THIS POST IS CONTINUED FROM PART 10,
BELOW—
CAPT AJIT VADAKAYIL SAYS AI MUST MEAN “INTELLIGENCE AUGUMENTATION “ IN FUTURE ..
Let this be IA
Let this be IA
OBJECTIVE AI CANNOT HAVE A VISION,
IT CANNOT PRIORITIZE,
IT CANT GLEAN CONTEXT,
IT CANT TELL THE MORAL OF A STORY ,
IT CANT RECOGNIZE A JOKE, OR BE A JUDGE IN A JOKE CONTEST
IT CANT DRIVE CHANGE,
IT CANNOT INNOVATE,
IT CANNOT DO ROOT CAUSE ANALYSIS ,
IT CANNOT MULTI-TASK,
IT CANNOT DETECT SARCASM,
IT CANNOT DO DYNAMIC RISK ASSESSMENT ,
IT IS UNABLE TO REFINE OWN KNOWLEDGE TO WISDOM,
IT IS BLIND TO SUBJECTIVITY,
IT CANNOT EVALUATE POTENTIAL,
IT CANNOT SELF IMPROVE WITH EXPERIENCE,
IT CANNOT UNLEARN
IT IS PRONE TO CATASTROPHIC FORGETTING
IT DOES NOT UNDERSTAND BASICS OF CAUSE AND EFFECT,
IT CANNOT JUDGE SUBJECTIVELY TO VETO/ ABORT,
IT CANNOT FOSTER TEAMWORK DUE TO RESTRICTED SCOPE,
IT CANNOT MENTOR,
IT CANNOT BE CREATIVE,
IT CANNOT THINK FOR ITSELF,
IT CANNOT TEACH OR ANSWER STUDENTs QUESTIONS,
IT CANNOT PATENT AN INVENTION,
IT CANNOT SEE THE BIG PICTURE ,
IT CANNOT FIGURE OUT WHAT IS MORALLY WRONG,
IT CANNOT PROVIDE NATURAL JUSTICE,
IT CANNOT FORMULATE LAWS
IT CANNOT FIGURE OUT WHAT GOES AGAINST HUMAN DIGNITY
IT CAN BE FOOLED EASILY USING DECOYS WHICH CANT FOOL A CHILD,
IT CANNOT BE A SELF STARTER,
IT CANNOT UNDERSTAND APT TIMING,
IT CANNOT FEEL
IT CANNOT GET INSPIRED
IT CANNOT USE PAIN AS FEEDBACK,
IT CANNOT GET EXCITED BY ANYTHING
IT HAS NO SPONTANEITY TO MAKE THE BEST OUT OF SITUATION
IT CAN BE CONFOUNDED BY NEW SITUATIONS
IT CANNOT FIGURE OUT GREY AREAS,
IT CANNOT GLEAN WORTH OR VALUE
IT CANNOT UNDERSTAND TEAMWORK DYNAMICS
IT HAS NO INTENTION
IT HAS NO INTUITION,
IT HAS NO FREE WILL
IT HAS NO DESIRE
IT CANNOT SET A GOAL
IT CANNOT BE SUBJECTED TO THE LAWS OF KARMA
ON THE CONTRARY IT CAN SPAWN FOUL AND RUTHLESS GLOBAL FRAUD ( CLIMATE CHANGE DUE TO CO2 ) WITH DELIBERATE BLACK BOX ALGORITHMS, JUST FEW AMONG MORE THAN 60 CRITICAL INHERENT DEFICIENCIES.
HUMANS HAVE THINGS A COMPUTER CAN NEVER HAVE.. A SUBCONSCIOUS BRAIN LOBE, REM SLEEP WHICH BACKS UP BETWEEN RIGHT/ LEFT BRAIN LOBES AND FROM AAKASHA BANK, A GUT WHICH INTUITS, 30 TRILLION BODY CELLS WHICH HOLD MEMORY, A VAGUS NERVE , AN AMYGDALA , 73% WATER IN BRAIN FOR MEMORY, 10 BILLION MILES ORGANIC DNA MOBIUS WIRING ETC.
SINGULARITY , MY ASS !
Simultaneous
Localization and Mapping (SLAM) technology aids in the localization and
positioning of a robot or a device in real-time using mathematical and
statistical algorithms with different sensors
In navigation, robotic
mapping and odometry for virtual reality or augmented reality, simultaneous
localization and mapping (SLAM) is the computational problem of constructing or
updating a map of an unknown environment while simultaneously keeping track of
an agent's location within it.
While this initially
appears to be a chicken-and-egg problem there are several algorithms known for
solving it, at least approximately, in tractable time for certain environments.
Popular approximate solution methods include the particle filter, extended
Kalman filter, Covariance intersection, and GraphSLAM.
SLAM algorithms are
tailored to the available resources, hence not aimed at perfection, but at
operational compliance. Published approaches are employed in self-driving cars,
unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers,
newer domestic robots and even inside the human body
Self-navigation and
communication has significantly improved thanks to technologies like
Simultaneously Localisation and Mapping (or SLAM), we are able to visually and
sensorily improve augmented reality (AR) experiences. Still, the risks of
having human beings in the midst of a robot swarm is fraught with a variety of
risks. Not just that different robots need to sense and respond to the location
and movement of other robots, they need to respond to “unpredictable” movements
and responses of humans.
Just like humans, bots
can’t always rely on GPS, especially when they operate indoors. And GPS isn’t
sufficiently accurate enough outdoors because precision within a few inches is
required to move about safely.
Instead they rely on
what’s known as simultaneous localization and mapping, or SLAM, to discover and
map their surroundings.
Using SLAM, robots
build their own maps as they go. It lets them know their position by aligning
the sensor data they collect with whatever sensor data they’ve already
collected to build out a map for navigation.
Sounds easy enough, but
it’s actually a multi-stage process that includes alignment of sensor data
using a variety of algorithms well suited to the parallel processing
capabilities of GPUs.
Computers see a robot’s
position as simply a timestamp dot on a map or timeline.
Robots continuously do
split-second gathering of sensor data on their surroundings. Camera images are
taken as many as 90 times a second for depth-image measurements. And LiDAR
images, used for precise range measurements, are taken 20 times a second.
When a robot moves,
these data points help measure how far it’s gone relative to its previous
location and where it is located on a map.
Motion Estimation
In addition, what’s known as wheel odometry takes into account the rotation of a robot’s wheels to help measure how far it’s travelled. Inertial measurement units are also used to gauge speed and acceleration as a way to track a robot’s position.
All of these sensor streams are taken into consideration in what’s known as sensor fusion to get a better estimate of how a robot is moving.
The mapping calculations described above happen 20-100 times a second, depending on the algorithms. This wouldn’t be possible to perform in real time without the processing power of NVIDIA GPUs. Ideal for robotics
Future development for Isaac on visual odometry will integrate it and elevate it to the level of SLAM. For now, SLAM is used as a check for map recovery of a robot’s location and orientation to eliminate errors in navigation from inaccurate visual odometry results.
Researchers at the
Massachusetts Institute of Technology (MIT) presented a project at the
International Symposium on Experimental Robotics involving an autonomous drone
fleet system that collaboratively mapped an environment under dense forest
canopy.
Designed with search
and rescue in mind, the drones used lidar, onboard computation and wireless
communication, with no requirement for GPS positioning.
Each drone carries
laser-range finders for position estimation, localization and path planning. As
it flies, each drone creates its own 3-D map of the terrain. A ground station
uses simultaneous localization and mapping (SLAM) technology to combine
individual maps from multiple drones into a global 3-D map that can be
monitored by operators.
The drones were
programmed to identify multiple trees’ orientations, as recognizing individual
trees in impossible for the technology, and individual trees’ orientation very
difficult. When the lidar signal returns a cluster of trees, an algorithm
calculates the angles and distances between trees to identify the cluster and
determine if it has already been identified and mapped, or is a new
mini-environment.
The technique also aids
in merging maps from the separate drones. When two drones scan the same cluster
of trees, the ground station merges the maps by calculating the relative
transformation between the drones, and then fusing the individual maps to
maintain consistent orientations.
Dragonfly is a visual
3D positioning/location system based on Visual SLAM:--
A valid alternative to
LiDAR and Ultra Wide Band for accurate indoor positioning and location of
drones, robots and vehicles.
Based on a patented
proprietary technology.
Computer vision and
odometry to create an accurate SLAM system.
Just an on-board camera
(mono or stereo) required.
