In Futures Thinking: The Basics, I offered up an overview of how to engage in a foresight exercise. In Futures Thinking: Asking the Question, I explored in more detail the process of setting up a futures exercise, and how to figure out what you're trying to figure out. In Futures Thinking: Scanning the World, I took a look at gathering useful data. This time, we dive into the heart of the process: creating alternative scenarios.
Note the plural. Foresight exercises that result in a single future story are rarely as useful as they appear, because we can't predict the future. The goal of futures thinking isn't to make predictions; the goal is to look for surprising implications. By crafting multiple futures (each focused on your core dilemma), you can look at your issues from differing perspectives, and try to dig out what happens when critical drivers collide in various ways.
Whatever you come up with, you'll be wrong. The future that does eventually emerge will almost certainly not look like the scenarios you construct. However, it's possible to be wrong in useful ways--good scenarios will trigger minor epiphanies (what more traditional consultants usually call "aha!" moments), giving you clues about what to keep an eye out for that you otherwise would have missed.
As a clarifying example, I once worked with a business services company to craft different scenarios of its future competitive environment. All of the scenarios included some level of economic upheaval and technological disruption, with one suggesting that a technology company offering business services could be a real threat--a story arising from tentative signs that some of their clients were starting to use more open source software. A few of the strategists realized that a company they had thought of as a minor nuisance at worst could actually be their most serious rival, and pushed their CEO to pay more attention to that threat.
(He didn't, and the business services company eventually went under, with the tech company gaining many of its former clients--and employees.)
So how do you actually do this?
In the aftermath of your "scanning the world" work, you will have come up with at least dozens and probably hundreds of interesting and potentially relevant data points and potential drivers. It's hard to work with hundreds, though; more useful would be about five or six. Time to call in some friends.
You're going to want to spend an afternoon with a group of no more than a dozen colleagues talking about the "distant early warnings" you've dug up. You'll probably find it easiest to put each one on a separate index card or sticky note (in this, low-tech still beats high-tech). What you'll then do is look for patterns and bigger picture categories that would encompass multiple topics. Try to focus the categories on subjects that are clearly important and hold a great deal of uncertainty.
Pile up the clusters of related subjects; you might find that a single topic might belong in multiple clusters, but resist the urge to duplicate it--you want to put it with the group where it seems most important. Feel free to brainstorm along the way--you might find that your colleagues are inspired by the scanning results, and offer more than a few additional suggestions. This is fine.
You will eventually have a smallish group of categories with lots of members, and a largish group of categories with just a few. The big categories will be your key scenario drivers, and should appear in all of your scenarios in some form. The smaller piles will be minor drivers, and should be included in at least one.
For example, a scenario project about whether and how to expand a business might end up with major drivers such as "the economy," "China," "aging baby boom market," "oil prices," and "computer tech." All of your scenarios will at least touch on each of these subjects. Minor drivers might include "labor relations," "advertising markets," "new manufacturing technologies," and so forth. These don't necessarily need to appear in every scenario, but should appear in at least one. Yes, these are all very high-level categories, but remember that they're clusters of individual data points and issues; these individual bits are what you'll eventually weave into the scenarios you construct.
So you now have your key drivers, and have been thinking about the ways in which they're both important and uncertain. It's time to start building your scenario worlds.
Next time: Building Your Scenario Worlds.
Images
Futurama, 1939 World's Fair, photo by Norman Bel Geddes, Public Domain
For those of you living under a rock, Apple's announcement of the iPad tablet has shaken up the tech world. The combination of slick hardware and more-than-usable software has long been Apple's stock-in-trade, and the iPad demonstrates again what we saw with the iPod back in 2001, and the iPhone back in 2007--interface matters. Tablet computers aren't new, but neither were MP3 players or smart phones. What's new (then as now) is the way in which the user communicates with and controls the device.
There's no doubt: The iPad is a beautiful, extremely well-designed device.
So why am I worried?
The iPad runs the iPhone OS and uses the iTunes App Store. That means that it will have a large selection of applications ready to go when it hits the shelves in March, but it also means that Apple will be the sole source of the applications, deciding what can and what can't run on the device.
Lots of people dislike that aspect of the iPhone experience, but I can't say that I was terribly bothered by it. I understand that most iPhone users want a phone that can do other nifty things, not a general purpose computer that happens to make phone calls. Strict control over apps minimizes the chances that someone will find their phone hacked or virus-laden. As we add more computational smarts into our physical surroundings, this kind of software management is almost certain to become commonplace. We've already seen digital picture frames pre-loaded with viruses; I'm not eager to have my refrigerator hacked or my alarm clock turned against me.
