Back in the 2000s, when Branimir Blagojevic was developing a sensor for the military to detect airborne bio-hazards, he had no idea the technology could later be used in the search for extraterrestrial life.

His original work focused on the novel use of lidar — which stands for Light Detection and Ranging — and is based on similar principles as radar. Instead of using radio waves like radar, lidar uses laser light to detect objects and measure the distance to a target. This why lidar is often referred to as "light radar."

Blagojevic, now a NASA technologist at the Goddard Space Flight Center in Maryland, realized that the technology behind seeking out toxins and pathogens in the air might have an application beyond Earth and may even contribute to NASA's mission to work out whether Mars has (or had) life.

"If past life existed on Mars, it could be possible to detect with this kind of instrument," Blagojevic told Seeker.

RELATED: Mars Rover Curiosity Can Now Fire Laser

Currently, any mission sent to Mars is pretty limited in how it searches for evidence of biology, past or present. The Mars Science Laboratory rover Curiosity, for example, can scoop a sample of regolith (the dusty powdery "soil" that covers the Red Planet) in the hope that the tiny amount of pulverized rock contains biological chemistry. Making physical contact with any material intended for analysis is a concern; though any robot sent to Mars is sanitized, there's a risk of contaminating the "pristine" sample with contaminants from Earth, potentially skewing test results.

Also, the slow and laborious process a robot must go through to collect and analyze samples means very few samples can be taken from any given location. It could be that the sample analyzed by the Mars rover scoops a completely sterile sample, but unbeknownst to the robot or its controllers back on Earth, there could be Mars dirt crawling with organic chemistry only a couple of meters to the left. They would never know.

Blagojevic likens this maddening search as trying to find the proverbial "needle in a haystack." But it's worse than that — we don't yet know where the haystack is.

So how do you narrow the odds of finding biological material on Mars? One method could be to use Blagojevic's Bio-Indicator Lidar Instrument, or, simply "BILI."

Mars is no stranger to lasers. Currently, Curiosity uses its ChemCam to laser-blast rocks so its sensors can study the vapor produced to decipher what chemicals it contains. Mars rocks will be safe from BILI, however.

Blagojevic, working with NASA planetary scientists Melissa Trainer, Alexander Pavlov and Melissa Floyd, hopes to mount this lidar system on a future Mars rover much in the same way ChemCam is used by Curiosity, but rather than aiming it at interesting geological features, this future rover would shoot its laser at particles in the Martian atmosphere. During its mission, the rover would scan its environment for dust plumes. Once it finds a wind-blown plume, likely above a hard-to-reach slope, it would fire its twin ultraviolet lasers into the dust.

As the laser light hits individual dust particles, energy from the laser will cause any particles in its path to produce light, a phenomenon known as florescence. The light from these fluorescing particles can then be measured, revealing what chemicals they are made of. If there's any organic matter in the dust (known as bio-indicators), BILI should be able to decipher its signal.

And the best thing is that this is all carried out remotely, possible hundreds of meters from the rover, meaning a huge area could be scanned for organic chemistry and contamination from the rover wouldn't factor into the measurements.

"This will basically increase the probability of finding life by roving the surface of Mars," said Blagojevic.

RELATED: Cassini Zeros in on Daring Dive into Enceladus' Plumes

The applications on Mars are obvious, and we could see future rovers using BILI to scan dusty red plumes, but could the technology be used to hunt for life elsewhere in the solar system?

"Beyond Mars, we did some calculations modeling whether this instrument could work on frozen worlds like the moon Enceladus and maybe Europa," Blagojevic said.

Enceladus is one of Saturn's enigmatic moons that possesses a thick icy shell surrounding a subsurface ocean. Through tidal interactions with Saturn, Enceladus produces heat in its core that keeps the subsurface water in a liquid state. Its ocean is of huge interest to astrobiologists because, on Earth, where there's liquid water there's life.

The internal heating and constant stresses on the icy shell causes some of the liquid water to erupt to the surface of the moon, like opening the cap of a soda bottle. Huge quantities of water vapor are lost to space, creating the moon's trademark plumes. If there's any extraterrestrial biology in that water, it's being vented to space also.

NASA's Cassini mission has used onboard sensors to "taste" these plumes as it flies past (pictured above), but detailed analysis is needed. Could BILI be mounted to a flyby mission to Saturn to possibly fire its UV lasers into the plumes to see if organic chemicals are present also?

"We found that it will be challenging because the water plumes on Enceladus have very low density" when compared with dust particles in Martian air, said Blagojevic. On average, Enceladus' plumes have an extremely low density, so to detect anything, a spacecraft would need to fly within 50 kilometers of the moon's surface and the laser would need to be in the 1 watt range to detect any florescence.

RELATED: Possible Europa Tectonics Could be Boon for Alien Life

"It's possible, but it requires a more powerful UV laser that may or may not be feasible for future flyby missions," he said.

As for Europa, lacking the obvious long-duration plumes that Enceladus has, whether this lidar system can be used to seek out biological chemistry would depend on if any aerosols exist close to its surface, cautions Blagojevic. The Jovian moon holds even more promise than Enceladus as a potentially habitable place for aquatic extraterrestrial life to thrive in its sub-surface ocean. Some optimistic hypotheses posit that multi-cellular life may even be possible.

