A startup company in California is using machine learning and arificial itelligence to advise fire departments about how to plan for earthquakes and respond to them. The company hopes its algorithms

A startup company in California is using machine learning and arificial itelligence to advise fire departments about how to plan for earthquakes and respond to them. The company hopes its algorithms can take a lot of the guesswork out of the planning process for disaster response by making accurate predictions about earthquake damage. I's one of a handful of companies rolling out atificial itelligence and machine learning systems that could help predict and respond to foods, cyber-attacks and other large- scale disasters.
Nicole Hu, the company's chief technology oflicer, says the key is to feed the computers three mam categories of data. The first is data about homes and other buildings, such as what materials they're made of, when they were built and how likely they are to collapse when the ground starts shaking. The next category is data about the natural environments. For example. "What is the soil like" What is the elevation like? What is the general humidity like?" explains Hu. The third thing we look at is live instant data." she says, such as the magnitude of the quake, the trafic in the area of the quake and the weather at the time of the quake. The computer uses the information to make predictions about what would happen if an earthquake occurred in a particular area. It then uses data from past earthquakes to see whether its predictions are any good, and revises its predictive models accordingly. In other words, it learns as it gocs, which is basically how machine learning works.
Stanford University earthquake engineer Gregory Deierlein consulted for the company. He says one of the most remarkable things about the company's software is its ability to incorporate data from an earthquake as its happening. and to adjust its predictions in real time. "Those sort of things used to be research projects." says Deierlein " After an event, we would collee data and a few years later we' d produce new models." Now the new models appear in a matter of minutes. He note the company's exact methods are opaque. "Like many startup companies they're not fully transparent in everything they're doing." He says.“I mean, that's their proprietary knowledge that they're proprietary knowledge that they're bringing to it."Nonetheless, some first responders are already convinced the software will be useful. Fire chief Dan Ghiorso leads the Woodside Fire Protection District near San Francisco, which covers 32 square miles. The San Andrea's fault is only a couple hundred feet behind the firehouse. Ghiorso says in the past, when an earthquake hit, he 'd have to make educated guesses about what parts of his district might have suffered the most damage, and then drive to each place to make a visual inspection. He hopes the company' s software will change that, although he has yet to put it to the test during an actual quake." Instead of driving thity two square miles, in fifteen minutes on a computer Ican get a good idea of the concerns." he says,“instead of me, taking my educated guess, they re putting science behind it, so I' m very confident." Unfortunately, it's going to take a natural disaster to see if his confidence is justified.
What is NOT needed for training the earthquake predicting software?

A Data about past earthquakes
B. Data about human impacts
C. Data about the geographic conditions
D. Data about the buildings

How does the writer feel about the business she runs?

A. It's the biggest company in the world.
B. It will possibly be more successful.
C. It's one of the most successful businesses.
D. It is the only company that is growing all around the world.

