PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE（Ⅱ）

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)中的應用

一個對人工智能和機器學習非常樂觀的領域是，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計算進步帶來的變革性影響。公共和私營部門對人工智能基礎和應用研發(fā)的投資已經開始在醫(yī)療保健、交通、環(huán)境、刑事司法和經濟融合等領域為公眾帶來重大利益。

在沃爾特·里德醫(yī)療中心，退伍軍人事務部正在使用人工智能來更好地預測醫(yī)療并發(fā)癥和改善嚴重的戰(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。同樣的預測并發(fā)癥以進行預防性治療的一般方法，也減少了約翰·霍普金斯大學醫(yī)院的獲得性感染。鑒于目前向電子健康記錄的過渡，健康數據的預測分析可能在許多健康領域發(fā)揮關鍵作用，如精密醫(yī)學和癌癥研究。

在交通領域，支持人工智能的智能交通管理應用程序在某些地方減少了25%的等待時間、能源消耗和排放。城市現在開始利用叫車服務系統(tǒng)所使用的響應式調度和路線，并將其與公共交通調度和跟蹤軟件相聯系，提供對公共交通的及時訪問，這通常會更快、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站的游客照片，改進動物遷徙跟蹤方式。該軟件可以識別照片中的單個動物，并使用照片上的數據和位置標記來建立它們遷移的數據庫。在OSTP（白宮科技辦公室）的關于智能用于社會公益的研究會上，研究人員談到了建立一些迄今為止最大的關于鯨魚和大型非洲動物的數量和遷徙的數據集，以及啟動一個跟蹤“海龜網絡”的項目，以獲得關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計棲息地保護策略，以最大限度地提高在高瀕危物種遺傳多樣性方面的應用。

自主帆船和船只已經攜帶先進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)變化的數據，這些數據若是雇傭船員來收集可能會過于昂貴或危險。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被用于提高天氣預報、氣候監(jiān)測的準確性或監(jiān)管非法捕魚。

人工智能也有潛力改善刑事司法系統(tǒng)的各個方面，包括犯罪報告、警務、保釋、量刑和假釋決定。美國政府正在探索人工智能如何負責任地造福于當前的計劃，例如數據驅動司法和警方數據倡議，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)決策提供信息的數據，同時也要注意盡可能減少人工智能因現有數據不足而產生偏見或不準確的可能性。

一些美國學術機構發(fā)起了利用人工智能來應對經濟和社會挑戰(zhàn)的倡議。例如，芝加哥大學創(chuàng)建了一個學術項目，利用數據科學和人工智能來應對失業(yè)和輟學等公共挑戰(zhàn)。南加州大學成立了社會人工智能中心，這是一個專門研究計算博弈論、機器學習、自動規(guī)劃和多智能體推理技術如何幫助解決無家可歸等社會相關問題的機構。與此同時，斯坦福大學的研究人員正在利用機器學習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。

人工智能在公共領域的許多應用依賴于數據的可用性，這些數據可用于訓練機器學習模型和測試人工智能系統(tǒng)的性能。機構和組織可以在不涉及個人隱私或商業(yè)秘密的情況下發(fā)布數據，這有助于讓研究人員能夠獲得這些數據，從而促進人工智能的發(fā)展。標準化數據模式和格式可以降低使新數據集有用的成本和難度。