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The human settlement of Space requires good tools, and the best tools are intelligent machines. This paper first covers some of the basic Artificial Intelligence technologies and their advantages and disadvantages. Some current space-related projects are covered. Finally, a few projections, both realistic and unrealistic are discussed.

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Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine,...

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By Jeff Youngs, August 10, 2012

Google has won the right to operate an autonomous Toyota Prius on the streets of Nevada, marking another major step in what is becoming a relentless march toward the driverless cars of the future. Nevada is the first state to approve and regulate self-driving vehicles that employ artificial intelligence software to guide them down the highway. Any such vehicle is identified by a special red license plate bearing an infinity symbol.

For years, Google has been developing self-driving cars, and its fleet of autonomous Toyota Prii has logged more than 190,000 miles with only a single mishap, caused when another motorist rear-ended one of the Googlemobiles. Other automakers are also working on this technology with the expectation that fully autonomous cars will be a reality by 2020, and the Society of Automotive Engineers is attempting to draft autonomous-vehicle standards well in advance of the technology becoming widely available.

Google's self-driving Prii wear a roof-mounted, Velodyne 64-beam laser range finder, which generates a 3-dimensional, 360-degree view of the car's surroundings, and then compares the radar measurements to high-resolution maps of the region and other data sets regarding traffic laws to drive by themselves. Additional hardware includes front and rear radar units mounted to the bumpers, a camera to "see" traffic lights, and other technologies to ensure safe travel. Of course, these test Prii have a driver on board, there to react in the event something goes wrong. But so far, nothing has.

During tests in California, Google tackled San Francisco with an autonomous Prius, successfully negotiating twisty Lombard Street, the Golden Gate Bridge, tollbooths, and hilly sections of the city. The car has also been driven up the California coast on Highway 1 and in the mountains near Lake Tahoe--all without incident.

Automakers are having similar success. Having completed track testing of its semi-autonomous ConnectedDrive Connect technology, BMW engineers are now flying down the Autobahn in a 5 Series that adheres to all traffic rules and regulations while the driver monitors systems and remains prepared to take over in the event such action is necessary. Audi is developing similar technology, and has successfully conquered Colorado's Pikes Peak in an autonomous TT sports car.

In truth, many of today's vehicles could already be called semi-autonomous. Thanks to adaptive cruise control systems that feature full stop-and-go technology; driver drowsiness detection systems; lane-departure assist and blind-spot intervention systems; forward-collision warning and emergency braking systems; self-park parallel parking systems and other high-tech options; many of today's cars are able to drive themselves to some degree using data gathered by sets of sensors, radar units and cameras.

Clearly, the future of artificial intelligence cars is already here, to some degree. Out in Nevada, not far from Area 51, a squadron of Prii wearing red license plates is legally prowling desert highways, ferrying Google engineers punching code into laptops while seeking to perfect the technology of tomorrow, today.

Untitled Document

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Google has won the right to operate an autonomous Toyota Prius on the streets of Nevada, marking another major step in what is becoming a relentless march toward the driverless cars of the future. Nevada is the first state to approve and regulate self-driving vehicles that employ artificial intelligence software to guide them down the highway.

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Artificial intelligence fights notorious crop pastes 

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Artificial intelligence fights notorious crop pastes 

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CALIFORNIA is facing a dire threat&colon; oriental fruit flies. Officials sprang into action laying traps and spreading insecticide after 13 of the insects were spotted in the state. They issued an emergency statewide alert on 8 August, and the next step is a lockdown of huge stretches of farmland in a crippling quarantine.

This is no overreaction. The oriental fruit fly, Bactrocera dorsalis, is among the most feared insects in agriculture, responsible for crop losses that can run into billions of dollars. It is known to infest 230 crop species, with the larvae that hatch in the fruits leaving them rotting.

"They are one of the world's worst fruit and vegetable pests because of their rapid breeding, broad range of host plants and invasive abilities," says Gary Steck, an entomologist at Florida's Department of Agriculture.

Now a system developed in Taiwan, where the pest is endemic, aims to harness artificial intelligence to warn of imminent outbreaks, limiting the need for such drastic action.

In Taiwan, fruit fly populations are normally monitored using traps that are manually checked every 10 days. Cheng-Long Chuang and colleagues at the National Taiwan University in Taipei wanted to automate the counting process, so they placed infrared beams in the traps. Each trap records when the beam is broken, indicating that an oriental fruit fly has entered, attracted by a chemical designed to lure the insect. The results collected are sent via radio to a local station every 30 minutes, allowing real-time measurements of the population.

