https://www.toutiao.com/i6641744189052682765/

 

2019-01-02 11:59:34

 

 

 

來源 | 資料觀（轉載請註明來源）

編譯 | 和璟禕

編輯 | 方茶雲

20 More AI Predictions For 2019

2019年20個人工智慧預測

 

1

Artificial intelligence (AI) is everywhere, driven by large investments, lots of startups, all established technology vendors, and enterprises big and small experimenting with what it can do for their bottom line.

人工智慧(AI)無處不在，在大型投資、大量初創公司、所有成熟的技術供應商以及大大小小的企業的推動下，他們都在試驗AI能為他們的利潤做些什麼。

“Some AI Applications will not live up to the hype, and that's OK. People have been planning to have self-driving cars for a while. Some still fear an AI take over might be just 20 years away but the truth is we're still a long way away from truly autonomous cars. And as for an AI takeover, that will only exist in SciFi movies for the foreseeable future. My prediction is that our expectations for AI and the reality of its capability will meet somewhere in the middle. The next 5 years will look a lot like they do now, but our day-to-day will become more and more efficient in subtle, yet significant, ways. AI bots will get better at answering questions and vetting customer service cases, smart assistants will be more equipped to complete tasks and self-driving car features will continue to improve, but they will not take over the road”—Richard Socher, Chief Scientist, Salesforce

“一些人工智慧應用會辜負炒作，但這沒關係。人們計劃擁有自動駕駛汽車已經有一段時間了。一些人仍然擔心人工智慧可能在20年後將會接管，但事實是，我們距離真正的自動駕駛汽車還有很長的路要走。至於人工智慧的收購，在可預見的未來，這隻會出現在科幻電影中。我的預測是，我們對人工智慧的期望和它的能力的現實將在中間的某個地方達到。未來5年看起來和現在很像，但我們的日常生活將變得越來越有效率，以微妙但重要的方式。人工智慧機器人將在回答問題和審查客戶服務案例方面變得更好，智慧助手將擁有更多完成任務的裝置，自動駕駛汽車的功能將繼續改進，但它們不會取代道路。”

——Salesforce首席科學家Richard Socher

2

“The adoption of artificially intelligent offerings will continue to scale into different verticals from manufacturing to education, retail and more in 2019. In the healthcare sector, for example, AI-enhanced applications have the capability to reduce emergency waiting room times and even free up doctors’ time through the use of AI in detecting and diagnosing tumors. As new advances and applications make their way into various verticals, expect to see accelerated adoption as technology costs come down and organizational and business outcomes improve. At Lenovo, we’re already using AI in our supply chain and parts planning process so that we can better develop best in class experience for customers also keen to transform their business with artificial intelligence”—Gianfranco Lanci, Corporate President and Chief Operating Officer, Lenovo

“2019年，人工智慧產品的應用將從製造業繼續擴充套件到教育、零售等各個垂直領域。例如，在醫療行業，人工智慧增強的應用程式能夠通過使用人工智慧來檢測和診斷腫瘤，減少急診候診室的時間，甚至解放醫生的時間。隨著新的科學進展和應用程式進入各個垂直領域，技術成本的降低與組織和業務結果的改善，我們預計這將加速人工智慧的採用度。在聯想，我們已經在供應鏈和零部件規劃過程中使用了人工智慧，以便更好地為那些同樣渴望使用人工智慧改造業務的客戶提供一流的體驗。”

——聯想公司總裁兼營運長Gianfranco Lanci

 

“Patients will find themselves talking via a variety of omni-channel UIs in addition to the pre-existing chat bots that are currently available on mobile apps and other health care IT platforms. Consumer frameworks for conversational experiences like Alexa and Google Home may add HIPAA privacy support that opens the gates for bots to keep the dialog going during the big gaps in time between patient visits. In care settings, nurse call buttons beside hospital beds, forms to collect health histories, and klunky scheduling apps will evolve into customer-focused robot medical assistants”—Dan Housman, chief technology officer for ConvergeHEALTH, Deloitte

3

“患者會發現，除了現有的移動端APP和其他網際網路醫療平臺的聊天機器人之外，他們還可以通過各種全通道使用者介面進行醫患交談。像Alexa和Google Home這樣的會話體驗，使用者框架或許可以新增HIPAA（《健康保險流通與責任法案》）隱私支援，這將為機器人開啟一扇大門，以便在患者在前後訪問的間隔時間內保持適時溝通。在護理機構，醫院病床旁的護士呼叫按鈕、病歷表格以及殘舊的排程應用程式，都將發展成為以客戶為中心的機器人醫療助手。”

