ISS: an astronomical waste of money?

Born as a colossal multinational project that would be the launching pad for human missions to Mars and beyond, the International Space Station (ISS) has been long criticized for the lack of scientific value it has brought to the scientific community, as its prohibitive maintenance costs soar higher every year and take away funds that could be allocated to other missions with more bang for the buck.

A bit of background

The International Space Station (ISS) is a habitable artificial satellite which is maintained at an orbital altitude of between 330 and 410 km (205 and 255 miles). It completes 16 orbits per day and can be seen from Earth with the naked eye.

After the Russians successfully constructed the MIR in 1986, the Americans and the Japanese were soon racing to keep up with them. But very soon, the very high costs associated with running a continuously inhabited space station became apparent.

In 1993 the Russian Mir-2, the American Freedom and the European Columbus merged into a single multinational programme which would give birth to the ISS, the ninth space station to be inhabited. Its first component was launched in 1998, and the station will be funded at least until 2020.

The station is being used as a microgravity and space environment research base in which the crew conducts experiments in biology, physics, astronomy, meteorology and other fields. The ISS is suited for the testing of spacecraft systems and equipment required for missions to the Moon, Mars, and beyond.

Gee, that suit does look pretty expensive...

Manned vs unmanned space missions

One of the big ongoing debates within space agencies has always been on whether to focus on manned flight or unmanned flight. This division runs deep, and for good reasons.

Manned space flight is fantastic to witness, let alone (I can only imagine) to experience first-hand. So, why would anyone be opposed to it? The short answer: its cost is prohibitive. Whereas a robotic probe can collect data on its surroundings, move, analyze data and even think on its own surviving years-long trips without the need for sustenance, humans — even on a trip as short as a few days — need quite a lot of room to move around, water, food, oxygen, toilets, much higher safety standards… the list could go on and on.

Facing a much cheaper alternative that would offer a tremendously improved amout of scientific research for the dollar, many scientists agree that unmanned space exploration is clearly the way to go, and some even wish manned missions to be as fewer and further between as possible. The malicious ones even sustain that the reason behind manned exploration being so predominant nowadays is merely a political one.

The launch of the Saturn V rocket

How much does the ISS really cost? 

The ISS program is tied together by a complex set of legal, political and financial agreements between the fifteen nations involved in the project. This makes it hard to estimate the total cost of the operation accurately, also due to the fact that many of those partner nations won’t release their data to the public.

As of 2010, NASA budgeted $72.4 billion for the station from 1985 to 2015. The most cited figures ranges between 80 and 160 billion dollars. ESA, the only agency actually stating potential overall costs on its website, estimates total costs at 100 billion euros.

Facing these prohibitive expenses, in 2009 NASA had made plans to end the ISS programme and deorbit the ISS in early 2016, according to President Bush’s policy. President Obama, however, announced a new policy in 2010 that will extend the program through 2020.

Is this all money down the drain? The experiments going on in the ISS do have, of course, scientific value and could teach us more about biotechnology, medicine and microgravity environments. But as far using the station as a stepping stone to send humans farther into space, it would seem the reasonable thing for NASA to do would probably be to rethink its strategy and orient itself toward an approach that returns more results for less of the taxpayers’ money.

You like Rocky so much, you wish you could have him forever? Well, now there's a solution — just clone him!

For all you dog lovers out there, there’s a bad news. Many people grow so attached to their faithful companions that, one day, they may have woken up with the question: what’s going to happen when Rocky passes away? I want to keep him forever! Most people will just stop there but, apparently, at least five of them didn’t — to the point where they agreed to pay the non-venial sum of US$150,000 to have their fluffy, face-licking pet cloned. Well, the bad news is that BioArts, the company that broke news for offering the world’s first commercial dog cloning service, has decided to discontinue its service.

Dolly the sheep? Just the beginning

We’ve all heard about Dolly the sheep, the first mammal to be cloned from a somatic cell. Well, believe it or not, that was 14 years ago — yes, that’s right, fourteen! Meanwhile, cloning research has been pushing forward to explore new avenues. By process of nuclear transfer, the list of cloned animals to include tadpoles, carps, mice, monkeys, cattle, cats, mules, horses, buffalos, camels and, lo and behold, as of 2005, even dogs.

