As politicians play whack-a-mole with COVID-19 infection rates and try to balance the economic damage caused by lockdowns, stay-at-home orders have also impacted those out there in the dating scene. No longer able to meet up for a drink, a coffee, or now even a walk in the park, organizing an encounter with anyone other than your household or support bubble is banned and can result in a fine in the United Kingdom -- and this includes both dates and overnight stays. Therefore, the only feasible option available is online connections, by way of social networks or dating apps. Dating is hard enough at the best of times but sexual desire doesn't disappear just because you are cooped up at home. Realizing this, a number of healthcare organizations worldwide have urged us not to contribute to the spread of COVID-19 by meeting up with others for discreet sex outside of our social bubbles, bringing new meaning to the phrase, "You are your safest sex partner."

The lengthy video breaks down how Choi built his "Labo Fit Adventure Kart Kit." The heart of his creation is TAPBO, a small robotic device he engineered that holds the Joy-Con in place and has motorized arms that press down on two of the controller's buttons for accelerating and slowing down in "Mario Kart 8 Deluxe." To play, you sit on the bike (which he comically calls the Bike-Con and covered in cardboard to resemble Nintendo Labo's line of products), hold the Ring-Con in both hands like a steering wheel, and peddle to move forward.

There are many amazing practical applications of AI and machine learning here now and on the horizon. But that doesn't mean every use is good or applied with good intent. The fastest growing type of financial crime in the United States is synthetic identity fraud - when a fraudster uses a combination of real and fake information to create an entirely new identity. This progressive uptick in synthetic identity fraud is likely due to multiple factors such as data breaches, dark web data access, and the competitive lending landscape. In Experian's recent Future of Fraud Forecast, they are predicting that these fraudsters will start to use fake faces for biometric verification, which is the first of five new threats they detail for 2021.

If you're looking to expanding your work beyond basic programming, it might be a great tome to dip into machine learning. Machine learning is the use of existing data to explain unknown data and predict future scenarios. It is also an integral part of technology and tech-related fields. If you're looking to pick up a useful skill or expand your tech prowess, learning about machine learning may broaden your career possibilities. With that in mind, we've crafted a list of the best machine learning books for beginners to get you started.

A study by Vuno, a Korean artificial intelligence (AI) developer, showed that a deep learning algorithm could predict Alzheimer's disease (AD) within one minute. Jointly with Asan Medical Center, Vuno verified an AI algorithm using MRI scans of 2,727 patients registered at domestic medical institutions. Vuno found that the algorithm predicted AD and mild cognitive impairment (MCI) accurately. Vuno's deep learning-based algorithm used an area under the curve (AUC) to predict dementia. The closer the AUC value is, the higher the algorithm's performance is.

Aging is a universal feature shared by all living beings. While the rate of aging may vary among individuals and species, the time elapsed since birth is a strong predictor of health status and mortality. Targeting aging may extend the average life expectancy more substantially than prevention or treatment of individual diseases1. However, within the established drug discovery and development framework, pharmaceutical companies are still searching for compounds and interventions for the treatment of individual chronic diseases such as cancer and cardiovascular or pulmonary diseases. Current biomedical research aims to identify the underlying mechanisms and molecular targets specific to a disease in order to modify the disease, treat its symptoms or cure it.

I've read the book Life 3.0 by physicist & AI philosopher Max Tegmark, where he sets out a series of possible scenarios and outcomes for humankind sharing the planet with artificial intelligence. Tegmark immediately shoots down any notion that we are likely to be victims of a robot-powered genocide, and claims the idea we would programme or allow a machine to have the potential to hate humans is preposterous - fuelled by Hollywood's obsession with the apocalypse. Actually, we have the power, now, to ensure that if AIs goals are properly aligned with ours from the start, so that it wants what we want, then there can never be a'falling out' between species. In other words, if AI does pose a threat - and in some of his scenarios it does - it will not come from The Matrix's marauding AIs, enslaving humanity and claiming, like Agent Smith, 'Human beings are a disease. You are a plague and we are the cure'.

The hype for how artificial intelligence can miraculously change the world continues to fill media outlets. Still, the reality of how rapidly the science behind AI is evolving and becoming mainstream in every industry and facet of business will not be impeded. By the year 2025, the intersection of "advanced" AI and intelligent machines will become a part of every user's "things I just know how to use." As more industries adopt AI solutions and become savvy about how AI impacts their engagement with suppliers and employees, it is important for organizations to follow four key steps to implement it. While roles like data scientist, chief data officer, and senior data engineer are vital to implementing AI/ML systems, the two following roles are imperative for practical implementation.

Globally, healthcare organisations have accelerated adoption of artificial intelligence (AI), with the ones still implementing frameworks planning to go live within 24 months. Hardly surprising given the improved consumer engagement that results from the technology. But more than that, the challenging economic climate is seeing healthcare organisations looking for better ways to make processes more efficient, enhance their existing products and services and lower costs. The key to this is AI that brings with it a more innovative environment to automate manual, error-prone processes and introduce a sophisticated layer of analytics that can deliver new insights to the wealth of data already available. These platforms use algorithms and machine learning to analyse and interpret data, while empowering the healthcare organisation with the means to provide more personalised customer experiences.

In the last decades, artificial intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat a human player, chess champion Garry Kasparov. A new piece of research shows now that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. The new study, carried out by SISSA scientists in collaboration with Kavli Institute for Systems Neuroscience & Centre for Neural Computation, Trondheim, Norway, has just been published in Physical Review Letters. Neural networks, real or artificial, learn by tweaking the connections between neurons.