Much of the public discourse surrounding artificial intelligence (AI) in business has tended towards the dystopian.
Within the next 25 years, AI could render60%的职业过时了,加强性别和racial prejudice with反映人类偏见的机器学习算法, exacerbate inequality (or a‘数字可怜的房屋’根据政治科学家弗吉尼亚·尤巴克斯（Virginia Eubanks）的说法），甚至成为一个超级智能rules the world.
今天，我们有能力大声思考（请参阅Softbank Analytics的胡椒粉，可以发声其思维过程），使人们入狱并深深斗争女王’圣诞节讯息。如果您相信埃隆·马斯克（Elon Musk），人类必须成为机器人或最终成为AI’s‘家猫’.
In the public mind, at least, there’s one group set to benefit from all this: business. Over the past decade, the corporate world has been seemingly embroiled in a Gadarene rush to embrace AI, whether it’S Big Tech赛车将AI产品推向市场或对冲赌注的公司，这些赌注是防流行，始终与聊天机器人和其他机器学习系统可以降低成本并提高生产率。
Yes, there are potentially sinister elements to AI, but it can also be a force for good: revolutionising healthcare, improving road safety with driverless cars, solving problems for scientists, engineers and weather forecasters alike, alleviating skills shortages (eg Fieldwork Robots’ raspberry-picking robot, which could tackle the UK’s lack of human seasonal farm labour) or perhaps removing cognitively repetitive tasks and drudgework for millions (read about an AI debating its own ethics at the Oxford）。
Augmented intelligence involves human input and judgement at every step of the process – systems are optimised to bring benefits, and avoid harm, to humans.
Augmented intelligence
To harness the best of AI, a human-centric approach – augmented intelligence – will be crucial, according to experts at the School, which last year introduced a popular augmented intelligence-centred AI course and Oxford’s first AI-focused degree programme, the牛津人工智能企业执行文凭.
‘增强智能只是人工智能学习与人类判断力和决策的结合：以人为中心的方法意味着人类的智力必须是系统的一部分，’explains Andrew Stephen, Associate Dean of Research and L’欧莱尔营销教授。
'Humans write, select and make decisions about data to go into the algorithms,’ says Andrew.‘但是，仅仅因为他们参与其中并且是AI的所谓受益者，这并不意味着它们对这些系统以及如何运行是核心。例如，“设置并忘记它”的AI驱动自动化方法不是以人为本。而是以人为本。增强智能在整个过程中都具有人类的投入和判断力，以及针对提供人类利益并避免对人类伤害的系统进行了优化的系统.’
增强情报也可能带来重要的商机：根据2030年，全球增强情报市场预计将达到1.215亿美元（91亿英镑）。盟军市场研究，安德鲁预测‘那些将来更注重以人为本的企业将是那些成长速度更快和繁荣的企业’.
现在趋势
Andrew gives the example of the AI-powered‘超级市场和零售商目前使用预测机的预测来预测未来的客户行为，也许可以确定将哪些产品放在货架上。正如过去两年所表明的那样，诸如Covid-19大流行（Covid-19）诸如阻塞苏伊士运河或更限制的国际贸易法规的集装箱船（例如英国脱欧）可能会使供应链陷入混乱。
Although prediction machines can be fairly accurate during predictable, stable times, you wouldn’t want them to automate decisions,’ says Andrew.‘增强情报的替代方法是让人类专家通过包括在历史数据中未捕获的不同宏观因素，甚至嵌入专家的直觉和先前的经验，从而为预测做出了贡献。涉及人类专家可以防止基于不良预测的决定。’
2020年，包括安德鲁（Andrew）在内的学校的学术研究人员发布了一种预测工具Hypertrendsthat is based on this logic.Hypertrends使用在线数据（新闻渠道，社交媒体，博必威体育是哪里的公司客和评论）和复杂的数学来做出预测。‘我们的AI系统的作用是在社交媒体的众所周知的大草原中找到针头，’安德鲁说。‘But you wouldn’t want to go automatically with the future scenarios we predict. [Instead], those predictions need to be fed to corporate decision-makers and experts who can then integrate these scenarios with their own knowledge to arrive at the best possible business decisions.’