The SLAM location
engine can run on board of the device or on a remote server (local or cloud).
Below: DRAGON FLY
Logistics Automation:
Tracking and locating autonomous mobile robots (AMR) and automated guided
vehicles (AGV) to allow indoor navigation. Equipment tracking indoors is also
enabled.
Warehouse management:
Tracking the location of mobile robots and lift trucks enables automating
inventory management, as items are placed on racks.
Forklift Tracking:
Forklift location tracking facilitates accident prevention and enables fleet
management.
Autonomous Robots:
Autonomous self-driven robots, often used in retail and healthcare, can
navigate indoors and outdoors using Dragonfly’s centimeter precision location
technology and allowing remote monitoring of their position in real time.
Dragonfly is a SLAM for ROS technology, as we provide ROS (Robot Operating
System) nodes for integration.
Various industries:
Drones 3D indoor location and navigation enables inspections and tasks
requiring visual identification. Drone positioning and tracking indoors is also
possible.
Dragonfly’s accurate
indoor location system is based on venue and place recognition:--
Dragonfly navigates and
creates a 3D map in real time. Note: the 3D map is not meant to be human
intelligible.
The map can be shared
among different devices.
Dragonfly comes with an
intuitive camera calibration tool for the configuration of the on-board camera.
5 cm accuracy.
WE ARE A NATION WHO
ALLOWED A NAXAL RED CORRIDOR TO PARALYSE 20% OF OUR LANDMASS.
WE ARE A NATION WHO
ALLOWED KASHMIRI PANDITS TO BE ETHNICALLY CLEANSED AND HUNDREDS OF HINDU TEMPLES TO
BE DESTROYED.
AND WE DON’T WANT TO
PUNISH THE FOREIGN PAYROLL CULPRITS—INCLUDING ILLEGAL COLLEGIUM JUDGES ?
ARE WE DOOMED TO GET
TRUCKLOADS OF UNSOLICITED INFORMATION ABOUT ROTHSCHILDs AGENT KATHIAWARI JEW GANDHI
FROM OUR PM DAILY DAILY ?
NLU and natural
language processing (NLP) are often confused. Instead they are different parts
of the same process of natural language elaboration. Indeed, NLU is a component
of NLP. More precisely, it is a subset of the understanding and comprehension
part of natural language processing.
While speech recognition captures spoken
language in real-time, transcribes it, and returns text, NLU goes beyond
recognition to determine a user’s intent. Speech recognition is powered by
statistical machine learning methods which add numeric structure to large
datasets. In NLU, machine learning models improve over time as they learn to
recognize syntax, context, language patterns, unique definitions, sentiment,
and intent.
NLP — Natural Language
“Processing”
NLU — Natural Language
“Understanding”
NLG — Natural Language
“Generation”
Mathematically the
combination of NLU and NLG will result in an NLP engine that works.
How the three of them
work in hand in hand:--
NLU takes up the
understanding of the data based on grammar, the context in which it was said
and decide on intent and entities.
NLP will convert the
text into structured data.
NLG generates text
generated based on structured data.
What Is Natural
Language Processing (NLP)?
Speech recognition is
an integral component of NLP, which incorporates AI and machine learning. Here,
NLP algorithms are used to understand natural speech in order to carry out
commands.
Natural language
processing starts with a library, a pre-programmed set of algorithms that plug
into a system using an API, or application programming interface. Basically,
the library gives a computer or system a set of rules and definitions for
natural language as a foundation.
Your development team
can customize that base to meet the needs of your product.
Neural networks figure
prominently in NLP systems and are used in text classification, question
answering, sentiment analysis, and other areas. Processing big data involved
with understanding the spoken language is comparatively easier and the nets can
be trained to deal with uncertainty, without explicit programming.
NLP, NLU, and NLG all
play a part in teaching machines to think and act more like humans.
So, if you’re Google,
you’re using natural language processing to break down human language and
better understand the true meaning behind a search query or sentence in an
email. You’re also using it to analyze blog posts to match content to known
search queries.
NLP is also used
whenever you ask Alexa, Siri, Google, or Cortana a question, and anytime you
use a chatbot. The program is analyzing your language against thousands of
other similar queries to give you the best search results or answer to your
question.
The beautiful thing
about AI and machine learning is that, with regular use, it learns your
language patterns to improve and tailor its results.
I. NLP, or Natural
Language Processing is a blanket term used to describe a machine’s ability to
ingest what is said to it, break it down, comprehend its meaning, determine
appropriate action, and respond back in a language the user will understand.
II. NLU, or Natural
Language Understanding is a subset of NLP that deals with the much narrower,
but equally important facet of how to best handle unstructured inputs and
convert them into a structured form that a machine can understand and act upon.
While humans are able to effortlessly handle mispronunciations, swapped words,
contractions, colloquialisms, and other quirks, machines are less adept at
handling unpredictable inputs.
III. NLG, or Natural
Language Generation, simply put, is what happens when computers write language.
NLG processes turn structured data into text.
What Is Natural
Language Understanding (NLU)?
Natural language
understanding is a smaller part of natural language processing. Once the
language has been broken down, it’s time for the program to understand, find
meaning, and even perform sentiment analysis.
The program breaks
language down into digestible bits that are easier to understand. It does that
by analyzing the text semantically and syntactically.
Semantically, it looks
for the true meaning behind the words by comparing them to similar examples. At
the same time, it breaks down text into parts of speech, sentence structure,
and morphemes (the smallest understandable part of a word).
Unlike structured data,
human language is messy and ambiguous. As a species, we are rarely
straightforward with our communication. Grammar and the literal meaning of
words pretty much go out the window whenever we speak.
In fact, “out the
window” is a great example. I, of course, didn’t mean that I throw things out a
literal window, especially since I was talking about intangible concepts rather
than solid objects.
And so it is when you
ask your smart device something like “What’s I-93 like right now?”.
If you were being
literal, you might get an answer like, “It’s long, gray, and has cars driving
on it. It was recently paved between exits 36 and 42.” But you probably wanted
to know what the traffic conditions are.
That’s where natural
language understanding comes in. It’s taking the slangy, figurative way we talk
every day and understanding what we truly mean.
Once a chatbot, smart
device, or search function understands the language it’s “hearing,” it has to
talk back to you in a way that you, in turn, will understand.
That’s where NLG comes
in. It takes data from a search result, for example, and turns it into
understandable language. So whenever you ask your smart device, “What’s it like
on I-93 right now?” it can answer almost exactly as another human would.
It may say something
like, “There is an accident at exit 36 that has created a 15-minute delay,” or
“The road is clear.”
NLG is used in chatbot
technology, as well. In fact, chatbots have become so advanced; you may not
even know you’re talking to a machine.
At its most basic, an
algorithm simply tells a computer what to do next with an “and,” “or,” or “not”
statement. ... Algorithms can be used to break down and automate sorting tasks.
When chained together,
algorithms – like lines of code – become more robust. They're combined to build
AI systems like neural networks . AI
often revolves around the use of algorithms. An algorithm is a set of
unambiguous instructions that a mechanical computer can execute.
A complex algorithm is
often built on top of other, simpler, algorithms. “AI at maturity is like a
gear system with three interlocking wheels: data processing, machine learning
and business action. ... “The key difference, is that an algorithm defines the
process through which a decision is made, and AI uses training data to make
such a decision.
An algorithm is a set
of instructions — a preset, rigid, coded recipe that gets executed when it
encounters a trigger. AI on the other hand — which is an extremely broad term
covering a myriad of AI specializations and subsets — is a group of algorithms
that can modify its algorithms and create new algorithms in response to learned
inputs and data as opposed to relying solely on the inputs it was designed to
recognize as triggers.
This ability to change,
adapt and grow based on new data, is described as “intelligence.” AI at
maturity is like a gear system with three interlocking wheels: data processing,
machine learning and business action. It operates in an automated mode without
any human intervention.
Data is created,
transformed and moved without data engineers. Business actions or decisions are
implemented without any operators or agents. The system learns continuously
from the accumulating data and business actions and outcomes get better and
better with time .
An algorithm defines
the process through which a decision is made, and AI uses training data to make
such a decision.. “AI can make life easy
by automating actions and making processes more efficient, even learn things
from our day to day that we don't necessarily notice. .
Pattern matching in
computer science is the checking and locating of specific sequences of data of
some pattern among raw data or a sequence of tokens. Unlike pattern
recognition, the match has to be exact in the case of pattern matching..
