Certainly, Nigerians look forward to more government investments in the development of digital infrastructure across other sections of the country. So, it is imperative for the government to provide necessary funding to expand this centre to other parts of the country. The National Information Technology Development Agency (NITDA) has again achieved another milestone with the launch of the National Centre for Artificial Intelligence and Robotics (NCAIR) as part of its contribution to the successful implementation of the digital economy. Coming at a time when the global economy is rapidly transforming into the new economy driven by creative innovations derived from Science, Technology, Engineering and Mathematics (STEM), the unveiling of this state-of-the-art technology innovation centre can be described as futuristic, in the sense that it is a remarkable demonstration of proactivity on the part of the Ministry of Communications and Digital Economy and NITDA. NCAIR represents government's determination to create a suitable environment for discovering and harnessing the abundant creative ideas of Nigeria's teeming youth population for national development through the promotion of innovative technologies. With the actualisation of this centre, the youth segment of the population would be challenged to channel their creative energies towards preparing solutions that seek to address future problems or challenges across all sectors of the economy.

Artificial General Intelligence (AGI) is at the core of a vision that leads the future of our world. Its main goal is to provide autonomous solutions for each mankind's problem, a fully automated system that will serve humans and live among them. Artificial Intelligence is the technology to fulfill such a vision. At the intersection of computer science and data science, AI's first step is to create a computational representation of everything. Algorithms & Big Data are the two keys.

A Japanese government-funded project to develop the world's first predictive maintenance system for industrial plants will employ artificial intelligence and advanced technology to store data in a secure and user-centric way. The IOTA Foundation, a German-based nonprofit organization behind the technology, said it has been chosen as a partner in the project funded by Japan's New Energy and Industrial Technology Development Organization, a national research and development agency operating under the Ministry of Economy, Trade and Industry. The project, which seeks to strengthen the durability of critical infrastructure, will optimize facility management systems deployed in power, industrial, petrochemicals and oil refining plants throughout Japan by digitizing maintenance data and using artificial intelligence to predict when checkups are needed, according to the foundation. Data for plants across Japan is currently stored manually, causing issues with integrity and sharing capability, it said. The project aims to shift data to a decentralized database using IOTA's distributed ledger service called the Tangle, while AI systems are expected to replace engineers amid Japan's shrinking labor force.

On the recent development, the National Center for Artificial Intelligence and Robotics is equipped with state-of-the-art facilities that include;.

Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that extracts data from a well-defined grid of sensors and optimizes traffic flow by controlling traffic signals. The data extraction mechanism is independent of the optimization algorithm and this paper primarily emphasizes the later one. We have used MiniZinc modeling language to define our system as a constraint satisfaction problem which can be solved using any off-the-shelf solver. The proposed system performs much better than the systems currently in use. Our system reduces the mean waiting time and standard deviation of the waiting time of vehicles and avoids deadlocks.

In the simplest version of this problem, given access to a graph, one seeks to cluster the vertices into interpretable communities or groups of vertices, which are believed to reflect latent similarities among the nodes. From a theoretical standpoint, this problem has been extensively analyzed under specific generative assumptions on the observed graph; the most popular generative model in this context is the stochastic block model (SBM) [22]. Inspired by intriguing conjectures arising from the statistical physics community [29], community detection under the stochastic block model has been studied extensively. As a consequence, the precise information theoretic limits for recovering the underlying communities have been derived, and optimal algorithms have been identified in this setting (for a survey of these recent breakthroughs, see [1]). In reality, the practitioner often has access to additional information in the form of node covariates, which complements the graph information.

We conjecture that the reason for the difference in generalisation between adaptive and non adaptive gradient methods stems from the failure of adaptive methods to account for the greater levels of noise associated with flatter directions in their estimates of local curvature. This conjecture motivated by results in random matrix theory has implications for optimisation in both simple convex settings and deep neural networks. We demonstrate that typical schedules used for adaptive methods (with low numerical stability or damping constants) serve to bias relative movement towards flat directions relative to sharp directions, effectively amplifying the noise-to-signal ratio and harming generalisation. We show that the numerical stability/damping constant used in these methods can be decomposed into a learning rate reduction and linear shrinkage of the estimated curvature matrix. We then demonstrate significant generalisation improvements by increasing the shrinkage coefficient, closing the generalisation gap entirely in our neural network experiments. Finally, we show that other popular modifications to adaptive methods, such as decoupled weight decay and partial adaptivity can be shown to calibrate parameter updates to make better use of sharper, more reliable directions.

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.