原文来自美国Canaan风险投资基金的合伙人RayfeAI热遍全球,到底是泡沫还是趋势,文章的三个主要观点:1)平台、算法和结果三者驱动AI发展 2)AI尚在早期 3)好的AI公司不仅拥有独特的数据和模型,同时具有颠覆特定行业的商业模式。

Artificial Intelligence (AI) is everywhere, from startups to enterprises, and even Hollywood. The search frequency of the term “Deep Learning” (DL) has grown 4x in the last two years. It seems to be the subject of every article (guilty), conference, and startup. It all feels a bit overhyped. Nonetheless, when you cut through this hype, AI is the next wave of innovation — and this is only the beginning.

人工智能(AI)无处不在,从创业公司到大企业,甚至好莱坞。 “深度学习”(DL)的搜索频率在过去两年中增长了4倍。它似乎是每一篇文章(我也有罪),会议和创业公司的主题。这一切都感觉有点过热了。尽管如此,当你透过亢奋的热潮看去,AI仍然是下一波的创新 — 这还只是开始。

Compounding Forces: Platforms, Algorithms, & Results 复合驱动力:平台,算法和结果

Over the last few years, we’ve seen the rapid development of AI platforms. Especially in the subfield of DL, where the differential equations behind backpropagation would make most developers’ heads spin, open source library TensorFlow empowers almost anyone to build the latest classifier and sophisticated conv-net. Today there are dozens of open source choices, ranging from university-led developments like Theano, Caffe, and DyNet, to company offerings such as TensorFlow, CNTK, and MXNet. As they compete to become the de-facto development platform, they push each other to improve functionality and feature sets. This competition is actually a Trojan horse within the enterprise. As these platforms become easier to use, AI will become more and more central to the enterprise.

在过去几年中,我们看到了AI平台的快速发展。特别是在深度学习子领域,反向传播算法背后的微分方程会使大多数开发人员头晕,但是开源库TensorFlow让几乎任何人都可以构建最新的分类器和复杂的神经网络。今天有几十种开源选择,从大学主导的开发,如Theano,Caffe和DyNet,到公司的产品,如TensorFlow,CNTK和MXNet。当他们竞争试图成为事实上的开发平台时,他们互推动彼此性能和功能集的改进。这种竞争实际上是企业内的一种特洛伊木马。随着这些平台变得更容易使用,AI将越来越成为企业的核心。

As algorithms move from classical rules-based AI (Expert Systems), to regressions (Machine Learning), to multi-layered nets (Deep Learning), to now Reinforcement Learning, we see new ways for AI to permeate the enterprise. For example, Deep Learning has redefined fields using unstructured data (i.e., computer vision and speech). Reinforcement Learning has even broader applicability, ranging from time-series data fields like finance and security, to multi-step processes like robotics and logistics. The race is on for startups to deploy the next generation of algorithms and create a defensible moat of proprietary data and models. Ready. Set. Train!

随着算法从传统的基于规则的AI(专家系统),到回归算法(机器学习),到多层网络(深度学习),到现在的增强学习,我们看到AI渗透到企业的新的方式。例如,深度学习使用非结构化数据(比如计算机视觉和语音)重新定义字段。增强学习具有更广泛的适用性,从财务和安全等时间序列数据领域到机器人和物流等多步骤流程。这场比赛已经开始, 初创公司需要部署下一代算法,并利用私有数据和模型建立可防守的护城河。准备。开始。训练!

The result? AI is, for the first time at scale, delivering real results in real products and services. Companies like Google, Facebook, and Baidu have embraced the spirit of applied research within the enterprise with people like Geoffrey Hinton, Yann LeCun, and Andrew Ng. Jeff Dean recently talkedabout the increased use of AI within Google — not just in research, but in production. In many ways, this parallels the software movement two decades ago — the successful companies embraced this new paradigm and thought of their business as software-first. A decade ago it was mobile-first. The next generation of successful companies will be AI-first.

