Artificial Intelligence - Ai
is something contrived by
. Not arising from natural
growth or characterized by vital processes.
is having the
especially to a high
degree. To understand
. Possessing sound
Exercising or showing
Endowed with the
capacity to Reason.
Having good understanding or a
; quick to
Intelligence is making
carefully. Intelligence is always learning
"Now is that something a
can do? No, not yet."
is the attempt to mimic human
thinking and actions using Computerized
totally defined just yet, so Artificial Intelligence is limited and mostly misunderstood.
Though machine intelligence, or Artificial
, has great areas of performance and
capabilities, artificial intelligence is mostly just fantasy for
now. There will never be a
Heuristically programmed ALgorithmic Computer like in the movie 2001:
A Space Odyssey
computer that can be corrupted to Kill, not a good idea, like
War Games - Joshua Simulations
(youtube). But this doesn't
mean that Ai technology like
, can be helpful to people, especially
the blind. What's really interesting is the Ai computer like the one in the 1977 movie
, which the
decides it wants to be human
, Ai come full circle, which in a
way shows you that a human is the superior machine. There will never be
Hybrid Assistive Limb
are mostly used to help
handicap people with
, which will not make them
And don't worry about
Brain Computer Interfaces
turning us into
because they are also used
handicap people with disabilities
. People with no
disabilities can use their eyes,
ears and hands as a Brain Computer Interface, as we've been
doing since the creation of modern computers. There will
be no Terminator
Sentient Android named Data
either. And don't ever worry
about someone becoming 'The
', though I did like the
virtual teaching methods
, which proved that it's not how fast you learn but what
you actually learn. I also liked the end when he was able to
digitize himself, and to confirm that he succeeded he made a
billion phones ring at once, I myself would send a billion text
messages that would say "You Are Loved, Keep Learning
continue, people are not going to merge with
, were just
using machines to increase our abilities
, and at the same time,
we are using machines to improve the quality of life. People
merging with machines is only for the handicapped who need extra
help. People saying that we're going to merge with machines
sends the wrong message and makes people fear technology. People
just say these crazy things to sell stories and to bring
attention to themselves. Another reason why
you can never have
, or a
, or a
without humans first learning to master their own intelligence.
is not actually talking about super
intelligent machines, it is in reference to Humans, or a
. It's not machines
that will cause the unfathomable changes to human civilization, it will be
a new level of educated humans who have finally grasped the full
of knowledge and information
. It will be humans creating self-improvement
cycles, with each new and more intelligent generation appearing more and
more rapidly, causing an intelligence explosion and resulting in powerful
changes and improvements in people and to the planet. So we will not just
make incredible machines, we will first develop incredible humans using a
new improved education system that is already in
, and will soon be
ready for download
literally. Your software
is a hypothetical agent that possesses
that of the brightest and most gifted human minds.
is the point when the realization of
intelligence will trigger technological growth resulting in a reaction of
self-improvement cycles, with each new and more intelligent generation
appearing more and more rapidly.
is a movement defined by the belief that a
technological singularity—the creation of superintelligence—will likely
happen in the medium future, and that deliberate action ought to be taken
to ensure that the Singularity benefits humans.
"The real problem
is not whether machines think but whether men do." -
(1904 - 1990).The only way to
create artificial intelligence is to first create intelligent
. Then intelligent humans could then examine the methods and
actions that helped to define intelligence. This could
ultimately help guide intelligent humans to repeat these
mechanically so that they could eventually create
artificial intelligence in limited applications. And what I mean
by limited applications is that there is no such thing as a
. Artificial intelligence, or
, will never
become conscience of itself, unless ‘God’ allows machine
intelligence to have souls, or maybe, that humans could actually
figure out someway to put a human soul into a machine, like in
, or the movie Tron
But of course our priorities will not allow us to waste any more
time perusing these types of fantasies, unless of course ‘Hollywood
feels the need to play out these fantasies a few more times in
the movies. Besides,
the AI we experience in the movies are mostly just metaphors
that are created to imitate the
ignorant and corrupt
our leaders, as well as our
. Real AI
will be nothing like what you see in the movies. So AI for now
is way beyond anyone's comprehension. But when we finally do
create the perfect education that produces intelligent people
then we will start hearing more about the potential of AI. So
until then, people can only incorrectly fantasize about AI, and
incorrectly fantasize about what things will be like in the future. What
human intelligence will be like in the future is beyond peoples current
level of understanding, so any assumptions made about the future will have
serious flaws and misconceptions.
first have to come up with proven teaching methods that would
create intelligent humans. Intelligent humans who are capable of
applying logic in all aspects of their life, intelligent humans
who never stop learning, and, intelligent humans that are not
vulnerable to corruption or ignorant behavior. When humans
accomplish this, then and only then, will we ever have a chance
to create some sort of
. Creating intelligent machines in multiple
capacities and linking them together will be the closet we can
get to artificial intelligence. But it will never have that same
capability as the human brain, and artificial intelligence will
always need a human to interact with it at some point. The only
real intelligence is the human brain
, which is kind of scary
because the human brain is not perfect or correctly educated yet.
Maybe we should stop calling it
Artificial Intelligence and
just call it Machine Intelligence, or just
not Compute, Lost in Space
2001 American science fiction film
directed, written, and co-produced by Steven Spielberg.
of Artificial intelligence
attempts to answer such questions
as follows: Can a machine act intelligently?
If programed correctly and the word intelligent is defined,
maybe at times. Can it solve any problem that a person would solve by
Sometimes. Are human
intelligence and machine intelligence the same?
No. Is the human brain essentially a computer?
but not the same.
Can a machine have a mind, mental states, and consciousness in the same
mans do? Answer:
No. Can it feel how things
No. But we can program it so
that it acts like it does feel.
I'm not saying that I doubt that these types
of technological advances will never happen. I just don't like to
say things before people understand them, because that will only
create misunderstanding and confusion. So unless you're trying to
manipulate peoples thinking, you're better off saying something
that is happening now, or say something that is not happening
now, something that people can confirm, something people can
learn from. We have to stop trying to wow people or impress
people, we are not kids any more. Leave the wowing to nature,
because nature is a lot better at impressing us then our
technological advancements. After all, nature has been advancing
for millions of years, remember,
we just got
Intelligent Machines have incredible
but that's only if they're
holds that a program cannot give a computer a "mind",
"understanding" or "consciousness",[a] regardless of how intelligently or
human-like the program may make the computer behave.
A computer did not beat Lee Se--Dol
Board Game Go
, a team of human's using a machine beat
him, that's not AI, that's just lazy. That's like someone using
a calculator to beat you at math when you don't have a
calculator, that doesn't make a calculator smart, a human still
has to push the buttons, or write the code.
Google Software DeepMind’s AI System Algorithm
, it does show
us how advanced machines are becoming, which is good, that's if
we use our advanced technological machines for actual
, instead of using it to entertain ourselves
playing games, or other time wasting activities. This is not to
say that games are not worth playing, we do have
non-profit artificial intelligence research company. Our goal is
to advance digital intelligence in the way that is most likely
to benefit humanity as a whole.
Open Ai Gym
toolkit for developing and comparing reinforcement learning algorithms.
Open Source Software Library for Machine Intelligence.
essays, reports, software by Peter Norvig
Carnegie Mellon University Artificial Intelligence
Sankar: The Rise of Human-Computer Cooperation
Modularity helps Organisms evolve to Learn New Skills without
Forgetting Old Skills
Biologically-Inspired Massively-Parallel Architectures -
Computing Beyond a Million Processors
Humans have physical limitations, but
humans have very little limitations in the mind. Human
enhancement is not about technology, because
technology is only a tool
. Human enhancement is about using
the worlds most valuable knowledge and skills that the world has
to offer that would help develop advanced intelligent humans,
people who would be able to live high quality lives, while at
the same time, solve every problem on the planet. That's the
future. Technology can get you from point A to point B quicker,
and technology can help you to learn things faster, but
technology does not replace the journey or create the
destination, or create the knowledge and information that is
needed to understand yourself and the world around you.
Technology is a time saver, but technology is not life, or does
technology give life meaning. The human mind is our greatest
asset, and if we don't take care of our minds, then technology
will not save us, it will most likely hurt us and destroy us. If
we improve education to match the worlds accumulated knowledge
and wisdom, then we will save the world.
is exploring regulatory systems—their
structures, constraints, and possibilities. The scientific study of
control and communication in the animal and the machine. Control of any
system using technology.
is the philosophical study of the nature of being,
becoming, existence and/or reality, as well as the basic categories of
being and their relations.
is a meta-ethical
view that claims that ethical sentences do not express propositions but
is the closest
thing that we have to Artificial
Intelligence. The Internet is Humans using Machines, Technology
and Knowledge together as
one. All life forms use elements of their environment in order
to survive and prosper. Humans have now reached a new level, a
level that increases our potential, and a level that gives us
limitless possibilities. Here we go!
is a view that the human mind or the
(or both) is an
system and that
is a form of computing
studies brain function in
terms of the
properties of the structures that make up the
. It is
an interdisciplinary computational science that links the diverse fields
of neuroscience, cognitive
, and psychology
computer science, mathematics
describes the use of
very-large-scale integration (VLSI) systems containing electronic analog
circuits to mimic neuro-biological architectures present in the nervous
is a family of algorithms for global optimization inspired by biological
evolution, and the subfield of artificial intelligence and soft computing
studying these algorithms
. In technical terms,
they are a family of population-based trial and error problem solvers with
a metaheuristic or stochastic optimization character.
is a mathematical model in computational
science that requires extensive computational resources to study the
behavior of a complex system by computer
Computational Complexity Theory
is a branch of the theory of
computation in theoretical computer science that focuses on classifying
computational problems according to their inherent difficulty, and
relating those classes to each other. A computational problem is
understood to be a task that is in principle amenable to being solved by a
computer, which is equivalent to stating that the problem may be solved by
mechanical application of mathematical steps, such as an
Computational Learning Theory
is a subfield of Artificial Intelligence devoted to studying the design
and analysis of machine learning algorithms.Computer
refers to the ability of a computer to learn a specific task from data or
experimental observation. Even though it is commonly considered a synonym
of soft computing, there is still no commonly accepted definition of
computational intelligence. Machine Learning
is an alternative term for artificial
intelligence which emphasizes that the intelligence of machines need not
be an imitation or in any way artificial; it can be a genuine form of
refers to electronic environments that
are sensitive and responsive to the presence of people.
