Artificial intelligence (AI) is poised to help deliver precision medicine and health.1,2 The clinical and biomedical research communities are increasingly embracing this modality to develop tools for diagnosis and prediction as well as to improve delivery and effectiveness of healthcare. New breakthroughs are being developed in an unprecedented fashion and the developed ones have obtained regulatory approval and found their way into routine medical practice.3,4,5 Yet, the medical school curriculum as well as the graduate medical education and other teaching programs within academic hospitals across the United States and around the world have not yet come to grips with educating students and trainees on this emerging technology. Several expert opinions have pointed to the benefits and limitations associated with the use of ML in medicine,1,2,6,7,8,9,10 but the aspect related to formally educating the younger generation of medical professionals has not been openly discussed.

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].

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. For more information see the IDTechEx report on Digital Health 2019: Trends, Opportunities and Outlook. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML.

Ten years ago, I invited the community to envision the future of Data, AI and Analytics (I called it then:"BI 2020"). From the Museum of Information in Paris, I asked: what could the world of AI, Data and Analytics look like by 2020?! Many predicted the advent of natural interfaces like search and voice for analysis. Conversational AI or Big Data weren't common industry terms back then but many saw that such trends would be transformative over the next decade. And we are far from having achieve the ambitious goals of the past decade.

It has been more than six decades since the concept of Artificial Intelligence has transformed from imagination to an academic discipline. Influencers, especially those active on social media help give direction to the policymakers and academicians. They keep common men updated on the trends and'what is what' in AI, Machine Learning and associated concepts like Big Data and BlockChain. AiThority introduces you to the 50 most popular AI-influencers of North America. A PhD in industrial-organizational psychology, his interests lies in Data Science, CX, Statistics and Machine Learning.

TALLINN (Reuters) - Finland, which holds the rotating EU presidency until the end of the year, said on Tuesday it aims to teach 1% of all Europeans basic skills in artificial intelligence through a free online course it will now translate into all official EU languages. The European Union is pushing for wide deployment of artificial intelligence across the bloc, to help European companies catch up with rivals in Asia and the United States. "Our investment has three goals: we want to equip EU citizens with digital skills for the future, we wish to increase practical understanding of what artificial intelligence is, and by doing so, we want to give a boost to the digital leadership of Europe," said Finnish Minister of Employment Timo Harakka. "As our Presidency ends, we want to offer something concrete. It's about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy," Harakka said in a statement.

In this blog, I'll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I'll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager. Machine learning (ML) has had an incredible impact across industries with numerous applications such as personalized TV recommendations and dynamic price models in your rideshare app. Because it is such a core component to the success of companies in the tech industry, advances in ML research and applications are developing at an astonishing rate. For industries outside of tech, ML can be utilized to personalize a user's experience, automate laborious tasks and optimize subjective decision making.

In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.

In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.

Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for further analysis. Statistical methods themselves are dependent on the theory of probability, which allows us to make predictions. Both statistics and probability are separate and complicated fields of mathematics.