The document provides an overview of artificial intelligence and related topics. It discusses (1) how computers compare to humans in their abilities, with computers excelling at tasks like calculations but humans superior in areas like visual recognition; (2) different approaches to AI like expert systems, semantic networks, search trees, neural networks, natural language processing, robotics; and (3) open challenges in AI like ambiguity in language and limitations of different techniques.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
The presentation gives a brief introduction to artificial neural networks, their types, their characteristics and properties, their advantages and disadvantages, their architectures, different activation functions, popular learning approaches, the gradient descent algorithm, the back propagation algorithm and the application domains.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
The presentation gives a brief introduction to artificial neural networks, their types, their characteristics and properties, their advantages and disadvantages, their architectures, different activation functions, popular learning approaches, the gradient descent algorithm, the back propagation algorithm and the application domains.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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.
2. Thinking Machines
• Are there tasks which cannot easily
be automated? If so, what are the
limitations?
• How do computers abilities compare
to that of humans?
4. Computers versus humans
• A computer can do some things better
than a human can
– Adding a thousand four-digit numbers
– Drawing complex, 3D images
– Store and retrieve massive amounts of data
• However, there are things humans can do
much better.
6. Computer or human?
• Which of the following occupations could
(or should) be performed by computers?
– Postman
– Bookstore clerk
– Librarian
– Doctor
– Lawyer
– Judge
– Professor
7. 7
Thinking Machines
Artificial intelligence (AI)
The study of computer systems that attempt
to model and apply the intelligence of the
human mind
For example, writing a program to pick out
objects in a picture
8. First things first…
• Of course, first we have to understand why
we use the term “intelligence” in regard to
humans.
– What defines “intelligence”?
– Why is it that we assume humans are
intelligent?
– Are monkeys intelligent? Dogs? Ants? Pine
trees?
9. Early History
• In 1950 English mathematician Alan Turing
wrote a landmark paper titled “Computing
Machinery and Intelligence” that asked the
question: “Can machines think?”
• Further work came out of a 1956 workshop at
Dartmouth sponsored by John McCarthy. In the
proposal for that workshop, he coined the
phrase a “study of artificial intelligence”
10. Can Machines Think?
• So Turing asked: “Can machines think?”
He felt that such machines would
eventually be constructed.
• But he also realized a bigger problem.
How would we know if we’ve succeeded?
11. 11
The Turing Test
Turing test
A test to empirically determine whether a
computer has achieved intelligence
Figure 13.2
In a Turing test, the
interrogator must
determine which
respondent is the
computer and which is
the human
12. 12
The Turing Test
•Passing the Turing Test does not truly show that
the machine was thinking. It simply shows that it
generated behavior consistent with thinking.
•weak equivalence: the two systems (human and
computer) are equivalent in results (output), but
they do not necessarily arrive at those results in
the same way
•Strong equivalence: the two systems use the
same internal processes to produce results
13. 13
The Turing Test
Loebner prize
The first formal instantiation
of the Turing test, held
annually
Chatbots
A program designed to carry on a
conversation with a human user
14. 14
Knowledge Representation
• We want to compare the way that
computers and humans work to see if we
can better understand why each have their
(computational) strengths.
– Processing Models
– Knowledge Representation
– Reasoning
15. 15
Semantic Networks
Semantic network
A knowledge representation technique that
focuses on the relationships between objects
A directed graph is used to represent a semantic
network or net
Remember directed
graphs? (See Chapter 8.)
18. 18
Semantic Networks
Network Design
– The objects in the network represent the
objects in the real world that we are
representing
– The relationships that we represent are
based on the real world questions that we
would like to ask
– That is, the types of relationships represented
determine which questions are easily
answered, which are more difficult to answer,
and which cannot be answered
19. Difficult questions
For example, it
would be difficult
to ask “how
many students
are female?” or
“who lives in
Doughtery Hall?”
The answers are
present - just
difficult to find by
searching here.
20. 20
Search Trees
Search tree
A structure that represents alternatives in
adversarial situations such as game playing
The paths down a search tree represent a
series of decisions made by the players
Remember trees?
(See Chapter 8.)
21. Example: (simplified) Nim
• There are a row of spaces. The game
alternates:
– Player 1 may place 1, 2, or 3 x’s in the
leftmost spaces.
– Player 2 may then place 1, 2, or 3 o’s in the
next spaces.
• Goal: place the last mark in the rightmost
space.
