You need to go from your house to the Airport. Do you take a Limo or a bike? Of course a Limo? The road is bad and the traffic worse…A Limo is not always the right choice.

您需要从您的房子去机场。 您乘坐豪华轿车还是骑自行车？ 当然是豪华轿车吗？ 路况不好，交通状况也越来越差……豪华轿车并非总是正确的选择。

Product Managers solve user problems. Sometimes AI is the answer to all your problems. Other times, it is not worth the trouble.The question becomes, when and where should we leverage AI in our Products?

产品经理解决用户问题。 有时，人工智能是您所有问题的答案。 在其他时候，这不值得麻烦。问题变成了，我们应该何时何地在产品中利用AI？

My first job as a Product Manager was in an AI-based startup whose core competency was image and video-based analytics. I was exploring the feasibility and applications in the Security Surveillance space.

我作为产品经理的第一份工作是在一家基于AI的初创公司，其核心能力是基于图像和视频的分析。 我正在探索安全监视领域的可行性和应用。

What I found surprised me.

我的发现使我感到惊讶。

One of my visits was to a company helping the Singapore govt with the Surveillance of the country. Singapore has one of the finest infrastructures in the world. And it maintains it beautifully. Littering is a punishable offense. One aspect, hence also becomes ensuring that people don’t throw garbage from the balconies of their highrise buildings.

我的访问之一是访问一家帮助新加坡政府进行国家监视的公司。 新加坡拥有世界上最好的基础设施之一。 而且它保持得很漂亮。 乱扔垃圾是应受惩罚的罪行。 因此，一方面也可以确保人们不要从高层建筑的阳台上扔垃圾。

The few rooms had its walls completely plastered with hundreds of screens. Around 1 person per wall was busily looking at multiple screens at a time trying to detect violations. 24X7 monitoring across thousands of cameras was not an easy task. Was it practical? I would say no, not if done manually.So here is how they handled it.They added pixel monitors on each of the balcony railings within range. Any pixel changes flagged the image and people would set forth to manually analyze them.

几个房间的墙壁完全贴满了数百个屏幕。 每堵墙大约有1个人正在忙于一次查看多个屏幕，以发现违规情况。 在数千台摄像机上进行24X7全天候监控并非易事。 实用吗？ 我会说不，如果不是手动完成，那么这就是他们的处理方式。他们在范围内的每个阳台栏杆上添加了像素监视器。 任何像素变化都会标记图像，人们将着手手动分析它们。

There were two main problems. First, this was, of course, not scalable. Second, There were too many false positives. Anyone randomly roaming around in their balcony would trigger the alarm. Needless to say, this was very expensive to implement. That was when I was convinced that an AI could do this better and more effectively.

主要有两个问题。 首先，这当然是不可扩展的。 第二，误报过多。 任何人在阳台上随机漫游都会触发警报。 不用说，这实现起来非常昂贵。 那时我深信AI可以更好，更有效地做到这一点。

Just like this use case, many problems could be solved by AI.

就像这个用例一样，人工智能可以解决许多问题。

But what are those problems? When do you even dabble with AI to solve your problems?It is worth serious consideration because AI is not without its limitations and challenges. AI done wrong often leads to extremely high costs without the added value.Un-Explainability of results and inconsistent responses are other factors often hampering the reliability.

但是这些问题是什么？ 您什么时候甚至涉足AI解决您的问题？值得认真考虑一下，因为AI并非没有局限性和挑战性。 人工智能做错了往往会导致无附加值的极高成本，结果无法解释和响应不一致是其他经常影响可靠性的因素。

So, what are some guidelines that will help you decide if to go the AI route?

那么，有哪些准则可以帮助您决定是否走AI路线？

Do not use AI if:- Your problems can be solved by simple rules- If you need an explanation of why you received the output that you did. AI is often unexplainable.- You need a 100% accuracy 100% times- If you do not have good quality and quantity of data

在以下情况下不要使用AI：-您的问题可以通过简单的规则解决-如果您需要解释为什么收到输出的原因。 AI通常无法解释。-您需要100％的准确率100％次-如果数据质量和数量不高

If your product includes one or more of the following problems, you could leverage AI

如果您的产品包含以下一个或多个问题，则可以利用AI

1.排名和推荐 (1. Ranking and recommendation)

When you visit the Amazon app intending to buy a product, it is important to Amazon that you make a purchase. With thousands of products in a single category, how does Amazon shows you the product that you will like? It hence utilizes your behavioral patterns, the characteristic of products, and other parameters to predict the products you are likely to purchase.

当您访问打算购买产品的亚马逊应用程序时，对亚马逊来说很重要。 在一个类别中有成千上万的产品，Amazon如何向您显示您想要的产品？ 因此，它利用您的行为方式，产品特征和其他参数来预测您可能购买的产品。

It can do so without AI as well but then keeping a track of your changing preferences, purchasing patterns need constant adaptation. AI hence solves this problem beautifully.

