People often confound various aspects of “thinking” when it comes to AI that ought to be distinguished. In humans, we find free will, creative thinking, self identity all go together under the umbrella we call “consciousness,” but each of those traits has different ramifications for an AI and don’t necessarily come bundled together. So I made this little video to start drawing out some of those distinctions without getting terribly academic about it.
Last December I was Ralph Walker’s guest on his talk show on KGEM in Monrovia, CA. The full interview is an hour long and I will be making excerpts and the whole show available at a later date, but to provide a bite-sized taste of what it was like, here’s a brief clip where Ralph is asking about the consequences of humanity having the power to create superintelligences:
The other mainstream article that crossed my desk today is this one about the use of AI in Sales. Business Intelligence, later called Big Data, has driven sales for a long time, of course, but this isn’t merely an example of AI-washing. This tone of this article makes it clear that AI is here to stay in the field of sales, and that tools like Machine Learning are becoming an integral and indispensable part of their practice.
AI is starting to soak into the fabric of modern society, and there’s little limit to how far it will penetrate. Now law firms are putting it on their radar, as evidenced in this blog entry from the California law firm of Hogan Injury. Their advice is confined to the relatively innocuous considerations of training around robots, and we have had industrial robots for decades, but of more interesting note is the framing of this more as a partnership with a co-worker rather than using a workplace tool.
My 2017 interview on the Concerning AI podcast was recently published and you can hear it here. Ted and Brandon wanted to talk about my timeline for AI risks, which has sparked a little interest for its blatant speculation.
Brandon made the point that the curves are falsely independent, i.e., if any one of the risks results in an existential threat eliminating a substantial portion of the population, the chart following that point would be invalidated. So these lines really represent some estimates as to the potential number of people impacted at each time, but under the supposition that everything until that point had failed to have a noticeable effect.
Why is such rampant guesswork useful? I think it helps to have a framework for discussing comparative risk and timetables for action. Consider the Drake Equation by analogy. It has the appearance of formal math, but really all it did was replace one unknowable (number of technological civilizations in the galaxy) with seven unknowables, multiplied together. At least, those terms were mostly unknowable at the time. But it suggested lines for research; by nailing down the rate of star formation, and launching spacecraft to look for exoplanets (another one of which just launched), we can reduce the error bars on some of those terms and make the result more accurate.
So I’d like to think that putting up a strawman timetable to throw darts at could help us identify the work that needs to be done to get more clarity. At one time, the weather couldn’t be predicted any better than saying that tomorrow would be the same as today. Because it was important, we can now do better than that through the application of complex models and supercomputers operating off enormous quantities of observations. Now, it’s important to predict the future of existential risk. Could we create models of the economy, society, and technology adoption that would give us as much more accuracy in those predictions? (Think psychohistory.) We have plenty of computing power now. We need the software. But could AI help?
Check out the Concerning AI podcast! They’re exploring this issue starting from an outsider’s position of concern and getting as educated as they can in the process.
It’s been busy lately! Interest in Crisis of Control has skyrocketed, and I’m sorry I have neglected the blog. There are many terrific articles in the pipeline to post.
If you’re new and finding your way around… don’t expect much organization, yet. I saved that for my book (https://humancusp.com/book1). That contains my best effort at unpacking these issues into an organized stream of ideas that take you from here to there.
On Saturday, February 3, I will be speaking at TEDx Pearson College UWC on how we are all parenting the future. This event will be livestreamed and the edited video available on the TED site around May.
I have recorded podcasts for Concerning AI and Voices in AI that are going through post-production and will be online within a few weeks, and my interview with Michael Yorba on the CEO Money show is here.
On March 13, I will be giving a keynote at the Family Wealth Report Fintech conference in Manhattan. Any Crisis of Control readers near Midtown who have a group that would like a talk that evening?
I’m in discussions with the University of Victoria about offering a continuing studies course and also a seminar through the Centre for Global Studies. My thanks to Professor Rod Dobell there for championing those causes and also for coming up with what I think is the most succinct description of my book for academics: “Transforming our response to AGI on the basis of reformed human relationships.”
All this and many other articles and quotes in various written media. Did I mention this is not my day job? 🙂
In other random thoughts, I am impressed by how many layers there are in the AlphaGo movie. A friend of mine commented afterwards, “Here I was thinking you were getting me to watch a movie about AI, and I find out it’s really about the human spirit!”
Watch this movie to see the panoply of human emotions ranging across the participants and protagonists as they come to terms with the impact of a machine invading a space that had, until weeks earlier, been assumed to be safe from such intrusion for a decade. The developers of AlphaGo waver between pride in their creation and the realization that their player cannot appreciate or be buoyed by their enthusiasm… but an actual human (world champion Lee Sedol) is going through an existential crisis before their eyes.
At the moment, the best chess player in the world is, apparently, neither human nor machine, but a team of both. How, exactly, does that collaboration work? It’s one thing for a program to determine an optimal move, another to explain to a human why it is so. Will this happen with Go also?
Hello! You can listen to my November 28 interview with Jim Blasingame on his Small Business advocate radio show in these segments:
My friend, fellow coach, and globetrotting parent Fionn Wright recently visited the Pacific NorthWest and generously detoured to visit me on my home turf. He has produced this video of nearly an hour and a half (there’s an index!) of an interview with me on the Human Cusp topics!
