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If There's An Innovation Gap, Where Is It?

A BusinessWeek article this week, "Turning Research into Inventions and Jobs," asserts that there's plenty of basic research in the world. What there's not enough of, the authors assert, is products - products that exploit this research.

But too often overlooked in discussions over research spending is a fundamental fact: We've already got an abundance of research. The next transistor, semiconductor, or breakthrough in MRI technology may already have been discovered. The problem is, we've dropped the ball on translating this science into invention. The vast majority of great research is languishing in filing cabinets, unable to be harnessed by the entrepreneurs and scientist-businesspeople who can set it free. We consider this shortfall academia's equivalent of Alaska's "bridge to nowhere."

The article, by Vivek Wadhwa and Robert E. Litan, was written in disagreement with an earlier BusinessWeek article, "How Science Can Create Millions of New Jobs," which asserted that "Reigniting basic research can repair the broken U.S. business model and put Americans back to work." I think Judy Estrin might agree with that, and she would certainly disagree with the new article.

Almost exactly a year ago, Estrin's book, Closing the Innovation Gap, was published by McGraw-Hill Press. Andy Grove of Intel called it “A passionate look at innovation by a proven innovator concerned about the level of short-sightedness surrounding us.” Grove is right — it's a topic Estrin is remarkably qualified to talk about. She's now at Stanford, but back in the day she co-founded seven different technology companies. When one of them was bought by networking goliath Cisco Systems she became its chief technology officer. (Chapter Two of the book, "The Innovation Ecosystem," is available here.)

I did a long podcast interview with Estrin when the book came out. In it, she asserts just the opposite of what Wadhwa and Litan says. Her view is that there is a dearth of fundamental research, in fact, that we're still living off the seed corn of the 1960s and 1970s - and it's running out. Here's a snippet from the interview.

SPECTRUM: Engineering and innovation are in your blood. Your mother was the second woman ever to get a Ph.D. in electrical engineering.... And your father taught at UCLA — he helped start up the computer science department. You went there in the early 1970s; you were on Vint Cerf's research team as the Internet Protocol was being invented there. But you say in your book that was also the time innovation started to decline.
ESTRIN: It started in the 70's with a narrowing of the horizons in the research community first and that's where we began to pull back on planting the seeds for the future. It came in a variety of ways, it came in terms of government funding for research, not just the magnitude of the funding but how it was allocated, and the types of funding that were coming out of the government agencies, and how it was allocated amongst different fields of science.
But the other thing that happened in the 70s and 80s is that corporations began to focus on becoming more and more efficient and more and more productive; a good thing and on the surface you would say. Of course they need to do that but as they did and as they started to focus on productivity and efficiency they essentially took all the slop out of the system and often innovation happens, comes out of some of those inefficiencies. And they became so efficient that people began to invest just for the short term and in order to have sustainable innovation, you have to be wiling to invest in things that you don't know what the outcome is going to be, that you don't know is going to succeed. And as corporations became more efficient they cut back on investing in things that didn't have direct correlation to their quarterly or this year's earnings.
So we stopped planting seeds for the future not just in research but in corporations for a while. The startup ecosystem was still thriving, so a lot of innovation was coming out of Silicon valley and other places where startups thrived.
But when we hit 2000 and the bursting of the internet bubble, the corporate scandals and the tragedy of 9/11 we saw a shift here in Silicon Valley of people becoming more risk averse and more short term focused. So as a result I have people coming and saying to me “well come on Judy there's been lots of innovation over the last couple of years look at the iPod, look at consumer internet, look at what's happened in biotech.”
And yes there is still innovation going on my claim is not that there is not but the innovation we're doing is tending to be more incremental and is built upon years and years of seeds that were planted but we're not continuing to plant enough seeds to sustain us out 10, 20, 30 years from now.
I have a quote in the book that I really liked. When I interviewed Mark Andressen, who developed the initial browser, he was telling me how quickly he was able to bring the browser to the market, and I looked at him and just said “That was an incredibly short time” and he said “you know the browser we developed was just the icing on a cake that had been baking for 30 years.”

Saudi Arabia Aims to Become Data Visualization Hub

Saudi Arabia's biggest experiment in higher education, the King Abdullah University of Science and Technology, has just opened its doors to an international student body, as we reported earlier this month. The King has gambled billions of dollars on raising a university out of the desert that he hopes will compete against other top-notch institutions worldwide. Intellectual freedom isn't exactly the first thing that jumps to mind when one thinks of Saudi Arabia, and for a country whose technological contributions basically begin and end with oil, the hurdle is significant.

In recognition of this challenge, the king has recruited an international collection of about 70 faculty members (rumor: an assistant professor makes about US $200 000) and built laboratories with staggering price tags. The campus supercomputer, Shaheen, is the fastest in the Middle East and had a starting price of about $50 million, which will certainly grow. A nanofabrication clean room, one of the cleanest clean rooms in academia, came with a price tag that was “much, much larger than Shaheen,” according to Khaled Salama, an electrical engineering professor at KAUST.