它也可以在没有AI的情况下做到这一点，但是要跟踪您不断变化的偏好，购买模式需要不断地适应。 AI因此可以很好地解决这个问题。

The majority of Products whose bread and butter depend on recommendations leverage AI to satisfy their users. Other examples include OTT like Netflix.

依靠推荐的面包和黄油的大多数产品都利用AI来满足用户的需求。 其他示例包括像Netflix这样的OTT。

2.自然语言理解/处理 (2. Natural Language Understanding/Processing)

There is a high probability that whatever you say, Alexa will understand it. How does Alexa understand you? How does it interact with you in a human fashion?NLP is the field that explores how machines can understand and respond to human languages.

无论您说什么，Alexa都很可能会理解。 Alexa如何理解您？ NLP是如何以人类的方式与您交互的？NLP是探索机器如何理解和响应人类语言的领域。

The field of AI is relatively new, exciting, and undergoing rapid developments owing to the extensive research.

由于广泛的研究，人工智能领域相对较新，令人兴奋，并且正在快速发展。

3.分类 (3. Classification)

Finding your older photos based on search keywords has become so easy with Google Photos.

使用Google相册，根据搜索关键字查找较旧的照片变得非常容易。

A single word “Beach” reveals all the photos including beaches from your album. How does it happen? When you train a model with millions of pictures of the beach, it learns that a beach has water, sand, and maybe coconut trees.

一个单词“ Beach”可显示您相册中所有照片，包括海滩。 它是怎么发生的？ 当训练带有数百万张海滩图片的模型时，它会发现海滩上有水，沙子和椰子树。

Now, can this be done without AI? Yes, conventional image processing methods would work as well. The accuracy is another matter, however.

现在，没有AI就能做到吗？ 是的，常规图像处理方法也可以使用。 然而，准确性是另一回事。

The classification applies whenever you need to throw things into multiple buckets. You can classify utterances into intents, manufactured bulbs into good quality vs garbage, news based on topics, and so on. Classification is also what keeps your inbox clean by identifying and segregating spam emails.

只要您需要将事物放入多个存储桶中，分类就适用。 您可以将话语分类为意图，将制成的灯泡分类为优质vs.垃圾，基于主题的新闻，等等。 分类还可以通过识别和隔离垃圾邮件来保持收件箱的整洁。

4.聚类 (4. Clustering)

Clustering helps group similar objects.

群集有助于将相似的对象分组。

Every day you read Google News, there are too many news items around the same topic. But they all appear to you in groups. This helps users consume content better.

每天您阅读Google新闻时，围绕同一主题的新闻太多。 但是它们全都出现在您的组中。 这有助于用户更好地消费内容。

Clustering is what helps Google segregate all information on the Web, and Banks identify credit card frauds.

群集可以帮助Google隔离网络上的所有信息，而银行可以识别信用卡欺诈行为。

5.回归 (5. Regression)

What will be the price of your house in 2023?Multiple factors will affect the price. Some of them are the size of the house, location, age, brand, the economic condition of the city, country, and so on.

您的房子在2023年的价格是多少？有很多因素会影响价格。 其中一些是房屋的大小，位置，年龄，品牌，城市，国家/地区的经济状况等。

If you give the corresponding values for a million houses, and their prices, the model will learn. It will create opinions like the size of the house is more important than the brand, or age is less considered than the brand.

如果您提供一百万所房屋的相应值及其价格，则模型将学习。 它将产生意见，例如房屋的大小比品牌重要，或者年龄比品牌少。

It can hence predict the price of your house in 2023.

因此，它可以预测您的房屋价格在2023年。

Regression is an important area with applications like predicting when the corona cases will peak, the risk associated with particular investments, life expectancy, and so on.

回归是重要的领域，例如预测电晕病例何时达到顶峰，与特定投资相关的风险，预期寿命等。

AI can slingshot your Products to great heights. It’s not, however, without its downsides.

人工智能可以将您的产品推向新的高度。 但是，它并非没有缺点。

The following guidelines are worth considering when relying on AI to augment your Products:

依靠AI扩展产品时，以下准则值得考虑：

- A half-hearted attempt helps no one. If you are serious about using AI, invest in good AI research team- It is ok to use rule engines to boost the accuracy of your AI algorithms. Most companies do.- AI is undergoing rapid development. Better algorithms are coming up every day. Remember to be on the lookout for recent research that might help your product.- Always focus on solving user problems. AI is always just one of the ways to solve it, and not always the best.

-三心二意的尝试无济于事。 如果您认真使用AI，请投资优质的AI研究团队-可以使用规则引擎来提高AI算法的准确性。 大多数公司都这样做。-AI正在快速发展。 每天都会出现更好的算法。 请记住要注意可能有助于您的产品的最新研究。-始终专注于解决用户问题。 人工智能永远只是解决它的方法之一，而并非总是最好的。

This story was originally published at www.aditi-priya.com. Follow me on Twitter and connect with me on LinkedIn here.

这个故事最初发表在www.aditi-priya.com上 。 跟随我的Twitter的和我在LinkedIn连接在这里 。