美国代写：人工智能在谷歌的历史和发展

美国代写：人工智能在谷歌的历史和发展。谷歌在其现有的应用发布中一直使用机器学习和人工智能。然而，并不是它所有的产品都受益于人工智能。例如，谷歌搜索引擎从一开始就一直是最成功的搜索引擎之一。从简单的蜘蛛疯狂，搜索引擎继续由自动生成查询响应的算法驱动。然而，这些自动响应查询都没有使用人工智能。搜索引擎利用了算法。反应是由算法驱动的。与人工智能相比，算法不是自学习。它是一个接一个重复的有限规则集，并基于这些有限规则集生成查询结果。接下来有关美国代写专家将为同学们分析下人工智能在谷歌的历史和发展。

谷歌工程师将根据结果和性能问题处理这些规则，并修改规则以产生更好的结果。然而，修改必须由他们完成，而算法的设计并不是为了让他们自学。

随着谷歌宣布为其人工智能相关项目设立一个单独的部门，情况正在发生变化。谷歌AI是2017年宣布的谷歌的一个部门，将完全致力于人工智能和相关的发展。现有项目有基于云的TPUs、Tensorflow research等(Craig & Karl, 2016)。

例如，考虑一下，宣布更好地整合AI会如何改变谷歌的格局。在过去，谷歌搜索引擎是由有限查询驱动的，但现在他们可以通过神经网络找到一些AI实现。负责公司搜索引擎的Amit Singhal即将退休，该职位被分配给John Giannandrea，他也负责谷歌的AI工作(Craig & Karl, 2016)。他的大部分工作都集中在深度神经网络上。深度神经网络在某种程度上近似于存在于人脑中的神经元，因此可以创建类似于人脑神经网络的数据网。在改进搜索引擎的背景下，现在将使用神经网络。大量的数据可以在接近人类大脑神经元网络的快速时间内被搜索。神经网络的这种能力将使他们在很短的时间内完成很多事情。它们能够更快地识别照片，能够同时接受文本和照片输入，并能够通过自学习的方式更新查询响应能力。它将能够在提供搜索结果的方式上超越人类，为人类用户定制自学习。

公司正在推广一种深度学习方法。谷歌打算投资的深度学习方法并不是什么新鲜事。类似的人工智能已经被用于互联网社交媒体服务网络，如Facebook、twitter和skype。虽然基于规则的评分指标可以并行地用于调整或纠正某些情况，但人工智能方法将确保自我学习是定期发生的。

Google engineers would work on these rules based on results and performance issues and they would modify the rules to generate better results. However, the modification had to be done by them, and the algorithms were not designed to make them self-learn.

The situation now is changing as Google now announced a separate division for its AI related projects. Google AI was a division of Google announced in 2017 that would be completely dedicated to Artificial Intelligence and related development. Some existing projects are the cloud based TPUs, Tensorflow research, etc (Craig & Karl, 2016).

Consider for instance, how the announcement of better incorporation of AI changes Google landscape. In the past, the google search engine was driven by finite queries but now they would find some AI implementation through neural nets. The person overseeing the search engine of the company, Amit Singhal is retiring and the post is assigned to John Giannandrea who also oversees the work in AI of Google (Craig & Karl, 2016). Much of his work is focused on deep neural networks. Deep neural networks in a way approximate the neurons that exist in the human brain and hence webs of data can be created similar to the neural network of the human brain. In the context of improving the search engine, now neural nets would be employed. Vast amounts of data could be searched in a rapid time that approximate the web of neurons in the human brain. This capability of the neural net will make them do many things in a short amount of time. They can identify photo faster, will be able to accept both text and photo input and can update its query response capability by means of self-learning. It will be able to outperform humans in the way it delivers search results with customized self-learning for the human user in question.

A deep learning approach is promoted for the company. The deep learning approach that Google intends to invest in is nothing new. A similar form of AI has been used on Internet social media service networks like that of Facebook, twitter and skype. While rule based scoring metrics can be used in parallel to tweak or correct certain situations, the AI approach would assure self-learning is happening on a regular basis.

以上内容就是美国代写专家对人工智能在谷歌的历史和发展的分析。同学们如需进一步的帮助，可以立即联系美国论文代写平台机构获取高质量的论文。而我们雇用不同专业领域的代写专家，为同学们提供不同学科的论文创作，如essay代写、硕士论文代写、案例研究等服务，同学们可以放心的选择美国论文代写服务机构!