Lifeboat Foundation

NASA has awarded Carnegie Mellon University (CMU) and Astrobotic a US$5.6 million contract to build a new suitcase-sized robotic lunar rover that could land on the Moon as soon as 2021. One of 12 proposals selected as part of the agency’s Lunar Surface and Instrumentation and Technology Payload (LSITP) program, the 24-lb (11-kg) MoonRanger rover is designed to operate autonomously on week-long missions within 0.6 mi (1 km) of its lander.

The Defense Advanced Research Projects Agency made headlines last fall when it announced that it was pledging $2 billion for a multi-year effort to develop new artificial intelligence technology.

Months later, DARPA’s “AI Next” program is already bearing fruit, said Peter Highnam, the agency’s deputy director.

DARPA — which has for decades fostered some of the Pentagon’s most cutting-edge capabilities — breaks down AI technology development into three distinct waves, he said during a meeting with reporters in Washington, D.C.

Seemingly “intelligent” devices like self-driving trucks aren’t actually all that intelligent. In order to avoid plowing into other cars or making illegal lane changes, they need a lot of help.

In China, that help is increasingly coming from rooms full of college students.

Continue reading “China’s Big AI Advantage: Humans” »

It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.

Aging manifests itself through a decline in organismal homeostasis and a multitude of cellular and physiological functions. Efforts to identify a common basis for vertebrate aging face many challenges; for example, while there have been documented changes in the expression of many hundreds of mRNAs, the results across tissues and species have been inconsistent. We therefore analyzed age-resolved transcriptomic data from 17 mouse organs and 51 human organs using unsupervised machine learning^{3 –5} to identify the architectural and regulatory characteristics most informative on the differential expression of genes with age. We report a hitherto unknown phenomenon, a systemic age-dependent length-driven transcriptome imbalance that for older organisms disrupts the homeostatic balance between short and long transcript molecules for mice, rats, killifishes, and humans. We also demonstrate that in a mouse model of healthy aging, length-driven transcriptome imbalance correlates with changes in expression of splicing factor proline and glutamine rich (Sfpq), which regulates transcriptional elongation according to gene length. Furthermore, we demonstrate that length-driven transcriptome imbalance can be triggered by environmental hazards and pathogens. Our findings reinforce the picture of aging as a systemic homeostasis breakdown and suggest a promising explanation for why diverse insults affect multiple age-dependent phenotypes in a similar manner.

The transcriptome responds rapidly, selectively, strongly, and reproducibly to a wide variety of molecular and physiological insults experienced by an organism. While the transcripts of thousands of genes have been reported to change with age, the magnitude by which most transcripts change is small in comparison with classical examples of gene regulation^2,8 and there is little consensus among different studies. We hence hypothesize that aging is associated with a hitherto uncharacterized process that affects the transcriptome in a systemic manner. We predict that such a process could integrate heterogenous, and molecularly distinctive, environmental insults to promote phenotypic manifestations of aging.

We use an unsupervised machine learning approach^{3 –5} to identify the sources of age-dependent changes in the transcriptome. To this end, we measure and survey the transcriptome of 17 mouse organs from 6 biological replicates at 5 different ages from 4 to 24 months raised under standardized conditions (Fig. 1A). We consider information on the structural architecture of individual genes and transcripts, and knowledge on the binding of regulatory molecules such as transcription factors and microRNAs (miRNAs) (Fig. 1B). We define age-dependent fold-changes as the log₂-transformed ratio of transcripts of one gene at a given age relative to the transcripts of that gene in the organs of 4-month-old mice. As expected for models capturing most measurable changes in transcript abundance, the predicted fold-changes (Fig. S1) match changes empirically observed between distinct replicate cohorts of mice (Figs. S2 and S3).

A team of two indigenous Nahua students from Guerrero came in first place at a national robotics contest held in Quintana Roo, winning them a berth to represent Mexico in an international competition in Japan next year.

The contest was organized by Conalep, a system of public high schools that offer technical education.

Victor Manuel Bautista Nieves and Próspero Romero Gerardo, both 18-year-old students at the Conalep school in Chilapa, Guerrero, won the contest by designing a robot able to locate and extinguish three randomly-placed candles on a determined field within three minutes.

Galaxy clusters are some of the most massive structures in the cosmos, but despite being millions of lightyears across, they can still be hard to spot. Researchers at Lancaster University have turned to artificial intelligence for assistance, developing “Deep-CEE” (Deep Learning for Galaxy Cluster Extraction and Evaluation), a novel deep learning technique to speed up the process of finding them. Matthew Chan, a Ph.D. student at Lancaster University, is presenting this work at the Royal Astronomical Society’s National Astronomy meeting on 4 July at 3:45pm in the Machine Learning in Astrophysics session.

Most galaxies in the universe live in low-density environments known as “the field”, or in small groups, like the one that contains our Milky Way and Andromeda. Galaxy clusters are rarer, but they represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy.

During 1950s the pioneer of galaxy cluster-finding, astronomer George Abell, spent many years searching for galaxy clusters by eye, using a magnifying lens and photographic plates to locate them. Abell manually analysed around 2,000 photographic plates, looking for visual signatures the of galaxy clusters, and detailing the astronomical coordinates of the dense regions of galaxies. His work resulted in the ‘Abell catalogue’ of galaxy clusters found in the northern hemisphere.

Credit where credit is due: Evolution has invented a galaxy of clever adaptations, from fish that swim up sea cucumber butts and eat their gonads, to parasites that mind-control their hosts in wildly complex ways. But it’s never dreamed up ion propulsion, a fantastical new way to power robots by accelerating ions instead of burning fuel or spinning rotors. The technology is in very early development, but it could lead to machines that fly like nothing that’s come before them.

You may have heard of ion propulsion in the context of spacecraft, but this application is a bit different. Most solar-powered ion spacecraft bombard xenon atoms with electrons, producing positively charged xenon ions that then rush toward a negatively charged grid, which accelerates the ions into space. The resulting thrust is piddling compared to traditional engines, and that’s OK—the spacecraft is floating through the vacuum of space, so the shower of ions accelerate the aircraft bit by bit.

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Last month, engineers at NASA’s Jet Propulsion Laboratory wrapped up the installation of the Mars 2020 rover’s 2.1-meter-long robot arm. This is the most powerful arm ever installed on a Mars rover. Even though the Mars 2020 rover shares much of its design with Curiosity, the new arm was redesigned to be able to do much more complex science, drilling into rocks to collect samples that can be stored for later recovery.

JPL is well known for developing robots that do amazing work in incredibly distant and hostile environments. The Opportunity Mars rover, to name just one example, had a 90-day planned mission but remained operational for 5,498 days in a robot unfriendly place full of dust and wild temperature swings where even the most basic maintenance or repair is utterly impossible. (Its twin rover, Spirit, operated for 2,269 days.)

To learn more about the process behind designing robotic systems that are capable of feats like these, we talked with Matt Robinson, one of the engineers who designed the Mars 2020 rover’s new robot arm.