Post by plutronus on Jan 1, 2019 6:18:13 GMT -6
Or Ask an artificially intelligent question…
NGA Deputy Director Justin Poole: “Relevance is now a matter of speed."
WASHINGTON — The U.S. military got is first big taste of artificial intelligence with Project Maven. An Air Force initiative, it began more than a year ago as an experiment using machine learning algorithms developed by Google to analyze full-motion video surveillance.
The project has received high praise within military circles for giving operators in the field instant access to the type of intelligence that typically would have taken a long time for geospatial data analysts to produce.
Project Maven has whetted the military’s appetite for artificial intelligence tools. And this is creating pressure on the National Geospatial-Intelligence Agency to jump on the AI bandwagon and start delivering Maven-like products and services.
“Relevance is now a matter of speed,” NGA Deputy Director Justin Poole told a C4ISRNET conference last week in Arlington, Va.
Data is flowing at unprecedented volumes from government and commercial sensors. There will be more constellations of remote sensing satellites and swarms of spy drones in the future piping in even more data. ‘We must adapt rapidly,” Poole said.
NGA’s answer is what the agency calls its “triple A” strategy: automation, augmentation, AI. “We intend to apply triple A by the end of this year to every image we ingest,” said Poole. It will be a massive undertaking. Just over the past year, NGA ingested more than 12 million images and generated more than 50 million indexed observations.
The agency has to step up the application of machine learning and advanced algorithms so it can provide faster support to forces in the field, Poole said. “We’re partnering with leading commercial vendors to produce next generation high resolution 3D models, 3D geospatial data, battlefield visualization,” said Poole. “As we expand from products to services, we have to push triple A. We have to transform how we interact with customers. We want to become a broker of diverse geospatial content.”
WASHINGTON — The U.S. military got is first big taste of artificial intelligence with Project Maven. An Air Force initiative, it began more than a year ago as an experiment using machine learning algorithms developed by Google to analyze full-motion video surveillance.
The project has received high praise within military circles for giving operators in the field instant access to the type of intelligence that typically would have taken a long time for geospatial data analysts to produce.
Project Maven has whetted the military’s appetite for artificial intelligence tools. And this is creating pressure on the National Geospatial-Intelligence Agency to jump on the AI bandwagon and start delivering Maven-like products and services.
“Relevance is now a matter of speed,” NGA Deputy Director Justin Poole told a C4ISRNET conference last week in Arlington, Va.
Data is flowing at unprecedented volumes from government and commercial sensors. There will be more constellations of remote sensing satellites and swarms of spy drones in the future piping in even more data. ‘We must adapt rapidly,” Poole said.
NGA’s answer is what the agency calls its “triple A” strategy: automation, augmentation, AI. “We intend to apply triple A by the end of this year to every image we ingest,” said Poole. It will be a massive undertaking. Just over the past year, NGA ingested more than 12 million images and generated more than 50 million indexed observations.
The agency has to step up the application of machine learning and advanced algorithms so it can provide faster support to forces in the field, Poole said. “We’re partnering with leading commercial vendors to produce next generation high resolution 3D models, 3D geospatial data, battlefield visualization,” said Poole. “As we expand from products to services, we have to push triple A. We have to transform how we interact with customers. We want to become a broker of diverse geospatial content.”
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The National Geospatial-Intelligence Agency is investing in machine learning technologies as it grapples with a deluge of data, said the agency’s deputy director May 10.
“As the commercial sector steadily fields new devices that enhance and connect our lives, the amount of data and types of data that we can use to drive analytic insight continues to grow,” said Justin Poole.
Poole — speaking at the 17th annual C4ISRNET Conference in Arlington, Virginia — noted that a recent study by Gartner found that by the end of the year, 8.4 billion devices will be connected to the internet, which is an increase of 30 percent from last year.
“It’s predicated that by 2025 these devices will generate over two zettabytes, or two trillion gigabytes of data, … [with] every one of them providing a … continuous stream of geospatial information about the users and their activities,” he said.
For the NGA — which focuses on the collection of location intelligence — that’s a game changer, he said.
