ai無法啟動產品

重點 (Top highlight)

Let me be upfront: I was the technical co-founder of an AI startup and it failed.

讓我先行：我是一家AI創業公司的技術聯合創始人，但失敗了。

PharmaForesight was an AI startup in the pharmaceutical business intelligence industry. Here was our elevator pitch:

PharmaForesight是制藥商業智能行業的AI初創公司。 這是我們的電梯間距：

“The rate of return for pharmaceutical companies on their R&D is currently below their cost of capital — therefore it is becoming less profitable for pharmaceutical companies to invest in innovative drugs. To decide what clinical trials to conduct, the likelihood of approval is a crucial metric which is currently being calculated in a very subjective and biased way. Our AI algorithm can estimate this figure much more accurately, saving time, money and ultimately benefits patients.”

“制藥公司在研發方面的回報率目前低于其資本成本-因此，制藥公司投資于創新藥物的利潤越來越少。 為了決定進行何種臨床試驗，批準的可能性是一項至關重要的指標，目前正在以一種非常主觀和偏頗的方式進行計算。 我們的AI算法可以更準確地估算該數字，從而節省時間，金錢并最終使患者受益。”

PharmaForesight failed despite following much of the best practices associated with startups.

盡管遵循了與創業公司相關的許多最佳實踐，但PharmaForesight還是失敗了。

We had a strong team and iterated fast using a lean startup strategy. We conducted just shy of 100 interviews with a variety of different stakeholders to identify the early adopters and validate demand for our product. After only four months, we partnered with the global portfolio management office of a large pharmaceutical company that paid us to build our model and we retained all of the IP.

我們擁有一支強大的團隊，并使用精益啟動策略快速迭代。 我們與各種不同的利益相關者進行了約100次采訪，以找出早期采用者并驗證對我們產品的需求。 僅僅四個月后，我們與一家大型制藥公司的全球資產管理辦公室建立了合作關系，該辦事處向我們付費以建立我們的模型，并保留了所有IP。

But ultimately, things didn’t work out—due to some bad luck but also poor judgments. AI startups require subtly different strategies and approaches to Software-as-a-Service (SaaS) startups — these aren’t widely appreciated. My aim in writing this article is to tell you about our mistakes so you don’t make the same ones.

但是最終，由于運氣不好但判斷力差，事情沒有解決。 人工智能初創企業對軟件即服務(SaaS)初創企業要求的策略和方法稍有不同-這些并未得到廣泛認可。 我寫這篇文章的目的是告訴您我們的錯誤，以免您犯同樣的錯誤。

首先，什么是AI初創公司？ (First of all, what is an AI startup?)

There are plenty of startups claiming that they use AI but in reality, they actually use outsourced human labor or basic statistical techniques. A study by the London-based MMC Ventures found that 40% of so-called AI startups were not in fact using AI.

?這里有很多初創公司聲稱，他們使用的AI，但在現實中，他們實際使用外包人力或基本統計技術。 總部位于倫敦的MMC Ventures進行的一項研究發現， 40％的所謂AI初創公司實際上并未使用AI 。

For the purposes of this article, an AI startup is one that could not exist without relatively modern machine learning techniques. As an example, Poly.ai could not exist without its deep learning algorithms.

就本文而言，如果沒有相對較新的機器學習技術，人工智能創業公司將是不存在的。 例如，沒有深度學習算法， Poly.ai就不可能存在。

This is in contrast to a number of companies whose product is merely AI-enabled. Spotify, for example, has invested heavily in machine learning and it is central to its strategy today. But Spotify could and did exist before it started using machine learning in a concerted way— to me, that’s more of a SaaS company.

這與許多公司的產品僅支持AI的公司形成對比。 例如，Spotify已在機器學習方面投入了大量資金 ，這對當今的戰略至關重要。 但是Spotify在開始以一致的方式使用機器學習之前可以并且確實存在-在我看來，這更多是一家SaaS公司。

With that in mind, here are the lessons I learned from launching an AI startup.

