The complexity of the human brain, in relation to that of other species, is one of the biggest wonders in science. Today, neural networks compete alongside us with processing powers that can calculate 200 million potential outcomes per second. With the data trail we feed into it as we go through our lives, and the vast data sets scientists are training machines on, they are learning to compete with the greatest human minds at our own games.

与其他物种相比，人脑的复杂性是科学界最大的奇迹之一。 如今，神经网络与我们并肩作战，其处理能力每秒可计算出2亿个潜在结果。 有了数据跟踪，我们就可以在生活中使用它，并且庞大的数据集正在训练机器，他们正在学习在自己的游戏中与最伟大的人竞争。

In this story we explore how humans compare with machines in games, healthcare, art and emotional intelligence.

在这个故事中，我们探讨了人类如何与游戏，医疗保健，艺术和情商中的机器进行比较。

游戏类 (Games)

AlphaGo, DeepMind’s Go playing AI, has been dubbed “The AI that has nothing to learn from humans”. Go is a complex strategy game over 3000 years old, with 10170 different board configurations. It played against itself thousands of times, getting stronger every time. It found inventive winning strategies that haven’t been played in hundreds of years, winning its first game 5–0 against European Champion Fan Hui, and later 4–1 against 18 world title winning Lee Sudol. DeepMind have since made more advanced versions; AlphaGo Master, AlphaGo Zero and AlphaZero.

DeepMind的Go玩AI游戏AlphaGo被称为“无需向人类学习的AI”。 Go是一款具有3000多年历史的复杂策略游戏，具有10170种不同的棋盘配置。 它与自己对抗了数千次，每次都变得更强大。 它发现了数百年来未曾发挥过的创新性制胜策略，在与欧洲冠军范慧的比赛中首场比赛5-0赢得了冠军，后来在与18个世界冠军头衔的Lee Sudol的比赛中赢得了4-1的胜利。 从那以后，DeepMind制作了更高级的版本。 AlphaGo Master，AlphaGo Zero和AlphaZero。

Chess is another game where computers are revealing tactics overlooked by humans, for instance maintaining pressure until the opponent makes an error. IBM’s DeepBlue AI defeated world Champion Garry Kasparov 4–2 in 1997; which is unsurprising when, despite the greatest minds, AI can calculate 200 million moves per second and map 5 to 6 potential consequences of each move.

象棋是另一种游戏，其中计算机揭示了人类忽略的战术，例如保持压力直到对手犯错。 IBM的DeepBlue AI在1997年以4比2击败世界冠军Garry Kasparov。 尽管有最伟大的头脑，但AI可以每秒计算2亿步动作并映射出每步动作5到6个潜在后果，这不足为奇。

Machines are learning new strategies, showing potential for humans and machines to collaborate to each improve and become more intelligent together.

机器正在学习新的策略，这显示了人与机器协作进行改进并共同变得更加智能的潜力。

卫生保健 (Healthcare)

AI can compute vast amounts of data, for instance databases of symptom data, disease causes, test results, medical images, latest medical papers, doctors reports. This means it can spot patterns from data that a single human never could.

AI可以计算大量数据，例如症状数据，疾病原因，测试结果，医学图像，最新医学论文，医生报告的数据库。 这意味着它可以发现一个人从未有过的数据模式。

Over the past few years AI has become even more accurate at identifying disease diagnosis in patient scan images and health reports. In a study, it was found that AI could diagnose with 87% accuracy, which doesn’t seem like good enough, but the same study found that healthcare professionals diagnosed the same data with 86% accuracy.

在过去的几年中，AI在识别患者扫描图像和健康报告中的疾病诊断方面变得更加准确。 在一项研究中，发现AI可以以87％的准确度进行诊断，这似乎还不够好，但是同一项研究发现，医疗保健专业人员以86％的准确度来诊断相同的数据。

The US Institute of Medicine report 1 in 10 diagnoses are wrong, leading to 80 thousand unnecessary deaths per year. An improvement of 1% accuracy with AI assistance could help limit this number.

美国医学研究所报告，十分之一的诊断是错误的，每年导致8万不必要的死亡。 借助AI协助，准确率提高1％可能有助于限制此数字。

One team at University of California are working with 1.3 million young patients to train an AI that can diagnose Glandula fever, roseola, influenza, chicken pox and hand-foot-mouth disease with 90–97% accuracy.

加利福尼亚大学的一个团队正在与130万年轻患者一起训练AI，该AI可以诊断出Glandula发热，迷迭香，流感，水痘和手足口病，其准确率达到90-97％。

The benefit of AI is that it doesn’t get tired, hungry, or lose concentration, like a doctor would. The downside is that a lot of health data has ethnic bias, meaning training databases may not be diverse enough to work for all patients. However, it is the beginning, and currently showing signs of being a vital tool that can be used alongside doctor’s diagnoses to improve detection.

