Pushing the envelope of Neural Language Processing model GPT-3 by OpenAI I explore its capability to interpret art -- and to create. One might say, "GPT-3" is still far away from AGI. One might say, it's "competence without comprehension". But during my experiments, I was already convinced about the creative power of GPT-3 -- mixed with human perception. In my texts, I always mark AI-generated contents as such.

In this article, I will take you through a brief explanation of Image Segmentation in Deep Learning. I will only explain the concept behind the image segmentation here in this article. If you want to go through the practical part of Image Segmentation you can see it's tutorial here. In Image segmentation, each pixel is classified according to the class of the object it belongs to (e.g., road, car, pedestrian, building, etc.), as shown in the figure below. Note that different objects of the same class are not distinguished.

An artificial intelligence (AI) technology made by a firm co-founded by billionaire Elon Musk has won praise for its ability to generate coherent stories, novels and even computer code but it remains blind to racism or sexism. GPT-3, as Californian company OpenAI's latest AI language model is known, is capable of completing a dialogue between two people, continuing a series of questions and answers or finishing a Shakespeare-style poem. Start a sentence or text and it completes it for you, basing its response on the gigantic amount of information it has been fed. This could come in useful for customer service, lawyers needing to sum up a legal precedent or for authors in need of inspiration. While the technology is not new and has not yet learnt to reason like a human mind, OpenAI's latest offering has won praise for the way its text resembles human writing.

The Power of AI and Machine Learning – Progress is kind of skyrocketing. And this is due to a couple of factors. First, we have this like volume of computation that's available. It's much larger now than it used to be. And this increasing that compute and data kind of directly has led to better and more powerful models.

Convolutional Neural Networks (CNNs) are the fundamental building blocks of many deep learning applications. Everyday computer vision solutions to tasks such as image classification, object detection, pose estimation, depth estimation are solved through the implementation of carefully designed CNN architecture. Throughout my academic and professional career, I have found that it's vital to have an in-depth awareness of the internal components that are employed to develop widely used CNN architecture. Transfer Learning and Fine Tuning have made access to state of the art CNN architecture easier. Nonetheless, it is beneficial to take a step back to the simplest architectures and earliest deep learning research papers and understand the initial building blocks of what we today observe as Deep Learning.

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Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., Abstract Syntax Trees) and/or semantic information (e.g., Dependency Graphs). Although graphs may be better at capturing various viewpoints of code semantics than trees, constructing graph inputs from code needs static code semantic analysis that may not be accurate and introduces noise during learning. Although syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques. We propose a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks, to achieve learning accuracy higher than existing graph-based techniques while it is based only on trees. TreeCaps introduces novel variable-to-static routing algorithms into the capsule networks to compensate for the loss of previous routing algorithms. Aside from accuracy, we also find that TreeCaps is the most robust to withstand those semantic-preserving program transformations that change code syntax without modifying the semantics. Evaluated on a large number of Java and C/C programs, TreeCaps models outperform prior deep learning models of program source code, in terms of both accuracy and robustness for program comprehension tasks such as code functionality classification and function name prediction.

Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of 96.1% (94.9%), compared to 93.0% (87.0%) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users, based on the Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease self-diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph, resulting in better reasoning ability of our HealGCN model, when confronting users with scarce descriptions. At last, we validate our model on a large-scale EHR dataset, the superior performance does confirm our model's effectiveness in practice.

The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting relevant regions that contribute significantly to its prediction. A layer-wise amplitude filtering method was previously introduced to improve the quality of heatmaps, performing error corrections by noise-spike suppression. In this study, we generalize the layerwise error correction by considering any identifiable error and assuming there exists a groundtruth interpretable information. The forms of errors propagated through layerwise relevance methods are studied and we propose a filtering technique for interpretability signal rectification taylored to the trend of signal amplitude of the particular neural network used. Finally, we put forth arguments for the use of groundtruth interpretable information.