision, it can be touch, and so forth. A visual word can be an object, a sound of a word can be an object, or the visual meaning of the word can be an object. For different medias the objects can be represented differently. There is also the consideration of combinations of objects together such as a visual object in conjunction with a sound object. A car zooming by is a combination of a visual object and the zoom sound is the sound object. Or dropping a pencil on the ground is a combination of visual and sound objects.

Another factor is that objects can be encapsulated. For example, a hand is an object that is encapsulated in another object, a human being. Another example is a foot is an object encapsulated in another object, a leg.

The way the program learns these objects is by repetition and patterns. Each object is represented by strength and if it ever repeats itself the strength gets stronger. If the object don’t repeat itself then it will forget and memory won’t have a trace. 1-d, 2-d, 3-d, 4-d, and N-d objects can be created by repetition and patterns.

Object association is the key to the conscious
For each object the AI has to find other objects in memory that have association. “The more times two objects are trained together” and “the closer the timing of the two objects are” the more association the two objects have with one another. The object that will be used to find associations is called the target object and the objects that have associations are called element objects.

When the AI recognizes the target object from the environment it will activate closest element objects that have association to the target object. There are three types of element objects:

1. equals (same meaning)
2. stereotypes
3. trees

Equals
Objects that are very close to each other are considered “equal”. When any element object past the assign threshold the element object and the target object are considered equal – they have the same meaning. One example of this is the sound “hat”, if the sound “hat” is the target object and the element object that passes the assign threshold is a visual image of a hat then both the sound “hat” and the visual image of hat is considered the same.

Stereotypes
Stereotypes are facts about the target object. Objects that are associated with the target object but are not consistent are stereotypes. These objects are also farther away from the target object. We look at the fixed object as a part of the overall object. If the target object is “cat” and “cat” is a part of “cats don’t like dogs”, then we can safely say that “cats don’t like dogs” is a stereotype of “cat”.

Trees
Trees are objects that are usually farther away from the target object. Sometimes trees have relations to the target object. A tree is just instructions that people teach you at certain situations. Timing of the object is the key difference between stereotypes and trees. This is the most important trait in my program to convey intelligence. One example of trees is when you cross the street, the tree “look left, look right and check to make sure there are no cars before crossing the street” pops up in your mind.

Activation of objects produces the conscious
I mentioned before that the HLAI inherits everything from the UAI. If I apply the above lessons along with the UAI program then you have a machine that can “think”. As the AI experience things in our environment it recognizes objects from memory and activates its element objects. The object the robot recognizes has associations to other objects such as meaning and stereotypes. When the AI see a cat the meaning of the cat pops up. This could be a sound “cat” or the visual word cat. All the meaning of the object cat will compete with one another to be activated (pop up in the mind). Next, the stereotypes of cat will start to pop up in the robots mind such as “cats don’t like dogs”. Element objects that are closest to the target object gets activated first before the element objects that are farther away gets activated. It also depends on how long the AI focuses on the target object.

In the case the AI is on the cross walk, getting ready to cross the street, the tree “look left, look right and check to make sure there are no cars before crossing the street” pops up in its mind. This tree is learned previously by teachers many times and the association between crossing the street and the tree becomes stronger. The timing of when to train the tree to pop-up is important.

The importance of language
Language is very important to producing machines that can do complex thinking. The words in the language encapsulate things around us in a “fuzzy” way. For example, the word cat can mean a lot of things. Cats can come in different variety of shapes and sizes and colors. All the different objects in our environment that is classified as a cat is actually encapsulated in the word “cat”. Another word that is encapsulated is “jump”. There are infinite ways that the word can be expressed in the environment. You can be watching a person from the back and seeing them jump, you can be watching a person from the front, side, bottom, top, 50 yards away, 1000 yards away, etc. The person doing the jumping can also be wearing different clothes or they could be different species. All these various ways of interpreting jump is all encapsulated in the word “jump”. The point I’m trying to make is that our world is infinite and in order to understand the world around us we use language.

A more complex form of language is entire sentences. The computer will find meaning to sentences by studying the patterns that surround the sentence. Specifically, analyze certain scenes related to the sentence. If the sentence is “the rat jumped over the dog”, then the scene of a rat jumping over the dog is used to find patterns to the sentence. The AI will take training data (diagram below) and other similar training data in memory and average out what the meaning of the “the rat jumped over the dog” is.

Understanding sentences is important because it encapsulates entire scenes and instructions. The sentence is a fixed thing in our environment but the meaning behind the sentence can be changed based on society. Language is considered fuzzy logic because there is no real meaning behind a word (or sentence). One example is the word bad. We know from the dictionary that the word “bad” means something that is negative. However, the meaning can change based on society. In modern times the sentence: “those dance moves are bad”, uses the word bad not in the context of what we know as bad, but the word means the opposite of negative. The letters that make up the word bad is always going to be the same but the meaning of it from one time period to the next will change. (of course, there can be multiple meaning to a word and that is also decided by society)

Patterns
The AI program can do a lot of things such as change data, forget data (there is no such thing as deleting data), insert data, look for data, take sections of data out and so forth. The AI relies on patterns from the environment to do all these things. The one thing that usually manipulates these functions is language. When we say something like: “that was the wrong answer, the right answer is packets”. This one sentence is actually instructing the AI to modify data in memory. When we say something like: “remember to buy cheese at the store”. This one sentence is actually instructing the AI to put a remember instruction when it gets to the store. When the AI goes to the store the instructions “remember to buy cheese at the store” pops up and this tells the AI to go to the cheese section and buy cheese.