I'm attending the inauguration ceremonies this week and got a quick tour of some of the university's laboratories, including the supercomputer and the clean room. From my perspective, if you've seen one clean room, you've seen them all. What did draw my attention were the visualization labs, which are using Shaheen's computing power to add a visual dimension to large data sets. The first example I'm posting here is of a visualization of the human brain, where researchers are attempting to trace how signals travel between different regions by mapping the flow of water through the brain.  

 

Million Dollar Netflix Prize Won

At last, it's official: the research team known as BellKor has won the $1 million Netflix Prize by improving the accuracy of the Netflix recommender algorithm by 10 percent. It won because it submitted its entry 20 minutes before that of Ensemble, the only other team to make the 10 percent mark.

Netflix sure got its money's worth. CEO, Reed Hastings told the New York Times, “You look at the cumulative hours and you're getting Ph.D.'s for a dollar an hour.” Beyond the technical harvest, Netflix got the kind of halo effect that no company has enjoyed since 1987, when IBM's “Deep Blue” machine won a chess match against then-world champion Gary Kasparov.

Bellkor and Ensemble were both coalitions formed by teams that had to join forces to stay in the race. BellKor's own core, though, had always been the team to beat. Lead members Robert M. Bell, Chris Volinsky, and Yehuda Koren began their work at Bell Laboratories, where the first two still work; Koren has since joined Yahoo Research, in Israel. They snagged the competition's first, $50 000 milestone by achieving the best improvement, short of 10 percent, by a certain date. The three scientists, together with Jim Bennett, then a vice-president at Netflix, described their work for IEEE Spectrum, in “The Million-Dollar Programming Prize” (May 2009). 

Of course, Netflix has launched a follow-up competition. This time, though, in the interest of brevity, it has set no particular target, deciding instead to  award $500 000 to whoever's in the lead after six months and an equal sum to the leader after 18 months.

The competition has advanced the entire field of recommender systems, to the benefit of other companies.  Earlier this year Tom Conrad, CTO of Pandora.com, the Internet radio company, told IEEE Spectrum that “we have benefited by peering inside the approaches tried by some of the thinking that went into the Netflix prize. We have incorporated some ideas into our own system.”

DOE Mad Science Wing Finally Gets a Director

On Friday, President Obama announced his pick to head the Advanced Research Projects Agency Energy (ARPA-E); Lawrence Berkeley National Laboratory director Arun Majumdar.

ARPA-E, a high-risk research incubator in the U.S. Department of Energy, was signed into law in 2007 but languished for two years as funding and interest lagged. In February, the incoming Obama administration lavished $415 million on the fledgling organization.

So what’s next for Majumdar? The new director will need to be confirmed, a process that will take at least one month. By the time he is confirmed, it will likely be time for the house to adjourn for winter. In a report released in July, former HSARPA director Jane "Xan" Alexander laid out a roadmap for success for the new director.

ARPA-E was recommended in an influential 2005 report co-authored by Obama Energy Secretary and Nobel prize winning physicist Steven Chu, and signed into law in 2007. The agency is modeled on the Defense Advanced Research Projects Agency (DARPA), credited with developing the Internet. ARPA-E’s goal is to create game-changing energy technologies from high-risk research gambles. Other agencies have adopted DARPA’s framework, leading to the creation of IARPA (Intelligence) and HSARPA (Homeland Security). With a director who reports only to the Energy secretary and a lean core staff with a personnel cap of 120 directly responsible for all funding, such agencies award grants fast and is free of the bureaucracy that famously slows government to a crawl.

The plans are there, and the money is there, and now a new director is in place. My advice? The first step needs to be to get your own web site.
 


FCC to Tackle Internet Rules

FCC Chairman Julius Genachowski announced that the Commission will kick off a rulemaking proceeding on Internet regulation shortly, as soon as the formality of a vote is completed. The long-anticipated announcement has more to do with cleaning up the state of Internet regulation, which the previous chairman left in a bit of a mess. The FCC is supposed to make rules in public proceedings before enforcing them, but the previous commission slapped Comcast's wrists with a set of rules that it had declared unenforceable when they were written, the Four Freedoms that make up the Internet Policy Statement. 

As expected, the Genachowski announced that he intends to propose an anti-discrimination rule and a transparency rule. These had been considered mutually exclusive, so the combination is a bit of a surprise.

