1. Introduction to Artificial Intelligence
인공지능특론
부산대학교
권 혁 철

2. Artificial Intelligence until now
“Narrow AI" was very different from the strong-AI fantasies that people painted. "AI programs are just a bunch of hacks," I thought. "This isn't intelligence; it's just people using computers to manipulate data and perform optimization, and they dress it up as 'AI' to make it sound sexy." Machine learning in particular seemed to be just a computer scientist's version of statistics. Neural networks were just an elaborated form of logistic regression.

3. Applications of AI previously
Game playing
Speech Recognition
Natural Language Processing
Computer Vision
Expert systems
Heuristic classification

4. Now on Health care Business integrity, business intelligence
Knowledge discovery
Enterprise knowledge management
Security
Customer support
Support decision making
Speech Understanding
Natural Language Understanding
Image Understanding

5. What do more AI in future
Strong AI
Sematic processing
Automatic learning

6. Deep learning
Gives the possibility of semantic processing by unsupervised learning ????
Word embedding in NLP
Image recognition?

7. Chess Humans are still better at making up excuses.
Name: Garry Kasparov
Title: World Chess Champion
Crime: Valued greed over common sense
Humans are still better at making up excuses.

8. Perspective on Chess: Pro
“Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn't really fly because it doesn't flap its wings”
Drew McDermott

9. Perspective on Chess: Con
“Chess is the Drosophila of artificial intelligence. However, computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophila. We would have some science, but mainly we would have very fast fruit flies.”
John McCarthy
미국 스탠포드대 AI연구소 맥카시(John McCarthy)는 “바둑이야말로 인공지능의 초파리”라고 말했다. 초파리는 20세기 이후 멘델의 유전법칙을 증명하는 ‘복잡계’의 곤충이다. 인간의 줄기세포 연구에도 생쥐와 함께 등장한다. 미국의 컴퓨터 과학자인 힐스(Hills)는 “체스는 논리를 연구하는 초파리였다면 바둑은 직감을 연구하는 초파리”라고 했다.
(참고문헌 이병두의 《인공지능 바둑》(북스홀릭, 2011))

10. 바둑 구글이 개발한 인공지능 ‘알파고(AlphaGo)’ ??? 생성과 평가 모듈을 분리!! Google DeepMind
Two neural networks paired with a new tree search algorithm has resulted in a Go program that was able to defeat the European champion 5-0 in October
다른 프로그램과 대결
Single computer: 499승 1패
Multiple computers: 1위
The distributed version was using 1,202 CPUs and 176 GPUs, about 25 times as many as the single-computer version
생성과 평가 모듈을 분리!!

11. The key to AlphaGo is reducing the enormous search space to something more manageable(David Silver and Demis Hassabis, from Google DeepMind)
"One neural network, the 'policy network', predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win.
"The other neural network, the 'value network', is then used to reduce the depth of the search tree -- estimating the winner in each position in place of searching all the way to the end of the game.“
AlphaGo uses a Monte-Carlo tree search to look ahead to possible moves, with the neural networks suggesting moves and judging the board position. The system was trained on 30 million moves from games played by human experts, Google said, until it could predict the human move 57 percent of the time. Then it played against itself thousands of times.
the most significant part of AlphaGo was not mastering Go, but rather using general-purpose learning techniques that could be applied to climate modelling or disease analysis.

12. 빅 데이터 분석을 활용한 Watson
Watson의 고급 분석 기술 능력은 3초 내에 답을 알아내기 위해서 2억 페이지의 데이터를 분석합니다.

13. Jeopardy Challenge
Jeopardy Challenge requires advancing and incorporating a variety of QA technologies including parsing, question classification, question decomposition, automatic source acquisition and evaluation, entity and relation detection, logical form generation, and knowledge representation and reasoning

14. Watson
Watson is a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering developed in IBM's DeepQA project

15. Watson
Watson is a question answering computer system capable of answering questions posed in natural language, Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage including the full text of Wikipedia, but was not connected to the Internet during the game.
encyclopedias, dictionaries, thesauri, newswire articles, and literary works. Watson also used databases, taxonomies, and ontologies. Specifically, DBPedia, WordNet, and Yago

16. IR-based Knowledge Acquisition
Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases. Watson's main innovation was not in the creation of a new algorithm for this operation but rather its ability to quickly execute hundreds of proven language analysis algorithms simultaneously to find the correct answer. Once Watson has a small number of potential solutions, it is able to check against its database to ascertain whether the solution makes sense.

17. Algorithm
The questions and content are ambiguous and noisy and none of the individual algorithms are perfect. Therefore, each component must produce a confidence in its output, and individual component confidences must be combined to compute the overall confidence of the final answer. The final confidence is used to determine whether the computer system should risk choosing to answer at all.