Pattern matching in
computer science is the checking and locating of specific sequences of data of
some pattern among raw data or a sequence of tokens. Unlike pattern
recognition, the match has to be exact in the case of pattern matching.
.
Unlabeled data is a
designation for pieces of data that have not been tagged with labels
identifying characteristics, properties or classifications. Unlabeled data is
typically used in various forms of machine learning.
An example of pattern
recognition is classification, which attempts to assign each input value to one
of a given set of classes (for example, determine whether a given email is
"spam" or "non-spam"). However, pattern recognition is a
more general problem that encompasses other types of output as well.
Pattern recognition is
the ability to detect arrangements of characteristics or data that yield
information about a given system or data set. ... In the context of AI, pattern
recognition is a sub-category of machine learning (ML).
Pattern recognition and
classification is the act of taking in raw data and using a set of properties
and features take an action on the data. ... Moving on, we seek to design
models and systems that will be able to recognize and furthermore classify
these patterns into different categories for further use.
Why do we need to look
for patterns? Finding patterns is extremely important. ... Problems are easier
to solve when they share patterns, because we can use the same problem-solving
solution wherever the pattern exists. The more patterns we can find, the easier
and quicker our overall task of problem solving will be..
Labeled data is a group
of samples that have been tagged with one or more labels. Labeling typically
takes a set of unlabeled data and augments each piece of that unlabeled data
with meaningful tags that are informative.
Data classification is
the process of sorting and categorizing data into various types, forms or any
other distinct class. Data classification enables the separation and
classification of data according to data set requirements for various business
or personal objectives. It is mainly a data management process.
Clustering is the
process of partitioning or grouping a given set of patterns into disjoint
clusters. This is done such that patterns in the same cluster are alike and
patterns belonging to two dierent clusters are dierent.
Clustering is an
important concept when it comes to unsupervised learning. It mainly deals with
finding a structure or pattern in a collection of uncategorized data.
Clustering algorithms will process your data and find natural clusters(groups)
if they exist in the data. You can also modify how many clusters your
algorithms should identify. It allows you to adjust the granularity of these
groups.
Cluster analysis or
clustering is the task of grouping a set of objects in such a way that objects
in the same group (called a cluster) are more similar (in some sense) to each
other than to those in other groups (clusters). It is a main task of
exploratory data mining, and a common technique for statistical data analysis,
used in many fields, including machine learning, pattern recognition, image
analysis, information retrieval, bioinformatics, data compression, and computer
graphics.
Cluster analysis itself
is not one specific algorithm, but the general task to be solved. It can be
achieved by various algorithms that differ significantly in their understanding
of what constitutes a cluster and how to efficiently find them.
Popular notions of
clusters include groups with small distances between cluster members, dense
areas of the data space, intervals or particular statistical distributions.
Clustering can therefore be formulated as a multi-objective optimization
problem. The appropriate clustering algorithm and parameter settings (including
parameters such as the distance function to use, a density threshold or the
number of expected clusters) depend on the individual data set and intended use
of the results.
Cluster analysis as
such is not an automatic task, but an iterative process of knowledge discovery
or interactive multi-objective optimization that involves trial and failure. It
is often necessary to modify data preprocessing and model parameters until the
result achieves the desired properties.
There are different
types of clustering you can utilize:-
Exclusive
(partitioning)
In this clustering
method, Data are grouped in such a way that one data can belong to one cluster
only.
Example: K-means
Agglomerative
In this clustering
technique, every data is a cluster. The iterative unions between the two
nearest clusters reduce the number of clusters.
Example: Hierarchical
clustering
Overlapping
In this technique,
fuzzy sets is used to cluster data. Each point may belong to two or more
clusters with separate degrees of membership.
Here, data will be
associated with an appropriate membership value. Example: Fuzzy C-Means
Probabilistic
This technique uses
probability distribution to create the clusters
Example: Following
keywords
"man's shoe."
"women's
shoe."
"women's
glove."
"man's
glove."
can be clustered into
two categories "shoe" and "glove" or "man" and
"women."
Clustering Types--
Hierarchical clustering
K-means clustering
K-NN (k nearest
neighbors)
Principal Component
Analysis
Singular Value
Decomposition
Independent Component
Analysis
Hierarchical
Clustering:
Hierarchical clustering
is an algorithm which builds a hierarchy of clusters. It begins with all the
data which is assigned to a cluster of their own. Here, two close cluster are
going to be in the same cluster. This algorithm ends when there is only one
cluster left.
K-means Clustering
K means it is an
iterative clustering algorithm which helps you to find the highest value for
every iteration. Initially, the desired number of clusters are selected. In
this clustering method, you need to cluster the data points into k groups. A
larger k means smaller groups with more granularity in the same way. A lower k
means larger groups with less granularity.
The output of the
algorithm is a group of "labels." It assigns data point to one of the
k groups. In k-means clustering, each group is defined by creating a centroid
for each group. The centroids are like the heart of the cluster, which captures
the points closest to them and adds them to the cluster.
K-mean clustering
further defines two subgroups:--
Agglomerative
clustering
Dendrogram
Agglomerative
clustering:--
This type of K-means
clustering starts with a fixed number of clusters. It allocates all data into
the exact number of clusters. This clustering method does not require the
number of clusters K as an input. Agglomeration process starts by forming each
data as a single cluster.
This method uses some
distance measure, reduces the number of clusters (one in each iteration) by
merging process. Lastly, we have one big cluster that contains all the objects.
Dendrogram:
In the Dendrogram
clustering method, each level will represent a possible cluster. The height of
dendrogram shows the level of similarity between two join clusters. The closer
to the bottom of the process they are more similar cluster which is finding of
the group from dendrogram which is not natural and mostly subjective.
K- Nearest neighbors
K- nearest neighbour is
the simplest of all machine learning classifiers. It differs from other machine
learning techniques, in that it doesn't produce a model. It is a simple
algorithm which stores all available cases and classifies new instances based on
a similarity measure.
It works very well when
there is a distance between examples. The learning speed is slow when the
training set is large, and the distance calculation is nontrivial.
Principal Components
Analysis:--
In case you want a
higher-dimensional space. You need to select a basis for that space and only
the 200 most important scores of that basis. This base is known as a principal
component. The subset you select constitute is a new space which is small in
size compared to original space. It maintains as much of the complexity of data
as possible.
Association
Association rules allow
you to establish associations amongst data objects inside large databases. This
unsupervised technique is about discovering interesting relationships between
variables in large databases. For example, people that buy a new home most
likely to buy new furniture.
Other Examples:--
A subgroup of cancer
patients grouped by their gene expression measurements
Groups of shopper based
on their browsing and purchasing histories
Movie group by the
rating given by movies viewers
.
Clustering and
Association are two types of Unsupervised learning.
Four types of
clustering methods are 1) Exclusive 2) Agglomerative 3) Overlapping 4)
Probabilistic.
Important clustering
types are: 1)Hierarchical clustering 2) K-means clustering 3) K-NN 4) Principal
Component Analysis 5) Singular Value Decomposition 6) Independent Component
Analysis.
Association rules allow
you to establish associations amongst data objects inside large databases.
Clustering – describes
an unsupervised machine learning technique for identifying structures among
unstructured data. Clustering algorithms group sets of similar objects into
clusters, and are widely used in areas including image analysis, information
retrieval, and bioinformatics.
Clustering Analysis -
is a type of unsupervised machine learning used for exploratory data analysis
to find hidden patterns or groupings in datasets. Using metrics like
probabilistic or Euclidian distance, clusters group together similar data
points.
Clustering, like
regression, describes the class of problem and the class of methods.
Clustering methods are
typically organized by the modeling approaches such as centroid-based and
hierarchal. All methods are concerned with using the inherent structures in the
data to best organize the data into groups of maximum commonality.
The most popular
clustering algorithms are:--
k-Means
k-Medians
Expectation
Maximisation (EM)
Hierarchical Clustering
Clustering Algorithms
The basic idea behind
clustering is to assign the input into two or more clusters based on feature
similarity. It falls into the category of Unsupervised Machine Learning, where
the algorithm learns the patterns and useful insights from data without any guidance
(labeled data set).
For example, clustering
viewers into similar groups based on their interests, age, geography, etc can
be done by using Unsupervised Learning algorithms like K-Means Clustering.Clustering is very
similar to classification, but involves grouping chunks of data together based
on their similarities. For example, you might choose to cluster different
demographics of your audience into different packets based on how much
disposable income they have, or how often they tend to shop at your store.
