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Talkdesk Launches New Generative AI Features that Make AI More Responsible, Accurate, and Accessible in the Contact Center

Talkdesk Launches New Generative AI Features that Make AI More Responsible, Accurate, and Accessible in the Contact Center
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United States , Talkdesk Gen , Ken Cohen , Tiago Paiva , Jennifer Lundberg , Charanya Kannan , Jk Moving Services , Talkdesk Inc , Supercharge Contact Center Efficiency , Gartner Inc , Industry Experience , Automatic Summary , Generative Knowledge Retrieval , Automatic Topic Discovery , Process Based Virtual Agent , Data Augmentation , Agent Assist , Talkdesk Virtual Agent , Contact Center Efficiency , North America , Industry Experience Clouds ,

A faster way to teach a robot

A new technique enables a human to efficiently fine-tune a robot that failed to complete a desired task with very little effort on the part of the human. Their system uses algorithms, counterfactual explanations, and feedback from the user to generate synthetic data it uses to quickly fine-tune the robot. ....

Julie Shah , Tianmin Shu , Pulkit Agrawal , Andreea Bobu , Aviv Netanyahu , International Conference On Machine , York University , National Science Foundation Graduate Research Fellowship , Stevens Institute Of Technology , University Of California At Berkeley , Hyundai Motor Corporation , Watson Ai Lab , Artificial Intelligence Laboratory , Interactive Robotics Group , National Science Foundation Institute For Artificial Intelligence , Andi Peng , New York University , Stevens Institute , Computer Science , International Conference , Open Philanthropy , National Science Foundation Institute , Artificial Intelligence , Julie Shah , Pulkit Agrawal , Human Robot Interaction ,

"Data Augmentation for Small Sample Iris Image Based on a Modified Spar" by Qi Xiong, Xinman Zhang et al.

Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-I ....

Equal Error Rate , Data Augmentation , Iris Images , Small Sample , Parrow Search Algorithm , Swarm Intelligence ,

"DAGAD: Data Augmentation for Graph Anomaly Detection" by Fanzhen Liu, Xiaoxiao Ma et al.

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data aug ....

Data Augmentation Based Graph Anomaly Detection , Anomalous Sample Scarcity , Anomaly Detection , Class Imbalance , Data Augmentation , Graph Mining , Graph Neural Networks , Semi Supervised Learning ,

"GAME: Generative-Based Adaptive Model Extraction Attack" by Yi Xie, Mengdie Huang et al.

The outstanding performance of deep learning has prompted the rise of Machine Learning as a Service (MLaaS), which significantly reduces the difficulty for users to train and deploy models. For privacy and security considerations, most models in the MLaaS scenario only provide users with black-box access. However, previous works have shown that this defense mechanism still faces potential threats, such as model extraction attacks, which aim at stealing the function or parameters of a black-box victim model. To further study the vulnerability of publicly deployed models, we propose a novel model extraction attack named Generative-Based Adaptive Model Extraction (GAME), which augments query data adaptively in a sample limited scenario using auxiliary classifier GANs (AC-GAN). Compared with the previous work, our attack has the following advantages: adaptive data generation without original datasets, high fidelity, high accuracy, and high stability under different data distributions. Acco ....

Service Mlaa , Machine Learning , Generative Based Adaptive Model Extraction , Adaptive Strategy , Uxiliary Classifier Gans , Data Augmentation , Odel Extraction Attack ,