Part-funded by the Taiwanese government, the team have so far set up 240 traps on fruit farms around the country. Machine learning algorithms pool the continuous data arriving from each of these traps and predict when the local fruit fly population is about to explode.

To help in this prediction, the traps are also fitted with weather sensors that monitor temperature, humidity, wind speed and rainfall. Fruit fly population surges tend to match changes in weather - when it is humid, the level of insects is expected to rise, for example.

In Taiwan's current system, a red alert is issued when the number of flies caught in a trap surges beyond 1024 in a 10-day period. But the AI system can learn what counts as a normal level of fruit flies in an area and adapt its warnings on the basis of the current weather and time of year. It can also work out where the pest is likely to be breeding.

When a potentially devastating infestation is predicted, it automatically sends a text message to government officials' cellphones, providing the time, location and severity of the potential outbreak. The warning should allow authorities to pre-empt the outbreak by putting down insecticide.

Tested on historical data taken from the network of traps, the AI system was accurate in predicting an outbreak 88 per cent of the time (Computers and Electronics in Agriculture, doi.org/h6b).

"It is good to know the real-time status of my farm without physically going there," says Zai-Lang Jiang, who owns a guava orchard in Yuanlin, and is taking part in the experiment. "Also, I can reduce some production cost by avoiding unnecessary pesticide spreading."

Chuang believes the system can have immediate benefits for local farmers and help slash the annual fruit-crop loss. "If this system is widely deployed in most of the fruit orchards in Taiwan, we expect it to reduce the damage caused by the oriental fruit fly by 50 per cent," he says.

"If this system is deployed in Taiwan, it could reduce damage caused by fruit flies by 50 per cent"

Farmers around the world will hope it works. The oriental fruit fly lives mainly in south-east Asia, but it is also seen in Hawaii, California and Florida, usually carried in on fruit that is illegally imported. As the effects of climate change play out, it is expected to expand its stomping ground into higher latitudes as temperatures rise (Bulletin of Entomological Research, doi.org/b6jjzd).

# data[].attributes.attachment_link.title
Artificial intelligence fights notorious crop pest

# data[].attributes.attachment_link.description
CALIFORNIA is facing a dire threat: oriental fruit flies. Officials sprang into action laying traps and spreading insecticide after 13 of the insects were spotted in the state. They issued an emergency statewide alert on 8 August, and the next step is a lockdown of huge stretches of farmland in a crippling quarantine.

# data[].attributes.body
Al intelligence fights notorious crop pest

# data[].attributes.attachment_link.readable_text
CALIFORNIA is facing a dire threat&colon; oriental fruit flies. Officials sprang into action laying traps and spreading insecticide after 13 of the insects were spotted in the state. They issued an emergency statewide alert on 8 August, and the next step is a lockdown of huge stretches of farmland in a crippling quarantine.

This is no overreaction. The oriental fruit fly, Bactrocera dorsalis, is among the most feared insects in agriculture, responsible for crop losses that can run into billions of dollars. It is known to infest 230 crop species, with the larvae that hatch in the fruits leaving them rotting.

"They are one of the world's worst fruit and vegetable pests because of their rapid breeding, broad range of host plants and invasive abilities," says Gary Steck, an entomologist at Florida's Department of Agriculture.

Now a system developed in Taiwan, where the pest is endemic, aims to harness artificial intelligence to warn of imminent outbreaks, limiting the need for such drastic action.

In Taiwan, fruit fly populations are normally monitored using traps that are manually checked every 10 days. Cheng-Long Chuang and colleagues at the National Taiwan University in Taipei wanted to automate the counting process, so they placed infrared beams in the traps. Each trap records when the beam is broken, indicating that an oriental fruit fly has entered, attracted by a chemical designed to lure the insect. The results collected are sent via radio to a local station every 30 minutes, allowing real-time measurements of the population.

Part-funded by the Taiwanese government, the team have so far set up 240 traps on fruit farms around the country. Machine learning algorithms pool the continuous data arriving from each of these traps and predict when the local fruit fly population is about to explode.

To help in this prediction, the traps are also fitted with weather sensors that monitor temperature, humidity, wind speed and rainfall. Fruit fly population surges tend to match changes in weather - when it is humid, the level of insects is expected to rise, for example.