K-Means Clustering
K-means is probably the
simplest unsupervised learning approach. The idea here is to gather similar
data points together and bind them together in the form of a cluster. It does
this by calculating the centroid of the group of data points.
To carry out effective
clustering, k-means evaluates the distance between each point from the centroid
of the cluster. Depending on the distance between the data point and the
centroid, the data is assigned to the closest cluster. The goal of clustering
is to determine the intrinsic grouping in a set of unlabelled data.
The ‘K’ in K-means
stands for the number of clusters formed. The number of clusters (basically the
number of classes in which your new instances of data can fall into) is
determined by the user.
K-means is used majorly
in cases where the data set has points which are distinct and well separated
from each other, otherwise, the clusters won’t be far apart, rendering them
inaccurate. Also, K-means should be avoided in cases where the data set
contains a high amount of outliers or the data set is non-linear.
Clustering is grouping
a set of objects in such a manner that objects in the same group are more
similar than to those object belonging to other groups. Whereas, association
rules is about finding associations amongst items within large commercial
databases.
Text analytics and text
mining approaches have essentially equivalent performance. Text analytics
requires an expert linguist to produce complex rule sets, whereas text mining
requires the analyst to hand-label cases with outcomes or classes to create
training data
Text Analytics is the
process of drawing meaning out of written communication. In a customer
experience context, text analytics means examining text that was written by, or
about, customers. You find patterns and topics of interest, and then take
practical action based on what you learn.
Text analytics is the
automated process of translating large volumes of unstructured text into
quantitative data to uncover insights, trends, and patterns. Combined with data
visualization tools, this technique enables companies to understand the story
behind the numbers and make better decisions.
Colossal amounts of
unstructured data are generated every minute – internet users post 456,000 new
tweets, 510,000 new comments on Facebook, and send 156 million emails – so
managing and analyzing information to find what’s relevant becomes a major
challenge.
Thanks to text
analytics, businesses are able to automatically extract meaning from all sorts
of unstructured data, from social media posts and emails to live chats and
surveys, and turn it into quantitative insights.
By identifying trends and
patterns with text analytics, businesses can improve customer satisfaction (by
learning what their customers like and dislike about their products), detect
product issues, conduct market research, and monitor brand reputation, among
other things.
Text analytics has many
advantages – it’s scalable, meaning you can analyze large volumes of data in a
very short time, and allows you to obtain results in real-time. So, apart from
gaining insights that help you make confident decisions, you can also resolve
issues in a timely manner.
One of the most
interesting applications of text analytics in business is customer feedback
analysis. This includes analyzing product and service reviews to see how your
customers evaluate your company, processing the results of open-ended responses
to customer surveys, or checking out what customers say about your brand on
social media.
Text Analysis is about
parsing texts in order to extract machine-readable facts from them. The purpose
of Text Analysis is to create structured data out of free text content. The
process can be thought of as slicing and dicing heaps of unstructured,
heterogeneous documents into easy-to-manage and interpret data pieces.
Text mining, text
analysis, and text analytics are often used interchangeably, with the end goal
of analyzing unstructured text to obtain insights. However, while text mining
(or text analysis) provides insights of a qualitative nature, text analytics
aggregates these results and turns them into something that can be quantified
and visualized through charts and reports.
Text analysis and text
analytics often work together to provide a complete understanding of all kinds
of text, like emails, social media posts, surveys, customer support tickets,
and more. For example, you can use text analysis tools to find out how people
feel toward a brand on social media (sentiment analysis), or understand the
main topics in product reviews (topic detection).
Text analytics, on the other
hand, leverages the results of text analysis to identify patterns, such as a
spike in negative feedback, and provides you with actionable insights you can
use to make improvements, like fixing a bug that’s frustrating your users.
Text mining, also referred
to as text data mining, roughly equivalent to text analytics, is the process of
deriving high-quality information from text. ... The overarching goal is,
essentially, to turn text into data for analysis, via application of natural
language processing (NLP) and analytical methods
NLP and text mining are
usually used for different goals. ... Text mining techniques are usually
shallow and do not consider the text structure.Usually, text mining will use
bag of words, n-grams and possibly stemming over that. In NLP methods usually
involve the test structure.
The goal of text mining
is to discover relevant information in text by transforming the text into data
that can be used for further analysis. Text mining accomplishes this through
the use of a variety of analysis methodologies; natural language processing
(NLP) is one of them
Text mining enables to
quickly extract customers' needs, preferences and requests. It could help
managers to make decisions and figure out a lot of measures to respond to
customers' discontent. It facilitates gleaning from many unstructured text data
and compiles them
Natural language
processing (NLP) is a subfield of linguistics, computer science, information
engineering, and artificial intelligence concerned with the interactions between
computers and human (natural) languages, in particular how to program computers
to process and analyze large amounts of natural language data.
The Concept of Text
Mining--For example, text categorization, text clustering, concept/entity
extraction, sentiment analysis, document summarization, production of granular
taxonomies, entity relation modeling
Text mining is required
if organisations and individuals are to make sense of these vast information
and data resources and leverage value. ... The processed data can then be
'mined' to identify patterns and extract valuable information and new
knowledge.
There are different
text mining methods as in data mining had been proposed such as clustering,
classification, information retrieval, topic discovery, summarization, topic
extraction. This phase includes evaluation and interpretation of results in
terms of calculating precision and recall, accuracy etc.
Some of the popular
Text Mining applications include:--
Enterprise Business
Intelligence/Data Mining, Competitive Intelligence.
E-Discovery, Records
Management.
National
Security/Intelligence.
Scientific discovery,
especially Life Sciences.
Search/Information
Access.
Social media
monitoring.
Natural language
processing (NLP ) is a type of artificial intelligence that derives meaning
from human language in a bid to make decisions using the information.
NLP algorithms are
typically based on machine learning algorithms. Instead of hand-coding large
sets of rules, NLP can rely on machine learning to automatically learn these
rules by analyzing a set of examples (i.e. a large corpus, like a book, down to
a collection of sentences), and making a statical inference
NLP is a sub-area of
Artificial Intelligence (AI) research that focusses on the processing of
language, either in the form of text or speech. More generally, it can be
defined as the modeling of how signs (sound/characters/words) representing some
meaning are used in order to fulfill a pre-defined task such as translation,
summarization or question answering. In contrast to linguistic studies, NLP is
an engineering discipline focusing on achieving a pre-defined task instead of leading
to a deeper understanding of language.
Natural language
processing is one of the most active research areas in AI and provides a rich
target for machine learning research as well
Natural Language
Processing (NLP) is the study and application of techniques and tools that
enable computers to process, analyze, interpret, and reason about human
language.
Natural Language
Processing involves the application of various algorithms capable of taking
unstructured data and converting it into structured data. If these algorithms
are applied in the wrong manner, the computer will often fail to derive the
correct meaning from the text.
In order for computers
to interpret human language, they must be converted into a form that a computer
can manipulate. However, this isn’t as simple as converting text data into
numbers. In order to derive meaning from human language, patterns have to be
extracted from the hundreds or thousands of words that make up a text document.
This is no easy task. There are few hard and fast rules that can be applied to
the interpretation of human language. For instance, the exact same set of words
can mean different things depending on the context. Human language ( especially
stupid language English ) is a complex and often ambiguous thing, and a
statement can be uttered with sincerity or sarcasm.
NLP technologies are
not sophisticated enough to understand all of the nuances of human speech. .
Natural Language
Processing involves the application of various algorithms capable of taking
unstructured data and converting it into structured data.
If these algorithms
are applied in the wrong manner, the computer will often fail to derive the
correct meaning from the text. This can often be seen in the translation of
text between languages, where the precise meaning of the sentence is often
lost. While machine translation has improved substantially over the past few
years, machine translation errors still occur frequently.
Many of the techniques
that are used in natural language processing can be placed in one of two
categories: syntax or semantics. Syntax techniques are those that deal with the
ordering of words, while semantic techniques are the techniques that involve
the meaning of words.
Examples of syntax
include:--
Lemmatization
Morphological
Segmentation
Part-of-Speech Tagging
Parsing
Sentence Breaking
Stemming
Word Segmentation
Lemmatization refers to
distilling the different inflections of a word down to a single form.
Lemmatization takes things like tenses and plurals and simplifies them, for
example, “feet” might become “foot” and “stripes” may become “stripe”. This simplified word form makes it easier for
an algorithm to interpret the words in a document.