But the iPad isn't a phone; it is a general purpose computer. It does email and Web and documents and presentations and games and all of the other kinds of things we do with our "regular" computers. Yet it will suffer under the same restrictions as the iPhone--prohibition of any application that Apple doesn't like, for whatever reason. Sometimes that means the application uses undocumented features, but startlingly often it just means "duplication of features"--the application does something that Apple's own software does, but does it differently. (This raises the uncomfortable question as to whether the Kindle app for the iPhone--which works quite nicely, actually--will run on the iPad.)
This is problematic to me for a couple of reasons. The first, and simplest, reason is that it narrows the scope of innovation. The main reason why the personal computer--including the Mac--served as a catalyst for economic and social transformation was that it was open to every imaginable use. The only limits came from hardware capacity and code complexity, not arbitrary restrictions. The iPad, as swoopy and neat as it may seem, won't trigger a similar revolution.
But this is just the iPad, right? So Apple wants to shoot itself in the innovation foot in order to maximize control. That doesn't affect my other machines. Right?
For now. The second reason this worries me is that successfully shifting one general purpose computer to a world of controlled software may lead to similar restrictions showing up on other kinds of general purpose hardware. I don't want to wake up one day and find that the next version of the Mac OS (or Windows, for that matter) will only run "approved" software.
Okay, slippery slope arguments always curve towards hyperbole, and I really don't think that the next version of any mainstream OS is going to be restricted like that. But the version after the next one? One uncomfortable question is just how readily a restricted application scenario would support the aggressive copyright rules now being negotiated in secret.
I'm not an Apple-hater--I'm writing this on a Macbook, with my iPhone on the desk. And there are definitely positive aspects about the iPad announcement that stand out for me.
The first is the price. While it's easy to find netbooks running well under $500, tablet computers and touch-screen notebooks rarely get that low (and, of course, none of them have this interface).
The second is Apple's choice to use the open ePub format for electronic books. This one surprised me more than the price, actually--with Amazon's Kindle already using a proprietary format, and Apple pushing the semi-proprietary AAC format for its iTunes music, all signs seemed to point to an Apple-controlled ebook standard. It's still unknown how difficult it will be to use non-iBook ePub documents, but in principle, the system should be open to any publisher.
So the iPad has a number of nifty characteristics, and it's certainly tempting. But I'm very conscious of how quickly the line between utility device that happens to have a microprocessor and a general purpose computer that offers basic utility services has blurred. And I worry about the rules for restricted devices finding their way into the general purpose systems that have come to be so important for innovation and experimentation.
You may not know who Art Rosenfeld is, but his work has probably made a difference in your life. He certainly made a difference in mine. A while ago, I read a piece he had co-written for Technology Review about the energy-efficiency benefits of white rooftops, so when the time came for me to replace the roof on my house, I took a chance and opted for the white "energy efficient" shingles.
As a result, my summertime electricity bills dropped dramatically.
Rosenfeld was, until his retirement, the head of the California Energy Commission, a state organization that shapes the rules surrounding electricity production and use in California. During Rosenfeld's 30-year tenure at the CEC, he made energy efficiency the overriding driver of regulatory policy, creating rules for everything from refrigerators (which now use only a quarter of the power that their less-fancy 1970s ancestors did) to "vampire loads" (the power still consumed by devices when turned off) to--most recently--the power consumed by flat screen televisions, which by some reports now account for nearly 10% of the power consumption in California.
And in doing so, is directly responsible for this remarkable fact: despite an explosion of consumer electronics, mobile gadgets, and personal computers of all types, energy use per-capita in California is the same as it was 30 years ago. In the rest of the U.S., per-capita energy consumption has continued to climb, albeit more slowly than it might have, as more states come to adopt California's energy rules. And these rules make a difference:
Rosenfeld, starting in the 1970s, provided California energy regulators the data they needed to enact some of the toughest efficiency standards in the world.
New homes and buildings were required to be better insulated and fitted with energy-wise lighting, heating and cooling systems. Appliances had to be designed to use less power. Utilities were forced to motivate their customers to use less electricity. [...]
[T]hese mandates have yielded about $30 billion annually in energy savings for California consumers. They've eliminated air pollution that's the equivalent of taking 100 million cars off the roads.
This helps to drive home Rosenfeld's key point: efficiency is the cheapest source of energy. The regulations assembled by the CEC have added about 2-3 cents per kilowatt-hour to the price of electricity in California. According to energy specialist Joe Romm, that's about one-fifth of the cost of energy from the new power plants that would have been built had the efficiency rules not gone into effect.