In Taiwan's current system, a red alert is issued when the number of flies caught in a trap surges beyond 1024 in a 10-day period. But the AI system can learn what counts as a normal level of fruit flies in an area and adapt its warnings on the basis of the current weather and time of year. It can also work out where the pest is likely to be breeding.

When a potentially devastating infestation is predicted, it automatically sends a text message to government officials' cellphones, providing the time, location and severity of the potential outbreak. The warning should allow authorities to pre-empt the outbreak by putting down insecticide.

Tested on historical data taken from the network of traps, the AI system was accurate in predicting an outbreak 88 per cent of the time (Computers and Electronics in Agriculture, doi.org/h6b).

"It is good to know the real-time status of my farm without physically going there," says Zai-Lang Jiang, who owns a guava orchard in Yuanlin, and is taking part in the experiment. "Also, I can reduce some production cost by avoiding unnecessary pesticide spreading."

Chuang believes the system can have immediate benefits for local farmers and help slash the annual fruit-crop loss. "If this system is widely deployed in most of the fruit orchards in Taiwan, we expect it to reduce the damage caused by the oriental fruit fly by 50 per cent," he says.

"If this system is deployed in Taiwan, it could reduce damage caused by fruit flies by 50 per cent"

Farmers around the world will hope it works. The oriental fruit fly lives mainly in south-east Asia, but it is also seen in Hawaii, California and Florida, usually carried in on fruit that is illegally imported. As the effects of climate change play out, it is expected to expand its stomping ground into higher latitudes as temperatures rise (Bulletin of Entomological Research, doi.org/b6jjzd).

# data[].attributes.attachment_link.title
Artificial intelligence fights notorious crop pest

# data[].attributes.attachment_link.description
CALIFORNIA is facing a dire threat: oriental fruit flies. Officials sprang into action laying traps and spreading insecticide after 13 of the insects were spotted in the state. They issued an emergency statewide alert on 8 August, and the next step is a lockdown of huge stretches of farmland in a crippling quarantine.

# data[].attributes.attachment_link.readable_text
"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it.
Get our daily newsletter
Dread that the abominations people create will become their masters, or their executioners, is hardly new. But voiced by a renowned cosmologist, a Silicon Valley entrepreneur and the founder of Microsoft-hardly Luddites-and set against the vast investment in AI by big firms like Google and Microsoft, such fears have taken on new weight. With supercomputers in every pocket and robots looking down on every battlefield, just dismissing them as science fiction seems like self-deception. The question is how to worry wisely.

You taught me language and...

The first step is to understand what computers can now do and what they are likely to be able to do in the future. Thanks to the rise in processing power and the growing abundance of digitally available data, AI is enjoying a boom in its capabilities (see article). Today's "deep learning" systems, by mimicking the layers of neurons in a human brain and crunching vast amounts of data, can teach themselves to perform some tasks, from pattern recognition to translation, almost as well as humans can. As a result, things that once called for a mind-from interpreting pictures to playing the video game "Frogger"-are now within the scope of computer programs. DeepFace, an algorithm unveiled by Facebook in 2014, can recognise individual human faces in images 97% of the time.

Crucially, this capacity is narrow and specific. Today's AI produces the semblance of intelligence through brute number-crunching force, without any great interest in approximating how minds equip humans with autonomy, interests and desires. Computers do not yet have anything approaching the wide, fluid ability to infer, judge and decide that is associated with intelligence in the conventional human sense.

Briefing: AI is improving rapidly, but remains more useful than terrifying

Yet AI is already powerful enough to make a dramatic difference to human life. It can already enhance human endeavour by complementing what people can do. Think of chess, which computers now play better than any person. The best players in the world are not machines however, but what Garry Kasparov, a grandmaster, calls "centaurs": amalgamated teams of humans and algorithms. Such collectives will become the norm in all sorts of pursuits: supported by AI, doctors will have a vastly augmented ability to spot cancers in medical images; speech-recognition algorithms running on smartphones will bring the internet to many millions of illiterate people in developing countries; digital assistants will suggest promising hypotheses for academic research; image-classification algorithms will allow wearable computers to layer useful information onto people's views of the real world.

Even in the short run, not all the consequences will be positive. Consider, for instance, the power that AI brings to the apparatus of state security, in both autocracies and democracies. The capacity to monitor billions of conversations and to pick out every citizen from the crowd by his voice or her face poses grave threats to liberty.