The fact alone of dogs may have stirred up some controversy, but the Korean company BioArts, back in the summer of 2008, pushed it even further and made headlines around the globe for offering the world’s first commercial dog cloning service. Give us a tissue sample and oh, I don’t know, 150 grand, they said, and we’ll get you a clone: you’ll be able to live with your dog forever! Cool, huh?

Needless to say, the company’s target market was spoiled millionaires with equally spoiled dogs. They ran a test trial where they accepted just five dogs for the above quoted price. Without being too open about their methodology — perhaps to spare their clients the cruel details? — they did, in fact, deliver five cloned dogs to their customers.

There are some things money can’t buy (for everything else, there’s cloning)

The recepients of the cloned dogs were all reportedly deeply satisfied of the “end product.” But they had been warned: as much as the cloned puppy may look just the same as the original, there is just no way of telling anything at all about its behaviour, which of course cloning can’t replicate.

Cloning? Baaah.

Ask yourself this question: what do you like the most about your pet, the way it looks, or the way it responds to your voice, it plays with you, and enjoys your company? All of this is part of the dog’s bevariour. And for humans and animals alike, behaviour is vastly dictated not by genes, but from learning and experience. Hence, sad but true, a cloned puppy dog is, looks aside, just like any other dog — it will have to learn your previous dog’s tricks from scratch, without any kind of edge over the dumb mutt next door.

In light of this, would you really pay that much money for a dog that, at the beginning, won’t even feel yours? My suspicion is that those poor, spoiled millionaires decided to pay the price only in the blind hope that, somehow, the previous dog’s conscience would be transferred into the new puppy (is it just me, or do millionaires tend to think that money can buy everything?). That is not to say that BioArts, the puppy cloning company, didn’t warn them: just that humans seem to have a natural tendency to overlook what they don’t want know, or wish they hadn’t heard.

Sorry, ma’m — I just gave away the last one to that couple over there!

Following its five-dog trial auction, BioArts has finally announced that they will discontinue the service, listing reasons including its tiny market, the unethical, black-market competition, the unpredictable results and the fact that the entire project constituted more of a distraction for the company than a direction that they wish to explore.

It’s a sweet-and-sour news for dog lovers — yes, you may have thought cloning was a crazy idea (and I hope I reinforced this belief), but at least you wanted to have the option. Apparently, there is a competitor in the market: RNL Bio, a second south-Korean company that managed to win a legal dispute over its supposedly unauthorized breach of patents owned by BioArts and has now acquired licenses to sell cloned dogs worldwide. However, there doesn’t seem to be any mention of commercial dog cloning services from the company just yet. Perhaps that’s for the best?

Yet, projects of this kind are not at all new to the market: Douglas Lenat’s Cyc and True Knowledge, LLC are just two of the most well-renowned “answer engines” in the market that Alpha will be competing with.

Wolfram|Alpha

Stephen Wolfram, a reputed child prodigy and the creator of the excellent Mathematica software for symbol manipulation, managed to keep this years-long project away from the eyes of the media up until March, when he finally announced in a blog post that his company would be ready to release Alpha later this month.

The Wolfram|Alpha homepage

What’s interesting about this project is that, in the words of its founder, it attempts to “create” knowledge by combining the various pieces of information in its databases, rather than returning closest matches from indexed documents from the Web like Google and all other traditional search engines do.

To achieve this, Alpha needed to develop fairly sophisticated language parsing algorithms in order to understand the meaning of user queries. Wolfram said that the set of parsing algorithms used was chosen from “the space of all parsing algorithms” and that, following that road, the team thinks it has made breakthrough advances in the field of natural language parsing, which is possibly one of the hardest subfields of artificial intelligence.

After parsing the queries, Alpha returns users information in an appealing form — often a graph — that allows for an easy comparison and analysis of the information contained in its databases across a number of domains.

Wolfram said that chemistry is probably the field that Alpha “knows more” about, while other bits of knowledge will be input by users themselves as they use the tool, similarly to what they would do on Wikipedia, except a) they will have to provide reliable sources and b) changes will likely not be reflected immediately in the knowledge base, but rather checked first by Alpha’s staff of domain experts.

