vad唤醒算法_唤醒算法经济后公司需要考虑的问题
vad喚醒算法
We are living in the era of the algorithm driving the digital economy. To keep up, businesses are turning to the backend of machine learning tools to power revenue streams.
我們正處在驅動數字經濟的算法時代。 為了跟上步伐，企業正在轉向機器學習工具的后端來增加收入來源。
According to Gartner’s 2019 CIO Agenda survey:
根據Gartner的2019年CIO議程調查：
Between 2018 and 2019, the percent of organizations adopting AI practices grew from 4% to 14%.
在2018年至2019年之間，采用AI做法的組織百分比從4％增加到14％。
As data continues to be the focal point of decision-making, we will only see more and more businesses harnessing AI capabilities to supercharge certain business functions; however, with this trend, there are growing concerns over the gap between decision makers and their ability to defend the inner-workings of algorithms put to practice by their organizations.
隨著數據繼續成為決策的重點，我們只會看到越來越多的企業利用AI功能來增強某些業務功能。 但是，隨著這一趨勢，人們越來越關注決策者之間的差距以及他們捍衛組織實施的算法內部工作的能力 。
簡化算法 (Algorithms Simplified)
By definition, an algorithm is a series of steps taken to complete a task or operation.
根據定義，算法是完成任務或操作所采取的一系列步驟。
In the context of AI, an algorithm is a process that learns from given data without having to be programmed explicitly through statistical methodology known as machine learning (ML). An ML algorithm can range in complexity from a few paltry steps to complete a single parameter regression to a deep neural network with thousands or millions of parameters interconnected through an even larger number of neurons to form relationships, which are largely unexplainable by today’s technology.
在AI的上下文中，算法是從給定數據中學習而不必通過稱為機器學習(ML)的統計方法進行顯式編程的過程。 ML算法的復雜性范圍很廣，從完成幾個簡單的步驟到完成單個參數的回歸，再到深度神經網絡，其中成千上萬個參數通過更多數量的神經元相互連接以形成關系，而這在當今的技術中是無法解釋的。
This latter type of model is known as a blackbox model and is quite common in the practice of AI: this is when the input yields a valuable output, with little to no knowledge of what took place inside of the model and how the data in question was processed.
后一種類型的模型稱為黑盒模型 ，在AI的實踐中很常見：這是當輸入產生有價值的輸出時，幾乎不知道模型內部發生了什么以及所涉及的數據如何已處理。
缺乏算法責任 (The Lack of Algorithm Accountability)
Traditionally, algorithm accountability has taken a backseat to profit. Researchers and business leaders haven’t needed to make public or defend the details of their proprietary models.
傳統上，算法問責制使利潤倒退。 研究人員和業務負責人無需公開或捍衛其專有模型的細節。
With no outside pressure, why would a company be motivated to pause and contemplate their data practices if their ML pipelines are healthy and customers are satisfied?
在沒有外部壓力的情況下， 如果公司的機器學習管道運行良好且客戶滿意 ， 為什么還要激勵公司暫停并考慮其數據實踐？
For starters, there is the interest in perfecting the product and strategy. Within the data pipeline, you should be able to guarantee quality and no bias at every step. If you can’t, there is a risk of welcoming bad quality data into the system or creating a highly biased model that turns away customers.
對于初學者來說，有完善產品和策略的興趣。 在數據管道中，您應該能夠保證質量，并且每一步都沒有偏差。 如果無法做到，則存在將不良質量的數據歡迎到系統中或創建高度偏向客戶的模型的風險。
A famous example came last fall, when Apple released the Apple Card (issued by Goldman Sachs) and customers noticed that female cardholders were given a lower credit line than their male counterparts. When interrogated, Goldman Sachs was largely unable to explain the disparity and ended up facing no consequences.
一個著名的例子是去年秋天，當時蘋果發布了由高盛發行的Apple Card， 顧客注意到女性持卡人的信用額度比男性持卡人低。 在接受審訊時，高盛基本上無法解釋這種差異，最終沒有任何后果。
This seems to be the pattern with companies and organizations who are called out for discriminatory systems: initial exposure by media and then swiftly brushed away as the product stays in production with few hassled changes on the company’s end.
這似乎是要求歧視性系統的公司和組織的一種模式： 最初是通過媒體曝光，然后隨著產品在生產中的使用而Swift被淘汰， 而在公司的末尾幾乎沒有麻煩的變化。
But, as AI becomes democratized, companies should be wary that regulation will follow suit.
