In many countries in the West, hysteria about the future of artificial intelligence (AI) is everywhere. There seems to be no shortage of sensationalist news about how AI could cure diseases, accelerate human innovation and improve human creativity. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society.

在西方许多国家，人们对人工智能（AI）未来的狂热情绪随处可见。关于AI如何治愈疾病、加速人类创新并提升人类创造力的煽动性新闻似乎层出不穷。仅从媒体标题来看，你可能会觉得我们已经生活在一个AI渗透到社会各个角落的未来之中。

While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as 'AI solutionism'. This is the philosophy that, given enough data, machine learning algorithms can solve all of humanity's problems. But, in fact, instead of supporting AI progress, this mindset actually jeopardises the value of machine intelligence by disregarding important AI safety principles and setting unrealistic expectations about what AI can really do for humanity.

尽管人工智能无疑带来了大量令人期待的机遇，但它也催生了一种可被描述为“AI解决方案主义”的思维方式。这种理念认为，只要拥有足够的数据，机器学习算法就能解决人类面临的全部问题。然而事实上，这种思维不仅未能推动人工智能的发展，反而通过忽视重要的AI安全原则，并对人工智能真正能为人类带来的价值抱有不切实际的期望，从而危及了机器智能的价值。

In only a few years, AI solutionism has made its way from the technology evangelists' mouths in Silicon Valley in California to the minds of government officials and policymakers around the world. The pendulum has swung from the dystopian notion that AI will destroy humanity to the utopian belief that our algorithmic saviour is here.

短短几年间，人工智能解决方案主义已从加利福尼亚硅谷科技狂热者的口中，传入全球各国政府官员和政策制定者的心中。人们的态度从“人工智能将毁灭人类”的反乌托邦观念，转向了“我们的算法救世主已经到来”的乌托邦信念。

We are now seeing governments pledge support to national AI initiatives and compete in a technological race to dominate the burgeoning machine-learning sector. While many politicians proclaim the transformative effects of the coming 'AI revolution', they fail to realise the complexity around deploying advanced machine learning systems in the real world.

如今，各国政府纷纷承诺支持本国的人工智能发展，并在技术竞赛中争夺主导新兴机器学习领域的地位。尽管许多政客宣称即将到来的“人工智能革命”将带来深远变革，但他们却未能意识到在现实世界中部署先进机器学习系统所面临的复杂挑战。

One of the most promising varieties of AI technologies are neural networks. This form of machine learning is loosely modelled on the neuronal structure of the human brain, but on a much smaller scale. But what many politicians do not understand is that simply adding a neural network to a problem will not automatically mean that you'll find a solution. Similarly, adding a neural network to a system of government does not mean it will be instantaneously more inclusive or fair.

人工智能技术中最具前景的类型之一是神经网络。这种机器学习方式大致模仿了人类大脑的神经结构，但规模要小得多。然而，许多政客并不明白的是，仅仅在问题中加入神经网络，并不意味着会自动找到解决方案。同样，将神经网络引入政府体系，并不意味着它会立即变得更加包容或公平。

AI systems need a lot of data to function, but the public sector typically does not have the appropriate data infrastructure to support advanced machine learning. Most of the data remains stored in offline archives. The few digitised sources of data that exist tend to be buried in bureaucracy. More often than not, data is spread across different government departments that each require special permissions to be accessed. Above all, the public sector typically lacks the human talent with the right technological capabilities to fully reap the benefits of machine intelligence.

人工智能系统需要大量数据才能正常运行，但公共部门通常缺乏支持先进机器学习所需的适当数据基础设施。大部分数据仍存储在离线档案中，而现有的少数数字化数据来源往往深藏于官僚体系之中。数据通常分散在不同的政府部门之间，每个部门都需要特殊权限才能访问。更重要的是，公共部门普遍缺乏具备相应技术能力的人才，无法充分挖掘机器智能带来的效益。

For these reasons, the sensationalism over AI has attracted many critics. Stuart Russell, a professor of computer science at the University of California, Berkeley, has long advocated a more sensible and realistic approach that focuses on simple everyday applications of AI instead of the hypothetical takeover by super-intelligent robots. Similarly, Rodney Brooks, professor of robotics at Massachusetts Institute of Technology, writes that 'almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine'.