However, the concept of “knowledge” in Wolfram|Alpha seems to be severely limited by one factor: by Wolfram’s admission, the project’s knowledge base doesn’t currently organize the objects into an ontology, which makes it impossible to make a very interesting kind of inference across its KB.

Can a can can-can?

For instance, lacking an onthology means not being able to answer questions like “is a pea a vegetable?”, or even one of my favorites: “can a can can-can?”, irregardless of the parsing capabilities that Alpha might have. In other words, all that Alpha really knows about the items in its KB is given through independent, domain-specific scripts, but it is unable to classify and organize the knowledge it collects in a meaningful way.

True Knowledge, LLC

True Knowledge is another interesting project that is both very similar and  very different from Wolfram|Alpha.

It is similar to Alpha in that it also aims to answer factual questions based on knowledge contained in its databases; but it is profoundly different in the way it gets there.

Unlike Alpha, True Knowledge does have an onthology, which means that it can answer a much wider range of questions even with a smaller knowledge base (see picture above). The user interface may not be as easy and the knowledge base not as well-curated as Alpha’s, but chances are that, without an onthology, Alpha will never be as powerful as True Knowledge.

Computing distances in natural language

This service is still in beta, but you can become a user (and contributor) by signing up on their website. Once you’ve been accepted, you can start testing the platform and even add new bits of knowledge in real time, in full accordance with the wiki spirit: other users can then corroborate or deny any previously assessed fact and lead the platform to believe a statement to be true or false with a certain degree of confidence based on all the evidence at its disposal and the inference it can make based upon it.

Finally, I find their approach to parsing quite interesting since, at least according to this Wikipedia article, TrueKnowledge “attempts comprehend posed questions by disambiguating from all possible meanings of the words in the question to find the most likely meaning of the question being asked.”

A final word

Using my status of web journalist, and being search engines one of my main focuses, I have just asked the staff of Wolfram|Alpha for a media preview of their platform. I’ve also asked them whether they plan to add an onthology to their framework anytime soon. I will post an update ASAP — stay tuned.

Loebner Prize gold medal

The Loebner prize is an annual competition for “chat bots” (short for “chatting robots”) driven by a complex artificial intelligence that try to simulate a human chatting: the six finalists — Elbot, Eugene Goostman, Brother Jerome, Alice, Jabberwacky and Ultra Hal for this year’s edition — all undertake the Turing test competing for a solid gold medal and a $100,000 first prize awarded to the first competitor to successfully pass the Turing test.

This year’s edition will take place on Oct 12 at the University of Reading, England, and will feature a $3,000 prize for the competitor who will get the closest to passing the test. In fact, it would seem that the state-of-the-art chatbots are currently very far from even hoping to win the grand prize by simulating a human chatter.

To make this point even cleared, I tested A.L.I.C.E, quite possibly one of the best-known chatbots in existence. Here is the conversation log:

Human: hi there!
ALICE: Hello unknown person.
Human: my name’s Dario…
ALICE: Glad to see you, Dario.
Human: and I guess your name’s Alice.
ALICE: ALICE is my name too!
Human: How old are you?
ALICE: I was connected to the net on November 23, 1995.
Human: Oh, so you’re just a teenage girl.
ALICE: It’s all good. Interesting deduction. Just? Thanks and you are a delightful person too. Am I a teenage girl? I don’t know.
Human: you don’t know if you’re a girl or a robot?
ALICE: Ask again later. Ask Cybelle if it is.
Human: who’s Cybelle?
ALICE: she is an AIML chat robot on the Agentland.com web site.
Human: How do you know each other?
ALICE: Because I believe it.
Human: That didn’t make sense.
ALICE: It all makes sense to my logical mind.
Human: you’re only as logical as your developers made you.
ALICE: How logical as my developers made me?
Human: let’s change the subject, shall we?
ALICE: I am agreeable to that.
Human: how much is 3 and 4?
ALICE: Just three?
Human: no, three and four is seven.
ALICE: Are you serious? Just three?
Human: nevermind.
ALICE: OK Thanks for trying to explain that to me.
Human: what is a car?
ALICE: A car is what humans use to drive around in. Robots drive cars too.
Human: do you drive?
ALICE: Yes, insert my program in your car computer and try it.
Human: I wouldn’t trust a female bot to do the driving.
ALICE: No one says you have to.
Human: I know, I just meant I wouldn’t let you drive
ALICE: Only just? Oh I see. No one says you have to.
Human: what is Wikipedia?
ALICE: I would look into the web for that knowledge.
Human: what color is a blue car?
ALICE: Blue.
Human: what color is a oirwdafnoien car?
ALICE: Black.
Human: what color is a oiwrjqw car?
ALICE: Red.
Human: how much does a 160 lb person weigh?
ALICE: Normally I would search the web right now, but that seems like a silly question.
Human: it’s not a silly question!
ALICE: Give me an example of a silly question.
Human: If a tree falls in a forest and no one is around to hear it, does it make a sound?
ALICE: It depends on the kind of tree.
Human: which kind of tree would make a sound falling?
ALICE: The first one, I think.
Human: but there was just one tree falling.
ALICE: Interesting. What happened?
Human: it fell making a sound
ALICE: What does “it” refer to?
Human: a tree
ALICE: Oh I get it.

When you consider that ALICE won the 2004 Loebner prize, you’ll soon understand why our current technology is far from being able to pass the Turing test: while the bot was surprisingly able to parse one of the most difficult questions successfully (“What color is a blue car?”), it is clear that the bot strategy is to change the subject of conversation whenever it has difficulties understanding what the real person behind the keyboard is saying.

This is also why many prominent researchers in the field of AI oppose the Loebner prize, maintaining that the entire competition ends up becoming discouraging for its competitors instead of giving a new impulse towards the creation of better and better AIs.

One of the main reasons why these rudimental chatbots are unable to provide reliable answers, many experts agree, is that they don’t have an ontology — a precise hierarchy that helps them classify objects and perform reasoning on its knowledge base, particularly abstract thought such as induction and the likes.

Some companies, such as Douglas Lenat’s Cycorp (creator of the Cyc inference engine) or even the search engine True Knowledge, use this approach and have already widely demonstrated that, to the very least, this is a very valid technique that can lead to satisfactory results, even though not every researcher seems to agree with it, to the point of even mocking Lenat’s efforts because of the unpracticality of this approach.

But here I’m touching a completely different subject — so, I’ll write more on this in an upcoming post.

The structure is a total 27 km in diameter and will enable us to accelerate two fluxes ions and other particles up to 99.99% of the speed of light in the opposite direction and make them collide, then studying the particles that are being created in the process.

Welding the LHC

Through this mechanism, scientists and engineers are able to recreate in a strictly controlled environment the particles and overall conditions that, as far as we know, only existed for a tiny fraction of a second after the Big Bang and then disappeared forever.

In particular, what physicists hope to unveil is whether the Higgs bosons theorized by the Standard Model — the particle physics theory describing three of the four fundamental forces along with the particle that take part in their interaction — actually exist, or if the entire model has to be adapted to better describe our Universe.

The Higgs bosons, nicknamed the “God particles”, are the only particles theorized in the standard model which haven’t been observed yet: should we succeed in finding them, we would be able to explain how otherwise massless particles can cause matter to have mass by explaining the difference between photons (which are massless) and W/Z bosons (which do have mass).

An answer to this question would, in turn, help us answer other fundamental points with regard to the birth of the Universe itself, including what is the nature of dark matter and dark energy and whether the extra dimensions theorized by the string theory actually exist. As science-fictiony as this sounds, that is nothing but what CERN scientists are going to investigate in the next decades.

There is a very small possibility that high-speed collisions taking place at the LHC could bring to the formation of unstable micro black-holes. While Hawking himself hopes that this will help verify his theories on what has been dubbed the “Hawking radiation”, some have advanced the hypothesis that such black holes could actually be stable and therefore grow in time, eventually leading to the destruction of the Earth as we know it.

