« swipe left for tags/categories
swipe right to go back »
Today’s post is a guest post from William Hertling, author of the award-winning Avogadro Corp: The Singularity Is Closer Than It Appears and A.I. Apocalypse, near-term science-fiction novels about realistic ways strong AI might emerge. They’ve been called “frighteningly plausible”, “tremendous”, and “thought-provoking”. By day he works on web and social media for HP. Follow him on twitter at @hertling or visit his blog williamhertling.com.
I’m a huge fan of William and his writing as you can see from my review of his book Avogadro Corp. So when William offered to write a guest post on how to predict the future, I enthusiastically said yes. Take a look – and take your time.
Pretty much everyone would like a sure-fire way to predict the future. Maybe you’re thinking about startups to invest in, or making decisions about where to place resources in your company. Maybe you just care about what things will be like in 10, 20, or 30 years.
There are many techniques to think logically about the future, to inspire idea creation, and to predict when future inventions will occur.
I’d like to share one technique that I’ve used successfully. It’s proven accurate on many occasions. And it’s the same technique that I’ve used, as a writer, to create realistic technothrillers set in the near future. I’m going to start by going back to 1994.
Predicting Streaming Video and the Birth of the Spreadsheet
There seem to be two schools of thought on how to predict the future of information technology: looking at software or looking at hardware. I believe that looking at hardware curves is always simpler and more accurate.
This is the story of a spreadsheet I’ve been keeping for almost twenty years.
In the mid-1990s, a good friend of mine, Gene Kim (founder of Tripwire and author of When IT Fails: A Business Novel) and I were in graduate school together in the Computer Science program at the University of Arizona. A big technical challenge we studied was piping streaming video over networks. It was difficult because we had limited bandwidth to send the bits through, and limited processing power to compress and decompress the video. We needed improvements in video compression and in TCP/IP – the underlying protocol that essentially runs the Internet.
The funny thing was that no matter how many incremental improvements we made (there were dozens of people working on different angles of this), streaming video always seemed to be just around the corner. I heard “Next year will be the year for video” or similar refrains many times over the course of several years. Yet it never happened.
Around this time I started a spreadsheet, seeding it with all of the computers I’d owned over the years. I included their processing power, the size of their hard drives, the amount of RAM they had, and their modem speed. I calculated the average annual increase of each of these attributes, and then plotted these forward in time.
I looked at the future predictions for “modem speed” (as I called it back then, today we’d called it internet connection speed or bandwidth). By this time, I was tired of hearing that streaming video was just around the corner, and I decided to forget about trying to predict advancements in software compression, and just look at the hardware trend. The hardware trend showed that internet connection speeds were increasing, and by 2005, the speed of the connection would be sufficient that we could reasonably stream video in real time without resorting to heroic amounts of video compression or miracles in internet protocols. Gene Kim laughed at my prediction.
Nine years later, in February 2005, YouTube arrived. Streaming video had finally made it.
The same spreadsheet also predicted we’d see a music downloading service in 1999 or 2000. Napster arrived in June, 1999.
The data has held surprisingly accurate over the long term. Using just two data points, the modem I had in 1986 and the modem I had in 1998, the spreadsheet predicts that I’d have a 25 megabit/second connection in 2012. As I currently have a 30 megabit/second connection, this is a very accurate 15 year prediction.
Why It Works Part One: Linear vs. Non-Linear
Without really understanding the concept, it turns out that what I was doing was using linear trends (advancements that proceed smoothly over time), to predict the timing of non-linear events (technology disruptions) by calculating when the underlying hardware would enable a breakthrough. This is what I mean by “forget about trying to predict advancements in software and just look at the hardware trend”.
It’s still necessary to imagine the future development (although the trends can help inspire ideas). What this technique does is let you map an idea to the underlying requirements to figure out when it will happen.
For example, it answers questions like these:
- When will the last magnetic platter hard drive be manufactured? 2016. I plotted the growth in capacity of magnetic platter hard drives and flash drives back in 2006 or so, and saw that flash would overtake magnetic media in 2016.
