有时你需要的只是与某人交谈。一个可以用自己的方式让你振作起来的人，一个充满活力和健谈的人，以至于你忘记了生活中的所有问题。一个比你的期望更好的来逗你开心的人。每个人都不愿意与其他“人类”谈论事物，但确实有一些好奇的人会与 AI 交谈。在这里，Ruuh出现了。
Ruuh能够倾听一个人的问题，检测他们的情绪，了解用户的背景并做出适当的回复等等。这增强了他们的联系以及他们与用户共享的关系。它直接意味着聊天机器人和用户之间更有价值和更明智的聊天。
Ruuh善于交谈
没有情感的介入，聊天机器人的存在是毫无用处的。仅仅(Just)能够在没有任何个人联系的情况下进行回复就使聊天变得正式并且很多时候无趣。只有当聊天机器人能够在与之相关的情感的基础上进行对话时，聊天机器人才是有趣的。对此，微软(Microsoft)表示，
Building a conversational layer in Ruuh helps her develop relationships so users can be more open, more casual and more engaged. This leads to better, more honest and natural conversations that ultimately lead to added value and a better experience for users.
建设Ruuh的目的
微软构建这款人工智能聊天机器人的主要目的是为印度(India)年轻、精通技术的早期采用者提供服务。它本来就应该类似于微软的中文聊天(Chatbot)机器人(Xiaoice)小冰。Ruuh更像是一个数字朋友，而不仅仅是一个数字助理。Ruuh是一个不仅仅是一段代码的软件。这是你的朋友。
深度学习的工作原理。
Ruuh是一个虚构的人物，我们都知道。但她的角色模仿了一个年轻的印度(India)城市女孩，她大约 18-24 岁。她似乎对流行(Pop)文化很感兴趣，并且擅长使用流利的印度(India)城市俚语。
创建Ruuh的第一步是收集数据。她的本意是和蔼可亲，也很诙谐。Ruuh的这种个性的来源是实时对话、社交媒体(Social Media)对话、论坛、社交平台和消息服务，在这些服务中收集数据以匿名改善用户体验。
接下来，他们必须改进他们收集的有用数据。此步骤将收集的总数据的 70% 视为无用并被删除。微软(Microsoft)确保没有针对美国、英国和澳大利亚人(Australia)的冒犯性评论以及任何性别歧视或政治评论。
现在，这些精炼且有用的数据将应用于所选模型。该模型是 cDSSM 或卷积深度结构化语义模型(Deep Structured Semantic Model)。这是一个更新的模型，有助于在 AI 中实现更好、更深入的类人行为。
cDSSM 如何产生更好的 AI
查询标识(Query Identification)
查询识别是让人工智能更像(Identification)人类(Humans)的第一步。算法接受输入查询并在数据库中查找类似的问题。这也称为信息检索(Information Retrieval)或 IR。
例如：如果查询是“我如何制作鸡肉意大利面？”，Ruuh分析(Example)数据并找到多个类似问题的样本。
排名响应(Ranking responses)
在这里，算法根据样本的相关性对响应进行分类。这就是将最相关的数据作为输出给出的方式。
了解上下文(Understanding Context)
现在，如果聊天机器人忘记了用户在说什么，那可能就没有意义了。
For Example: Question: “Do you like ice cream, Ruuh?”
Ruuh: “Yes, I like it.”
Question: “which flavors do you like?”
Ruuh: “Chocolate and Vanilla.”
现在，Ruuh知道第二个问题是关于冰淇淋的，因此回答是适当的。
为了在她的功能上如此出色，Ruuh 的算法不断地在用户之前的查询中查找数据，并了解用户正在谈论的内容的上下文。
检测和响应情绪线索(Detection and response to emotional cues)
现在，更像人类意味着检测情绪。之所以如此，是因为人类有情绪化的心态。因此，为了检测用户的情绪，Ruuh 会(Ruuh)查找她收到的聊天消息中的模式以及聊天中使用的表情符号类型。所以，当你和她说话时，她知道你是高兴、悲伤、兴奋还是沮丧。
判决(Verdict)
Ruuh是一种强大的方式，也是展示人工智能今天可以做的像人类一样的力量的好方法。借助 cDSSM 的强大功能，Ruuh变得更加聪明。
微软说：
To summarize, the model combined with deep learning integrates context and the user’s message to extract the appropriate response. The model extracts the context from the message, retrieves previous messages, creates a group of appropriate responses, ranks them according to relevance, and generates the final output.
让我们通过一个例子更好地理解这一点。如果用户问Ruuh，“哪种(Which)披萨配料最受欢迎？”，Ruuh会将查询标识为关于“披萨配料”，并根据该查询检索最相关的答案。Ruuh会根据相关性对来自数据库的相似答案进行排名，以生成最合适的答案。借助情境意识，Ruuh可以轻松回答后续问题，例如“你喜欢哪些？” 回复“我喜欢蘑菇和菠萝”。
Ruuh现在一岁了，我必须说，人工智能的未来是光明的，因为我们看到越来越多的先进人工智能以这种速度出现，我们很快就会看到周围更聪明的东西。我们祝微软(Microsoft)的团队好运，我希望他们在未来能继续用这些伟大的产品给我们带来惊喜。
您可以在 Microsoft的官方文章中阅读更多关于Ruuh的信息(Ruuh)-并(Microsoft –)在 Facebook 上(on Facebook)(on Facebook.)尝试一下。
Meet Microsoft Ruuh chatbot on Facebook - All you need to know!