Morphological
segmentation is the process of dividing words into morphemes or the base units
of a word. These units are things like free morphemes (which can stand alone as
words) and prefixes or suffixes.
Part-of-speech tagging
is simply the process of identifying which part of speech every word in an
input document is.
Parsing refers to
analyzing all the words in a sentence and correlating them with their formal
grammar labels or doing grammatical analysis for all the words.
Sentence breaking, or
sentence boundary segmentation, refers to deciding where a sentence begins and
ends.
Stemming is the process
of reducing words down to the root form of the word. For instance, connected,
connection, and connections would all be stemmed to “connect”.
Word Segmentation is
the process of dividing large pieces of text down into small units, which can
be words or stemmed/lemmatized units.
Semantic NLP techniques
include techniques like:==
Named Entity
Recognition
Natural Language
Generation
Word-Sense
disambiguation
Named entity
recognition involves tagging certain text portions that can be placed into one
of a number of different preset groups. Pre-defined categories include things
like dates, cities, places, companies, and individuals.
Natural language
generation is the process of using databases to transform structured data into
natural language. For instance, statistics about the weather, like temperature
and wind speed could be summarized with natural language.
Word-sense
disambiguation is the process of assigning meaning to words within a text based
on the context the words appear in.
Deep Learning Models
For Natural Language Processing--
Regular multilayer
perceptrons are unable to handle the interpretation of sequential data, where
the order of the information is important. In order to deal with the importance
of order in sequential data, a type of neural network is used that preserves information
from previous timesteps in the training.
Recurrent Neural
Networks are types of neural networks that loop over data from previous
timesteps, taking them into account when calculating the weights of the current
timestep. Essentially, RNN’s have three parameters that are used during the
forward training pass: a matrix based on the Previous Hidden State, a matrix
based on the Current Input, and a matrix that is between the hidden state and
the output.
Because RNNs can take information from previous timesteps into
account, they can extract relevant patterns from text data by taking earlier
words in the sentence into account when interpreting the meaning of a word.
Another type of deep
learning architecture used to process text data is a Long Short-Term Memory
(LSTM) network. LSTM networks are similar to RNNs in structure, but owing to
some differences in their architecture they tend to perform better than RNNs.
They avoid a specific problem that often occurs when using RNNs called the
exploding gradient problem.
These deep neural
networks can be either unidirectional or bi-directional. Bi-directional
networks are capable of taking not just the words that come prior to the
current word into account, but the words that come after it. While this leads
to higher accuracy, it is more computationally expensive.
NLP is is the acronym for neuro-linguistic
programming. NLP is also an abbreviation
used for natural language processing. NLP is a means of training computers to
understand human language. This is no easy thing. Human language is fluid;
words change over time or with context.
NEURO-LINGUISTIC
PROGRAMMING AND NLP IN THIS CONTEXT DOES NOT HAVE ANY CONNECTION WITH NATURAL
LANGUAGE PROCESSING. THE WORD NEURO DOES
NOT WORK WITH AI.
NEURO-LINGUISTIC
PROGRAMMING IS A BULLSHIT FRAUDENT
PRACTICE TO HONE COMMUNICATION, FACILITATE PERSONAL DEVELOPMENT AND TO
MAKE PSYCHOTHERAPY MORE EFFECTIVE ( SIC).
I HAD WRITTEN A
UNFINISHED –ONLY 70% COMPLETE --17 PART POST ON NLP ( NEURO-LINGUISTIC
PROGRAMMING ) THROWING SHIT ON IT..
NLP (Natural language
processing) is simply the part of AI that has to do with language (usually
written)
At its simplest form,
NLP will help computers perform commands given to them through text commands.
The next stage of NLP
is natural language interaction, which allows humans to communicate with
computers using normal, everyday language to perform tasks.
Natural language processing
is the linguistically oriented discipline in computer science that is concerned
with the capacity of software to understand natural human language – written as
well as spoken.
Deep NLP is a branch of
both Deep Learning and NLP that deals with using Deep Learning to achieve some
of the NLP related tasks. For example, sentiment classification.
Natural Language
Processing. NLP is a field in machine learning with the ability of a computer
to understand, analyze, manipulate, and potentially generate human language.
Information Retrieval(Google finds relevant and similar results). .
NLP powers the
voice-based interface for virtual assistants and chatbots. The technology is
increasingly being used to query data sets as well.
Because Natural
Language Processing involves the analysis and manipulation of human languages,
it has an incredibly wide range of applications. Possible applications for NLP
include chatbots, digital assistants, sentiment analysis, document
organization, talent recruitment, and healthcare.
Chatbots and digital
assistants like Amazon’s Alexa and Google Assistant are examples of voice
recognition and synthesis platforms that use NLP to interpret and respond to
vocal commands. These digital assistants help people with a wide variety of
tasks, letting them offload some of their cognitive tasks to another device and
free up some of their brainpower for other, more important things. Instead of
looking up the best route to the bank on a busy morning, we can just have our
digital assistant do it.
Sentiment analysis is
the use of NLP techniques to study people’s reactions and feelings to a
phenomenon, as communicated by their use of language. Capturing the sentiment
of a statement, like interpreting whether a review of a product is good or bad,
can provide companies with substantial information regarding how their product
is being received.
Automatically
organizing text documents is another application of NLP. Companies like Google
and Yahoo use NLP algorithms to classify email documents, putting them in the
appropriate bins such as “social” or “promotions”. They also use these
techniques to identify spam and prevent it from reaching your inbox.
Groups have also
developed NLP techniques are being used to identify potential job hires,
finding them based on relevant skills. Hiring managers are also using NLP
techniques to help them sort through lists of applicants.
NLP techniques are also
being used to enhance healthcare. NLP can be used to improve the detection of
diseases. Health records can be analyzed and symptoms extracted by NLP
algorithms, which can then be used to suggest possible diagnoses.
One example
of this is Amazon’s Comprehend Medical platform, which analyzes health records
and extracts diseases and treatments. Healthcare applications of NLP also
extend to mental health. There are apps such as WoeBot, which talks users
through a variety of anxiety management techniques based in Cognitive
Behavioral Therapy.
Natural language is the
language humans use to communicate with one another. On the other hand,
programming language was developed so humans can tell machines what to do in a
way machines can understand. For example, English is a natural language while
Java is a programming one.
Natural Language
Processing facilitates human-to-machine communication without humans needing to
“speak” Java or any other programming language as it allows machines to obtain
and process information from written or verbal user inputs.
In essence, developers
create NLP models that enable computers to decode and even mimic the way humans
communicate.
How Does Natural
Language Processing Work?
One of the best things
about NLP is that it’s probably the easiest part of AI to explain to
non-technical people.
Take one of the most
common natural language processing application examples, the prediction
algorithm in your email. The software is not just guessing what you will want
to say next but analyzes the likelihood of it based on tone and topic.
Engineers are able to do this by giving the computer and “NLP training”.
In
other words, they provide the software with a huge amount of data about
language including sentences and phrases as well as transcripts from live
conversations/emails. This way, over time, computer programs are able to learn
how to pair words together; what it is we are trying to convey, and what we
need to achieve with that communication.
Naturally, predicting
what you will type in a business email is significantly simpler than
understanding and responding to a conversation. Still, the
decoding/understanding of the text is, in both cases, largely based on the same
principle of classification.
Generally, the
“understanding” of the natural language (NLU) happens through the analysis of
the text or speech input using a hierarchy of classification models.
Unlike common word
processing operations, NLP doesn’t treat speech or text just as a sequence of
symbols. It also takes into consideration the hierarchical structure of the
natural language – words create phrases; phrases form sentences; sentences turn into coherent ideas. In other
words, NLP software doesn’t just look for keywrods.
It uses pre-programmed or
acquired knowledge to decode meaning and intent from factors such as sentence
structure, context, idioms, etc. For instance, good NLP software should be able
to recognize whether the user’s “Why not?” indicates agreement or a question
that requires an answer.
NLP is divided in two
key categories:--
Natural Language
Understanding (NLU)
Natural Language
Generation (NLG)
NLU is an essential
sub-domain of NLP and have a general idea of how it works.
It is important to
point out that the ability to parse what the user is saying is probably the
most obvious weakness in NLP based chatbots today. Human languages are just way
too complex. Besides enormous vocabularies, they are filled with multiple
meanings many of which are completely unrelated.
