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.

Few would dispute the idea that artificial intelligence will be a transformative technology for financial services. Yet the view of how that transformation will shake out may be evolving significantly. A report from Deloitte and the World Economic Forum contends that in the near future, technology expertise will grow so commonly available that raw AI and multiple technologies built around that hub will not be what separates the winners from the other players. Instead, as envisioned by the report, the transformative technologies that excite so many today will become as basic to the industry as the longstanding payments rails they all share today. What institutions do with that transformative technology will mean much more and that will hinge on some surprisingly basic ideas.

The extractive summarization involves getting key phrases from the actual document and combining those key phrases to make a brief summary. Most used technique for extractive text summarization is sentence scoring. So, extractive summarization involves assigning saliency measure to some units of the documents and extracting those with highest scores to include in the summary of the document.Extractive text summarization methods can be classified into two: Supervised Learning methods and Unsupervised Learning methods.

Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider two-player multi-objective normal form games with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i.e., learning while considering the impact of one's policy when anticipating the opponent's learning step). Empirical results in five different MONFGs demonstrate that opponent learning awareness and modelling can drastically alter the learning dynamics in this setting. When equilibria are present, opponent modelling can confer significant benefits on agents that implement it. When there are no Nash equilibria, opponent learning awareness and modelling allows agents to still converge to meaningful solutions that approximate equilibria.

GovCon Expert Chuck Brooks has published his latest article as a member of Executive Mosaic's GovCon Expert program on Wednesday. Brooks discussed the development and procurement of emerging technologies as they influence every sector of the federal marketplace, including the Department of Defense (DoD), the Department of Homeland Security (DHS), academia and the intelligence community. You can read Chuck Brooks' latest GovCon Expert article below: The development and procurement of emerging technologies is being institutionalized throughout government, particularly in national security areas. There are a variety of new initiatives and programs that have been created to ensure that the United States is prepared for a new era of technology leadership. If you are interested in transformative technologies, it is an exciting time to follow what is happening both in industry and in government.

One of the greatest barriers to adopting and scaling AI applications is the scarcity of varied, high-quality raw data. To overcome it, firms need to share their data. But the many regulatory restrictions and ethical issues surrounding data privacy pose a major obstacle to doing this. A novel solution that my firm is piloting that could solve this problem is a data trust: an independent organization that serves as a fiduciary for the data providers and governs their data's proper use. Research shows that companies are becoming increasingly aware of the value of sharing data and are exploring ways to do so with other players in their industry or across industries.

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. Though prevalent and effective in many downstream applications (e.g., information retrieval, question answering, and knowledge graph embedding), conventional sequence labeling approaches heavily rely on hand-crafted or language-specific features. Recently, deep learning has been employed for sequence labeling tasks due to its powerful capability in automatically learning complex features of instances and effectively yielding the stat-of-the-art performances. In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition, and text chunking. Then, we systematically present the existing approaches base on a scientific taxonomy, as well as the widely-used experimental datasets and popularly-adopted evaluation metrics in the SL domain. Furthermore, we also present an in-depth analysis of different SL models on the factors that may affect the performance and future directions in the SL domain.

We have seen the advent of state-of-the-art (SOTA) deep learning models for computer vision ever since we started getting bigger and better compute (GPUs and TPUs), more data (ImageNet etc.) and easy to use open-source software and tools (TensorFlow and PyTorch). Every year (and now every few months!) we see the next SOTA deep learning model dethrone the previous model in terms of Top-k accuracy for benchmark datasets. The following figure depicts some of the latest SOTA deep learning vision models (and doesn't depict some like Google's BigTransfer!). However most of these SOTA deep learning models are brought down to their knees when it tries to make predictions on a specific class of images, called as adversarial images. The whole idea of an adversarial example can be a natural example or a synthetic example.

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

In this comprehensive review, we describe a new mathematical problem in artificial intelligence (AI) from a mathematical modeling perspective, following the philosophy stated by Rudolf E. Kalman that "Once you get the physics right, the rest is mathematics". The new problem is called "Generalized Constraints (GCs)", and we adopt GCs as a general term to describe any type of prior information in modelings. To understand better about GCs to be a general problem, we compare them with the conventional constraints (CCs) and list their extra challenges over CCs. In the construction of AI machines, we basically encounter more often GCs for modeling, rather than CCs with well-defined forms. Furthermore, we discuss the ultimate goals of AI and redefine transparent, interpretable, and explainable AI in terms of comprehension levels about machines. We review the studies in relation to the GC problems although most of them do not take the notion of GCs. We demonstrate that if AI machines are simplified by a coupling with both knowledge-driven submodel and data-driven submodel, GCs will play a critical role in a knowledge-driven submodel as well as in the coupling form between the two submodels. Examples are given to show that the studies in view of a generalized constraint problem will help us perceive and explore novel subjects in AI, or even in mathematics, such as generalized constraint learning (GCL).