The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at contributing to the current discussion by highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also deals with the equally necessary and meaningful extension of curricula in schools and universities.

At a Friday event, Elon Musk revealed more details about his mysterious neuroscience company Neuralink and its plans to connect computers to human brains. While the development of this futuristic-sounding tech is still in its early stages, the presentation was expected to demonstrate the second version of a small, robotic device that inserts tiny electrode threads through the skull and into the brain. Musk said ahead of the event he would "show neurons firing in real-time. And he did just that. At the event, Musk showed off several pigs that had prototypes of the neural links implanted in their head, and machinery that was tracking those pigs' brain activity in real time. The billionaire also announced the Food and Drug Administration had awarded the company a breakthrough device authorization, which can help expedite research on a medical device. Like building underground car tunnels and sending private rockets to Mars, this Musk-backed endeavor is incredibly ambitious, but Neuralink ...

The federal government took several important steps that prioritized AI development and deployment and positioned the United States to strengthen its global AI leadership, beginning with President Trump's "Executive Order on Maintaining American Leadership in Artificial Intelligence," which set the tone for the rest of the year. February 11: President Trump issued Executive Order 13859, "Maintaining American Leadership in Artificial Intelligence," which launched the American AI Initiative, the official U.S. national AI strategy. The initiative includes five pillars: investing in AI research and development, making federal AI resources more available, setting standards for AI, training an AI workforce, and promoting a pro-innovation international environment. The executive order stresses the importance of "continued American leadership" in AI to "maintaining the economic and national security of the United States," as President Trump wrote in a press release accompanying the order. April 2: The Food and Drug Administration (FDA) released a proposed regulatory framework for AI-based software as a medical device, including as a tool for disease detection, diagnosis, targeted therapies, or personalized medicine.

Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as " training data," in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML related tasks were translated to the cloud, creating latency, consuming scarce power, and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the Edge slower, more expensive, and less predictable. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into an almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain.

In a groundbreaking ruling, CMS has granted Viz.ai the first New Technology Add-on Payment (NTAP) for artificial intelligence software. NTAP, part of the CMS Inpatient Prospective Payment System (IPPS), was set up to support the adoption of cutting-edge technologies that have demonstrated substantial clinical improvement and ensure early availability to Medicare patients. In the US, stroke is the number one cause of long term disability, but is a treatable condition if identified early enough. Viz.ai has been recognized by Forbes, Fast Company, and AuntMinnie as one of the leading AI healthcare companies in the US. The company provides software that improves clinical and financial outcomes1,2 by streamlining acute care, leading to shorter time to treatment, improved patient outcomes, reduced length of stay, and increased number of procedures.

A hospital in Massachusetts has found another job for Spot, Boston Dynamics' dog-like robot: Doctor. The yellow-and-black quadruped has been proven able to take patients' vital signs from a distance of over six feet. That could allow healthcare workers to keep a safe distance from patients who may be infected with the coronavirus or other contagion. So far, Spot has only been tested on healthy patients at Harvard Medical School's Brigham and Women's Hospital - the next step would be to try it out in an emergency room setting. Researchers at MIT say they've developed cameras that allow Spot, Boston Dynamics' dog-like robot, to take vital signs from more than six feet away.

It is going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool. Artificial intelligence (AI) can be defined to mean the use of intelligent machines to replicate and augment the intelligence of human beings. The Turing test was propounded to show what factors determine whether a machine operates on artificial intelligence or not. AI applications are being used in various fields such as telecommunication, banking, agriculture, manufacturing, health care, and transportation. The implementation of AI in health care aims to enhance the lives of the patients and enable physicians, doctors, hospitals, and administrators to improve health care delivery in a cost-effective and time-efficient manner. The traditional drug industry is also experiencing a wave of change due to the implementation of AI-based processes in drug discovery and development. Substitution of AI technology-based solutions in place of the traditional methods for drug discovery is expected to reduce the time for drug development. Using AI in clinical trials has reduced the time required for drug trials from 4–6 months to three months. After the analysis of the genomic data from different patients, AI helps by selecting only those patients whose genetic profile suggests it will help them to undergo testing in the clinical trial.2 Machine learning technologies, deep learning algorithms, various neural networks (such as artificial neural networks or computational neural networks), and content screening are a few examples of AI that have brought radical changes to the process of drug discovery and development.

Nowadays the term computing is very broad. The definition covers everything that is necessary to handle information with computers, e.g. Whole disciplines like Computer Engineering, Information Technology or Cybersecurity also belong to this term. I want to start this article with a very brief history of computing and based on this extrapolate where the journey could go. Computing is as old as mankind.

New York – Elon Musk isn't content with electric cars, shooting people into orbit, populating Mars and building underground tunnels to solve traffic problems. He also wants to get inside your brain. His startup, Neuralink, wants to one day implant computer chips inside the human brain. The goal is to develop implants that can treat neural disorders -- and that may one day be powerful enough to put humanity on a more even footing with possible future superintelligent computers. In a video demonstration Friday explicitly aimed at recruiting new employees, Musk showed off a prototype of the device.

Elon Musk isn't content with electric cars, shooting people into orbit, populating Mars and building underground tunnels to solve traffic problems. He also wants to get inside your brain. His startup, Neuralink, wants to one day implant computer chips inside the human brain. The goal is to develop implants that can treat neural disorders -- and that may one day be powerful enough to put humanity on a more even footing with possible future superintelligent computers. In a video demonstration Friday explicitly aimed at recruiting new employees, Musk showed off a prototype of the device.