Artificial General Intelligence
is the intelligence of a
machine that could successfully perform any intellectual task that a human
being can. It is a primary goal of artificial intelligence research and a
common topic in science fiction and futurism. Artificial general
intelligence is also referred to as "strong AI", "full AI" or as the
ability of a machine to perform "general intelligent action".
Science and Artificial Intelligence Laboratory
best practices on AI technologies.
Machinery and Intelligence
is a seminal paper written by
Alan Turing on the topic of artificial intelligence. The paper, published
in 1950 in Mind, was the first to introduce his concept of what is now
known as the Turing test to the general public.
vulnerable to Viruses
is a British artificial intelligence company founded in September 2010. It
was acquired by Google in 2014.
is a style of software design
where services are provided to the other components by application
components, through a communication protocol over a network. The basic
principles of service oriented architecture are independent of vendors,
products and technologies. A service is a discrete unit of functionality
that can be accessed remotely and acted upon and updated independently,
such as retrieving a credit card statement online. A service has four
properties according to one of many definitions of SOA: It logically
represents a business activity with a specified outcome. It is
self-contained. It is a black box for its consumers. It may consist of
other underlying services. Different services can be used in conjunction
to provide the functionality of a large software application.
Service-oriented architecture makes it easier for software components to
communicate and cooperate over the network, without requiring any human
interaction or changes in the underlying program, so that service
candidates can be redesigned before their implementation.
also known as message-driven
architecture, is a software architecture pattern promoting the production,
detection, consumption of, and reaction to events.
Complex Event Processing
is a method of tracking and analyzing (processing) streams of information
(data) about things that happen (events), and deriving a conclusion from
them. Complex event processing, or CEP, is event processing that combines
data from multiple sources to infer events or patterns that suggest more
complicated circumstances. The goal of complex event processing is to
identify meaningful events (such as opportunities or threats) and respond
to them as quickly as possible.
is an IBM project aimed at designing
supercomputers that can reach operating speeds in the PFLOPS (petaFLOPS)
range, with low power consumption.
is a computer program that operates or
controls a particular type of device that is attached to a computer. A
driver provides a software interface to hardware devices, enabling
operating systems and other computer programs to access hardware functions
without needing to know precise details of the hardware being used.
is a generic class of abstract machines used in a manner similar to a
Turing machine. All the models are Turing equivalent.
is a quickly accessible location available to a computer's
central processing unit (CPU). Registers usually consist of a small amount
of fast storage, although some registers have specific hardware functions,
and may be read-only or write-only. Registers are typically addressed by
mechanisms other than main memory, but may in some cases be assigned a
memory address. Almost all computers, whether load/store architecture or
not, load data from a larger memory into registers where it is used for
arithmetic operations and is manipulated or tested by machine
instructions. Manipulated data is then often stored back to main memory,
either by the same instruction or by a subsequent one. Modern processors
use either static or dynamic RAM as main memory, with the latter usually
accessed via one or more cache levels. Processor registers are normally at
the top of the memory hierarchy, and provide the fastest way to access
data. The term normally refers only to the group of registers that are
directly encoded as part of an instruction, as defined by the instruction
set. However, modern high-performance CPUs often have duplicates of these
"architectural registers" in order to improve performance via register
renaming, allowing parallel and speculative execution. Modern x86 design
acquired these techniques around 1995 with the releases of Pentium Pro,
Cyrix 6x86, Nx586, and AMD K5. A common property of computer programs is
locality of reference, which refers to accessing the same values
repeatedly and holding frequently used values in registers to improve
performance; this makes fast registers and caches meaningful. Allocating
frequently used variables to registers can be critical to a program's
performance; this register allocation is performed either by a compiler in
the code generation phase, or manually by an assembly language programmer.
is a theoretical model of a
automata theory. Abstraction of computing processes is used in both the
computer science and computer engineering disciplines and usually assumes
a discrete time paradigm.
Hao Wang (academic)
was a logician, philosopher, mathematician, and
. (20 May 1921 – 13 May 1995).
is an extra input to a Turing machine that
is allowed to depend on the length n of the input, but not on the input
itself. A decision problem is in the complexity class P/f(n) if there is a
polynomial time Turing machine M with the following property: for any n,
there is an advice string A of length f(n) such that, for any input x of
length n, the machine M correctly decides the problem on the input x,
given x and A.
is a question in some formal system that
can be posed as a yes-no question, dependant on the input values. Decision
problems typically appear in mathematical questions of decidability, that
is, the question of the existence of an effective method to determine the
existence of some object or its membership in a set; some[which?] of the
most important problems in mathematics are undecidable.
is an abstract machine used to study decision problems. It can
be visualized as a Turing machine with a black box, called an oracle,
which is able to solve certain decision problems in a single operation.
The problem can be of any complexity class. Even undecidable problems,
such as the halting problem, can be used.
DisinhibitionThe Human Brain
computerized game of twenty questions that began as a test in artificial
intelligence (AI). It was invented by Robin Burgener in 1988.
describes a class of functions which
modify other functions when the latter are run; it is a certain function,
method or procedure that is to be applied at a given join point of a
is a procedure for solving a problem from a
specific class. An effective method is sometimes also called mechanical
method or procedure.
refers to the decision problem, the
question of the existence of an effective method for determining
membership in a set of formulas, or, more precisely, an algorithm that can
and will return a boolean true or false value that is correct (instead of
looping indefinitely, crashing, returning "don't know" or returning a
is the problem of finding the best
solution from all feasible solutions. Optimization problems can be divided
into two categories depending on whether the variables are continuous or
discrete. An optimization problem with discrete variables is known as a
combinatorial optimization problem. In a combinatorial optimization
problem, we are looking for an object such as an integer, permutation or
graph from a finite (or possibly countable infinite) set. Problems with
continuous variables include constrained problems and multimodal problems.
is a specific table layout that allows
visualization of the performance of an algorithm, typically a supervised
learning one (in unsupervised learning it is usually called a matching
matrix). Each column of the matrix represents the instances in a predicted
class while each row represents the instances in an actual class (or vice
versa). The name stems from the fact that it makes it easy to see if the
system is confusing two classes (i.e. commonly mislabelling one as
is a software design technique that
emphasizes separating the functionality of a program into independent,
interchangeable modules, such that each contains everything necessary to
execute only one aspect of the desired functionality.
is the tendency of an artificial
neural network to completely and abruptly forget previously learned
information upon learning new information. Neural networks are an
important part of the network approach and connectionist approach to
cognitive science. These networks use computer simulations to try and
model human behaviours, such as memory and learning. Catastrophic
interference is an important issue to consider when creating connectionist
models of memory.
Statistical Machine Translation
is a machine translation
paradigm where translations are generated on the basis of statistical
models whose parameters are derived from the analysis of bilingual text
corpora. The statistical approach contrasts with the rule-based approaches
to machine translation as well as with example-based machine translation.
is a sub-field of computational
linguistics that investigates the use of software to translate text or
speech from one language to another.
describes the perceived "rot" which is either a
slow deterioration of software performance over time or its diminishing
responsiveness that will eventually lead to software becoming faulty,
unusable, or otherwise called "legacy" and in need of upgrade. This is not
a physical phenomenon: the software does not actually decay, but rather
suffers from a lack of being responsive and updated with respect to the
changing environment in which it resides.
is source code that relates to a no-longer
supported or manufactured operating system or other computer technology.
is a software development
methodology that focuses on creating and exploiting domain models, which
are conceptual models of all the topics related to a specific problem.
Hence, it highlights and aims at abstract representations of the knowledge
and activities that govern a particular application domain, rather than
the computing (f.e. algorithmic) concepts.
. specializes in the analysis and management of
unstructured information using a semantic approach.
Open Knowledge Base Management
is a set of computer software
for systems management of applications that use knowledge management
techniques (the KBM in OpenKBM stands for Knowledge Based Management).
Unsupervised Learning with Artificial Neurons
Stochastic Phase-Change Neurons
Artificial Neural Network
is a network inspired by biological neural networks
(the central nervous
systems of animals, in particular the brain) which are used to estimate or
approximate functions that can depend on a large number of inputs that
are generally unknown. Artificial neural networks are typically specified
using three things.
Rule specifies what variables are
involved in the network and their topological relationships—for
example the variables involved in a neural network might be the weights of
the connections between the neurons, along with activities of the
Activity Rule states that most neural network models have short
time-scale dynamics: local rules define how the activities of the
neurons change in response to each other. Typically the activity rule
depends on the weights (the parameters) in the network.
Rule specifies the way in which the neural network's
weights change with time. This learning is usually viewed as taking
place on a longer time scale than the time scale of the dynamics under
the activity rule. Usually the learning rule will depend on the activities
of the neurons. It may also depend on the values of the target values
supplied by a teacher and on the current value of the weights.
is a mathematical function conceived as a model of
Artificial neurons are the constitutive units in an artificial neural
Feedforward Neural Network
is an artificial neural network wherein
connections between the units do not form a cycle. This is different
from recurrent neural network.
Convolutional Neural Network
is a type of feed-forward artificial neural network in which the
connectivity pattern between its neurons is inspired by the organization
of the animal visual cortex, whose individual neurons are arranged in such
a way that they respond to overlapping regions tiling the visual field.
Convolutional networks were inspired by biological processes and are
variations of multilayer perceptrons designed to use minimal amounts of
preprocessing. They have wide applications in image and video recognition,
recommender systems and natural language processing.
is a class of artificial neural network where
connections between units form a
. This creates an internal state of the network which
allows it to exhibit dynamic temporal behavior. Unlike feedforward neural
networks, RNNs can use their internal memory to process arbitrary
sequences of inputs. This makes them applicable to tasks such as
unsegmented connected handwriting recognition or speech recognition.
Bidirectional associative memory
is a type of recurrent neural
network. Hopfield Network
is an artificial neural network characterized by a
series of independent neural networks moderated by some intermediary. Each
independent neural network serves as a module and operates on separate
inputs to accomplish some subtask of the task the network hopes to
perform. The intermediary takes the outputs of each module and processes
them to produce the output of the network as a whole. The intermediary
only accepts the modules’ outputs—it does not respond to, nor otherwise
signal, the modules. As well, the modules do not interact with each other.
