See also article by Sveinsson et al in this issue. Paul H. Yi, MD, was a musculoskeletal radiology fellow at Johns Hopkins Hospital and is affiliate faculty at the Malone Center for Engineering in Healthcare. His research focuses on application and limitations of deep learning in radiology, including the potential for algorithmic bias. He serves on the RSNA Machine Learning Steering Subcommittee and the trainee editorial board of Radiology: Artificial Intelligence and is the journal's podcast co-host. In July 2021, Dr Yi joined the radiology faculty at the University of Maryland and serves as director of the University of Maryland Intelligent Medical Imaging Center.

Modelling the noise is a really hard task. And I'm not even trying to argue with that. As a physicist, I deal with noise all the times, and it is not easy to capture it, model it, or deal with it in general. For this reason, a lot of complex, smart, and efficient methods have been developed during the years. With the technological progresses that computer have experienced during the years, great performances of Machine (Deep) Learning de-noising algorithms have been obtained.

OpenAI's GPT-3 is the most powerful AI system I've ever used. Trained on billions of web pages and tens of thousands of books, the system can generate nearly any kind of text, from news articles to computer code to sea shanties.

Machine Learning and Deep Learning and Natural Language Processing (NLP. The report covers all the aspects of Enterprise Artificial Intelligence (AI) …

Max-AI is an artificial intelligence enabled robot that identifies recyclables and other items for recovery. Through deep learning technology, Max employs both multi-layered neural networks and a vision system to see and identify objects similar to the way a person does. The technology is driving improvements in Material Recovery Facility (MRF) design, operational efficiency, recovery, system optimization, maintenance. Credit: Bulk Handling Systems For ML/ AI/ Data Science learning materials, please check my previous posts. I share my learning journey into Data Science with my amazing LinkedIn friends, please follow me and let's grow together!

"In 2021, language models will start to become aware of the visual world. The next generation of models will understand text better because of the many images they've seen." The line dividing pixels and prose is receding with every passing day. And, the reason behind this is the flourishing of vision transformers. Over the years, neural networks got better with natural language processing.

Fine-grained image classification uses the step-by-step approach and understanding the different areas of the image, for example, features of the bird, and then analyzing those features to classify the image completely. Also, it is difficult to tag the location information of the image pixels manually. But in comparison to the standard image classification process, the advantage of using fine-grained classification is that the model is supervised by using image notes without additional training.

Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590. No additional data available.

Objective: To propose a novel deep neural network (DNN) architecture -- the filter bank convolutional neural network (FBCNN) -- to improve SSVEP classification in single-channel BCIs with small data lengths. Methods: We propose two models: the FBCNN-2D and the FBCNN-3D. The FBCNN-2D utilizes a filter bank to create sub-band components of the electroencephalography (EEG) signal, which it transforms using the fast Fourier transform (FFT) and analyzes with a 2D CNN. The FBCNN-3D utilizes the same filter bank, but it transforms the sub-band components into spectrograms via short-time Fourier transform (STFT), and analyzes them with a 3D CNN. We made use of transfer learning. To train the FBCNN-3D, we proposed a new technique, called inter-dimensional transfer learning, to transfer knowledge from a 2D DNN to a 3D DNN. Our BCI was conceived so as not to require calibration from the final user: therefore, the test subject data was separated from training and validation. Results: The mean test accuracy was 85.7% for the FBCCA-2D and 85% for the FBCCA-3D. Mean F1-Scores were 0.858 and 0.853. Alternative classification methods, SVM, FBCCA and a CNN, had mean accuracy of 79.2%, 80.1% and 81.4%, respectively. Conclusion: The FBCNNs surpassed traditional SSVEP classification methods in our simulated BCI, by a considerable margin (about 5% higher accuracy). Transfer learning and inter-dimensional transfer learning made training much faster and more predictable. Significance: We proposed a new and flexible type of DNN, which had a better performance than standard methods in SSVEP classification for portable and fast BCIs.

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors \cite{Plate1995} by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differentiable component of a deep learning architecture. HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop an output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.