what artificial intelligence can learn from human evolution

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By Abhimanyu Singh Enrolment No.: 0441189907 MBA (SEM) What we can learn from human evolution.. Under the able supervision of Mr. Nidhish Shroti Faculty & ERP Consultant, CDAC-Noida

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Page 1: What Artificial intelligence can Learn from Human Evolution

By

Abhimanyu SinghEnrolment No.: 0441189907 MBA (SEM)

What we can learn from human evolution..

Under the able supervision of

Mr. Nidhish ShrotiFaculty & ERP Consultant, CDAC-

Noida

Page 2: What Artificial intelligence can Learn from Human Evolution

ObjectiveTo try to understand the behavior of human

intelligence and to find out points where artificial intelligence can be benefitted from it.

Page 3: What Artificial intelligence can Learn from Human Evolution

IntroductionHuman intelligence has developed for billions of

years through the process of evolution. Bit by bit, features were sifted and deployed, ensuring we got the best.

We humans now want to replicate this marvel. We want to create an intelligence of our own, we want to create the Artificial Intelligence. The only catch is that we don’t have as much time.

I would like to put light on some of these features that can be replicated to get closer to the goal of AI.

Page 4: What Artificial intelligence can Learn from Human Evolution

Artificial IntelligenceAttempt to make computers do things that

right now humans do better.Related not only to Computer Science, but

also to Psychology, Physics, Anthropology, Biology, Philosophy and so on..

Currently broken into specialized sub fields.Amusingly enough, though AI has not evolved

much, people have already started working on the so called “Robot Rights”.

Page 5: What Artificial intelligence can Learn from Human Evolution

Examples of Current Approaches to AINeural Networks

An artificial neural network (ANN) is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.

Fuzzy Logic Fuzzy logic is a form of multi-valued logic derived from fuzzy set

theory to deal with reasoning that is approximate rather than precise. In binary sets with binary logic, in contrast to fuzzy logic named also crisp logic, the variables may have a membership value of only 0 or 1. Just as in fuzzy set theory with fuzzy logic the set membership values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values {true (1), false (0)} as in classic predicate logic. And when linguistic variables are used, these degrees may be managed by specific functions, as discussed below.

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Human Intelligence characteristicsMotivated.Emotional.Simulator.Regularly updated by the 5 senses.Self Aware and conscious.Predicts future, and can plan.Can group things and show belongingness to

groups.Not bound by rules.Social

Page 7: What Artificial intelligence can Learn from Human Evolution

MotivationProvokes a person to do or not to do

something.Some of the popular theories of Motivation

Maslow’s Need Hierarchy (5 Factors)ERG Theory (3 Factors)Hertzberg’s Two factors Theory (2 Factors)

Page 8: What Artificial intelligence can Learn from Human Evolution

The Two Basic FactorsWe can reduce all of them into the following two

basic factors of motivation,GreedFear

We struggle to achieve things we want (Greed).We Avoid things that we fear.As we grow, we learn what we want to attain and

what we want to abstain.Interestingly, we are not “Hard wired” to absolutely

follow these rules, e.g. when needed, we may even plunge into fire to save someone we love, despite the fear of getting burnt.

Page 9: What Artificial intelligence can Learn from Human Evolution

Motivation for Computers??This approach can make them autonomous or

“Self Motivated”, i.e. they don’t need to be told to do or not to do something every time, they react to a situation they sense around them.

Two Priority based stacks can be used to replicate fear and greed.

Will assist in making decisions based on past experiences.

Can be preloaded with a few basic instincts like humans.

Page 10: What Artificial intelligence can Learn from Human Evolution

Social BehaviorSince it is possible that we may have

conflicting motives, we need to authenticate the sources to deal with such situations.

Each person it “knows” is assigned with a priority, which we can refer to as “trust”.

In case of conflicting motives the, higher priority source will be preferred.

In such cases, the other person’s priority may be decreased to mark out non trustable sources.

Page 11: What Artificial intelligence can Learn from Human Evolution

Emotional computers

Page 12: What Artificial intelligence can Learn from Human Evolution

EmotionsWe often consider emotions as hindrances to

our intellectuality.Hitler tried to speed up the process of natural

selection and hence the speed of human evolution by eliminating the old, weak and sick people.. Was his decision intelligent??

Emotions bring rationalities to our decision making process.

Behave as constraints to our motivations.E.g. We do not start gobbling up sweets

wherever we see them, even if we like them very much.