Now, you may not live in California, and the energy rules where you live may not come close to those promulgated by the CEC. You can still take advantage of them, however.
Manufacturers subject to California Energy Commission regulations typically make noises about how the latest rules will mean the death of their industries, then turn around and start innovating. And while the use of the most energy-efficient appliances and building materials may not be mandated where you are, chances are you can still get a hold of them. When you need to replace your refrigerator, your TV, your roof, or the other various parts of your household infrastructure, you can choose to go with more efficient options.
It's a small thing, a simple choice. But simple choices matter. And, over time, can become transformative.
You've probably seen "neodymium" (actually neodymium-iron-boron) magnets advertised in techie-oriented magazines and gadget blogs. They're actually the strongest type of magnet available, and a pair of them can easily smash fingers. They're also incredibly useful, with small neodymium magnets found in everything from hard drives to wind turbines. Neodymium is one of 17 "rare-earth metals," and these elements have turned out to be critical to the rapidly-growing green technology industries. Rare-earth metals are used in hybrid and electric cars and low-energy lightbulbs, along with windmills (and numerous other greentech applications).
And China is the source for over 95% of the rare-earth metals now in use--something that increasingly looks like a problem. How we respond to this problem can tell us something about how we can respond to other imminent resource and sustainability crises.
Conventional wisdom says that we live in a globalized economy and if China can offer the metals at cheaper prices than other sources (namely, now-closed mines in South Africa, Greenland, and Canada), it's good for us all, right? The fact that many high-tech military technologies rely on Chinese rare-earth metals may give some people pause, but so far, so good. But that model assumes that China is willing to sell as much mineral as it can produce, to whomever wants to buy--and that assumption may no longer be true.
The U.K.'s Independent reports that China has been gradually cutting the amount of rare-earth elements it exports, now down 40% from seven years ago. China now exports only 25% of the rare-earth elements it mines. More worrisome, they say:
Industry sources have told The Independent that China could halt shipments of at least two metals as early as next year, and that by 2012 it is likely to be producing only enough REE ore to satisfy its own booming domestic demand, creating a potential crisis as Western countries rush to find alternative supplies... Beijing announced last month that it was setting exports at 35,000 tonnes for each of the next six years, barely enough to satisfy demand in Japan. From this year, Toyota alone will produce annually one million of its hybrid Prius cars, each of which contains 16kg of rare earths. By 2014, global demand for rare earths is predicted to reach 200,000 tonnes a year as the green revolution takes hold.
With industries relying on rare-earth elements making up a rapidly-growing part of the global economy, this isn't good.
So what are our options? We (as in, the non-China parts of the industrialized world) could try to pressure China to sell more, but that's unlikely to work--and China tends not to respond well to even mild criticism. We could try to rapidly reopen the now-closed rare-earth element mines, but mining is, frankly, an environmental nightmare and incredibly dangerous--hardly a sustainable practice.
Our best option is to innovate our way out of the problem. Ideally, we'd figure out a way to make what we need without those elements. In the shorter term, however, we'd want to figure out a way to obtain those necessary elements without either trying to push China around or reopening dirty mines. If the innovation manages to help solve an otherwise unrelated problem, too--a so-called "economy of scope"--so much the better.
Researchers from Leeds' Faculty of Engineering have discovered how to recover significant quantities of rare-earth oxides, present in titanium dioxide minerals. [...] The Leeds breakthrough came as Professor Jha and his team were fine-tuning a patented industrial process they have developed to extract higher yields of titanium dioxide and refine it to over 99 per cent purity. Not only does the technology eliminate hazardous wastes, cut costs and carbon dioxide emissions, the team also discovered they can extract significant quantities of rare earth metal oxides as co-products of the refining process.
This is, to me, a perfect example of how we should deal with resource problems. Not by simply fighting over the remaining scraps, or trying to get at marginal sources, but by looking at ways to increase supplies while reducing waste, with methods that have a smaller impact on the planet.
Can we do it for every limited resource? Probably not--but focusing research into how to use resources more efficiently, how to extract the resources with less waste, and ultimately how to move beyond them entirely will bring enormous benefits.
Looking for the distant early warnings of tomorrow.
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Futures Thinking
In Futures Thinking: The Basics, I offered up an overview of how to engage in a foresight exercise. In Futures Thinking: Asking the Question, I explored in more detail the process of setting up a futures exercise, and how to figure out what you're trying to figure out. In this entry in the occasional series, we'll take a look at gathering useful data.
