OCTOBER 20228MANUFACTURING TECHNOLOGY INSIGHTSIN MY OPINIONMachine learning models can be used in manufacturing to predict equipment failures, optimize production lines, and improve quality control. By analyzing data from sensors and other sources, machine learning can detect patterns human operators may not be able to see. This can help identify potential problems, increase efficiency, and fewer downtime incidents.The shortage of AI talent and resources is a significant challenge for organizations. As a result, they may outsource their machine learning work or use open-source pre-trained ML models published on online repositories. Using pre-trained machine learning models is a common and attractive alternative for many organizations. Often these models have been trained on large datasets and can be easily accessed via online services. However, there are some potential drawbacks to using pre-trained models. First, the data used to train the model may not represent the data you are using for your application. This can lead to poor performance of the model on your data. Second, pre-trained models can be expensive to use. If you are using a paid service, you may need to pay for each prediction made by the model. Finally, pre-trained models can be unstable. The model's weights can change over time, leading to changes in the predictions made by the model. While pre-trained models have many advantages, you should know these potential drawbacks before using them for your applications.In addition, when relying on pre-trained models and online hosted services for machine learning applications, it is essential to be aware of the potential risks posed by adversarial threats. Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. This attack allows an adversary to manipulate data deliberately to trick a machine learning algorithm into making incorrect predictions. As machine learning becomes more widespread, so will the threats posed by adversarial attacks. Unfortunately, many artificial intelligence (AI) developers and users fail to take the necessary precautions against these assaults. This lack of attention could have severe consequences as adversarial threats become more sophisticated and widespread.Adversarial machine learning is a field of study that focuses on developing methods to attack machine learning models. These attacks can range from simple evasion attacks, where an attacker attempts to fool a model into misclassifying an input, to more sophisticated poisoning By Jarrod Anderson, Senior Director, Artificial Intelligence, ADMMACHINE LEARNING BACKDOORS AND ADVERSARIAL THREATS
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