And even when there are broad gains for society, many individuals will lose out from AI. The original "computers" were drudges, often women, who performed endless calculations for their higher-ups. Just as transistors took their place, so AI will probably turf out whole regiments of white-collar workers. Education and training will help and the wealth produced with the aid of AI will be spent on new pursuits that generate new jobs. But workers are doomed to dislocations.

Surveillance and dislocations are not, though, what worries Messrs Hawking, Musk and Gates, or what inspires a phalanx of futuristic AI films that Hollywood has recently unleashed onto cinema screens. Their concern is altogether more distant and more apocalyptic: the threat of autonomous machines with superhuman cognitive capacity and interests that conflict with those of Homo sapiens.

Such artificially intelligent beings are still a very long way off; indeed, it may never be possible to create them. Despite a century of poking and prodding at the brain, psychologists, neurologists, sociologists and philosophers are still a long way from an understanding of how a mind might be made-or what one is. And the business case for even limited intelligence of the general sort-the sort that has interests and autonomy-is far from clear. A car that drives itself better than its owner sounds like a boon; a car with its own ideas about where to go, less so.

...I know how to curse

But even if the prospect of what Mr Hawking calls "full" AI is still distant, it is prudent for societies to plan for how to cope. That is easier than it seems, not least because humans have been creating autonomous entities with superhuman capacities and unaligned interests for some time. Government bureaucracies, markets and armies: all can do things which unaided, unorganised humans cannot. All need autonomy to function, all can take on life of their own and all can do great harm if not set up in a just manner and governed by laws and regulations.

These parallels should comfort the fearful; they also suggest concrete ways for societies to develop AI safely. Just as armies need civilian oversight, markets are regulated and bureaucracies must be transparent and accountable, so AI systems must be open to scrutiny. Because systems designers cannot foresee every set of circumstances, there must also be an off-switch. These constraints can be put in place without compromising progress. From the nuclear bomb to traffic rules, mankind has used technical ingenuity and legal strictures to constrain other powerful innovations.

The spectre of eventually creating an autonomous non-human intelligence is so extraordinary that it risks overshadowing the debate. Yes, there are perils. But they should not obscure the huge benefits from the dawn of AI.

# data[].attributes.attachment_link.description
"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it.

# data[].attributes.attachment_link.readable_text
"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it.
Get our daily newsletter
Dread that the abominations people create will become their masters, or their executioners, is hardly new. But voiced by a renowned cosmologist, a Silicon Valley entrepreneur and the founder of Microsoft-hardly Luddites-and set against the vast investment in AI by big firms like Google and Microsoft, such fears have taken on new weight. With supercomputers in every pocket and robots looking down on every battlefield, just dismissing them as science fiction seems like self-deception. The question is how to worry wisely.

You taught me language and...

The first step is to understand what computers can now do and what they are likely to be able to do in the future. Thanks to the rise in processing power and the growing abundance of digitally available data, AI is enjoying a boom in its capabilities (see article). Today's "deep learning" systems, by mimicking the layers of neurons in a human brain and crunching vast amounts of data, can teach themselves to perform some tasks, from pattern recognition to translation, almost as well as humans can. As a result, things that once called for a mind-from interpreting pictures to playing the video game "Frogger"-are now within the scope of computer programs. DeepFace, an algorithm unveiled by Facebook in 2014, can recognise individual human faces in images 97% of the time.

Crucially, this capacity is narrow and specific. Today's AI produces the semblance of intelligence through brute number-crunching force, without any great interest in approximating how minds equip humans with autonomy, interests and desires. Computers do not yet have anything approaching the wide, fluid ability to infer, judge and decide that is associated with intelligence in the conventional human sense.

Briefing: AI is improving rapidly, but remains more useful than terrifying

Yet AI is already powerful enough to make a dramatic difference to human life. It can already enhance human endeavour by complementing what people can do. Think of chess, which computers now play better than any person. The best players in the world are not machines however, but what Garry Kasparov, a grandmaster, calls "centaurs": amalgamated teams of humans and algorithms. Such collectives will become the norm in all sorts of pursuits: supported by AI, doctors will have a vastly augmented ability to spot cancers in medical images; speech-recognition algorithms running on smartphones will bring the internet to many millions of illiterate people in developing countries; digital assistants will suggest promising hypotheses for academic research; image-classification algorithms will allow wearable computers to layer useful information onto people's views of the real world.