考慮到這一點，這是我從創建AI初創公司中學到的經驗教訓。

1.專有數據是關鍵 (1. Proprietary data is key)

Photo by Mika Baumeister on Unsplash Mika Baumeister在Unsplash上拍攝的照片

The best way to think of AI in a business context is as an underlying enabling technology, much like the advent of SQL databases was in the 1980s. SQL enabled billion dollar industries such as Customer Relationship Management. In the same way, AI will create new industries and enable improvements in a large number of business use cases.

在業務環境中思考AI的最好方法是作為一種底層支持技術，就像1980年代SQL數據庫的出現一樣。 SQL支持了數十億美元的行業，例如客戶關系管理。 以同樣的方式，人工智能將創造新的行業并實現大量業務用例的改進。

Like SQL databases though, AI relies on data. It’s long settled that data is far more important than a better algorithm. Good quality proprietary data is absolutely crucial for AI startups.

但是，像SQL數據庫一樣，AI依賴于數據。 長期以來 ，數據遠比更好的算法重要。 高質量的專有數據對于AI初創公司絕對至關重要。

In hindsight, our data strategy was wrong. Initially, we chose the quicker and easier option: building the first version of our tool on publicly-available data. It took a huge amount of time to clean and transform the data to be ready for machine learning and we figured there was a certain amount of defensibility in that.

事后看來，我們的數據策略是錯誤的。 最初，我們選擇了更快，更輕松的選項：在可公開獲得的數據上構建我們工具的第一個版本。 花費大量時間清理和轉換數據以準備進行機器學習，我們認為其中存在一定的防御性。

We also thought that once we had gained a certain amount of credibility, it would be easier to access more interesting and defensible proprietary datasets.

我們還認為，一旦我們獲得了一定程度的信譽，就可以更輕松地訪問更多有趣且可辯駁的專有數據集。

Many of these assumptions were proved to be wrong. When we started building our model, we couldn’t find anyone else (publicly, anyway) who was tackling it, but when we had finished there were a number of competitors. Even if our algorithm was more accurate, it was incredibly hard to differentiate ourselves against more established competitors, particularly because it seemed that everyone was using similar data.

這些假設中有許多被證明是錯誤的。 當我們開始構建模型時，我們找不到其他人(無論如何，公開地)來解決它，但是當我們完成模型時，就會有許多競爭對手。 即使我們的算法更準確，也很難將自己與更成熟的競爭對手區分開來，尤其是因為似乎每個人都在使用相似的數據。

Building our prototype did not seem to make it easier to access proprietary industry data either (although we were running out of funding at this point so we could have no doubt tested this more thoroughly).

構建我們的原型似乎也沒有使訪問專有行業數據變得更容易(盡管此時我們的資金已用盡，所以毫無疑問我們可以對此進行更徹底的測試)。

The takeaway is that access to a proprietary dataset is absolutely key for an AI company.

得出的結論是，訪問專有數據集對于AI公司而言絕對是關鍵。

In general, there are three ways you can get a proprietary dataset and they are not mutually exclusive:

通常，您可以通過以下三種方式獲取專有數據集，并且它們并不互斥：

Gather the data yourself by creating an initial product or service which generates data when users interact with it. This data can subsequently be used to improve the product or service. Think Facebook, Google, Spotify, and many others.

通過創建初始產品或服務來自己收集數據，當用戶與產品或服務交互時會生成數據。 此數據隨后可用于改善產品或服務。 想想Facebook，Google，Spotify等。

Gather the data yourself by manually collecting a small proprietary dataset. This can be used to train an initial machine learning model, which needs to perform well enough to satisfy the needs of at least some early adopters. Subsequent partnerships will enable the scaling of data gathering which can then improve the model etc. Hoxton Analytics is an example of a company that followed this approach.

您可以通過手動收集小型專有數據集來自己收集數據。 這可以用于訓練初始的機器學習模型，該模型需要表現良好，以滿足至少一些早期采用者的需求。 后續的合作伙伴關系將能夠擴展數據收集的規模，進而改善模型等。Hoxton Analytics是采用這種方法的公司的一個示例。

Do a deal with an existing data holder (typically a large company or public institution). For example, Sensyne Health has done a deal with a couple of NHS trusts in the UK.

