What is the difference between image processing and computer vision? Both are concerned with images. And that's the only thing they have in common. Computer vision and image processing are two distinct tools with different applications. In this post, we'll look at each of these in greater detail and explore the differences between them.

Death by suicide is the seventh of the leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI application in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of researches that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression signs and suicidal behavior non-verbal cues. In this paper, we review the recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues.

Melbourne (Australia) The human brain is a true miracle machine. It is always active, can solve complex tasks, is capable of learning and has the ability to process several streams of information at once. For this reason, researchers have tried to make biological nerve cells usable for computer science. According to the scientists at Cortical Labs, they recently made a breakthrough. They taught microscopic brains grown in Petri dishes to play the computer game Pong.

According to a new market research report, "Artificial Intelligence in Accounting Market by Component, Deployment Mode, Technology, Enterprise Size, Application (Automated Bookkeeping, Fraud and Risk Management, and Invoice Classification and Approvals), and Region - Global Forecast to 2024", published by MarketsandMarkets, the global the Artificial Intelligence (AI) in Accounting Market is expected to grow from USD 666 million in 2019 to USD 4,791 million by 2024, at a Compound Annual Growth Rate (CAGR) of 48.4% during the forecast period. The major factors driving the growth of AI in accounting market include the growing need to automate accounting processes and the need for enhanced data-based advisory and decision making. The AI in accounting market has been segmented based on components into 2 categories: solutions and services. The solutions segment is estimated to hold a larger market size, which is driven by the ease of integrating pre-built solutions with existing accounting infrastructure. The growing number of innovations and partnerships in the accounting sector and the focus on automating repetitive accounting processes to enhance efficiency, are also the factors contributing to the adoption.

As artificial intelligence plays an increasingly important role in the R&D process, the premise that invention is a uniquely human characteristic is being challenged. Patent offices and courts around the world have recently been grappling with the question of whether an AI system can be the inventor of a patent. This has been prompted by Dr. Stephen Thaler's applications to designate his AI system (known as'DABUS') as the inventor of patents filed in multiple jurisdictions. Most recently, the appeal board of the European Patent Office (EPO) refused Dr. Thaler's patent applications because there was no valid inventor. Dr. Thaler, as part of the Artificial Inventor Project, is pursuing parallel patent applications across over fifteen jurisdictions which designate his AI system, DABUS, as the inventor.

Gibson Dunn lawyers examine a number of the most significant developments from 2021 affecting companies as they navigate the evolving AI and autonomous systems landscape.

The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models. Therefore, it is pivotal to have a precise evaluation method to gauge the performance and predictability power of these complex methods. To do so, several evaluation metrics have been proposed in the past (such as Energy Score, Dawid-Sebastiani score, variogram score), however, they cannot reliably measure the performance of a probabilistic forecaster. Recently, CRPS-sum has gained a lot of prominence as a reliable metric for multivariate probabilistic forecasting. This paper presents a systematic evaluation of CRPS-sum to understand its discrimination ability. We show that the statistical properties of target data affect the discrimination ability of CRPS-Sum. Furthermore, we highlight that CRPS-Sum calculation overlooks the performance of the model on each dimension. These flaws can lead us to an incorrect assessment of model performance. Finally, with experiments on the real-world dataset, we demonstrate that the shortcomings of CRPS-Sum provide a misleading indication of the probabilistic forecasting performance method. We show that it is easily possible to have a better CRPS-Sum for a dummy model, which looks like random noise, in comparison to the state-of-the-art method.

Deep learning models have been shown to be vulnerable to recent backdoor attacks. A backdoored model behaves normally for inputs containing no attacker-secretly-chosen trigger and maliciously for inputs with the trigger. To date, backdoor attacks and countermeasures mainly focus on image classification tasks. And most of them are implemented in the digital world with digital triggers. Besides the classification tasks, object detection systems are also considered as one of the basic foundations of computer vision tasks. However, there is no investigation and understanding of the backdoor vulnerability of the object detector, even in the digital world with digital triggers. For the first time, this work demonstrates that existing object detectors are inherently susceptible to physical backdoor attacks. We use a natural T-shirt bought from a market as a trigger to enable the cloaking effect--the person bounding-box disappears in front of the object detector. We show that such a backdoor can be implanted from two exploitable attack scenarios into the object detector, which is outsourced or fine-tuned through a pretrained model. We have extensively evaluated three popular object detection algorithms: anchor-based Yolo-V3, Yolo-V4, and anchor-free CenterNet. Building upon 19 videos shot in real-world scenes, we confirm that the backdoor attack is robust against various factors: movement, distance, angle, non-rigid deformation, and lighting. Specifically, the attack success rate (ASR) in most videos is 100% or close to it, while the clean data accuracy of the backdoored model is the same as its clean counterpart. The latter implies that it is infeasible to detect the backdoor behavior merely through a validation set. The averaged ASR still remains sufficiently high to be 78% in the transfer learning attack scenarios evaluated on CenterNet. See the demo video on https://youtu.be/Q3HOF4OobbY.

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.

Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc. Understanding the field of distance measures is more important than you might realize. Take k-NN for example, a technique often used for supervised learning. As a default, it often uses euclidean distance. However, what if your data is highly dimensional?