Even in the short run, not all the consequences will be positive. Consider, for instance, the power that AI brings to the apparatus of state security, in both autocracies and democracies. The capacity to monitor billions of conversations and to pick out every citizen from the crowd by his voice or her face poses grave threats to liberty.

And even when there are broad gains for society, many individuals will lose out from AI. The original "computers" were drudges, often women, who performed endless calculations for their higher-ups. Just as transistors took their place, so AI will probably turf out whole regiments of white-collar workers. Education and training will help and the wealth produced with the aid of AI will be spent on new pursuits that generate new jobs. But workers are doomed to dislocations.

Surveillance and dislocations are not, though, what worries Messrs Hawking, Musk and Gates, or what inspires a phalanx of futuristic AI films that Hollywood has recently unleashed onto cinema screens. Their concern is altogether more distant and more apocalyptic: the threat of autonomous machines with superhuman cognitive capacity and interests that conflict with those of Homo sapiens.

Such artificially intelligent beings are still a very long way off; indeed, it may never be possible to create them. Despite a century of poking and prodding at the brain, psychologists, neurologists, sociologists and philosophers are still a long way from an understanding of how a mind might be made-or what one is. And the business case for even limited intelligence of the general sort-the sort that has interests and autonomy-is far from clear. A car that drives itself better than its owner sounds like a boon; a car with its own ideas about where to go, less so.

...I know how to curse

But even if the prospect of what Mr Hawking calls "full" AI is still distant, it is prudent for societies to plan for how to cope. That is easier than it seems, not least because humans have been creating autonomous entities with superhuman capacities and unaligned interests for some time. Government bureaucracies, markets and armies: all can do things which unaided, unorganised humans cannot. All need autonomy to function, all can take on life of their own and all can do great harm if not set up in a just manner and governed by laws and regulations.

These parallels should comfort the fearful; they also suggest concrete ways for societies to develop AI safely. Just as armies need civilian oversight, markets are regulated and bureaucracies must be transparent and accountable, so AI systems must be open to scrutiny. Because systems designers cannot foresee every set of circumstances, there must also be an off-switch. These constraints can be put in place without compromising progress. From the nuclear bomb to traffic rules, mankind has used technical ingenuity and legal strictures to constrain other powerful innovations.

The spectre of eventually creating an autonomous non-human intelligence is so extraordinary that it risks overshadowing the debate. Yes, there are perils. But they should not obscure the huge benefits from the dawn of AI.

# data[].attributes.attachment_link.description
"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it.

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AL and Military

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AL and Farming

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AL and Car

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Today military use al to process large amount of information including videos, signals and intelligence that must be analyzed quickly.

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A groundbreaking artificial intelligence (AI) project seeks to have sensors accepting and transmitting information in a method similar to the human brain. Being developed by the Defense Advanced Research Projects Agency (DARPA) along with SRI International, the Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, project is surpassing traditional mathematical algorithms and attempting to process information in complex environments autonomously by learning relevant and probabilistically stable features and associations automatically.

Today, the military primarily uses AI to process large amounts of information, including videos, signals and intelligence that must be deciphered and analyzed quickly. As developments in AI continue, these tasks may seem almost menial. "A robot would be able to recognize, from the activities its video cameras capture, what the people it's observing are doing," explains Dr. Raymond Perrault, director, , SRI. "It can tell you that someone is walking through the door, or that two people have met and exchanged a package or that a person is digging a hole by the side of the road." A robot like this with sensors could decide what information is pertinent and report the data to a group of warfighters.

These intelligence systems can perceive their environment and adjust. "They manage to do [their mission] while the world changes around them," Perrault says. To accomplish this task, they organize ideas utilizing mathematical logic. Using sensory data, the programs prove simple theorems by plugging the data into the algorithms, which results in a solution and consequent action.

An alternative U.S. Defense Department use for AI involves virtual personal assistants that respond to voice commands and complete complex tasks on the user's behalf. These AI programs analyze enormous amounts of data and make decisions. They interact and learn but don't necessarily need to perceive the environment. "You communicate what you would like to have done, and they have a dialogue with you to get the job done," he explains.

DARPA and SRI completed this kind of application in the 2009 Personalized Assistant that Learns (PAL) program; SRI calls the application the Cognitive Assistant that Learns and Organizes (CALO). The software program can reason, learn, understand instructions, explain what it is doing and react. It responds to unknown situations and can debrief after an operation is complete.

