Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
In the last few years, Artificial Intelligence applications have become more and more sophisticated and often operate like algorithmic “black boxes” for decision-making. Due to this fact, some questions naturally arise when working with these models: why should we trust a certain decision taken by these algorithms? Why and how was this prediction made? Which variables mostly influenced the prediction? The most crucial challenge with complex machine learning models is therefore their interpretability and explainability. This talk aims to illustrate an overview of the most popular explainability techniques and their application in Learning to Rank. In particular, we will examine in depth a powerful library called SHAP with both theoretical and practical insights; we will talk about its amazing tools to give an explanation of the model behaviour, especially how each feature impacts the model’s output, and we will explain to you how to interpret the results in a Learning to Rank scenario.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
In the last few years, Artificial Intelligence applications have become more and more sophisticated and often operate like algorithmic “black boxes” for decision-making. Due to this fact, some questions naturally arise when working with these models: why should we trust a certain decision taken by these algorithms? Why and how was this prediction made? Which variables mostly influenced the prediction? The most crucial challenge with complex machine learning models is therefore their interpretability and explainability. This talk aims to illustrate an overview of the most popular explainability techniques and their application in Learning to Rank. In particular, we will examine in depth a powerful library called SHAP with both theoretical and practical insights; we will talk about its amazing tools to give an explanation of the model behaviour, especially how each feature impacts the model’s output, and we will explain to you how to interpret the results in a Learning to Rank scenario.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology
Given at the annual Open Universiteit Informatics faculty research meeting on March 6, 2012. Video is at http://video.intranet.ou.nl/mediadienst/_website/php/external_video.php?Q=1056|videoID
Ontologies for multimedia: the Semantic Culture WebGuus Schreiber
Keynote, International Conference on Semantic and Digital Media Technology (SAMT 2006), Athens, 7 December 2006. Slide design with lots of help of Lora Aroyo.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
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Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
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• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
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2. Lecture
1
Agenda
• Course
introduc:on:
what
is
an
ontology?
• Administra:on
• RDF/RDFS
3. Literature
• James
Odell,
Ontology
White
Paper,
CSC
Catalyst,
2011,
V2011-‐07-‐15,
hNp://www.jamesodell.com/
Ontology_White_Paper_2011-‐07-‐15.pdf.
• For
this
lecture
Sec.s
1-‐4
are
relevant
• Acknowledgement:
some
figures
in
this
lecture
come
from
the
paper
above.
4. What
is
an
Ontology?
• In
philosophy:
theory
of
what
exists
in
the
world
• In
IT:
consensual
&
formal
descrip:on
of
shared
concepts
in
a
domain
• Aid
to
human
communica:on
and
shared
understanding,
by
specifying
meaning
• Machine-‐processable
(e.g.,
agents
use
ontologies
in
communica:on)
• Key
technology
in
seman:c
informa:on
processing
• Applica:ons:
knowledge
management,
e-‐business,
seman:c
world-‐wide
web.
5. What
is
an
Ontology?
II
“explicit
specifica-on
of
a
shared
conceptualiza-on
that
holds
in
a
par-cular
context”
(several
authors)
6. Knowledge
sharing
and
reuse
• Knowledge
engineering
is
costly
and
:me-‐
consuming
• Distributed
systems
• Increasing
need
for
defini:on
of
a
common
frame
of
reference
– Internet
search,
document
indexing,
….
14. Domain
standards
and
vocabularies
as
ontologies
• Contain
ontological
informa:on
• Ontology
needs
to
be
“extracted”
– Not
explicit
• Lists
of
domain
terms
are
some:mes
also
called
“ontologies”
– Implies
a
weaker
no:on
of
ontology
– Scope
typically
much
broader
than
a
specific
applica:on
domain
– Contain
some
meta
informa:on:
hyponyms,
synonyms,
text
• Structured
knowledge
is
available
(on
the
web)
–
use
it!
14
18. Context
and
Domain
Principle
1:
“ The
representa:on
of
real-‐world
objects
always
depends
on
the
context
in
which
the
object
is
used.
This
context
can
be
seen
as
a
“viewpoint”
taken
on
the
object.
It
is
usually
impossible
to
enumerate
in
advance
all
the
possible
useful
viewpoints
on
(a
class
of
)
objects.”
Principle
2:
“Reuse
of
some
piece
of
informa:on
requires
an
explicit
descrip:on
of
the
viewpoints
that
are
inherently
present
in
the
informa:on.
Otherwise,
there
is
no
way
of
knowing
whether,
and
why
this
piece
of
informa:on
is
applicable
in
a
new
applica:on
seing.”
19. Mul:ple
views
on
a
domain
• typical
viewpoints
captured
in
ontologies:
• func:on
• behavior,
• causality
• shape,
geometry
• structure:
part-‐of
(mereology),
aggrega:on
• connectedness
(topology)
• viewpoints
can
have
different
abstrac:on
(generaliza:on)
levels
• viewpoints
can
overlap
• applica:ons
require
combina:ons
of
viewpoints
19
28. The
concept
triad
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
29. Concept
specifica:on
• Symbol
– Name
used
for
the
concept
– Can
be
different
names,
different
languages
– E.g.,
“bike”,
fiets”
• Intension
(defini:on)
– Intended
meaning
of
the
concept
(seman:cs)
– E.g.
a
bike
has
at
least
one
wheel
and
a
human-‐
powered
movement
mechanism
• Extension
– Set
of
examples
of
the
concept
– E.g.