與現有的數據持有人(通常是大公司或公共機構)進行交易。 例如， Sensyne Health已與英國的幾個NHS信托公司達成了交易。

Of the three options, I would recommend the third. Here’s why.

在這三個選項中，我將推薦第三個。 這就是為什么。

If you go down route one, you are not an AI startup. AI is clearly not crucial to what you do as you can provide the service without AI. Sure, AI might massively improve your product/ or service but it needs to be good enough to collect a critical mass of user data.

如果沿著第一條路線走，那您就不是AI初創公司。 顯然，人工智能對您的工作并不重要，因為您無需使用人工智能就可以提供服務。 當然，人工智能可能會極大地改善您的產品/服務，但它必須足夠好以收集大量的用戶數據。

Photo by Massimo Botturi on Unsplash Massimo Botturi在Unsplash上拍攝的照片

With the second option, it’s possible to create an AI startup, but to maximize your chance of success, the initial dataset needs to be sufficiently niche or your approach needs be sufficiently innovative compared to existing solutions. The risk in following this approach is that before you develop partnerships to gather a critical mass of data, your idea and dataset can easily be copied by a competitor, particularly if you are tackling a well-known use case.

使用第二個選項，可以創建一個AI初創公司，但是為了最大程度地提高成功的機會，與現有解決方案相比，初始數據集需要足夠的利基，或者您的方法必須具有足夠的創新性。 采用這種方法的風險在于，在建立合作伙伴關系以收集大量數據之前，競爭對手可以輕松復制您的想法和數據集，尤其是在解決眾所周知的用例時。

That leaves option three as a key way to build an AI startup — doing a deal with a large data holder to access their data. This is the reason that the vast majority of AI startups are B2B. Big institutions and companies are usually quite slow-moving so this will usually take some time. There might well be ethical or commercial concerns about allowed another company to access their data, which will need to be ironed out. In general, companies are becoming increasingly aware of the value of the data they hold.

剩下的第三種選擇是建立AI初創公司的關鍵方法-與大型數據持有人進行交易以訪問其數據。 這就是絕大多數AI創業公司都是B2B的原因。 大型機構和公司通常動作緩慢，因此通常需要一些時間。 允許另一家公司訪問其數據可能會在道德或商業上引起關注，這將需要解決。 通常，公司越來越意識到他們所擁有數據的價值。

Of course, some AI startups have followed none of the above and have done well based on the strength of their algorithms — examples include DeepMind (bought by Google in 2014 for $500m), MagicPony (bought by Twitter in 2016 for $150m). But this path is tricky. It is much harder to maintain a competitive advantage without a proprietary dataset.

當然，一些人工智能初創公司沒有遵循上述條件，并且基于其算法的優勢也做得很好-例如DeepMind (2014年由Google以5億美元收購)， MagicPony (2016年由Twitter以1.5億美元收購)。 但是這條路很棘手。 沒有專有數據集，要保持競爭優勢要困難得多。

2.為人工智能初創公司籌集資金很難 (2. Raising money for AI startups is hard)

Photo by Micheile Henderson on Unsplash Micheile Henderson在Unsplash上拍攝的照片

Trying to raise funding was one of the hardest parts of growing our startup. It combines so many skills: telling a good story, pitching, commercial nous, legal, and more.

試圖籌集資金是發展我們的創業公司最困難的部分之一。 它結合了許多技能：講故事，推銷，宣傳廣告，法律等等。

We found there were particular challenges raising money for an AI startup.

我們發現在為AI創業公司籌集資金方面存在特別的挑戰。

We assumed that if the idea and team were strong enough and we had good enough traction, then we would be able to raise money.

我們假設，如果這個想法和團隊足夠強大并且我們有足夠的吸引力，那么我們將能夠籌集資金。

How wrong we were.