CALO takes information from a user's e-mails, contacts, tasks and projects. It then creates a relational model of the user's world and learns from it. From there, it can manage scheduling constraints, handle conflicts and negotiate with other parties on the user's behalf.

Recent AI technology currently does not come in the form of state-of-the-art robot companions. Most of the technology is still in standard computer hardware. What is changing, however, is the way this technology helps people: through programs that learn and hardware that will eventually imitate the human brain, Perrault notes.

Because of the variety in AI, many unforeseen uses could be right around the corner. Perrault observes that the practical usage of interactive computers has imitated human nature so well that the incorporation of AI, though impressive, won't look like a sci-fi movie. It will just naturally integrate into everyday life.

A supercomputer that runs operations like the HAL 9000 from 2001: A Space Odyssey does not yet exist, Perrault confirms. " The things that are being done are progress and are useful, but they will not allow machines to take over the world. When it comes to real tasks in the real world like dealing with perception and language, we still have a long way to go," he emphasizes.

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Artificial Intelligence Advances in Military Ranks

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A groundbreaking artificial intelligence (AI) project seeks to have sensors accepting and transmitting information in a method similar to the human brain. Being developed by the Defense Advanced Research Projects Agency (DARPA) along with SRI International, the Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, project is surpassing traditional mathematical algorithms and attempting to process information in complex environments autonomously by learning relevant and probabilistically stable features and associations automatically.

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Artificial Intelligence presents a list of real use cases of robots that will help agriculture changing. Artificial Intelligence is a website focusing on agriculture, artificial intelligence, farming, robots and smart building.

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NASA is developing ever more potent artificial intelligence planning software to make distant missions more practical.

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In the vast blackness of space, where Earth appears to be just a small dot, artificial intelligence someday will help astronauts and robots make swift decisions and plan quickly, without much assistance from home.

Even at the speed of light, about 186,000 miles per second, radio messages take at least 11 minutes to reach Mars (depending on its varying distance from Earth.) Because of time delays like this, human and robotic crews exploring deep space must depend less on help radioed from colleagues on Earth. Far away in space, astronauts will not only have to craft complex plans in 10 minutes or less, but they may need to make difficult, split-second decisions on their own.

According to NASA scientists, this vision of the future also includes potential cost reductions, since there would be less need for large teams on Earth to make detailed daily plans for deep space missions.

NASA is developing ever more potent artificial intelligence planning software to make distant missions more practical. In the near term, NASA scientists are creating software to reduce Mars teams' daily planning time and costs for missions that are about to begin. For example, in comparison with the Mars Exploration Rover (MER) missions, scientists hope to reduce planning time by a factor of 10 for missions in the near future. Increasingly streamlined, resource-saving approaches like this will play an important role as early as the Phoenix mission to the red planet's North Pole region, scheduled for launch in 2007.

Phoenix's 2008 landing zone is located in an ice-rich region. The lander's robotic arm will dig into the arctic terrain in search of clues to the geologic history of water on Mars. The spacecraft also will look for evidence of zones that could support microbial life.

Meanwhile, more than a year after landing on the red planet in January 2004, two Mars Exploration Rovers continued to look for clues that water may have existed in large amounts on the planet. Mars science teams made detailed plans to orchestrate the rovers' moves.

Image right: Artist rendition of the Mars Rover exploring the surface of Mars. Image courtesy: NASA.

"The Mars Exploration Rover activity planning has taken the human team on Earth about one-and-a-half hours every day for each of the two rovers, " said Alonso Vera, who leads a group developing artificial intelligence software at NASA Ames Research Center, located in California's Silicon Valley. "Our goal is to reduce planning time to about 10 minutes for a typical Mars surface mission, and we are a good part of the way there now," he ventured.

A team of a half dozen scientists at NASA Ames also is improving planning software, which has been used by the Mars rover teams. This software is called the Mixed-initiative Activity Plan Generator (MAPGEN). More than a year after the spacecraft landed on the red planet, scientists continued to use this automated planning and scheduling software to help build and edit science activity plans for the Mars Exploration Rovers based on a 'wish list' of observations and the rovers' available resources.

"MAPGEN is based on 'Europa,' an artificial intelligence planning tool that supported the Deep Space 1 mission before being used on the Mars Exploration Rover (mission)," explained Vera.

NASA launched the 945-pound Deep Space 1 probe on Oct. 24, 1998, to test 12 advanced technologies, including Remote Agent, an artificial intelligence program, intended to lower the cost and risk to future science-driven missions. Remote Agent's basic goal was to demonstrate that spacecraft could operate and be controlled with minimal human help.

