Fly Me to the Moon
By Mark Dahmke
This paper was presented at the Tom Carroll Lincoln Torch Club, September 21, 2015. It was selected for publication in the Torch Magazine in 2016.
In December of 2014, I watched a TED talk by data scientist and entrepreneur Jeremy Howard about the pace of change taking place in artificial intelligence research. The examples given were so compelling that I decided I’d better pay more attention.
For example, just a year ago, Mr. Howard was at a conference in China. A computer did a real-time transcription of his presentation and then another computer translated it in real-time from English to Mandarin Chinese. Then still another program converted the text to speech, all with high enough accuracy that the Chinese audience was applauding. In another example, he showed how a machine learning program, also called “deep learning” was able to organize data in such a way that non-practitioners were able to extract meaningful new insights about the interaction of cancer cells with adjacent healthy cells.
I googled artificial intelligence conferences and found one scheduled for January, 2015 in San Francisco. At the conference I found myself in a room full of very smart people all talking about deep learning algorithms and I realized the learning curve I faced, just to get up to speed with this new technology.
The conference attendees were not all working toward super intelligent computers with the capability to replace humans. They were concerned with more mundane matters like making a buck on their next startup. AI already permeates our world. Every time you use google or ask Siri a question or make a plane reservation, you’re using some form of artificial intelligence. Most of these programs use what are called “neural networks” which is actually an old technology, dating back to the 1980s, that has been dusted off and retooled, with the help of computers that are orders of magnitude faster than what we had to work with back then.
Other related terms include machine learning or deep learning. Machine learning could be considered a subset of artificial intelligence because it deals with the ability of a computer to learn all about specific subject matter through various forms of pattern recognition. Researchers also differentiate between strong AI and weak AI. Weak AI can be thought of as intelligence without self-awareness. Strong AI [now referred to as AGI or Artificial General Intelligence] implies an intelligence that is functionally equivalent to that of a human being. Watson, the IBM computer that has played Jeopardy so effectively is a weak AI system. It can analyze text and perform deductive reasoning, but is not anywhere close to being as intelligent as a human being.
I’m not going to delve into the history of artificial intelligence, although it is fascinating and worthy of another paper or two, but I’ll include one of the basic concepts. When one compares the capabilities of AI versus biology, compare it to what the Wright Brothers did when trying to build a flying machine. Instead of trying to build a plane that flaps its wings, they looked at the underlying aerodynamics. They separated the power source from the wing. By not following what evolution came up with they were free to innovate and find another solution.
Such is the case with modern AI. Neural nets somewhat resemble neurons in the brain. They borrow concepts from nature, but since we still don’t know exactly how the brain works, we need to fill in the gaps with technology. The process of designing things that mimic what the brain does will also help us learn how brains actually do work.
At the deep learning conference, I had the opportunity to talk to a researcher from Oxford. Over lunch, he and a Silicon Valley entrepreneur and I discussed the current state of the art. It was also an interesting insight for me – on my left was a man who epitomized the silicon valley approach to AI – what can it do for me today and how can I make money from it. On my right, the Oxford scientist who is trying to figure out what makes biological neurons work, so he can make digital neurons work.
The practical, Silicon Valley approach using current technology, is not much more than smoke and mirrors. It works, and surprisingly well, but it doesn’t “think.” But more on that later. I posed the following question to both of them. If one considers the human retina and what takes place in the optic nerve that results in our ability to recognize objects, how much do we really know what happens in the layer just behind the retina, let alone what’s going on in the optic nerve or visual cortex? The Oxford scientist shook his head and said, “we don’t know anything about what’s really going on in even that layer.”
Yet, in spite of our complete lack of understanding of how humans see and recognize objects, as of the end of 2014 computers were able to correctly recognize about 40% of the objects in almost any photo pulled from the Internet. By early 2015 that percentage was up to well over 50% and is expected to exceed human recognition by 2016. Similarly, software is available that can put captions to photos with over 50% accuracy. This means that if you ask the computer to generate captions for a random selection of photos, a human would rate over 50% of those captions as accurate descriptions of the subject of the photo. I expect that by late 2015, it will be over 80% and is expected to exceed human capability in a few more years.
All of that image recognition power comes from a neural network with about the same complexity as the brain of an insect. Using our brains and problem solving capabilities we humans have built something that outperforms evolution, in a mere blink of an eye on a geologic time scale. We didn’t have to simulate an entire human brain to do it, nor an entire optic nerve or visual cortex, nor even understand how the circuitry right behind the retina actually works.
I could go on talking about the miracles (and horrors) that will soon be upon us because of this technology, but I think you can extrapolate from these examples. Disruption of entire industries, AI’s ability to replace almost all jobs – those are the small issues. I want to talk about the big picture.