Like the first step, Asking the Question, Scanning the World seems like it would be easier than it really is. In my opinion, it may actually be the hardest step of all, because you have to navigate two seemingly contradictory demands:
You need to expand the horizons of your exploration, because the factors shaping how the future of the dilemma in question will manifest go far beyond the narrow confines of that issue.
You need to focus your attention on the elements critical to the dilemma, and not get lost in the overwhelming amount of information out there.
You should recognize up front that the first few times you do this, you'll miss quite a few of the key drivers; even experienced futurists end up missing a some important aspects of a dilemma. It's the nature of the endeavor: We can't predict the future, but we can try to spot important signifiers of changes that will affect the future. We won't spot them all, but the more we catch, the more useful our forecasts.
The biggest problem you'll face is wrestling with the limitless number of issues and forces related to your key question. In nearly every case, there will be too many for you to investigate them all. Moreover, only a few of them will be truly critical to determining the outcome of your problem. So how do you narrow down the drivers?
Look Backwards
In many ways, the best training for futures work is the study of history. Scenario-based forecasting can be thought of as anticipatory history--scenarios are often written as if looking back from the narrative "present" (which could be 2015 or 2020 or 2050 or whichever point your scenario is set) at how that world came to be. It stands to reason, then, that getting a handle on understanding what led to the real present will help you understand what will shape the future.
Digging up college history textbooks can't hurt, but (as noted before) you probably aren't trying to develop scenarios of the future of the world. Instead, you will need to dig up multiple perspectives (if possible) on how the subject of your dilemma got to where it is today, and then work your way backwards. How did your company come to need to look for new place to build a widget factory, for example? If the primary answer is "increased demand," start looking at what drove that increased demand, and then what triggered that change, and so on.
The reason you want to find different perspectives is that you're looking for patterns not answers. Are there cause-and-effect loops that seem to show up time and again? Do your various sources all point to similar processes? Your future scenarios won't simply be re-tellings of the past--but they should reflect the kinds of drivers that have already proven to be important.
How far should you look back? The futurist rule of thumb is to look back twice as far as you want to look forward. If you've decided that your scenarios will be set 12 years out, then you'll want to look back roughly 24 years. That may require you to look at parallel or competing organizations, but again: your goal is not to come up with universal causes, but to spot patterns.
Ask Around
If you're reading this, you're clearly of above-average intelligence (and good-looking too!), but even the most brilliant among us can't and won't know everything. The next step in scanning is to find other people who may have useful insights into your dilemma. Some of these may be experts in the field, or people with a good grasp of the history of your organization.
But make a point of talking to outsiders, too. In your "looking backwards" exercise, you will have come across a number of recurring patterns and important drivers shaping the past. Find people--in academia, in industry, even in the blogosphere--who seem to have interesting things to say about these forces external to your organization. And if you're really feeling daring, find a few people who have nothing to do with your dilemma or drivers at all, but offer intriguing insights about the world in general--science fiction writers often fit that role.
You're not going to ask them to solve your problem. That's your job. You're going to ask them what they would be looking for in trying to figure out the answer to your dilemma. What are the paths they would follow? What are the issues they'd be concerned about? What would they want to know?
You'll also want to ask them what they see as important changes happening in the coming years, both in their area of expertise and in general. Here, you're trying to gather both important data points specific to the problem, and (again) recurring patterns. If the design specialist, the environmental scientist, and the science fiction writer all call out "smart objects" as something to watch, it's probably worth investigating.
Follow Your Nose
This is the step that will be the hardest--and probably the most cursory--at first, but increasingly important as you continue to engage in futures thinking.
Simply put, this is the process of gathering information and looking for items that stand out as interesting. That's it. Your sources will be quality general-interest magazines (such as The Economist or New Scientist) and Web sites (such as Fast Company or Worldchanging), as well as specialty resources related to your main topics of interest. This will mean drinking from a firehose of information--I follow something on the order of 300 RSS newsfeeds, as an example. It also means learning how to tease out the useful and interesting from that flood.
Unfortunately, there isn't an easy heuristic for doing so. I can't tell you how to determine "usefulness" and "interestingness"--it's something you'll figure out through practice and experimentation. Fortunately, the more you dig through your newsfeeds and information resources and apply what you've found, the better you'll get at spotting the useful and interesting.
And as you continue your futures thinking practice, you'll almost certainly find it useful to engage in scanning even when you're not actively working on a project. You'll find yourself reading through magazines and blogs, noticing the stories and headlines that others might miss in the noise, but stand out to you like beacons. Keeping track of the "distant early warnings" of future changes will become a habit, and hopefully a pleasure.
