In today's ultra-competitive business universe, Probability and Statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance. But why do you need to master probability and statistics in Python? The answer is an expert grip on the concepts of Statistics and Probability with Data Science will enable you to take your career to the next level. The course'Mastering Probability and Statistics in Python' is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regards to Python.

In the digital world, the two buzzwords discussed everywhere include Artificial Intelligence and Machine Learning. These technologies have revolutionized the ways businesses function and also the ways we execute our routine tasks. These have gradually seeped into the business world as well as our personal lives. It is through Artificial Intelligence and Machine Learning that every company is on the way to becoming a tech company. The profound implications of Artificial Intelligence in both business and society have made this technology the next digital frontier.

These concerns have spread throughout the AI field, leading even large corporations such as Microsoft to develop internal guidelines for using this technology. In June, the company publicly shared its new "Responsible AI Standard" framework that is aimed at "keeping people and their goals at the center of system design decisions and respecting enduring values like fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability," according to a Microsoft blog post. As a result of these standards, the company phased out an emotion recognition tool from its AI facial analysis services following criticism that such software was discriminatory against marginalized groups and not proven to be scientifically accurate. Businesses are not the only organizations looking to solve ethical questions about AI. Multiple colleges and universities are also creating research centers, educational programming, and other efforts that will help develop a new generation of scientists and engineers who are dedicated to using this form of technology to better society.

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Active inference offers a principled account of behavior as minimizing average sensory surprise over time. Applications of active inference to control problems have heretofore tended to focus on finite-horizon or discounted-surprise problems, despite deriving from the infinite-horizon, average-surprise imperative of the free-energy principle. Here we derive an infinite-horizon, average-surprise formulation of active inference from optimal control principles. Our formulation returns to the roots of active inference in neuroanatomy and neurophysiology, formally reconnecting active inference to optimal feedback control. Our formulation provides a unified objective functional for sensorimotor control and allows for reference states to vary over time.

Working on a group project as part of a #DataScience internship I'm on and I just received the greatest compliment I've received on my data science journey...

If you're looking for a free introductory AI course for beginner's, Microsoft has got you covered. The aptly-named Artificial Intelligence for Beginners is put together by Microsoft Azure Cloud Advocates, and consists of a 12 week, 24 lesson curriculum designed to introduce learners to the wonderful world of AI. You can see specific course content, laid out lesson by lesson, here. Lessons are taught using a variety of materials. You can find a mind map of the course here.

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