- When will a general purpose computer be small enough to be implanted inside your brain? 2030. Based on the continual shrinking of computers, by 2030 an entire computer will be the size of a pencil eraser, which would be easy to implant.
- When will a general purpose computer be able to simulate human level intelligence? Between 2024 and 2050, depending on which estimate of the complexity of human intelligence is selected, and the number of computers used to simulate it.
Wait, a second: Human level artificial intelligence by 2024? Gene Kim would laugh at this. Isn’t AI a really challenging field? Haven’t people been predicting artificial intelligence would be just around the corner for forty years?
Why It Works Part Two: Crowdsourcing
At my panel on the future of artificial intelligence at SXSW, one of my co-panelists objected to the notion that exponential growth in computer power was, by itself, all that was necessary to develop human level intelligence in computers. There are very difficult problems to solve in artificial intelligence, he said, and each of those problems requires effort by very talented researchers.
I don’t disagree, but the world is a big place full of talented people. Open source and crowdsourcing principles are well understood: When you get enough talented people working on a problem, especially in an open way, progress comes quickly.
I wrote an article for the IEEE Spectrum called The Future of Robotics and Artificial Intelligence is Open. In it, I examine how the hobbyist community is now building inexpensive unmanned aerial vehicle auto-pilot hardware and software. What once cost $20,000 and was produced by skilled researchers in a lab, now costs $500 and is produced by hobbyists working part-time.
Once the hardware is capable enough, the invention is enabled. Before this point, it can’t be done. You can’t have a motor vehicle without a motor, for example.
As the capable hardware becomes widely available, the invention becomes inevitable, because it enters the realm of crowdsourcing: now hundreds or thousands of people can contribute to it. When enough people had enough bandwidth for sharing music, it was inevitable that someone, somewhere was going to invent online music sharing. Napster just happened to have been first.
IBM’s Watson, which won Jeopardy, was built using three million dollars in hardware and had 2,880 processing cores. When that same amount of computer power is available in our personal computers (about 2025), we won’t just have a team of researchers at IBM playing with advanced AI. We’ll have hundreds of thousands of AI enthusiasts around the world contributing to an open source equivalent to Watson. Then AI will really take off.
(If you doubt that many people are interested, recall that more than 100,000 people registered for Stanford’s free course on AI and a similar number registered for the machine learning / Google self-driving car class.)
Of course, this technique doesn’t work for every class of innovation. Wikipedia was a tremendous invention in the process of knowledge curation, and it was dependent, in turn, on the invention of wikis. But it’s hard to say, even with hindsight, that we could have predicted Wikipedia, let alone forecast when it would occur.
(If one had the idea of an crowd curated online knowledge system, you could apply the litmus test of internet connection rate to assess when there would be a viable number of contributors and users. A documentation system such as a wiki is useless without any way to access it. But I digress…)
Objection, Your Honor
A common objection is that linear trends won’t continue to increase exponentially because we’ll run into a fundamental limitation: e.g. for computer processing speeds, we’ll run into the manufacturing limits for silicon, or the heat dissipation limit, or the signal propagation limit, etc.
I remember first reading statements like the above in the mid-1980s about the Intel 80386 processor. I think the statement was that they were using an 800 nm process for manufacturing the chips, but they were about to run into a fundamental limit and wouldn’t be able to go much smaller. (Smaller equals faster in processor technology.)
But manufacturing technology has proceeded to get smaller and smaller. Limits are overcome, worked around, or solved by switching technology. For a long time, increases in processing power were due, in large part, to increases in clock speed. As that approach started to run into limits, we’ve added parallelism to achieve speed increases, using more processing cores and more execution threads per core. In the future, we may have graphene processors or quantum processors, but whatever the underlying technology is, it’s likely to continue to increase in speed at roughly the same rate.
Why Predicting The Future Is Useful: Predicting and Checking
There are two ways I like to use this technique. The first is as a seed for brainstorming. By projecting out linear trends and having a solid understanding of where technology is going, it frees up creativity to generate ideas about what could happen with that technology.
It never occurred to me, for example, to think seriously about neural implant technology until I was looking at the physical size trend chart, and realized that neural implants would be feasible in the near future. And if they are technically feasible, then they are essentially inevitable.
