A highly autonomous system (HAS) has to assess the situation it is in and derive beliefs, based on which, it decides what to do next. The beliefs are not solely based on the observations the HAS has made so far, but also on general insights about the world, in which the HAS operates. These insights have either been built in the HAS during design or are provided by trusted sources during its mission. Although its beliefs may be imprecise and might bear flaws, the HAS will have to extrapolate the possible futures in order to evaluate the consequences of its actions and then take its decisions autonomously. In this paper, we formalize an autonomous decisive system as a system that always chooses actions that it currently believes are the best. We show that it can be checked whether an autonomous decisive system can be built given an application domain, the dynamically changing knowledge base and a list of LTL mission goals. We moreover can synthesize a belief formation for an autonomous decisive system. For the formal characterization, we use a doxastic framework for safety-critical HASs where the belief formation supports the HAS's extrapolation.

As we randomly search terms on the internet, we often encounter "machine learning" and "deep learning" and how they are revolutionizing the way in which we live our lives. At present, machine learning is almost used everywhere from self-driving cars, email spam detection, recommender systems that we see in Netflix and Amazon, credit card fraud detection used by banks and so on. The list goes on and on with potential new applications being created. Therefore, it is very important to stay updated with the latest trends and understand what machine learning actually is and get a good broader understanding of some of the types of machine learning. In this article, I would explain machine learning and the different categories of machine learning.

Artificial intelligence, distributed ledger technologies, novel biomarkers of aging and longevity, internet of things; all of these technologies are rapidly evolving, converging and giving raise to the new applications in every industry. From helping us track the rate of our mental physiological aging, to impacting our overall health and longevity, AI has the potential and the capacity to transform life and health insurance as we know it. In a recent article titled "How AI And Aging Research Can Help Life Insurance Companies", I explored many ways in which AI and aging research are going to disrupt the traditional models of how life insurance companies operate and can help them, as well as policy owners, make better informed decisions. Traditional life insurance companies used broad actuarial tables to assign policy seekers to a risk category. However, there is a new upcoming brand of insurance that makes it even more relevant for the entire society and economy at large. You might have probably heard of fintech, the technology startups disrupting the finance industry.

Artificial intelligence, distributed ledger technologies, novel biomarkers of aging and longevity, internet of things; all of these technologies are rapidly evolving, converging and giving raise to the new applications in every industry. From helping us track the rate of our mental physiological aging, to impacting our overall health and longevity, AI has the potential and the capacity to transform life and health insurance as we know it. In a recent article titled "How AI And Aging Research Can Help Life Insurance Companies", I explored many ways in which AI and aging research are going to disrupt the traditional models of how life insurance companies operate and can help them, as well as policy owners, make better informed decisions. Traditional life insurance companies used broad actuarial tables to assign policy seekers to a risk category. However, there is a new upcoming brand of insurance that makes it even more relevant for the entire society and economy at large. You might have probably heard of fintech, the technology startups disrupting the finance industry.

So, today's article is all about artificial intelligence, automation, how things work! We know it's a vast field, so in today's article I will just touch on some of the basic things, or some of the major changes this AI can create, some advantages of this AI, about robotics, how these things will be changing our lives, etc, so let's start... Firstly, let's know what is AI? Basically, AI( artificial intelligence) is the ability of a computer or a robot to do things that are usually done by human beings because it needs to have human intellect. This is the simple definition of understanding AI. We will see the benefits of AI in healthcare, banking, human intelligence, automation, arts, businesses, and almost in every field where humans are involved today. "AI is going to change the world more than anything in the history of mankind.

Do you ever ask yourself why your machine learning model isn't used? Why do so few people really believe in the power of machine learning rather than these old dashboards? When I was working in a football club, I made a data visualization showing player performances during the season. It was a really simple tile plot. But when football people saw it on my screen they engaged quite quickly.

In this blog we are going to talk about decision tree algorithm. Yeah, you read it right. It is a tree, or it looks like a tree (upside down tree) which helps to take decision. How come a tree helps us to take decision? So how do we take any decision?

In operational studies, for example, AI can help operate a port or an airline. With so many dimensions to take into account, AI technologies can further push the limits of strategies to optimise resources. Health care is similar in this regard. Genetic data is massively complex, but it is also incredibly useful for understanding individual human health. This data can only be analysed in real time by AI technologies.

Esteban Granero has some good news, a little light at the end of a long, dark tunnel in Spain, where the coronavirus crisis has left more than 21,000 people dead. "The situation is terrible," says the midfielder, a league title winner with Real Madrid, "but the curve is clearly downward now; we reached the peak on the fourth [of April] and now we're on the way down. Things shift daily but we think at the end of the month, early May, the number of cases will be very low and there will be room for optimism." Granero does not speak lightly. He has been watching the trends carefully.

In spite of incidents of inaccuracy and bias, agencies like Artificial Intelligence (AI) court judges are starting to get accepted. However, AI has a lot to learn before we allow it to judge our moral behavior. Ganes Kesari, Co-Founder and Head of Analytics at Gramener, tells The Sociable that right now AI is not ready to take decisions on cases, and even in the future, it would be better off in the court in an assistant's role. AI needs to acquire skills in'understanding' context and interpreting scenarios "Today, AI is more suited to play the role of a judicial assistant than that of a criminal judge. It is smart at processing details, summarizing cases and looking up references. It is not ready to take decisions on cases just as yet," he says.