This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
A seminar ppt fully imformative about ai.1. Artificial Intelligence<br />Shannon Baker, Laura Paviglianiti, Tim Stuart, Harrison Baker<br />
2. What is Artificial Intelligence?<br />
3. The intelligence of machines and the branch of computer science that aims to create it<br />"the study and design of intelligent agents”<br />No single goal of artificial intelligence<br />Some say it’s putting the human mind into computers<br />What is intelligence?<br />The computational part of the ability to achieve goals in the world<br />We do not yet fully understand what intelligence consists of<br />
4. 1941:Development of the electronic computer<br /><ul><li>Some trace the origin to John Atanasoff and Clifford Berry at Iowa State University
5. Required large, separate </li></ul>air-conditioned rooms<br /><ul><li>Required separate </li></ul>configuration of <br />thousands of wires<br /><ul><li>Data fed into system </li></ul>By punched cards<br />
6. First Commercial, Stored Program Computer<br />Made job of entering a program easier<br />Advancements in computer theory computer science <br />(and eventually <br />to AI)<br />Invention of a <br />means of processing <br />data makes AI <br />possible<br />
7. Dartmouth Conference<br />John McCarthy (“father of AI”) organizes conference<br />A month of brainstorming in VT<br />Talent and expertise of others interested in machine intelligence<br />Biggest gain: field <br />now called<br />Artificial Intelligence<br />
8. LISP Language Developed<br />McCarthy announces new development: LISP language<br />Still used today<br />LISt Processing – <br />language of <br />choice <br />among AI <br />developers<br />
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
A presentation on Artificial Intelligence which covers definition, introduction , advantages/disadvantages, AI Tree and application of artificial intelligence
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
A seminar ppt fully imformative about ai.1. Artificial Intelligence<br />Shannon Baker, Laura Paviglianiti, Tim Stuart, Harrison Baker<br />
2. What is Artificial Intelligence?<br />
3. The intelligence of machines and the branch of computer science that aims to create it<br />"the study and design of intelligent agents”<br />No single goal of artificial intelligence<br />Some say it’s putting the human mind into computers<br />What is intelligence?<br />The computational part of the ability to achieve goals in the world<br />We do not yet fully understand what intelligence consists of<br />
4. 1941:Development of the electronic computer<br /><ul><li>Some trace the origin to John Atanasoff and Clifford Berry at Iowa State University
5. Required large, separate </li></ul>air-conditioned rooms<br /><ul><li>Required separate </li></ul>configuration of <br />thousands of wires<br /><ul><li>Data fed into system </li></ul>By punched cards<br />
6. First Commercial, Stored Program Computer<br />Made job of entering a program easier<br />Advancements in computer theory computer science <br />(and eventually <br />to AI)<br />Invention of a <br />means of processing <br />data makes AI <br />possible<br />
7. Dartmouth Conference<br />John McCarthy (“father of AI”) organizes conference<br />A month of brainstorming in VT<br />Talent and expertise of others interested in machine intelligence<br />Biggest gain: field <br />now called<br />Artificial Intelligence<br />
8. LISP Language Developed<br />McCarthy announces new development: LISP language<br />Still used today<br />LISt Processing – <br />language of <br />choice <br />among AI <br />developers<br />
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
A presentation on Artificial Intelligence which covers definition, introduction , advantages/disadvantages, AI Tree and application of artificial intelligence
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
3. Undrstanding Intelligence
Many great philosopher over the ages attempted to explain the
process of thought and understanding.
Intelligence
Plato
428–348 BC
Aristotle
384–322 BC
Philosophy
Math
Nature and Universe
Human knowledge Intelligence
*
4. Undrstanding Intelligence
Plato
428–348 BC
Aristotle
384–322 BC
Copernic
1473-1543
Galileo
1564-1642
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
The real key that started the quest for the
simulation of inteligence did not occure until …
*
5. Undrstanding Intelligence
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
Thomas Hobbes (English Philosopher) put forth an interesting concept that
thinking consists of symbolic operations and that everything in the life
can be represented mathematically.
Hobbes
1588-1679
*
6. Undrstanding Intelligence
Philosophy & Natural Science
Math
Intelligence
Nature and Universe
Human knowledge
Thomas Hobbes (English Philosopher) put forth an interesting concept that
thinking consists of symbolic operations and that everything in the life
can be represented mathematically.
Hobbes
1588-1679
*
7. Hobbes (British Philosopher):
Thinking consists of symbolic operations!
Based on this logic, a machine capable of caring out
mathematical operations on symbols could imitate human
thinking.
