Using Bluetooth speakers based on speech recognition technology has been popularized to the extent that many tasks at hand are now processed with a simple voice command; be it connecting with video or music channels, cable TVs, food delivery or online shopping services. Similarly, the use of AI can be seen in motor vehicles which are regularly run in restricted environments for autonomous driving, driver assisted lane changing, obstacle avoiding and so on.<\/p>
The field that most mobile services adopt machine learning at first is recommendation algorithm. The algorithm acts based on the assumption that user groups with similarity will also show a similar purchasing pattern. Contrary to the businesses' beliefs, however, the field does not give an instant boost to customer satisfaction or business results, as recommendation algorithm is a somewhat of a generic technology with a wide range of application. Therefore, the focus should be more on imminent issues and pursuing a practical solution.<\/p>
We take increasing operation efficiency of a customer service center with the new technology for example: In the Indian market, we have to bear with specificity of processing a variety of languages simultaneously, including 7 of the official languages. As the customer base grows, the number of inquiries and complaints the customer care center receives increases. If we assign these tasks directly to the personnel, we must recruit additional staff for additional customers. This translates into a linear increase in operation cost with respect to the increase in customers. Moreover, managing Customer Service staff training, overall service quality, response time and customer satisfaction is something more than a matter of cost.<\/p>
Streamlining CS work and managing quality<\/strong> It is also crucial to monitor if a new group can be found in the voices received. The computer regularly modifies and updates the classification system and forming new categories not found in previous classifications from customers' complaints and inquiries ensures a swift adaptation to user response. This is a great advancement from the traditional rule-based approach of call centers in which CS staff have a hard time detecting the customer response. The fraudulent users usually switch to new tactics when caught, so supervised learning, in which the classification is based on pre-defined properties or patterns, proves inadequate. Traditional human-powered analysis methods also have limits since the terminal or user information may be altered or forged. Outlier Analysis is commonly used in this case to scan for suspicious behaviors or patterns which sometimes shed light on suspicious patterns, revealing groups of fraudsters who have been forging information to avoid getting caught.<\/p> Authentication technology confirms the legitimacy of terminal users, using information of mobile terminal usage pattern, call patterns, location data and social relationships; it is something that has been developed and applied to actual services in response to various needs. A well-established data infrastructure is necessary in order to apply a machine learning-based algorithm to the services. The result of the learning depends on the type, quantity and quality of data to be learned.<\/p> We first need a data loader to confirm the source of usable data, collect them at regular intervals, pre-process to an optimal form for learning and then finally load the data. One also needs an algorithm library to store the learning algorithm of the computer and the revisions by version. The last ingredient is a feedback process to apply the learning results to the actual service and record and reflect user responses recursively.