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Breathing Life into Artificial Intelligence and Next Generation Autonomous Aerospace Systems
参考中译:为人工智能和下一代自主航天系统注入生命


          

刊名：Aerospace & Defense Technology
作者：Chitra Sethi
刊号：877B0292
ISSN：2472-2081
出版年：2023
年卷期：2023, vol.8, no.3
页码：4-6,8
总页数：4
分类号：V2
语种：eng
文摘：For many years, artificial intelligence (AI) experts have worked on models for machine learning (ML) and adapting those models to make sense for humans. A computer that makes decisions seems intelligent, even intuitive based on certain circumstances, however a closer look under the hood reveals how unintelligent AI really is. People have always both romanticized and feared machines becoming intelligent in the advent they take over humans, it might seem we are not even close to this much-prophesized next-generation autonomy. Or are we? When it comes to image recognition in today's AI world, a human is required to initially train the underlying system to recognize an object. This is achieved though tagging countless images and recording what the object in the image really is. On a detection of the same or similar object, the AI algorithm is then able to lookup like images to see if there's an exact or close match. If yes, it recognizes that object. The problem however is when something is changed enough, AI fails to recognize that object for what it truly is. A clear example of this includes items with complicated geometry such as human hands. When it comes to hands, there are no universal collections of lines or shapes that AI can use to identify. AI must combine various shapes and combinations to identify hands with a high degree of confidence. An interesting mathematical problem for AI scientists. The human brain however overcomes this with basic logic.
参考中译：多年来，人工智能(AI)专家一直在研究机器学习(ML)的模型，并对这些模型进行调整，使其对人类有意义。做出决策的计算机看起来很智能，甚至在某些情况下是直觉的，然而仔细观察一下引擎盖下的情况就会发现，人工智能实际上是多么的不智能。人们总是既浪漫化又害怕机器在它们取代人类的到来时变得智能，似乎我们甚至还没有接近这种早已预言的下一代自主。或者我们是吗？在当今的S人工智能世界里，当谈到图像识别时，人类需要首先训练底层系统来识别对象。这是通过标记无数的图像并记录图像中对象的真实情况来实现的。在检测到相同或相似的对象时，人工智能算法随后能够像图像一样查找，以查看是否存在与S完全匹配或接近匹配的对象。如果是，则它识别该对象。然而，问题是，当某事改变得足够多时，人工智能无法识别出那个物体的真实面目。这方面的一个明显例子包括具有复杂几何形状的物品，如人手。说到手，没有人工智能可以用来识别的通用线条或形状集合。AI必须结合各种形状和组合来高度自信地识别手。对于人工智能科学家来说，这是一个有趣的数学问题。然而，人脑用基本的逻辑克服了这一点。



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