Applications of AI for Public Good

One area of great optimism about AI and machine learning is their potential to improve people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies. The promise of AI has been compared to the transformative impacts of advances in mobile computing.Public- and private-sector investments in basic and applied RD on AI have already begun reaping major benefits for the public in fields as diverse as health care， transportation， the environment， criminal justice， and economic inclusion.

At Walter Reed Medical Center， the Department of Veteran Affairs is using AI to better predict medical complications and improve treatment of severe combat wounds， leading to better patient outcomes， faster healing， and lower costs. The same general approach—predicting complications to enable preventive treatment—has also reduced hospital-acquired infections at Johns Hopkins University. Given the current transition to electronic health records， predictive analysis of health data may play a key role across many health domains like precision medicine and cancer research.

In transportation， AI-enabled smarter traffic management applications are reducing wait times， energy use， and emissions by as much as 25 percent in some places. Cities are now beginning to leverage the type of responsive dispatching and routing used by ride-hailing services， and linking it with scheduling and tracking software for public transportation to provide just-in-time access to public transportation that can often be faster， cheaper and， in many cases， more accessible to the public.

Some researchers are leveraging AI to improve animal migration tracking by using AI image classification software to analyze tourist photos from public social media sites. The software can identify individual animals in the photos and build a database of their migration using the data and location stamps on the photos. At OSTP’s AI for Social Good workshop， researchers talked about building some of the largest available datasets to-date on the populations and migrations of whales and large African animals， and about launching a project to track “The Internet of Turtles” to gain new insights about sea life.

Other speakers described uses of AI to optimize the patrol strategy of anti-poaching agents， and to design habitat preservation strategies to maximize the genetic diversity of endangered populations.

Autonomous sailboats and watercraft are already patrolling the oceans carrying sophisticated sensor instruments， collecting data on changes in Arctic ice and sensitive ocean ecosystems in operations that would be too expensive or dangerous for crewed vessels. Autonomous watercraft may be much cheaper to operate than manned ships， and may someday be used for enhanced weather prediction， climate monitoring， or policing illegal fishing.

AI also has the potential to improve aspects of the criminal justice system， including crime reporting， policing， bail， sentencing， and parole decisions. The Administration is exploring how AI can responsibly benefit current initiatives such as Data Driven Justice and the Police Data Initiative that seek to provide law enforcement and the public with data that can better inform decision-making in the criminal justice system， while also taking care to minimize the possibility that AI might introduce bias or inaccuracies due to deficiencies in the available data.

Several U.S. academic institutions have launched initiatives to use AI to tackle economic and social challenges. For example， the University of Chicago created an academic program that uses data science and AI to address public challenges such as unemployment and school dropouts. The University of Southern California launched the Center for Artificial Intelligence in Society， an institute dedicated to studying how computational game theory， machine learning， automated planning and multi-agent reasoning techniques can help to solve socially relevant problems like homelessness. Meanwhile， researchers at Stanford University are using machine learning in efforts to address global poverty by using AI to analyze satellite images of likely poverty zones to identify where help is needed most.

Many uses of AI for public good rely on the availability of data that can be used to train machine learning models and test the performance of AI systems. Agencies and organizations with data that can be released without implicating personal privacy or trade secrets can help to enable the development of AI by making those data available to researchers. Standardizing data schemas and formats can reduce the cost and difficulty of making new data sets useful.

人工智能在公益事業(yè)中的應(yīng)用

一個(gè)對(duì)人工智能和機(jī)器學(xué)習(xí)非常樂觀的領(lǐng)域是，它們有潛力通過幫助解決世界上最大的挑戰(zhàn)和效率低下的問題來改善人們的生活。人工智能的前景被比作移動(dòng)計(jì)算進(jìn)步帶來的變革性影響。公共和私營(yíng)部門對(duì)人工智能基礎(chǔ)和應(yīng)用研發(fā)的投資已經(jīng)開始在醫(yī)療保健、交通、環(huán)境、刑事司法和經(jīng)濟(jì)融合等領(lǐng)域?yàn)楣妿碇卮罄妗?/p>

在沃爾特·里德醫(yī)療中心，退伍軍人事務(wù)部正在使用人工智能來更好地預(yù)測(cè)醫(yī)療并發(fā)癥和改善嚴(yán)重的戰(zhàn)斗創(chuàng)傷的治療，從而獲得更好的治療效果、更快的治愈速度和更低的成本。同樣的預(yù)測(cè)并發(fā)癥以進(jìn)行預(yù)防性治療的一般方法，也減少了約翰·霍普金斯大學(xué)醫(yī)院的獲得性感染。鑒于目前向電子健康記錄的過渡，健康數(shù)據(jù)的預(yù)測(cè)分析可能在許多健康領(lǐng)域發(fā)揮關(guān)鍵作用，如精密醫(yī)學(xué)和癌癥研究。