23. 23
Search Trees
Search tree analysis can be applied to other, more
complicated games such as chess
However, full analysis of the chess search tree
would take more than your lifetime to determine
the first move
Because these trees are so large, only a fraction of
the tree can be analyzed in a reasonable time limit,
even with modern computing power
Therefore, we must find a way to prune the tree
24. 24
Search Trees
Techniques for pruning search
space
•Depth-first A technique that involves the
analysis of selected paths all the way down
the tree
•Breadth-first A technique that involves
the analysis of all possible paths but only for
a short distance down the tree
26. Search Tree strategies
• Breadth-first strategies tend to be more
successful (and computationally feasible).
• Search tree analysis can be applied nicely
to other, more complicated games such as
chess.
• In 1997, IBM’s Deep Blue became the first
program to defeat a master-level chess
player.
27. 27
Expert Systems
Knowledge-based system
Software that uses a specific set of information, from which
it extracts and processes particular pieces
Expert system
A software system based the knowledge of human experts;
it is a
– Rule-based system
– A software system based on a set of if-then rules
– Inference engine
– The software that processes rules to draw conclusions
29. 29
Expert Systems
Named abbreviations that represent
conclusions
– NONE—apply no treatment at this time
– TURF—apply a turf-building treatment
– WEED—apply a weed-killing treatment
– BUG—apply a bug-killing treatment
– FEED—apply a basic fertilizer treatment
– WEEDFEED—apply a weed-killing and
fertilizer combination treatment
30. 30
Expert Systems
Boolean variables needed to represent state
of the lawn
– BARE—the lawn has large, bare areas
– SPARSE—the lawn is generally thin
– WEEDS—the lawn contains many weeds
– BUGS—the lawn shows evidence of bugs
31. 31
Expert Systems
Data that is available
– LAST—the date of the last lawn treatment
– CURRENT—current date
– SEASON—the current season
Now we can formulate some rules for our
gardening expert system Rules take the form of
if-then statements
32. 32
Expert Systems
Some rules
– if (CURRENT – LAST < 30) then NONE
– if (SEASON = winter) then not BUGS
– if (BARE) then TURF
– if (SPARSE and not WEEDS) then FEED
– if (BUGS and not SPARSE) then BUG
– if (WEEDS and not SPARSE) then WEED
– if (WEEDS and SPARSE) then WEEDFEED
33. 33
Expert Systems
An execution of our inference engine
– System: Does the lawn have large, bare areas?
– User: No
– System: Does the lawn show evidence of bugs?
– User: No
– System: Is the lawn generally thin?
– User: Yes
– System: Does the lawn contain significant weeds?
– User: Yes
– System: You should apply a weed-killing and
fertilizer combination treatment.
34. 34
Artificial Neural Network
Artificial neural networks
A computer representation of knowledge
that attempts to mimic the neural networks
of the human body
Yes, but what is a human neural network?
36. 36
Neural Network
Neuron
A single cell that conducts a chemically-based
electronic signal
At any point in time a neuron is in either an
excited state or an inhibited state
Excited state
Neuron conducts a strong signal
Inhibited state
Neuron conducts a weak signal
37. 37
Neural Network
–A series of connected neurons forms a
pathway
–A series of excited neurons creates a
strong pathway
–A biological neuron has multiple input
tentacles called dendrites and one
primary output tentacle called an axon
–The gap between an axon and a
dendrite is called a synapse
38. Neural network
• A neuron accepts multiple input signals
and then controls the contribution of each
signal based on the “importance” the
corresponding synapse gives to it
39. 39
Neural Network
The pathways along the neural nets are in a
constant state of flux
As we learn new things, new strong neural
pathways in our brain are formed
40. 40
Artificial Neural Networks
Each processing element in an artificial
neural net is analogous to a biological
neuron
– An element accepts a certain number of input
values (dendrites) and produces a single
output value (axon) of either 0 or 1
– Associated with each input value is a numeric
weight (synapse)
41. 41
Artificial Neural Networks
– The effective weight of the element is the
sum of the weights multiplied by their
respective input values
v1 * w1 + v2 * w2 + v3 * w3
– Each element has a numeric threshold value
– If the effective weight exceeds the threshold,
the unit produces an output value of 1
– If it does not exceed the threshold, it produces
an output value of 0
42. 42
Artificial Neural Networks
•The process of adjusting the weights and
threshold values in a neural net is called
training
•A neural net can presumably be trained to
produce whatever results are required
•But think about the complexity of this! Train
a computer to recognize any cat in a picture,
based on training run with several pictures.
