Every business in the world should be an artificial intelligence company.
世界上的每個企業都應該是人工智慧公司。
That’s quite a definitive statement, isn’t it? It’s also a true one.
這是一個非常明確的聲明，不是嗎？這也是真實的。
I had the pleasure of recently speaking at a Startup Grind conference on this very topic, and today, I get to share that same message with you. As developers within the Intuit community, I hope you’ll enjoy my “demystification” of artificial intelligence (AI) and machine learning (ML).
我最近很高興在 Startup Grind 會議上就這個主題發表演講，今天，我將與您分享相同的資訊。作為 Intuit 社群的開發人員，我希望您會喜歡我對人工智慧 (AI) 和機器學習 (ML) 的「揭秘」。
The AI revolution 人工智慧革命
Before diving in to why every business in
the world should be an AI company, let’s quickly define AI and ML.
在深入探討為什麼世界上的每個企業都應該是人工智慧公司之前，讓我們先快速定義一下人工智慧和機器學習。
The Gartner Glossary defines AI as “applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.” For our purposes today, I’m defining AI as taking data, learning from it, and redeploying outputs that help your customers.
Gartner 術語表將人工智慧定義為“應用先進的分析和基於邏輯的技術（包括機器學習）來解釋事件、支援和自動化決策，並採取行動。”就我們今天的目的而言，我將人工智慧定義為獲取數據、從中學習並重新部署可幫助客戶的輸出。
This is what the Gartner Glossary writes about ML: “Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.” ML is a subset of AI, and all ML counts as AI, but not all AI counts as ML; instead, ML learns over time.
Gartner 術語表對ML 的描述是這樣的：「高級機器學習演算法由許多技術（例如深度學習、神經網路和自然語言處理）組成，用於無監督和監督學習，並根據現有資訊的經驗教訓進行操作”。 ML是AI的子集，所有ML都算是AI，但並非所有AI都算在ML；相反，機器學習會隨著時間的推移而學習。
AI and ML are hot topics right now, but I consider this time to be more than just implementing great technologies. I believe we’re in an AI revolution for several reasons.
人工智慧和機器學習是目前的熱門話題，但我認為這次不僅僅是實施偉大的技術。我相信我們正處於人工智慧革命之中有幾個原因。
- AI and ML have been around for a long time, but the challenge has been the lack of collected data to apply AI, or more specifically, ML. That’s changing as businesses begin to understand the power of data and the necessity of collecting mass quantities of data.
人工智慧和機器學習已經存在很長時間了，但挑戰是缺乏收集的數據來應用人工智慧，或更具體地說，機器學習。隨著企業開始了解資料的力量以及收集大量資料的必要性，這種情況正在改變。 - ML requires neural networks that require high-intensity compute. ML does parallel processing. CPUs can’t do that, but GPUs can. They can take large chunks of data, split them up, and process them over thousands and thousands of cores so that you come up with the output. Because the cost and availability of GPU has come down and is widely available, you can now build ML models on your laptop.
機器學習需要需要高強度計算的神經網路。 ML 進行平行處理。 CPU 做不到這一點，但 GPU 可以。它們可以獲得大量數據，將其分割，並透過成千上萬個核心對其進行處理，以便您可以得出輸出。由於 GPU 的成本和可用性已經下降並且廣泛使用，您現在可以在筆記型電腦上建立 ML 模型。 - There have been great advances in algorithms. Note: people that want to get into AI and ML need to know math— it’s all equations and algorithms.
演算法已經取得了巨大的進步。注意：想要進入人工智慧和機器學習領域的人需要了解數學——所有的都是方程式和演算法。
With these advances in AI and ML capabilities, every industry, not just technology, is going to be changed. These technologies are revolutionary. Between now and 2030, they will create an estimated $13 trillion of GDP growth. AI will also create 2.3 million jobs by 2020.
隨著人工智慧和機器學習能力的進步，每個行業（而不僅僅是技術）都將改變。這些技術是革命性的。從現在到 2030 年，它們將創造估計 13 兆美元的 CGD 成長。到2020年，人工智慧也將創造230萬個就業機會。
Interestingly, Gartner’s 2018 survey of 3,000 CIOs across the globe found that only one in 25 have started to deploy AI. The question is, why aren’t they jumping on the AI and ML bandwagon?
有趣的是，Gartner 2018 年對全球 3000 名 CIO 的調查發現，只有二分之一的人開始部署人工智慧。問題是，他們為什麼不加入人工智慧和機器學習的潮流呢？
The answer lies in how broad the topic is and the lack of understanding of how to use AI in their businesses. For example, most distributors don’t need AI to know that adding another truck or two can increase their supply chain. What they do need AI and ML for is to collect the data from those trucks (for example, the distance they travel) to make better, more informed business decisions.
答案在於該主題的廣泛性以及對如何在業務中使用人工智慧缺乏了解。例如，大多數分銷商不需要人工智慧就知道增加一兩輛卡車可以增加他們的供應鏈。他們真正需要人工智慧和機器學習的是從這些卡車收集數據（例如，它們行駛的距離），以做出更好、更明智的業務決策。
Gartner’s study provides great examples of how using AI benefits businesses and customers alike. Example include using chatbots to answer consumer questions, predicting when a key sensor in a machine needs to be replaced, forecasting when units will sell out, assisting healthcare providers diagnose and search images for early cancer detection, and more.