在交通領(lǐng)域，支持人工智能的智能交通管理應(yīng)用程序在某些地方減少了25%的等待時(shí)間、能源消耗和排放。城市現(xiàn)在開始利用叫車服務(wù)系統(tǒng)所使用的響應(yīng)式調(diào)度和路線，并將其與公共交通調(diào)度和跟蹤軟件相聯(lián)系，提供對(duì)公共交通的及時(shí)訪問，這通常會(huì)更快、更便宜，而且在許多情況下，公眾更容易知曉。

一些研究人員利用人工智能，通過使用人工智能圖像分類軟件來分析來自公共社交媒體網(wǎng)站的游客照片，改進(jìn)動(dòng)物遷徙跟蹤方式。該軟件可以識(shí)別照片中的單個(gè)動(dòng)物，并使用照片上的數(shù)據(jù)和位置標(biāo)記來建立它們遷移的數(shù)據(jù)庫(kù)。在OSTP（白宮科技辦公室）的關(guān)于智能用于社會(huì)公益的研究會(huì)上，研究人員談到了建立一些迄今為止最大的關(guān)于鯨魚和大型非洲動(dòng)物的數(shù)量和遷徙的數(shù)據(jù)集，以及啟動(dòng)一個(gè)跟蹤“海龜網(wǎng)絡(luò)”的項(xiàng)目，以獲得關(guān)于海洋生物的新見解。其他發(fā)言者描述了人工智能在優(yōu)化反偷獵巡邏策略以及設(shè)計(jì)棲息地保護(hù)策略，以最大限度地提高在高瀕危物種遺傳多樣性方面的應(yīng)用。

自主帆船和船只已經(jīng)攜帶先進(jìn)的傳感器在海洋中巡邏，收集北極冰層和敏感海洋生態(tài)系統(tǒng)變化的數(shù)據(jù)，這些數(shù)據(jù)若是雇傭船員來收集可能會(huì)過于昂貴或危險(xiǎn)。自主船只的操作成本可能比載人船只低得多，并且有朝一日可能會(huì)被用于提高天氣預(yù)報(bào)、氣候監(jiān)測(cè)的準(zhǔn)確性或監(jiān)管非法捕魚。

人工智能也有潛力改善刑事司法系統(tǒng)的各個(gè)方面，包括犯罪報(bào)告、警務(wù)、保釋、量刑和假釋決定。美國(guó)政府正在探索人工智能如何負(fù)責(zé)任地造福于當(dāng)前的計(jì)劃，例如數(shù)據(jù)驅(qū)動(dòng)司法和警方數(shù)據(jù)倡議，這些舉措旨在為執(zhí)法部門和公眾提供能夠更好地為刑事司法系統(tǒng)決策提供信息的數(shù)據(jù)，同時(shí)也要注意盡可能減少人工智能因現(xiàn)有數(shù)據(jù)不足而產(chǎn)生偏見或不準(zhǔn)確的可能性。

一些美國(guó)學(xué)術(shù)機(jī)構(gòu)發(fā)起了利用人工智能來應(yīng)對(duì)經(jīng)濟(jì)和社會(huì)挑戰(zhàn)的倡議。例如，芝加哥大學(xué)創(chuàng)建了一個(gè)學(xué)術(shù)項(xiàng)目，利用數(shù)據(jù)科學(xué)和人工智能來應(yīng)對(duì)失業(yè)和輟學(xué)等公共挑戰(zhàn)。南加州大學(xué)成立了社會(huì)人工智能中心，這是一個(gè)專門研究計(jì)算博弈論、機(jī)器學(xué)習(xí)、自動(dòng)規(guī)劃和多智能體推理技術(shù)如何幫助解決無家可歸等社會(huì)相關(guān)問題的機(jī)構(gòu)。與此同時(shí)，斯坦福大學(xué)的研究人員正在利用機(jī)器學(xué)習(xí)來解決全球貧困問題，他們利用人工智能來分析可能為貧困地區(qū)的衛(wèi)星圖像，以確定最需要幫助的地方。

人工智能在公共領(lǐng)域的許多應(yīng)用依賴于數(shù)據(jù)的可用性，這些數(shù)據(jù)可用于訓(xùn)練機(jī)器學(xué)習(xí)模型和測(cè)試人工智能系統(tǒng)的性能。機(jī)構(gòu)和組織可以在不涉及個(gè)人隱私或商業(yè)秘密的情況下發(fā)布數(shù)據(jù)，這有助于讓研究人員能夠獲得這些數(shù)據(jù)，從而促進(jìn)人工智能的發(fā)展。標(biāo)準(zhǔn)化數(shù)據(jù)模式和格式可以降低使新數(shù)據(jù)集有用的成本和難度。