Image left: Artist rendition of Remote Agent Spacecraft. Image courtesy: NASA.

NASA's continuing efforts to evolve artificial intelligence for use in future space mission operations are pushing the development envelope, according to Vera. Currently, new software is under development for daily team use during upcoming Mars surface missions. This software is called the 'Science Planning Interface for Engineering' (SPIFe), pronounced 'spiffy.' Scientists hope it will "dramatically" reduce the number of people needed to achieve mission goals, according to Vera.

"The heritage we're looking at with SPIFe is the Constraint Editor software and the MAPGEN software, used for the two Mars Exploration Rovers," said Mike McCurdy, a member of the SPIFe software development team at NASA Ames.

Constraint Editor has allowed Mars Exploration Rover mission scientists to make direct, single changes in their daily rover activity plans. The software also adjusts entire plans to take into account individual changes that scientists make. Meanwhile, MAPGEN software has made some mission choices, and scientists have made the rest of the mission decisions, according to McCurdy.

With artificial intelligence software like MAPGEN - 'mixed initiative' systems -- "the human and the computer are equal partners in a dialog that happens around the generation of a plan," Vera asserted. "Each brings different skills to the table. The human can focus on what humans are good at (high-level decisions, for example), while the computer can focus on what computers are good at -- making thousands of smaller decisions -- or automatically enforcing rules and constraints," he explained.

Image right: Team of NASA scientists working with software. Image courtesy: NASA.

"Before, people have been sitting down and deciding step-by-step what is going to happen during a mission," McCurdy noted.

"Generally, what we are looking at is what kind of information people need when they are planning the rover's work," McCurdy said. "We're looking at how we can make that information readily available."

"A scientist may say, 'Go look at that rock,' and the computer could say, 'in order to look at the rock, you must consider x-y-z.' There is a dialogue between the human beings and the software," McCurdy explained.

"We're still in a prototyping phase at this point," McCurdy said. "Because of our Mars Exploration Rover involvement, we can use that experience as a baseline. We can take some of the Mars Exploration Rover data and software and use that to prototype SPIFe."

"This is a team effort," said McCurdy. "The whole collaboration is called Ensemble, which is shared between the NASA Jet Propulsion Laboratory, Pasadena, Calif., (JPL) and NASA Ames. The JPL component is based on the Mars Exploration Rover Science Activity Planner, now called Maestro," McCurdy explained.

SPIFe software also is vital, because it may well be the forerunner of future software that could help astronauts work independently far from Earth. An important part of the Vision for Space Exploration is to enable crews in spaceships or on the moon to be more self-sufficient. Crews using advanced planning software would not have to depend on people on Earth for plans, according to Vera. Human-robot teams could inspect their vehicles or habitats and make repairs unassisted, Vera ventured.

Image left: Scientists testing software to allow for wireless communication. Humans do not have to dedicate continuous attention to the robot. Image courtesy: NASA.

"A lot of space station time is spent doing repairs," said Vera. "If you are going to provide tools that allow an astronaut to plan a complex sequence of activities for a human-robot team, that astronaut has to be able to plan very quickly in order to adapt to the specific conditions. An hour-and-a-half to plan is excessive," Vera added. "You've got to be able to do it in 10 minutes or less."

One of the capabilities added to SPIFe is multiple timelines for scientists to use instead of the one timeline that MAPGEN provides, according to Vera. Also, SPIFe enables users to view information in many different ways so scientists can better see and grasp problems and processes, said Vera.

"You can view plans in a timeline, or a waterfall-like time link," Vera said. "An activity plan has hundreds of parts. One change can affect all the other activities," he explained.

"We built in a lot of feedback about how a change affects everything else," Vera said. "But in the end, it's the human who is responsible for the plan."

John Bluck
NASA Ames Research Center, Moffett Field, Calif.
Phone: 650/604-5026
Email: jbluck@mail.arc.nasa.gov

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NASA - National Aeronautics and Space Administration

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In the vast blackness of space, where Earth appears to be just a small dot, artificial intelligence someday will help astronauts and robots make swift decisions and plan quickly, without much assistance from home. Even at the speed of light, about 186,000 miles per second, radio messages take at least 11 minutes to reach Mars (depending on its varying distance from Earth.)