我們錯了。

It is absolutely vital to think about your likely funding right at the beginning of your startup journey. Different funders have different objectives and constraints — it’s important to appreciate these from the start. The two main groups of funders for early-stage startups are:

在啟動創業之初就考慮可能的資金投入絕對至關重要。 不同的出資者有不同的目標和限制-從一開始就意識到這些是很重要的。 早期創業公司的主要資助者有兩個類別：

• Technology Venture Capital (VC) — institutional investors in early-stage companies. At the early stages, they look for three main things — a strong team, a large market-size, and good initial traction. A large market size is crucial. As VCs typically put large amounts of money into very risky ventures, they expect most of their investments to go bust. So for the investments that go right, they need to a) see a return of more than 10x and b) see that return within ~ a 5 year time period. This means that VC-backed companies today are typically Software-as-a-Service startups (SaaS) which are focused on disrupting large industries. If you accept VC investment, the founding team will usually have less control over the company. Most VCs insist on preference shares (ability to claw back your equity if the business is sold for under the valuation they invested) and ability to get rid of the founding team (although this is rarely exercised).

  技術風險投資(VC) -早期公司的機構投資者。 在早期階段，他們尋找三大要素-強大的團隊，龐大的市場規模和良好的初期吸引力。 大市場規模至關重要。 由于風險投資人通常將大量資金投入風險很高的企業中，因此他們期望大多數投資會破產。 因此，對于正確的投資，他們需要a)看到10倍以上的回報，b)在5年內看到回報。 這就是說，如今，由VC支持的公司通常是專注于顛覆大型行業的軟件即服務初創公司(SaaS)。 如果您接受風險投資，則創始團隊通常對公司的控制較少。 大多數風險投資人堅持優先股(如果以低于其所投資估值的價格出售企業，則可以收回您的股權的能力)和擺脫創始團隊的能力(盡管很少使用)。

• Angel Investors — Angel investors come in all shapes and sizes — some look to invest alongside VCs, others look to provide more patient capital. Angel investment typically means that you retain more control over your business, although in the UK at least, it is hard to raise angel investment in excess of ￡0.5m unless you are either well connected or had previous entrepreneurial success. Most angel investors will be looking for an exit within 10 years or so.

  天使投資者 -天使投資者的形態和規模各不相同-有些人希望與風險投資人一起投資，另一些人希望提供更多的耐心資金。 天使投資通常意味著您可以更好地控制自己的業務，盡管至少在英國，除非您有良好的人脈關系或已有創業成功，否則很難籌集超過50萬英鎊的天使投資。 大多數天使投資者將在10年左右的時間內尋求退出。

Based on the above constraints, there are particular challenges in finding funding for an AI startup.

基于上述限制，在為人工智能初創公司尋找資金方面存在特殊的挑戰。

First, AI startups typically take longer to get off the ground than SaaS startups. AI algorithms rely on data and large data holders are typically big companies. As discussed above, getting any sort of access to data held by large companies is time-consuming. Even when you have access to data, you not only need to focus on business development and your software platform (like in a SaaS startup) but also your AI algorithm.

首先，與SaaS初創公司相比，AI初創公司通常需要更長的時間才能投入使用。 人工智能算法依賴數據，大數據持有者通常是大公司。 如上所述，對大公司持有的數據進行任何形式的訪問都是很耗時的。 即使可以訪問數據，您不僅需要專注于業務開發和軟件平臺(例如在SaaS創業公司中)，還需要專注于AI算法。

Photo by Jp Valery on Unsplash Jp Valery在Unsplash上拍攝的照片

Given that you need more specialist skills and it takes longer to get an AI startup off the ground, this means that you typically need more money to launch an AI startup and that money needs to be “patient capital.” For most founders, this rules out long-term angel investment (unless you are insanely well-connected) — the amount of capital required is just too great.

鑒于您需要更多的專業技能，并且要花更多的時間才能啟動AI初創公司，這意味著您通常需要更多的錢來啟動AI初創公司，并且這筆錢需要用作“患者資本”。 對于大多數創始人而言，這排除了長期的天使投資(除非您瘋狂地擁有良好的人脈關系)—所需的資金太多。

When you do pitch to VCs though, you are competing against traditional SaaS companies, which are likely going to offer a quicker return if all goes well. SaaS is an attractive business model due to its regular recurring revenue and tendency by users to forget to cancel their subscription even if they do not use the service frequently.