Gartner 的研究提供了使用人工智慧如何使企業和客戶受益的絕佳範例。例如，使用聊天機器人回答消費者問題、預測機器中的關鍵感測器何時需要更換、預測設備何時售完、協助醫療保健提供者診斷和搜尋影像以進行早期癌症檢測等。
And to do all of this, businesses need to
be collecting data. Lots of it.
為了做到這一切，企業需要收集數據。很多。
Mounds of data and AI strategy
大量數據和人工智慧策略
So, to properly utilize AI and ML, you need to set up a strategy.
因此，要正確利用人工智慧和機器學習，您需要製定策略。
#1: Build a team of AI and ML experts. These experts should not be people who may know a little about simple rule-based algorithms. They must understand math, specifically the linear regression algorithm. The team should include a software engineer, data scientists and developers.
#1：建立一支人工智慧和機器學習專家團隊。這些專家不應該是對簡單的基於規則的演算法略知一二的人。他們必須了解數學，特別是線性迴歸演算法。該團隊應包括軟體工程師、資料科學家和開發人員。
#2: Start collecting mounds of data. Have people in your organization dedicated to collecting data.
#2：開始收集大量數據。讓您的組織中的人員致力於收集數據。
Developers, your job is to help collect the data; you’re not data scientists.
Data scientists are the ones who will know how to collect, gather, and organize
data in a way that makes it easily consumable to both people and machines.
開發者，你的工作就是幫忙收集數據；你不是資料科學家。資料科學家知道如何收集、收集和組織數據，以便人和機器都可以輕鬆使用數據。
Also, and this is important, you must inform your customers that you’re gathering the data and what you’re doing with the data. In fact, in most countries, it’s the law.
此外，這一點很重要，您必須告知客戶您正在收集資料以及您正在使用這些資料做什麼。事實上，在大多數國家，這是法律。
#3: Do experiments and start building models. You can use companies like Kaggle to find the code and data to do your data science work.
#3：做實驗並開始建立模型。您可以使用 Kaggle 等公司來尋找程式碼和資料來完成您的資料科學工作。
AI and ML can bring powerful prosperity
人工智慧和機器學習可以帶來強大的繁榮
Here’s a great quote: With the advent of AI, intelligent applications will be the fountain of the next generation of great software companies because they will be the new moats. This means that companies collecting data and using that data to improve their business processes will not get overtaken by their competitors. Essentially, every business should be an AI business.
這是一句很好的話：隨著人工智慧的出現，智慧應用程式將成為下一代偉大軟體公司的源泉，因為它們將成為新的護城河。這意味著收集數據並使用這些數據來改善業務流程的公司不會被競爭對手超越。本質上，每個企業都應該是人工智慧企業。
However, to be clear, AI and ML aren’t
magic wands. They won’t solve every business problem, but they are tools within
your toolbox to solve many business needs.
然而，需要明確的是，人工智慧和機器學習並不是魔杖。它們無法解決所有業務問題，但它們是您工具箱中的工具，可以解決許多業務需求。
My 13+ years at Microsoft and Google kept me at the very center of the technology industry – and Intuit is no different. I can tell you that Intuit has been leveraging the latest technology, including AI and ML, for years as they continue to fulfill their mission of powering prosperity around the world in a transparent and ethical manner.
我在 Microsoft 和 Google 工作了 13 年多，讓我一直處於科技產業的中心 — Intuit 也不例外。我可以告訴您，Intuit 多年來一直在利用包括人工智慧和機器學習在內的最新技術，繼續履行以透明和道德的方式推動世界繁榮的使命。
Just as Intuit CEO Sasan Goodarzi mentioned in talking to Business Insider, “It’s actually a cultural change that we have to go through internally to understand the impact of artificial intelligence.” While we at Intuit approach all the amazing opportunities that AI delivers, we approach them with thoughtful consideration. AI is not something to do to just do. It is a technology that every company has to approach thoughtfully.
正如 Intuit 執行長 Sasan Goodarzi 在接受 Business Insider 採訪時提到的那樣，“這實際上是一種文化變革，我們必須在內部經歷這種變革，才能了解人工智慧的影響。”雖然 Intuit 正在抓住人工智慧帶來的所有令人驚奇的機會，但我們會深思熟慮地對待它們。人工智慧不是為了做而做的事情。這是每家公司都必須深思熟慮的一項技術。
I’ve been gathering financial data from small businesses (which these businesses know about) and implementing business-changing innovations, such as QuickBooks Assistant. Unveiled in 2017 for iOS and Android users, and available for QuickBooks Online in 2018, QuickBooks Assistant is a chatbot that combines data-driven insights and natural language processing to ease business operations by asking financial questions or making requests, allowing users to uncover financial data points.
我一直在收集小型企業的財務數據（這些企業了解這些數據）並實施改變業務的創新，例如 QuickBooks Assistant。 QuickBooks Assistant 於2017 年為iOS 和Android 用戶推出，並於2018 年為QuickBooks Online 推出，它是一款聊天機器人，它將資料驅動的見解和自然語言處理相結合，透過詢問財務問題或提出請求來簡化業務運營，從而使用戶能夠發現財務數據點。
Pretty awesome stuff. So is the idea set forth by Sam Altman, former president of Y Combinator and CEO of Open AI. He says AI and ML have the potential to eliminate poverty, solve climate change, cure human disease, and educate people.
非常棒的東西。 Y Combinator 前總裁兼 Open AI 執行長 Sam Altman 提出的想法也是如此。他表示，人工智慧和機器學習有潛力消除貧窮、解決氣候變遷、治癒人類疾病和教育人們。
Every company in the world should be an
AI business. Do you agree?
世界上每家公司都應該是人工智慧企業。你同意？
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