“As the commercial sector steadily fields new devices that enhance and connect our lives, the amount of data and types of data that we can use to drive analytic insight continues to grow,” said Justin Poole.
Poole — speaking at the 17th annual C4ISRNET Conference in Arlington, Virginia — noted that a recent study by Gartner found that by the end of the year, 8.4 billion devices will be connected to the internet, which is an increase of 30 percent from last year.
“It’s predicated that by 2025 these devices will generate over two zettabytes, or two trillion gigabytes of data, … [with] every one of them providing a … continuous stream of geospatial information about the users and their activities,” he said.
For the NGA — which focuses on the collection of location intelligence — that’s a game changer, he said.
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The NGA manages both the US DoD's NAVSTAR (civilians think of that as being 'their GPS') & L2C "Civil Tracker" GPS. L2C "Civil Tracker" system, is the new GPS system intended for civilian public usage, but which hasn't yet been turned-on for civilian usage, as that GPS system is still being developed.
In GPS speak, a GPS satellite is called an 'SV' or 'Space Vehicle'.
L2C GPS Block-III SV01, known as “Vespucci,” in honor of "Amerigo Vespucci", the Italian explorer for whom the 'Americas' were named, is now ready to be rolled out to its pad at Space Launch Complex-40, where it will be mated with a SpaceX Falcon 9 rocket.
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Other GPS Systems in operation, being used to procure user data:
BeiDou Navigation Satellite System (BDS)
BeiDou, or BDS, is a regional GNSS owned and operated by the People's Republic of China. China is currently expanding the system to provide global coverage with 35 satellites by 2020. BDS was previously called Compass.
BeiDou, or BDS, is a regional GNSS owned and operated by the People's Republic of China. China is currently expanding the system to provide global coverage with 35 satellites by 2020. BDS was previously called Compass.
Galileo
Galileo is a global GNSS owned and operated by the European Union. The EU declared the start of Galileo Initial Services in 2016 and plans to complete the system of 24+ satellites by 2020.
Galileo is a global GNSS owned and operated by the European Union. The EU declared the start of Galileo Initial Services in 2016 and plans to complete the system of 24+ satellites by 2020.
GLONASS
GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema, or Global Navigation Satellite System) is a global GNSS owned and operated by the Russian Federation. The fully operational system consists of 24+ satellites.
GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema, or Global Navigation Satellite System) is a global GNSS owned and operated by the Russian Federation. The fully operational system consists of 24+ satellites.
Indian Regional Navigation Satellite System (IRNSS) / Navigation Indian Constellation (NavIC)
IRNSS is a regional GNSS owned and operated by the Government of India. IRNSS is an autonomous system designed to cover the Indian region and 1500 km around the Indian mainland. The system consists of 7 satellites and should be declared operational in 2018. In 2016, India renamed IRNSS as the Navigation Indian Constellation (NavIC, meaning "sailor" or "navigator")
IRNSS is a regional GNSS owned and operated by the Government of India. IRNSS is an autonomous system designed to cover the Indian region and 1500 km around the Indian mainland. The system consists of 7 satellites and should be declared operational in 2018. In 2016, India renamed IRNSS as the Navigation Indian Constellation (NavIC, meaning "sailor" or "navigator")
Quasi-Zenith Satellite System (QZSS)
QZSS is a regional GNSS owned by the Government of Japan and operated by QZS System Service Inc. (QSS). QZSS complements GPS to improve coverage in East Asia and Oceania. Japan plans to have an operational constellation of 4 satellites by 2018 and expand it to 7 satellites for autonomous capability by 2023.
QZSS is a regional GNSS owned by the Government of Japan and operated by QZS System Service Inc. (QSS). QZSS complements GPS to improve coverage in East Asia and Oceania. Japan plans to have an operational constellation of 4 satellites by 2018 and expand it to 7 satellites for autonomous capability by 2023.
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Ask an artificially intelligent question…
January 29, 2018 - By Alan Cameron
There was plenty for a philosophy major to sink his teeth into at ION’s January workshop on Cognizant Autonomous Systems for Safety Critical Applications (CASSCA).