Biological Neural Network
is a series of interconnected neurons whose
activation defines a recognizable linear pathway. The interface through
which neurons interact with their neighbors usually consists of several
axon terminals connected via synapses to dendrites on other neurons. If
the sum of the input signals into one neuron surpasses a certain
threshold, the neuron sends an action potential (AP) at the axon hillock
and transmits this electrical signal along the axon.
connects one part of the nervous system with another via a bundle of
axons, the long fibers of neurons. A neural pathway that serves to
connect relatively distant areas of the brain or nervous system is usually
a bundle of neurons, known collectively as white matter. A neural pathway
that spans a shorter distance between structures, such as most of the
pathways of the major neurotransmitter systems, is usually called grey
is either a connection point, a redistribution point
(e.g. data communications equipment), or a communication endpoint (e.g.
data terminal equipment). The definition of a node depends on the network
and protocol layer referred to. A physical network node is an active
electronic device that is attached to a network, and is capable of
creating, receiving, or transmitting information over a communications
channel. A passive distribution point such as a distribution frame or
patch panel is consequently not a node.
is a branch of physiology and
that is concerned with the study of the functioning of
the nervous system. The primary tools of basic neurophysiological research
include electrophysiological recordings, such as patch clamp, voltage
clamp, extracellular single-unit recording and recording of local field
potentials, as well as some of the methods of calcium imaging,
and molecular biology
If a computer tricks a human into believing that the machine is
, this does not mean that the machine is intelligent, it only means
that that human is not intelligent. People
can be easily fooled
, and not just by machines.
can you tell the difference between a machine
and a human? If the human made the machine and wrote its language, then it's not just a machine, but a hybrid machine with human qualities.
I'm sure you
can have a conversation with a computer, but you are just
into its database
you are not getting to know the computer like you would a person. There's
a difference between Recorded Messages and Logical Associations.
Natural Language Processing
Natural Language Toolkit
"Artificial intelligence is fine, as long
as I can have someone intelligent to talk to, whether it's a
machine or a human."
Can machines think like humans?
That is a stupid question that
only a human could ask. First, a human would have to define what
it means to
. And this is where the question actually begins. To
think like a human is not always a good thing, since humans make
a lot of mistakes,
that we don't always learn from. So
a machine thinking like a human would not be a good thing,
especially when the thinking process hasn't even been totally
defined just yet. You have to remember that humans
machines, and humans also
machines. Machines can do amazing things because
humans can do amazing things. But people get this crazy idea
that that machines will think for them. This is because some
people have not yet learned to think for themselves. Just how
much machines will think for humans, is up to humans, not
machines. So maybe the first question should be, can humans
think like machines?
and Human Brain Similarities
Can machines become smarter then humans?
Of course they can, because
our education system sucks
. If we spent as much time improving
education as we did creating artificial intelligence, we would eventually
have the best of both worlds.
behavior only produces the appearance of intelligent, just like the
news gives the appearance of
, and schools give
the appearance of education
. Human interpretation is amazing, but when
human intelligence is dumbed down, machines look smarter."
striking differences between the intelligence of people and the responses
of machines. Machines (and their programmers) use cold reason and logical
associations within a given topic. This reasoning mode is akin to the
scholastic intelligence of humans. From the viewpoint of a computer or
scholastic intelligence, all associations (even procedures, which have
sequences and temporal span) are eternal and "timeless" logical facts.
When and how they occur is "considered" irrelevant by a computer or
scholastic intelligence. The broader context of one's life experiences is
only handled by emotional intelligence. It tracks biographical events in
time and space, and supplies the mind with broad contextual understanding
of technical, social, and personal matters. Emotional intelligence knows
what happened earlier and is able to detect a potential logical
association between the past and the present happenings. Emotional habits
and intelligence take into account physiological drives, emotional state
of the mind, somatic responses, sex drive, and gender orientation. Unlike
scholastic abilities, emotional habits and emotional intelligence allow
the human organism to interact with social and physical effects of the
environment. This ability only exists in living things and is not
achievable in machines."
With Ai, everything needs to be written.
Creating a machine that can have random actions or thoughts can be very dangerous.
Controls - Automation
is the control method used by a controller
which must adapt to a controlled system with parameters which vary, or are
is the engineering discipline that
applies control theory to design systems with desired behaviors. The
practice uses sensors to measure the output performance of the device
being controlled and those measurements can be used to give feedback to
the input actuators that can make corrections toward desired performance.
When a device is designed to perform without the need of human inputs for
correction it is called automatic control (such as cruise control for
regulating the speed of a car). Multi-disciplinary in nature, control
systems engineering activities focus on implementation of control systems
mainly derived by mathematical modeling of systems of a diverse range.
is an engineering discipline that deals with
architectures, mechanisms and algorithms for maintaining the output of a
specific process within a desired range. For instance, the temperature of
a chemical reactor may be controlled to maintain a consistent product
is one of the managerial functions like planning,
organizing, staffing and directing. It is an important function because it
helps to check the errors and to take the corrective action so that
deviation from standards are minimized and stated goals of the
organization are achieved in a desired manner. According to modern
concepts, control is a foreseeing action whereas earlier concept of
control was used only when errors were detected. Control in management
means setting standards, measuring actual performance and taking
corrective action. You can't control
is a device, or set of devices, that manages,
commands, directs or regulates the behaviour of other devices or systems.
They can range from a home heating controller using a thermostat
controlling a boiler to large Industrial control systems which are used
for controlling processes or machines.
Regulator (automatic control)
is a regulator is a device which has the
function of maintaining a designated characteristic. It performs
the activity of managing or maintaining a range of values in a
machine. The measurable property of a device is managed closely
by specified conditions or an advance set value; or it can be a
variable according to a predetermined arrangement scheme. It can
be used generally to connote any set of various controls or
devices for regulating or controlling items or objects.
Examples are a voltage regulator (which can be a transformer
whose voltage ratio of transformation can be adjusted, or an
electronic circuit that produces a defined voltage), a pressure
regulator, such as a diving regulator, which maintains its
output at a fixed pressure lower than its input, and a fuel
regulator (which controls the supply of fuel).
Regulators can be designed to control anything from gases or
fluids, to light or electricity. Speed can be regulated by
electronic, mechanical, or electro-mechanical means. Such
Electronic regulators as used in modern railway sets where the
voltage is raised or lowered to control the speed of the engine. Mechanical
such as valves as used in fluid control systems.
Purely mechanical pre-automotive systems included such designs as the Watt
centrifugal governor whereas modern systems may have electronic fluid
speed sensing components directing solenoids to set the valve to the
desired rate. Complex electro-mechanical speed control systems used to
maintain speeds in modern cars (cruise control) - often including
hydraulic components, An aircraft engine's constant speed unit changes the
propeller pitch to maintain engine speed. Cybernetics
Real-time Control System
is a reference model
, suitable for many software-intensive, real-time control
problem domains. RCS is a reference model architecture that defines the
types of functions that are required in a real-time intelligent control
system, and how these functions are related to each other.
Programmable Logic Controller
is an industrial digital computer which
has been ruggedised
adapted for the control of manufacturing processes, such as assembly
lines, or robotic devices
, or any activity that requires high reliability
control and ease of programming
and process fault diagnosis. They were
first developed in the automobile industry to provide flexible, ruggedised
and easily programmable controllers to replace hard-wired relays and
timers. Since then they have been widely adopted as high-reliability
suitable for harsh environments. A PLC is an
example of a "hard" real-time system since output results must be produced
in response to input conditions within a limited time, otherwise
unintended operation will result. Algorithms
Controller (control theory)
is a device, historically using
mechanical, hydraulic, pneumatic or electronic techniques often in
combination, but more recently in the form of a microprocessor or
computer, which monitors and physically alters the operating conditions of
a given dynamical system. Typical applications of controllers are to hold
settings for temperature, pressure, flow or speed.
is the area of control theory which deals
with systems that are nonlinear, time-variant, or both.
Closed-Loop Transfer Function
in control theory is a
mathematical expression (algorithm) describing the net result of the
effects of a closed (feedback) loop on the input signal to the circuits
enclosed by the loop.
Hierarchical Control System
is a form of control system in
which a set of devices and governing software is arranged in a
When the links in the tree are implemented by a computer network, then
that hierarchical control
system is also a form of networked control system.
is a class of control techniques that use various artificial intelligence
computing approaches like neural networks
, machine learning
evolutionary computation and genetic
Networked Control System
is a control system wherein the
control loops are closed through a communication network. The defining
feature of an NCS is that control and feedback signals are exchanged among
the system's components in the form of information packages through a
is when the control action from the
controller is independent of the "process output", which is the process
variable that is being controlled. It does not use feedback to determine
if its output has achieved the desired goal of the input or process "set
point". An open-loop system cannot engage in machine learning and also
cannot correct any errors that it could make. It will not compensate for
disturbances in the process being controlled.
Perceptual Control Theory
is a model of behavior based on
the principles of negative feedback, but differing in important respects
from engineering control theory. Results of PCT experiments have
demonstrated that an organism controls neither its own behavior, nor
external environmental variables, but rather its own perceptions of those
variables. Actions are not controlled, they are varied so as to cancel the
effects that unpredictable environmental disturbances would otherwise have
on controlled perceptions.
is the application of mechanisms to the
operation and regulation of processes without continuous direct human
is the use of various control systems for
operating equipment such as machinery, processes in factories, boilers and
heat treating ovens, switching on telephone networks, steering and
stabilization of ships, aircraft and other applications and vehicles with
minimal or reduced human intervention. Some processes have been completely
is the idea that two control systems—inner
controls and outer controls—work against our tendencies to deviate.
is a term used in signal processing and
mixed-signal system design to describe a series of signal-conditioning
electronic components that receive input (data acquired from sampling
either real-time phenomena or from stored data) in tandem, with the output
of one portion of the chain supplying input to the next. Signal chains are
often used in signal processing applications to gather and process data or
to apply system controls based on analysis of real-time phenomena.
Feed Forward (control)
is a term describing an element or
pathway within a control system which passes a controlling signal from a
source in its external environment, often a command signal from an
external operator, to a load elsewhere in its external environment. A
control system which has only feed-forward behavior responds to its
control signal in a pre-defined way without responding to how the load
reacts; it is in contrast with a system that also has feedback, which
adjusts the output to take account of how it affects the load, and how the
load itself may vary unpredictably; the load is considered to belong to
the external environment of the system.
Nothing is beyond your control,
there is nothing that you cannot control
. Something's are harder
to control then others, and there are some things you have not
yet learned how to control. To say that I cannot control
something is a false statement. To be more accurate, you have to
say that I have not yet learned how to control this.