—— 德勤(Deloitte)總經理兼德勤醫療系統專案ConvergeHEALTH首席技術官Dan Housman

 

4

“2019 will see the pendulum shift to a focus on performing analytics at the edge. Organizations will save time and money by processing and analyzing data at the edge versus moving it back to a core, storing it and applying traditional analytics. Use cases include anomaly detection (fraud), pattern recognition (predicting failures/maintenance) and persistent streams. Autonomous vehicles, Oil and gas platforms, medical devices are all early examples of this trend that we will see expand in 2019. Cost drivers for this trend are bandwidth (semi-connected environments as well as expensive cellular) considerations and storage (reduce the amount of data sent to the cloud)”—Jack Norris, Senior Vice President, Data and Applications, MapR

“2019年我們將看到鐘擺式的轉折——聚焦邊緣分析的執行。與其傳統地將資料移回核心儲存、應用，企業更青睞於邊緣處理、分析資料以節省時間和成本。其用例包括異常檢測(欺詐)、模式識別(故障預測/維護)和持久流。自動駕駛汽車、油氣平臺、醫療裝置都是這一趨勢的早期例子，我們將在2019年看到這一趨勢的擴充套件。這一趨勢的成本驅動因素是頻寬(半連線環境以及昂貴的蜂窩網路)和儲存(減少傳送到雲端的資料量)。”

——開源大資料技術公司MapR資料與應用程式高階副總裁Jack Norris

 

5

“Public demands for responsible AI will increase. 2018 was the year of awakening. 2019 will be the year of action. It won't be just data ethicists and human rights advocates demanding fairness, accountability, and transparency. Consumers are already changing how they use Facebook or deleting their accounts altogether and this trend is likely to spread to other social media and other services that leverage personal data. Greater numbers of pledges and declarations about the responsible creation and use of AI will be written and companies will be pressured to adopt them. The public will fight back over government use of biased AI in decisions impacting human rights. More employees will demand influence over what they create and refuse to contribute to harmful automation. Companies will have to lead with their conscious-- whether they are buying AI solutions or building them-- and seek assurances that the systems are fair in order to avoid being the next headline on AI gone awry”—Kathy Baxter, Architect of Ethical AI Practice, Salesforce

“公眾對可靠人工智慧的需求將會增加。如果說2018年是AI覺醒之年，那2019年就將成為行動之年。不僅是資料倫理學家和人權倡導者要求公平、問責和透明，消費者已經在改變他們使用Facebook的方式，或者乾脆刪除他們的賬戶，這種趨勢可能會蔓延到其他通過註冊個人資料資訊來登入的社交媒體和其他服務。更多關於有責創造和使用人工智慧的承諾書及宣告將形成書面檔案，企業將被迫採用遵循，公眾也將對政府在影響人權的決策中使用有偏見的人工智慧進行反擊。更多員工將要求對他們所創造的東西施加影響，並拒絕為有害的自動化做出貢獻。企業將不得不以自己的意識為先導——無論是購買人工智慧解決方案還是建立人工智慧，都要確保這些系統是合法的，以避免成為下一個‘問題AI’頭條。”

——Salesforce倫理AI實踐架構師Kathy Baxter。

 

6

“Advanced analytics and artificial intelligence will continue becoming more highly focused and purpose-built for specific needs, and these capabilities will increasingly be embedded in management tools. This much-anticipated capability will simplify IT operations, improve infrastructure and application robustness, and lower overall costs. Along with this trend, AI and analytics will become embedded in high availability and disaster recovery solutions, as well as cloud service provider offerings to improve service levels. With the ability to quickly, automatically and accurately understand issues and diagnose problems across complex configurations, the reliability, and thus the availability, of critical services delivered from the cloud will vastly improve”—Jerry Melnick, President and CEO, SIOS Technology

“高階分析和人工智慧將繼續變得更加集中，併為特定的需求而專門構建，這些功能將越來越多地嵌入管理工具中。這個備受期待的功能將簡化IT操作，改進基礎設施和應用程式穩健性，並降低總體成本。隨著這一趨勢的發展，人工智慧和分析技術將被嵌入到高可用性和故障恢復解決方案中，以及雲服務提供商提供的提高服務水平的產品中。由於能夠快速、自動和精確地理解問題，跨越複雜配置診斷問題，雲端關鍵服務的可靠性和可用性將極大提高。”

——SIOS Technology總裁兼執行長Jerry Melnick

 

