Limitations of neural networks. Why using CNNs and why usingRNNs?4 answersNeural networks have limitations in their ability to perform tasks compared to biological neural networks (BNNs). However, artificial neural networks (ANNs) such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have specific advantages. CNNs are well-suited for tasks involving image and pattern recognition due to their ability to extract spatial features. On the other hand, RNNs are effective in processing sequential data and have been successfully applied in speech audio processing tasks. Both CNNs and RNNs have the potential to improve predictions accuracy compared to traditional statistical models. While CNNs excel in tasks involving spatial features, RNNs are better suited for sequential data processing, making them valuable tools in various fields such as computer vision and speech processing.
Issues in artificial neural networks5 answersArtificial Neural Networks (ANNs) face several issues. One issue is the lack of trustworthiness in extrapolations, which can be problematic for safety critical systems. Another issue is the difficulty in training neural networks on physics problems, particularly when high-order differential operators are involved. When using ANNs in model predictive controls (MPCs) for buildings, issues can arise in training the ANN and managing energy flexibility. Additionally, NNs have disadvantages such as overfitting, lack of explainability, and high computing resource consumption. To overcome these difficulties, determining the appropriate NN structure is crucial, as too poor or too rich NNs can lead to training failures or unexplainable results. Simplifying NN parameters can also help by reducing resource consumption and increasing transparency.
What are the challenges of machine learning?4 answersMachine learning (ML) faces several challenges in its development and implementation. These challenges include limited data availability and the need to go beyond supervised learning for scalability and generalization. ML techniques, including deep learning, struggle to quantify process uncertainty in modeling complex nonlinear systems. Another challenge is characterizing the interaction between stressed systems and various hazards at different scales. In the healthcare industry, challenges arise due to the adoption of ML, such as data quality, model design, and limited implementation. ML practitioners in clinical research face challenges related to data, feature generation, model design, performance assessment, and limited implementation. Operationalizing and maintaining ML-centered products also pose challenges, requiring a broader set of practices and technologies. Overall, challenges in machine learning range from data limitations and uncertainty quantification to domain-specific issues and operationalization difficulties.
What are some of the challenges of artificial intelligence?4 answersArtificial intelligence (AI) faces several challenges. One major challenge is the lack of data infrastructure and trained people, as well as a better understanding of applications. Another challenge is the potential risks and dangers associated with AI, which need to be addressed to ensure responsible and equitable deployment. Additionally, there are challenges related to diversity and inclusivity in AI design, such as bias, discrimination, and perceived untrustworthiness, which require attention. The study of false information using AI models also presents problems, including difficulties in defining and classifying terms and differences in labeling. Finally, there are challenges in realizing the full potential of human-AI collaboration, including understanding the conditions that support complementarity, assessing human mental models of AI, and designing effective human-AI interaction.
What are the challenges in machine learning?4 answersMachine learning faces several challenges. These include poor data quality, underfitting and overfitting of training data, lack of sufficient training data, slow implementation, imperfections in algorithms as data grows, irrelevant features, non-representative training data, making incorrect assumptions, and becoming obsolete as data grows. In the field of drug discovery, challenges arise from the need for rigorous model validation and potential biases in training data sets. In the context of human resources, legal concerns arise regarding employment discrimination laws and data protection regulations, while ethical concerns revolve around privacy and justice for employees. Implementing machine learning in embedded systems presents challenges such as restricted memory and processor speed, as well as considerations of time, space, cost, security, privacy, and power consumption. Despite these challenges, machine learning offers the potential to extract useful information from vast amounts of data and enable computers to learn and make accurate predictions.
What are the challenges to using machine learning in health care?3 answersMachine learning (ML) has great potential in healthcare, but there are several challenges to its adoption. These challenges include issues related to data, feature generation, model design, performance assessment, and limited implementation. One major challenge is the prediction and classification of diseases, particularly cardiovascular disease, which can be addressed by using vital sign parameters and machine learning algorithms. Another challenge is the volume of people needing medical support, which has been exacerbated by the pandemic. Machine learning can help address this challenge by providing disease prediction, detection, and personalized healthcare services. Additionally, the integration of machine learning with internet of things technologies enables personalized healthcare through the processing of data collected from wearable devices and sensors. Overall, machine learning has the potential to improve healthcare services, but challenges related to data, model design, and implementation need to be addressed for its successful adoption in clinical research.