IBM：有效AI的5個屬性

來源：數(shù)據(jù)觀 時間：2018-04-26 17:29:21 作者：Dinesh Nirmal（IBM分析開發(fā)部門的副總裁）

?幾周前，一個垂頭喪氣的首席技術(shù)官告訴我，他的團(tuán)隊(duì)花了三個禮拜的時間才建立起一個機(jī)器學(xué)習(xí)的模型，我告訴他才三周就建起一個模型聽起來相當(dāng)不錯了，他表示同意。那么，為什么要拉長了臉滿是沮喪呢？因?yàn)?1個月后，這個模型就會被束之高閣。

?隨著人工智能和機(jī)器學(xué)習(xí)與現(xiàn)實(shí)世界接觸，優(yōu)秀人工智能雛形和在運(yùn)人工智能之間的鴻溝開始成為一個共同主題。原因是……實(shí)際上有很多因素，我們可以任中擇取一些來看，但是在其他所有原因下，最本質(zhì)的問題還是數(shù)據(jù)變?nèi)绯庇壳冶剂鞑幌⑦@個事實(shí)。

?世界日新月異，數(shù)據(jù)瞬息萬變。建立人工智能或機(jī)器學(xué)習(xí)模型意味著建立一種看待世界的方式。但隨著世界和數(shù)據(jù)的變化，模型也需要適應(yīng)。我認(rèn)識的首席技術(shù)官開始意識到建立一個優(yōu)異的模型也僅僅只是跨出了第一步。

IBM位于曼哈頓的沃森總部

?一個模型本身對于現(xiàn)實(shí)世界來說太脆弱了，它需要作為一個更龐大的系統(tǒng)并且保證流暢。那么我們?nèi)绾问谷斯ぶ悄芟到y(tǒng)的流暢呢?——通過在頭腦中構(gòu)建五個屬性：

?1.控管

?對于人工智能和機(jī)器學(xué)習(xí)來說，要做實(shí)際而持久的工作，他們需要周到、持續(xù)和透明的基礎(chǔ)設(shè)施，這始于識別數(shù)據(jù)管道，糾正不良數(shù)據(jù)或丟失數(shù)據(jù)的問題。它還意味著對模型進(jìn)行集成數(shù)據(jù)治理和版本控制，每個模型的版本——你也可以同時使用數(shù)以千計(jì)的模型——顯示其輸入。你會想知道，監(jiān)管者也會想知道。

?2.彈性

?流體性意味著從一開始就能接受人工智能模型的不同步。這種“漂移”可以根據(jù)現(xiàn)實(shí)世界的變化時而迅速或緩慢地發(fā)生，經(jīng)常進(jìn)行數(shù)據(jù)科學(xué)回歸測試，但不會耗費(fèi)你的時間。

?這需要一個系統(tǒng)來設(shè)置準(zhǔn)確的閾值和自動警報(bào)，讓你知道什么時候需要注意模型。你是否要在舊的數(shù)據(jù)上重訓(xùn)模型，獲取新的數(shù)據(jù)，又或者從頭對特性進(jìn)行重組？答案取決于數(shù)據(jù)和模型，但第一步是知道問題出在哪里。

?3.競爭力

?大多數(shù)人工智能在計(jì)算上都非常緊張——無論是在訓(xùn)練期間還是在部署之后。大多數(shù)模型需要以毫秒為單位，而不是分鐘來進(jìn)行交易，以防止欺詐或投機(jī)倒把。理想情況下，你可以在GPU上訓(xùn)練模型，然后將它們部署到高性能的CPU上，并提供足夠的內(nèi)存來進(jìn)行實(shí)時評分。

?當(dāng)然，不管部署在何處，on-prem、云計(jì)算或multi-cloud，你都希望一切能準(zhǔn)確無誤地快速運(yùn)行。

?4.可測性

?目前，對于人工智能和機(jī)器學(xué)習(xí)項(xiàng)目的預(yù)算還算慷慨，但如果數(shù)據(jù)科學(xué)團(tuán)隊(duì)遲遲不能提供具體結(jié)果，這些預(yù)算也將缺口。從一開始就考慮如何量化和形象化你正在學(xué)習(xí)的東西以及變化過程，這改進(jìn)了數(shù)存取和數(shù)據(jù)卷，提高了模型的精確度，并最終提升了底線。

?當(dāng)你的數(shù)據(jù)科學(xué)工作已日趨成熟，你不僅要考慮現(xiàn)在需要權(quán)衡什么，還要考慮將來的權(quán)衡問題，系統(tǒng)是足夠“流動”以便跟進(jìn)這些長期目標(biāo)?