Sometimes all you need is to talk tо someone. Someone who can cheer you up in their own wаy, someone who is so full of life and chatty that you forget all your problems in life. Someone thаt amuses you by coming better than your expectations. Everyone is not ѕo comfortable about talking to other ‘humans’ about things, but there are some curious people whо dо talk to ΑI. Here, Ruuh comes to the picture.
Ruuh is capable of listening to one’s question, detect their emotions, learn about the user’s background and make appropriate replies and more. This enhances their bonding and the relationship they share with the user. It directly implies to more valuable and sensible chats between the chatbot and the user.
Ruuh is good at making conversations
Without the involvement of emotions, the existence of chatbots is useless. Just being able to reply without any personal connection makes the chat formal and many times uninteresting. A chatbot is interesting only if they are able to make conversations on the foundation of emotions being involved with it. About this, Microsoft says,
Building a conversational layer in Ruuh helps her develop relationships so users can be more open, more casual and more engaged. This leads to better, more honest and natural conversations that ultimately lead to added value and a better experience for users.
Aim of building Ruuh
Microsoft’s main aim behind building this AI-powered chatbot was to make it for the young, tech-savvy early adopters in India. It was already meant to be similar to Microsoft’s Chinese Chatbot named Xiaoice. Ruuh is more of a digital friend rather than just a digital assistant. Ruuh is a software that is not just a piece of code; it is your friend.
How deep learning works.
Ruuh is a fictional character, we all know that. But her character is modeled after a young, urban Indian girl who is about 18-24 year old. She seems to be interested in Pop culture and is great at the usage of fluent urban slangs used in India.
The first step in creating Ruuh was to collect data. She was meant to by affable as well as witty. The source for this personality for Ruuh was real-time conversations, Social Media conversations, forums, social platforms and messaging services where the data is collected to improve user experience anonymously.
Next, they had to refine the useful data that they collected. This step took 70% of total data collected as useless and was removed. Microsoft made sure that there are no offensive comments for people in the US, UK and Australia and any sexist or political comments.
Now, this refined and useful data was to be applied in the selected model. This model was the cDSSM or Convolutional Deep Structured Semantic Model. This is a newer model and helps in more better and deeper human-like behavior in AI.
How cDSSM results in better AI
Query Identification
Query Identification is the first step in making AI more like Humans. An algorithm takes the input query and looks in the database for similar questions. This is also referred to as Information Retrieval or IR.
For Example: if the query is, “how do I make chicken pasta?”, Ruuh analyzes the data and finds multiple samples of similar questions.
Ranking responses
Here, the algorithm sorts out the responses based on how relevant the samples are. This is how the most relevant data is given as an output.
Understanding Context
Now, it might be pointless if the chatbot forgets what the user is talking about.
For Example: Question: “Do you like ice cream, Ruuh?”
Ruuh: “Yes, I like it.”
Question: “which flavors do you like?”
Ruuh: “Chocolate and Vanilla.”
Now, Ruuh knew that the second question was regarding ice creams and hence, the reply was appropriate.
To be so good at her functionality, Ruuh’s algorithm constantly looks up for data in the previous queries from the user and understands the context about what the user is talking about.
Detection and response to emotional cues
Now, more human-like means detection of emotions. This is so because humans have emotional mindsets. So, in order to detect users’ emotions, Ruuh looks up for patterns in chat messages received by her and the type of emojis used in the chat. So, when you are talking to her, she knows if you are happy, sad, excited or upset.
Verdict
Ruuh is powerful and a great way to show the power of what AI can do today to behave like a human being. With the power of cDSSM, Ruuh is much smarter.
Microsoft says:
To summarize, the model combined with deep learning integrates context and the user’s message to extract the appropriate response. The model extracts the context from the message, retrieves previous messages, creates a group of appropriate responses, ranks them according to relevance, and generates the final output.
Let’s understand this better with an example. If a user asked Ruuh, “Which pizza toppings are most popular?”, Ruuh would identify the query as about ‘pizza toppings’ and retrieve the most relevant answers based on this query. Ruuh would rank similar answers from the database based on relevance to generate the most appropriate response. With contextual awareness, Ruuh can easily answer follow-on questions such as, “Which ones do you like?” by replying “I love mushroom and pineapple”.
Ruuh is now one year old, and I must say that the future of AI is bright because of this rate at which we are seeing more and more advanced AI emerging, we are about to see smarter things around us very soon. We wish the team at Microsoft, a very best of luck and I hope they will keep surprising us in the future with these great products.
You can read more about Ruuh here on the official article by Microsoft – and give her a try here on Facebook.