A real-time computer-simulated cat brain? Could IBM have come up with a project more likely to trigger Internet excitement?
For the handful of you who missed the news, IBM's Almaden Research Center announced this week that it had produced a "cortical simulation" of the scale and complexity of a cat brain. This simulation ran on one of IBM's "Blue Gene" supercomputers, in this case at the Lawrence Livermore National Laboratory (LLNL). (An aside: LLNL is best known as the center for ongoing research into advanced nuclear weapons and related projects; if the lab is now turning its attention to brain simulations, I don't know whether to be happy that it's moving away from weapons or worried that it will try to weaponize AI.)
Worries about the Robopocalypse may be only partially tongue-in-cheek, but it's worth taking a moment to examine what exactly has happened here. This is what the IBM press release says about the simulation:
Scientists, at IBM Research - Almaden, in collaboration with colleagues from Lawrence Berkeley National Lab, have performed the first near real-time cortical simulation of the brain that exceeds the scale of a cat cortex and contains 1 billion spiking neurons and 10 trillion individual learning synapses.
This isn't a simulation of a cat brain, it's a simulation of a brain structure that has the scale and connection complexity of a cat brain. It doesn't include the actual structures of a cat brain, nor its actual connections; the various experiments in the project filled the memory of the cortical simulation with a bunch of data, and let the system create its own signals and connections. Put simply, it's not an artificial (feline) intelligence, it's a platform upon which an A(F)I could conceivably be built.
Long-time readers may be having a deja vu moment here, and for good reason. The same team responsible for the cat-scale brain sim created a mouse-scale brain sim a few years ago. One of the researchers, Dharmendra Modha, runs a blog on cognitive computing, and has posted a PDF of the research paper on this project. If you want the hard-core science, not just a press release, have fun.
Ultimately, this is a very interesting development, both for the obvious reasons (an artificial cat brain!) and because of its associated "Blue Matter" project, which uses supercomputers and magnetic resonance to non-invasively map out brain structures and connections. The cortical sim is intended, in large part, to serve as a test-bed for the maps gleaned by the Blue Matter analysis. The combination could mean taking a reading of a brain and running the shadow mind in a box.
Science fiction writers will have a field day with this, especially if they develop a way to "write" neural connections, and not just read them. Brain back-ups? Shadow minds in a box, used to extract secret knowledge? Hypercats, with brains operating at a thousand times normal speed? The mind reels.
But the reality is that in many ways the IBM team has done the easy part, and still has a far greater challenge ahead of them. As I said in response to the mouse sim announcement in 2007, the brain isn't simply a haphazard mass of neural junctions; a functional structure simulation may well prove to be a far greater task than simply getting the neural connection sim working. Don't expect to be able to upload your cat's brain into your Roomba any time soon.
It's a pretty widely-accepted notion that the atmosphere is a ridiculously complex system, and the best we can do with our models is a rough approximation. The more teraflops we throw at the problem, the more granular the results--but even the best models operate at a scale of a hundred or so kilometers; we're still just seeing a shadow of the atmosphere's true complexity.
But what if that's wrong?
The atmospheric complexity idea has a lengthy provenance. Lewis Fry Richardson, the father of numerical analysis of the weather, proposed way back in 1922 that weather could be forecast using difficult math. This insight, and the work that he produced, led directly to the climate and weather models in use today. But Richardson had another insight: perhaps there's a simpler underlying system at work, something involving what would later be called fractal geometry. (He once wrote: "Big whirls have little whirls that feed on their velocity, and little whirls have lesser whirls, and so on to viscosity.") In the 1980s, when we finally had enough computational firepower to test this, the initial results weren't good, and the idea was more-or-less abandoned.
McGill University physicist Shaun Lovejoy kept coming back to the idea, though, and he and his team found suggestive indications that there was a multifractal process at work. (Standard fractal systems involve a single exponent defining the "fractal dimension" of a system; multifractal systems involve a range of exponents, given the label "singularity exponent." Seriously.) The available data weren't clear though, because the readings were muddied by the effects of the very aircraft and instruments used to gather them. So Lovejoy looked up--to satellites. And digging through data from 1,200 consecutive orbits of the Tropical Rainfall Measuring Mission, the team came up with something pretty remarkable: very strong evidence that the atmosphere follows power laws and shows fractal behavior, visible at scales from under 10km to over 20,000km.
Um, okay. Nice, I suppose. But what does that mean?
