molto interessante -- I mufloni malvagi On Jun 23, 2012 6:42 PM, "Open: libera aperta anonima senza censura" < [email protected]> wrote:
> 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 ofWhen 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. > _______________________________________________ > Open mailing list > [email protected] > http://lists.partito-pirata.it/cgi-bin/mailman/listinfo/open > _______________________________________________ Open mailing list [email protected] http://lists.partito-pirata.it/cgi-bin/mailman/listinfo/open