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         AL and Car          Automatic artificial intelligence cargo inspection isn't actually a thing that's happening right now, but research at university college London has proven that its a viable solution to a very real problem.

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         AL and Car          Automatic artificial intelligence cargo inspection isn't actually a thing that's happening right now, but research at university college London has proven that its a viable solution to a very real problem.

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Okay, automatic, artificial intelligence cargo inspection isn't actually a thing that's happening right now, but research at University College London has proven that it's a viable solution to a very real problem. A team at the school's Department of Computer Science successfully trained a convolutional neural network to spot automobiles in X-ray images of shipping containers.

The neural network was startlingly accurate -- correctly identifying cars 100-percent of the time with very few false alarms. The system even spotted cars in images that were challenging for human observers, finding the vehicles that were intentionally obscured by other objects. It wasn't a revolutionary study, to be sure, but the project is a great example of how deep learning image recognition will be used to make our lives easier in the future. Check out the source link below for a detailed write-up of the project.

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Artificial intelligence could be used to stop car smugglers

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Okay, automatic, artificial intelligence cargo inspection isn't actually a thing that's happening right now, but research at University College London has proven that it's a viable solution to a very real problem. A team at the school's Department of Computer Science successfully trained a convolutional neural network to spot automobiles in X-ray images of shipping containers.

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Artificial Intelligence in medicine primary concerned with the construction of AL program that perform diagnosis and make therapy recommendations.

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artificial intelligence applications in clinical practice

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Artificial Intelligence in Medicine: An Introduction

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AL is the most exciting field in the robotics. its certainly the most controversial

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Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent.

Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual ability.

Computers can already solve problems in limited realms. The basic idea of AI problem-solving is very simple, though its execution is complicated. First, the AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it's programmed to solve -- it doesn't have any generalized analytical ability. Chess computers are one example of this sort of machine.

Some modern robots also have the ability to learn in a limited capacity. Learning robots recognize if a certain action (moving its legs in a certain way, for instance) achieved a desired result (navigating an obstacle). The robot stores this information and attempts the successful action the next time it encounters the same situation. Again, modern computers can only do this in very limited situations. They can't absorb any sort of information like a human can. Some robots can learn by mimicking human actions. In Japan, roboticists have taught a robot to dance by demonstrating the moves themselves.

Some robots can interact socially. Kismet, a robot at M.I.T's Artificial Intelligence Lab, recognizes human body language and voice inflection and responds appropriately. Kismet's creators are interested in how humans and babies interact, based only on tone of speech and visual cue. This low-level interaction could be the foundation of a human-like learning system.

Kismet and other humanoid robots at the M.I.T. AI Lab operate using an unconventional control structure. Instead of directing every action using a central computer, the robots control lower-level actions with lower-level computers. The program's director, Rodney Brooks, believes this is a more accurate model of human intelligence. We do most things automatically; we don't decide to do them at the highest level of consciousness.

The real challenge of AI is to understand how natural intelligence works. Developing AI isn't like building an artificial heart -- scientists don't have a simple, concrete model to work from. We do know that the brain contains billions and billions of neurons, and that we think and learn by establishing electrical connections between different neurons. But we don't know exactly how all of these connections add up to higher reasoning, or even low-level operations. The complex circuitry seems incomprehensible.

Because of this, AI research is largely theoretical. Scientists hypothesize on how and why we learn and think, and they experiment with their ideas using robots. Brooks and his team focus on humanoid robots because they feel that being able to experience the world like a human is essential to developing human-like intelligence. It also makes it easier for people to interact with the robots, which potentially makes it easier for the robot to learn.

Just as physical robotic design is a handy tool for understanding animal and human anatomy, AI research is useful for understanding how natural intelligence works. For some roboticists, this insight is the ultimate goal of designing robots. Others envision a world where we live side by side with intelligent machines and use a variety of lesser robots for manual labor, health care and communication. A number of robotics experts predict that robotic evolution will ultimately turn us into cyborgs -- humans integrated with machines. Conceivably, people in the future could load their minds into a sturdy robot and live for thousands of years!

In any case, robots will certainly play a larger role in our daily lives in the future. In the coming decades, robots will gradually move out of the industrial and scientific worlds and into daily life, in the same way that computers spread to the home in the 1980s.

The best way to understand robots is to look at specific designs. The links below will show you a variety of robot projects around the world.

Related HowStuffWorks Articles

More Great Links

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Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent. Like the term "robot" itself, artificial intelligence is hard to define.