Put simply, it means that the classic "chaos theory" problem--that small variations and inaccuracies can lead to wildly divergent results, aka "the butterfly effect"--could be set aside, and we'll be able to create accurate models down to... well, here's what New Scientist says:
Now Lovejoy's team is keen to see cascades extend the reach and reliability of current models. While the existing models cannot handle structures much smaller than 100 kilometers across, the cascades may continue down to scales smaller than a millimeter. "Cascades could help fill in that missing factor of 100 million or so," says Lovejoy.
This will have a major impact on climate models--both for improving their accuracy, and (interestingly) for verification. If a given climate model's version of the atmosphere doesn't result in a system that shows power laws and multifractal behavior, then it's definitely inaccurate. Fortunately (or unfortunately, if you hope that climate science has this whole global warming thing wrong), the climate models currently in use do show power law and fractal results.
This is one of those discoveries that will undoubtedly take years to integrate into existing global circulation models, so we're not going to have ultra-accurate climate simulations overnight. But this does give a great deal of impetus to the idea that we can, in fact, generate useful insights into the functioning of global systems using simulations. I'm really curious about how well the multifractal concept could be applied to other ultra-complex systems. Psychohistory, anyone?
It may be odd to focus a political movement on a relatively obscure bit of science, but a world-wide push to limit concentration of atmospheric carbon dioxide to 350 parts-per-million made a big splash last week, with rallies and gatherings all over the planet focusing on drilling this number into the public consciousness. The number comes from work done by (among others) NASA's James Hansen, looking for potential climate "tipping points." 350ppm for CO2 is a safe limit--get too much beyond it, and the dangers multiply.
It's an audacious goal, for reasons of both communication and science.
In terms of communication, while a simple meme like "350" or "350ppm" fits nicely on protest signs and bumper stickers, it's a term without much context for the vast majority of the populace. In and of itself, that's not a problem; however, it can make a visceral connection to the concept more difficult. Activists adopting the 350 meme will need to match rhetoric with education, to make the number meaningful. Again, not impossible, but likely an ongoing challenge.
The scientific audacity with the 350 meme comes from a single, simple fact: current concentration of atmospheric CO2 is roughly 385ppm. That is, we already exceed the 350 limit, and most climate scientists say we'll be hard-pressed to keep from going over 450ppm by the middle of the century. And carbon dioxide takes centuries to cycle out of the atmosphere--even if we stopped all anthropogenic sources of CO2 right this minute, we'd still see too-high concentrations for years to come.
(Even more troubling: even if we stopped all anthropogenic carbon sources immediately, we'd still see continued warming for at least decades, possibly longer, simply from the thermal inertia of the oceans. Absent a radical step, we're guaranteed to see at least another degree or two of warming, no matter what we do.)
If this sounds like I think the 350 movement is a bad idea... I don't. I rather like the simplicity of the meme, and the target is--if difficult--smart. It's not saying "let's keep things from getting too much worse," it's saying "let's make things better." That's the kind of goal I like.
But getting back to 350ppm requires more than a rapid cessation of anthropogenic sources of atmospheric carbon. It requires an acceleration of the processes that cycle atmospheric CO2. Planting trees is an obvious step, but it's slow and actually doesn't do enough alone. We'll also need to bring in more advanced carbon sequestration techniques, such as bio-char. The combination of the two would likely bring down atmospheric carbon levels, given enough time.
Unfortunately, we may not have enough time.
If efforts to eliminate carbon emissions continue to happen at a pace most generously described as "leisurely," we will almost certainly face a situation where we approach and even pass critical tipping point concentrations. Ocean thermal inertia means that climate benefits from emission cessation won't be seen for decades. There's a very real scenario where finally get it right, both cutting out anthropogenic emissions and sequestering megatons of carbon via plants and bio-char ... and still face terrible environmental consequences, simply because we didn't act fast enough.
That's where we start to talk about much more radical, and potentially dangerous, steps. Geoengineering to hold temperatures down is one; to meet the 350ppm goal, we will likely also start looking at large-scale methods to sequester carbon, such as with triggered algae blooms.
350ppm is an audacious goal, but one worth striving for. But its challenge comes not just in the effort to eliminate anthropogenic carbon emissions around the world--a massive endeavor alone--but also in figuring out how to remove the extra carbon already there. I hope that the 350 leaders have thought through the implications of what that means.
In Futures Thinking: The Basics, I offered up an overview of how to engage in a foresight exercise. Today, as the next piece in this occasional series, I'll take a look at the first step in such a process.