Page 13: What Artificial intelligence can Learn from Human Evolution

The 5 Basic EmotionsPleasure: The Reward for doing things we like.

(Dopamine)

Pain: Physical pain draws attention towards a malfunction, Mental pain associated with social reasons.

Anger: The feeling that provokes us to fight against the immediate danger.

Fear: The feeling that provokes us get out of a situation where we cannot fight in case of danger. (Amygdala)

Disgust: The feeling that repels us from possibly harmful objects.

Page 14: What Artificial intelligence can Learn from Human Evolution

The Law of Diminishing Returns..States that for each unit being added for an

activity, the returns keep diminishing.Required for emotions.This helps in getting used to things and

moving on.Makes us dynamic, regularly urging us to try

something new.Lack of it will lead to an almost static life,

keeping us in a state we already are.Experiments have shown lack of it could lead

to death.

Page 15: What Artificial intelligence can Learn from Human Evolution

SensesWe have 5 Senses, namely the sense of Smell,

Taste, Touch, Sound and Vision.Of all the senses, 3 senses can prove to be very

important for AI, i.e. Touch, Vision & Sound.The sense of touch can be recreated easily,

including the feeling of heat and pressure.The problem arises with the Sense of Vision and

Sound.These provide the highest details of the

surrounding environment, and we will be focusing on these two.

Page 16: What Artificial intelligence can Learn from Human Evolution

Natural Language ProcessingWe use dictionary based lexical parsing.

Store words and their meanings in data dictionary.

Store people, place etc. and their identity in object dictionary.

Learn new words while conversation, typically by typing or in some cases through voice recognition.

Create replies based on grammatical rules and on past experiences.

Page 17: What Artificial intelligence can Learn from Human Evolution

Problems with NLPNo physical world-logical world connection.“Understanding” heavily marred by ambiguity

present in the sentences humans use.Conversation has to be error free for proper

absorption.We humans not always talk sense.Most of the things we often say, has internal

meanings which are not understandable by computer.

Recent and older conversations has to be available so as to talk sense and not be repetitive.

Page 18: What Artificial intelligence can Learn from Human Evolution

Eye SightWe believe that we see whatever is present in

front of the eyes.In reality, what we see is actually a recreated

interpretation of things in front of our eyes.Even 2-Dimensional images are also

interpreted in 3 Dimension.Follows HSI color model, and not the RGB

model.Has two modes, Day Vision (Using Cones)

and Night Vision (Using Rods).

Page 19: What Artificial intelligence can Learn from Human Evolution

Eye Sight Continued..We can identify reflection, and feel presence

of transparent & fluid objects.We have different algorithm for face

recognition.Alphabets are interpreted differently (lack of it is

Dyslexia).Numbers are interpreted differently (lack of it is

Dyscalculia).

Page 20: What Artificial intelligence can Learn from Human Evolution

Image processingHeavily noisy environment.Need a lot of interpretation.Consume a lot of Processor power.Edges not clearly defined.3-Dimensional objects have almost

uncountable 2-dimensional footprints, leading to almost useless comparison of interpreted objects.

Reflections and transparency increase complexity.

Page 21: What Artificial intelligence can Learn from Human Evolution

Optical Interpretation

Optical Illusion

Page 22: What Artificial intelligence can Learn from Human Evolution

What can be done..The entire visible area doesn’t need to go

through detailed scan.We can run an iteration of algorithms such as

RGB2HSI convertor, Edge detector, Histogram based Object finder, Motion detector, Distance finder etc. on the entire visible area.

Using all the above parameters, we can find Points of Interests (POI) in the Field of View (FOV).

The points of interest are then sent for further deeper analysis like Face recognition, character recognition, Object Combiner etc.

Page 23: What Artificial intelligence can Learn from Human Evolution

The Language of the BrainWhat is the language that the brain of a newborn

infant thinks through?It doesn’t have any knowledge of any language, and

yet it thinks.There must be some language that the brain thinks

through, and the NLP can be superimposed on it.It is also compatible with the senses of vision, smell,

touch and taste.Do we have such language for computers?The Chinese room hypothesis asks exactly this

question.

Page 24: What Artificial intelligence can Learn from Human Evolution

3 Dimensional Object Based Thought ArenaPerhaps we can use our very own OOP

concepts for this purpose.We need to create a 3D canvas where we can

import objects that we desire or see.For our convenience, we can use the term

Thought Arena for it.We can create or remove rules in/from this

arena. (Laws of physics, like gravity etc.)We can assign attributes to the objects,

morph them, and tag them.