7

“As chatbots and AI continue to evolve, the depth and breadth of functions they can perform will increase. What does this mean for the workforce, positively and negatively? On one hand, machine learning will help people sift through massive amounts of data and do their jobs more effectively. On the other, customer service and support roles will be phased out as people grow more comfortable with bot interactions. This will begin to occur on a wider scale in 2019, as more enterprises adopt AI and chatbots to either boost productivity among their existing workforce, or phase out positions that can be taken with the assistance of these technologies”—David Cohn, Co-founder and Chief Strategy Officer, Pigeon

“隨著聊天機器人和人工智慧的不斷髮展，它們能夠執行的功能深度和廣度將會增加。這對勞動力意味著什麼，積極還是消極?一方面，機器學習將幫助人們篩查大量資料，並更有效地完成工作。另一方面，隨著人們對機器人互動越來越熟悉，客戶服務和售後角色將逐步消失。到2019年，隨著越來越多的企業將採用人工智慧和聊天機器人來提高現有員工的生產率，或者逐步淘汰這些技術可以替代的職位，這種情況將大範圍出現。”

—— Pigeon聯合創始人兼首席戰略官David Cohn

 

8

“A dirty little secret about industrial-strength AI is that many of these systems are trained and evaluated on datasets created and labeled by thousands (or more) human raters. As we tackle more complex AI problems, the need for massive amounts of high-quality human judgments will increase, but there will be breakthroughs in leveraging machine learning techniques to make collecting those judgments more time- and cost-efficient. At the same time, methods which use minimal or even no labeled data (aka unsupervised techniques) will reduce our reliance on large swaths of labeled data, enabling deep learning models to be more robust on new and different types of problems”—Joel Tetreault, Head of Research, Grammarly

“關於工業級人工智慧的一個不可告人之處是，這些系統多數是由成千上萬甚至更多人類評分者建立和標記的資料集進行培訓和評估的。隨著我們解決更復雜的人工智慧問題，對大量高質人類判斷的需求將會增加，但在利用機器學習省時省錢收集這些判斷方面將會出現突破。與此同時，使用最少甚至不使用標記資料的方法(又稱無監督技術)將減少我們對大量標記資料的依賴，從而使深度學習模型能夠更穩健地處理新的、不同型別的問題。”

——Grammarly研究主管Joel Tetreault

 

9

“Knowledge Graphs are the new black! The technologies needed – NLP, Graph DB, Content Analytics – are now aligned to enable knowledge graphs to easily codify domain knowledge. From usable chatbot, guided processes to automated advisors, we’ll see increased use in many industries and domains, including healthcare, financial services, and supply chain”—Jean-Luc Chatelain, Managing Director & Chief Technology Officer, Accenture Applied Intelligence

“知識圖是新的黑馬!所需的技術——NLP、圖形資料庫、內容分析——現在已看齊以使知識圖能夠輕鬆地編碼領域知識。從可用的聊天機器人、引導流程到自動化顧問，我們將看到他們在眾多行業和領域裡的使用率增加，包括醫療保健、金融服務和供應鏈。”

——Accenture Applied Intelligence總經理兼首席技術官Jean-Luc Chatelain

 

10

“AI has moved into the mainstream with innovations in self-driving cars, smart speakers, and facial recognition. Less visible, yet equally impactful, are AI applications around logistics, manufacturing, healthcare, and cybersecurity. And what makes cybersecurity unique is that it’s an essential component of all the other technologies. Whether we choose to live in an ‘intelligent’ or an ‘artificially intelligent’ world, one thing is certain: If AI and deep learning isn’t enhancing your cybersecurity strategy, you’re far more likely to get hacked. AI makes it considerably more difficult for cybercriminals to earn their disreputable income. With an AI-powered defense, attackers are left to seek out softer targets (those who don’t think they need AI) or they’re forced to develop even more sophisticated and costly methods of attack – and so the cyber arms race continues”—Joe Levy, CTO, Sophos

“隨著自動駕駛汽車、智慧揚聲器和麵部識別技術的創新，人工智慧已進入主流。雖然人工智慧在物流、製造、醫療保健和網路安全方面的應用不那麼引人注目，但同樣具有影響力。網路安全的獨特之處在於它是所有其他技術的重要組成部分。無論我們選擇生活在一個“智慧”還是“人工智慧”的世界，有一件事是肯定的:如果人工智慧和深度學習不能增強你的網路安全戰略，你就更有可能被黑客攻擊。人工智慧讓網路犯罪分子更難獲得聲名狼藉的收入。有了人工智慧的防禦，攻擊者只能尋找更容易的目標(那些認為自己不需要人工智慧的目標)，或者被迫開發出更復雜、成本更高的攻擊方法——因此，網路軍備競賽仍在繼續。”

——Sophos首席技術官Joe Levy

 

