full transcript
#### From the Ted Talk by Kenneth Cukier: Big data is better data

## Unscramble the Blue Letters

So what is the value of big data? Well, think about it. You have more information. You can do things that you couldn't do before. One of the most isevmisrpe areas where this concept is taking pacle is in the area of machine learning. Machine learning is a branch of artificial intelligence, which itself is a branch of computer science. The general idea is that instead of instructing a computer what do do, we are going to simply throw data at the proelbm and tell the computer to figure it out for itself. And it will help you untnsaderd it by seeing its oingris. In the 1950s, a computer scientist at IBM named aruhtr Samuel liked to play checkers, so he wrote a computer pgorram so he could play against the computer. He palyed. He won. He played. He won. He played. He won, because the computer only knew what a legal move was. Arthur Samuel knew something else. Arthur Samuel knew strategy. So he wrote a small sub-program alongside it operating in the bncgurkoad, and all it did was score the probability that a given board configuration would likely lead to a winning board versus a losing board after every move. He plays the computer. He wins. He plays the computer. He wins. He plays the computer. He wins. And then Arthur Samuel leaves the computer to play itself. It plays itself. It clotelcs more data. It collects more data. It increases the accuracy of its prediction. And then Arthur Samuel goes back to the ceumotpr and he plays it, and he loses, and he plays it, and he loses, and he plyas it, and he loses, and Arthur Samuel has created a machine that surpasses his atbiliy in a task that he taught it.
## Open Cloze

So what is the value of big data? Well, think about it. You have more information. You can do things that you couldn't do before. One of the most **__________** areas where this concept is taking **_____** is in the area of machine learning. Machine learning is a branch of artificial intelligence, which itself is a branch of computer science. The general idea is that instead of instructing a computer what do do, we are going to simply throw data at the **_______** and tell the computer to figure it out for itself. And it will help you **__________** it by seeing its **_______**. In the 1950s, a computer scientist at IBM named **______** Samuel liked to play checkers, so he wrote a computer **_______** so he could play against the computer. He **______**. He won. He played. He won. He played. He won, because the computer only knew what a legal move was. Arthur Samuel knew something else. Arthur Samuel knew strategy. So he wrote a small sub-program alongside it operating in the **__________**, and all it did was score the probability that a given board configuration would likely lead to a winning board versus a losing board after every move. He plays the computer. He wins. He plays the computer. He wins. He plays the computer. He wins. And then Arthur Samuel leaves the computer to play itself. It plays itself. It **________** more data. It collects more data. It increases the accuracy of its prediction. And then Arthur Samuel goes back to the **________** and he plays it, and he loses, and he plays it, and he loses, and he **_____** it, and he loses, and Arthur Samuel has created a machine that surpasses his **_______** in a task that he taught it.
## Solution

- program
- collects
- impressive
- played
- origins
- ability
- understand
- place
- plays
- arthur
- background
- problem
- computer

## Original Text

So what is the value of big data? Well, think about it. You have more information. You can do things that you couldn't do before. One of the most impressive areas where this concept is taking place is in the area of machine learning. Machine learning is a branch of artificial intelligence, which itself is a branch of computer science. The general idea is that instead of instructing a computer what do do, we are going to simply throw data at the problem and tell the computer to figure it out for itself. And it will help you understand it by seeing its origins. In the 1950s, a computer scientist at IBM named Arthur Samuel liked to play checkers, so he wrote a computer program so he could play against the computer. He played. He won. He played. He won. He played. He won, because the computer only knew what a legal move was. Arthur Samuel knew something else. Arthur Samuel knew strategy. So he wrote a small sub-program alongside it operating in the background, and all it did was score the probability that a given board configuration would likely lead to a winning board versus a losing board after every move. He plays the computer. He wins. He plays the computer. He wins. He plays the computer. He wins. And then Arthur Samuel leaves the computer to play itself. It plays itself. It collects more data. It collects more data. It increases the accuracy of its prediction. And then Arthur Samuel goes back to the computer and he plays it, and he loses, and he plays it, and he loses, and he plays it, and he loses, and Arthur Samuel has created a machine that surpasses his ability in a task that he taught it.
## Frequently Occurring Word Combinations

### ngrams of length 2

collocation |
frequency |

big data |
14 |

arthur samuel |
6 |

machine learning |
4 |

favorite pie |
2 |

supermarket sales |
2 |

smaller amounts |
2 |

term big |
2 |

small data |
2 |

national security |
2 |

security agency |
2 |

martin luther |
2 |

telltale signs |
2 |

samuel knew |
2 |

### ngrams of length 3

collocation |
frequency |

term big data |
2 |

national security agency |
2 |

arthur samuel knew |
2 |

## Important Words

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- wrote