“my
bike”,
“your
bike”
30. Incomplete
concept
specifica:ons
• Are
common
• Think
of
an
example:
– Concept
with
no
instances
– Concept
with
no
symbol
• Primi:ve
vs.
defined
concepts
31. Domain
=
area
of
interest
• Can
be
any
size
– e.g.,
medicine
• Concepts
may
have
different
symbols
in
different
domains
• The
same
symbol
may
be
used
for
different
concepts
in
different
domains
(some:mes
also
in
the
same
domain)
32. Ontology
Specifica:on
• Class
(concept)
• Aggrega:on
• Subclass
with
inheritance
• Rela:on-‐aNribute
dis:nc:on
• Trea:ng
rela:ons
as
classes
• Rela:on
(slot)
• Sloppy
class/instance
dis:nc:on
– Class-‐level
aNributes/
rela:ons
– Meta
classes
• Constraints
• Data
types
• Modularity
– Import/export
of
an
ontology
• Ontology
mapping
33. Ontology
Languages
– UML
– RDF
Schema,
OWL
– …..
• Common
basis
– Class
(concept)
– Subclass
with
inheritance
– Rela:on
(slot)
33
34. Ontology
Tools
Best
known
tool
• Protégé
(Stanford)
• We
will
use
this
tool
Decision
points:
– Expressivity
– Graphical
representa:on
– DB
backend
– Modulariza:on
support
– Versioning
35. Administra:on
• Course
website:
hNp://seman:cweb.cs.vu.nl/OE2012/
• Use
blog
posts
for
content
ques:ons
• Use
oe-‐list@few.vu.nl
for
admin
ques:ons
36. Engineering
needs
prac:ce!
Lots
of
exercises
throughout
the
course:
• Two
mee:ngs
per
week
• Lectures
on
Monday
• Work
sessions
on
Thursday
• You
are
encouraged
to
do
assignments
together
with
colleagues
• Individual
porsolio
37. RDF(S)
Recap
• Which
RDF/RDF-‐Schema
constructs
do
you
remember?
38. URIs,
URLs
• URI:
global
iden:fier
for
a
web
resource
• hNp://www.w3.org/2006/03/wn/wn20/instances/synset-‐
anniversary-‐noun-‐1
• URL:
dereferencable
URI,
used
to
locate
a
file
on
the
web.
• hNp://www.w3.org/2006/03/wn/wn20/instances/synset-‐
anniversary-‐noun-‐1
• URI
abbrevia:ons:
– Qnames
• Namespace:iden:fier
• Wordnet:synset-‐anniversary-‐noun-‐1
41. Blank
nodes
How
would
you
model
“Sonnet78
was
inspired
by
a
woman
who
lives
in
England”?
Lit:Sonnet78 lit:hasInspiration [ rdf:type bio:Woman;
bio:livedIn geo:England ] .
44. Domain
and
Range
IF
IF
P rdfs:domain D
P rdfs:range R
x P y
x P y
THEN
THEN
x rdf:type D
y rdf:type R
45. More
RDF(S)
• rdfs:label
• rdfs:comment
• rdfs:seeAlso
46. RDF-‐Schema
• Provides
a
way
to
talk
about
the
vocabulary
– Define
classes,
proper:es
bb:author rdf:type rdfs:Property
• Enables
inferencing
– Inferring
new
triples
from
asserted
triples.
• subClassOf,
subPropertyOf,
domain,
range.
47. Guidelines
for
ontological
engineering
(1)
• Do
not
develop
from
scratch
• Use
exis:ng
data
models
and
domain
standards
as
star:ng
point
• Start
with
construc:ng
an
ontology
of
common
concepts
• If
many
data
models,
start
with
two
typical
ones
• Make
the
purpose
and
context
of
the
ontology
explicit
– E.g.
data
exchange
between
ship
designers
and
assessors
– Opera:onally
purpose/context
with
use
cases
• Use
mul:ple
hierarchies
to
express
different
viewpoints
on
classes
• Consider
trea:ng
central
rela:onships
as
classes
47
48. Guidelines
for
ontological
engineering
(2)
• Do
not
confuse
terms
and
concepts
• Small
ontologies
are
fine,
as
long
as
they
meet
their
goal
• Don’t
be
overly
ambi:ous:
complete
unified
models
are
difficult
• Ontologies
represent
sta:c
aspects
of
a
domain
– Do
not
include
work
flow
• Use
a
standard
representa:on
format,
preferably
with
a
possibility
for
graphical
representa:on
• Decide
about
the
abstrac:on
level
of
the
ontology
early
on
in
the
process.
– E.g.,
ontology
only
as
meta
model
48