Augmented intelligence could also help prevent some of the more pernicious aspects of AI for business, such as the way in which its judgements reflect the biases on which machine-learning systems are built and programmed (unsurprising given an估计有88％的人工智能研究人员是男性）。
‘Algorithms are not inherently sexist or racist,’ says Andrew.‘他们可以成为这种方式（或看起来似乎是），并且需要进行人工干预以监测他们的学习以防止这种情况。’
早期诊断
人类对增强情报的判断和决策也可以用于防止社交媒体使用者被算法操纵的工具中，这些算法将他们带到极端主义或阴谋论的兔子洞中。
在医疗保健中，AI已被用来发现食道癌和痴呆症的早期迹象，有助于通过使用机器人宠物来快速开发Covid-19疫苗，并缓解老年人的孤独感。
尽管很乐观，但在医疗保健中使用AI仍然可能适得其反。例如，使用AI治愈癌症可能会导致最近概述的世界末日情况本文在守护者由加利福尼亚州人工智能中心的创始人斯图尔特·罗素（Stuart Russell）教授那里‘它可能会找到在整个人口中诱导肿瘤的方法，因此它可以并行运行数百万个实验，将我们所有人用作豚鼠’.
增强智能还可以用来减少AI的巨大环境影响，例如机器学习或加密货币所需的电力。正如安德鲁所解释的那样‘我所说的很多人工智能和机器学习是“蛮力”分析 - 云服务使运行数千甚至数以百万计的模型变得容易，只是看看什么’s“最好的”。那’s costly in terms of energy, when each model estimated or trained does have a CO2 impact. Humans are needed to design“实验”’t rely on brute force approaches, but instead analyse only what is needed to be looked at.’
人的因素
As such, a human hand is essential.‘If an AI system is amazing at detecting hard-to-identify tumours on scans, the doctor and patient need to be involved in the process because that algorithm might be great at applying computer vision to scans, but it might not be intelligent enough to know about the patient and their lifestyle: likely factors that matter when the doctor determines the optimal treatment plan,’ says Andrew.
在他们对AI的采用中，许多企业忽略了客户的旅程。正如成千上万的在线评论论坛会谴责银行聊天机器人将无辜的询问转化为官僚主义的噩梦，许多客户在讨论固定利率抵押贷款或投资信息方面更喜欢人类专家的建议。这是在2021年的Blame the Bot纸，由安德鲁（Andrew）撰写，cammy crolic,Rhonda Hadi和费利佩·托马兹（Felipe Thomaz），发现‘部署人类聊天机器人会对客户满意度，整体企业评估和随后的购买意图产生负面影响’.
让机器和人类都做自己擅长的事情。在一起，谁知道我们可以实现什么。
嵌入AI
尽管如此，AI在业务中的广泛嵌入仍可能导致大量失业。根据World Economic Forum，下一波自动化可能会导致全球8500万（主要低技能）的工作在2025年流离失所。然而，同一份报告还估计，损失的就业人数将超过9700万 - 创造的新工作数量（9700万）在接下来的四年中。安德鲁预见了新角色‘算法培训师/教练，负责算法学习的AI主管，以及道德和偏见专家’.
同时，随着政府起草围绕AI的安全，保障和税收的新立法，可以发明新的工作（例如，在评估无人驾驶汽车的税收的会计中）。
Ensuring AI is integrated into organisations in a way that favours both business and humanity is no easy task, says Andrew.‘Leaders have to learn how to collaborate with algorithms,’ he says.‘他们将需要深入了解AI系统的运作方式，以便他们可以在引擎盖下看，并确定其系统或过程中的正确位置，以将人类带入，以及需要什么人类专业知识。’
新的法规可以帮助增强情报的兴起。去年4月，欧盟制定了针对AI的第一草案综合法规，该法规可能禁止模仿人类，并看到企业受到人类监督，透明度，网络安全，风险管理，监测和报告等要求。AI可能必须用算法伦理编码，并可以追溯到其创作者，或者用一系列增强的情报检查建造，这些机器停止并寻求人类建议。
这样的法律问题 - 以及人为AI的首先方法的重要性 - 在学校中教授’s aforementioned diploma in AI in business, which helps develop the strategic skills that will be needed by future leaders in the age of AI. The diploma, and the need for augmented intelligence, comes at something of a tipping point for the technology, which is advancing rapidly (a computer could match thehuman brain by 2052根据《公开慈善项目》的技术分析师的说法，尽管世界有足够的能力参加这样的时尚活动。
'We’re not placing blind faith in the machines,’ says Andrew. 'We are advocating a human-centric approach, which will ensure our society will do its best to prevent AI from doing harmful things.
‘There’s no magic to AI: it’s just a set of tools that organises, structures and analyses data. But the future isn’t captured in data yet: it’s up to our imaginations as business leaders. AI alone won’t have better business judgement than us humans. But we can’t sift through the world’s data like AI can. Let machines do what they’re good at, and let humans do what they’re good at. If we join forces, who knows what we can achieve.
Watch教授安德鲁·斯蒂芬（Andrew Stephen）主题演讲“使用数字技术变得更加人性化”他通过研究的3个案例研究进行了交谈。