What OS will they run? From what app store will I get my neural apps? Who will sell the advertising space in our brains? What else can we do with uber-powerful computers about the size of a penny?
The second way I like to use this technique is to check other people’s assertions. There’s a company called Lifenaut that is archiving data about people to provide a life-after-death personality simulation. It’s a wonderfully compelling idea, but it’s a little like video streaming in 1994: the hardware simply isn’t there yet. If the earliest we’re likely to see human-level AI is 2024, and even that would be on a cluster of 1,000+ computers, then it’s seems impossible that Lifenaut will be able to provide realistic personality simulation anytime before that.* On the other hand, if they have the commitment needed to keep working on this project for fifteen years, they may be excellently positioned when the necessary horsepower is available.
At a recent Science Fiction Science Fact panel, other panelists and most of the audience believed that strong AI was fifty years off, and brain augmentation technology was a hundred years away. That’s so distant in time that the ideas then become things we don’t need to think about. That seems a bit dangerous.
* The counter-argument frequently offered is “we’ll implement it in software more efficiently than nature implements it in a brain.” Sorry, but I’ll bet on millions of years of evolution.
How To Do It
This article is How To Predict The Future, so now we’ve reached the how-to part. I’m going to show some spreadsheet calculations and formulas, but I promise they are fairly simple. There’s three parts to to the process: Calculate the annual increase in a technology trend, forecast the linear trend out, and then map future disruptions to the trend.
Step 1: Calculate the annual increase
It turns out that you can do this with just two data points, and it’s pretty reliable. Here’s an example using two personal computers, one from 1996 and one from 2011. You can see that cell B7 shows that computer processing power, in MIPS (millions of instructions per second), grew at a rate of 1.47x each year, over those 15 years.
I like to use data related to technology I have, rather than technology that’s limited to researchers in labs somewhere. Sure, there are supercomputers that are vastly more powerful than a personal computer, but I don’t have those, and more importantly, they aren’t open to crowdsourcing techniques.
I also like to calculate these figures myself, even though you can research similar data on the web. That’s because the same basic principle can be applied to many different characteristics.
Step 2: Forecast the linear trend
The second step is to take the technology trend and predict it out over time. In this case we take the annual increase in advancement (B$7 – previous screenshot), raised to an exponent of the number of elapsed years, and multiply it by the base level (B$11). The formula displayed in cell C12 is the key one.
I also like to use a sanity check to ensure that what appears to be a trend really is one. The trick is to pick two data points in the past: one is as far back as you have good data for, the other is halfway to the current point in time. Then run the forecast to see if the prediction for the current time is pretty close. In the bandwidth example, picking a point in 1986 and a point in 1998 exactly predicts the bandwidth I have in 2012. That’s the ideal case.
Step 3: Mapping non-linear events to linear trend
The final step is to map disruptions to enabling technology. In the case of the streaming video example, I knew that a minimal quality video signal was composed of a resolution of 320 pixels wide by 200 pixels high by 16 frames per second with a minimum of 1 byte per pixel. I assumed an achievable amount for video compression: a compressed video signal would be 20% of the uncompressed size (a 5x reduction). The underlying requirement based on those assumptions was an available bandwidth of about 1.6mb/sec, which we would hit in 2005.
In the case of implantable computers, I assume that a computer of the size of a pencil eraser (1/4” cube) could easily be inserted into a human’s skull. By looking at physical size of computers over time, we’ll hit this by 2030:
This is a tricky prediction: traditional desktop computers have tended to be big square boxes constrained by the standardized form factor of components such as hard drives, optical drives, and power supplies. I chose to use computers I owned that were designed for compactness for their time. Also, I chose a 1996 Toshiba Portege 300CT for a sanity check: if I project the trend between the Apple //e and Portege forward, my Droid should be about 1 cubic inch, not 6. So this is not an ideal prediction to make, but it’s still clues us in about the general direction and timing.
The predictions for human-level AI are more straightforward, but more difficult to display, because there’s a range of assumptions for how difficult it will be to simulate human intelligence, and a range of projections depending on how many computers you can bring to pair on the problem. Combining three factors (time, brain complexity, available computers) doesn’t make a nice 2-axis graph, but I have made the full human-level AI spreadsheet available to explore.