"Asking the Question" is the first step in a formal futures thinking project. At first glance, it should be easy--after all, you should know what you're trying to figure out. Unfortunately, while it may be simple to ask a question, asking the right question is much more challenging It's easy to ask questions that are too vague, too narrow, or assume the answer; it's much more difficult to ask a question that can elicit both surprises and useful results.
Remember, the goal of structured futures thinking is to come up with a picture of possible futures that will help to inform strategic decisions. The answers you'll get from a futures exercise are rarely cut-and-dried, but ideally will help you make your decision more thoughtfully. Futures thinking isn't a Magic 8-Ball, a process where all you need to ask is "Should we do X?" (and getting "Ask Again Later" as a result is neither useful nor surprising).
It's a subtle point, but I tend to find it useful to talk about strategic questions in terms of dilemmas, not problems. Problem implies solution--a fix that resolves the question. Dilemmas are more difficult, typically situations where there are no clearly preferable outcomes (or where each likely outcome carries with it some difficult contingent elements). Futures thinking is less useful when trying to come up with a clear single answer to a particular problem, but can be extremely helpful when trying to determine the best response to a dilemma. The difference is that the "best response" may vary depending upon still-unresolved circumstances; futures thinking helps to illuminate possible trigger points for making a decision.
One important point about the difference between problems and dilemmas: with dilemmas, you will generally have a sense of the different possible responses, and have to make an intelligent choice between them. With problems, the solution is almost by definition hidden, and must be discovered. Futures thinking is much more robust when dealing with dilemmas.
That's because what you're doing with a futures exercise is trying to draw out the range of conditions in which your choices play out--the internal and (especially) external factors that will shape outcomes. You can then play your initial strategic choices against the different resulting futures. Bear in mind that this can sometimes have surprising results. Although a futures exercise asking "where should I build my widget factory?" may be too broad, the more narrow exercise of "should I build my widget factory in China or India?" may well lead to a determination that neither really provides the desired results.
As I noted in Futures Thinking: The Basics, another aspect of asking the question is figuring out the time frame for the exercise. This typically comes down to two key issues: how long will it take to implement the plan you're pondering; and how long into the implementation do you want to test the results. If it takes three years to set up the widget factory, a five year target for the future exercise would be useful to think through initial operating environment, while a 12 year exercise will help to think about what things will be like over time.
One trick that I find useful in determining the target date is to think about political cycles in the operating environment. For something that looks primarily at the United States, for example, eight years out works well because it guarantees that whoever has the presidency at the moment will be out of power, so narratives about the current political leadership (pro or con) have less salience. A similar process works nicely for most regions with relatively predictable political cycles.
Another technique that can be useful is to think about two or three key drivers that you're already familiar with, and look for any upcoming inflection points in their ongoing evolutions. If you're looking at mobile technologies, you might target a point after planned roll-outs of 4G wireless networks. Regulations coming into effect, changes in resource availability or pricing, and demographic shifts can all play similar roles.
One last note: even if you're satisfied with the question you're going to examine through a futures exercise, don't be afraid to reconsider it as the exercise progresses. "Am I asking the right question?" should come up repeatedly over the course of the process. You may end up discovering that a better question is out there, or that you're on the right track. Either way, the practice of reexamining your own assumptions and expectations will inevitably prove a useful part of the process.
My talk last weekend at the New York Future Salon explored the likelihood and the implications of the transformative event known as the "Singularity." I tend to part ways with many Singularity enthusiasts over two small issues: what comes before a Singularity, and what comes after.
In terms of what comes before, I'm generally in the camp that
machine-substrate intelligence is very likely possible, but is probably
a much more complex problem than some of the more enthusiastic
Singularitarians would have us think. We currently have a single model
of a mind emerging from a physical structure--the human brain--and
(as noted by one of the 2009 Singularity Summit speakers, David Chalmers)
we're not even sure how that happens. Add to that issues around
learning, around complexity, around the very definition of
intelligence, and you have the potential for a situation where--even
if there are no physical laws preventing the emergence of artificial general intelligence
-- "real" AI remains the computer science version of nuclear fusion:
perpetually just a couple of decades away (with plenty of dead-ends and
showy hoaxes along the way).
I've noted elsewhere that I suspect that "a stand-alone artificial mind will be more a tool of narrow utility than something especially apocalyptic." Part of the reason is the difficulty, but another part is the near-certainty that the technologies of human intelligence augmentation will continue apace. The technologies that may be dead-ends for efforts to construct a self-aware artificial mind could easily be of great value as non-conscious assistants to human minds.