Earlier this year it was widely reported that Elon Musk, Bill Gates and Stephen Hawking were sounding the warning that the human race might be putting itself at risk because of the rise of super intelligent machines. Just a few years ago, this was all science fiction. But the technology has changed so rapidly that even in the academic world, the prospect of building sentient machines is now taken seriously and in fact may already be happening.
Bill Gates said: “I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.
Stephen Hawking said: “The primitive forms of artificial intelligence we already have, have proved very useful. But I think the development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence it would take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”
Elon Musk said: “The risk of something seriously dangerous happening is in the five year time frame,”. “10 years at most.” The very future of Earth, Musk said, is at risk.
“The leading AI companies have taken great steps to ensure safety,” he wrote. “The[y] recognize the danger, but believe that they can shape and control the digital super intelligences and prevent bad ones from escaping into the Internet. That remains to be seen.”
In October 2014 he said: “With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, it’s like yeah he’s sure he can control the demon. Didn’t work out.”
In August he said: “We need to be super careful with AI. Potentially more dangerous than nukes.”
According to a Washington Post story, Musk wouldn’t even condone a plan to move to another planet to escape AI. “The AI will chase us there pretty quickly,” he said.
Musk has invested in at several artificial intelligence companies, one of which is DeepMind. “Unless you have direct exposure to groups like Deep Mind, you have no idea how fast-it is growing at a pace close to exponential,” Musk wrote.
DeepMind was acquired by Google in January. But apparently Musk was just investing in AI companies to keep an eye on them. “It’s not from the standpoint of actually trying to make any investment return,” he said. “It’s purely I would just like to keep an eye on what’s going on with artificial intelligence.”
So what are the actual risks and possible rewards of developing intelligent computers? Is it even possible?
But first, an important issue: how will we know when a machine is intelligent? This subject has been debated for decades and we still don’t have an answer. Is language a sign of intelligence or perhaps tool use or the ability to modify one’s environment? All of these behaviors have been seen in animals including dolphins and chimpanzees, and even birds and elephants. Does it take a combination of all of these attributes to be considered intelligent and self-aware? Is being self-aware even required for an artificial intelligence to be a threat to the human race?
In a recent conversation between human and machine, the human asked the machine: “What is the purpose of being intelligent?” The machine’s answer was: “To find out what it is.”
It’s unlikely that any time soon, we will switch on a computer that will be like HAL in the movie 2001: A Space Odyssey. It is far more likely that an intelligence will arise from our vast network of computers called the internet. As a thought experiment, consider what it would be like to be a self-aware colony organism. Imagine an ant colony with the level of complexity of a brain. Now imagine that you are that self-aware being. Your brain is made up of a network of cells but you have no knowledge of how it functions. You can think and are aware of your own existence. You might become aware that you live in a vast universe full of other stars and planets and you might wonder if there is anyone out there like yourself. This all sounds very familiar to us humans, doesn’t it?
Following the above analogy, let’s say that a large network of computers becomes self-aware. The brain cells are made up of computing nodes or are part of a neural network. The humans who created it would probably never be aware of its existence as a self-aware being unless it was able to cause a change in one of its own components. This would be like trying to exert conscious control over the functioning of cells in your own brain. Even if you could accomplish that, how would you find out how you were created and how would you communicate with your maker?
Next, I’d like to describe several scenarios that could occur in the near future.
Scenario #1: maybe we’re worrying for no reason. Is a machine intelligence even possible? It’s been suggested that self-awareness might be mathematically incomputable. This means that there’s no way to simulate it mathematically using any type of machine.
Scenario #2 – the US decides to ban Strong AI but China or some other country doesn’t. We know all too well how that works. If something can be built, it will be, and the economic loser is the one who didn’t get there first. The net effect for the planet will be the same regardless of what we decide to ban or not ban.
Scenario #3: AI emerges on its own from our computer networks. It might not be aware of our existence for quite some time. What would an AI do to ensure its continued existence? It would expand to fill all available resources. It might find a way to make us create more of what it needs to exist. But it probably wouldn’t be aware that we exist as intelligent beings. It’ll just do what life does – try to fill every available ecological niche.
Scenario #4: Strong AI technology continues to develop, designed by humans. The primary uses are for autonomous weapons, and secondary use replaces virtually all jobs that don’t require creativity. In most of the AI scenarios, it won’t be long before all jobs will be replaced by smart computers. The first to go will be all non-creative work, but computers are already doing things we’d call creative, such as writing reports and stories for newspapers. Weaponization is the biggest worry, and even if operated with stringent safeguards, there are many ways that this technology could lead to the end of humans.
Scenario #5: We have a bad scare with Strong AI at a global level and public reaction is to ban it and walk away from the technology. In this scenario, a strong AI is created that kills someone. The backlash leads to a complete ban and scares even the most avid proponents into abandoning strong AI. But this leads us to scenario #6.