43. Human vs. Computer
Human Brain Computer
speed Neurotransmitters
travel at about 1000
ft/second
Electrons at
speed of light
memory Roughly 100 billion
neurons - about 50
trillion bits
Top super
computers might
approach this
much memory
other Each neuron
connected to 1000
others (roughly)
Perhaps 100
parallel
processors
44. 44
Natural Language Processing
•There are three basic types of processing going
on during human/computer voice interaction
– Voice recognition—recognizing human words
– Natural language comprehension—interpreting
human communication
– Voice synthesis—recreating human speech
•Common to all of these problems is the fact that
we are using a natural language, which can be any
language that humans use to communicate
45. 45
Voice Synthesis
One Approach to Voice Synthesis
Dynamic voice generation
A computer examines the letters that make up a
word and produces the sequence of sounds that
correspond to those letters in an attempt to
vocalize the word
Phonemes
The sound units into which human speech has
been categorized
47. 47
Voice Synthesis
Another Approach to Voice Synthesis
Recorded speech
A large collection of words is recorded digitally and
individual words are selected to make up a message
Since words are pronounced differently in different
contexts, some words may have to be recorded multiple
times
– For example, a word at the end of a question rises in
pitch compared to its use in the middle of a sentence
48. 48
Voice Recognition
Problems with understanding speech
– Each person's sounds are unique
– Each person's shape of mouth, tongue,
throat, and nasal cavities that affect the pitch
and resonance of our spoken voice are
unique
– Speech impediments, mumbling, volume,
regional accents, and the health of the
speaker are further complications
50. 50
Voice Recognition
Other problems
– Humans speak in a continuous, flowing manner,
stringing words together
– Sound-alike phrases like “ice cream” and “I scream”
– Homonyms such as “I” and “eye” or “see” and “sea”
Humans can often clarify these situations by the
context of the sentence, but that processing
requires another level of comprehension
Modern voice-recognition systems still do not do
well with continuous, conversational speech
51. 51
Voice Recognition
Voiceprint
The plot of frequency changes over time
representing the sound of human speech
A human trains a voice-recognition system
by speaking a word several times so the
computer gets an average voiceprint for a
word
Used to authenticate the declared
sender of a voice message
52. 52
Natural Language Comprehension
Natural language is ambiguous!
Lexical ambiguity
The ambiguity created when words have multiple meanings
Syntactic ambiguity
The ambiguity created when sentences can be constructed
in various ways
Referential ambiguity
The ambiguity created when pronouns could be applied to
multiple objects
53. Lexical Ambiguity
• A single word can have two meanings.
– “The bat slipped from his hand”
– “Cinderella had a ball”
– “Ron lies asleep in his bed”
• Worse yet, those meanings may even
constitute different parts of speech.
– “Time flies like an arrow”
– “They are racing horses”
– “Stampeding cattle can be dangerous”
54. Syntatic Ambiguity
• Even if all words have a clear meaning,
ambiguity may exist because the phrases
can be combined in several ways when
parsing.
– “I saw the Grand Canyon flying to New York”
– “The clams are ready to eat”
– “I saw the man in the park with the telescope”
55. Referential Ambiguity
• Pronouns may cause ambiguity when it is
not clear which noun is being referenced.
– “The brick fell on the computer but it is not broken”
– “Jon met Bill before he went to the store”
56. Rules of Conversation
– “Do you know what time it is?”
• Presumably, a correct response is “Yes.”
• Does this sentence’s meaning change if it is said by your
boss when you walk into a meeting 30 minutes late?
– “Do you know you have a flat tire?”
– “I’d like to ask everyone to raise their hand for two
seconds.”
• How did you respond?
57. “Real world” knowledge
• “Norman Rockwell painted people.”
– Did he do tatoos? Face painting?
– Without background knowledge, difficult to
parse this into real information.
58. 58
Natural Language Comprehension
Lexical ambiguity
Stand up for your country
Take the street on the left
Syntactic ambiguity
I saw the bird watching from the corner
I ate the sandwich sitting on the table
Referential ambiguity
The bicycle hit the curb, but it was not damaged
John was mad at Bill, but he didn't care
Can you think
of
some others?
59. 59
Robotics
Mobile robotics
The study of robots that move relative to their environment,
while exhibiting a degree of autonomy
Sense-plan-act (SPA) paradigm
The world of the robot is represented in a complex
semantic net in which the sensors on the robot are used to
capture the data to build up the net
Figure 13.8 The sense-plan-act (SPA) paradigm
60. 60
Subsumption Architecture
Rather than trying to model the entire world all the time, the
robot is given a simple set of behaviors each associated
with the part of the world necessary for that behavior
Figure 13.9
The new control
paradigm