但是，隨著AI民主化，公司應警惕監管將隨之而來。
在公司設定AI道德標準 (Setting the AI Ethics Standard at Your Company)
“Garbage in = garbage out” is an overused adage in machine learning that will definitely be thrown at you in an intro class.
“垃圾回收=垃圾淘汰”是機器學習中過度使用的格言，在入門課程中肯定會扔給您。
There is a common misconception that in order to remedy model accuracy, you must simply rein in more data. This is not necessarily true. it is quite more strategic to have good quality data than be able to mine a copious amount of data.
常見的誤解是，為了糾正模型的準確性，您必須簡單地控制更多數據。 這不一定是真的。 擁有高質量的數據比挖掘大量 數據更具戰略意義。
Without being thorough, you risk putting your company in a spot where the data team is struggling with an algorithm that isn’t performing optimally or is backed up with scrubbing messy data to a usable format. It is a laughable point in the industry that a data scientist spends most of their time cleaning data than any other glamorized task. While data scientists tend to be trained on clean toy datasets, in practice, data is extremely difficult to receive cleaned.
如果不徹底，您可能會冒著使公司陷入數據團隊陷入困境的風險，因為該算法的性能無法達到最佳或已將雜亂數據整理成可用的格式。 在行業中，可笑的一點是，數據科學家花費大部分時間來清理數據，而不是其他任何繁瑣的任務。 盡管數據科學家傾向于接受有關清潔玩具數據集的培訓，但在實踐中，很難獲得清潔的數據。
If you are building a company or running the department that works with data, it is your responsibility to find resources to create thoughtful data processes.
如果您要建立公司或運營與數據打交道的部門，那么您有責任找到創建周到的數據流程的資源。
If you’re an early-stage company, it is crucial to set this data quality precedence as the company grows and is centered around AI technology. If you do come from a non-technical background and are starting up a company, it might help to consult experts in this area to ensure that your business is running on a healthy pipeline without being fully hands-off. There is a lot of contention in how much AI should be present in our lives, but it looks like it will become more of a reality sooner than later. We are the past the struggle of getting usable information out of these systems, but are stuck fully on the complicated side-effects that do come with it.
如果您是一家處于起步階段的公司，那么隨著公司的發展以及以AI技術為中心，設置此數據質量優先級至關重要。 如果您確實來自非技術領域并且正在創辦公司，那么可能會幫助咨詢該領域的專家，以確保您的業務運轉順利，而無需完全放手。 在我們的生活中應該出現多少AI方面存在很多爭論，但是看起來它早日成為現實。 從這些系統中獲取有用信息是我們過去的努力，但是我們完全陷入了隨之而來的復雜副作用。
AI民主化如何在道德AI中發揮作用？ (How Does AI Democratization Play in Ethical AI?)
The previous section ties into the goals of democratizing AI — challenging the incorrect perception that AI is highly complicated and unapproachable by those who are not specifically trained in it. There are several no-code/low-code platforms that could help you build initial business intelligence and machine learning models. These platforms are ideal for early stage to early growth-stage companies for specific needs like predicting churn rate or customer lifetime value.
上一節與使AI民主化的目標相關聯-挑戰錯誤的認識，即AI高度復雜且未被未經專門培訓的人無法接近。 有幾種無代碼/低代碼平臺可以幫助您建立初始的商業智能和機器學習模型。 這些平臺對于滿足特定需求(例如預測客戶流失率或客戶生命周期價值)的早期到早期增長階段的公司是理想的。
However, there are limitations to using no-code platforms with pre-built models in being able to do precise parameter tuning or scale well as the company and data grow. This is would likely be the time your company is able to grow a data department to take care of custom pipelines, but using a platform like Obviously AI to do initial data work is quite effective.
但是，使用無代碼平臺和預構建模型存在局限性，因為它可以進行精確的參數調整或隨著公司和數據的增長而很好地擴展。 這可能是您的公司能夠發展數據部門來處理自定義管道的時候，但是使用像明顯的AI這樣的平臺進行初始數據工作是非常有效的。
The main challenge for for startups has become how to thoughtfully and emphatically build these machines—and less about if it is possible to.
對于初創企業而言，主要的挑戰在于如何深思熟慮地重點開發這些機器，而不是如果可能的話。
As a small to medium sized business, you might not be able to allocate resources to your data team, but you do have the ability to set up a data-powered company ethically and carefully very early on.
作為一家中小型企業，您可能無法為數據團隊分配資源，但是您確實有能力在很早的時候就以道德和謹慎的態度成立一家由數據驅動的公司。
Still, there is a fair amount of apprehension with shifting towards the AI economy from analog and what this could mean for privacy and avoiding discrimination. To protect your company and customers, it is important to think about the steps that take to deploy a model.
