3. COURSE OUTCOMES
CSC604.1 Ability to develop a basic understanding of AI building blocks
CSC604.2 Ability to identify the suitable intelligent agent
CSC604.3 Ability to choose an appropriate problem solving method and to analyze
the strength and weaknesses of AI approaches to knowledge– intensive
problem solving.
CSC604.4 Ability to choose appropriate knowledge representation technique and
to design models for reasoning with uncertainty as well as the use of
unreliable information.
CSC604.5 To understand the role of planning and learning in intelligent systems .
CSC604.6 Ability to design and develop AI applications in real world scenarios
6. ARTIFICIAL INTELLIGENCE IN REAL LIFE
Exciting and dynamic field, lots of uncharted territory left
Impressive success stories
“Intelligent” in specialized domains
Many application areas
7. WHY THE INTEREST IN AI?
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances What else?
8. WHAT IS ARTIFICIAL INTELLIGENCE?
• There is no clear consensus on the definition of AI
• John McCarthy coined the phrase AI in 1956
Artificial Intelligence
It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using computers to
understand human or other intelligence.
Intelligence
Intelligence is the computational part of the ability to achieve goals in the world.
It is the ability to learn, perceive, plan, problem solving, decision making
Varying kinds and degrees of intelligence occur in people, many animals and some
machines.
10. WHAT’S EASY AND WHAT’S HARD?
• It’s been easier to mechanize many of the high level cognitive tasks we
usually associate with “intelligence” in people
– e. g., symbolic integration, proving theorems, playing chess, some
aspect of medical diagnosis, etc.
• It’s been very hard to mechanize tasks that animals can do easily
– walking around without running into things
– catching prey and avoiding predators
– interpreting complex sensory information (visual, aural, …)
– modeling the internal states of other animals from their behavior
– working as a team (ants, bees)
• Is there a fundamental difference between the two categories?
• What are those things which are still difficult to achieve for AI
even today?
14. History of AI can be explained
mainly in six groups
1. Beginning: 1943–1952
2. 1952–1969: Early enthusiasm, high hopes – ELIZA chatbot
3. 1952–1969: Sobering up
4. 1970–1979: Knowledge-based systems
5. 1980–2010: (AI becomes an industry – era of Intelligent Agents,
Machine Learning, Robotics)
6. 2010–till date: (era of Deep Learning)
15. History of AI
• AI has roots in a number of scientific disciplines
– computer science and engineering (hardware and software)
– philosophy (rules of reasoning)
– mathematics (logic, algorithms, optimization)
– cognitive science and psychology (modeling high level
human/animal thinking)
– neural science (model low level human/animal brain activity)
– linguistics
• The birth of AI (1943 – 1956)
– McCulloch and Pitts (1943): simplified mathematical model of
neurons (resting/firing states) can realize all propositional logic
primitives (can compute all Turing computable functions)
– Alan Turing: Turing machine and Turing test (1950)
– Claude Shannon: information theory; possibility of chess playing
computers
– Boole, Aristotle, Euclid (logics, syllogisms)
16. • Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Alan Newell and Herbert Simon (GPS);
– Emphasis on intelligent general problem solving
GSP (means-ends analysis);
Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem
proving);
heuristic search (A*, AO*, game tree search)
• Emphasis on knowledge (1966 – 1974)
– domain specific knowledge is the key to overcome existing
difficulties
– knowledge representation (KR) paradigms
– declarative vs. procedural representation
17. • Knowledge-based systems (1969 – 1999)
– DENDRAL: the first knowledge intensive system (determining 3D
structures of complex chemical compounds)
– MYCIN: first rule-based expert system (containing 450 rules for
diagnosing blood infectious diseases)
EMYCIN: an ES shell
– PROSPECTOR: first knowledge-based system that made significant
profit (geological ES for mineral deposits)
• AI became an industry (1980 – 1989)
– wide applications in various domains
– commercially available tools
– AI winter
• Current trends (1990 – present)
– more realistic goals
– more practical (application oriented)
– distributed AI and intelligent software agents
– resurgence of natural computation - neural networks and emergence
of genetic algorithms – many applications
– dominance of machine learning (big apps)
24. AI PROGRAM SHOULD HAVE
knowledge base
navigational capability
inferencing
25. KNOWLEDGE BASE
AI programs should be learning in nature and
update its knowledge accordingly.
Knowledge base consists of facts and rules.
