【标题】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
【类型】 期刊
【作者】 Chitra Sethi
【摘要】 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。
【来源】 Aerospace & Defense Technology 2023, vol.8, no.3
【入库时间】 2023/8/29
【标题】Digital Manufacturing Solution
【参考中译】数字化制造解决方案
【作者】 Bruce A. Bennett
【摘要】 Advanced manufacturing has yet to even crack the surface of what is possible. From generative design and lightweighting to software simulation and decentralized production, the transition to digital manufacturing promises to unlock an enormous amount of potential. However, large aerospace and defense organizations are struggling to fully capitalize on these benefits due in large part to the complexities of integrating these technologies into an enterprise-wide solution. An ideal platform would enable design cycle times to be slashed from weeks to hours, project costs would greatly decrease, while at the same time the quality of each part would be perfected. Additionally, supply chain complexities would be virtually eliminated because components could be designed in one part of the world and printed anywhere else on the planet, even in space or on Mars, with a push of a button.
【参考中译】 先进制造业甚至还没有破解一切可能的表面。从生成式设计和轻量化到软件模拟和分散生产，向数字制造的过渡有望释放出巨大的潜力。然而，大型航空航天和国防组织正在努力充分利用这些好处，这在很大程度上是因为将这些技术整合到企业范围的解决方案中的复杂性。一个理想的平台将使设计周期从几周缩短到几个小时，项目成本将大大降低，同时每个部件的质量都将得到完善。此外，供应链的复杂性实际上将被消除，因为零部件可以在世界的一个地方设计，然后只需按一下按钮，就可以在地球上的任何其他地方打印，甚至是在太空或火星上。
【来源】 Aerospace & Defense Technology 2022, vol.7, no.8
【标题】ARTIFICIAL INTELLIGENCE AND CYBERSECURITY, UNIVERSITY KLAGENFURT
【参考中译】克拉根福大学人工智能与网络安全
【作者】 Konstantin Schekotihin
【摘要】 The department of Artificial Intelligence and Cyber-security (AI&CS), University Klagenfurt, Austria, focuses on research and application of AI methods to various practical problems. Many of our activities are conducted within research projects with industrial partners, where logic-based methods are combined with machine learning to solve complex problems related to the digitalization of business processes. In the failure analysis (FA) domain, our activities aim to develop an intelligent FA assistant that can automate many tedious and routine, but nevertheless essential activities, helping an FA engineer to localize physical failures as efficiently as possible. Therefore, the assistant's main task is to predict the possible failure using information collected by the FA engineer so far and recommend the most probable hypothesis. This task requires an application of both symbolic and machine-learning methods. The former are used to collect and store knowledge from FA engineers about the domain in a machine-readable form, whereas the latter combine this knowledge with raw data resulting in the application of FA methods.
【参考中译】 奥地利克拉根福大学人工智能与网络安全(AI&CS)系专注于人工智能方法的研究和应用于各种实际问题。我们的许多活动都是在与行业合作伙伴的研究项目中进行的，在这些项目中，基于逻辑的方法与机器学习相结合，以解决与业务流程数字化相关的复杂问题。在故障分析(FA)领域，我们的活动旨在开发一种智能FA助手，该助手可以自动执行许多乏味和常规但仍然必不可少的活动，帮助FA工程师尽可能有效地定位物理故障。因此，S助理的主要任务是利用FA工程师到目前为止收集的信息来预测可能的故障，并推荐最可能的假设。这项任务需要应用符号和机器学习方法。前者用于收集和存储FA工程师关于该领域的机器可读形式的知识，而后者将这些知识与原始数据相结合，从而导致FA方法的应用。
【来源】 Electronic Device Failure Analysis 2023, vol.25, no.2