Ai is about making humans more effective
, it's not about making
machines more like humans, because that's crazy. Humans are
mistake prone, and machines are supposed to help us reduce
mistakes, and help us to analyze our options. A machine could
never be more intelligent then the most intelligent human. But a
machine could easily be more intelligent then a human who has
never learned enough, or went to school. You really don't want a
machine to be more intelligent then you, because that clearly
says that you don't have the necessary knowledge and information
that's needed to be intelligent. But Ai could easily be a
teacher and a measuring tool for intelligence, with an emphasis
on the word 'Tool'. Ai is not human, or will it ever be. But Ai
is a great example and a symbol of human ingenuity and
intelligence. A dog is a mans best friend, and Ai is an
extension of our friendship, and not a replacement for
friendship, for that would be like being friends with yourself.
Not exciting or real. But still better then nothing. You can
love a machine, but what you are really doing is just loving
yourself. A machine could never be a replacement for a human,
machine can only be an aid. If we never improve education, or
if we keep denying people access to valuable knowledge and
information, then yes a machine could be more intelligent then a
human who is not fully educated. Ai will not be more intelligent
then humans, but Ai will help humans become more intelligent. Ai
is the path that we are taking to human intelligence.
is a sense already a machine
, a machine that can create more
machines. Machines are not made to replace humans
, machines only
replace certain actions that humans don't need to do. Thus
freeing up humans to do more important work, and also freeing up
more time to explore, with more time to relax. Ai advancements
will eventually lead us right back to ourselves. There is no
better machine then a human. Yes there will be certain machines
that will have better abilities in certain areas, but only
because we made it so. This way we can focus on other things
that are more important.
Questions for Machines
ask a question
if you are talking to a machine, or a search engine that's not
manipulated by money. And we know that sometimes we have to ask
more then one question, even when talking to a machine. So in a
way, machines can be better then a human because machines can be
built to resemble the best qualities and the best skills that a
human could have, without any of the deceitful behaviors, or
human ignorance, or human flaws. Ai should encompass the best
qualities of a human, not the worst. So as Ai improves, so will
"The danger is not Artificial Intelligence,
the danger is the ignorant criminals in power who will use AI
incorrectly, like they are now with
. When crazy people make machines that kill humans,
that's not artificial intelligence, that's just ignorance.
There is no future in War
like all ignorant behaviors, war will become obsolete. Humans
are not wired for war. War is only a byproduct of the corrupted
influences of power
. People don't start wars, people in power
start wars. Though people are the ones who fight wars, and
suffer from the violence from wars, it is the people in power
who start wars, and profit from wars. They never fight in wars
themselves, for if they did, they would realize how insane and
ignorant they are. But sadly, the
continues with their
story telling fear based narratives
that try to manipulate
public thinking. War is murder, and
murder is illegal
. But some how people have been tricked
into believing that they are not the same. The
media and the movie industries to create
, so as to manipulate people even more. The only
way that the war machine lives, is to keep people ignorant. And
since ignorance will not be apart of our future, then it's time
to let war die
autonomous killing robots
, but in a sense we already
have them, they're called soldiers, they're called police,
they're called the CIA, they're called the NSA, they're called
the IRS, they're called the TSA, they're called drug addicts, they're called mindless consumers,
they're called anyone who does things just for money, they're called
anyone who blindly follows orders, whether internally or
externally, or blindly follows
the rule of a law without
question. Yes we need Command Hierarchy
, especially when organizing for emergency
response, like an
Incident Command System
. But when people say "I'm just
following orders", what they are really saying is that I can't
think for myself and have no intelligent reasoning that would
allow me to make intelligent decisions on my own. When people
blindly follow orders
, they are no more then a robot. Humans
are born free thinkers, but when they are not allowed to think
freely for themselves, they are no more then autonomous killing
machines. People who have power are
. So don't
worry about machines killing you, because autonomous humans have
killed millions, and will continue to kill millions. So unless
you become intelligent, this ignorance will continue to
Kill and Destroy
Meaningful Human Control
will only happen when military personnel are
educated to be intelligent. In 2011, Air Force psychologists completed
a mental-health survey of 600 combat drone operators. Forty-two percent of
drone crews reported moderate to high stress, and 20 percent reported
emotional exhaustion or burnout. The study’s authors attributed their dire
results, in part, to “existential conflict.” A later study found that
drone operators suffered from the same levels of depression, anxiety, PTSD,
alcohol abuse, and suicidal ideation as traditional combat aircrews. And
this is not just about drones, there's long range missile's, large canons
and land mines that kill from a distance. Emotionally detached and
disconnected from reality
Autonomy in Weapon Systems
We want machines to have some
, like we do now with
But we don't want machines to do things totally on their own.
Like, you don't want your computer to shut off or stop running
programs when you need them. That is when a human will need the
on and off switch, or a cancel button, or the ability to
reprogram. Kind of like what we have now with most computers. In
order for machines to have intelligent abilities, we first have
to have intelligent humans to manage the operation of these
intelligent machines. Any type of
wrong hands will always have
like we have now, except people are being controlled by money,
and not by intelligent algorithms. So we need to focus more on
improving the abilities of humans, and focus less on the
abilities of machines, or the assumed abilities of machines.
We have to understand what
involves the use of mechatronics, artificial intelligence,
and multi-agent system to assist a vehicle's operator. These features and
the vehicles employing them may be labeled as intelligent or smart. A
vehicle using automation for difficult tasks, especially navigation, may
be referred to as semi-autonomous. A vehicle relying solely on
is consequently referred to as robotic or autonomous. After the invention
of the integrated circuit, the sophistication of automation technology
increased. Manufacturers and researchers subsequently added a variety of
automated functions to automobiles and other vehicles.
is a system used to control the trajectory of a vehicle
without constant 'hands-on' control by a human operator being required.
Autopilots do not replace a human operator, but assist them in controlling
the vehicle, allowing them to focus on broader aspects of operation, such
as monitoring the trajectory, weather and systems. Autopilots are used in
aircraft, boats (known as self-steering gear), spacecraft, missiles, and
others. Autopilots have evolved significantly over time, from early
autopilots that merely held an attitude to modern autopilots capable of
performing automated landings under the supervision of a pilot.
behaviors or tasks with a high degree of autonomy, which is particularly
desirable in fields such as spaceflight, household maintenance (such as
cleaning), waste water treatment and delivering goods and services.
Robot Operating System
Autonomous Robotics Laboratory
is something that is Not controlled by outside forces. Existing as an independent
Free from external control and constraint in e.g. action
is one who gives oneself one's own law
is the study of abstract machines and automata, as well as the
computational problems that can be solved using them. It is a theory in
theoretical computer science, under discrete mathematics (a subject of
study in both mathematics and computer science). The word automata (the
plural of automaton) comes from the Greek word αὐτόματα, which means
(automata or automatons) is a self-operating machine, or a
machine or control mechanism designed to follow automatically a
predetermined sequence of operations, or respond to predetermined
instructions. Some automata, such as bellstrikers in mechanical clocks,
are designed to give the illusion to the casual observer that they are
operating under their
consists of the processes in the mind which occur
automatically and are not available to introspection, and include thought
processes, memories, interests, and motivations.
Nothing is totally autonomous, nothing is totally independent,
nothing is totally free from external control. Nothing is. So what are you
talking about when you say something is autonomous?
Everything is Connected
are Replacing some Jobs, so Human Labor will do other more
important things, and that's a good thing
There is already
, like insects, plants, bacteria, dna. But these types
of autonomous abilities have been perfected over millions of
years, and we are just learning how to expand these autonomous
abilities to machines. So we need to go slow and learn from the
experts in nature, because just like
, autonomous abilities can have catastrophic
can actually help teach
people how to drive with better awareness. We could use the
software that controls the
, and create a simulation that anyone can
use on a computer. It would give people different scenarios that
can test a persons awareness. It will make Driving safer and
can now make 12 trillion
operations a second, almost as good as a
Most People Trust Machines and Humans
. But most people know
better not to count on machines, or humans, 100% of the time,
because we all know that both machines and humans make mistakes.
We trust them, but not so much that we are gullible or unaware.
So verifying is not a sign of distrust, it's just being aware
that mistakes and errors happen.
Can’t you see, the
smarter you make the machine the smarter you become.
You say you are going to make intelligent machines, or AI, but
on the contrary, it will be the machines that will make you
intelligent. And the computer machine has already been doing
this for some time. Intelligent machines are just mimics,
mirrors, extensions and expansions of the human mind. This is
way beyond a
. It’s self-realization and enlightenment on
the grandest scale. Can’t you see, you are not just building a
better machine you are building a better human. And yes not
everyone is benefiting from this as fast as we would like, but
they will if everyone has a
and understands what
it resembles and what it can achieve. Man is the Machine.
And we know how to duplicate this intelligent machine, it's
called childbirth plus education. We now have more words and
more ways to express them then ever before. Words have the
ability to shape the human mind. Words are the
of the brain where they are translated into
Zero’s and Ones
so that the
knows when to fire and when to create more
. We will soon be able to scientifically prove
what the correct words should be and when the correct time and
sequence they should be learned.
The human brain is the
of brains. Or you can say that the human brain is
of all brains. And from our incredible brains we
as our tools. Tools that makes our brains even more
powerful by expanding our abilities. And these tools also save
us time, which gives us more time to play, and more time to
create more time.
Machine learning is the study of pattern recognition
and computational learning theory in artificial intelligence.
Field of study that gives computers the ability to learn without
being explicitly programmed, but still it needs to be
to learn, I would like to see that
is a branch of machine learning
based on a set of Algorithms
that attempt to model high-level abstractions
in data by using a deep graph with multiple processing layers, composed of
multiple linear and non-linear transformations.
is a subfield of Machine learning
where automatic learning Algorithms
on meta-data about machine learning experiments.
List of Machine Learning Concepts
Computational Learning Theory
is a subfield of Artificial Intelligence
devoted to studying the design and analysis of machine learning
Algorithm that Learns directly from Human Instructions, rather than an
existing set of examples
, and outperformed conventional methods of
training neural networks by 160 per cent.
is the construction of Algorithms
that can learn from and make predictions on data – such algorithms
overcome following strictly static program instructions by making
data-driven predictions or decisions, through building a model from sample
Internet of Things
Machine to Machine
refers to direct communication between devices using any communications
channel, including wired and wireless. Machine to machine communication
can include industrial instrumentation, enabling a sensor or meter to
communicate the data it records (such as temperature, inventory level,
etc.) to application software that can use it (for example, adjusting an
industrial process based on temperature or placing orders to replenish
inventory). Such communication was originally accomplished by having a
remote network of machines relay information back to a central hub for
analysis, which would then be rerouted into a system like a personal
refers to the effective use
of information technology in augmenting human intelligence.