但是，當您投身于VC時，您正在與傳統的SaaS公司競爭，如果一切順利，它們可能會提供更快的回報。 SaaS是一種有吸引力的業務模型，因為它具有固定的經常性收入，而且即使用戶不經常使用該服務，他們也傾向于忘記取消訂閱。

As the SaaS business model has been so successful for VCs in the past decade, we found that many VCs were stuck in this way of thinking, even when not appropriate for many AI startups.

由于SaaS業務模型在過去十年中對于VC如此成功，因此我們發現，即使不適合許多AI初創公司，也有許多VC陷入了這種思維方式。

We heard a lot of “Come back to us when you have some subscription revenue.”

我們聽到了很多“當您獲得一些訂閱收入時請回來給我們。”

Most AI startups will find it hard to generate subscription revenue for at least the first few years and may well require a different business model altogether. Your proposition needs to be even more compelling to raise money.

大多數AI初創企業至少在頭幾年會發現很難獲得訂閱收入，并且可能完全需要不同的業務模型。 您的主張必須更具吸引力，才能籌集資金。

3.根據您的用例，可解釋性可能是關鍵 (3. Depending on your use case, explainability can be key)

Photo by Emily Morter on Unsplash 艾米麗·莫特 ( Emily Morter)在Unsplash上拍攝的照片

Even if you have a proprietary dataset and a brilliant product, it doesn’t necessarily mean that your product will be a hit. If you’re launching an AI startup, you are (hopefully) knowledgeable about AI and machine learning.

即使您擁有專有數據集和出色的產品，也不一定意味著您的產品會很受歡迎。 如果您要啟動AI初創公司，那么(希望)您對AI和機器學習有所了解。

The average person, however, isn’t familiar with these topics and might be sceptical of their potential.

但是，普通人對這些主題并不熟悉，可能會對它們的潛力表示懷疑。

Simply put: you need convincing proof that your model works well. A live demonstration might work but if that‘s not plausible, try using specific hand-picked examples rather than high-level accuracy figures. This might sound counter-intuitive, particularly if you’re from a mathematical background.

簡而言之：您需要令人信服的證據證明您的模型運行良好。 現場演示可能會奏效，但如果這樣做不太合理，請嘗試使用經過精心挑選的特定示例，而不是使用高準確性的數字。 這聽起來可能違反直覺，特別是如果您是數學背景的人。

Being able to explain the predictions from your model will increase trust in your model. Depending on your use case, the ability to explain each prediction from your model will often be as important as the accuracy. Explainability is a huge topic but in general, the more “human-like” your explanations, the better.

能夠解釋模型的預測將增加對模型的信任。 根據您的用例，解釋模型中每個預測的能力通常與準確性一樣重要。 可解釋性是一個巨大的話題，但總的來說，您的解釋越“像人一樣”就越好。

A good rule of thumb is that the more important each individual prediction, the more important explainability.

一個好的經驗法則是，每個單獨的預測越重要，可解釋性就越重要。

最后的想法 (Final thoughts)

AI is an amazing enabling technology that will no doubt have an incredible impact on our lives in the years to come. But that doesn’t mean that setting up an AI startup is easy—far from it.

人工智能是一項了不起的使能技術，無疑將對未來幾年的生活產生不可思議的影響。 但這并不意味著成立AI創業公司很容易-遠非如此。

Indeed, in many respects, I’ve come to learn that setting up an AI startup has a number of unique difficulties, many of which I don’t think are fully appreciated.

的確，從很多方面來看，我已經學會了建立一個人工智能初創公司有許多獨特的困難，其中許多我認為并沒有得到充分的理解。

After setting up PharmaForesight, I’m a big believer in the Henry Ford quote:

建立了PharmaForesight之后，我堅信Henry Ford的話：

“The only real mistake is the one from which we learn nothing.”

“唯一真正的錯誤是我們從中學不到的錯誤。”

Hopefully we made the mistakes above so you don’t have to.

希望我們在上面犯了錯誤，所以您不必這樣做。

翻譯自: https://medium.com/swlh/three-crucial-lessons-for-launching-an-ai-startup-976d1d44f370

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