We need thus to keep track of technological development and innovation. Yet, this is only a pre-requisite. We also need to be able to sort through these many “new techs” and identify, as early as possible, which ones will be key for the future. If we invest in the wrong technology, or in the wrong way, or with the wrong timing, then the consequences are likely to be negative.


As we are at a generic level, we shall, for now, not specify exactly “when in the future”.





慕尼黑再保险公司与ERGO IT战略合作,为我们提供了一个非常有用的年度扫描,即 技术趋势雷达 (hereinafter “雷达“), which aims to raise “awareness of key trends” in the new tech sector. They focus on those technologies that are especially relevant to the insurance sector. Nonetheless, considering the breadth of insurance companies’ interest, their scan is relevant for many sectors and excellent for a general and comprehensive overview of current new technologies.


The methodology for the “雷达” is grounded in the compilation of trends, which are then screened according to four rules “to define the most relevant trends categorised in four primary fields” (2020年技术趋势雷达, p.62). These rules are notably inspired by a management framework, the “Run-Grow-Transform” (RGT) model (Ibid., RGT model adapted to IT by Hunter et al, Gartner Research, 2008).

First, Munich-Re and Ergo Tech Trend Radar 2020 present their result sorted according to “four trend fields” (Tech Trend Radar 20202019):

  • 以用户为中心。
  • 连接的世界。
  • 人工智能。
  • 2019年版的赋能技术、前颠覆性技术。









For example, “precision farming” – also known as “smart farming” or “smart agriculture” – is a novelty in Munich-re-Ergo 雷达 2020年,没有包括在2019年的版本中(同上)。

然而,像迪尔公司这样的公司至少在2017年已经开始为智能农业做准备(Helene Lavoix, 人工智能、物联网和农业的未来。智能农业安全? 第一部分第二部分, The Red Team Analysis Society, 2019). Interest and investments in the field increased in 2018 and then in 2019 (Ibid.). Thus, the “雷达” is three years late. If we had used the 2019 “Radar”, then we would have entirely missed a technology, possibly key for the future.

The case of “Deep Fake”, stemming from generative adversarial networks (GANs)

Similarly “DeepFake Defence” enters the “雷达” in 2020, in the trend field “User Centricity”.

However, the name “deep fake” emerged in 2017 to convey concern with forgeries involving Artificial Intelligence (AI) (Laurie A. Harris, “深度造假与国家安全", 国会研究处,2021年5月7日更新,第三版)。美国国防部高级研究计划局(DARPA)有两个专注于打击深度造假的计划。第一个。 媒体鉴证学 (MediFor)于2016年开始,第二个 语义鉴证学 (SemaFor)在2019年。

Thus, here again, the “雷达” is late, for our purpose, in identifying a key trend.

Meanwhile, Deep Fakes are most often grounded in generative adversarial networks (GANs), indeed identified in the “Radar” in AI this time. The GANs entered the “雷达” in 2019 (Ibid.)


GANs group, alone, objects, finding “concepts”: pixel trees with pixel trees, doors with doors etc. (e.g. Gan Paint).

生成的图像质量惊人,在现实中并不存在,这使人们有了令人难以置信的可能性。它们可能有负面的应用,例如用于伪造。它们也可能导致许多其他活动的建设性用途,如城市规划、建筑、电影、时尚等(也见Helene Lavoix, 在现实中插入人工智能, The Red Team Analysis Society, 2019年1月)。

Identifying GANs should thus have led to look at it use and misuse, as early as the GAN new tech was found. Furthermore, the classification of two related “tech” in different categories – even if those categories are called “fields” – may create problems, as we shall see below.

Of course, only those who do nothing never make any mistake. Yet, if some technologies were detected late previously, then, could a methodology similar to the “雷达” lead us to miss something else now for the future?


Can we identify what is missing or what can be improved when we use an approach such as the one used for the “雷达“?


The “雷达” we use here as a case study presents us with a long list of technologies sorted out through categories labelled “trend fields”. But we do not know exactly how and why these “trend fields” are chosen.


Categories are used in and result from classification, a fundamental cognitive function for the brain (Fabrice Bak, 2013: 107-113). Indeed, “Categorization is a process by which people make sense of things by working out similarities and differences” (McGarty, Mavor, & Skorich, 2015). The highest level of categorization is hierarchical (organised as a tree) and called a taxonomy or hierarchical classification. In classical terms, categories must be clearly defined (which criteria are necessary to make an item part or not of the category), mutually exclusive (one item can belong to only one category) and fully exhaustive (all the categories together represent the whole set for which the categories are built) (OECD,”分类“, using “United Nations Glossary of Classification Terms” prepared by the Expert Group on International Economic and Social Classifications; unpublished on paper).