11

“AI is entering the Age of Commodity. You do not need to know how the technology of a microwave works in order to use it, it is simply a tool. With the huge influx of no-code, point-and-click tools we are entering into the same phase with AI where it will become a widely used utility by everyone, regardless of technical background. As a result, most of the AI applications in the coming years will be built by people with little or no AI training”—Vitaly Gordon, VP Data Science, Salesforce

“人工智慧正在進入商品時代。你不需要知道微波技術如何工作才能使用它，它只是一個工具。隨著大量無程式碼、點選式工具的湧入，我們正進入與人工智慧相同的階段，在這個階段，無論技術背景如何，它都將成為一種廣泛使用的實用工具。因此，未來幾年的大部分人工智慧應用程式將由很少或沒有人工智慧培訓的人開發。”

——Salesforce資料科學副總裁Vitaly Gordon

 

12

“Robotic Process Automation (RPA) has been one of the hottest areas of tech in the last two years – because of its simple, easy-to-understand value prop – process automation, efficiency; freeing resources up to focus on higher value activities, etc. But It has fundamental limits – it’s only effective with rote, repetitive processes and it cannot impact workflows involving unstructured content which makes up over 80% of data in most enterprises. At the same time, AI and machine learning are seen as too esoteric; requiring too much data science expertise, too much hand-holding, too much uncertainty and risk about ROI. Companies will look to bridge the gap in 2019 – between the horsepower of RPA and the intellect of AI/machine learning through what many experts are calling ‘intelligent process automation”—Tom Wilde, CEO and Founder, Indico

“機器人流程自動化(RPA)在過去兩年中一直是最熱門的技術領域之一——因為其簡單、易於理解的價值主張——流程自動化、效率高，釋放資源以專注於更有價值的活動等等。但它有基本的侷限性——它只對死記硬背、重複的流程有效，不會影響涉及非結構化內容的工作流，在大多數企業中，非結構化內容佔資料的80%以上。與此同時，人工智慧和機器學習被視為過於深奧，需要太多的資料科學專業知識、太多扶持、太多關於利潤率的不確定性和風險。2019年，各公司有望通過專家們提出的“智慧流程自動化”，在RPA馬力和人工智慧/機器學習之間架起一座橋樑。”

—— Indico執行長和創始人Tom Wilde

 

13

“Artificial Intelligence (AI) and machine learning (ML) are overhyped for many real-life applications, including the contact center industry. For example, instead of trying to identify specific patterns in images or data (an AI/ML sweet spot), it will be much more useful to increase the volume of satisfying self-service support sessions through intelligently applied automation to resolve common questions and provide guided user flows through defined business processes. By leveraging human intelligence primarily for those support scenarios that can’t be effectively automated, call center operations will be further optimized”—Anand Janefalkar, Founder and CEO, UJET

“人工智慧(AI)和機器學習(ML)在許多實際應用中被誇大了，包括呼叫中心行業。例如，與其試圖識別影象或資料中的特定模式(AI/ML的最佳點)，不如通過智慧應用自動化來解決常見問題並通過定義的業務流程提供指導使用者流程，從而增加滿足自助服務支援會話的數量。通過主要利用人類智慧來支援那些不能有效自動化的場景，呼叫中心的操作將得到進一步優化。”

—— UJET創始人和執行長Anand Janefalkar

 

14

“In 2019, there will be a shift from AI toolkits to AI solving specific enterprise challenges, such as IT and human resources employee experiences. To date, the model has been that enterprises can apply hard-to-come-by skills to leverage AI toolkits to build a custom application. This is shifting to using AI to solve common enterprise problems”—Pat Calhoun, Founder and CEO, Espressive

“2019年，將會有一個從人工智慧工具包到人工智慧解決具體企業挑戰的轉變。如IT和人力資源員工的經驗，迄今為止，模型成了企業利用AI工具包構建自定義應用程的難獲技術。這正轉向使用AI來解決常見的企業問題。”

——Espressive創始人和執行長Pat Calhoun

 

15

“In 2019, AI’s early adopters in the enterprise will look to gain more value from their AI investments as they expect more abundant and richer, built-in AI solutions within cloud applications in terms of functionality, user experience, and accessibility (multi-device, chatbots, digital assistants, etc.). We’ll see companies investing in third party data sources and smart data (dynamic signals and flexible classifications that are regularly refreshed) to optimize outputs. Trust, transparency, and explainable AI will become bigger issues as organizations wrestle with machine learning bias. Customers will realize that machine learning requires human supervision and features like supervisory controls, coupled with data insights, to help early adopters tweak machine learning outputs and generate more value from AI investments”—Melissa Boxer, VP of Adaptive Intelligent Applications, Oracle