盡管如此，在從模擬經濟轉向AI經濟的過程中仍存在相當多的擔憂，這對于隱私和避免歧視意味著什么。 為了保護您的公司和客戶，請務必考慮部署模型所要采取的步驟。
在構建AI系統的每個步驟中都可能存在偏差 (Bias is Possible at Every Step of Building Your AI system)
Suppose you are implementing the pipeline for determining credit limit:
假設您正在實施確定信用額度的管道：
You frame the problem as “What is the credit limit granted to the customer based off personal and financial factors?” You are trying to optimize for maximum repayment.
您將問題定義為“ 基于個人和財務因素授予客戶的信用額度是多少？ ”您正在嘗試優化以實現最大還款。
The model is trained on previously collected data because you have no new user data of this specific credit card since it is unreleased. You have access to tons of previous credit issuing and repayment data on thousands of customers, so you decide to use this to train your model. This data was collected in a way that ended up with a lot of missing data points.
該模型是根據先前收集的數據進行訓練的，因為您沒有該特定信用卡的新用戶數據，因為它尚未發布。 您可以訪問成千上萬的大量客戶先前的信用發放和還款數據，因此您決定使用此數據來訓練模型。 收集這些數據的方式最終導致許多缺失的數據點。
Bias can already be introduced to your system if you didn’t stop to question a few things:
如果您不停止質疑以下問題，那么就已經可以將偏差引入您的系統：
Has your previous data—which will be your training data—been checked for bias? Did you question why there’s missing data?
是否檢查過您以前的數據(將作為您的訓練數據)是否存在偏倚？ 您是否質疑為什么缺少數據？
Now, we move on to processing the data. Here, you try to figure out what to do with the missing data and determine what variables to use in your model. For those instances of missing data points, you choose to drop them entirely from your dataset. You choose to remove “gender” and “race” from the input because you definitely don’t want a biased system. However, you did not consider the other variables which will implicitly group genders and races anyway — which happens quite often in problematic AI that have been made public.
現在，我們繼續處理數據。 在這里，您嘗試找出如何處理丟失的數據，并確定要在模型中使用哪些變量。 對于那些缺少數據點的實例，您選擇完全從數據集中刪除它們。 您選擇從輸入中刪除“性別”和“種族”，因為您絕對不希望系統帶有偏見。 但是，您沒有考慮其他變量，它們無論如何都會隱式地將性別和種族進行分組，而這在已公開的有問題的AI中經常發生。
You choose a proprietary model after some testing to output credit limits, which will maximize repayment for the company. The model is trained on the data you processed and cleaned in the previous step. You test for error rates and are satisfied and the model is put into production as the credit card is released.
經過一些測試后，您可以選擇專有模型來輸出信用額度，這將最大程度地為公司償還債務。 根據您在上一步中處理和清理的數據對模型進行訓練。 您測試錯誤率并感到滿意，并且隨著信用卡的發放，該模型已投入生產。
The model may be able to now discriminate on the basis of race and/or gender. The missing data points could’ve been due to customers who are younger and are lacking information on their financial background or it could be customers who had to cancel a previous credit card or because of hardship have gaps in repaying on time.
該模型現在可以根據種族和/或性別進行區分。 丟失的數據點可能是由于年輕的客戶以及缺乏財務背景信息的客戶造成的，或者是由于不得不取消以前的信用卡或由于困難而無法按時還款的客戶。
This leads the system to not internalize these instances and form an unknown bias for new customers who may have similar backgrounds since those data points were dropped and excluded from the model.
這導致系統無法內部化這些實例，并且對于那些背景相似的新客戶形成未知的偏見，因為這些數據點已被刪除并從模型中排除。
Machine-intelligence emulates the societal prejudice that plagues us, but it does reduce bias to a certain degree. There are plentiful examples of why AI is better than allowing humans to be the sole decision maker in cases such as policing or determining what high-risk patient should receive care because human bias is exactly the thing that AI-based recommendations were set out to abolish. With this shift, we have introduced a different, more complex type of bias which is inherent to how AI functions.
機器智能模仿了困擾我們的社會偏見，但確實在一定程度上減少了偏見。 有很多例子說明為什么在監管或確定哪些高風險患者應接受治療等方面，人工智能比讓人類成為唯一的決策者更好，因為基于人類的偏見正是基于人工智能的建議被取消的原因。 。 通過這種轉變，我們引入了一種不同的，更復雜的偏差類型，這是AI運作方式固有的。
呼吁開源和可解釋模型 (The Call for Open Source and Explainable Models)
Algorithms often become questionable when it enters spaces where data is sensitive — finance, healthcare, or the justice system.