The notion that creating "real" AI may turn out to be extraordinarily difficult, and the idea that human intelligence augmentation could itself turn out to be a more promising line of research doesn't get a lot of push-back from the more thoughtful Singularity proponents I've encountered. After all, both have been demonstrably true so far. A tougher sticking point, however, comes when I explore what could come afterwards.
If greater-than-human artificial intelligence emerges out of aggressively competitive projects, each seeking to be first, and is put to use without much thought to what might happen next, then the traditional Singularity scenario seems pretty likely. But that's not the only one:
The upper-left scenario, "Out of Control," is the more-or-less conventional Singularity story. AI gets smart, gets loose, and does as it will. Could be hell, could be heaven, but pretty much is out of our hands. In short, this is the scenario in which AIs eliminate our civilization.
Upper-right, "Taxes and Allies," is a world where competitive projects lead to real AI, but they're undertaken with a greater awareness of implications and impacts. AIs in this world are held onto as business tools--may in fact be corporations (as in Charlie Stross' novel Accelerando)--but remain embedded in human civilization. This could, by the way, be one of the pathways to a "robonomics" economy. This is the scenario in which AIs become part of our civilization.
Lower-left is "Eat Your Vegetables*." In this scenario, the greater-than-human AIs emerge not from tools of competition, but tools of collaboration--imagine, for example, AIs emerging out of software intended to help humanity manage climate disruption. The result here is a world where these systems are less about artificial intelligence and more about artificial wisdom: assisting us in doing the right things for ourselves and our planet. (Listen to the audio scenario "The Chorus" for an example of this kind of world.) This is the scenario in which AIs take care of our civilization.
Finally, "Djinni in a Bottle" offers a scenario where AIs come from collaborative tools, but in a context of management of impacts and implications. As in the classic tales of djinnis, this is a world in which beneficial and detrimental results occur based on just how wisely we use our power. This is the scenario in which AIs empower the best and worst of our civilization.
Three of the four scenarios (leaving aside "Out of Control") assume that human social intelligence, augmentation technology, and competition continue to develop. And in all three, human civilization--with its resulting conflicts and mistakes, communities and arts, and, yes, politics--remains a vital force even after a Singularity has begun.
One key aspect of the three is that they're not necessarily end states. Each could, given the right drivers, eventually evolve into one of the others. Moreover, all three could in principle exist side-by-side.
I noted earlier that I differ from many of the Singularity enthusiasts in my take on what happens before and what happens after a Singularity. I suppose I differ in my take on what happens during one, as well. I don't think that a Singularity would be visible to those going through one. Even the most disruptive changes are not universally or immediately distributed, and late-followers learn from the reactions and dilemmas of those who had initially encountered the disruptive change.
Perhaps the most notable aspect of a Singularity is that, ultimately, it's only clearly visible in the distance: off in the future, where it remains a mysterious veil; and in the past, after we've moved along far enough to see just how differently we're living our lives.
(When video from my latest talk, a lively over-capacity event, is online, I'll post a link at my home Web site, Open the Future. My talk slides are available via Slideshare.)
*For those of you unfamiliar with this bit of American idiom, it refers to being told (often forced) to do something that's actually good for us, but not necessarily pleasant.
Pile up the clusters of related subjects; you might find that a single topic might belong in multiple clusters, but resist the urge to duplicate it--you want to put it with the group where it seems most important. Feel free to brainstorm along the way--you might find that your colleagues are inspired by the scanning results, and offer more than a few additional suggestions. This is fine.
For those of you living under a rock, Apple's announcement of the 
You may not know who 
And in doing so, is directly responsible for this remarkable fact: despite an explosion of consumer electronics, mobile gadgets, and personal computers of all types, energy use per-capita in California is the same as it was 30 years ago. In the rest of the U.S., per-capita energy consumption has continued to climb, albeit more slowly than it might have, as more states come to adopt California's energy rules. And these 
Now, you may not live in California, and the energy rules where you live may not come close to those promulgated by the CEC. You can still take advantage of them, however.
You've probably seen "neodymium" (actually neodymium-iron-boron) magnets advertised in techie-oriented magazines and gadget blogs. They're actually the strongest type of magnet available, and a pair of them can 
The U.K.'s 
Remember, the goal of structured futures thinking is to come up with a picture of possible futures that will help to inform strategic decisions. The answers you'll get from a futures exercise are rarely cut-and-dried, but ideally will help you make your decision more thoughtfully. Futures thinking isn't a Magic 8-Ball, a process where all you need to ask is "Should we do X?" (and getting "Ask Again Later" as a result is neither useful nor surprising). 