To nail the NLU is more
important than making the bot sound 110% human with impeccable NLG. Why? If a
bot understands the users and fulfills their intent, most won’t care if that
response is a bit taciturn…
It doesn’t work the other way around. A bot that
can’t derive meaning from the natural input efficiently can have the smoothest
small talk skills and nobody will care. Not even a little!
NLU is the
post-processing of text, after the use of NLP algorithms (identifying
parts-of-speech, etc.), that utilizes context from recognition devices
(automatic speech recognition [ASR], vision recognition, last conversation,
misrecognized words from ASR, personalized profiles, microphone proximity
etc.), in all of its forms, to discern meaning of fragmented and run-on
sentences to execute an intent from typically voice commands.
NLP is short for
natural language processing while NLU is the shorthand for natural language
understanding. Similarly named, the concepts both deal with the relationship
between natural language (as in, what we as humans speak, not what computers
understand) and artificial intelligence.
NLU is the
post-processing of text, after the use of NLP algorithms (identifying
parts-of-speech, etc.), that utilizes context from recognition devices
(automatic speech recognition [ASR], vision recognition, last conversation,
misrecognized words from ASR, personalized profiles, microphone proximity
etc.), in all of its forms, to discern meaning of fragmented and run-on
sentences to execute an intent from typically voice commands.
NLU has an
ontology around the particular product vertical that is used to figure out the
probability of some intent. An NLU has a defined list of known intents that
derives the message payload from designated contextual information recognition
sources.
The NLU will provide back multiple message outputs to separate
services (software) or resources (hardware) from a single derived intent (response
to voice command initiator with visual sentence (shown or spoken) and
transformed voice command message to different output messages to be consumed
for M2M communications and actions)
Natural-language
understanding (NLU) or natural-language interpretation (NLI) is a subtopic of
natural-language processing in artificial intelligence that deals with machine
reading comprehension. Natural-language understanding is considered an AI-hard
problem.
NLU describes the quest to build machines with
true reading comprehension, so that humans can communicate with them in natural
human language and the machine can respond appropriately. Commercial
applications of interest include applications ranging from text categorization,
where emails are routed to the proper department based on their content, to
full comprehension of newspaper articles.
Given that the NLP
chatbot successfully parsed and understood the user’s input, its programing
will determine an appropriate response and “translate” it back to natural
language.
Needless to say, that
response doesn’t appear out of thin air.
For the NLP to produce
a human-friendly narrative, the format of the content must be outlined be it
through rules-based workflows, templates, or intent-driven approaches. In other
words, the bot must have something to work with in order to create that output.
Currently, every NLG
system relies on narrative design – also called conversation design – to
produce that output. This narrative design is guided by rules known as
“conditional logic”. These rules trigger different outputs based on which
conditions are being met and which are not.
The flip side of NLP is
natural language generation (NLG), the AI discipline that enables computers to
generate text that is meaningful to humans.
Natural language
generation (NLG) is the use of artificial intelligence (AI) programming to
produce written or spoken narrative from a dataset.
At its simplest, an NLG
platform is a computer process that can generate natural language text and speech
from pre-defined data.
In natural language
understanding the system needs to disambiguate the input sentence to produce
the machine representation language, whereas in Natural Language Generation the
system needs to make decisions about how to put a concept into words.
Natural Language
Processing (NLP) is what happens when computers read language. NLP processes
turn text into structured data. Natural Language Generation (NLG) is what
happens when computers write language. NLG processes turn structured data into
text
The output of NLG
algorithms can either be displayed as text, as in a ., or converted to speech
through voice synthesis and played for the user, as smart speakers and AI
assistants do.
NLG can turn charts and spreadsheets into
textual descriptions. AI assistants such as Siri and Alexa also use NLG to
generate responses to queries.
Google’s AI assistant
puts both the capabilities and the limits of artificial intelligence’s grasp of
human language. Duplex combines speech-to-text, NLP, NLG and voice synthesis in
a very brilliant way, duping many people into believing it can interact like a
human caller.
But Google Duplex is narrow artificial intelligence, which means
it is will be good at performing the type of tasks the company demoed, such as
booking a restaurant or setting an appointment at a salon. These are domains
where the problem space is limited and predictable.
NLP is a tool for
computers to analyze, comprehend, and derive meaning from natural language in
an intelligent and useful way. This goes way beyond the most recently developed
chatbots and smart virtual assistants. In fact, natural language processing
algorithms are everywhere from search, online translation, spam filters and spell
checking.
So, by using NLP,
developers can organize and structure the mass of unstructured data to perform
tasks such as intelligent:---
Automatic summarization
(intelligently shortening long pieces of text)
Automatic suggestions
(used to speed up writing of emails, messages, and other texts)
Translation
(translating phrases and ideas instead of word for word)
Named entity
recognition (used to locate and classify named entities in unstructured natural
languages into pre-defined categories such as the organizations; person names;
locations; codes; quantities; price; time; percentages)
Relationship extraction
(extraction of semantic relationships among the identified entities in natural
language text/speech such as “is located in”, “is married to”, “is employed
by”, “lives in”, etc.)
Sentiment analysis
(helps identify, for instance, positive, negative and neutral opinion form text
or speech widely used to gain insights from social media comments, forums or
survey responses)
Speech recognition
(enables computers to recognize and transform spoken language into text –
dictation – and, if programmed, act upon that recognition – e.g. in case of
assistants like Google Assistant Cortana or Apple’s Siri)
Topic segmentation
(automatically divides written texts, speech or recordings into shorter,
topically coherent segments and is used in improving information retrieval or
speech recognition)
Using NLP for simple
and straightforward use cases is over the top and completely unnecessary.
In fact, if used in an
inappropriate context, natural language processing chatbot can be an absolute
buzzkill and hurt rather than help your business. If a task can be accomplished
in just a couple of clicks, making the user type it all up is most certainly
not making things easier.
On the other hand, if
the alternative means presenting the user with an excessive number of options
at once, NLP chatbot can be useful. It can save your clients from
confusion/frustration by simply asking them to type or say what they want. It’s
not much different from coming up to the staff member at the counter in the
real world.
NLP is a decades-old
field that sits at the cross-section of computer science, artificial
intelligence, and, more and more, data mining. It focuses on how we can program
computers to process large amounts of natural language data, such as a poem or
novel or a conversation, in a way that is productive and efficient, taking
certain tasks off the hands of humans and allowing for a machine to handle
certain processes – the ultimate “artificial intelligence”.
NLP can refer to a
range of tools, such as speech recognition, natural language recognition, and
natural language generation. Common NLP algorithms are often manifest in
real-world examples like online chatbots, text summarizers, auto-generated
keyword tabs, and even tools that attempt to identify the sentiment of a text,
such as whether it is positive, neutral, or negative.
Considered a subtopic
of NLP, natural language understanding is a vital part of achieving successful
NLP. NLU is narrower in purpose, focusing primarily on machine reading
comprehension: getting the computer to comprehend what a body of text really
means.
After all, if a machine cannot comprehend the content, how can it
process it accordingly? (But, drawing distinct, clear insights from data that
is anything but clear or distinct – often governed by only half-rules and
exceptions, as is common for language– is tricky;
Natural language
understanding can be applied to a lot of processes, such as categorizing text,
gathering news, archiving individual pieces of text, and, on a larger scale,
analyzing content.
Real-world examples of
NLU range from small tasks like issuing short commands based on comprehending
text to some small degree, like rerouting an email to the right person based on
a basic syntax and decently-sized lexicon. Much more complex endeavors might be
fully comprehending news articles or shades of meaning within poetry or novels.
It’s best to view NLU
as a first step towards achieving NLP: before a machine can process a language,
it must first be understood.
Natural Language
Understanding (NLU) or Natural Language Interpretation (NLI), deals with machine reading comprehension by
breaking the elemental pieces of speech. NLU communicates with the untrained
and unstructured data in order to understand their insights and meanings, i.e.
this technique determines a user’s intent.
Successful NLP must
blend techniques from a range of fields: language, linguistics, data science,
computer science, and more.
This is why NLP has
been so elusive – an academic advance in a small part of NLP may take years for
a company to develop into a successful tool that relies on NLP. Such a product
likely aims to be effortless, unsupervised, and able to interact directly with
customers in an appropriate and successful manner.
Context awareness is the ability of a system
or system component to gather information about its environment at any given time
and adapt behaviors accordingly. Contextual or context-aware computing uses
software and hardware to automatically collect and analyze data to guide
responses.
