『新东方在线托福听力』为托福考生准备了ETS托福TPO/Official 46 Lec 1听力练习：What is the lecture mainly about?原文题目答案解析，TPO/Official 46 Lec 1听力原文范文音频翻译。

托福TPO/Official 46 Lec 1听力原文What is the lecture mainly about?【新东方在线】

旁白：下面听一段生物学讲座的片段。

Narrator: Listen to part of a lecture in a biology class.

教授: 我想继续讨论动物行为，从今天的课堂起，开始关注一个我们还没有涉及的概念——集群智能。

Professor: I'd like to continue our discussion of animal behavior, and start off today's class by focusing on a concept we haven't yet touched upon--swarm intelligence.

集群智能是一群集体行为，从一群动物中出现，如白蚁群，鱼群或一群鸟。

Swarm intelligence is a collective behavior that emerges from a group of animals like a colony of termites, a school of fish, or a flock of birds.

让我们先来考虑集群智能背后的原理，我们将使用蚂蚁作为我们的范例。

Let's first consider the principles behind swarm intelligence and we'll use the ant as our model.

Now, an ant on its own is not that smart.

然而，当你有一群蚂蚁，你的行动就会变得有效率。

When you have a group of ants, however, there you have efficiency in action.

你看，没有领导管理一个蚁群。

You see, there is no leader running an ant colony.

当觅食的时候，每个个体，每个个体蚂蚁本能地遵循一套简单的规则。

Each individual, each individual ant operates by instinctively following a simple set of rules when foraging for food.

第一条规则：释放一种被称为费洛蒙的化学标记。（这里有个问题，费洛蒙可以翻译成“信息素”但是全文都翻译的“费洛蒙”，是否翻译需要全文替换？）

Rule number one: deposit a chemical marker called a pheromone.

规则二：跟随最强的费洛蒙路径。

And rule two: follow the strongest pheromone path.

最强的费洛蒙路径有利于蚂蚁找寻食物。

The strongest pheromone path is advantageous to ants seeking food.

例如，当蚂蚁离开巢穴时，它们会沿着它们走过的路径释放费洛蒙（信息素）标记。

So for example, when ants leave the nest, they deposit a pheromone trail along the route they take.

如果它们找到食物，它们会沿着相同的路径返回到巢穴，并且费洛蒙标记会变得更加浓烈。

If they find food, they return to the nest on the same path, and the pheromone trail gets stronger.

因为采取较短路径的蚂蚁首先返回，其费洛蒙轨迹更强，其它蚂蚁根据规则二将会跟随它。

Because an ant that took a shorter path returns first, its pheromone trail is stronger and other ants will follow it according to rule two.

随着越来越多的蚂蚁走这条路，费洛蒙轨迹会变得更加强。

And as more ants travel that path the pheromone trail gets even stronger.

每个蚂蚁遵循两个非常基本的规则。

每只蚂蚁根据它在其即时局部环境中找到的信息行动。

And each ant acts on information it finds in its immediate local environment.

重要的是要注意，即使没有个体蚂蚁知道更大的计划，但它们仍共同选择巢穴和食物来源之间的最短路径，因为它是最强的路径。

And it's important to note, even though none of the individual ants is aware of the bigger plan, they collectively choose the shortest path between the nest and the food source, because it's the most reinforced path.

顺便提一下有几个你们问我的问题，关于我们正在学习的东西与日常生活的相关性。

By the way, a-a few of you have asked me about the relevance of what we are studying to everyday life.

如何将生物学中的概念应用到其它领域，集群智能提供了几个好的例子。

And swarm intelligence offers several good examples of how concepts in biology can be applied to other fields.

企业已经能够在设计复杂系统时使用这种遵循简单规则的方法。

Well, businesses have been able to use this approach of following simple rules when designing complex systems.

例如，在电话网络中，当电话从一个城市呼叫到另一个城市时，它必须沿着该路径通过多个节点连接。

For instance, in telephone networks, when a call is placed from one city to another, it has to connect through a number of nodes along the way.

在每一个节点，必须做出决定：从这里这通电话该去往何方？

At each point, a decision has to be made: which direction does the call go from here?

恩，基于类似蚂蚁寻找食物的原理，人们开发了一个计算机程序以解决这个问题。

Well, a computer program was developed to answer this question based on rules that are similar to the ones that ants use to find food.

记住，个体蚂蚁释放信息素，它们会跟随被最多强化的路径。

Remember, individual ants deposit pheromones and they follow the path that is most reinforced.

当前，在电话网络中，计算机监视每个路径的连接速度并且识别当前最快的，最不拥挤的部分的路径。

Now, in the phone network, a computer monitors the connection speed of each path and identifies the paths that are currently the fastest, the least crowded parts of the network.

并且该信息被转换为数字代码，被存储在网络节点处。

And this information, converted into a numeric code, is deposited at the network nodes.

这加强了在那时最不拥挤的路径。

This reinforces the paths that are least crowded at the moment.

电话网络遵循的规则总是选择最加强的路径。

The rule the telephone network follows is to always select the path that is most reinforced.

因此，类似于蚂蚁的行为，在每个中间节点，这个呼叫遵循最加强的路径。

So similar to the ant's behavior, at each intermediate node, the call follows the path that is most reinforced.

这导致了对整个网络有益的结果，电话能更快地接通。

This leads to an outcome which is beneficial to the network as a whole and calls get through faster.