正因如此，关于人工智能的煽动性报道吸引了许多批评者。加州大学伯克利分校的计算机科学教授斯图尔特·拉塞尔长期以来一直倡导一种更理性、更现实的方法，主张将重点放在人工智能在日常生活中的简单应用上，而非设想超级智能机器人可能夺取控制权。同样，麻省理工学院的机器人学教授罗德尼·布鲁克斯也指出：“机器人和人工智能领域的几乎所有创新，其真正大规模推广所需的时间，远比该领域内外人士所预期的要长得多。”

One of the many difficulties in deploying machine learning systems is that AI is extremely susceptible to adversarial attacks. This means that a malicious AI can target another AI to make it behave in a certain way, such as forcing it to make wrong predictions. Many researchers have warned against the rolling out of AI without appropriate security standards and defence mechanisms. Still, AI security remains an often overlooked topic when machine learning systems are installed.

部署机器学习系统的一大难题在于，人工智能极易受到对抗性攻击的影响。这意味着恶意的人工智能可以针对另一台人工智能，使其以特定方式运行，例如强制其做出错误的预测。许多研究人员已警告，若缺乏适当的安全标准和防御机制，就不要贸然推广人工智能技术。然而，在安装机器学习系统时，人工智能安全仍常常被忽视。

If we are to reap the benefits and minimise the potential harms of AI, we must start thinking about how machine learning can be meaningfully applied to specific areas of government, business and society. This means we need to have a discussion about AI ethics and the distrust that many people have towards machine learning.

如果我们希望充分受益并最大限度地减少人工智能带来的潜在危害，就必须开始思考如何将机器学习有意义地应用于政府、企业和社会的特定领域。这意味着我们需要讨论人工智能伦理问题，以及许多人对机器学习所抱有的不信任情绪。

Most importantly, we need to be aware of the limitations of AI and where people still need to take the lead. Instead of painting an unrealistic picture of the power of AI, it is important to take a step back and separate the actual technological capabilities of AI from fantasy.

最重要的是，我们必须认识到人工智能的局限性，以及人们仍需发挥主导作用的领域。与其对人工智能的力量描绘不切实际的画面，不如退后一步，将人工智能的实际技术能力与幻想区分开来。

The medical profession has also recognised the drawbacks to AI. The IBM Watson for Oncology programme was a piece of AI that was meant to help doctors treat cancer. Even though it was developed to deliver the best recommendations, human experts found it hard to trust the machine. As a result, the AI programme was abandoned in most hospitals where it was trialled.

医疗行业也意识到了人工智能的弊端。IBM Watson for Oncology 项目是一项旨在帮助医生治疗癌症的人工智能应用。尽管该项目旨在提供最佳建议，但人类专家发现难以信任机器，因此在大多数试用该系统的医院中，该项目最终被放弃。

Similar difficulties arose in the legal domain when algorithms were used in courts in the US to sentence criminals. An algorithm calculated risk assessment scores and advised judges on the sentencing. The system was found to amplify structural racial discrimination and was later abandoned.

类似的问题也出现在法律领域，当时美国法院使用算法对罪犯进行量刑。该算法计算风险评估得分，并向法官提提供建议。但该系统被发现加剧了结构性的种族歧视，随后被废弃。

There are some crucial lessons here for everyone aiming to boost investments in national AI programmes. These examples demonstrate that there is no AI solution for everything. Using AI simply for the sake of AI may not always be productive or useful, and not every issue is best addressed by applying machine intelligence to it.All solutions come with a cost and not everything that can be automated should be.

对于所有希望增加国家人工智能项目投资的人来说，这里有一些至关重要的教训。这些案例表明，并非所有问题都有适用于AI的解决方案。仅仅为了追求AI而使用AI，未必总是高效或有益的，而且并非所有问题都应通过应用机器智能来解决。每种解决方案都伴随着成本，因此并非所有可以自动化的事情都应当被实现。