NLP helps developers to
organize and structure knowledge to perform tasks like translation,
summarization, named entity recognition, relationship extraction, speech
recognition, topic segmentation, etc. NLP is a way of computers to analyze,
understand and derive meaning from a human languages such as English, Spanish,
Hindi, etc.
Natural language refers
to language that is spoken and written by people, and natural language
processing (NLP) attempts to extract information from the spoken and written
word using algorithms.
NLP encompasses active
and a passive modes: natural language generation (NLG), or the ability to
formulate phrases that humans might emit, and natural language understanding
(NLU), or the ability to build a comprehension of a phrase, what the words in
the phrase refer to, and its intent.
In a conversational system, NLU and NLG
alternate, as algorithms parse and comprehend a natural-language statement, and
formulate a satisfactory response to it. Natural language processing tries to
do two things: understand and generate human language. Natural language
processing (NLP) is a form of AI that extracts meaning from human language to
make decisions based on the information.
Here are some more applications of
natural language processing:Email assistant: Auto-correct, grammar and spell
check, as well as auto-complete, are all functions enabled by NLP. Machine
translation is a huge application for NLP that allows us to overcome barriers
to communicating with individuals from around the world as well as understand
tech manuals and catalogs written in a foreign language.
Google Translate is
used by 500 million people every day to understand more than 100 world
languages.
Natural Language
Processing helps in gaining insights from meaningless and unstructured data. It
basically aims to convert human language into a formal representation which is
easy for computers or machines to manipulate.
Besides text recognition, NLP has
the ability to recognise meaningful insights from videos and other unstructured
materials. This technique helps a machine to understand the sentences and
convert them into meaningful information.
NLP includes various
approaches such as tokenizer, entity extraction, sentence boundary detection,
etc to extract information from free-form text. The use cases of this technique
involve summarising text by identifying the entities present in the document,
analysing the sentiment in a given text, classifying documents by labelling it
as sensitive, spam, etc.
Conversational AI –
describes a branch of artificial intelligence (AI) that focuses on interpreting
human (colloquial) language and subsequently communicating back with them.
Powered by advanced features such as natural language processing (NLP),
conversational AI is the logic that creates virtual conversations.
Conversational AI examples of these are the voice assisted devices that are
commercially available.
Conversational UI – is
the platform that allows a user to communicate with a computer that mimics a
‘human like’ conversation. Powered by natural language processing (NLP), a
conversational UI framework allows for an interaction that accounts for the
user’s sentiments as well as the context carried during the conversation.
“Common-sense reasoning
is a field of artificial intelligence that aims to help computers understand
and interact with people more naturally by finding ways to collect these assumptions
and teach them to computers. Common Sense Reasoning has been most successful in
the field of natural language processing (NLP), though notable work has been
done in other areas.
This area of machine learning, with its strange name, is
starting to quietly infiltrate different applications ranging from text
understanding to processing and comprehending what’s in a photo. Without common
sense, it will be difficult to build adaptable and unsupervised NLP systems in
an increasingly digital and mobile world. …
NLP is where common-sense reasoning
excels, and the technology is starting to find its way into commercial
products. Though there is still a long way to go, common-sense reasoning will
continue to evolve rapidly in the coming years and the technology is stable
enough to be in business use today. It holds significant advantages over
existing ontology and rule-based systems, or systems based simply on machine
learning.”
The field of NLP brings
together artificial intelligence, computer science, and linguistics with the
goal of teaching machines to understand and process human language. NLP
researchers and engineers build models for computers to perform a variety of
language tasks, including machine translation, sentiment analysis, and writing
enhancement. Researchers often begin with analysis of a text corpus—a huge
collection of sentences organized and annotated in a way that AI algorithms can
understand.
The problem of teaching
machines to understand human language—which is extraordinarily creative and complex—dates
back to the advent of artificial intelligence itself. Language has evolved over
the course of millennia, and devising methods to apprehend this intimate facet
of human culture is NLP’s particularly challenging task, requiring astonishing
levels of dexterity, precision, and discernment.
As AI approaches—particularly
machine learning and the subset of ML known as deep learning—have developed
over the last several years, NLP has entered a thrilling period of new
possibilities for analyzing language at an unprecedented scale and building
tools that can engage with a level of expressive intricacy unimaginable even as
recently as a decade ago.
Computational
linguistics is a field of vital importance in the information age.
Computational linguists create tools for important practical tasks such as
machine translation, speech recognition, speech synthesis, information
extraction from text, grammar checking, text mining and more. confluence of
artificial intelligence and computational linguistics which handles
interactions between machines and natural languages of humans in which
computers are entailed to analyze, understand, alter, or generate natural
language.
Computer science includes the study of formal languages
(mathematically defined as production systems). Formal languages can be used to
model natural languages, and that is done in computational linguistics, a
branch of linguistics The difference is that Computational Linguistics tends
more towards Linguistics, and answers linguistic questions using computational
tools. Natural Language Processing involves applications that process language
and tends more towards Computer Science.
Computational linguistics is an
interdisciplinary field concerned with the statistical or rule-based modeling
of natural language from a computational perspective, as well as the study of
appropriate computational approaches to linguistic questions. Traditionally, computational linguistics was
performed by computer scientists who had specialized in the application of
computers to the processing of a natural language.
Today, computational
linguists often work as members of interdisciplinary teams, which can include
regular linguists, experts in the target language, and computer scientists. In
general, computational linguistics draws upon the involvement of linguists,
computer scientists, experts in artificial intelligence, mathematicians,
logicians, philosophers, cognitive scientists, cognitive psychologists,
psycholinguists, anthropologists and neuroscientists, among others.
NLP, also known as
computational linguistics, is the combination of AI and linguistics that allows
us to talk to machines as if they were human. NLP powers predictive word suggestions
on our mobile devices and voice-activated assistants like Siri, Bixby and
Google's voice search Computational linguists create tools for important
practical tasks such as machine translation, speech recognition, speech
synthesis, information extraction from text, grammar checking, text mining and
more
The difference is that NLP seeks to do useful things using human language,
while Computational Linguistics seeks to study language using computers and
corpora. Computational linguistics (CL) is the application of computer science
to the analysis, synthesis and comprehension of written and spoken language.
Computational linguistics is used in instant machine translation, speech
recognition (SR) systems, text-to-speech (TTS) synthesizers, interactive voice
response (IVR) systems, search engines, text editors and language instruction
materials. The interdisciplinary field of study requires expertise in machine
learning (ML), deep learning (DL), artificial intelligence (AI), cognitive
computing and neuroscience.
computational linguistics is the scientific study of language from a
computational perspective.”
Work in computational linguistics (CL) is concerned
with modeling natural language, and draws on a variety of other disciplines,
among them cognitive computing and artificial intelligence. The goal of
computational linguistics is to develop software able to understand natural
language, the everyday language we use to communicate.
The difference is that
Computational Linguistics tends more towards Linguistics, and answers
linguistic questions using computational tools. Natural Language Processing
involves applications that process language and tends more towards Computer
Science..
Computational
linguistics explores how human language might be automatically processed and
interpreted. Research in this area considers the mathematical and logical
characteristics of natural language, and develops algorithms and statistical
processes for automatic language processing.
Human language is
processed by computers in every sector of contemporary society. Smartphones are
required to register the meaning of language inputs, machine translation helps
us to communicate, and information from large data sets is extracted and
summarised.
Computational
linguistics concerns the development and analysis of the methods which
facilitate these applications and others like them. Analysis might therefore
focus on anything from fundamental linguistic issues such as modelling the
meaning of the word and recognising the grammatical structure of sentences, to
complex applications such as machine translation or the assessment of
statements for factual accuracy.
Analysis is conducted using statistical and
computational processes such as neural networks or processes borrowed from
logic. Computational linguistics therefore makes an important contribution to
the further development of artificial intelligence and serves as a driver of
innovation in this field.
THIS POST IS NOW CONTINUED TO PART
12 , BELOW--
CAPT AJIT VADAKAYIL
..
SOMEBODY CALLED ME UP AND CRIED..
ReplyDeleteCAPTAIN, PLEASE TELL US — HOW DO YOU KNOW ABOUT GREEK AND ROMAN SCHOLARS ACCOUNTS OF “MAMANKAM FEST” AT TIRUNNAVAYA ..
