We study the decentralized multi-agent multi-armed bandit problem for agents that communicate with probability over a network defined by a $d$-regular graph. Every edge in the graph has probabilistic weight $p$ to account for the ($1\!-\!p$) probability of a communication link failure. At each time step, each agent chooses an arm and receives a numerical reward associated with the chosen arm. After each choice, each agent observes the last obtained reward of each of its neighbors with probability $p$. We propose a new Upper Confidence Bound (UCB) based algorithm and analyze how agent-based strategies contribute to minimizing group regret in this probabilistic communication setting. We provide theoretical guarantees that our algorithm outperforms state-of-the-art algorithms. We illustrate our results and validate the theoretical claims using numerical simulations.

Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach for the partitioning of k-uniform hypergraphs. Most of the existing methods work by reducing the hypergraph to a graph followed by applying standard graph partitioning algorithms. The reduction step restricts the algorithms to capturing only some weighted pairwise interactions and hence loses essential information about the original hypergraph. We overcome this issue by utilizing the tensor-based representation of hypergraphs, which enables us to capture actual super-dyadic interactions. We prove that the hypergraph to graph reduction is a special case of tensor contraction. We extend the notion of minimum ratio-cut and normalized-cut from graphs to hypergraphs and show the relaxed optimization problem is equivalent to tensor eigenvalue decomposition. This novel formulation also enables us to capture different ways of cutting a hyperedge, unlike the existing reduction approaches. We propose a hypergraph partitioning algorithm inspired from spectral graph theory that can accommodate this notion of hyperedge cuts. We also derive a tighter upper bound on the minimum positive eigenvalue of even-order hypergraph Laplacian tensor in terms of its conductance, which is utilized in the partitioning algorithm to approximate the normalized cut. The efficacy of the proposed method is demonstrated numerically on simple hypergraphs. We also show improvement for the min-cut solution on 2-uniform hypergraphs (graphs) over the standard spectral partitioning algorithm.

While Artificial Intelligence (AI) is a much touted technology in mining, it would seem that the sector is yet to fully embrace this advance technology. Why is this and how can we insure that AI can be beneficial to mining in Africa. According to Prof. Frederick Cawood, Director of Wits Mining Institute at the University of the Witwatersrand, it will take a policy change to ensure that it can benefit mining in Africa. Cawood was a panellist on a recent Mining Review Africa webinar titled Mining 2025: A 5-year vision for AI in mining. Cawood was joined on the panel by Eric Croeser, MD for Africa at Accenture Industry X and Jean-Jacques Verhaeghe, programme manager for real-time information management systems at Mandela Mining Precinct.

As we enter the age of advanced technological breakthroughs such as Artificial Intelligence (AI), Nigeria cannot be left out in harnessing the advantages …

The Machine learning model classifies lithic assemblages of Eastern Africa to identify major incidents of history. Archaic incidents have shaped the existence of human civilization. It tells about the evolution of humans over centuries. Understanding these incidents helps us to have a peek in the past. Moreover, it helps in analyzing the various processes that led to the development of humans.

The National Centre for Artificial Intelligence and Robotics will engage youth, support in the diversification of the economy, create more jobs and inspire Nigeria's digital entrepreneurs. This was disclosed by the Minister of Communications and Digital Economy, Dr. Isa Pantami at the commissioning ceremony of the National Centre for Artificial Intelligence and Robotics on Friday, 13th November, 2020 in Abuja. Pantami reiterated that the proposed activities of this Centre map to Pillar No. 7 on Digital Society and Emerging Technologies and this explains the choice of the Centre's name. "Artificial Intelligence is the Refinery of the digital economy and Robotics is very useful in supporting companies as they carry out repetitive tasks. These are 2 very important emerging technologies that will shape the face of future technologies and we have decided to be proactive to enable us play a key role in how these technologies evolve, Pantami said.

Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live. You can subscribe to the DataTalk podcast on iTunes, Google Play, Stitcher, SoundCloud and Spotify. This data science video and podcast series is part of Experian's effort to help people understand how data-powered decisions can help organizations develop innovative solutions and drive more business. To keep up with upcoming events, join our Data Science Community on Facebook or check out the archive of recent data science videos. To suggest future data science topics or guests, please contact Mike Delgado. In this week's #DataTalk, we talked with Dr. Anima Anandkumar, Principal Scientist at Amazon AI and Bren Professor at Caltech, about what data scientists need to know about deep learning and how to scale deep learning frameworks. Today we're excited to talk about deep learning with Dr. Anima Anandkumar. Anima serves as the principal scientist at Amazon Web Services. Anima earned her BTech in electrical engineering from the Indian Institute of Technology. She also earned her Ph.D. in electrical engineering from Cornell University, and then after that she served as a postdoctoral researcher at MIT. She's the recipient of dozens of awards.

Technology is evolving at such a rapid pace that it is sometimes hard to keep up. As technology evolves, it causes faster change and progress. Individuals and companies that do not keep up with these changes are at the risk of being left behind. Understanding and keeping up with new technological trends will enable individuals to prepare and grasp the opportunities they provide. Here are some big technology trends of 2020.

COVID-19, declared a pandemic by the World Health Organization (WHO) [25] on 11 March 2020, is still spreading around the world up to date 26 September 2020. The number of people infected is beyond 32 million on 26 September 2020 with 989,380 deaths [24]. In Senegal, the number of cumulative cases is currently 14839 with 2624 individuals undergoing treatment on 25 September 2020[13]. The first cases, from Wuhan, were notified to WHO on 31 December 2019 [25, 26] while, Senegal notified its first case on 02 March 2020 [13]. Because of its limited resources, as in many sub-Saharan African countries, it is therefore valuable to understand the growth and the timing in responding to the logistic needs of their health system. We note that several developed countries that nevertheless have high-capacity health structures have been overwhelmed and this considerably impacted negatively in the combat against the pandemic.

At some point, you might have asked yourself, What are the origins of parameters for artificial neural networks? What is the purpose of the weight? What if no bias is used? In this tutorial, we set out to answer those questions by working from the most simple artificial neural network (ANN), to something much more complex. Let's start by building a machine learning model with no parameters--which is Y X. Then, we'll gradually add some parameters to the model until we build a single neuron. This neuron is made to accept one or more inputs. The mathematical representation of the neuron is then mapped to a graphical representation. By connecting multiple neurons, a complete ANN can be created.