Despite being nearly identical in size to Earth, our sister world suffers from a choking greenhouse effect, a surface covered in permanent sulfuric acid clouds, and average temperatures hot enough to melt lead. Most digital devices will get swiftly destroyed under such conditions, which makes planning a robotic rover that can survive long-term a challenge. So, thought Jonathan Sauder, a mechatronics engineer at NASA's Jet Propulsion Laboratory in California, why not go analog? Rather than relying entirely on state-of-the-art components, a mechanical automaton built from high-temperature steel and titanium could travel over Venus' scorching terrain, using clockwork sensors to avoid obstacles while collecting power from wind and storing it in a wind-up spring. Though it sounds like the basis of some retro-future sci-fi novel in which the Victorians explore the solar system, a rudimentary version of Sauder's vision is being built and tested in the modern day.

The longest amount of time that a spacecraft has survived on the surface of Venus is 127 minutes. On March 1, 1982, the USSR's Venera 13 probe parachuted to a gentle landing and managed to keep operating for just over two hours by hiding all of its computers inside of a hermetically sealed titanium pressure vessel that was pre-cooled in orbit. The surface temperature on Venus averages 464 C (867 F), which is hotter than the surface of Mercury (the closest planet to the sun), and hot enough that conventional electronics simply will not work. It's not just the temperature that makes Venus a particularly nasty place for computers--the pressure at the surface is around 90 atmospheres, equivalent to the pressure 3,000 feet down in Earth's ocean. And while you can be relieved that the sulfuric acid rain that you'll find in Venus' upper atmosphere doesn't reach the surface, it's also so dark down there (equivalent to a heavily overcast day here on Earth) that solar power is horrendously inefficient.

The longest amount of time that a spacecraft has survived on the surface of Venus is 127 minutes. On March 1, 1982, the USSR's Venera 13 probe parachuted to a gentle landing and managed to keep operating for just over two hours by hiding all of its computers inside of a hermetically sealed titanium pressure vessel that was pre-cooled in orbit. The surface temperature on Venus averages 464 C (867 F), which is hotter than the surface of Mercury (the closest planet to the sun), and hot enough that conventional electronics simply will not work. It's not just the temperature that makes Venus a particularly nasty place for computers--the pressure at the surface is around 90 atmospheres, equivalent to the pressure 3,000 feet down in Earth's ocean. And while you can be relieved that the sulfuric acid rain that you'll find in Venus' upper atmosphere doesn't reach the surface, it's also so dark down there (equivalent to a heavily overcast day here on Earth) that solar power is horrendously inefficient.

For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. "If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home," says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health. Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, "the old ways of delivering care will not work," says Krishnapuram. "We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctor's office or hospital to an outpatient setting or to the patient's home." Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools. Identifying risks, determining who is at risk, and identifying interventions that will reduce risk. Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health. Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.