YOU HAVE TO TELL IT NOW, OR THE MALAYALAM MOVIE STARRING MEGASTAR MAMMOOTY DUE FOR RELEASE SOON AFTER TWO POSTPONEMENTS ..
THIS LYING MOVIE WILL CEMENT WHITE INVADERs LIES. AND TRUTH WILL BE BURIED FOR EVER.
https://keralakaumudi.com/en/news/news.php?id=185046&u=release-date-of-mamangam-extended-film-crew-apologises-to-fans...-185046
CAPTAIN, IT IS NOW OR NEVER..
THE GREATEST AND MOST ANCIENT FEST IS NOW REDUCED TO SOME BULLSHIT 18TH CENTURY EVENT OF PETTY QUARRELS BETWEEN MINOR MALAYALI FEUDAL LORDS.
http://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html
IT WAS MENTOR SHUKRACHARYA WHO FORCED KERALA KING MAHABALI OF CALICUT TO DO THE ASHWAMEDHAM YAGNA ( VOTE OF CONFIDENCE USING A WHITE HORSE ) , TO TAUNT HIS BITTER RIVA LBRIHASPATI WHOSE SON KACHCHA TRIED TO DO LOVE JIHAD ON HIS DAUGHTER DEVYANI.
http://ajitvadakayil.blogspot.com/2014/09/ashwamedha-yagam-bloodless-sacrifice.html
THE CALICUT KING TRADITION WAS TO HOLD A GLADIATOR CONTEST EVERY 12 YEARS ..WHERE THE KING OFFERED HIMSELF AS A BAIT INSTEAD OF A WHITE HORSE.
http://ajitvadakayil.blogspot.com/2011/09/why-kerala-does-not-celebrate-diwali-or.html
THIS EVENT WAS KNOWN WORLD OVER.. SENATORS FROM ROME CAME TO WITNESS THIS EVENT, AFTER ALL IT WAS HOMECOMING FOR THEM..
THE FIRST KING OF ROME WAS RAMA , A KERALA THIYYA ( ETRUSCAN ) WHO WAS CROWNED ON 21ST APRIL 830 BC...
I AM AN ETRUSCAN..
MAHABALIs FATHER VIROCHANA RULED THE WHOLE PLANET.. HE IS A GOD IN PERU TODAY.
http://ajitvadakayil.blogspot.com/2019/07/secrets-of-12000-year-old-machu-picchu.html
THE GREATEST ROMAN EMPEROR WAS HINDU MARCUS AURELIUS WHO RULED FROM 161 AD TO 180 AD.. THE ROMAN EMPIRE WAS LARGEST DURING HIS RULE..
https://en.wikipedia.org/wiki/Marcus_Aurelius
THE SON OF MARCUS AURELIUS WAS HINDU EMPEROR COMMODUS WHO RULED FROM 180 AD TO 192 AD..
https://en.wikipedia.org/wiki/Commodus
THE SPEAKER OF THE ROMAN SENATE ( ALL 600 WERE KERALA THIYYA BLOOD ) WAS CASSIUS DIO.. HE WAS A GREAT HISTORIAN..
https://en.wikipedia.org/wiki/Cassius_Dio
SENATOR CASSIUS DIO HOWEVER MAINTAINED A SECRET DIARY, IN WHICH HE WROTE THE TRUTH ABOUT HIS EMPEROR COMMODUS WHO TRIED TO “ DO A MAMANKAM ” , IMITATING THE BRAVE AND HONORABLE FEATS OF CALICUT KINGS IN THEIR 12TH YEAR OF REIGN.
CALICUT KINGS, THE RICHEST ON THE PLANET, DERIVED POWER FROM THE SOULS OF THE PEOPLE .
MARCUS AURELIUS WAS AN EXTRAORDINARY MAN .. FULL OF WISDOM AND VALOR. ..
WHEN HE DIED AT A ROMAN ARMY FRONTIER OUTPOST IN GERMNAY, IT WAS DIFFICULT FOR HIS BIOLOGICAL SON COMMODUS TO MATCH HIS FATHER IN THE EYES OF THE PEOPLE OF ROME , THE ARMY, AND THE SENATE ..
BEFORE COMMODUS WAS BORN ON 80 YEARS OF ROMAN EMPERORS COULD NOT HAVE A BIOLOGICAL SON ( DUE TO FOUL PLAY BY JEW INFILTRATORS ).. COMMODUS WOULD ALSO DIE WITHOUT A CHILD.
COMMODUS WAS TOLD BY A CHILD HOOD FRIEND THAT THE ONLY WAY TO GRAB POWER AND GLORY OVERNIGHT WAS TO REPLICATE WHAT THE CALICUT KINGS DID FOR MILLENNIUMS AT MAMANKAM FEST AT THE END OF THE 12TH YEAR OF HIS REIGN. .
GLADIATOR CONTESTS IN 192 AD WOULD BE ARRANGED BY EMPEROR COMMODUS IN THE ROMAN COLOSSEUM.
http://ajitvadakayil.blogspot.com/2016/10/the-huge-statue-of-colossus-of-rome-at.html
THIS STADIUM WHICH COULD HOLD 50,000 PEOPLE WAS BUILT TO HEAR APOLLONIUS OF TYANA , A KERALA SAGE ON WHOSE LIFE JESUS CHRIST WAS COOKED UP IN 325 AD.
http://ajitvadakayil.blogspot.com/2019/09/istanbul-deep-seat-of-jewish-deep-state.html
COMMODUS WAS TAUGHT SWORD FIGHTING BY HIS FATHER USING EXPERTS FOR TWO YEARS AT THE GERMANY ROMAN OUTPOSTS.. HE PARTICIPATED IN CRUSHING GERMAN TRIBAL REVOLTS ALONG WITH HIS FATHER .
BUT FIGHTING IN THE COLOSSEUM AGAINST HARDENED GLADIATOR SLAVES WAS A DIFFERENT KETTLE OF FISH..
CONTINUED TO 2--
CONTINUED FROM 1--
DeleteSO COMMODUS EMPLOYED A RETIRED GLADIATOR NAMED NARCISSUS WHO HAD NEVER LOST A FIGHT, TO TEACH HIM THE FIGHT WINNING SECRETS.
ALMOST ALL OF YOU HAVE SEEN THE GLADIATOR MOVIE BELOW.
https://en.wikipedia.org/wiki/Gladiator_(2000_film)
IN THE FILM ABOVE THEY SHOW EMPEROR COMMODUS FIGHTING AND DYING IN THE COLOSSEUM…
THE RETIRED GLADIATOR NARCISSUS IS ANTONIUS PROXIMO ( ACTED BY OLIVER REED ) IN THE MOVIE ABOVE..
AFTER HIS TRAINING WAS OVER EMPEROR COMMODUS GAVE NARCISSUS A WOODEN SWORD LIBERATING HIM FORM SLAVERY AS A GIFT .
EMPEROR COMMODUS FOUGHT IN THE QUARTER FINALS, SEMI-FINALS AND FINALS AND WON -- BY FOUL MEANS .. HIS OPPONENTS WERE GIVEN SWORDS WITH ROLLED UP EDGES WHICH COULD NOT CUT EVEN HUMAN SKIN..
ANYWAYS – EMPEROR COMMODUS NOW WON OVER THE SOULS OF THE PEOPLE OF ROME AND HE HARNESSED THE HOSTILE SENATE.
WHEN NARCISSUS FOUND OUT ABOUT THIS CHEATING , HE WAS FURIOUS AND CONFRONTED HIS STAR PUPIL , THE EMPEROR, FOR LACK OF HONOR AND COWARDICE..
THIS SENT COMMODUS TO A DRINKING BINGE.. AND HE MADE A LIST OF PEOPLE WHO KNEW THAT HE CHEATED AND WHO HAD TO BE ELIMINATED ..
THIS LIST INCLUDED HIS WIFE MARISA , GURU NARCISSUS AND ALMOST THE WHOLE OF THE SENATE..
SENATOR CASSIUS DIO AND WIFE MARISA WOULD SECRETLY ENLIST NARCISSUS TO KILL COMMODUS IN HIS BEDROOM .
THUS ENDED THE GLORIOUS ROMAN EMPEROR LINEAGE.. THE GLORY OF ROME ENDED AT THAT HOUR. FROM THEN ALL IT WAS DOWNHILL..
90 % OF WIKIPEDIA IS LIES.. INDIA AND HINDUS ARE ALWAYS AT THE RECEIVING END ..
Capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF ( WARN THEM ) -
DeleteMAMMOOTY
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