Artificial intelligence is bound to change the future of the world. By fiscal year 2025, the artificial intelligence market size is expected to be $390.9 However, this number does not tell the whole story. By FY2030, the projected growth of global GDP as a result of artificial intelligence is expected to be $15.7 trillion. This projection bodes well for AI stocks.

In a far-reaching and candid interview after IBM's #THINK2020 digital event, Rob and I discussed his insights into what's happening with AI in the enterprise and where the biggest long-term impacts will be. Editorial comments are set off by square brackets [such as these.] There's one word that describes what I'm seeking here: insight. I am seeking your insights. I've looked at your blog posts and reviews of your books. You have insights I want to share with all The Analyst Syndicate's readers. Let's start with insights on business model disruption in particular industries.

Image classification has become more interesting in the research field due to the development of new and high performing machine learning frameworks. With the advancement of artificial neural networks and the development of deep learning architectures such as the convolutional neural network, that is based on artificial neural networks has triggered the application of multiclass image classification and recognition of objects belonging to the multiple categories. Every latest machine learning framework has a comparative advantage over the older ones in terms of performance and complexity. In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes.

With a shortage of medical staff, hospitals around the world turn to robots to assist with the ongoing increase of work due to the Coronavirus outbreak. Here are a few examples of how Robots are helping human doctors and nurses throughout this outbreak. Links to more details can be found on Welcome.AI https://www.welcome.ai/news_info/the-... Streamline Machine Learning Projects https://spell.run/

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.

eXplainable AI focuses on generating explanations for the output of an AI algorithm to a user, usually a decision-maker. Such user needs to interpret the AI system in order to decide whether to trust the machine outcome. When addressing this challenge, therefore, proper attention should be given to produce explanations that are interpretable by the target community of users. In this chapter, we claim for the need to better investigate what constitutes a human explanation, i.e. a justification of the machine behaviour that is interpretable and actionable by the human decision makers. In particular, we focus on the contributions that Human Intelligence can bring to eXplainable AI, especially in conjunction with the exploitation of Knowledge Graphs. Indeed, we call for a better interplay between Knowledge Representation and Reasoning, Social Sciences, Human Computation and Human-Machine Cooperation research -- as already explored in other AI branches -- in order to support the goal of eXplainable AI with the adoption of a Human-in-the-Loop approach.

Misinformation such as fake news has drawn a lot of attention in recent years. It has serious consequences on society, politics and economy. This has lead to a rise of manually fact-checking websites such as Snopes and Politifact. However, the scale of misinformation limits their ability for verification. In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims. Behind the scenes BRENDA uses a tested deep neural network architecture to automatically identify fact check worthy claims and classifies as well as presents the result along with evidence to the user. Since BRENDA is a browser extension, it facilities fast automated fact checking for the end user without having to leave the Webpage.

The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.

We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SATsolving algorithms. We report improvements of learned neural heuristics compared with two standard human-designed heuristics. We compare the performance in terms of number of branching decisions and show the possibility of enhancing the performance of SAT solvers with the help of learned heuristics. A similar graph representation, but more general in order to accommodate for higher-order logic is used in FormulaNet presented in [WTWD17]. To the best of our knowledge the FormulaNet architecture was never used for neural guidance.

Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services-such as computer vision-continually learn with time. When the internals within the abstracted 'black box' become hidden and evolve, pitfalls emerge in the robustness of applications that depend on these evolving services. Without adapting the way developers plan and construct projects reliant on intelligent services, significant gaps and risks result in both project planning and development. Therefore, how can software engineers best mitigate software evolution risk moving forward, thereby ensuring that their own applications maintain quality? Our proposal is an architectural tactic designed to improve intelligent service-dependent software robustness. The tactic involves creating an application-specific benchmark dataset baselined against an intelligent service, enabling evolutionary behaviour changes to be mitigated. A technical evaluation of our implementation of this architecture demonstrates how the tactic can identify 1,054 cases of substantial confidence evolution and 2,461 cases of substantial changes to response label sets using a dataset consisting of 331 images that evolve when sent to a service.