蹭人工智能热点的公司太多，如何辨别？

作为一个扑克迷，我深知世上没有“必赢”一说。然而，如果我们随机选择100家科技初创企业，我敢打赌，大多数公司都会大肆宣传自身业务与人工智能技术结合的多么紧密。

这种人工智能的营销炒作已是愈演愈烈，这一点我们倒是可以理解。不管是Netflix根据观众此前的选择来预测观众可能的节目喜好，还是谷歌根据数千万网民的点击对搜索结果进行持续的改善，亦或是具有争议的人工智能系统，包括亚马逊善解人意的Alexa，人工智能业已成为我们日常生活的一部分。与此同时，商界正在竞相使用人工智能技术来解决一系列问题，只是对于主流消费者来说不怎么显眼罢了。

竞相搭乘新兴热门事物的顺风车是人类的天性，而且这种现象以前在其他技术领域也是屡见不鲜，例如大数据、云、软件即服务、移动、网络2.0，等等。

但是初创企业对“人工智能”一词的过度使用——或赤裸裸的滥用——如今尤为猖獗。几乎每一天，我都会遇到一些企业，它们会在自身的营销说辞大谈特谈人工智能，并将自己描述为一家人工智能公司，但却拿不出真正的人工智能技术作为证据。

例如，这些人工智能公司实际上做的是基础的数据分析。他们的技术都来源于数据，而且结果都被用于实现特定的目的，例如，根据预设定的规则识别发送营销信息邮件的最佳时机。

这种根据上下文来整理数据的做法也让此类公司具有了一定的价值，但这并不是人工智能。它们之间的关键区别在于：人工智能系统具有迭代性，分析的数据越多，系统就会变得越智能，越能干，而且越自主化。特斯拉的自动驾驶便是一个例子，它能够根据其车辆在路上行驶的每一英里来不断进行完善。真正的人工智能功能会彻底地颠覆市场。

很多软件即服务和自动化公司也用人工智能来标榜自己，但事实上他们所做的只不过是使用数据分析来编排应用和工作流程。这一技术随着时间的推移不会变的更加智能，而且也无法达到真正人工智能技术的自主水平。

这些公司错误地认为，只要其工作与数据或工作流程相关，都可以被称之为人工智能。它们还肆无忌惮地使用通常与人工智能相关的“算法”一词。然而，即便某个系统拥有能够实现某些功能的算法，但它并不一定就能被称之为人工智能。

因此，在我们投资高举人工智能旗帜的公司之前，我们应注意以下几点：公司的业务是否超出了基础数据分析的范畴？这些公司是否会产生数据废气——公司从感兴趣的数据源获取的大量专属数据？它们是否使用这一数据来打造日益智能化的系统，并转而产生自身的数据废气？它们是否拥有能够降低流程中人类干预需求的迭代技术（机器学习或深度学习）。

如果一家公司符合上述所有条件，我们还需注意公司是否拥有：在技术层面上深谙机器学习模型的创始人；将这些模型应用至庞大的数据集的独特方式；面向这些业务极有可能成功的业务模式。

如果有公司声称自己在使用人工智能技术，我们应该向公司负责人询问以下几个问题：那些声称自己是人工智能技术专家的员工在应对巨大的人工智能挑战方面是否有经验，而且相对于竞争对手，他们是否有绝对的优势？他们是否了解打造自主系统所需要的复杂技术细节？他们是否通过吸引人才在市场上开展竞争？

真正的人工智能技术能够为现实问题提供突破性的解决方案。如果这些公司在现实当中能够提供这类解决方案，那么它们再怎么炒作也不为过。（财富中文网）

阿里夫·简莫哈默德是Lightspeed Venture Partners的合伙人。

译者：Pessy

审校：夏林

As a fan of poker, I know there’s no such thing as a sure bet. But randomly pick 100 tech startups and I’d confidently wager that the vast majority are t weaving AI heavily into their narrative.

AI hype has become intense, and understandably so. The technology has become part of everyday life, whether it’s Netflix predicting what shows we might like based on previous choices, Google’s search results consistently improving based on millions of people’s clicks, or conversational AI systems like Amazon’s Alexa getting to know you. Meanwhile, virtually invisible to mainstream consumers, the business world is agog over harnessing AI to solve a range of problems.

It’s human nature to latch onto the next big thing, and we’ve seen it many times before with other tech buzzwords of the moment: big data, the cloud, software as a service (SaaS), mobile, Web 2.0—the list goes on and on.

But overuse—or flat-out misuse—of the term “AI” in the startup world right now is especially rampant. Barely a day goes by when I don’t come across a company that is molding its marketing messages to the hype and pitching itself as an AI company, without a genuine AI story to back it up.

In a typical scenario, these artificial artificial intelligence companies are in actuality doing basic data analysis. Their technology sifts through data, and the results are used to drive certain outcomes—say, identifying the best time to send marketing emails based on pre-programmed rules.

Such companies may provide value by making data contextually relevant, but that’s not AI. Here’s the crucial difference: AI systems are iterative—they get smarter with the more data they analyze and become increasingly capable and autonomous as they go. Think of Tesla’s Autopilot improving with every mile that its fleet spends on the road. Authentic AI capability is what enables true market disruption.

A number of SaaS and automation companies out there are positioning themselves under the AI banner, even though all they really do is use data analytics to orchestrate applications and workflows. The technology doesn’t get more intelligent over time, and it never reaches the level of autonomy of bona fide AI.

For these companies, AI incorrectly has become a catchall phrase for anything that has to do with data or workflow. They also tend to liberally throw around “algorithm,” a word often associated with AI. But just because a system has algorithms that drive certain outcomes doesn’t necessarily mean it is AI.

Here’s what we look for before we invest in a company making an AI play: Are they doing more than basic data analysis? Are they creating their own data exhaust—a large trail of proprietary data that they collect from interesting sources? Do they use this data to create systems that constantly get smarter and in turn create their own data exhausts? Do they have iterative technology (machine learning or deep learning) that reduces the need for humans in the loop?

If those boxes can be checked off, we look for the following: founders with deep technical understanding of machine learning models, a unique approach for applying those models to a very large data set, and the strong possibility of a successful business model from all of it.

We should ask the following questions about the heads of companies that claim to use AI: Do the people claiming to be AI experts have experience taking on huge AI challenges, to the point that they have an extreme advantage over competitors? Do they understand the intricate technical details of what it takes to build an autonomous system? Are they attracting the talent to attack the market?

Authentic AI can provide groundbreaking solutions to real problems. The companies that can deliver in the field deserve all of the hype coming their way.

Arif Janmohamed is a partner at Lightspeed Venture Partners.