I’ll leave you with a reminder of a few important caveats:
- Not everything in life is subject to exponential improvements.
- Some trends, even those that appear to be consistent over time, will run into limits. For example, it’s clear that the rate of settling new land in the 1800s (a trend that was increasing over time) couldn’t continue indefinitely since land is finite. But it’s necessary to distinguish genuine hard limits (e.g. amount of land left to be settled) from the appearance of limits (e.g. manufacturing limits for computer processors).
- Some trends run into negative feedback loops. In the late 1890s, when all forms of personal and cargo transport depended on horses, there was a horse manure crisis. (Read Gotham: The History of New York City to 1898.) Had one plotted the trend over time, soon cities like New York were going to be buried under horse manure. Of course, that’s a negative feedback loop: if the horse manure kept growing, at a certain point people would have left the city. As it turns out, the automobile solved the problem and enabled cities to keep growing.
So please keep in mind that this is a technique that works for a subset of technology, and it’s always necessary to apply common sense. I’ve used it only for information technology predictions, but I’d be interested in hearing about other applications.
Over the weekend, Kwin Kramer, the CEO of Oblong, wrote a great essay on TechCrunch titled Hey Kids, Get Off My Lawn: The Once And Future Visual Programming Environment. He starts off with a great Mark Twain quote.
“When I was a boy of 14, my father was so ignorant I could hardly stand to have the old man around. But when I got to be 21, I was astonished at how much the old man had learned in seven years.”
Mark Twain, ”Old Times on the Mississippi”
Atlantic Monthly, 1874
This describes my continuous interaction with the computer industry. I was 14 once, then 21, and now 46. It’s remarkable to me to reflect on how far things have come since I wrote my first program on APL on an IBM mainframe (no idea what kind) in the basement of a Frito-Lay datacenter in Dallas at age 12. Then there are moments where I can’t believe that we are just now discovering things – again – that were figured out 30 years ago. And last night, while laying in bed in a hotel in Iceland and reading the wikipedia page on Iceland on my iPad, I kept thinking “what’s old is new again.”
Kwin nails it in his essay. Oblong, which is one of the most amazing and unique companies I’ve ever been involved in, is constantly dealing with the constraints of today while working a decade into the future. A year ago the present caught up with the future and their first product, Mezzanine, came to life.
I love working with companies where the CEO still writes code and uses his perspective on the past to inform the product, but isn’t afraid to completely leap over the current constraints to create something entirely new, amazing, and delightful.
Here’s what’s so amazing about this to me. I don’t think I know Jason. Maybe we’ve met once – I don’t know. I do know that he’s jason_silva on Twitter, is a Host / Producer for Current TV (so I might know him via the TechStars segment CurrentTV did a few years ago on TechStars), but I don’t recognize his picture. We might have met a few times – that’s my issue, not his, since I’m out of namespace in my brain (I have to forget someone to learn someone new.)
All that said, I think Jason is just awesome. Every video I’ve seen of his lights me up. They are beautiful, thought provoking, and something I wish I had the talent to do.
I just tweeted him back that I want to get together with him. I think he’s in NY (Twitter says he’s on 53rd between 5th and 6th) so hopefully he’ll respond and we can hang out the next time I’m in NY. He certainly has gotten my attention!
Marc Andreessen recently wrote a long article in the WSJ which he asserted that “Software Is Eating The World.” I enjoyed reading it, but I don’t think it goes far enough.
I believe the machines have already taken over and resistance is futile. Regardless of your view of the idea of the singularity, we are now in a new phase of what has been referred to in different ways, but most commonly as the “information revolution.” I’ve never liked that phrase, but I presume it’s widely used because of the parallels to the shift from an agriculture-based society to the industrial-based society commonly called the “industrial revolution.”
At the Defrag Conference I gave a keynote on this topic. For those of you who were there, please feel free to weigh in on whether the keynote was great, sucked, if you agreed, disagreed, were confused, mystified, offended, amused, or anything else that humans are capable of having as stimuli-response reactions.