<\/p> With a user count of 1.2 billion, the Indian communications market is also a major challenge in data analysis. The amount of collected data for a basic level of analysis needed for mobile app service operation is out of the operation limits for a typical system. For this reason, a good data analysis in the Indian market requires not only the capacity to analyze, but also the capacity to manage data infrastructure.<\/p> In this era, it truly is difficult for businesses to expand without using data and machine learning to analyze patterns and networks. In summary, we recommend a small and practical field when one applies machine learning algorithm to a mobile service for the first time. We should also note that the quantity and quality of data and its update frequency is at the very core of machine learning and data infrastructure should be built to facilitate the process.<\/p> 即时满足的需求和方便使用导致增长ofArtificial智能(人工智能见过的),这样的蓝牙音箱,自主车辆,人形机器人和无人驾驶的商店。 基于语音识别技术的使用蓝牙音箱已经普及,许多任务现在处理一个简单的语音命令;结合视频或音乐频道、有线电视、食品外卖或网上购物服务。同样,使用人工智能中可以看到机动车定期运行在受限制的环境中自主驾驶,驾驶员辅助车道改变,障碍避免等等。 大多数移动服务采用的领域机器学习起初是推荐算法。算法基于行为相似性的假设用户组也将显示出类似的采购模式。与企业的信念,然而,不给瞬间提升客户满意度或业务结果,作为一个通用的推荐算法是一种技术与广泛的应用。因此,应该更加关注迫在眉睫的问题,追求实用的解决方案。 我们将增加一个客户服务中心的运营效率新技术例如:在印度市场,我们必须忍受特异性同时处理多种语言,包括7种官方语言。随着客户群的增长,客户服务中心咨询和投诉的数量得到增加。如果我们直接将这些任务分配人员,我们必须为其他客户招募额外的员工。这转化为一个线性增加运营成本的增加客户。此外,管理客户服务人员培训,总体服务质量、响应时间和客户满意度是超过一个成本的问题。 简化CS和质量管理工作 也是至关重要的监控,如果可以找到一个新组的声音。电脑经常修改并更新分类系统和形成新的类别中没有以前的分类从客户的投诉和咨询确保迅速适应用户响应。从传统的基于规则的方法这是一个很好的进步CS的呼叫中心的员工很难检测客户的反应。 发现通常欺诈用户切换到新策略,监督学习,的分类是基于预定义的属性或模式,证明不足。传统的人力分析方法也有限制自终端或用户信息可能被改变或伪造。离群值分析是常用的在这种情况下扫描可疑行为或模式有时阐明可疑模式,揭示组织的骗子伪造信息,以避免被抓到。 认证技术证实了终端用户的合法性,使用移动终端使用模式信息,调用模式、位置数据和社会关系;它已经被开发和应用于实际服务,以应对各种需求。一个完善的数据基础设施是必要的为了一个基于机器学习的算法应用到服务。学习的结果取决于类型、数量和质量的数据。 我们首先需要一个数据加载程序来确认源的可用数据,定期收集、预处理的最优形式学习最后加载数据。还需要一个算法库存储计算机的学习算法和修正的版本。最后一个成分是一个反馈的过程将学习成果应用到实际的服务和递归地记录和反映用户的反应。 用户数量为12亿,印度通信市场数据分析也是一个重大的挑战。收集到的数据量分析所需的基本水平移动应用服务操作的操作限制为一个典型的系统。出于这个原因,一个好的数据分析在印度市场不仅需要分析能力,而且管理数据基础设施的能力。 在这个时代,真的是困难的为企业扩大不使用数据分析和机器学习模式和网络。总之,我们建议一个小和实践领域当一个机器学习算法适用于第一次的移动服务。我们也应该注意,数据的数量和质量及其更新频率是机器学习和数据基础设施的核心应该是建立促进这个过程。
<\/strong>Customers' voices sent through CS channels are classified by the computer, based on the rules it picked by itself. Natural language processing is applied in Data Preprocessing where the language characteristics are important and converting the language into a type of signal or network makes it possible to process different languages collectively. When a simple response is available for the classified voice, the computer sends an immediate response based on a pre-set pattern. This technology is nearly identical to that of chatbots or robot writers. In this way, about 70% of CS can be processed with a computer.<\/p>
<\/strong>Addressing concerns in the field of mobile security using AI<\/strong>
<\/strong>For mobile services, fraud or misconduct by users always presents a major issue. Such users intercept the benefits provided, an example being compromising service integrity in a marketing promotion. Companies frequently suffer losses because of them.<\/p>
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<\/p>","blog_img":"","featured":0,"status":"Y","seo_title":"Machine Learning is not futuristic science fiction anymore","seo_url":"machine-learning-is-not-futuristic-science-fiction-anymore","cms_link":"machine-learning-is-not-futuristic-science-fiction-anymore\/2862","updated_at":"2018-01-31 13:11:48","time":"2018-01-31 12:09:41","authors":[{"author_name":"Charlie Lee","author_description":"CEO, True Balance","author_designation":"CEO","author_company":"True Balance","profile_pic":"retail_files\/author_1517380613_temp.jpg"}],"tags":["Machine Learning","AI","Artificial Intelligence.","Data"],"url_seo":"machine-learning-is-not-futuristic-science-fiction-anymore"}">
客户的声音通过CS渠道发送由计算机进行分类,基于规则本身。自然语言处理应用数据预处理是重要的语言特征和语言转换成一个类型的信号或网络可以集体过程不同的语言。当一个简单的反应是用于分类的声音,电脑发送立即响应基于预设模式。这项技术是几乎相同的聊天机器人或机器人的作家。通过这种方式,大约70%的CS可以用电脑处理。
解决移动安全使用人工智能领域的担忧
用户对移动服务,欺诈或不当行为的始终提供一个主要问题。这样用户拦截提供的好处,一个例子是影响营销推广服务的完整性。因为他们公司经常蒙受损失。
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