| Time | Item | Who | Notes |
|
|---|
| 5 mins | Introduction | | etc.cuit.columbia.edu |
|
| 60 mins | Introduction to AI | Prof. Ansaf | Intro - What is A.I.? - Definition -
- the science and engineering of making intelligent machines
- A system that perceives its environment and acts accordlingly
- AI has the potential to free up humanity for new tasks (like Industrial Revolution)
- 4 basic tenets:
- Think Humanly - currently cognitive science and machine learning are distinct disciplines
- Alan Turing - imitation test
- Acting Humanly - imitating actions behaviors that we see in nature (flight)
- Thinking Rationally - Law of thoughts; logic, inference, derivations
- Acting Rationally - systems maximize goal achievement; best outcomes
- Applications - Alexa, Siri, Netflix/Amazon recommendations, ATM handwriting recognition, USPS ZIP code readers from 1990's
- machine translations have grown and improved since they first began; tap into large neural networks of language data
- Robotics
- search engines, ranked listings
- email filters (SPAM)
- face detection for smartphone cameras
- facial recognition
- medical imaging (mammograms)
- Chess - IBM Deep Blue
- Jeopardy - IBM Watson
- AlphaGo
- Autonomous driving - DARPA Grand Challenge
- AI foundation is quickly becoming cross disciplinary
- History
- 1940's-50's; boolean circuits; Alan Turing
- 1950-1970 - great enthusiasm, high expectations; emphasis on language translation; checkers
- 1970-1990 - expert systems, AI as an industry
- 1990's to present - Neural networks, intelligent agents, scientific rigor in R & D
| |
|
|
| AI Agents - initial uses and applications - Rational
- Search agents - goal oriented; learn outcomes of potential actions
- Pathfinders (point A to point B)
- Adversarial search (games w/ opponents who we can't control)
- Constraint satisfaction
- Logical agents
|
|
|
|
| AI and Machine Learning - using data as inputs to solve problems/write programs
- programs learn from experience without specifying the rules to solve the problem
- data is used to identify patterns
- statistics are use to analyze data and make predictions
- Supervised learning (labeled data)
- Unsupervised learning (unlabeled data)
- Training and Testing
- Training set
- ML Algorithm
- Model (f) - how can we be confident in (f)?
- Neural Networks
|
|
|
|
| Challenges and Potential - can AI assist in achieving better broad social outcomes?
- what is impact on: Jobs, cities, politics?
- autonomous weapons?
|
|
|
|
| AI and Inclusion - How can we develop AI that can allow for a more inclusive society?
- Develop knowledge; digital divide may increase with AI
- Decipher - current Neural network black box models need to evolve into more transparent, explainable, unbiased, interpretable outcomes
- De-identify- protect privacy; avoid stigma
- De-bias - ensure fairness; avoid profiling, exclusion; data itself can have bias (baby vs. black baby)
|
|