但回到动物行为，另一个群体智能的例子是鸟群如何能够如此紧密地一起飞行。

But getting back to animal behavior, another example of swarm intelligence is the way flocks of birds are able to fly together so cohesively.

它们如何协调它们的运动，并且知道它们应该在哪里？

How do they coordinate their movements and know where they're supposed to be?

它基本上归结为三个每只鸟似乎遵循的原则。

Well, it basically boils down to three rules that each bird seems to follow.

规则二：避免与附近鸟类碰撞。

Rule two: avoid collision with nearby birds.

规则三：沿着附近鸟类的平均速度和方向移动。

And rule three: move in the average speed and direction of nearby birds.

哦，顺便说一句，如果你想知道这种方法如何可以为人类实际使用，电影业一直试图在电影场景中创造计算机生成的鸟群。

Oh, and by the way, if you're wondering how this approach can be of practical use for humans, the movie industry's been trying create computer-generated flocks of birds in movie scenes.

问题是：如何轻易的大规模做到？

The question was: how to do it easily on a large scale?

研究人员在计算机图形程序中使用这三个规则，并且起作用了。

A researcher used these three rules in a computer graphics program, and it worked.

还有人试图用集群智能的鸟群模型创造出计算机生成的人群。

There have also been attempts to create computer-generated crowds of people using this bird flocking model of swarm intelligence.

然而，对于需要更多的研究，我并不感到惊讶。

However, I'm not surprised that more research is needed.

我所提到的三原则可能对鸟群模拟可能很棒，但它们没有考虑到人类行为的复杂性和不可预测性。

The three rules I mentioned might be great for bird simulations, but they don't take into account the complexity and unpredictability of human behavior.

所以，如果你想以逼真的方式创建人群，那个计算机模型可能会太有限。

So, if you want to create crowds of people in a realistic way, that computer model might be too limited.

According to the professor, what behavior plays an important role in the way ants obtain food?

What are two principles of swarm intelligence based on the ant example?

According to the professor, what path is followed by both telephone calls on a network and ants seeking food?

What is the professor’s attitude about attempts to create computer-generated crowds of people?

Listen to part of a lecture in a biology class. 
I'd like to continue our discussion of animal behavior and start off today's class by focusing on a concept we haven't yet touched upon----swarm intelligence. 
Swarm intelligence is a collective behavior that emerges from a group of animal, like, a colony of termites, a school of fish or a flock of birds. Let's first consider the principles behind swarm intelligence and will use ant as our model. 
Now ant on its own is not that smart. When you have a group of ants however, there you have a deficiency in action. You see, there's no leader running an ant calling each individual. Each individual ant operates by instinctively following a simple set of rules when foraging for food. Rule number one: Deposit a chemical marker called a Pheromone. And Rule Two: Follow the strongest pheromone path. The strongest pheromone path is advantageous to him seeking food, so, for example, when ants leave the nest, they deposited pheromone trail along the route they take. If they fine food they return to the nest on the same path in the pheromone trail gets stronger. It's double strength because it adds that to the shorter path returns first experiment trail is stronger and other ants will follow it according to rule two. And as more ants travel that path, the pheromone trail gets even stronger. So what's happening here: each ant follows two very basic rules and each ant acts on information it finds in its immediate local environment, and it’s important to note. Even though none of the individual ants is aware of the bigger plan, they collectively choose the shortest path between the nests at a food source, because it's the most reinforced path. By the way, a few of you asked me about the relevance of what we're studying to everyday life, and swarm intelligence offers several good examples of how concepts of biology can be applied to other fields. 
Well, businesses have been able to use this approach of following simple rules when designing complex systems, for instance, in telephone networks. When a call is placed from one city to another, it has to connect through a number of nodes along the way. At each point, a decision has to be made which direction does the call go from here. Well, the computer program was developed to answer this question based on rules that are similar to the ones that the ants use to find food. Remember, individual ants deposit pheromones, and they follow the path that is most reinforced. Now in the phone network, a computer monitors that connection speed of each path and identifies the paths that are currently the fastest. The least crowded parts of the network. And this information converted into a numeric code is deposited at the network notes. This reinforces the paths that are least crowded at the moment. 
The rule the telephone network follows is to always select the path that is most reinforced. So, similar to the ants’ behavior, at each intermediate note, the call follows the path that is most reinforced. This leads to an outcome which is beneficial to the network as a whole and calls get through faster. 
But getting back to animal behavior, another example of swarm intelligence is the way flocks of birds are able to fly together so cohesively. How did they coordinate their movements and know where they're supposed to be. 
Well, it basically boils down to three rules that each bird seems to follow. Rule one: stay close to nearby birds. Rule two: avoid collision with nearby birds. And rule three: move in the average speed and direction of nearby birds. Oh, and by the way, if you're wondering how this approach can be of practical use for humans, the movie industry has been trying to create computer-generated flocks of birds in movie scenes. The question was how to do it easily on a large-scale? 
A researcher use these three rules in a computer graphics program, and it worked. There have also been attempts to create computer-generated crowds of people using this bird flocking model of swarm intelligence. However I'm not surprised that more research is needed. The three rules I mentioned might be great for bird simulations, but they don't take into account the complexity and unpredictability of human behavior. So if you want to create crowds of people in a realistic way, that computer model might be too limited.

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