This book deals with machine learning and on how to use deep learning tools and techniques to solve the challenges faced by the cybersecurity …

The National Robotics Engineering Center is looking for a Machine Learning Engineer to develop machine learning algorithms and applications for …

In this course, you will learn how to apply Artificial Intelligence and Machine Learning methods to design new products or systems and solve …

After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as components of a machine's mind. Every affective manifold models a characteristic group of mind and contains multiple states. We define the machine's mind as a set of affective manifolds. We use a learning model for mapping the input signals to the embedding space of affective manifold. Using this mapping, a machine or a robot takes an input signal and can react emotionally to it. We use deep metric learning, with Siamese network, and propose a loss function for affective manifold learning. We define margins between states based on the psychological and philosophical studies. Using triplets of instances, we train the network to minimize the variance of every state and have the desired distances between states. We show that affective manifolds can have various applications for machine-machine and human-machine interactions. Some simulations are also provided for verification of the proposed method. It is possible to have as many affective manifolds as required in machine's mind. More affective manifolds in the machine's mind can make it more realistic and effective. This paper opens the door; we invite the researchers from various fields of science to propose more affective manifolds to be inserted in machine's mind.

Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-frequency bins in a spectrogram or time-pitch bins in a piano roll. This can be difficult to understand, particularly for musicologists without technical knowledge. To address this issue, we focus on more human-friendly explanations based on high-level musical concepts. Our research targets trained systems (post-hoc explanations) and explores two approaches: a supervised one, where the user can define a musical concept and test if it is relevant to the system; and an unsupervised one, where musical excerpts containing relevant concepts are automatically selected and given to the user for interpretation. We demonstrate both techniques on an existing symbolic composer classification system, showcase their potential, and highlight their intrinsic limitations.

Abstract--Graph-based anomaly detection has been widely used for detecting malicious activities in real-world applications. Existing attempts to address this problem have thus far focused on structural feature engineering or learning in the binary classification regime. In this work, we propose to leverage graph contrastive learning and present the supervised GraphCAD model for contrasting abnormal nodes with normal ones in terms of their distances to the global context (e.g., the average of all nodes). To handle scenarios with scarce labels, we further enable GraphCAD as a self-supervised framework by designing a graph corrupting strategy for generating synthetic node labels. To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph. We conduct extensive experiments on four public datasets, demonstrating that 1) GraphCAD significantly and consistently outperforms various advanced baselines and 2) its self-supervised version without fine-tuning can achieve comparable performance with its fully supervised version. A real example of detecting the papers (red) that don't belong Technology, Tsinghua University, Beijing, China, 100084. Jian Song is with Zhipu.AI, Beijing, China. Kaibo Xu is with Mininglamp Technology, Beijing, China.

With the development of social media, social communication has changed. While this facilitates people's communication and access to information, it also provides an ideal platform for spreading rumors. In normal or critical situations, rumors will affect people's judgment and even endanger social security. However, natural language is high-dimensional and sparse, and the same rumor may be expressed in hundreds of ways on social media. As such, the robustness and generalization of the current rumor detection model are put into question. We proposed a novel \textbf{h}ierarchical \textbf{a}dversarial \textbf{t}raining method for \textbf{r}umor \textbf{d}etection (HAT4RD) on social media. Specifically, HAT4RD is based on gradient ascent by adding adversarial perturbations to the embedding layers of post-level and event-level modules to deceive the detector. At the same time, the detector uses stochastic gradient descent to minimize the adversarial risk to learn a more robust model. In this way, the post-level and event-level sample spaces are enhanced, and we have verified the robustness of our model under a variety of adversarial attacks. Moreover, visual experiments indicate that the proposed model drifts into an area with a flat loss landscape, leading to better generalization. We evaluate our proposed method on three public rumors datasets from two commonly used social platforms (Twitter and Weibo). Experiment results demonstrate that our model achieves better results than state-of-the-art methods.

Look to your favorite sci-fi movie and you'll get a good sense of how Hollywood sees robots of the future: Most communicate with us. But the future of robots doesn't just lie in more lifelike, human and helpful drones, droids and automatons, which we'll increasingly encounter at every turn. It also lies in smaller, smarter and more self-aware high-tech helpers that will aid and assist with nearly every facet of everyday life. Alone, robots designed for industrial purposes are projected to be a $35.68 billion market by 2029, per Fortune Business Insights. The market for medical robots is anticipated to trail closely behind.

Madam Ama Pomaa Boateng, the Deputy Minister of Communication and Digitalisation, says Artificial Intelligence (AI) is now driving global digital revolution and solving problems and challenges for emerging economies. AI is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Madam Boateng said this in Accra at the first face-to-face meet-up networking event on AI, organised by the Ghana-India Kofi Annan Centre of Excellence in ICT (GI-KACE). "Financial Inclusion using AI is a very good thing because it is part of the Sustainable Development Goals," she said. "It is actually seven out of the 17 goals that government and other institutions are working on by using AI to solve problems ….".

Artificial intelligence disrupted the music industry this week when a major recording label signed--and then quickly dropped--a "robot rapper" who casually dropped the N-word in their lyrics. Many Black artists felt the decision to sign the AI rapper in the first place was a racist slap in the face. "Real talk, anybody who was involved with research, development, and signing this artist at Capitol music should have their resignation submitted or their jobs terminated," rock singer Ali Adkins of Ali A and the Agency in Phoenix told The Daily Beast. "Because that just means you don't get 50 fucks about the music. You just care about making a [dollar]."