The archetypal example of a taxonomy is Linnaeus’ classifications of plants, animals and minerals (兽性大发摄取植物人 和 Regnum Lapideum),根据不同的阶级,他的这项工作是由他的 植物人物种他于1753年发表了《我的生活》一书,并在他的一生中一直坚持着(见他的《我的生活》)。 参考书目). Building upon Linnaeus ‘ work, organisms are now organised in the following inclusive taxonomies, organised from the most to the least inclusive: Kingdom, Phylum, Class, Order, Family, Genus, Species, and Strain.


Now, if we look at the “trend fields” used in the “拉达r”, what we observe is that they respect none of the specificities a category should have:

1- 它们没有得到很好的界定. There is not one criteria that allows to class easily one item in one “trend field” or another. For example, are various types of IAs not actually also enabling technologies?

2- 它们并不相互排斥, i.e. some items could belong to two or more “trend fields”: 5 G is enabling and also part of a connected world; 智能纺织品 也是以用户为中心的,可以被视为 可编程材料;IA使自主事物和精确耕作成为可能,正如所见,等等。

3- 它们可能不是详尽的这就造成了我们的问题,即不知道我们是否没有错过什么。

The four “trend fields”, here, seem to be mainly habits of thoughts, existing names or disparate categories that allow readers and users to identify quickly and easily the new technologies selected by the process. 


In our case study, Munich-Re and Ergo then sort out the first “proto-categorisation” according to a second categorisation: maturity/degree of adoption of the tech.




Let us tell the story – or a story – of human beings and technologies.


生活在地球上的每个人都有需求,正如马斯洛(Abraham Maslow, 动机和个性。 1954, 1987).


一个社会意味着社会协调必须发挥作用。 社会协调是根据三个组成部分来表达的(Barrington Moore, 不公正。服从和反抗的社会基础, 1978):

  1. 权力问题。
  2. 商品和服务生产的劳动分工。
  3. 以及这些货物和服务的分配。




Thus, we can assume that the technologies that will be key for the future will be all those tech that will effectively help us to satisfy needs. Meanwhile, the actions required to meet these needs are more and more complex. They become increasingly complex because of previous actions – including the creation and use of previous technologies – and of their impact on the environment, and thus on the conditions for the actions. The evolution of needs resulting from this process also, in the same time, contributes to make actions and tasks more complex.

我们现在有一个模型,可以让我们找出哪些技术最有可能在未来成为关键技术,正如我们将看到的那样。 下篇.


特色图片。宇宙飞船和行星,以及安全,由 莱蒙德-贝特朗斯 德 淘宝网  / 公共领域。

Chappellet-Lanier, Tajha, “DARPA希望通过语义取证解决 "深度造假 "问题", 联邦储蓄银行(Fedscoop),2019年8月7日。

Diamond, Jared 枪炮、病菌和钢铁。人类社会的命运》(The Fates of Human Societies,(W. W. Norton: 1997)。

Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua, “生成式对抗网", 神经信息处理系统国际会议论文集 (NIPS), 2014.

Harris, Laurie A., “深度造假与国家安全", 国会研究处,2021年5月7日更新,第3版

Hunter R.等人,"将IT效益转化为业务的简单框架
价值影响",Gartner Research,2008年5月16日。

Lavoix, Helene, 在现实中插入人工智能, The Red Team Analysis Society,2019年1月。

McGarty, Craig, et al, “Social Categorization”, in 国际社会和行为科学百科全书》(International Encyclopedia of Social & Behavioral Sciences)。, December 2015, DOI: , 10.1016/B978-0-08-097086-8.24091-9

Maslow, Abraham, 动机和个性。 (London, Harper & Row, 1954, 1987)。

Moore, B., 不公正。服从和反抗的社会基础,(伦敦:麦克米伦出版社,1978年)。

慕尼黑-Re和ERGO的IT战略。 技术趋势雷达 2020年和2019年。

由Dr Helene Lavoix (MSc PhD Lond)发布

Helene Lavoix博士伦敦大学博士(国际关系) ,是Red Team Analysis Society的总裁/CEO。她专门研究国际关系、国家和国际安全问题的战略预见和早期预警。她目前的工作重点是乌克兰战争、国际秩序和中国的崛起、行星越轨行为和国际关系、战略预见和预警方法、激进化以及新技术和安全。


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