Characteristics of Knowledge:
It is voluminous in nature and requires proper
structuring
It may be incomplete and imprecise
It may keep on changing (dynamic)
32. Game playing
Mathematics
Autonomous control
Diagnosis
Logistics planning
Autonomous planning and
scheduling
Language understanding and
problem solving
Robotics
Natural Language Generation
Speech recognition
Virtual agents
Text analytics and NLP
Robotic process automation
Biometrics
Deep learning platforms
Decision management
AI-optimized hardware
Machine-learning platforms
APPLICATION AREAS OF AI
33. REAL LIFE EXAMPLES OF APPLICATIONS OF
AI
• AI in Marketing: Netflix
https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe
• AI in Banking:
HDFC Bank Chatbot EVA
https://v1.hdfcbank.com/htdocs/common/eva/index.html?query=hi
Mastercard
https://cio.economictimes.indiatimes.com/news/strategy-and-management/how-mastercard-is-using-
artificial-intelligence-to-stop-fraud-and-reduce-false-
declines/69929529#:~:text=We%20use%20AI%20for%20both%20internal%20and%20external%20business
%20operations%20of%20Mastercard.&text=We%20have%20an%20artificial%20intelligence,acquainted%2
0with%20machine%20learning%20quickly.
• AI in Finance:
Indian Stock Exchange
https://analyticsindiamag.com/why-d-street-is-tapping-into-ai-ml-to-improve-stock-market-
functioning/#:~:text=BSE%20is%20known%20to%20use%20emerging%20technologies%20like
%20AI%20and%20machine%20learning.&text=Further%2C%20machine%20learning%20and%
20deep,BSE%20site%20and%20Sentifi%20platform.
34. • AI In Agriculture: Berlin-based agricultural tech start-up called
PEAT, has developed an application called Plantix
https://plantix.net/en/
• AI in Healthcare: Cambio Health Care developed a clinical
decision support system for stroke prevention, Coala life
• https://www.newworldai.com/how-is-artificial-intelligence-empowering-
healthcare-today/
• AI in Gaming: Alpha Go , Alpha Go Zero
https://deepmind.com/blog/article/alphago-zero-starting-scratch
• AI in Space Exploration: NASA’s next rover mission to Mars,
the Mars 2020 Rover. The AEGIS, which is an AI-based Mars
rover is already on the red planet.
• https://mars.nasa.gov/news/8689/nasas-mars-rover-drivers-need-your-help/
35. • AI in autonomous Vehicles: Tesla’s self-driving car. Elon Musk
talks a ton about how AI is implemented in tesla’s self-driving cars
and autopilot features.
https://www.tesla.com/autopilot
• AI in Chatbots: Siri , Cortana, Amazon Echo
https://www.ometrics.com/blog/ai-and-amazon-alexa-echo/
• AI in Social Media: Facebook, Twitter’s AI, which is being used to
identify hate speech and terroristic language in tweets. The
company discovered and banned 300,000 terrorist-linked accounts,
95% of which were found by non-human, artificially intelligent
machines.
https://www.bernardmarr.com/default.asp?contentID=1373#:~:text=One%20of%
20the%20ways%20Twitter,tweets%20in%20reverse%20chronological%20order.
• AI in creativity: Musenet, wordsmith
https://openai.com/blog/musenet/
https://automatedinsights.com/wordsmith/
38. AI PROGRAMMING LANGUAGES
• AI could be a branch of engineering, which essentially aims for creating
computers which may think intelligently in a similar manner the intelligent
humans think.
• A number of programming languages exist that are used to build AI
systems. General programming languages, such as C++, R, Java, Python,
and LISP (List Processing) are frequently used, because these are the
languages with which most computer scientists have got experience.
• Here are some languages that are most typically used for creating the AI
projects:
Python
R
Java
C++
PROLOG
LISP
39. ARTIFICIAL INTELLIGENCE GROWTH
The number of AI startups since 2000 has increased 14 times.
72% of execs believe that AI will be the most significant business advantage of
the future.
AI will automate 16% of American jobs.
Machine learning is predicted to grow by 48% in the automotive industry
15% of enterprises are using AI, and 31% of them say that it is their agenda for
the next 12 months.
By 2021, customer insights-driven businesses will see $1.2 trillion more per year
than their less-informed peers.
Financial services are the future of AI
40. TOP 10 COMPANIES IN AI RESEARCH
1. Deepmind
2. Google
3. Facebook
4. OpenAI
5. Baidu
6. Microsoft
7. Apple
8. IBM
9. Amazon
10. NVIDIA
