what artificial intelligence can learn from human evolution
TRANSCRIPT
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
ObjectiveTo try to understand the behavior of human
intelligence and to find out points where artificial intelligence can be benefitted from it.
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.
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”.
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.
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
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)
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.
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.
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.
Emotional computers
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.
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.
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.
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.
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.
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.
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).
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).
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.
Optical Interpretation
Optical Illusion
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.