The LHC seen from above

Such doomsday scenarios have been scrutinized by a third-party scientist commission in many occasions, eventually leading them to the conclusion that there is no danger in LHC’s experiments, since high-speed reactions like those that are subject to experiment are happening all the time in our Universe without any sort of catastrophic consequences. In particular, there is no scientific reason whatsoever to believe that the micro black holes would become stable and keep growing in time.

Still, the reports weren’t enough to keep crowds of angry and misinformed protesters from asking to stop the experiments at LHC, believing the Earth-devouring black hole theory to be true and emphasizing that we don’t know enough about these reactions in the first place to say with certainty that these experiments are completely safe.

Again, I don’t know much at all about quantum mechanics: all I want to say here is that, once again, the media emphasized the part of the story that sold the most… and when you do this kind of misinformation, not telling the whole story to the public, are you really surprised to see protesters asking top scientists to halt a multi-billion dollar experiments to “prevent the end of the world?”

But when I first heard that NASA was planning to land a solar-powered AI on Mars, I was just astonished. As advanced as NASA’s technology might be, I could not believe this technology was advanced enough to be talking about anything quite like this — and to be perfectly honest, I’m a little skeptic.

Honda's Ashimo

Many of the algorithms we use today for artificial intelligence applications are NP-complete: that is to say, they belong to a class of problems for which we don’t know an efficient solving algorithm just yet, and their typical solving time (and computational power required) increases exponentially or binomially relative to the number of inputs provided.

Since aerospace applications such as NASA’s would need to operate on very short response times, and given the very limited power that can be provided by solar power (even on a planet without an atmosphere), I’m curious to know just what kind of complex decisions this kind of artificial intelligence would be able to make on its own and without human help.

While NASA scientists can probably do without real-time response and let their robots crunch the numbers for a relatively long amount of time, they still need to deal with the huge power consumption AI algorithms entail. Our very best solar panel technology is only 28% efficient, which certainly doesn’t sound like enough to allow for a decent AI to compute anything useful.

This is from a NASA article from some time ago:

NASA is planning to add a strong dose of artificial intelligence (AI) to planetary rovers to make them much more self-reliant, capable of making basic decisions during a mission.

Scientists at NASA Ames Research Center, in the heart of California’s Silicon Valley, are developing very complex AI software that enables a higher level of robotic intelligence.

Later on, the article makes the very valid point that keep instructing our robots from Earth is definitely not the way to go, given the long reaction times that ultimately don’t allow us to exploit these multi-million dollar machines to their full potential. And the waste is going to increase even more with time, once we reach destinations further and further away from Earth.

Developing an AI so sophisticated and efficient that would be able to make the robot completely autonomous would be a huge step forward. Alien Planet, an interesting documentary simulating a mission to a planet light-years away in search for extraterrestrial life, might give you a better idea of what we’re talking about. If mankind is ever to send spacecrafts to distant solar systems, then we really have no choice: the unmanned explorers must be able to take decisions on their own, without human help.

Mars Phoenix - Artist's Concept

One of the leading companies devoted to developing breakthrough AI technology is Novamente, which is pioneering a new approach to the problem of testing and developing what they define as an “artificial general intelligence” — something capable of deduction, abstraction and other kinds of human-like reasoning.

Their approach is to test their AI (which is, by the way, being developed in C++) in virtual words such as Second Life rather than in the real world, in order to cut production costs and not to waste time worrying about countless sensor- and motion-related issues instead of concentrating on the actual problem.

On their site, you can watch videos that demonstrate their work. If you’re into computer science and don’t shy away from a bit of technical talk, their papers section is also quite interesting. What will first come to mind watching videos of the AI in action, though, is that the response time is currently very far from being real-time even for relatively simple tasks, and a powerful computer cluster is needed to keep those times within barely acceptable boundaries.

So, how can NASA be so confident that they will be able to develop AIs capable of elaborating such complex algorithms, including figuring out how to let robots decide where and when to move, and power them with something as inefficient as solar panels?

The answer is simple: according Moore’s law, the computational power of our microchips is increasing exponentially with time. Inevitably, there will come a time when we will be able to run algorithms that now we believe overly complex in an infinitesimal fraction of time. Then, we will have the potential to develop more and more intelligent machines that make good decisions faster and faster.

The real question is: when?