Since the pattern subject matter is so long I’m just going to give one example. The answering of questions relies on patterns in order to be understood. We are able to find the patterns and universalize the pathways so that when someone ask us a question we can give them the appropriate answer.

8 = 8 is an equal object or Dave = Dave is an equal object. They are equal is the relationship between the two objects. Whenever the computer finds two objects equal it will establish a relationship between the two objects and find patterns that revolve around these two objects. From the example below we have taken all the equal objects and we have tried to find patterns between those equal objects. Answering questions is a pattern that relies on equality to find the answers. This may not be very clear when you look at the first example, but after looking at the second example and comparing that with the first example there is clearly a pattern there.

By establishing a relationship between equal objects the computer will be able to find patterns between different training data and forge a universal pattern that can answer a universal question. The examples above have a pattern.

The pattern found above can answer any question that has that kind of configuration. Examples of this would be:

what is 8+8? 8+8 is 16.
what is the 21st state in the USA? The 21st state in the USA is Illinois.
what is the first letter in the alphabets? The first letter in the alphabets is ‘A’
what is the last letter in the alphabets? The last letter in the alphabets is ‘Z’

Averaging the meaning of sentences
When teachers say:

“look left, right, and make sure there are no cars before crossing the street” (R1)
“remember to see if there are no cars from the left and right before you cross the street” (R2)
“don’t forget to look at all corners to make sure there are no cars before crossing the street” (R3)

All the sentences are saying the same thing. This is why language is so important, we can interpret language infinite ways and they are all talking about the same things. The computer will recognize all of these things and it will average out what the meaning of the sentence is

After many training of the pathway the AI has universalized the groups of pathways. R1, R2, and R3 disappear and what you have left is the average of all the training data located in that area.

The AI not only averages out trees in pathways but entire pathways. The purpose is to universalize similar pathways into one pathway. This one pathway will contain the fuzziness of infinite possibilities. We can also take this universalized pathway and encapsulate that to make even more complex pathways.

Applying my AI program to real world situations
Language encapsulates the instructions to do something. We can keep encapsulating sequences in itself to form a very complex problem solving technique. In the problem below you will get an understanding of how encapsulation works.

ABC block
In this problem we want to use a basic intelligent problem that kids can solve. The ABC block is just 3 square blocks and the robot has to find a way to stack the blocks in an A B C format.

We accomplish this problem with the English language. We simple tell the machine: “I want you to stack the blocks up starting with C then B and finally A”. From this one sentence the robot should be able to finish the task. It doesn’t matter what order the blocks are put in. It doesn’t matter where the blocks are. If the robot understands the sentence it will carry out the command. Of course we have to train it to understand the steps to accomplishing this easy task. Let’s say that we had the blocks in this order and we wanted the robot to stack the blocks up from ABC:

We learn from teachers that in order to solve this problem we: “locate the C block”, “Take the C block and put it on the ground”, “then find the B block and put it on the C block”, “finally find the A block and put it on the B block”. These sentences are trees that tell you what to do in order to solve this problem. These trees were trained by a teacher many many times before you can attempt to solve this problem. By the way, these trees are your conscious.

These trees encapsulate the instructions to accomplish a goal. We train them by teaching the robot that this sentence is followed by these instructions. The robot will create pathways in memory that will store the instructions step by step. This may not sound impressive but let’s say you wanted to solve something like lining up the entire alphabet letters in a certain order. If you preprogram the solutions there will be couple trillion possibilities you have to manually preprogram. With trees we can encapsulate instructions in the form of sentences. And these sentences can be encapsulated into even more complex problems. Thus making a complex problem into a simple problem.

HLAI Pseudo codes
Chess game:
Player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, and so forth until the game is over.

Human robots:
There are two players in the game of life: the robot and life. The robot makes a move, life makes a move, the robot recalculates its future steps, the robot makes a move, life makes a move, then the robot recalculates its future steps, the robot makes a move, life makes a move, then the robot recalculates its future steps, the robot makes a move, life makes a move, and so forth until the robot is shut down.

In addition to this the AI robot activates objects that it recognizes from the environment. Anything that has association with the object it recognizes will pop up in its mind. You can call this type of thinking the conscious. The robot gets all its training data straight from the environment. The more sophisticated the society is the more intelligent the robot becomes. Without a society this robot won’t have the ability to use language. Without language then complex thoughts can’t be created. This will eventually put the robot in the animal intelligence category.

The computer codes look exactly like the Universal Artificial Intelligence program. There are differences in the way the pseudo codes are built. One is the fact that the HLAI has 5 senses in its pathways instead of two senses like in the videogame program. The storage part has another addition. The UAI works by grouping data into two categories

1. primitive learning group
2. commonality group

The HLAI stores information by a more defined learning group. It too also has two categories:

1. learning group
2. commonality group

The learning group and the commonality group must co-exist in the same storage area. This means that similar learned groups will be put in an area that might not be similar because the learned groups may not be similar. For example, a tiger and a giraffe are animals and we classify them as animals but in their commonality groups they look totally different.

[In the notes webpage I will outline the stages of how the robot will learn information]