Sticks and stones may break my bones, but words will never hurt me. This was a mantra I picked up on the playground at elementary school--something I repeated over and over again anytime I came face to face with racism. It was a coping mechanism meant to guard my heart from the cacophony of discriminatory comments that shaped me as a young Korean American girl growing up in predominantly white spaces. But now that I'm well into adulthood, I think about the girls of color who are also being taught to pretend that words don't hurt--and the people this way of thinking actually protects. It's hard to escape the unrelenting consequences of racism: In the past year alone, we lost Breonna Taylor, George Floyd, Ahmaud Arbery, and the six women of Asian descent murdered in Atlanta (Xiaojie "Emily" Tan, Daoyou Feng, Suncha Kim, Yong Ae Yue, Soon Chung Park, Hyun Jung Grant) at the hands of this insidious disease--and those are just the names that were in the headlines. If we don't acknowledge ...

Despite the bleak outcome of 2020, the pandemic has brought some good as it has sped up the level of AI-powered automation. If anything, it clearly pointed to a need for a powerful HR leadership amid a drastically changing workplace that relied on tech tools to pivot over the course of a few days, in some cases even overnight. These changes will spill over to 2021, where AI will continue to transform HR on an industry-wide level. Let's start with predictive skills gaps. The transition to skills-based HR rather than job-based HR has been quiet in the past few years. However, with the pandemic throwing a wrench in the works, many departments are beginning to catch up.

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Heterogeneous Ultra-Dense Network (HUDN) is one of the vital networking architectures due to its ability to enable higher connectivity density and ultra-high data rates. However, efficiently managing the wireless resource of HUDNs to reduce the wireless interference faces challenges. In this paper, we tackle this challenge by jointly optimizing user association and power control. The joint user association and power control problem is a typical non-convex problem that is hard and time-consuming to solve by traditional optimization techniques. This paper proposes a novel idea for resolving this question: the optimal user association and Base Station (BS) transmit power can be represented by some network parameters of interest, such as the channel information, the precoding matrices, etc. Then, we solve this problem by transforming it into an optimal representation function learning problem. We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning (SSL), in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration in the initial phase. Besides, we use the entropy regularization to guarantee the effectiveness of exploration in the configuration space. To embrace both the generalization of the learning algorithm and higher performance of HUDNs, we separate the learning process into two parts, the generalization-representation learning (GRL) part, and the specialization-representation learning (SRL) part. In the GRL part, the GNN learns a representation with a tremendous generalized ability to suit any scenario with different user distributions, which processes offline. Based on the learned GRL representation, the SRL finely turn the parameters of GNN on-line to further improving the performance for quasi-static user distribution. Simulation results demonstrate that the proposed GRL-based solution has higher computational efficiency than the traditional optimization algorithm. Besides, the results also show that the performance of SRL outperforms the GRL.

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experience, which are interleaved with new data during training. The amount of patterns stored in the buffer is a critical parameter which largely influences the final performance and the memory footprint of the approach. This work introduces Distilled Replay, a novel replay strategy for Continual Learning which is able to mitigate forgetting by keeping a very small buffer (up to $1$ pattern per class) of highly informative samples. Distilled Replay builds the buffer through a distillation process which compresses a large dataset into a tiny set of informative examples. We show the effectiveness of our Distilled Replay against naive replay, which randomly samples patterns from the dataset, on four popular Continual Learning benchmarks.

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.

Artificial Intelligence is no more a new thing to the world. However, advancing AI still remains the buzzword of the century. Now, it has become an inevitable technology in every industry worldwide. From yielding accurate models and providing competitive advantage to improving operational efficiencies, its early adopters has benefited in endless ways. In this article, we will discuss in brief how AI application is benefiting different platforms in 2021.

A project-based course to build an AIoT system from theory to prototype. Artificial Intelligence and Automation with Zang Cloud Sample codes are provided for every project in this course. You will receive a certificate of completion when finishing this course. There is also Udemy 30 Day Money Back Guarantee, if you are not satisfied with this course. This course teaches you how to build an AIoT system from theory to prototype particularly using Naive Bayes algorithm.

As the coronavirus pandemic has brought the workforce online, organizations are struggling to manage dynamic remote workloads. On Thursday, a team of data scientists led by a Purdue University professor, Somali Chaterji, introduced a solution called OPTIMUSCLOUD. This new software technology, which runs with a database server, harnesses machine learning to create algorithms to improve the efficiency of virtual machine selection and options for database management systems. The system is designed to help organizations reap the greatest benefit from cloud-based databases. Chaterji directs the Innovatory for Cells and Neural Machines and teaches agricultural and biological engineering.

San Francisco - Amazon Web Services (AWS) has announced the general availability of Lookout for Metrics, a new Machine Learning (ML) service to help businesses monitor their performance. The services is designed to helps customers monitor the most important metrics for their business like revenue, web page views, active users, transaction volume, and mobile app installations with greater speed and accuracy, AWS, the Cloud computing business arm of Amazon, said on Thursday. The service also makes it easier to diagnose the root cause of anomalies like unexpected dips in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures, increases in new user sign-ups, and many more - all with no machine learning experience required. With Amazon Lookout for Metrics, customers need to pay only for the number of metrics analysed per month. "We're excited to deliver Amazon Lookout for Metrics to help customers monitor the metrics that are important to their business using an easy-to-use machine learning service," Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS, said in a statement.