Undrstanding Intelligence
Hobbes
1588-1679
What is a symbolic operation?
• Numeric operation (2+3)2 = 25
• Symbolic operation (a+b)2 = a2 + b2 + 2ab
8. Rene Descartes (French Philosopher and Mathematician):
He believed that the mind and the real world are in parallel
planes. The physical word (i.e. machines) cannot imitate the
mind because there is no common reference point.
Undrstanding Intelligence
Descartes
1596-1650
9. Charles Babbage (British Mathematician):
In Babbage's time, numerical tables were calculated by humans who
were called 'computers’. He saw the high error-rate of this human-
driven process and started work of trying to calculate the tables
mechanically. He created a “difference engine” to compute values
of polynomial functions.
Imitating Inteligence
Babbage
1791-1871
A part of Babbage's difference
engine
He also introduced the idea of “Analytical
Machine”, but he could never realize this
idea.
10. George Boole (British Mathematician):
Boole formulated the “Laws of Thought” that set up rules of logic for
representing thoughts (symbolic logic). This was the birth of digital
logic, a key component of AI.
In the early 1900s, Alfred Whitehead and Bertrand Russell extended
Boole’s logic to include mathematical operations. This led to the
formulation of digital computers. Also, this made possible one of the
first ties between computers and thought process.
Imitating Inteligence
Boole
1815-1864
Russell
1872-1970
Whitehead
1861-1947
11. Design a digital computer using logical operations to compute y=x1+x2
where x1 and x2 are 4-digit binary numbers (4-bit adder).
Design a digital computer using logical operations to compute y=x1.x2
where x1 and x2 are 4-digit binary numbers (4-bit multiplier).
Design a digital computer using logical operations to compute y=ex where
x1 and x2 are 4-digit binary numbers (ex=1+x+x2/2+x3/6+…).
Imitating Inteligence
12. Claude Shannon (American Electrical Engineer):
He wrote his master’s thesis demonstrating that electrical
applications of Boolean algebra could construct and resolve any
logical, numerical relationship.
It has been claimed that this was the most important master's thesis
of all time. His PhD these was on mathematical relationships of
genetics.
He is known as the father of Information Technology.
Imitating Inteligence
Shannon
1916-2001
13. John Neumann (American Mathematician)
He suggested that the computers
should be general purpose logic machines.
could react intelligently to the results of their calculations
could choose among alternatives, and even play checker and chess
This represented something unheard of at that time: a machine with
built-in intelligence, able to operate on internal instructions.
Before introducing this concept, even the most complex mechanical
devices had always been controlled from the outsides, by knobs and
dials.
He didn't’ invent the computer but what he introduced was equally
significant: computing by use of computer programs.
Imitating Inteligence
Neumann
1903-1957
14. John Mauchly (American Electrical Engineer):
John Mauchly designed and built the first general purpose digital
computer in 1946 at the University of Pennsylvania:
ENIAC (Electronic Numerical Integrator and Computer)
Weight = 30 Tons
Floor Space = 1500 Square Feet
Shannon’s idea Hardware
Neumann’s idea Software
Imitating Inteligence
Mauchly
1907-1980
15. Alan Turing (British Mathematician):
He introduced “Universal Machine Concept” that describe a
machine for solving all problems based on variable instructions.
Turing’s universal machine concept, along with Neumann’s concept
of computing using programs led to programmable computers.
Operational machines were now being realized. The question was
“Are they intelligent?” and “in what extend?”. Turing also designed
Turing’s test for determining the intelligence of a system.
Imitating Inteligence
Turing
1912-1954
16. Turing Test – Step 1 (man/woman)
A is a man and B is a woman and C is of either sex.
C is unable to see either A or B, and can communicate
with them only through online computer chat.
By asking questions of A and B, C tries to determine
which of the two is the man and which is the woman.
A's role is to trick C into making the wrong decision,
while B attempts to assist C in making the right one.
Imitating Inteligence
17. Turing Test – Step 2 (human/computer)
Substitute a computer for A.
By asking questions of Computer and B, C tries to
determine which of the two is the computer.
Computer's role is to trick C into making the wrong
decision, while B attempts to assist C in making the right
one.
If the C’s success rate in human/computer game is not
better than his success rate in the man/woman game
Imitating Inteligence
18. Turing Test
If the C’s success rate in human/computer game is not better than his
success rate in the man/woman game, then the computer can be said to be
“thinking”.