There was plenty for a philosophy major to sink his teeth into at ION’s January workshop on Cognizant Autonomous Systems for Safety Critical Applications (CASSCA).
What is knowledge?
What is meaning?
What is understanding?
What is intelligence?
What is learning?
What is thinking?
These questions excited Plato and Kant, Buddha and Descartes, perhaps out of intellectual or spiritual curiosity. Who’s to say? But the people asking them now are driven, quite literally, by practicalities. They have come to realize that we cannot ride in driverless cars or fly in pilotless plane-taxis, we cannot live in an autonomous, artificially intelligent environment without knowing a bit more exactly what knowledge is, in this brave new world.
Without thinking about what thinking may be, for a machine.
Without thinking about what thinking may be, for a machine.
Why does this matter to a GPS/GNSS/PNT readership? Because as positioning and navigation engage more deeply with artificial intelligence (AI) generally, and with autonomy in particular, these issues emerge as part of the environment that such solutions explore, and in which they must verify and validate themselves.
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Second Wave. We are in the second wave of AI, according to Steven Rogers, senior scientist for sensor fusion at the Air Force Research Laboratory. In the first wave, 60s and 70s, large and complex algorithms, relatively low on data, drove new developments — but they hit real-world problems, hard. Since the mid-80s, we have been in the “classify” stage with relatively simpler programs generating and consuming lots of data. Intense statistical learning will eventually lead to the third wave of AI: Explain.
On a timeline yet to be determined, contextual adaptation will give rise to “explainable” AI, capable of answering unexpected queries. That is, it will have learned how to teach itself.
Some of this stuff gets pretty scary.
Most future knowledge will be machine-generated.
Let’s run through that one more time.
“Most future knowledge on Earth will come from machines extracting it from the environment,” said Rogers. “Machine generation of knowledge is key for autonomy.”
Here’s where the thought processes really started to levitate. “Current sense-making solutions are not keeping pace, not growing as knowledge is growing,” Rogers asserted. And he challenged us with the questions posed at the beginning of this column: in AI, the context we will use to explore much of the future, what is knowledge? What is meaning? And so on.
He gave us one of his answers: “Knowledge is what is used to generate the meaning of the observable for an autonomous system. Correspondingly, machine-generated knowledge is what is used to turn observables into machine-generated meaning.”
On a timeline yet to be determined, contextual adaptation will give rise to “explainable” AI, capable of answering unexpected queries. That is, it will have learned how to teach itself.
Some of this stuff gets pretty scary.
Most future knowledge will be machine-generated.
Let’s run through that one more time.
“Most future knowledge on Earth will come from machines extracting it from the environment,” said Rogers. “Machine generation of knowledge is key for autonomy.”
Here’s where the thought processes really started to levitate. “Current sense-making solutions are not keeping pace, not growing as knowledge is growing,” Rogers asserted. And he challenged us with the questions posed at the beginning of this column: in AI, the context we will use to explore much of the future, what is knowledge? What is meaning? And so on.
He gave us one of his answers: “Knowledge is what is used to generate the meaning of the observable for an autonomous system. Correspondingly, machine-generated knowledge is what is used to turn observables into machine-generated meaning.”
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"Ultimately, the term artificial intelligence may be a misnomer. To be sure, these machines can solve complex, seemingly abstract problems that had previously yielded only to human cognition. But what they do uniquely is not thinking as heretofore conceived and experienced. Rather, it is unprecedented memorization and computation. Because of its inherent superiority in these fields, AI is likely to win any game assigned to it. But for our purposes as humans, the games are not only about winning; they are about thinking. By treating a mathematical process as if it were a thought process, and either trying to mimic that process ourselves or merely accepting the results, we are in danger of losing the capacity that has been the essence of human cognition. (June 2018)"
Kissinger also makes a strong statement that the United States needs to develop a national vision for AI like other countries (i.e. China, Russia, India) to stay competitive in computing power.
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More to come...