“2019年，人工智慧在企業中的早期採用者希望從他們的人工智慧投資中獲得更多價值，因為他們希望雲應用程式中內建的人工智慧解決方案在功能、使用者體驗和可訪問性(多裝置、聊天機器人、數字助理等)方面更豐富。我們將看到公司投資於第三方資料來源和智慧資料(動態訊號和定期重新整理的靈活分類)以優化輸出。隨著企業與機器學習偏差作鬥爭，信任、透明度和可解釋的人工智慧將成為更大的問題。客戶將會意識到機器學習需要人類的監督和監督控制等功能，再加上資料洞察，以幫助早期採用者調整機器學習輸出，並從人工智慧投資中產生更多價值。”

—— Oracle自適應智慧應用副總裁Melissa Boxer

 

16

“Marketers have long talked about taking the ideal next best action when it comes to marketing programs based on where people are in the buying cycle. However, this has been impossible to achieve without massive amounts of data being synthesized by AI in real time. The emergence of AI to take over manual tasks involving huge data sets means that automated next best action triggered by specific activity in the buying cycle will become a reality in 2019”—Peter Isaacson, CMO, Demandbase

“長期以來，營銷人員一直在談論，當涉及到基於人們在購買週期中所處位置的營銷計劃時，應該採取僅次於最佳的理想行動。然而，如果沒有人工智慧實時合成的海量資料，這是不可能實現的。人工智慧的出現接管了涉及龐大資料集的手工任務，這意味著在2019年，由購買週期中的特定活動觸發的自動次優行動將成為現實。”

——Demandbase首席營銷官Peter Isaacson

 

17

“In 2019, incorporating AI will be an essential part of the marketing strategy. Trained models around predictive analytics, sentiment analysis, programmatic advertising, to name a few, will revolutionize how marketers automate more aspects of the marketing pipeline and develop highly targeted Account Based Marketing (ABM) strategies. This will require investment in new technologies but will also lower custom acquisition costs by making marketing dollars more effective”—Daniel Raskin, CMO, Kinetica

“2019年，整合人工智慧將成為營銷戰略的重要組成部分。圍繞預測分析、情緒分析、程式化廣告等方面，訓練有素的模型，將徹底變革營銷人員在營銷渠道上多方面的自動化，並開發出具有高度針對性，基於客戶的營銷(ABM)策略。這需要對新技術進行投資，但也將通過提高營銷資金的有效性來降低定製採購的成本。”

——Kinetica首席營銷官Daniel Raskin

 

18

“Artificial intelligence and machine learning will become a requirement for new solutions for simplified operations: The IT skills gap will require progressive enterprises to implement new, innovative solutions that automate complex operations. Machine learning and artificial intelligence will become key requirements for new IT solutions to help businesses close the skills gap through smarter operations and modern IT solutions. Enterprise software firms will force their strategic vendors to integrate AI and ML into their existing offerings to provide a more efficient operating model and a higher level of success for meeting their desired outcomes”—Don Foster, Senior Director, Commvault

“人工智慧和機器學習將成為簡化操作新解決方案的需求:IT技能的差距會導致先進企業實現自動化複雜操作的創新解決方案。機器學習和人工智慧將成為新的IT解決方案的關鍵需求，以幫助企業通過更智慧的操作和現代IT解決方案縮小技術差距。企業軟體公司將迫使他們的戰略供應商將人工智慧和機器學習整合到他們現有的產品中，以提供更有效的操作模型和更高層次的成功來滿足他們期望的結果。”

——Commvault資深總監Don Foster

 

19

“Nearly every IT department will adopt AI to automate enterprise monitoring, reduce manual work of IT staff, and enable a vision of applications that can repair themselves”—Dave Anderson, Digital Performance Expert, Dynatrace

“幾乎每個IT部門都將採用人工智慧來實現自動化企業監控，減少IT人員的手上工作，並實現使應用程式能夠自我修復的願景。”

—— Dynatrace數字顯示專家Dave Anderson

 

20

“The robotics industry will see many startups trying to find a niche and trying to capture as much market share possible. However, in order to succeed, robotics startups must consider regulatory regulations from the start of designs so that they meet applicable safety regulations or else they will fail when they go to market”. Ryan Braman, Test Engineering Manager, TUV Rheinland

“我們在機器人行業將會看到許多初創公司試圖找到一個利基市場，並儘可能多地搶佔市場份額。然而，為了取得成功，機器人初創企業必須從設計的一開始就考慮監管規定，以滿足適用的安全法規，否則在進入市場時就會失敗。”

——TUV Rheinland測試工程經理Ryan Braman