结果如何? AI正第一次大规模的在真正的产品和服务中提供真正的结果。像谷歌、Facebook和百度这样的公司已经招募了Geoffrey Hinton,Yann LeCun和Andrew Ng这样人才开展企业内部的应用研究。 Jeff Dean最近谈到Google内部AI的使用不断增加 — 不仅仅是研究而是实际生产。在许多方面,这与20年前的软件运动相似 — 成功的公司接受了这种新的范式,并认为他们的业务是以软件为先。十年前,它是移动为先。下一代成功的公司将是AI为先。

Investing Through the Hype 在亢奋热潮中投资

There is no shortage of startups claiming to be AI companies. The challenge that investors — and founders face is cutting through the noise to determine what really is an AI company. This is especially true for companies building AI solutions at the application-level, which is where Canaan is focusing our time and dollars. In order to sift through the hype, I use a simple 2x2 framework to assess the potential of AI startups. On one axis, I look for companies that have a differentiated data set (i.e., uniquely labeled data, proprietary data) or algorithms, which will allow them to build a long-term competitive moat as they better train, process, and improve their model. The second, equally important factor is business model innovation. In particular, I am excited when I see companies building AI-centric applications that are fundamentally disrupting industries with manual, time-consuming processes. If companies excel along one axis but not the other, they may enjoy short-term success, but competitors with better data or unique business strategies will capitalize on their weakness and soon supplant them. The next generation of winners in AI will excel along both axes. Not only do they change the way an industry views their business, but by the time the competition figures it out and tries to challenge them, it will be too late to break the AI-first company’s defensive moat of better data and algorithms.

声称是AI公司的创业公司比比皆是。投资者和创始人面临的挑战是透过噪音找到真正的AI公司。这对于在应用层面构建AI解决方案的公司尤其如此,这就是Canaan集中我们的时间和金钱的地方。为了过滤噪声,我使用一个简单的2x2框架来评估AI创业公司的潜力。在一个轴上,我寻找具有差异化数据集(比如,唯一标记的数据,私有数据)或算法的公司,这将允许他们建立一个长期的竞争性护城河,因为他们可以更好地训练、处理和改进他们的模型。第二,同样重要的因素是商业模式创新。特别是,当我看到公司构建以AI为中心的应用去从根本上颠覆那些以手动、耗时的流程为中心的行业时,我非常兴奋。如果公司在一个维度突出而另一个不行,他们可能享受短期成功,但是具有更好数据或独特业务战略的竞争对手将利用它们的弱点很快取代它们。 AI的下一代获胜者必须在两个维度都很强。他们不仅改变了一个行业看待他们的业务的方式,而且在竞争对手明白过来从而尝试挑战他们的时候,会发现为时已晚,因为很难打破AI公司更好的数据和算法的护城河。

Ladder, a recent addition to the Canaan portfolio, is an example of a company that excels on both these fronts. They have differentiated data sets and unique AI models to underwrite term life insurance in real-time rather than the industry average 6–8 weeks to process an application. This vastly expands the accessibility and ease of buying this important product. And as they continue to ingest more data from their consumers, their real-time underwriting models continue to exponentially improve. Data moat? Check. Business model disruption? Check.

Ladder,是最近加入Canaan投资组合的公司,是一个在这两个维度都擅长的公司的例子。他们具有差异化的数据集和独特的AI模型,可以实时承保定期的人寿保险,而不是行业平均6-8周来处理申请。这大大扩展了购买这个重要产品的可获得性和易用性。随着他们继续从消费者那里获取更多的数据,他们的实时承保模式将呈指数级的提高。数据护城河?Yes。商业模式颠覆?Yes。

End Game: Democratization of AI 游戏的终局:AI的民主化

We are entering a critical inflection point in the AI ecosystem. The simultaneous forces of platforms, algorithms, and results are not isolated. They are deeply connected and create a viral network effect with each other. It’s still early days (despite all the hype). The majority of value creation in this nascent industry is still to come. But there is no question the potential and vast reach of AI is real. At Canaan, we’re looking to invest in startups that leverage AI to disrupt archaic business models with new and unique data. If that’s you, let’s chat (bot).

我们正在进入AI生态系统的一个关键拐点。 平台,算法和结果的同时作用力量不是孤立的。 它们是密切相关的,并且相互之间产生病毒网络效应。 AI还在早期(尽管所有的炒作)。 这个新兴行业的大部分价值创造尚未来临。 但是毫无疑问,AI的潜力和广泛的影响是真实的。 在Canaan,我们希望投资利用新的和独特的数据来破坏旧的商业模式的AI创业公司。 如果是你,让我们聊一聊。