{"id":35847,"date":"2022-08-04T08:52:30","date_gmt":"2022-08-04T06:52:30","guid":{"rendered":"https:\/\/wp.test-leogistics.com\/?p=35847"},"modified":"2022-08-04T09:44:57","modified_gmt":"2022-08-04T07:44:57","slug":"minds-mastering-machines-2022-2-2","status":"publish","type":"post","link":"https:\/\/leogistics.com\/en\/news-en\/minds-mastering-machines-2022-2-2\/","title":{"rendered":"minds mastering machines 2022: AI trends for the World of Logistics (part 2\/2)"},"content":{"rendered":"\t\t
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Ethical and Safety Issues<\/b> Related to AI and Machine Learning <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In our first part, our colleagues have already presented a selection of exciting presentations at m3. In this further section, we will again separately address the topics of ethical and safety-related issues of AI as well as transfer learning and domain adaptation of AI models. <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Reliable AI<\/b>: Securing artificial neural networks <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Even though the topic of artificial intelligence is becoming increasingly important in the public eye, ethical and safety-related questions must also be considered and answered as AIs become more widespread in real-world use cases. This was demonstrated by Prof. Dr.-Ing. Marco Huber from Fraunhofer IPA in his presentation.<\/p>

In the near past, well-known failures in ethical contexts were, for example, an AI at Amazon that was supposed to help in the selection of applicants. The AI was trained with employee profiles from the IT department that were predominantly male, which resulted in female applicants subsequently being excluded by the AI as unsuitable. Another experiment was a Twitter bot developed by Microsoft to learn from users (“Bot to Learn from User”). The model was trained by interacting with users, which turned the AI racist in just a few hours. The system was immediately stopped. Other failures that became known and were relevant to safety were various accidents with Tesla’s Autopilot, some of them serious.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Who do people trust<\/b>: AI or a human decision<\/b>? <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA survey collected by Bosch as part of its AI future forecast revealed notable differences in various AI application areas. While there is a high level of trust in AIs for topics relating to industry and transport, people’s trust in AI dwindles to a minimum when it comes to health issues or personnel decisions. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Varying levels of trust in AI <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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To safeguard an AI against errors, ethical missteps, security breaches, and human mistrust, there are the following action items:<\/p>

  1. Security<\/li>
  2. Reliability<\/li>
  3. Transparency<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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    Security<\/b>: Intentional manipulation<\/b> of an AI through an attack<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tWhen developing an AI, it is necessary to ensure and test that the model behaves as it should. One way to test this is to falsify the results. An adversarial attack is the use of adversarial examples to manipulate the AI. An adversarial example is a specially manipulated input signal to an artificial neural network that intentionally misleads it into misclassifications. The manipulation can even be done in such a way that a human observer does not notice it or does not recognize it as intentional manipulation. Even single erroneous pixels on the training data can cause a completely different classification result. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Reliability<\/b>: through model validation<\/b> to reliable artificial intelligence <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tWhen developing an AI model, it must also always be clear what it was developed for. The limits of the AI should also be clearly defined: Does the model know what it doesn’t know? In tests, the same stop sign was fed to an AI for evaluation over and over again, but in different color contrasts and\/or exposure scenarios. Not all of them were correctly recognized. Theoretically, an infinite amount of such manipulated data can be generated for an attack scenario, making verification impossible. Only small changes in input lead to large changes in output, so it is imperative to verify both the nature and quality of the training data when developing an AI model. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Transparency<\/b>: Blackbox AI and the explainability of artificial decisions<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    It is in the nature of a neural network that decisions are intransparent, and thus it is difficult for users to understand how one decision was made and not another. So how can humans understand what the AI is doing – for example, to check the results? After all, making the AI’s decisions explainable is essential for humans to trust it.<\/p>

    There are various approaches to solving these problems, for example by means of optimization, which, however, sometimes leads to mathematical problems that are almost impossible to solve. Another possibility is Bayesian inference: Here, previously known information is used to check whether a solution can be possible. All three questions, i.e. regarding security, reliability and transparency, should be considered and clarified during the development of AI.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    From the playground to implementation<\/b>: Deployment of ML in battery production <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    In the presentation by Dr. Antje Fitzner,<\/strong> Digitalization of Battery Cell Production, Fraunhofer Research Institution for Battery Cell Production FFB M\u00fcnster and Alexander D. Kies<\/strong>, M.Sc., Production Quality, Fraunhofer Institute for Production Technology IPT Aachen, two possible use cases for the deployment of AI in battery production were presented.<\/p>

    For example, the FFB is developing manufacturing options for subsequent battery cell production in small and large manufacturing facilities up to “giga-factories”. In this context, the possible integration of AI in the manufacturing and monitoring processes was also classified as an interesting area of development. The presentation went from the basic consideration of where the use of AI makes sense to the implementation of the use case.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    Possible fields of application for ML <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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    Finally, the following two applications seemed promising:<\/strong><\/p>