Page 25: What Artificial intelligence can Learn from Human Evolution

Thought ArenasResearch has shown that we can think about,

up to 4 different things at a time.This could mean that, we may be having up to

4 Thought Arenas in our brains.One of these is obviously

dedicated to the real world, and has got the highest priority.

Others may be dormant or running in the background.

During sleep, these could getactivated causing dreams.

Page 26: What Artificial intelligence can Learn from Human Evolution

How does it work?When we hear a voice coming from behind us,

what happens?We instantly know that there is a person behind

us.We can gauge out who (s)he is, we can

determine distance etc.For vehicles we even find out the speed and

direction.All this without even taking a look behind !!The reason is that we instantly import the human

object or vehicle object into the Thought Arena and assign attributes to it.

Page 27: What Artificial intelligence can Learn from Human Evolution

Contd..The same happens with vision too.We keep collecting data from the surrounding

environment.We create a list of objects and their position in

time and space.Even motion is stored as object, and helps us

find patterns in it.This is remodeled in the Brain and a simulation

is started, which may lead to certain results.Based on the results and instructions in the

Motivation stacks, the computer can react to situations.

Page 28: What Artificial intelligence can Learn from Human Evolution

3D ObjectsWe normally create

3D objects using Simple Nodes and Vertices.

Each object has a center of mass, which is used as a reference for activities like collision detection, movement control etc. for the entire object.

Page 29: What Artificial intelligence can Learn from Human Evolution

Required 3D PropertiesWe need special types of Nodes

which are used for points where other nodes may or may not be connected.

It will help in working with ambiguous environment.

Also, instead of each object having a single center of mass, we need each node to have a center of mass.

The vertices can then behave as bonding element.

Much like an atom, but bigger in size.

Page 30: What Artificial intelligence can Learn from Human Evolution

Time Based MemoriesShort Term, Mid Term and Long Term

Memories are present to boost the response time of brain, so it can be used for AI too.

Objects, that are of regular and daily use, are kept in short term memory and so on.

Similar concepts are used in computers, e.g. HDD, RAM and Cache. However they work on the physical level.

We need to replicate the same thing at logical level.

Page 31: What Artificial intelligence can Learn from Human Evolution

Learning ProcessNot all that comes in front of our eyes, goes into

our brains.Cause-Effect relation is used to get lessons.Brain mostly learns in two ways, either by

Interest or by Repetition.Things of interest get direct entry into the brain.However, things that we don’t like have to be

kept long enough in the short term memory so as to qualify for entering into the higher levels of memory.

So, we may have to define a computer’s points of interest, hence controlling its learning process.

Page 32: What Artificial intelligence can Learn from Human Evolution

PlanningIt is easier to plan if we can visualize our problem.

Simulation is essential for planning, and this can be done in the thought arena itself.

With the presence of multiple TA’s we can simulate the behavior of not only non living things but also of living things like human etc.

Page 33: What Artificial intelligence can Learn from Human Evolution

Motivators

Time Based Memories

Conclusion contd..The following simplified model is proposed for designing an

effective AI Droid.

LTM

MTM

STM

Greed

Fear

Emotional Evaluators

Thought Arenas

TA 4 TA 3 TA 2 TA 1

Body Interface

Stereo Vision Stereo Sound

Object Data Dictionary

Image Process

or

Audio Process

or

Page 34: What Artificial intelligence can Learn from Human Evolution

ConclusionFor practical AI to

be implemented, we need to develop the entire “body” rather than just the “brain”.

Concepts like Dreams and Imagination may not be alien to them.

Page 35: What Artificial intelligence can Learn from Human Evolution

Conclusion (contd.)Since there is no limit

to their learning capability, AI systems may eventually develop cyber psyche, exhibiting humane emotions like trust, fear, happiness etc.

Page 36: What Artificial intelligence can Learn from Human Evolution

Conclusion (contd.)Can be used in combat zones, or

patrolling difficult terrains, as found on the border areas.

Possibility of being hacked and misused, so encryption must be used for internal data. But volume of data may lead to degraded performance.

There is a possibility of commercial gains too. We may eventually be able to bring down the cost of such systems using economies of scale, thus allowing them to be used as “Manpower” for households and industrial usage.

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