Thank you, Fionn. Here is the index of topics:
0:18 - What is your book ‘Crisis of Control’ about? 3:34 - Musk vs. Zuckerberg - who is right? 7:24 - What does Musk’s new company Neuralink do? 10:27 - What would the Neural Lace do? 12:28 - Would we become telepathic? 13:14 - Intelligence vs. Consciousness - what’s the difference? 14:30 - What is the Turing Test on Intelligence of AI? 16:49 - What do we do when AI claims to be conscious? 19:00 - Have all other alien civilizations been wiped out by AI? 23:30 - Can AI ever become conscious? 28:21 - Are we evolving to become the cells in the greater organism of AI? 30:57 - Could we get wiped out by AI the same way we wipe out animal species? 34:58 - How could coaching help humans evolve consciously? 37:45 - Will AI get better at coaching than humans? 42:11 - How can we understand non-robotic AI? 44:34 - What would you say to the techno-optimists? 48:27 - How can we prepare for financial inequality regarding access to new technologies? 53:12 - What can, should and will we do about AI taking our jobs? 57:52 - Are there any jobs that are immune to automation? 1:07:16 - Is utopia naive? Won’t there always be problems for us to solve? 1:11:12 - Are we solving these problems fast enough to avoid extinction? 1:16:08 - What will the sequel be about? 1:17:28 - What is one practical action people can take to prepare for what is coming? 1:19:55 - Where can people find out more?
The debate about existential risks from AI is clouded in uncertainty. We don’t know whether human-scale AIs will emerge in ten years or fifty. But there’s also an unfortunate tendency among scientific types to avoid any kind of guessing when they have insufficient information, because they’re trained to be precise. That can rob us of useful speculation. So let’s take some guesses at the rises and falls of various AI-driven threats. The numbers on the axes may turn out to be wrong, but maybe the shapes and ordering will not.
The Y-axis is a logarithmic scale of number of humans affected, ranging from a hundred (102) to a billion (109). So some of those curves impact roughly the entire population of the world. “Affected” does not always mean “exterminated.” The X-axis is time from now.
We start out with the impact of today’s autonomous weapons, which could become easily-obtained and subverted weapons of mass assassination unless stringent controls are adopted. See this video by the Future of Life Institute and the Campaign Against Lethal Autonomous Weapons. It imagines a scenario where thousands of activist students are killed by killer drones (bearing a certain resemblance to the hunter-seekers from Dune). Cheap manufacturing with 3-D printers might stretch the impact of these devices towards a million, but I don’t see it easy enough for average people to make precision-shaped explosive charges to go past that.
At the same time, a rising tide of unemployment from automation is projected by two studies to affect half the workforce of North America and by extension, of the developed world, in ten to twenty years. An impact in the hundreds of millions would be a conservative estimate. So far we have not seen new jobs created beyond the field of AI research, which few of those displaced will be able to move into.
Starting around 2030 we have the euphemistically-labeled “Control Failures,” the result of bugs in the specifications, design, or implementation of AIs causing havoc on any number of scales. This could culminate in the paperclip scenario, which would certainly put a final end to further activity in the chart.
The paperclip maximizer does not require artificial consciousness – if anything, it operates better without it – so I put the risk of conscious AIs in a separate category starting around 20 years from now. That’s around the median time predicted by AI researchers for human scale AI to be developed. Again, “lives impacted” isn’t necessarily “lives lost” – we could be looking at the impact of humans integrating with a new species – but equally, it might mean an Armageddon scenario if conscious AI decides that humanity is a problem best solved by its elimination.
If we make it through those perils, we still face the risk of self-replicating machines running amok. This is a hybrid risk combining the ultimate evolution of autonomous weapons and the control problem. A paperclip maximizer doesn’t have to end up creating self-replicating factories… but it certainly is more fun when it does.
Of course, this is a lot of rampant speculation – I said as much to begin with – but it gives us something to throw darts at.
Art Kleiner, writing in Strategy+Business, cited much-reported research that a deep neural network had learned to classify sexuality from facial images better than people can, and went on to some alarming applications of the technology:
The Chinese government is reportedly considering a system to monitor how its citizens behave. There is a pilot project under way in the city of Hangzhou, in Zhejiang province in East China. “A person can incur black marks for infractions such as fare cheating, jaywalking, and violating family-planning rules,” reported the Wall Street Journal in November 2016. “Algorithms would use a range of data to calculate a citizen’s rating, which would then be used to determine all manner of activities, such as who gets loans, or faster treatment at government offices, or access to luxury hotels.”
It is no surprise that China would come up with the most blood-curdling uses of AI to control its citizens. Speculations as to how this may be inventively gamed or creatively sidestepped by said citizens welcome.
But the more ominous point to ponder is whether this is in the future for everyone. Some societies will employ this as an extension of their natural proclivity for surveillance (I’m looking at you, Great Britain), because they can. But when technology makes it easier for people of average means to construct weapons of global destruction, will we end up following China’s lead just to secure our own society? Or can we become a race that is both secure and free?