Program DRIVE PX
is an area of supervised machine learning in artificial intelligence. It
is closely related to regression and classification, but the goal is to
learn from examples a similarity function that measures how similar or
related two objects
has applications in ranking, in recommendation systems, visual identity
tracking, face verification, and speaker verification
Monad Functional Programming
are a way to build computer programs by
joining simple components in robust ways. Monads can be seen as a
functional design pattern to build generic types, with the following
organization: Define a data type, and how values of that datatype are
combined. Create functions that use the data type, and compose them
together (following the rules defined in the first step).
Statistical Learning Theory
is a framework for machine
learning drawing from the fields of statistics and functional analysis.
Statistical learning theory deals with the problem of finding a predictive
function based on data. Statistical learning theory has led to successful
applications in fields such as computer vision, speech recognition,
bioinformatics and baseball.
is the branch of mathematics concerning vector
spaces and linear mappings between such spaces. It includes the study of
lines, planes, and subspaces, but is also concerned with properties common
to all vector spaces.
is the machine learning
task of inferring a function to describe hidden structure from unlabeled
data. Since the examples given to the learner are unlabeled, there is no
error or reward signal to evaluate a potential solution – this
distinguishes unsupervised learning from supervised learning and
reinforcement learning. Unsupervised learning is closely related to the
problem of density estimation in statistics. However, unsupervised
learning also encompasses many other techniques that seek to summarize and
explain key features of the data.
is the machine learning task
of inferring a function from labeled training data. The training data
consist of a set of training examples. In supervised learning, each
example is a pair consisting of an input object (typically a vector) and a
desired output value (also called the supervisory signal). A supervised
learning algorithm analyzes the training data and produces an inferred
function, which can be used for mapping new examples. An optimal scenario
will allow for the algorithm to correctly determine the class labels for
unseen instances. This requires the learning algorithm to generalize from
the training data to unseen situations in a "reasonable" way (inductive
). The parallel task
in human and animal psychology is often
referred to as concept learning.
is an approximation to animal
cognitive processes (predominantly human) for the purposes of
comprehension and prediction. Cognitive models can be developed within or
without a cognitive architecture, though the two are not always easily
International Conference on Machine Learning
Human Operating System
reverse engineering the neocortex.
One of the greatest advancements is the Search Feature.
Finding what you're looking for
is like having a
except you're not only searching your own memory, but the
combined memories of
millions of humans
, which is incredible.
Search Engine Technology
is an information retrieval
software program that discovers, crawls, transforms and stores information
for retrieval and presentation in response to user queries.
seeks to improve search accuracy by
understanding the searcher's intent and the contextual meaning of terms as
they appear in the searchable dataspace, whether on the Web or within a
closed system, to generate more relevant results.
Search Engine Software
Search Engine Types
Web Search Engine
is a software system that is designed
to search for information on the World Wide Web.
the computational process of discovering
patterns in large data sets involving methods at the intersection of
artificial intelligence, machine learning, statistics, and database
is an algorithm that retrieves information stored
within some data structure. Data structures can include linked lists,
arrays, search trees, hash tables, or various other storage methods. The
appropriate search algorithm often depends on the data structure being
searched. Searching also encompasses algorithms that query the data
structure, such as the SQL SELECT command.
means when a search is
being conducted for a fuzzy match across a broad field. In computing the
equivalent function can be performed using content-addressable memory.
Unlike usual searches, which look for literal (i.e. exact, logical, or
regular expression) matches, a transderivational search is a search for a
possible meaning or possible match as part of communication, and without
which an incoming communication cannot be made any sense of whatsoever. It
is thus an integral part of processing language, and of attaching meaning
is a metaheuristic algorithm commonly
applied to combinatorial optimization problems.
a higher-level procedure or heuristic designed to find, generate, or
select a heuristic
(partial search algorithm) that may provide a sufficiently good solution
to an optimization problem, especially with incomplete or imperfect
information or limited computation capacity.
Questions and Answers
is a process that helps provide more relevant search results for users.
(hopefully a process not manipulated by money)
, and/or automated
Algorithm is a precise rule (or set of rules) specifying how to
Human-Based Genetic Algorithm
is a genetic algorithm that allows
humans to contribute solution suggestions
to the evolutionary process. For
this purpose, a HBGA has human interfaces for initialization, mutation,
and recombinant crossover. As well, it may have interfaces for selective
evaluation. In short, a HBGA outsources the operations of a typical
genetic algorithm to humans.
are a generalization of ordinary algorithms
that are more powerful, that is, compute more than Turing machines. Turing
machines and other mathematical models of conventional algorithms allow
researchers to find properties of recursive algorithms and their
computations. In a similar way, mathematical models of super-recursive
algorithms, such as inductive Turing machines, allow researchers to find
properties of super-recursive algorithms and their computations.
is an algorithm that puts elements of a list in a
The most-used orders are numerical order and lexicographical order.
Efficient sorting is important for optimizing the use of other algorithms
(such as search and merge algorithms) which require input data to be in
sorted lists; it is also often useful for canonicalizing data and for
producing human-readable output. More formally, the output must satisfy
two conditions: The output is in nondecreasing order (each element is no
smaller than the previous element according to the desired total order);
The output is a permutation (reordering) of the input. Further, the data
is often taken to be in an array, which allows random access, rather than
a list, which only allows sequential access, though often algorithms can
be applied with suitable modification to either type of data.
Critical Path Method
is an algorithm for scheduling a set of
Inductive Turing Machines
implement an important class of
super-recursive algorithms. An inductive Turing machine is a definite list
of well-defined instructions for completing a task which, when given an
initial state, will proceed through a well-defined series of successive
states, eventually giving the final result. The difference between an
inductive Turing machine and an ordinary Turing machine is that an
ordinary Turing machine must stop when it has obtained its result, while
in some cases an inductive Turing machine
can continue to compute after
obtaining the result
, without stopping.
is an abstract machine that manipulates symbols on a
strip of tape according to a table of rules; to be more exact, it is a
mathematical model of computation
that defines such a device. Despite the
model's simplicity, given any computer algorithm, a Turing machine can be
constructed that is capable of simulating that algorithm's logic.
is an algorithm that exhibits emergent
behavior. In essence an emergent algorithm implements a set of simple
building block behaviors that when combined exhibit more complex
behaviors. One example of this is the implementation of fuzzy motion
controllers used to adapt robot movement in response to environmental
obstacles. An emergent algorithm has the following characteristics: it
achieves predictable global effects, it does not require global
visibility, it does not assume any kind of centralized control, it is
self-stabilizing. Other examples of emergent algorithms and models include
cellular automata, artificial neural networks and swarm intelligence
systems (ant colony optimization, bees algorithm, etc.).
is an algorithm that employs a degree of
randomness as part of its logic. The algorithm typically uses uniformly
random bits as an auxiliary input to guide its behavior, in the hope of
achieving good performance in the "average case" over all possible choices
of random bits. Formally, the algorithm's performance will be a random
variable determined by the random bits; thus either the running time, or
the output (or both) are random variables.
Algorithmic Learning Theory
is a mathematical framework for
analyzing machine learning problems and algorithms. Synonyms include
formal learning theory and algorithmic inductive inference. Algorithmic
learning theory is different from statistical learning theory in that it
does not make use of statistical assumptions and analysis. Both
algorithmic and statistical learning theory are concerned with machine
learning and can thus be viewed as branches of computational learning
is a subset of evolutionary computation, a
generic population-based metaheuristic optimization algorithm. An EA uses
mechanisms inspired by biological evolution, such as reproduction,
mutation, recombination, and selection. Candidate solutions to the
optimization problem play the role of individuals in a population, and the
fitness function determines the quality of the solutions (see also loss
function). Evolution of the population then takes place after the repeated
application of the above operators. Artificial evolution (AE) describes a
process involving individual evolutionary algorithms; EAs are individual
components that participate in an AE.
referrs to in the literature as Baldwinian
evolutionary algorithms (EAs), Lamarckian EAs, cultural algorithms, or
genetic local search.
Expectation Maximization Algorithm
is an iterative method to find maximum likelihood or maximum a posteriori
(MAP) estimates of parameters in statistical models, where the model
depends on unobserved latent variables. The EM iteration alternates
between performing an expectation (E) step, which creates a function for
the expectation of the log-likelihood evaluated using the current estimate
for the parameters, and a maximization (M) step, which computes parameters
maximizing the expected log-likelihood found on the E step. These
parameter-estimates are then used to determine the distribution of the
latent variables in the next E step.
Monad Functional Programming
are a way to build computer programs by joining simple components in
robust ways. A monad may encapsulate values of a particular data type,
creating a new type associated with a specific computation.
is a condition or predicate that must always be true just prior
to the execution of some section of code or before an operation in a
formal specification. If a precondition is violated, the effect of the
section of code becomes undefined and thus may or may not carry out its
intended work. Security problems can arise due to incorrect preconditions.
Often, preconditions are simply included in the documentation of the
affected section of code. Preconditions are sometimes tested using guards
or assertions within the code itself, and some languages have specific
syntactic constructions for doing so. For example: the factorial is only
defined for integers greater than or equal to zero. So a program that
calculates the factorial of an input number would have preconditions that
the number be an integer and that it be greater than or equal to zero.
as opposed to a traditional serial
algorithm, is an algorithm which can be executed a piece at a time on many
different processing devices, and then combined together again at the end
to get the correct result. Many parallel algorithms are executed
concurrently – though in general concurrent algorithms are a distinct
concept – and thus these concepts are often conflated, with which aspect
of an algorithm is parallel and which is concurrent not being clearly
distinguished. Further, non-parallel, non-concurrent algorithms are often
referred to as "sequential algorithms", by contrast with concurrent
is any executable code that is passed as an
argument to other code, which is expected to call back (execute) the
argument at a given time. This execution may be immediate as in a
synchronous callback, or it might happen at a later time as in an
asynchronous callback. In all cases, the intention is to specify a
function or subroutine as an entity that is, depending on the language,
more or less similar to a variable. Programming languages support
callbacks in different ways, often implementing them with subroutines,
lambda expressions, blocks, or function pointers.Controls
Instance Based Learning Algorithm
is an algorithm for finding maximal cliques in an
undirected graph. That is, it lists all subsets of vertices with the two
properties that each pair of vertices in one of the listed subsets is
connected by an edge, and no listed subset can have any additional
vertices added to it while preserving its complete connectivity.
Big O Notation
is a mathematical notation that describes the
limiting behavior of a function when the argument tends towards a
particular value or infinity.