Imitating Inteligence
19. There was now a need for a high-level programming language.
Logic Theorist was written in 1955 by A. Newell, H. A. Simon
and J. C. Shaw. It was the first program deliberately
engineered to mimic the problem solving skills of a human
being and is called "the first artificial intelligence program.” It
would eventually prove 38 of the first 52 theorems of
Whitehead and Russell, and find new and more elegant proofs
for some.[2]
Imitating Inteligence
20. John McCarthy (American Computer Scientist)
He coined the term “Artificial Intelligence” in the first conference on
machine intelligence, 1956.
He also developed LISP (List Processing) programming language,
which has become a standard tool for AI development.
LISP distinctions:
Memory organization – in a tree fashion
Control structure – instead of working from perquisites to a goal, it
starts with the goal and works backward to determine what perquisites
are required to achieve the goal.
Artificial Intelligence
McCarthy
1927-2011
21. GPS (General Problem Solver) was another AI programming language that
introduced in 1959.
It was capable of solving theorems, playing chess, or doing puzzles.
Its core was based on the use of means-end analysis, which involves
comparing a present state with a goal state. The difference between the two
state is determined and a search is done to find a method to reduce this
difference. This process is continued until there is no difference between the
current state and the goal state.
It was capable of backtracking to an earlier state to correct its mistakes.
It was also able to define sub-goals.
GPS did a good job of imitating the human subjects.
Artificial Intelligence
22. ELIZA was the first intelligent computer program that
was enable of interacting in a two-way conversation.
It could sustain very realistic conversations by very
smart techniques.
For example, ELIZA used a pattern matching method
that would scan for keywords like “I”, “You”, “Like”
and so on. If one of these words was found, it would
execute rules associated with it. If no match was
found, it would request for more information.
Artificial Intelligence
Link to ELIZA
23. The various attempts at formally defining the use of machines to simulate
human intelligence let to several AI branches
1. Natural Language Processing (NLP)
2. Computer Vision
3. Robotics
4. Problem-solving and planning
5. Learning
6. Expert Systems
Branches of AI
25. How successful we have been in creating human-like artificial creatures?
Branches of AI
26. Natural Language Processing (NLP)
NLP understands, and generates languages that humans use naturally so that
eventually you will be able to address your computer as though you were
addressing another person (e.g. ELIZA)
Branches of AI
Speech NLP Knowledge
27. Natural Language Processing (NLP)
NLP Categories:
1- Phonology: modeling the pronunciation of words (chair, car, cell)
2- Morphology: identifying the structure of words (dog, dogs, hot dogs)
3- Syntax (identifying grammars)
4- Semantics (understanding and representing the meaning)
Applications:
automatic text indexing, grammar and style analyser, automatic text generation,
machine translation, optical character recognition (OCR) and etc.
Branches of AI
28. Computer Vision
Computer vision is a field that includes methods for acquiring, processing,
analysing, and understanding images and, in general, high-dimensional
data from the real world in order to produce numerical or symbolic
information, e.g., in the forms of decisions.
Branches of AI
Images
Computer
Vision
Knowledge
30. Computer Vision
Applications:
1. Recognize objects (e.g. people we know and things we own)
2. Locate objects in space (to pick them up?)
3. Track objects in motion (catching a baseball, avoiding collisions
with cars on the road)
4. Recognize actions (e.g. walking, running, pushing)
Branches of AI
31. Robotics
Robotics involves the control of actuators on robots to move, manipulate or
grasp objects, locomotion of independent machines and use of sensory
input to guide actions.
Branches of AI
32. Problem-solving and Planning
This technology involves application such s refinement of high-level goals
into lower-level ones, determination of actions to achieve goals, revision of
plans based on intermediate results, and focused search of important
goals. A good example is chess players software.
Branches of AI
33. Learning
Learning deals with research into various forms of learning including rote learning,
learning through advise, learning by example, learning by task performance, and
learning by following concepts.
Branches of AI
34. Expert Systems
Expert systems deal with the processing of knowledge as opposed to
processing of data. It involves the development of computer software to
solve complex decision problems. In fact, an expert system is a computer
system that make decisions on behalf of human.
Branches of AI
Link to ANNA Android Doctor
Editor's Notes
https://www.youtube.com/watch?v=MaTfzYDZG8c
Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…)
Morphology: Identification of the structure of words (dog, dogs, hot dog, ….)
Syntax: Study of grammars
Semantics: Understanding and representing the meaning
Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…)
Morphology: Identification of the structure of words (dog, dogs, hot dog, ….)
Syntax: Study of grammars
Semantics: Understanding and representing the meaning