A Cheat Sheet to Deep Learning Algorithms: Types, Applications, and Examples.

Self-supervised learning models were initially introduced in response to the challenges of … Machine Learning Developers Summit (MLDS) 2023

The field of artificial intelligence (AI) has made remarkable progress in recent years, with applications ranging from language translation to self-driving cars. But AI's use in the art world may be less known. Artificial intelligence art generators generate original artwork by using techniques such as neural networks and generative adversarial networks (GANs). Computer-generated imagery has been used in art since the 1960s, when early experiments were conducted. Artificial intelligence (AI) technology, however, has enabled AI art generators to create highly sophisticated and complex artworks.

This has been a hugely transformative year for artificial intelligence, with rapid advancements in AI capabilities raising a variety of novel legal questions that Law360 Expert Analysis writers explored, including copyright conundrums, murky legal rights and the troubling possibility of deepfake evidence.

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISumm, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISumm uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISumm. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages. Our experiments of GAE-ISumm in seven languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries.

With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative position of Kinect v2 sensors with a calibration wand and register the sensors' positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of the motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach, which discovers occluded joint data. The synchronization approach is based on an NTP protocol that synchronizes the time between the clocks of a server and clients dynamically, ensuring that the proposed system is a real-time system. Experiments for validating the proposed system are conducted from the perspective of calibration, occlusion, accuracy, and efficiency. Furthermore, to demonstrate the practical performance of our system, a comparison of previously developed motion capture systems (the linear trilateration approach and the geometric trilateration approach) with the benchmark OptiTrack system is conducted, therein showing that the accuracy of our proposed system is $38.3\%$ and 24.1% better than the two aforementioned trilateration systems, respectively.

I hate the stance people take with this argument, because it comes off as unnecessarily antagonistic. Preventing anyone from sharing the technology or indeed from using it would be very hard, this is true, but heavy handed regulation could still kill public development. Rather than trying to argue that it's too late to stop the technology, it's much more productive to try and convince people on the positives of the technology, that there's great things that can come from it. You convince no one with an argument that there's nothing they can do about it, because they can certainly try, and they certainly can make the experience worse. No one should want things to be stuck where they are now, with people just trading dated copies of the tech in shadier parts of the internet, and possible consequences for sharing the art.

1. Introduction. The EU AI Act (AIA) has received international attention, and many who have never before taken an interest in EU legislation are …

One of the most serious difficulties in image processing is picture deterioration. Image blur is an unfavorable loss of bandwidth that reduces image quality and is difficult to avoid. Blur is produced by bothatmospheric instability and improper camera settings. Noise alters the recorded image in addition to blur effects. Image restoration is the process of reducing blur from a deteriorated image and returning it to its original state.