當算法進入數據敏感的空間(金融，醫療保健或司法系統)時，算法通常會產生問題。
Without our direct knowledge of it, these algorithms have entered these systems which typically should require more scrutiny and we are finding that they introduce bias in a more complex manner than human decision making — this poses to affect part of society quite adversely, including women and people of color.
在沒有我們直接了解的情況下，這些算法已進入了這些系統，通常需要更多的審查，而且我們發現它們以比人為決策更復雜的方式引入偏見- 這對社會的某些部分造成了不利影響，包括婦女和婦女。有色人種。
The usual proposal for this type of recurring discrimination through AI is to make the use of proprietary modeling obsolete. The most infamous example of a company conducting secretive data operations is Palantir, who filed for an IPO in 2020. It faced a lot of public scrutiny because of their government ties. There hasn’t been a need for it to come forward and disclose publicly how it mines or uses data, likely because it does work with organizations such as the CIA and Pentagon.
通過AI進行這種反復歧視的通常建議是過時使用專有模型。 公司進行秘密數據操作的最臭名昭著的例子是Palantir，他于2020年申請IPO。由于其與政府的關系，該公司面臨著許多公眾審查。 并不需要公開提出它如何挖掘或使用數據，可能是因為它確實與CIA和五角大樓等組織合作。
Publishing your work in public gives it a better chance of it being checked for flaws or areas that it could collect bias. Popular AI frameworks like TensorFlow and Keras are open-source, and there’s people using it and pointing out any deprecations or bugs in the models frequently. The more likely scenario is that your company gets off the ground with no-code/low-code tools and then this may be something to worry about later down the line when scaling. It is also likely you might not even need blackbox AI tools to meet your business needs.
在公共場合發布您的作品，可以更好地檢查可能會引起偏差的缺陷或區域。 TensorFlow和Keras等流行的AI框架是開源的，有人在使用它，并經常指出模型中的任何過時或錯誤。 更有可能的情況是，您的公司使用無代碼/低代碼工具起步，然后在擴展時可能會擔心這事。 您甚至可能甚至不需要黑盒AI工具來滿足您的業務需求。
In this paper, Cynthia Rudin, professor of computer science at Duke University, vouches for picking interpretable models instead of blackbox models, especially when it comes to sensitive spaces. Of course, there is a compromise in model accuracy when we delve into using a simpler model with more explainability. However, a simpler ML model allows the researcher to be able to understand bias creation better and tune parameters as needed — doing the same for a highly complex model might be impossible. In most cases, Rudin argues, an interpretable model works good enough anyway for the problem being solved for. AI is marketed as an unattainable feat for the most part, but deep neural networks are not needed for you to automate internal processes.
在本文中 ，杜克大學計算機科學教授辛西婭·魯丁(Cynthia Rudin)保證選擇可解釋的模型，而不是黑盒模型，特別是在涉及敏感空間時。 當然，當我們深入研究使用具有更多可解釋性的更簡單模型時，模型準確性會受到損害。 但是，更簡單的ML模型使研究人員能夠更好地理解偏差的產生并根據需要調整參數-對高度復雜的模型進行相同的操作可能是不可能的。 魯丁認為，在大多數情況下，無論如何解決問題，可解釋的模型都足夠有效。 人工智能在大多數情況下是無法實現的壯舉，但是您不需要深度神經網絡來使內部流程自動化。
Algorithms are prevalent in our digital day-to-day lives and they improve it for the most part. It is inevitable that loosely defined problems, uncleaned data, and unintended bias will enter the system somehow, but these should be dealt with before being put in production. This is because when facial recognition technology discriminates against Black people or your healthcare bot prioritizes men over women for medical care—and that is plain irresponsible.
