The promises of quantum computing, artificial intelligence, and other advancing technologies sound like magic. However, even magic is subject to the laws of economics. And even quantum computers are "legal things…technological tools that are bound to affect our lives in a tangible manner," as Valentin Jeutner explains in The Quantum Imperative: Addressing the Legal Dimension of Quantum Computers. Analogous to Asimov's Three Laws of Robotics, Professor Jeutner proposes a three-part "quantum imperative," which "provides that regulators and developers must ensure that the development of quantum computers: 1. does not create or exacerbate inequalities, 2. does not undermine individual autonomy, 3. does not occur without consulting those whose interests they affect." Should regulators seek to apply these principles?

This year may go down as the point that regulators started to try to get a handle on the use of AI and ML in medical devices. Over the past 10 months, FDA has issued an AI/ML action plan for regulating the technology in medical devices, the European Commission has released contentious plans for the entire AI field and the U.K. has proposed an overhaul of how it regulates AI as a medical device. Now, the U.S. and U.K. have begun working together on a global initiative. Working with their peers at Health Canada, officials at FDA and the U.K.'s MHRA have laid out the following guiding principles: Collectively, the principles cover concerns about the possible biases of algorithms, their applicability to clinical practice and the potential for them to evolve as they are used in the real world. FDA and its collaborators have expanded on each of the principles, explaining, for example, that developers need to have "appropriate controls in place to manage risks of overfitting, unintended bias or degradation of the model" when their systems are "periodically or continually trained after deployment."

Richard Knecht predicts another 12-24 months before data begins to reveal the long-term effects of the pandemic, but it did worsen operational flaws in health systems that need to be addressed, such as the importance of data sharing. Aligning for Health, a health care membership association, and many other social-services organizations advocated for Social Determinants Accelerator Act of 2021, which, among other things, encourages greater coordination and accountability through cross-sector information exchange. Other laws coming out, such as Assembly Bill 2083 in California, require the sharing of data across many disciplines for certain populations, pointing to the end of silos of data.

The Rolling Stones' Mick Jagger is famous for his hip-snaking sorcery on stage, but the lead singer may have been shown up by Boston Dynamic robot'Spot' in a new video. To celebrate the 40th anniversary of the British band's'Tattoo You' album, Boston Dynamics' engineers taught Spot to dance and lip-sync like Jagger in the'Start Me Up' music video. The company also trained three other Spot robots to recreate the moves of fellow band members Keith Richards, Ronnie Wood and Charlie Watts. During the video, the lead Spot moves its long neck to mimic the motions Jagger makes with his arms and the robot also opens its mouth to lip-sync along with the Rockstar. The Rolling Stones' Mick Jagger is famous for his hip-snaking sorcery on stage, but the lead singer may have been shown up by Boston Dynamic robot'Spot' in a new video The veteran British band first began performing in 1962 and are the first to score a number one album on the British charts across six different decades.

In the global AI investment, innovation and implementation stakes, the UK lies in a creditable third place. Trailing the US and second-placed China, it holds a slight lead over Canada and South Korea, according to the Global AI Index published in December 2020 by Tortoise Media. The moral of Aesop's most famous fable involving a tortoise may be'more haste, less speed', but Westminster is seeking to hare ahead in this race over the coming decade. Its national AI strategy, published in September 2021, is a 10-year plan to make the country an "AI superpower". But what does that mean exactly?

The U.S. Food and Drug Administration (FDA), Health Canada, and the UK's Medicines and Healthcare products Regulatory Agency (MHRA) have jointly identified ten guiding principles that can inform the development of Good Machine Learning Practice (GMLP). These guiding principles are intended to help promote safe, effective and high-quality medical devices that use artificial intelligence and machine learning (AI/ML). There is a great deal of interest with these technologies in the medical field, especially with the design and operation of medical devices. This is to the extent that regulatory guidance is required and the opportunity has arisen for a transatlantic protocol to be fashioned between three national regulatory agencies. Artificial intelligence and machine learning technologies have the potential to transform healthcare.

Zeit Medical, which makes an early warning system for strokes during sleep, has raised $2M in a seed round just after leaving Y Combinator's Summer 2021 cohort. The company's work suggests the brain-monitoring headband could save lives by alerting people to possible strokes hours before they might otherwise be noticed, and the new funding will help propel them towards commercial availability. The company's device is a soft headband with a lightweight electroencephalogram (EEG) in it. It works with a smartphone app to analyze brain activity and, using a machine learning model trained by human experts, watch for signs of an impending stroke. I wrote up Zeit's system in detail in August, and little has changed since then, though co-founder and CEO (and now Ferolyn fellow) Orestis Vardoulis noted that a usage study found that people wore the headband on 90 percent of nights, including people using CPAP machines, and there were few complaints about fit or comfort.

Andrew Leakey, Jiayang (Kevin) Xie and their colleagues developed an improved method for analyzing features of plant leaves that contribute to water-use efficiency in crops like corn, sorghum (pictured) and Setaria. They used advanced statistical approaches to identify regions of the genome and lists of genes that contribute to these traits. Scientists have developed and deployed a series of new imaging and machine learning tools to discover attributes that contribute to water-use efficiency in crop plants during photosynthesis and to reveal the genetic basis of variation in those traits. The findings are described in a series of four research papers led by University of Illinois Urbana-Champaign graduate students Jiayang (Kevin) Xie and Parthiban Prakash, and postdoctoral researchers John Ferguson, Samuel Fernandes and Charles Pignon. The goal is to breed or engineer crops that are better at conserving water without sacrificing yield, said Andrew Leakey, a professor of plant biology and of crop sciences at the University of Illinois Urbana-Champaign, who directed the research.

The US Army is planning to demonstrate a 300-kilowatt laser weapon, its most powerful ever, next year. General Atomics Electromagnetic Systems (GA-EMS) and Boeing are building the device, which is the size of a shipping container and mounted on a heavy truck. "The high power, compact laser weapon… will produce a lethal output greater than anything fielded to date," Scott Forney, president of GA-EMS, said in a statement. The US Navy deployed the first high-energy laser weapon, known as LaWS, on the USS Ponce in 2014, with a reported 30 kilowatt output. Most military lasers tend to be in the 30 to 100 kilowatt range, which is mainly useful for shooting down small drones, so the new weapon is a significant increase.

The US Army is planning to demonstrate a 300-kilowatt laser weapon, the most powerful ever built, next year. General Atomics Electromagnetic Systems (GA-EMS) and Boeing are building the device, which is the size of a shipping container and mounted on a heavy truck. "The high power, compact laser weapon… will produce a lethal output greater than anything fielded to date," Scott Forney, president of GA-EMS, said in a statement. The US Navy deployed the first high-energy laser weapon, known as LaWS, on the USS Ponce in 2014, with a reported 30 kilowatt output. Most military lasers tend to be in the 30 to 100 kilowatt range, which is mainly useful for shooting down small drones, so the new weapon is a significant increase.