I believe the phase we are currently in began in the early 1990′s with the invention of the World Wide Web and subsequent emergence of the commercial Internet. Those of us who were involved in creating and funding technology companies in the mid-to-late 1990′s had incredibly high hopes for where computers, the Web, and the Internet would lead. By 2002, we were wallowing around in the rubble of the dotcom bust, salvaging what we could while putting energy into new ideas and businesses that emerged with a vengence around 2005 and the idea of Web 2.0.
What we didn’t realize (or at least I didn’t realize) was that virtually all of the ideas from the late 1990′s about what would happen to traditional industries that the Internet would distrupt would actually happen, just a decade later. If you read Marc’s article carefully, you see the seeds of the current destruction of many traditional businesses in the pre-dotcom bubble efforts. It just took a while, and one more cycle for the traditional companies to relax and say “hah – once again we survived ‘technology’”, for them to be decimated.
Now, look forward twenty years. I believe that the notion of a biologically-enhanced computer, or a computer-enhanced human, will be commonplace. Today, it’s still an uncomfortable idea that lives mostly in university and government research labs and science fiction books and movies. But just let your brain take the leap that your iPhone is essentially making you a computer-enhanced human. Or even just a web browser and a Google search on your iPad. Sure – it’s not directly connected into your gray matter, but that’s just an issue of some work on the science side.
Extrapolating from how it’s working today and overlaying it with the innovation curve that we are on is mindblowing, if you let it be.
I expect this will be my intellectual obsession in 2012. I’m giving my Resistance is Futile talk at Fidelity in January to a bunch of execs. At some point I’ll record it and put it up on the web (assuming SOPA / PIPA doesn’t pass) but I’m happy to consider giving it to any group that is interested if it’s convenient for me – just email me.
Once a quarter my partners (Seth, Ryan, Jason) and I spend 48 hours together. Unlike a typical offsite that ten zillion organizations have, we tend to spend less time on formalities and more time on wider ranging, forward looking discussions about what we are doing, both professionally and personally.
Last night, over an amazing meal, we ended up talking about what we’ve been investing in over the past four years. When we reflect on the 37 companies we’ve invested in since we raised our first Foundry Group fund in 2007, we’re delighted with the mix of companies and entrepreneurs we are working with. We have a very clear thematic strategy that we’ve discussed openly, along with a few other key principles such as being willing to invest anywhere in the US and being syndication agnostic.
At dinner we zoned in on all of the current activity in early stage tech. There’s an awesome amount of exciting stuff going on right now and a real entrepreneurial revival throughout the US. Sure, there’s all the inevitable bubble talk going on which I’ve encouraged entrepreneurs to simply ignore and play a long term game instead, and once again many VC firms are spreading themselves wide and chasing after whatever the latest interesting thing is. But entrepreneurship, especially throughout the US, is vigorous, exciting, and creating many really interesting companies, some of which will be important in the future.
When we think about what has driven the success of some of our investments, we realize that we’ve chose the macro environments to invest in really well. Our HCI, Adhesive, and Distribution themes are all great examples of this. With HCI, we are at the very beginning of a massive shift over the next 20 years around how humans and computers interact. Adhesive plays the macros of digital advertising – every year meaningful ad spend is shifting annually from offline to online and that will continue for quite some time. And with distribution we’ve benefitted from the application of the concept of social to extremely large existing online markets where innovation had stagnated.
Our conversation shifted to 2015. While we still believe there are many exciting opportunities within our existing themes, we think that given the velocity of technology innovation and the way we use technology, things will shift dramatically over the next four years. Completely new and unexpected innovations are emerging and entrepreneurs who are obsessed with transforming existing industries, creating radical new technologies, or dramatically changing the use case of existing technology are starting to work in 2011 on things that will matter immensely in 2015.
We have one new investment coming up that reflects this and, when we start talking about it, you’ll see the kind of entrepreneur and company we are searching for. We decided last night to look for a lot more of it. While our deeply held beliefs about what we invest in and how we invest are the same, we’ve decided to open up our intellectual aperture and make sure we’ve incorporated a stronger view of “what is 2015 going to be like” into our thinking.