As the purpose of the speech was to announce the rule making procedure and not the precise nature of the rules themselves, it wasn't the most stirring piece of oratory. There were some curious moments early in the narrative when the chairman walked through the history of ARPANET and touted the architectural wonder of the Internet (the speech is on a new web site the FCC created today, openinternet.gov):

Historian John Naughton describes the Internet as an attempt to answer the following question: How do you design a network that is “future proof” -- that can support the applications that today’s inventors have not yet dreamed of? The solution was to devise a network of networks that would not be biased in favor of any particular application. The Internet’s creators didn’t want the network architecture -- or any single entity -- to pick winners and losers. Because it might pick the wrong ones. Instead, the Internet’s open architecture pushes decision-making and intelligence to the edge of the network -- to end users, to the cloud, to businesses of every size and in every sector of the economy, to creators and speakers across the country and around the globe. In the words of Tim Berners-Lee, the Internet is a “blank canvas” -- allowing anyone to contribute and to innovate without permission.

While this is pretty much standard Internet mythology, it's not accurate enough for regulatory work. Network engineers know that no single-service network, which is what the Internet has become post-BGP, can ever be application neutral. The Internet's best-effort delivery service is fine for generic content applications like the Web, and much less fine for real-time services and for high bandwidth content applications like P2P file sharing. There is no such thing as a truly neutral network, and we can only approach neutrality to the extent that the network can tailor delivery services to the needs of applications. That's why we have Quality of Service logic in IEEE 802 LANs, WLANs, WPANs, and WWANs. One-size-fits-all is a myth. A network with no QoS does pick winners and losers, make no mistake about it.

The chairman's lack of precision is par for the course in political circles, but there's a significant danger to innovation from trying to apply these metaphoric descriptions too literally. When your network has a structural bias in favor of a particular class of applications, it needs to permit management practices to overcome it. It's not clear that the FCC has the digital chops to appreciate this.

So the shoe has finally dropped and the FCC is on the road to fulfilling President Obama's campaign promise to protect the open Internet. This could result in clarity, certainty, and a good environment for investment, or it could degenerate into a circus as the Comcast proceeding did in 2008. Chairman Genachowski is a bright and earnest public servant, the odds are better than even money that the rulemaking will not do significant harm, but you never know how these things will turn out until the votes are counted. Those of us who do network engineering for a living need to keep a close watch on this proceeding.

Can You Trust Crowd Wisdom?

Can you trust crowd wisdom? An article this week on the MIT Technology Review website asks that question and answers it in the negative - or rather, says that new research indicates the answer is no: "Researchers say online recommendation systems can be distorted by a minority of users."

When searching online for a new gadget to buy or a movie to rent, many people pay close attention to the number of stars awarded by customer-reviewers on popular websites. But new research confirms what some may already suspect: those ratings can easily be swayed by a small group of highly active users.
Vassilis Kostakos, an assistant professor at the University of Madeira in Portugal and an adjunct assistant professor at Carnegie Mellon University (CMU), says that rating systems can tap into the "wisdom of the crowd" to offer useful insights, but they can also paint a distorted picture of a product if a small number of users do most of the voting. "It turns out people have very different voting patterns," he says, varying both among individuals and among communities of users.

What¿s the official informal fallacy name for bait-and-switch? This Tech Review article commits it. It wants you to think this about recommendation systems, but it isn't. It wants you to think that there's a hidden problem of only a few people voting, when the research is really talking about the fact that a relatively small fraction of people are doing a large share of the total voting at places like IMDb.

That's not to say that there aren't problems with the voting at IMDb. Is Inglourious Basterds really the 43rd best movie ever made, better than The Departed (#57), Slumdog Millionaire (#75), Braveheart (#100), Unforgiven (#110), No Country For Old Men (#116), Million Dollar Baby (#150), or Crash (#232), each of which won the Academy Award for Best Picture in its respective year? Of course not. But the problem is''t a handful of voters influencing the vote - these fewest number of votes for any one of these is 85 000. The problem is 18-years-olds with no historical memory of cinema giving a movie a 10 the same night they see it, while those of us over 40 are carefully weighing whether Yojimbo gets an 8 or a 9.

Suppose for the sake of argument there's an 80/20 rule for IMDb voting - that is, 80 percent of all votes are cast by 20 percent of the people who vote. Is that a problem? What if it turns out there's an 80/20 rule for electoral voting in the United States. Does that invalidate the election process?

In other words, consider the entire aggregation of election votes cast by everyone alive who has ever voted. It might very well be the case that a handful of people turn out to every election, casting votes for every county supervisor and municipal judge election, while a large number of people turn out once every four years to vote for the U.S. President, while another large group votes even less frequently than that. It might well turn out that 20 percent of all citizens cast 80 percent of the votes. In fact, in the absence of Soviet-style mandatory voting, it would be surprising if something like that weren't the case.

As might be expected, the paper itself, which was presented at the 2009 IEEE International Conference on Social Computing and is available from Kostakos's website here [PDF], isn't about the unreliability of crowd wisdom at all. It looked at three different online voting systems with different barriers of entry to voting. (Its conclusion that experts can be encouraged to vote more often by lowering the barriers to voting seems to me to be rather circular and obvious, given that it defines experts simply as people who vote often.)