?5.持續(xù)性

?一開始我就指出了數(shù)據(jù)并不是靜止的。流體人工智能的第五個也是最后一個方面是隨著世界的變化不斷地進(jìn)行學(xué)習(xí)。一定要使用像Jupyter和Zeppelin這樣的工具，這些工具可以接入到進(jìn)程的調(diào)度評估和再培模型中。

?同時，當(dāng)你從各種算法、語言，數(shù)據(jù)集和工具中汲取優(yōu)缺勢，你也期待自己的學(xué)習(xí)不斷地成長和發(fā)展。流體人工智能要求對數(shù)據(jù)、工具和系統(tǒng)進(jìn)行持續(xù)改進(jìn)，但也需要每個人在工作上不斷改進(jìn)。

?數(shù)據(jù)科學(xué)是一段旅程。俗套但真實(shí)。注意以上這五個特質(zhì)，你將聚焦于每一個時刻，迫使自己去發(fā)現(xiàn)未來的清晰脈絡(luò)。

注：本文由數(shù)據(jù)觀編譯自VentureBeat網(wǎng)站，作者/Dinesh Nirmal，譯者/黃玉葉，圖片來源于原文配圖。轉(zhuǎn)載請務(wù)必注明來源、出處及作者等信息。

?以下為原文：

?IBM outlines the 5 attributes of useful AI

?A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Because 11 months later, the model was still sitting on a shelf.

?That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons, and we can look at a bunch of them, but underneath all the other reasons is the fact that data doesn’t sit still and never will.

?Data changes as the world changes. Building an AI or machine learning model means building a way of looking at the world. But as the world and the data change, the models need to adapt. The CTO I met was realizing that building a great model is only the first step.

?A model on its own is too brittle for the real world. It needs to live as a larger system that’s actually fluid. So how do we make AI systems that are fluid? By building them with five attributes in mind:

?1. Managed

?For AI and machine learning to do real and lasting work, they need thoughtful, durable, and transparent infrastructure. That starts with identifying the data pipelines and correcting issues with bad or missing data. It also means integrated data governance and version control for models. The version of each model — and you might use thousands of them concurrently — indicates its inputs. You’ll want to know, and so will regulators.

?2. Resilient

?Being fluid means accepting from the outset that AI models fall out of sync. That “drift” can happen quickly or slowly depending on what’s changing in the real world. Do the data science equivalent of regression testing, and do the testing frequently, but without burning up your time.

?That takes a system that allows you to set accuracy thresholds and automatic alerts to let you know when models need attention. Will you need to retrain the model on old data, acquire new data, or re-engineer your features from scratch? The answer depends on the data and the model, but the first step is knowing there’s a problem.

?3. Performant

?Most AI is computationally intense — both during training and after deployment. And most models need to score transactions in milliseconds, not minutes, to prevent fraud or leverage some fleeting opportunity. Ideally, you can train models on GPUs and then deploy them on high-performance CPUs, along with enough memory for real-time scoring.

?And of course you want everything to run fast and error-free regardless of where you deploy: on-prem, cloud, or multicloud.

?4. Measurable

?For the moment, budgets for AI and machine learning projects are generous, but those budgets will dry up if data science teams can’t deliver concrete results. Think from the outset about how you’ll quantify and visualize what you’re learning and how it changes: improvements in data access and data volume, improvements in model accuracy, and ultimately improvements to the bottom line.

?Don’t just think about what you need to measure now but also about what you’ll want to measure in the future as your data science work matures. Is the system fluid enough to track those long-term goals?

?5. Continuous

?I started by pointing out that data doesn’t sit still. The fifth and final aspect of fluid AI is about continuous learning as the world changes. Make sure to use tools like Jupyter and Zeppelin notebooks that can plug into processes for scheduling evaluations and retrain models.

?At the same time, expect your own learning to grow and evolve as you absorb the advantages and limitations of various algorithms, languages, datasets, and tools. Fluid AI demands continuous improvement for data, tools, and systems, but also continuous improvement from everybody doing the work.

?Data science is a journey. Cheesy, but true. Pay attention to these five attributes and you’ll bring focus to each moment and force yourself to find clarity about the future.

責(zé)任編輯：陳近梅