人工智能的好处在于，它不会像医生那样感到疲劳，饥饿或注意力不集中。 不利的一面是，许多健康数据存在种族偏见，这意味着培训数据库的多样性可能不足以适合所有患者。 但是，这是一个开始，并且目前显示出是一种重要工具的迹象，可以与医生的诊断一起使用以改善检测。

AI can be used beyond the detection process, but also to identify new treatments, because it can predict a number of outcomes. For instance, AtomWise works to predict how molecules could combine with a protein, in order to advise new medical treatments.

AI不仅可以用于检测过程，而且可以识别新的治疗方法，因为它可以预测许多结果。 例如，AtomWise致力于预测分子如何与蛋白质结合，以建议新的治疗方法。

艺术 (Art)

Edward Belamy’s portrait sold for $432,500 at Christie’s in New York. The creativity was entirely AI; it was trained on thousands of images of portraits and generated Edward Belamy based on its knowledge of art. This makes us question if it was just another pastiche, like AI generated music based on works of Mozart and Beethoven, or if it is true machine creativity. This sparks a debate about what human creativity really is, and whether AI can ever truly have it. A key area artists and programmers are currently working with AI on is trying to get out more than they put in — to be truly creative and original, it must make something beyond that which it has been fed on. This is where artists are collaborating with AI. For example, artist Amy Ridler used many photographs of tulips, and combined it with a program that could fluctuate the images based on bitcoin prices. In another example, AI learnt Sougwen Chung’s painting style, an in combination with a robotic arm, paints alongside her creating a beautiful balance of machine and human.

爱德华·贝拉米(Edward Belamy)的肖像在纽约佳士得拍卖行以432,500美元成交。 创造力完全是人工智能； 它接受了数千幅肖像图像的培训，并根据其艺术知识生成了爱德华·贝拉米(Edward Belamy)。 这使我们提出疑问，这是否只是另一个模仿，例如根据莫扎特和贝多芬的作品创作的AI音乐，还是真正的机器创造力。 这引发了关于人类创造力到底是什么以及人工智能是否能够真正拥有它的辩论。 艺术家和程序员目前正在与AI一起工作的一个关键领域正在尝试超越他们的投入—要真正发挥创造力和原创性，它必须做出超出其要求的工作。 这是艺术家与AI合作的地方。 例如，艺术家艾米·里德勒(Amy Ridler)使用了许多郁金香的照片，并将其与可以根据比特币价格波动图像的程序组合在一起。 在另一个示例中，人工智能学会了Sougwen Chung的绘画风格，结合了机械臂，在她旁边绘画，创造了机器与人的完美平衡。

AI may not have its own creative mind yet, but in combination with artists it can generate new levels of creativity.

人工智能可能还没有自己的创造力，但是与艺术家结合可以创造出更高水平的创造力。

情商 (Emotional Intelligence)

Emotional intelligence, the ability to respond to emotional signals, is one of the few skills that define what it means to be human, in addition to creativity. But as with art, programmers are working on giving AI these human skills. Deep fakes already fool many with their close replication of human facial expressions and mouth movements in real time. But AI must respond to real facial expressions in order to be emotionally intelligent. If your car could sense you are unhappy, it might suggest a more scenic route or upbeat music. AI could help with treatment for depression, anxiety, and other emotion-linked conditions. It would use speech patterns, facial expressions, physiology, which are already being programmed by design teams working on facial recognition, in addition to physiological signals like breath and heart rate, by tracking blood flow from detecting the skin. If AI begins to respond to emotional and physiological cues, it opens many doors to healthcare solutions and becoming smart and closer to being human.

情绪智力，即对情绪信号做出React的能力，是除创造力之外定义人的含义的少数技能之一。 但是与艺术一样，程序员正在努力为AI提供这些人类技能。 深度伪造品已经通过实时复制人类面部表情和嘴部动作而蒙骗了许多人。 但是，人工智能必须对真实的面部表情做出React，才能在情感上变得聪明。 如果您的汽车可以感觉到您不满意，则可能表示路线更优美或音乐更加悦耳。 人工智能可以帮助治疗抑郁症，焦虑症和其他与情感有关的疾病。 它会使用语音模式，面部表情，生理机能，除通过呼吸和心率等生理信号通过跟踪检测皮肤的血流外，已经由从事面部识别的设计团队已对其进行编程。 如果AI开始对情绪和生理线索做出React，它将为医疗保健解决方案打开许多大门，并变得越来越聪明，更接近人类。

Our next story will take a closer look at how policy changes are keeping up with this rapid growth of technological innovation.

我们的下一个故事将仔细研究政策变化如何跟上技术创新的快速增长。

This was written by a researcher at a specialist data company. The Digital Bucket Company operates in the UK and works with clients in overcoming data challenges including privacy concerns.

这是由一家专业数据公司的研究人员撰写的。 Digital Bucket Company 在英国运营，并与客户合作解决数据挑战，包括隐私问题。