As we know chemistry from inside the moon cycles to the surface — through apparently active icy tectonics — if there is biology in Europa's oceans, evidence of its presence may be found on the icy surface. And if there's some mechanism causing these organic chemicals to rise above the ice, perhaps a lander-mounted BILI could be used to reveal its secrets.

Originally published on Seeker.

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us?

The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks." But its assumptions about how those capabilities will develop missed some important points.

As an AI researcher, I'll admit it was nice to have my own field highlighted at the highest level of American government, but the report focused almost exclusively on what I call "the boring kind of AI." It dismissed in half a sentence my branch of AI research, into how evolution can help develop ever-improving AI systems, and how computational models can help us understand how our human intelligence evolved.

The report focuses on what might be called mainstream AI tools: machine learning and deep learning. These are the sorts of technologies that have been able to play "Jeopardy!" well, and beat human Go masters at the most complicated game ever invented. These current intelligent systems are able to handle huge amounts of data and make complex calculations very quickly. But they lack an element that will be key to building the sentient machines we picture having in the future.

We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

Type I AI: Reactive machines

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM's chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn't have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn't rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a "representation" of the world.

The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties. The innovation in Deep Blue's design was not to broaden the range of possible movies the computer considered. Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome. Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.

Similarly, Google's AlphaGo, which has beaten top human Go experts, can't evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blue's, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they can't be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can't function beyond the specific tasks they're assigned and are easily fooled.

They can't interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But it's bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won't ever be bored, or interested, or sad.

Type II AI: Limited memory

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars' speed and direction. That can't be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars' preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They're included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren't saved as part of the car's library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

Type III AI: Theory of mind

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called "theory of mind" – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other's motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they'll have to be able to understand that each of us has thoughts and feelings and expectations for how we'll be treated. And they'll have to adjust their behavior accordingly.

Type IV AI: Self-awareness

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the "theory of mind" possessed by Type III artificial intelligences. Consciousness is also called "self-awareness" for a reason. ("I want that item" is a very different statement from "I know I want that item.") Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that's how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

Arend Hintze, Assistant Professor of Integrative Biology & Computer Science and Engineering, Michigan State University

This article was originally published on The Conversation. Read the original article.

Bendable, morphing wings covered with overlapping pieces resembling scales or feathers could be used to build more agile, fuel-efficient aircraft, a new study finds.

Nowadays, conventional aircraft typically rely on hinged flaps known as ailerons to help control the way the planes tilt as they fly. However, when the Wright brothers flew the first airplane, Flyer 1, more than a century ago, they did not use ailerons. Instead, they controlled the aircraft using wires and pulleys that bent and twisted the wood-and-canvas wings.

Scientists have long sought to develop aircraft that can alter or morph their wings during flight, just as birds can. In theory, morphing wings would create smoother aerodynamic surfaces, making an aircraft more agile and efficient than an aircraft that flies with many separate moving surfaces. [Up She Goes! 8 of the Wackiest Early Flying Machines]

However, most previous attempts to develop morphing wings have failed because they relied on mechanical control structures within the wings that were so heavy they canceled out any advantages that morphing provided. These structures were also complex and unreliable, said study co-author Neil Gershenfeld, a physicist and director of the Center for Bits and Atoms at MIT.

"People have worked on morphing aerodynamics for years, but progress has been slow," Gershenfeld told Live Science.

But these new morphing wings allowed the researchers to "make the whole wing the mechanism," Gershenfeld said in a statement. "It's not something we put into the wing."

The new wing architecture consists of a system of tiny, strong, lightweight modules. The shape of the wing can be changed uniformly along its length using two small motors, which apply a twisting pressure to each wingtip.

These wings are covered in "skins" of overlapping strips of flexible material resembling fish scales or bird feathers. These strips move across each other as the wings morph, providing a smooth outer surface, the researchers explained.

Wind-tunnel tests of these wings showed that they at least matched the aerodynamic properties of conventional wings, at about one-tenth the weight. Initial tests using remotely piloted aircraft made with these wings have shown great promise, said study lead author Benjamin Jenett, a graduate student at the Center for Bits and Atoms at MIT. "The first tests were done by a certified test pilot, and he found it so responsive that he decided to do some aerobatics," Jenett said in a statement.

Even small improvements in fuel efficiency can have significant impacts on the economics of the airline industry and its contribution to greenhouse gas emissions.

"The goal is to build the whole aircraft this way, both manned and unmanned, so you can deform their shapes," Gershenfeld said. "The wings are just the first step."

The researchers noted that building large structures such as airplane wings from an array of small, identical building blocks — what Gershenfeld called "digital materials" — greatly simplifies the manufacturing process. Whereas the construction of light composite wings for aircraft currently requires large, specialized equipment for layering and hardening the material, the new modular structures the scientists developed could be manufactured quickly in mass quantities and then assembled by teams of small robots.

"We mass-produce pieces and assemble them like they're Legos," Gershenfeld said.

These modular structures also can be disassembled more easily, making repairs simpler. "An inspection robot could just find where the broken part is and replace it, and keep the aircraft 100 percent healthy at all times," Jenett said in a statement.

Still, the first aircraft built using this strategy will not be a passenger jet, Gershenfeld said. Instead, the technology will likely first be tested on unmanned aircraft, leading to drones that can fly for long times, to help deliver internet access or medicine to remote villages, he said.

The scientists detailed their findings online Oct. 26 in the journal Soft Robotics.

Original article on Live Science.

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