Lifeboat Foundation

First, we had the Green Goblin carrying a rifle. Now, Iron Man’s wearing one.

There’s reason to think fruits of the collaboration may interest the military. The Pentagon’s cloud strategy lists four tenets for the JEDI contract, among them the improvement of its AI capabilities. This comes amidst its broader push to tap tech-industry AI development, seen as far ahead of the government’s.

Microsoft’s $10 billion Pentagon contract puts the independent artificial-intelligence lab OpenAI in an awkward position.

We stand at the dawn of the space age, a time when we can see the very, very beginning of exploring the vastness of the unknown.

The live-streamed launch of a space rocket is the new entertainment for the revolutionary generation, the millennials who think they can really change the world.

Empowered by the digital revolution and even the crypto revolution, astute many of them and some of them actual geniuses, a new era is at inception where kids play almost at the same level as vast governments.

From remote measurements of the Moon’s mass and radius, researchers also know its density is anomalously low, indicating it lacks iron. While about 30 percent of Earth’s mass is trapped in its iron-rich core, the Moon’s core accounts for only a few percent of its total mass. Despite this substantial difference in iron, Apollo samples later revealed that mantle rocks from the Moon and Earth have remarkably similar concentrations of oxygen.

And because these lunar and terrestrial rocks differ significantly from meteorites originating from Mars or the asteroid belt, it shows the Moon and Earth’s mantle share a past connection. Additionally, compared with Earth, lunar rocks are more depleted in so-called volatile elements — those that vaporize easily upon heating — which hints that the Moon formed at high temperatures.

Finally, researchers know that tidal interactions forced the Moon to spiral outward over time, which in turn caused Earth to spin more slowly. This implies the Moon formed much closer to Earth than it is now. Precise measurements of the Moon’s position using surface reflectors placed during the Apollo program subsequently confirmed this, verifying the Moon’s orbit expands by about 1.5 inches (3.8 centimeters) each year.

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Apart from the asteroid that wiped out the dinosaurs 65 million years ago, there aren’t many connections between space and dinosaurs outside of the imagination. But that all changed when NASA research scientist Jessie Christiansen brought the two together in an animation on social media this month.

For the past decade, Christiansen has studied planet occurrence rates, or how often and what kinds of planets occur in the galaxy, while studying data from exoplanet hunters such as NASA’s Kepler, K2 and TESS missions.

During a stargazing party at the California Institute of Technology, Christiansen was explaining how young the stars were that they observed. The skywatchers were looking at the Pleiades, a bright young cluster of stars that are some of the youngest in our sky.

At this year’s Intel AI Summit, the chipmaker demonstrated its first-generation Neural Network Processors (NNP): NNP-T for training and NNP-I for inference. Both product lines are now in production and are being delivered to initial customers, two of which, Facebook and Baidu, showed up at the event to laud the new chippery.

The purpose-built NNP devices represent Intel’s deepest thrust into the AI market thus far, challenging Nvidia, AMD, and an array of startups aimed at customers who are deploying specialized silicon for artificial intelligence. In the case of the NNP products, that customer base is anchored by hyperscale companies – Google, Facebook, Amazon, and so on – whose businesses are now all powered by artificial intelligence.

Naveen Rao, corporate vice president and general manager of the Artificial Intelligence Products Group at Intel, who presented the opening address at the AI Summit, says that the company’s AI solutions are expected to generate more than $3.5 billion in revenue in 2019. Although Rao didn’t break that out into specific products sales, presumably it includes everything that has AI infused in the silicon. Currently, that encompasses nearly the entire Intel processor portfolio, from the Xeon and Core CPUs, to the Altera FPGA products, to the Movidius computer vision chips, and now the NNP-I and NNP-T product lines. (Obviously, that figure can only include the portion of Xeon and Core revenue that is actually driven by AI.)

Every 15 minutes, someone in the United States dies of a superbug that has learned to outsmart even our most sophisticated antibiotics, according to a new report from the US Centers for Disease Control and Prevention.

That’s about 35,000 deaths each year from drug-resistant infections, according to the landmark report.

The report places five drug-resistant superbugs on the CDC’s “urgent threat” list — two more germs than were on the CDC’s list in 2013, the last time the agency issued a report on antibiotic resistance.

Reinforcement learning (RL) is a widely used machine-learning technique that entails training AI agents or robots using a system of reward and punishment. So far, researchers in the field of robotics have primarily applied RL techniques in tasks that are completed over relatively short periods of time, such as moving forward or grasping objects.

A team of researchers at Google and Berkeley AI Research has recently developed a new approach that combines RL with learning by imitation, a process called relay policy learning. This approach, introduced in a paper prepublished on arXiv and presented at the Conference on Robot Learning (CoRL) 2019 in Osaka, can be used to train artificial agents to tackle multi-stage and long-horizon tasks, such as object manipulation tasks that span over longer periods of time.

“Our research originated from many, mostly unsuccessful, experiments with very long tasks using reinforcement learning (RL),” Abhishek Gupta, one of the researchers who carried out the study, told TechXplore. “Today, RL in robotics is mostly applied in tasks that can be accomplished in a short span of time, such as grasping, pushing objects, walking forward, etc. While these applications have a lot value, our goal was to apply reinforcement learning to tasks that require multiple sub-objectives and operate on much longer timescales, such as setting a table or cleaning a kitchen.”