1.
요즘 인공지능을 다루는 기사가 넘칩니다. 가깝게는 알파고 소식입니다. 바둑팬이면 알 이세돌 9단과 5번기를 둡니다. AlphaGo: using machine learning to master the ancient game of Go을 보면 구굴의 기계학습엔진인 Deepmind를 기반으로 한 프로그램입니다. 또다른 소식은 다보스포럼입니다. 2016년 다보스포럼의 주제는 ‘4차 산업혁명이’었습니다.
The possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited. And these possibilities will be multiplied by emerging technology breakthroughs in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.
Already, artificial intelligence is all around us, from self-driving cars and drones to virtual assistants and software that translate or invest. Impressive progress has been made in AI in recent years, driven by exponential increases in computing power and by the availability of vast amounts of data, from software used to discover new drugs to algorithms used to predict our cultural interests. Digital fabrication technologies, meanwhile, are interacting with the biological world on a daily basis. Engineers, designers, and architects are combining computational design, additive manufacturing, materials engineering, and synthetic biology to pioneer a symbiosis between microorganisms, our bodies, the products we consume, and even the buildings we inhab
The Fourth Industrial Revolution: what it means, how to respond중에서
미국 월스트리트도 인공지능의 영향력이 점차로 커지고 있는 듯 합니다. 뉴욕타임즈가 소개한 Jane Street는 컴퓨터 언어인 Ocaml으로 전략을 토론합니다.
Harnessing Ph.D.-toting mathematicians to the most powerful computers money can buy has become the accepted way for hedge funds and banks to get a trading edge these days, but Jane Street takes this marriage of high tech and high intellect to a new level.
Writing computer code, or at the least being conversant in the firm’s program of choice, OCaml, is a requisite for all traders. Indeed, new traders must complete a monthlong OCaml boot camp before they start trading.
A New Breed of Trader on Wall Street: Coders With a Ph.D.중에서
Prequin이 발간한 보고서를 인용한 와이어드 기사는 헤지펀드산업에서 기계 및 인공지능이 보편화함을 보여주고 있습니다. 한국의 경우 로보 어드바이저로 첫발을 내딛은 상태입니다.
Hedge funds have long relied on computers to help make trades. According to market research firm Preqin, some 1,360 hedge funds make a majority of their trades with help from computer models—roughly 9 percent of all funds—and they manage about $197 billion in total. But this typically involves data scientists—or “quants,” in Wall Street lingo—using machines to build large statistical models. These models are complex, but they’re also somewhat static. As the market changes, they may not work as well as they worked in the past. And according to Preqin’s research, the typical systematic fund doesn’t always perform as well as funds operated by human managers
The Rise of the Artificially Intelligent Hedge Fund중에서
기계학습이나 인공지능이 금융산업을 바꿀 겁니다. 그동안 사람이 경쟁을 하던 구도가 이제는 기계가 경쟁을 하는 구도로 바뀌고 있습니다. 앞서 소개한 해외 기사를 보면 한국 금융회사에 없는 직함이 있습니다. 대부분 Chief Technology Officer를 두고 있지만 Chief Science Officer(CSO)입니다. R&D를 책임질 임원입니다
2.
그러면 인공지능의 역사가 궁금합니다. 유명한 튜링시험부터 알파고까지 흐름을 정리한 글이 포브스에 실렸습니다.
A Short History of Machine Learning — Every Manager Should Read
1950 — Alan Turing creates the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.
1952 — Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM IBM +0.52% computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.
1957 — Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulate the thought processes of the human brain.
1967 — The “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.
1979 — Students at Stanford University invent the “Stanford Cart” which can navigate obstacles in a room on its own.
1981 — Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data.
1985 — Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does.
1990s — Work on machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions — or “learn” — from the results.
1997 — IBM’s Deep Blue beats the world champion at chess.
2006 — Geoffrey Hinton coins the term “deep learning” to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.
2010 — The Microsoft Kinect can track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures.
2011 — IBM’s Watson beats its human competitors at Jeopardy.
2011 — Google Brain is developed, and its deep neural network can learn to discover and categorize objects much the way a cat does.
2012 – Google’s X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.
2014 – Facebook develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.
2015 – Amazon launches its own machine learning platform.
2015 – Microsoft creates the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers.
2015 – Over 3,000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk and Steve Wozniak (among many others), sign an open letter warning of the danger of autonomous weapons which select and engage targets without human intervention.
2016 – Google’s artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world’s most complex board game and is many times harder than chess. The AlphaGo algorithm developed by Google DeepMind managed to win five games out of five in the Go competition.
3.
Cambridge Coding Academy가 제공하는 기계학습 입문입니다. 제목은 Machine Learning – A gentle & structured introduction입니다. 기계학습을 주제로
Definition and promises of Machine Learning
Recent examples of Machine Learning
Insight into the types of Machine Learning
을 다루었고 Evening Talk라는 행사때 발표한 자료입니다. 소프트웨어를 공부하는 사람을 대상으로 한 발표라 전문적인 내용을 포함하고 있습니다. 비교할 수 없지만 제가 개설한 실무자를 위한 데이터마이닝 과정은 실무적으로 기계학습을 배울 수 있는 과정입니다. 참고하세요.
