The global pandemic has given us all an opportunity to pause for thought and take stock of what is and what is not important. More and more businesses are turning to AI to become more sustainable, smarter and to better react to changing market conditions, as well as to ensure health, safety and social impact of our planet. We need a future where you can do the things you love; live the life you deserve and take the time to grow with nature and nurture the things that inspire you to help others. From pandemic prevention and fighting cancer, to fighting hunger, wildlife conservation and boosting accessibility, this article will explore exactly how AI is doing well by doing good. AI use cases can help towards overall adaptation in preventing wildfires, diagnosing deadly diseases, mitigating risks posed in critical areas as well as predictive analysis and monitoring to make our planet more resilient in the near future.

We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. As rational agents, drivers are trying to maximize some reward function unknown to an external observer as they make up their trajectories. We apply the concept of random utility from microeconomic theory to model the unknown reward function as a function of observable features plus an error term which represents features known only to the driver. We develop a parameterized generative model for the trajectories based on a random utility Markov decision process formulation of drivers decisions. We show that maximum entropy inverse reinforcement learning is a particular case of our proposed formulation when we assume a Gumbel density function for the unobserved reward error terms. We illustrate Bayesian inference on model parameters through a case study with real trajectory data from a large city obtained from sensors placed on sparsely distributed points on the street network.

Autonomous discovery and direct instruction are two extreme sources of learning in children, but educational sciences have shown that intermediate approaches such as assisted discovery or guided play resulted in better acquisition of skills. When turning to Artificial Intelligence, the above dichotomy is translated into the distinction between autonomous agents which learn in isolation and interactive learning agents which can be taught by social partners but generally lack autonomy. In between should stand teachable autonomous agents: agents learning from both internal and teaching signals to benefit from the higher efficiency of assisted discovery. Such agents could learn on their own in the real world, but non-expert users could drive their learning behavior towards their expectations. More fundamentally, combining both capabilities might also be a key step towards general intelligence. In this paper we elucidate obstacles along this research line. First, we build on a seminal work of Bruner to extract relevant features of the assisted discovery processes. Second, we describe current research on autotelic agents, i.e. agents equipped with forms of intrinsic motivations that enable them to represent, self-generate and pursue their own goals. We argue that autotelic capabilities are paving the way towards teachable and autonomous agents. Finally, we adopt a social learning perspective on tutoring interactions and we highlight some components that are currently missing to autotelic agents before they can be taught by ordinary people using natural pedagogy, and we provide a list of specific research questions that emerge from this perspective.

Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several astronomical catalogues. However, some scientific articles have also shown that the training data therein contain multiple sources of bias. Hence, the performance of those classifiers on objects not belonging to the training data is uncertain, potentially resulting in the selection of incorrect models. Besides, it gives rise to the deployment of misleading classifiers. An example of the latter is the creation of open-source labelled catalogues with biased predictions. In this paper, we develop a method based on an informative marginal likelihood to evaluate variable star classifiers. We collect deterministic rules that are based on physical descriptors of RR Lyrae stars, and then, to mitigate the biases, we introduce those rules into the marginal likelihood estimation. We perform experiments with a set of Bayesian Logistic Regressions, which are trained to classify RR Lyraes, and we found that our method outperforms traditional non-informative cross-validation strategies, even when penalized models are assessed. Our methodology provides a more rigorous alternative to assess machine learning models using astronomical knowledge. From this approach, applications to other classes of variable stars and algorithmic improvements can be developed.

In the end, the report includes Global Conversational Ai Platform Market new project SWOT analysis, investment feasibility analysis, investment return analysis, and development analysis. The report also presents a round-up of vulnerabilities which companies operating in the market must avoid in order to enjoy sustainable growth through the course of the forecast period. Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Asia (China, India, Japan etc.) or Oceania [Australia and New Zealand]. Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a market's size, key trends, participants and future outlook of an industry. We intend to become our clients' knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code – Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

Global health care expenditure has been projected to grow from US $7.7 trillion in 2017 to US $10 trillion in 2022 at a rate of 5.4% [1]. This translates into health care being an average of 9% of gross domestic product among developed countries [2,3]. Some key global trends that have led to this include tax reform and policy changes in the United States that could impact the expansion of health care access and affordability (Affordable Care Act) [4], implications on the United Kingdom's health care spend based on the decision to leave the European Union [5], population growth and rise in wealth in both China and India [6-8], implementation of socioeconomic policy reform for health care in Russia [9], attempts to make universal health care effective in Argentina [10], massive push for electronic health and telemedicine in Africa [11], and the impact of an unprecedented pace of population aging around the world [12]. From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 [13].

Some 500 C-level business and security experts from companies with over $5 billion in revenue in multiple industries expressed concern in a recent survey from Accenture about the potential security vulnerabilities posed by the pursuit of AI, 5G and augmented reality technologies all at the same time. To properly train AI models, for example, the company needs to protect the data needed to train the AI and the environment where it is created. When the model is being used, the data in motion needs to be protected. Data cannot be collected in one place, either for technical or security reasons, or for the protection of intellectual property. "Therefore, it forces companies to insert safe learning so that the different parties can collaborate," stated Claudio Ordóñez, Cybersecurity Leader for Accenture in Chile, in a recent account in Market Research Biz.

The global Artificial Intelligence Market is expected to reach USD 348.99 Billion by 2027, according to a new report by Emergen Research. The increasing need for understanding consumer needs and market trends is one of the major factors driving the market growth. Moreover, the extensive adoption of smartphones, along with the popularity of social media, will also boost the growth of the market in the coming years. The global Artificial Intelligence market is classified on a product basis, application and end-user. Based on product, the market is segmented as systems, and services & software.

Due to the practical importance of vehicle routing problems (VRP), there exists an ever-growing body of research in algorithms and (meta)heuristics for solving such problems. However, the diversity of VRP domains creates the separate problem of modeling such problems -- describing the domain entities (and, in particular, the planning decisions), the set of valid planning decisions, and the preferences between different plans. In this paper, we review the approaches for modeling vehicle routing problems. To make the comparison more straightforward, we formulate several criteria for evaluating modeling methods reflecting the practical requirements of the development of optimization algorithms for such problems. Finally, as a result of this comparison, we discuss several future research avenues in the field of modeling VRP domains.

The constant increase in the amount and complexity of information obtained from IT data networkelements, for its correct monitoring and management, is a reality. The same happens to data net-works in electrical systems that provide effective supervision and control of substations and hydro-electric plants. Contributing to this fact is the growing number of installations and new environmentsmonitored by such data networks and the constant evolution of the technologies involved. This sit-uation potentially leads to incomplete and/or contradictory data, issues that must be addressed inorder to maintain a good level of monitoring and, consequently, management of these systems. Inthis paper, a prototype of an expert system is developed to monitor the status of equipment of datanetworks in electrical systems, which deals with inconsistencies without trivialising the inferences.This is accomplished in the context of the remote control of hydroelectric plants and substationsby a Regional Operation Centre (ROC). The expert system is developed with algorithms definedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic with Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and generate the operating conditions (faulty,normal, unstable or inconsistent / indeterminate) of the equipment that are identified as importantfor the remote control of hydroelectric plants and substations. A prototype of this expert systemwas installed on a virtualised server with CLP500 software (from the EFACEC manufacturer) thatwas applied to investigate scenarios consisting of a Regional (Brazilian) Operation Centre, with aGeneric Substation and a Generic Hydroelectric Plant, representing a remote control environment.