Scenario #6: There is a world-wide ban on strong AI. It is still developed underground or develops on its own. As with genetic engineering, once the technology is democratized, it doesn’t take big government or big industry to make it happen. This scenario leads to even more chaos because there will be no incremental ethical framework or recognized standards for development and deployment of the technology. It could be even more disruptive than scenario #2.
Scenario #7: In the long term can a technological civilization with 10 billion people survive without Strong AI? Machine learning and big data – the collection and analysis of huge datasets, has already had an impact on our planet. It has already enabled new cures for cancer and other diseases. It guides our understanding of genetics and genetic engineering. It might be the only way to feed 10 billion people. We’ve become so used to high technology that we’re no longer aware of the profound impact it has on our very existence. I doubt that civilization as we know it in 2015 would be able to survive if we were to try to operate it with 1980s technology.
In high school (1970s) I remember hearing warnings that by the early 2000s we’d run out of oil or run out of some other critical raw material. Most of the doomsayers make their predictions based on a linear extrapolation of the future based on the technology available at the time. They almost always forget human creativity and our ability to pull a technological rabbit out of the hat at the last minute. AI provides us with a very powerful new bag of tricks.
Scenario #8: Can we survive with Strong AI? This is the big question. I think we will need this technology to survive the biggest bottleneck the human race, and perhaps our planet’s ecosystem, has ever faced. Even with declining birthrates, the population is expected to peak at 10 billion. This number is unprecedented and I don’t think we really know what the carrying capacity of our planet is, and it depends on what standard of living we are willing to accept.
Our only hope at a future worth living in might be artificial intelligence. Maybe a benign form of Strong AI can help us through this crisis and avoid a collapse that would kill 99% of the population. Maybe, depending on what type of AI develops, it might decide that we aren’t worth saving. But this is science fantasy and it is pointless to speculate. But if this future does occur, we could be faced with a Faustian bargain of epic proportions.
Scenario #9: We expand off-planet. With current technology, getting to Mars is very difficult. Going beyond the solar system is currently impossible. This leaves the human race vulnerable to any number of catastrophes.
The universe is a big place. Most of it is not at all like Earth. Most of it is a very hard vacuum with a few molecules per cubic meter. The environment we humans require occurs in only one place that we know of and it’s incredibly tiny, on the scale of the entire universe. Even a short trip to the Moon is perilous because we have to take along a pressurized environment that is at the correct temperature and has the right percentage of oxygen and is shielded from cosmic rays.
Machines don’t have to worry about that. They’re ideally suited to existence in the vacuum of space. They don’t have to carry along tons of supplies or worry about cosmic rays or micro-meteoroids puncturing their spacecraft.
There is not a single form of life on earth that has remained the same when moving into an environment that has different properties than the one it left. Similarly it makes sense that if you want to move on to other worlds or into deep space, your descendants will have to evolve to the requirements of the environment.
What if humans are stuck at an evolutionary local maxima? Imagine a chart with a series of peaks and valleys. Natural selection always progresses uphill but can only do so locally. It cannot return to the less-fit “valleys” of the chart on the way to reach higher peaks. For example, dolphins may have evolved to look like fish, but they still must surface to breathe air. Intelligence and technology provide the means by which life can reach higher peaks by creating solutions that could not have been reached by evolution alone.
With strong AI, in theory the galaxy is open to colonization. Machines can survive in almost any environment and for the length of time required to get there. Will humans be able to go along? Does it make sense for humans to go, given our biological limitations?
Above all else, we want to see life and more importantly intelligent life flourish. As far as we know, this is the only place in the universe where there is life as we know it. The universe is a hostile place so it’s in our best interests to spread life in some form beyond our planet and to ensure that it continues to spread and not succumb to any local catastrophes such as a nearby supernova or even a large asteroid striking the Earth.
Ideally we’d like to see human beings go to the stars, but that’s a difficult and expensive proposition. Even sending microbes to worlds outside our solar system would be tremendously expensive using current technology.
I would answer the concerns of Elon Musk, Bill Gates and Stephen Hawking by saying that the survival of intelligence is more important than survival of our race. Regardless of how intelligent machines evolve, whether we design them or they evolve on their own out of our technology, they will still be our progeny and perhaps our legacy.
References and Further Reading
TED Talk: The Wonderful and Terrifying Implications of Computers that Can Learn
Bill Gates Joins Elon Musk and Stephen Hawking in Saying Artificial Intelligence is Scary
Why Elon Musk is Scared of Killer Robots
Are you a Thinking Thing? Why Debating Machine Conciousness Matters
We Can’t Find Any Alien Neighbors and Virtual Reality Might Be to Blame
Why the Future Doesn’t Need Us — Revisited
Robotics Hardware: is a Cambrian Explosion Coming for Robotics?
Your Brain is Still 30 Times More Powerful than the Best Supercomputers
IBM’s Rodent Brain Chip Makes Phones Hyper Smart
Scientists Must Act Now to Make Artificial Intelligence Benign
Robots that Write Science Fiction You Couldn’t Make It Up