Transmetrics' demand forecasting and predictive optimization platform is powered by artificial intelligence and machine learning algorithms. With four decades of experience and a strong operational presence in Egypt, KSA, UAE and Sudan, Transmar has built a solid reputation in the market, founded on family values that drive the company's ambition to offer the best in class service to its customers. Transmar owns and operates a large fleet of both dry and refrigerated containers, serves thousands of customers, and moves hundreds of commodities throughout the Middle East. "We strongly believe in the power of Data. Transmetrics' AI solution helps us leverage our 4 decades of operational experience, to make decisions both faster and smarter. As a regionally focused carrier we are more exposed to volatility. We're excited about the capabilities Transmetrics will provide by helping see up to 12 weeks into the future, ensuring we have optimum planning and repositioning plans" said Ahmed el Ahwal, Commercial Manager at Transmar.

Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.

In this work, we analyze the reinstatement mechanism introduced by Ritter et al. (2018) to reveal two classes of neurons that emerge in the agent's working memory (an epLSTM cell) when trained using episodic meta-RL on an episodic variant of the Harlow visual fixation task. Specifically, Abstract neurons encode knowledge shared across tasks, while Episodic neurons carry information relevant for a specific episode's task.

T.A: If there's one thing the pandemic has demonstrated, it's the value of staying connected. We see connectivity as a basic human right. The collaboration with telecommunications service providers was key to developing the connectivity solutions we are relying on more than ever today, and it will be key for enabling future innovation to bring us even closer together. ICT standardization efforts are at the heart of creating network solutions that can keep our society running, even under pressure. Safeguarding and strengthening our key digital infrastructures – as well as enabling the continuous development of the underlying technology – will also be crucial as Africa emerges from the crisis – and has the potential to propel Africa into a steep and sustainable growth cycle.

If you are keen to watch more, the entire playlist for the 2020 event can be found here. The lectures include tutorials on the use of Python and R, statistics for machine learning, Gaussian processes, reinforcement learning. You can also find out about geospatial analysis, AI for social good, machine learning for industry users, and much more. You can find out more about Data Science Nigeria here.

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in modern educational, psychological, social and biological sciences. A key component of CDMs is a binary $Q$-matrix characterizing the dependence structure between the items and the latent attributes. Additionally, researchers also assume in many applications certain hierarchical structures among the latent attributes to characterize their dependence. In most CDM applications, the attribute-attribute hierarchical structures, the item-attribute $Q$-matrix, the item-level diagnostic model, as well as the number of latent attributes, need to be fully or partially pre-specified, which however may be subjective and misspecified as noted by many recent studies. This paper considers the problem of jointly learning these latent and hierarchical structures in CDMs from observed data with minimal model assumptions. Specifically, a penalized likelihood approach is proposed to select the number of attributes and estimate the latent and hierarchical structures simultaneously. An efficient expectation-maximization (EM) algorithm and a latent structure recovery algorithm are developed, and statistical consistency theory is also established under mild conditions. The good performance of the proposed method is illustrated by simulation studies and a real data application in educational assessment.

The graph represents a network of 2,735 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 04 April 2021 at 01:32 UTC. The requested start date was Sunday, 04 April 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 11-day, 1-hour, 34-minute period from Monday, 22 March 2021 at 09:56 UTC to Friday, 02 April 2021 at 11:30 UTC.

SAN FRANCISCO, April 02, 2021 (GLOBE NEWSWIRE) -- Holberton, making software engineering education affordable and accessible globally, today announced the appointment of a new Machine Learning and Mathematics Team to build out a comprehensive program to accelerate training students in the key tenets of Artificial Intelligence (AI), the engine of the New Economy. On LinkedIn, there are currently 60,000 machine learning jobs open in the U.S. alone. Many are technology giants such as Twitter and TikTok. But increasingly traditional tech companies are investing in machine learning and recruiting machine learning engineers: even companies like McDonald's. According to LinkedIn, machine learning has created one of the biggest employment opportunities of 2021.