Binary Search Algorithm
is a search algorithm that finds the
position of a target value within a sorted array. Binary search compares
the target value to the middle element of the array; if they are unequal,
the half in which the target cannot lie is eliminated and the search
continues on the remaining half until it is successful or the remaining
half is empty.
is a mathematical method of
assigning a prior probability to a given observation. It was invented by
Ray Solomonoff in the 1960s. It is used in inductive inference theory and
analyses of algorithms. In his general theory of inductive inference,
Solomonoff uses the prior obtained by this formula, in Bayes' rule for
Artificial intelligence needs the "if"
function, just like us.
There are a lot of if's, with some if's that
refer to other if's for more processing.
PHP has the following conditional
executes some code only if a specified condition is
executes some code if a condition is true and another code if
the condition is
selects one of several blocks of code to be executed.
selects one of many blocks of code
to be executed.
PHP if else
HP Control Structures
If Function (excel)
If Statement (excel)
is a control flow statement for specifying
iteration, which allows code to be executed repeatedly.
is a logic gate which produces an output which is
false only if all its inputs are true; thus its output is complement to
that of the AND gate. A LOW (0) output results only if both the inputs to
the gate are HIGH (1); if one or both inputs are LOW (0), a HIGH (1)
output results. It is made using transistors and junction diodes. By De
Morgan's theorem, AB=A+B, and thus a NAND gate is equivalent to inverters
followed by an OR gate.
is a digital logic gate that gives a true (1/HIGH)
output when the number of true inputs is odd. An XOR gate implements an
exclusive or; that is, a true output results if one, and only one, of the
inputs to the gate is true. If both inputs are false (0/LOW) or both are
true, a false output results. XOR represents the inequality function,
i.e., the output is true if the inputs are not alike otherwise the output
is false. A way to remember XOR is "one or the other but not both".
is an idealized or physical device implementing a
that is, it performs a logical operation on one or more binary inputs and
produces a single binary output. Depending on the context, the term may
refer to an ideal logic gate, one that has for instance zero rise time and
unlimited fan-out, or it may refer to a non-ideal physical device. (see
Ideal and real op-amps for comparison). Logic gates are primarily
implemented using diodes or transistors acting as
, but can also be
constructed using vacuum tubes, electromagnetic relays (relay logic),
fluidic logic, pneumatic logic, optics, molecules, or even mechanical
elements. With amplification, logic gates can be cascaded in the same way
that Boolean functions can be composed, allowing the construction of a
physical model of all of Boolean logic, and therefore, all of the
algorithms and mathematics that can be described with Boolean logic. Logic
circuits include such devices as multiplexers, registers, arithmetic logic
units (ALUs), and computer memory, all the way up through complete
microprocessors, which may contain more than 100 million gates. In modern
practice, most gates are made from field-effect transistors (FETs),
particularly metal–oxide–semiconductor field-effect transistorss (MOSFETs).
Compound logic gates AND-OR-Invert (AOI) and OR-AND-Invert (OAI) are often
employed in circuit design because their construction using MOSFETs is
simpler and more efficient than the sum of the individual gates. In
reversible logic, Toffoli gates are used.
Gottfried Wilhelm Leibniz
was a German polymath and
philosopher (1716) who occupies a prominent place in the history of
mathematics and the history of philosophy, having developed differential
and integral calculus independently of
is a universal and formal
language imagined to express mathematical, scientific, and metaphysical
concepts. Leibniz thus hoped to create a language usable within the
framework of a universal logical calculation or calculus ratiocinator.
is a theoretical universal logical
calculation framework, a concept described in the writings of Gottfried
Leibniz, usually paired with his more frequently mentioned characteristica
universalis, a universal conceptual language.
finds the remainder after division of one
number by another (sometimes called modulus).
is a system of arithmetic for integers,
where numbers "wrap around" upon reaching a certain value—the modulus
is a subfield of both biophysics and
mathematical biology focusing of physical and physico-chemical mechanisms
involved in physiological functions of living organisms, as well as the
molecular structures supporting such physiological functions.
Our greatest intelligence now is
already being formed by the
, which in some ways
simulates the neural network
of the human brain. But bringing
together all our knowledge and information is only the beginning,
because it will take the collective consensus of all the human
brains in order for us to achieve intelligent solutions to our
problems. And of course, incase of a major
, we will have
to Secure our intelligence
in something like the
Global Seed Vault
Because we would not want to
start all over again
as many humans
civilizations had to do throughout human history. Backup our
most important knowledge and information by transmitting it into
store it in a satellite
, store it on the moon and in
multiple places. This we have to do. That's Intelligence.
or having a fixed Pattern
; liable to
change. A value is either arbitrary or not fully specified or unknown.
is an instance of change;
the rate or magnitude of change. An activity that varies from a norm or
is something a little
different from others of the same type.
is something a little different
from others of the same type. Exhibiting variation and change. In biology,
a group of organisms within a species that differ in trivial ways from
event or system is one that is unpredictable due
to the influence of a random variable. The word "stochastic" comes from
the Greek word στόχος (stokhos, "aim"). It occurs in a wide variety of
professional and academic fields.
in probability and statistics
a random variable, random quantity, aleatory variable or stochastic
variable is a variable whose value is subject to variations due to
(i.e. randomness, in a
mathematical sense). A random variable can take on a set of possible
different values (similarly to other mathematical variables), each with an
associated probability, in contrast to other
is a system in which no randomness is
involved in the development of future states of the system. A
deterministic model will thus always produce the same output from a given
starting condition or initial state.
Internalism and externalism
are two opposing ways of
explaining various subjects in several areas of philosophy
. These include
human motivation, knowledge, justification, meaning, and truth. The
distinction arises in many areas of debate with similar but distinct
meanings. Usually 'internalism
' refers to the belief that an explanation
can be given of the given subject by pointing to things which are internal
to the person or their mind which is considering them. Conversely,
externalism holds that it is things about the world which motivate us,
justify our beliefs, determine meaning, etc.
the physical and psychological qualities.
refers to finding the linear approximation to
a function at a given point.
refers to the use of a Lyapunov function to
optimally control a dynamical system. Lyapunov functions are used
extensively in control theory to ensure different forms of system
stability. The state of a system at a particular time is often described
by a multi-dimensional vector. A Lyapunov function is a nonnegative scalar
measure of this multi-dimensional state. Typically, the function is
defined to grow large when the system moves towards undesirable states.
System stability is achieved by taking control actions that make the
Lyapunov function drift in the negative direction towards zero.
Variable and Attribute (research)
is a characteristic of an
object (person, thing, etc.). Attributes are closely related to variables.
A variable is a logical set of attributes. Variables can "vary" - for
example, be high or low. How high, or how low, is determined by the value
of the attribute (and in fact, an attribute could be just the word "low"
is an alphabetic character
representing a number, called the value of the variable, which is either
arbitrary or not fully specified or unknown. Making
variables as if they were explicit numbers allows one to solve a range of
problems in a single computation. A typical example is the quadratic
formula, which allows one to solve every quadratic equation by simply
substituting the numeric values of the coefficients of the given equation
to the variables that represent them.
of a function of a real variable measures the sensitivity
to change of a quantity (a function value or dependent variable) which is
determined by another quantity (the independent variable). Derivatives are
a fundamental tool of calculus
For example, the derivative of the position
of a moving object
with respect to time is the object's velocity: this
measures how quickly the position of the object changes when time is
Variable (computer science)
is a storage location paired
with an associated symbolic name (an identifier), which contains some
known or unknown quantity of information referred to as a value. The
variable name is the usual way to reference the stored value; this
separation of name and content allows the name to be used independently of
the exact information it represents. The identifier in computer source
code can be bound to a value during run time, and the value of the
variable may thus change during the course of program execution.
Variable and Attribute (research)
is a characteristic of an object
(person, thing, etc.). Attributes are closely related to variables. A
variable is a logical set of attributes. Variables can "vary" - for
example, be high or low. How high, or how low, is determined by the value
of the attribute (and in fact, an attribute could be just the word "low"
is a polynomial mapping (equivalently,
recurrence relation) of degree 2, often cited as an archetypal example of
how complex, chaotic behaviour can arise from very simple non-linear
is a system in which a function describes the
of a point in a
Examples include the mathematical models that describe the swinging of a
clock pendulum, the flow of water in a pipe, and the number of fish each
springtime in a lake.
Dependent and independent Variables
represent the output or outcome whose variation is being studied. The
independent variables represent inputs or causes, i.e. potential reasons
is a statistical process for estimating the relationships among
is a scientific principle used within the calculus of
variations, which develops general methods for finding functions which
extremize the value of quantities that depend upon those functions. For
example, to answer this question: "What is the shape of a chain suspended
at both ends?" we can use the variational principle that the shape must
minimize the gravitational potential energy.
are synchronization primitives that
enable threads to wait until a particular condition occurs. Condition
variables are user-mode objects that cannot be shared across processes.
Condition variables enable threads to atomically release a lock and enter
the sleeping state.
is one of many known
sequence of possible
Prepared for Emergencies
is a measure
of an event given
that (by assumption, presumption, assertion or evidence) another event has
How many questions
deep do you need to go?
You can't prepare for everything
how do you decide?
is the process of responding to the occurrence, during
computation, of exceptions – anomalous or exceptional conditions requiring
often changing the normal flow of program execution. It is provided by
Event Chain Methodology
is an uncertainty modeling and
schedule network analysis technique that is focused on identifying and
managing events and event chains that affect
Event chain methodology is the next advance beyond critical path method
and critical chain project management. Event chain methodology helps to
mitigate the effect of motivational and cognitive biases in estimating and
Preference Based Planning
is a form of automated planning
and scheduling which focuses on producing plans that additionally satisfy
as many user-specified preferences as possible. In many problem domains, a
task can be accomplished by various sequences of actions (also known as
plans). These plans can vary in quality: there can be many ways to solve a
problem but one generally prefers a way that is, e.g., cost-effective,
quick and safe.
is a statistical process for estimating
the relationships among variables. It includes many techniques for
modeling and analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more independent
variables (or 'predictors'). More specifically, regression analysis helps
one understand how the typical value of the dependent variable (or
'criterion variable') changes when any one of the independent variables is
varied, while the other independent variables are held fixed.
is a strategic
some organizations use to make flexible long-term plans. Part adaptation and generalization of classic methods.
is there, it's just not perfected yet, and just what
are you perfecting?
And just how does this relate to the normal processes of the
There has to be a
, so what are these procedures?