算法在我們的數字日常生活中非常普遍，并且在很大程度上改善了算法。 不可避免地，松散定義的問題，未清除的數據和意外的偏差將以某種方式進入系統，但是這些問題應在投入生產之前進行處理。 這是因為當面部識別技術歧視黑人時，或者您的醫療保健機器人將男性優先于女性進行醫療保健時，這是毫無責任的。
算法業務早就該進行監管了 (Algorithmic Businesses are Long Overdue for Regulation)
A lot of society is wholly skeptical of AI technology and with rightful reasoning because a lot of media perpetuates the controlling and invasive nature of AI repeatedly. While not understanding the true capabilities and limitations of the technology we have so far, lawmakers have slowly started to call for regulation but have fallen short:
許多社會完全懷疑AI技術并具有正當的理由，因為許多媒體反復地延續了AI的控制性和侵入性。 議員們雖然不了解我們迄今為止所擁有技術的真正功能和局限性，但他們慢慢開始呼吁監管，但未能做到：
In 2017, New York City put together an act to combat discriminatory AI systems. This received a lot of backlash because it was essentially forcing public agencies and tech companies to publicize their code. There was a huge concern for diminishing competitive advantage and also heightened security risk. In this case, it was obvious that the preparers of this act didn’t consider all the nuance that came with AI regulation.
2017年，紐約市制定了一項打擊歧視性AI系統的法案 。 由于這實際上迫使公共機構和科技公司公開其代碼，因此受到了強烈反對。 人們對減少競爭優勢以及增加安全風險深感憂慮。 在這種情況下，很明顯，該法案的準備者并未考慮到AI法規帶來的所有細微差別。
In 2019, Congress proposed the Algorithmic Accountability Act, which would allow the Federal Trade Commission (FTC) to enforce impact assessment on companies who are suspected to be deploying biased systems. This was exceptionally better fleshed out than the act in 2017, but there is still question of how third party affiliation could affect this sensitive investigation inside of a company.
國會在2019年提出了《 算法責任法案》 ，該法案將允許聯邦貿易委員會(FTC)對懷疑正在部署偏見系統的公司實施影響評估。 這比2017年的法案更為充實，但是仍然存在一個問題，即第三方關系如何影響公司內部的敏感調查。
From these two initiatives, it is clear that the runway to more refined regulation is in place as companies are adopting and deploying AI exponentially more each year. Although a lot appears to fly under the radar as AI regulation is still quite nebulous, we will see more companies facing more speculation.
從這兩項計劃中可以明顯看出，隨著公司每年采用和部署AI的次數呈指數級增長，更完善的法規已到位。 盡管由于AI法規仍然很模糊，很多人似乎不愿接受，但我們將看到更多的公司面臨更多的投機活動。
人工智能算法以多種方式影響消費者 (AI Algorithms Affect Consumers in Many Ways)
This article should encourage thoughtfulness about data processes as it not only serves as a revenue funnel, but also can affect people in many ways and jeopardize the company accidentally. In the situation of Apple and Goldman Sachs, there was little accountability on their end aside from a short statement. For a business leader, this means you should be ready to defend any processes or models that led to customers or users feeling any sort of discrimination. There is no way to be better prepared for this, but by being involved in laying the groundwork for thoughtful AI.
本文應該鼓勵對數據流程進行謹慎的考慮，因為它不僅可以充當收入漏斗，而且還可以在許多方面影響人們并意外危害公司。 在蘋果公司和高盛公司的情況下，除了簡短的聲明外，他們最終沒有承擔任何責任。 對于業務主管來說，這意味著您應該準備捍衛任何導致客戶或用戶感到任何歧視的流程或模型。 沒有辦法為此做好更好的準備，而是要參與為深思熟慮的AI奠定基礎。
🤖 Eshita Nandini is currently working on Option Impact at Shareworks by Morgan Stanley. Previously, Eshita studied applied math at UC Merced. Follow Eshita on Twitter here.
shi Eshita Nandini 目前 摩根士丹利(Morgan Stanley)在Shareworks研究“期權影響”。 之前，Eshita在UC Merced學習了應用數學。 按照Eshita在Twitter 這里 。
🤖 Don’t forget to follow Downsample on Twitter!
🤖 不要忘記在Twitter上關注Downsampl e！
🤖 While you’re at it, follow Obviously AI!
at在使用它時，請遵循AI ！
🤖 If you have any ideas, submissions, insights you want to provide, email jack@obviously.ai.
如果您有任何想法，意見，要提供的見解，請發送電子郵件至jack@obviously.ai。
🤖 For more reading, check out this 2019 AI Index published by Stanford.
🤖要了解更多信息，請查看斯坦福大學發布的這份2019年AI指數 。
🤖 Or the current controversy going on in the AI bias debate.
🤖或AI偏見辯論中的當前爭議 。
🤖 Lastly, don’t forget to follow us on Medium!
🤖最后，別忘了在Medium上關注我們！
翻譯自: https://medium.com/downsample/what-companies-need-to-consider-in-wake-of-the-algorithm-economy-78372d7fdc05
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