Editor’s note: A recording of this virtual event is embedded above.
Artificial intelligence is proving a potent weapon against the pandemic, enabling researchers to comb through massive data sets to understand the virus and how to combat it. From drug development to immune response, STAT’s Casey Ross will talk to researchers and AI experts about how AI is accelerating a worldwide effort to crack Covid-19’s molecular code.
Featured Speakers:
Machine logic can be too precise to seem fully human. Chatbots that learn logic from Aristotle instead might behave more like people.
NASA’s Mars rovers have been one of the great scientific and space successes of the past two decades.
Four generations of rovers have traversed the red planet gathering scientific data, sending back evocative photographs, and surviving incredibly harsh conditions—all using on-board computers less powerful than an iPhone 1. The latest rover, Perseverance, was launched on July 30, 2020, and engineers are already dreaming of a future generation of rovers.
While a major achievement, these missions have only scratched the surface (literally and figuratively) of the planet and its geology, geography, and atmosphere.
The Tetrad Automated Causal Discovery Platform, a software and text project developed by Peter Spirtes, Clark Glymour, Richard Scheines and Joe Ramsey of Carnegie Mellon University’s Department of Philosophy, earned the “Leader” Award at the 2020 World Artificial Intelligence Conference this past July.
The Leader Award is one of four awards presented at the conference that aim to recognize “the best in terms of impact and innovation in AI”. There were over 800 nominees for the awards, including projects by Amazon, Bosch, Huawei, Nvidia, Open AI Lab, and Siemens, among others.
Researchers at Oxford University, in collaboration with DeepMind, University of Basel and Lancaster University, have created a machine learning algorithm that interfaces with a quantum device and ‘tunes’ it faster than human experts, without any human input. They are dubbing it “Minecraft explorer for quantum devices.”
Classical computers are composed of billions of transistors, which together can perform complex calculations. Small imperfections in these transistors arise during manufacturing, but do not usually affect the operation of the computer. However, in a quantum computer similar imperfections can strongly affect its behavior.
In prototype semiconductor quantum computers, the standard way to correct these imperfections is by adjusting input voltages to cancel them out. This process is known as tuning. However, identifying the right combination of voltage adjustments needs a lot of time even for a single quantum device. This makes it virtually impossible for the billions of devices required to build a useful general-purpose quantum computer.
This approach to increasing capacity will be particularly important as robots shrink to the microscale and below—scales at which current stand-alone batteries are too big and inefficient.
“Robot designs are restricted by the need for batteries that often occupy 20% or more of the available space inside a robot, or account for a similar proportion of the robot’s weight,” said Nicholas Kotov, the Joseph B. and Florence V. Cejka Professor of Engineering, who led the research.
Applications for mobile robots are exploding, from delivery drones and bike-lane take-out bots to robotic nurses and warehouse robots. On the micro side, researchers are exploring swarm robots that can self-assemble into larger devices. Multifunctional structural batteries can potentially free up space and reduce weight, but until now they could only supplement the main battery.
Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking the formative stage of slow-slip ruptures in the earth’s crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.
“The machine learning model found that, close to the end of the slow slip cycle, a snapshot of the data is imprinted with fundamental information regarding the upcoming failure of the system,” said Claudia Hulbert, a computational geophysicist at ENS and the Los Alamos National Laboratory and lead author of the study, published today in Nature Communications. “Our results suggest that slow-slip rupture may well be predictable, and because slow slip events have a lot in common with earthquakes, slow-slip events may provide an easier way to study the fundamental physics of earth rupture.”
Slow-slip events are earthquakes that gently rattle the ground for days, months, or even years, do not radiate large-amplitude seismic waves, and often go unnoticed by the average person. The classic quakes most people are familiar with rupture the ground in minutes. In a given area they also happen less frequently, making the bigger quakes harder to study with the data-hungry machine learning techniques.