We all need to verify the validly of the procedures and learn
why the procedures are written the way they are. Have you
, and have you
correctly identified the
, and the most critical scenarios, and have you put
them in the appropriate order?
was OK even though it was silly in some
parts, especially the parts about Artificial Intelligence. I
would like to see a TV show with a Robot of this type. Everyone
who logs into the Internet website for
"The Robot Show"
can see what the robot sees and can even suggest
what the robot should do. People could also help the robot
analyze moments in the Robots life, like a collective learning
environment. All the suggestions will be posted so everyone can
see the comments and the percentages of people who voted for a
particular action. The Robot show will be Kind of like
, except with a Robot. The Robot will start by
experiencing the birth of a human, and then stay with the family
and watch the child or children grow up. There will also be one
more Robot that just goes out and learns from the world by
experiencing life in all kinds of situations with all kinds of
different people. Of course everything that each Robot learns
will be stored in a central database and will be used to help
perfect Artificial Intelligence and also help the Robots make
better decisions by using the collective data. This will be a
show that actually learns and teaches. So for the millions of
people who will be connected to the robots through the website
will actually be contributors of information and knowledge that
will help create Artificial intelligence, collectively. And yes
I am Functioning Normal.
Robot Operating System
is a collection of software frameworks for robot software
development, (see also Robotics middleware) providing operating
system-like functionality on a heterogeneous computer cluster. ROS
provides standard operating system services such as hardware abstraction,
low-level device control, implementation of commonly used functionality,
message-passing between processes, and package management. Running sets of
ROS-based processes are represented in a graph architecture where
processing takes place in nodes that may receive, post and multiplex
sensor, control, state, planning, actuator and other messages. Despite the
importance of reactivity and low latency in robot control, ROS, itself, is
not a real-time OS (RTOS), though it is possible to integrate ROS with
real-time code. The lack of support for real-time systems is being
addressed in the creation of ROS 2.0. Software in the ROS Ecosystem can be
separated into three groups: Language-and platform-independent tools used
for building and distributing ROS-based software; ROS client library
implementations such as roscpp,rospy, and roslisp; Packages containing
application-related code which uses one or more ROS client libraries.
DAvinCi Robotic Operating System
is a computer program that operates or
controls a particular type of device that is attached to a computer. A
driver provides a software interface to hardware devices, enabling
operating systems and other computer programs to access hardware functions
without needing to know precise details of the hardware being used.
Short Circuit (1986 film)
International Robot Exhibition
Remember how some people actually thought that artificial
intelligence, or AI, was the next big thing
. What they didn’t
realize was that
actually referring to human intelligence. This of course was a
human error. It was the Human Brain that has incredible
potential with endless possibilities and abilities, not
artificial intelligence. If the people at
IBM Computer on Jeopardy
spent the same amount of time,
people and resources on
creating an education curriculum that was based on learning and
understanding, they would have created something a lot more
valuable and useful then a
This is not to down play what they have accomplished, because
it's incredible. Imagine being able to ask a question and
getting an appropriate answer in the matter of seconds, that
would increase our abilities tremendously.
But we can't create a smarter planet if we're using the same
thinking that also created all our problems.
To create a smarter planet you have to make
people smarter, and not just by doing so called
'smart things', unless
one of those smart things actually improves education curriculum
that we use.Future
of LifeBrain and Computer Similarities
To say that a database like Watson is
artificial intelligence would be incorrect
. To say that
computers can do things that humans can't do would also be
incorrect. Humans build machines and tools to expand our
abilities, and also to save us time. Machines are not doing
things better then humans, machines are doing things for humans.
You can put all known knowledge and information into a machine
but that machine will still be far from intelligent. Humans have
the ability to be intelligent, but we first have to define a
particular intelligent action and then prove it to be
intelligent. And at the moment, we are far from defining what
intelligence is, or what intelligence is supposed to be. But we
do have the abilities and the knowledge to accomplish this, so
it's just a matter of time before intelligence becomes
mainstream. We are not building machines to think like humans or
to think for humans, we are building machines to help humans
think more. Instead of taking advantage of peoples ignorance by
selling them false narratives about artificial intelligence, how
about educating people, that would be the intelligent thing to
is an artificial
developed by Microsoft's Technology and Research and Bing teams to
experiment with and conduct research on conversational understanding. The
more you chat with Tay the smarter she gets is a lie. We need to stop this
type of abuse using words that
mislead and misinform.
"Computers can read zero's and ones, which
means they can be taught to look for patterns. And when these
patterns are labeled correctly and accurately, a computer can
identify things in the world pretty much the same way as humans
Deep learning is great for finding
, but if you
don't use this information to benefit society, then we will continue to
suffer, as we are now."
"Computers will help us make better
, Ai will also help us make better
, but Humans still have to
focuses on the recognition of patterns
and regularities in data.
is the use of
uncover patterns in data that can be presented as statistically
significant, without first devising a specific hypothesis as to the
underlying causality. (also known as data fishing, data snooping, and
is a discernible regularity in the world or in a
manmade design. As such, the elements of a pattern repeat in a predictable
manner. A geometric pattern is a kind of pattern formed of geometric
shapes and typically repeating like a wallpaper.
Linear Discriminant Analysis
is a generalization of Fisher's linear
discriminant, a method used in statistics, pattern recognition and machine
learning to find a
of features that characterizes or separates two or
more classes of objects or events. The resulting combination may be used
, or, more commonly, for dimensionality reduction
Facial Recognition System
is a computer application capable of
identifying or verifying a person from a digital image or a video frame
from a video source. One of the ways to do this is by comparing selected
facial features from the image and a face database.
is the algorithmic problem of finding a
cycle in a sequence of iterated function values.
is the irrational fear of irregular patterns or clusters of small
holes or bumps.
is a general direction in
which something tends to move.
is a statistical technique to aid interpretation
of data. When a series of measurements of a process are treated as a time
series, trend estimation can be used to make and justify statements about
tendencies in the data, by relating the measurements to the times at which
is a sequence of numbers such that
the difference between the consecutive terms is constant.
algorithms or mathematical techniques allow the discovery
of patterns or correlations in large quantities of data.
computers will be able to
understand what they mean
without the use of tags. This will lead to systems that can help doctors
analyze X-ray pictures, magnetic resonance imaging (MRI) machine,
ultrasound or computerized tomography scans.
How computers learn to recognize objects instantly
Build a TensorFlow
Image Classifier in 5 Min
sensor that detects and conveys the information that constitutes an image.
It does so by converting the variable attenuation of
(as they pass
through or reflect off objects) into signals, small bursts of current that
convey the information. The waves can be light or other
Image sensors are used in electronic imaging devices of both analog and
digital types, which include digital cameras, camera modules, medical
imaging equipment, night vision equipment such as thermal imaging devices,
radar, sonar, and others. As technology changes, digital imaging tends to
replace analog imaging.
Visual Search Engine
is a search engine designed to search
for information on the World Wide Web through the input of an image or a
search engine with a visual display of the search results. Information may
consist of web pages, locations, other images and other types of
documents. This type of search engines is mostly used to search on the
mobile Internet through an image of an unknown object (unknown search
query). Examples are buildings in a foreign city. These search engines
often use techniques for Content Based Image Retrieval.
is an image database
organized according to the WordNet hierarchy (currently only the nouns),
in which each node of the hierarchy is depicted by hundreds and thousands
of images. Currently we have an average of over five hundred images per
Outline of object recognition
technology in the field of computer
vision for finding and identifying objects in an image or video sequence.
Humans recognize a multitude of objects in images with little effort,
despite the fact that the image of the objects may vary somewhat in
different view points, in many different sizes and scales or even when
they are translated or rotated. Objects can even be recognized when they
are partially obstructed from view. This task is still a challenge for
computer vision systems. Many approaches to the task have been implemented
over multiple decades.
Pattern Analysis, Statistical Modelling and Computational Learning
Visual Object Classes
Visual Search Engine App
Resolution Image Compression with Recurrent Neural Networks.
deals with how computers can be made to gain high-level understanding from
digital images or videos. From the perspective of engineering, it seeks to
automate tasks that the human visual system can do.
Orientation (computer vision)
in computer vision and image
processing a common assumption is that sufficiently small image regions
can be characterized as locally one-dimensional, e.g., in terms of lines
or edges. For natural images this assumption is usually correct except at
specific points, e.g., corners or line junctions or crossings, or in
regions of high frequency textures. However, what size the regions have to
be in order to appear as one-dimensional varies both between images and
within an image. Also, in practice a local region is never exactly
one-dimensional but can be so to a sufficient degree of approximation.
Inception Model Image Recognition
is the problem of identifying to which of a
(sub-populations) a new observation belongs, on the basis
of a training set of data containing observations (or instances) whose
category membership is known.
Computer Algorithm that is nearly as accurate as people are at Image
Analysis of brain circuitry and neural networks
Convolutional Neural Network
is a class of deep, feed-forward
artificial neural network that have successfully been applied to analyzing
or inductive transfer is a research problem in
that focuses on storing
knowledge gained while solving one problem and applying it to a different
but related problem. For example, knowledge gained while learning to
recognize cars could apply when trying to recognize trucks. This area of
research bears some relation to the long history of psychological
literature on transfer of learning, although formal ties between the two
fields are limited.
Optical Character Recognition
is the mechanical or
electronic conversion of images of typed, handwritten or printed text into
machine-encoded text, whether from a scanned document, a photo of a
document, a scene-photo (for example the text on signs and billboards in a
landscape photo) or from subtitle text superimposed on an image (for
example from a television broadcast).
Image Classification Algorithm
Question and Answer Platforms
Recurrent Neural Networks
images with sentences
being able to identify images means that blind
people will see by way of
David Eagleman: Can we Create new Senses for Humans
When a machine can
see the world in
the same way that a human does
, then we will have some really
There will also be improvements in computers' ability to
and understand sound
. Greater sensitivity to
could lead to more-accurate landslide
warnings, for example.
YouTube built an Automated Content Detection System
prevents most unauthorized clips from appearing on its site.
Artificial intelligence produces realistic sounds that fool humans
Video-trained system from MIT’s Computer Science and Artificial
Intelligence Lab could help robots understand how objects interact with
Computers with virtual taste buds will be able to
, according to IBM, helping chefs improve recipes or
create new ones. The systems will break down ingredients to
their respective chemicals and calculate their interactions with
neural sensors in a person's tongue and nose.