A startup company in California is using machine learning and arificial itelligence to advise fire departments about how to plan for earthquakes and respond to them. The company hopes its algorithms can take a lot of the guesswork out of the planning process for disaster response by making accurate predictions about earthquake damage. I's one of a handful of companies rolling out atificial itelligence and machine learning systems that could help predict and respond to foods, cyber-attacks and other large- scale disasters.
Nicole Hu, the company's chief technology oflicer, says the key is to feed the computers three mam categories of data. The first is data about homes and other buildings, such as what materials they're made of, when they were built and how likely they are to collapse when the ground starts shaking. The next category is data about the natural environments. For example. "What is the soil like" What is the elevation like? What is the general humidity like?" explains Hu. The third thing we look at is live instant data." she says, such as the magnitude of the quake, the trafic in the area of the quake and the weather at the time of the quake. The computer uses the information to make predictions about what would happen if an earthquake occurred in a particular area. It then uses data from past earthquakes to see whether its predictions are any good, and revises its predictive models accordingly. In other words, it learns as it gocs, which is basically how machine learning works.
Stanford University earthquake engineer Gregory Deierlein consulted for the company. He says one of the most remarkable things about the company's software is its ability to incorporate data from an earthquake as its happening. and to adjust its predictions in real time. "Those sort of things used to be research projects." says Deierlein " After an event, we would collee data and a few years later we' d produce new models." Now the new models appear in a matter of minutes. He note the company's exact methods are opaque. "Like many startup companies they're not fully transparent in everything they're doing." He says.“I mean, that's their proprietary knowledge that they're proprietary knowledge that they're bringing to it."Nonetheless, some first responders are already convinced the software will be useful. Fire chief Dan Ghiorso leads the Woodside Fire Protection District near San Francisco, which covers 32 square miles. The San Andrea's fault is only a couple hundred feet behind the firehouse. Ghiorso says in the past, when an earthquake hit, he 'd have to make educated guesses about what parts of his district might have suffered the most damage, and then drive to each place to make a visual inspection. He hopes the company' s software will change that, although he has yet to put it to the test during an actual quake." Instead of driving thity two square miles, in fifteen minutes on a computer Ican get a good idea of the concerns." he says,“instead of me, taking my educated guess, they re putting science behind it, so I' m very confident." Unfortunately, it's going to take a natural disaster to see if his confidence is justified.
How does Dan Ghlorso view the software?

A. It saves both money and labor.
B. It helps improve the situation
C. It relies too much on past data.
D. It needs more attention

A startup company in California is using machine learning and arificial itelligence to advise fire departments about how to plan for earthquakes and respond to them. The company hopes its algorithms can take a lot of the guesswork out of the planning process for disaster response by making accurate predictions about earthquake damage. I's one of a handful of companies rolling out atificial itelligence and machine learning systems that could help predict and respond to foods, cyber-attacks and other large- scale disasters.
Nicole Hu, the company's chief technology oflicer, says the key is to feed the computers three mam categories of data. The first is data about homes and other buildings, such as what materials they're made of, when they were built and how likely they are to collapse when the ground starts shaking. The next category is data about the natural environments. For example. "What is the soil like" What is the elevation like? What is the general humidity like?" explains Hu. The third thing we look at is live instant data." she says, such as the magnitude of the quake, the trafic in the area of the quake and the weather at the time of the quake. The computer uses the information to make predictions about what would happen if an earthquake occurred in a particular area. It then uses data from past earthquakes to see whether its predictions are any good, and revises its predictive models accordingly. In other words, it learns as it gocs, which is basically how machine learning works.
Stanford University earthquake engineer Gregory Deierlein consulted for the company. He says one of the most remarkable things about the company's software is its ability to incorporate data from an earthquake as its happening. and to adjust its predictions in real time. "Those sort of things used to be research projects." says Deierlein " After an event, we would collee data and a few years later we' d produce new models." Now the new models appear in a matter of minutes. He note the company's exact methods are opaque. "Like many startup companies they're not fully transparent in everything they're doing." He says.“I mean, that's their proprietary knowledge that they're proprietary knowledge that they're bringing to it."Nonetheless, some first responders are already convinced the software will be useful. Fire chief Dan Ghiorso leads the Woodside Fire Protection District near San Francisco, which covers 32 square miles. The San Andrea's fault is only a couple hundred feet behind the firehouse. Ghiorso says in the past, when an earthquake hit, he 'd have to make educated guesses about what parts of his district might have suffered the most damage, and then drive to each place to make a visual inspection. He hopes the company' s software will change that, although he has yet to put it to the test during an actual quake." Instead of driving thity two square miles, in fifteen minutes on a computer Ican get a good idea of the concerns." he says,“instead of me, taking my educated guess, they re putting science behind it, so I' m very confident." Unfortunately, it's going to take a natural disaster to see if his confidence is justified.
What can be the best title of the passage?

A. Using Artificial Intelligence to Guide Earthquake Response.
B. Comparing Different Earthquake Predicting Software.
C. Challenges for Making Accurate Predictions about Earthquakes.
D. Causes of Naturally Occurring Earthquakes