Opportunities for digital technologies implementation, including implementation of artificial intelligence, are being implemented in the mining sector. Technologies help to save money and to solve problems that humans can't solve. McKinseyestimates that by 2035, the use of data analysis and digital technologies will help coal, iron ore, and copper producers save between $290 billion and $390 billion annually. Digital technologies and artificial intelligence enable companies to extract minerals in hard-to-reach places and under extreme weather conditions. This article first appeared in Mining Review Africa Issue 10, 2019 Read the full digimag here or subscribe to receive a print copy here This means that in an environment when mineral resources are becoming increasingly scarce, it is possible to develop deposits that used to be inaccessible, to do it without endangering lives of employees and to minimize human errors that often lead to costly mistakes.

Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine. But although technology clearly has the potential to do a great deal of good, there's a sound business reason that tech companies often pour large amounts of resources into social projects that don't seem to align with their core business of selling software and services. This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

We study a noisy symmetric tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank symmetric tensor from highly incomplete and randomly corrupted observations of its entries. While a variety of prior work has been dedicated to this problem, prior algorithms either are computationally too expensive for large-scale applications, or come with sub-optimal statistical guarantees. Focusing on "incoherent" and well-conditioned tensors of a constant CP rank, we propose a two-stage nonconvex algorithm --- (vanilla) gradient descent following a rough initialization --- that achieves the best of both worlds. Specifically, the proposed nonconvex algorithm faithfully completes the tensor and retrieves all individual tensor factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e. minimal sample complexity and optimal estimation accuracy). The estimation errors are evenly spread out across all entries, thus achieving optimal $\ell_{\infty}$ statistical accuracy. The insight conveyed through our analysis of nonconvex optimization might have implications for other tensor estimation problems.

Deep neural networks have gained remarkable success over a l arge variety of applications, including computer vision [ 1 ], natural language processing [ 2 ], speech recognition [ 3 ] and Go games [ 4 ]. But the reason why deep networks perform well over various tasks is still not exactly understood. The optimization performance of deep networks is one of the subj ects which requires an involved theoretical study, given that gradient descent can achieve zero training loss even for random labels [ 5 ], and the loss of deep networks is highly non-convex. There are different lines of works investigating the optimization of deep networks from different perspec tives. For example, a large number of works consider the optimization landscape correspondin g to different activation functions [ 6 - 11 ], whereas some others [ 12 - 15 ] ensure global convergence by imposing some restrictions o n the input distribution. In the recent years, there has been considerably many papers providing convergence guarantees for over-parameterized two-layer and deep networks. It is s hown in [ 16 ] that gradient descent can find the near-global minima of a single hidden layer network i n polynomial time with respect to the accuracy and sample size.

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis e.g. in increasing the classification accuracy. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification accuracy. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification accuracy of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy subjects performed 6 sessions of 15 gait trials for one day. For each trial, two force plates recorded the 3D ground reaction forces (GRF). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each individual preprocessing step were analyzed and compared with respect to their prediction accuracy in a six-session classification using Support Vector Machines, Random Forest Classifiers and Multi-Layer Perceptrons. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

The determination of the computational complexity of multi-agent pathfinding on directed graphs has been an open problem for many years. For undirected graphs, solvability can be decided in polynomial time, as has been shown already in the eighties. Further, recently it has been shown that a special case on directed graphs is solvable in polynomial time. In this paper, we show that the problem is NP-hard in the general case. In addition, some upper bounds are proven.

With the burning desire and passion to equip ourselves and others with Artificial Intelligence skills, the mission and vision of global AI6 resonated with Azeez Oluwafemi and I the moment it was announced. We were quick to kick-start AI6 in our local community, Lagos, Nigeria on January 6th 2018. We organized and facilitated 16 weeks of study group from January till April and then from August till November. As ambassadors, we played a pivotal role in empowering the AI community in our city to get updated with the latest methods in artificial intelligence, and be able to implement the most cutting-edge AI models out there. For our first cycle, we walked our participants through Computer Vision from Stanford CS231n and Fast AI deep learning for coders.

Co-Founder and Group CEO, AntWorks -- Asheesh is the co-founder and CEO of AntWorks, a global leader in AI and Robotics. He believes humane, responsible AI is the future, and is excited by its limitless applications to solve for issues t… (show all) Asheesh is the co-founder and CEO of AntWorks, a global leader in AI and Robotics. He believes humane, responsible AI is the future, and is excited by its limitless applications to solve for issues that impact business, our lives and the planet we inhabit. Prior to boarding the entrepreneurial ship, Asheesh headed Infosys BPO – Asia Pacific, Japan and the Middle East. His experience over twenty years has also spanned across leadership roles in large ITeS organizations, such as Mphasis, TCS and WNS, having worked extensively across the UK and the United States.

Latest Strategic Study Released on Global Software for Autonomous Cars Market with forecast till 2025, the report comprises of historical data and estimation of the Global Software for Autonomous Cars Market. The following Industry is shown to progress with a noteworthy rise in the Compound Annual Growth Rate (CAGR) during the forecast period owing to various factors driving the market. Some of the key players mentioned in this research are "Alphabet, Delphi Automotive, Intel, NVIDIA, QNX Software Systems, Tesla, Apple, Autotalks, Cisco, Cohda Wireless, Covisint, DeepMap & Nauto", etc. Rapid Growth Factors In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor that's helping the market grow faster than usual is the tough competition. Business Strategies Key strategies in theGlobal Software for Autonomous Cars Market that includes product developments, partnerships, mergers and acquisitions, etc discussed in this report.

Smart cities are no longer a utopian dream of the future. Thanks to a slew of innovative and game-changing technologies, they are already active and growing quickly. Smart cities could be described as the junction between three main areas, namely digital transformation, environmental sustainability and economic performance. They can be described as a framework made up of connected technologies designed to address the challenges of rapid urbanisation and promote more sustainable, smarter practices. And the more urbanisation soars, standards of living sustainability become more than pipe dreams, but rather calls for action.