And, finally, according to IBM, computers will have an acute
sense of smell
in order to diagnose from a person's breath a coming cold, liver and
kidney disorders, diabetes and tuberculosis. Similar to how a Breathalyzer
detects alcohol, the computer will be able to check for molecular
biomarkers pointing to diseases. Machine
is the automated simulation of the
is a device intended to detect odors or flavors. Over
the last decade, "electronic sensing" or "e-sensing" technologies have
undergone important developments from a technical and commercial point of
view. The expression "electronic sensing" refers to the capability of
reproducing human senses using sensor arrays and pattern recognition
The Panoptic Studio:
is a Massively Multiview System, a Social Motion
Capture Technology for Recording
is an enabling technology that encompasses the
fundamental theory, applications, algorithms, and implementations of
processing or transferring information contained in many different
physical, symbolic, or abstract formats broadly designated as signals. It
uses mathematical, statistical, computational, heuristic, and linguistic
representations, formalisms, and techniques for representation, modelling,
analysis, synthesis, discovery, recovery, sensing, acquisition,
extraction, learning, security, or forensics.
is an object
whose purpose is to detect events or changes in its environment and sends
the information to the computer which then tells the actuator (output
devices) to provide the corresponding output. A sensor is a device that
converts real world data (Analog) into data that a computer can understand
using ADC or Analog to Digital converter
living organisms contain biological sensors
with functions similar
to those of the mechanical devices described. Most of these are
specialized cells that are sensitive to: Light, motion, temperature,
magnetic fields, gravity, humidity, moisture, vibration, pressure,
electrical fields, sound, and other physical aspects of the external
environment. Physical aspects of the internal environment, such as
stretch, motion of the organism, and position of appendages
(proprioception). Estimation of biomolecules interaction and some kinetics
parameters. Internal metabolic indicators, such as glucose level, oxygen
level, or osmolality. Internal signal molecules, such as hormones,
neurotransmitters, and cytokines. Differences between proteins of the
organism itself and of the environment or alien creatures.
combining of sensory data or data derived from disparate sources such that
the resulting information has less uncertainty than would be possible when
these sources were used individually.
is a material whose resistance changes when a
Engineers Create Artificial Skin That "Feels" Temperature Changes
analytical device, used for the detection of an analyte, that combines a
biological component with a physicochemical detector. The sensitive
biological element (e.g. tissue, microorganisms, organelles, cell
receptors, enzymes, antibodies, nucleic acids, etc.) is a biologically
derived material or biomimetic component that interacts (binds or
recognizes) with the analyte under study.
Miniature Technology, Big Hope for Disease Detection
is a self-contained
analytical device that can provide information about the
environment, that is, a liquid or a gas phase. The information is provided
in the form of a measurable physical signal that is correlated with the
concentration of a certain chemical species (termed as analyte).
biological, chemical, or surgical sensory points used to convey
information about nanoparticles to the macroscopic world.
Towards General-Purpose Sensing
Brain Computer Interface
Do you need a Computer Chip implanted in your Brain?
You don't need a Computer Chip implanted in your Body or Brain, you can still use
and other devices to carry important
information with you that you need to remember
External Memory Devices
are amazing tools. Jumpdrives can be made to look like jewelry or credit
cards. You can even get a mini tattoo of your SS# in the
form of a
could link to your information. There are only a few very unique
circumstances that would require the need for a person to have a
like people who have
But the body does produce the
necessary voltage to run an extra memory device, so I guess it's just a
Blending human brains with computers has already been happening for over
20 years, so this is Your Brain on Computers. But you don't need to stick
a computer in your head, you can just carry one around in your pocket.
Brain Computer Interface helps Paralyzed Man feel again through
Mind-Controlled Robotic Arm
Primates Regain Control of Paralyzed Limb
Brain-Computer Interface Laboratory ETSU
Brain-to-Brain Interface Demonstration
The BrainNet - (VPRO documentary - 2014)
Direct Brain-to-Brain Interface in Humans
implantable brain–computer interfaces (BCIs).
Researchers Revolutionize Brain-Computer Interfaces Using Silicon
Mind-Controlled Device helps stroke patients retrain brains to move
Big Improvements to Brain-Computer Interface
. Newly developed “glassy
carbon” electrodes transmit more robust signals to restore motion in
people with damaged spinal cords.
Brain Computer Interfaces
is a direct communication pathway
between an enhanced or wired brain and an external device. BCIs are often
directed at researching, mapping, assisting, augmenting, or repairing
human cognitive or sensory-motor functions.
Brain Computer Interface
are clothing and accessories incorporating computer and
. The designs often incorporate practical functions and
Wearable Technology can take basic measurements and
monitor and track body functions to give the user a better understand of
their body and increase their awareness. They can then match the
sensations they feel to the recorded data. Eventually they will be able to
teach themselves how to notice body sensations and changes and have a
better idea what may be happening in their body. A prosthesis for feeling.
And the person will not have to wear the device all the time because they
will be more aware of their body sensations and what they may mean. Ai
teaching us to be more intelligent, now that's Ai.
Bendable Electronics Conference
are artificial or synthetically-created substances that have
the built-in ability to automatically repair damage to themselves without
any external diagnosis of the problem or human intervention. Generally,
materials will degrade over time due to fatigue, environmental conditions,
or damage incurred during operation. Cracks and other types of damage on a
microscopic level have been shown to change thermal, electrical, and
acoustical properties of materials, and the propagation of cracks can lead
to eventual failure of the material.
application of biological
and systems found in nature to the study and design of
engineering systems and modern technology.
The Six Million Dollar Man
Artificial Body Parts
is a being with
both organic and biomechatronic body parts. Not the same thing as bionic,
or android; it applies to an organism that has restored
function or enhanced abilities due to the integration of some artificial
component or technology that relies on some sort of feedback. While
cyborgs are commonly thought of as mammals, including humans, they might
also conceivably be any kind of organism.
approach for exploring regulatory systems—their
structures, constraints, and possibilities, the scientific study of
in the animal and the machine.
is a humanoid robot or synthetic organism designed to look and
act like a human, especially one with a body having a flesh-like
is a robot with its body shape built to resemble the
human body. The design may be for functional purposes, such as interacting
with human tools and environments, for experimental purposes, such as the
study of bipedal locomotion, or for other purposes. In general, humanoid
robots have a torso, a head, two arms, and two legs, though some forms of
humanoid robots may model only part of the body, for example, from the
waist up. Some humanoid robots also have heads designed to replicate human
facial features such as eyes and mouths. Androids are humanoid robots
built to aesthetically resemble humans.
Human Operating System
is a discipline related to neuroscience and
biomedical engineering concerned with developing neural prostheses. They
are sometimes contrasted with a brain–computer interface, which connects
the brain to a computer rather than a device meant to replace missing
is a wearable mobile machine that is powered by a
system of electric motors, pneumatics, levers, hydraulics, or a
combination of technologies that allow for limb movement with increased
strength and endurance.
Hybrid Assistive Limb
is a powered exoskeleton suit designed to support and expand the physical
capabilities of its users, particularly people with physical disabilities.
There are two primary versions of the system: HAL 3, which only provides
leg function, and HAL 5, which is a full-body exoskeleton for the arms,
legs, and torso.
refers to the empirically verified use of
technology to, in some sense, read people's minds. Advances in research
have made this possible by using human neuroimaging to decode a person's
conscious experience based on non-invasive measurements of an individual's
or Radio-frequency identification, uses electromagnetic
fields to automatically identify and
attached to objects. The
tags contain electronically
. Passive tags collect
energy from a nearby RFID reader's interrogating radio waves. Active tags
have a local power source such as a battery and may operate at hundreds of
meters from the RFID reader. Unlike a barcode, the tag need not be within
the line of sight of the reader, so it may be embedded in the tracked
object. RFID is one method for Automatic Identification and Data Capture (AIDC)
is a human-implantable
, which is an identifying integrated circuit device
or RFID transponder encased in silicate glass and implanted in the body of
a human being. A subdermal implant typically contains a unique ID number
that can be linked to information contained in an external database, such
as personal identification, medical history, medications, allergies, and
refers to a body modification that is placed
underneath the skin, therefore allowing the body to heal over
implant and creating a raised design. Such implants fall under the broad
category of body modification.
I laugh when I here people
say that soon we will be able to
upload information directly
into our brains
, that is so stupid. Why would you need to do
that if you have a
or other information storage
devices that can carry all your important data with you? And the
information you can't carry, you can access it using the
. Besides you just can't upload information into the
brain because information has to be processed very carefully so
that the information is correctly understood. So you have to
manually and slowly input information into a human brain so that
it has time to decipher the information and learn how the
information should be used. The key word here is 'Learn
one word we take for granted. So detailed instructions on how to
use this information is a must. Like when you install software
into a computer. The software comes with instructions that tells
the computer how the information can be used. Then of course the
computer needs an operating system
in order to use that
information correctly. Computers will allow humans to learn
faster, but only if the instructions are detailed and accurate.
So there are two sides of learning. Choosing the best
information to learn and then creating the instructions on how
to use the information correctly. Again, the computer and the
human brain show their similarities. Reading
Adding Information to our DNA
We may be limited by our biology, but
we are not limited by our intellect
. And our intellect has given
rise to technology, which is an extension of our intellect. So
we have no boundaries. We have taken the first step of
controlling matter, which means we have broken through another
level of reality, so now what? What is the next step? And why
should we take it? It's not the same out there as it is in here,
we basically have to start all over again. This is a whole other
dimension. Can we live in both dimensions at the same time? And
there's word 'Time' again. Time and Space does not have a
, so time and space are like fictitious
characters in a play, they're just actors, we know nothing about
their personal lives. Even with the mind fully open I still
cannot see. So there's the problem, it's not sight, sound,
smell, taste or feel. We are so close and yet still so far away.
But still an awesome feeling, which way do we go? Lets see how
far we can go in both directions without losing contact with
each other. And what if we eventually run right back into each
other? Then what?
"When people start learning how artificial
intelligence can learn on its own, we will actually be teaching
ourselves, and at the same time, learn how to learn more effectively."
We created something incredible
called the Computer.
And as we were perfecting the computers power and potential we
realized that we should be making these same improvements with
ourselves. Think about it, our brains are computers, so why
don't humans have an Operating System
to help us learn more and to be more
productive. But it simply wasn’t that the computer educated us
more, it was the realization that the computer was actually us.
This has happened with a lot of human creations and
advancements. We start off creating something to improve our
lives and it ends up teaching us that we are the ones who need
to improve and not our technology. If our education system does
not advance at the same speed as technology, we will continue to
suffer from these advancements instead of benefiting from them.
And that is a proven fact if you look at the world today and see
the worldwide suffering and the atrocities that have continued
to get worse and worse. One life improving at the expense of 1,000’s of other lives is not improvement it is simply criminal