GlobalData has identified ten of the biggest influencers in AI on Twitter during Q3 2019, using its Influencer Platform. GlobalData research has found the top AI influencers based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in AI on Twitter during Q3 2019. Ronald van Loon is a Big Data expert and Director at Advertisement, a data and analytics consultancy firm. He helps data-driven companies in executing data and analytics strategies to become more successful.

Technological advancements have been influencing our lives for quite some time already and still continue to do the same. Lately, the role and importance of Artificial Intelligence (AI) have risen at significant heights. It has an impact on every industry and every single aspect of our lives. Australia, as well as other countries all around the world, is facing challenges that are not easy to overcome, for example, management of natural hazards, aging population, health, infrastructure, etc, and surprisingly or not, AI has the ability to provide them with the proper help. The term Artificial Intelligence has been used to describe the technique that analyzes the threat or challenge and helps us to solve them.

"Together they created Appen, arguably one of the greatest IT success stories in Australia," said University of Sydney Vice-Chancellor and Principal Professor Stephen Garton AM. "Their company was founded long before artificial intelligence became fashionable and is a testament to the foresight of the Vonwillers." Honorary degrees are awarded to individuals who have made an outstanding contribution to the wider community or who have achieved exceptional academic or creative excellence. Husband and wife team Chris and Julia Vonwiller have been admitted to the degree of Doctor of Engineering (honoris causa). Dr Vonwiller is a respected linguist who studied at Macquarie University and graduated in 1980 with a Bachelor of Arts (Honours). She completed her PhD in linguistics at Macquarie University in 1989 before working as a researcher at the University of Sydney.

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the first AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement~13 (PS-013), organized by Universitat Pompeu Fabra (UPF), which primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we overview the ML models proposed by participants (including Artificial Neural Networks, Graph Neural Networks, Random Forest regression, and gradient boosting) and analyze their performance on an open dataset generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

Studying the implicit regularization effect of the nonlinear training dynamics of neural networks (NNs) is important for understanding why over-parameterized neural networks often generalize well on real dataset. Empirically, existing works have shown that weights of NNs condense on isolated orientations with a small initialization. The condensation dynamics implies that NNs can learn features from the training data with a network configuration effectively equivalent to a much smaller network during the training. In this work, we show that the multiple roots of activation function at origin is a key factor to understanding the condensation at the initial stage of training. Our experiments suggest that the maximal number of condensed orientations is twice of the multiplicity. Our theoretical analysis confirms experiments for two cases, one is for the activation function of multiplicity one and the other is for the one-dimensional input. This work makes a step towards understanding how small initialization implicitly leads NNs to condensation at initial stage of training, which lays a solid foundation for the future study of the nonlinear dynamics of NNs and its implicit regularization effect at a later stage of training.

Welcome to our May 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we look at research into the oceans, AI and philosophy of mind, and highlight some interesting podcasts. This month we focused on the UN sustainable development goal (SDG) of life below water. We interviewed Nayat Sánchez-Pi, director of Inria Chile and leader of the OcéanIA project. The project team is developing new artificial intelligence and mathematical modelling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling climate change.

LITERATURE UPDATE May 20, 2021 - May 26, 2021 Literature search terms: biomech* & locomot* Publications are classified by BiomchBERT, a neural network trained on past Biomch-L Literature Updates. BiomchBERT is managed by Ryan Alcantara, a PhD Candidate at the University of Colorado Boulder. Each publication has a score (out of 100%) reflecting how confident BiomchBERT is that the publication belongs in a particular category (top 2 shown). Risteski P, Jagrić M, Pavin N, Tolić IM, Current biology: CB. (76.3% CELLULAR/SUBCELLULAR; 4.7% MUSCLE) Physical analysis reveals distinct responses of human bronchial epithelial cells to guanidine and isothiazolinone biocides. Kwon TY, Jeong J, Park E, Cho Y, Lim D, Ko UH, Shin JH, Choi J, Toxicology and applied pharmacology.

Poshmark is a leading social marketplace for new and secondhand style for women, men, kids, home, and more. By combining the human connection of physical shopping with the scale, ease, and selection benefits of ecommerce, Poshmark makes buying and selling simple, social, and fun. The Machine Learning team is a central player in the Poshmark organization. Our mission is to build a world-class machine learning platform to bring value out of data for us and for our customers. The Machine Learning Engineering team at Poshmark is looking for an experienced machine learning engineer to take care of Poshmak's requirement to take machine learning models with varying requirements to production .

Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning. In our framework, two artificial agents play a Lewis signaling game where the goal is to convey a numeral concept. The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017). They are also shown to be similar to human numeral systems of same type. Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.

Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Na\"ive Bayes algorithms are introduced and evaluated.