The paper takes for granted that if an item has been only reviewed or voted on a couple of times, the result is unreliable, and it doesn't seem to have anything particular to say about the reliability of a recommendation based on a large number of votes or reviews. It doesn't, by the way, even contain the word "distorted" - that seems to have come from a conversation or interview with Kostakos, not from the paper itself.

Nor does the paper have anything to say about "online recommendation systems" - when discussing Amazon, for example, it considers only the voting and reviewing on the Amazon site, and not the feature by which it recommends other products based on what other people looked at or bought. This reviewer's recommendations: One shaky thumb up for the research, two firm thumbs down for Tech Review's report on it.

IEEE Standards Board Member to Rejoin Iggy Pop and The Stooges

 

Many of James Williamson’s colleagues—at Sony, where, until a few months ago, he was Vice President of Technology Standards, and at IEEE, where he serves as a member of the Standards Association Board of Governors and the Association's Corporate Advisory Group—didn’t know about the years he spent as a punk guitarist and member of The Stooges. His calm manner and even temper at standards meetings belied his previous reputation as one of the loudest and raunchiest punk rockers in the business.

Williamson co-wrote the songs and played guitar on the 1973 album, Raw Power, now considered a punk classic. He collaborated with Iggy Pop on the 1975 album Kill City, then turned to electrical engineering, getting his BSEE degree from California State Polytechnic University.

He did return to music briefly, contributing to Iggy Pop’s 1979 album New Values, then focused on his technical career.

But now, recently retired from Sony, he’s picking up the guitar again. Williamson, who hasn’t performed in front of a paying audience in 35 years, has reportedly started practicing for his musical comeback. The Stooges are currently booked to appear next year at the All Tomorrow’s Parties Festival in London, possibly the first stop on a tour.

No word yet as to whether IEEE members will be able to purchase concert tickets at a discount.

Apple Just Announced a Flip-killer, the iPod Nano Video Camera

I've been thinking about putting a Flip video camera high on my Christmas list, so much more convenient than lugging around my old digital video cassette camera for family events. But Apple's intro today of its Flip-killer–a video camera that oh, by the way, is built into an iPod Nano–just sunk that idea. Not just because it's an iPod too (I'm thinking I wouldn't use it for music, I'd be saving the memory for movies), but because I have complete faith in Apple making the user interface easy, I won't need to load more software (Flip requires a special app), and it'll go right into iTunes without the conversion that Flip videos require. Plus it's thinner, boasts a five hour battery life, and is about the same price ($149 for 8 GB). And oh yeah, I like the colors. Which could present a problem--do I want pink, or red, or blue...

Followup: I saw my first video Nano in the wild shortly after 7 p.m., just eight hours after the announcement--in the hands of parent taking videos at a back-to-school event. It was a red one. It got away before I could check it out.

Tech Museum of Silicon Valley Announces 2009 Laureates


This week, the Tech Museum of Silicon Valley announced its 2009 laureates. Among the 15 honorees:
—Joseph Adelegan, whose project in Nigeria takes the waste stream from slaughterhouses and turns it into methane for electricity generation or cooking gas.
—Sean White, who is digitizing the plant collection of the Smithsonian to create an Electronic Field Guide that will identify species through object recognition.
—The Alternative Energy Development Corp. of South Africa, which is using zinc air fuel cells for household electricity.
—Solar Ear, a Brazilian company building inexpensive hearing aids that come with solar rechargers.
—Geogebra, an organization developing open-source software for teaching geometry, algebra, and calculus.

The Tech Awards annually honor efforts to use technology to improve the lives of people around the world. One laureate in each of five categories—environment, economic development, education, equality, and health—will receive a cash prize of $50,000, to be announced at a gala on November 19th. This year’s James C. Morgan Global Humanitarian Award recipient, Al Gore, will also be recognized at the gala.

The announcement came at the unveiling of a new Tech Museum gallery, “Technology Benefiting Humanity. The exhibit includes interactive looks at the inventions of eleven previous laureates including Solar Sailor, a company that combines wind, solar, and hybrid technology to power boats, and Adaptive Eyecare, a company that is developing glasses with lenses whose power can be adjusted by the wearer. 

Ray tracing, Parallel Computing and a Bugatti Veyron

At last week's Hot Chips symposium, Nvidia founder and CEO Jen-Hsun Huang delivered the first keynote about the GPU computing revolution.

The keynote was definitely the highlight of the conference, but before I get all swoony over the incredible directional flame sprites and the finger-licking Bugatti Veyron their GPUs can render, first I need to pick on Nvidia a little.

That’s because the company was selling $200 3-D glasses at their booth. Or, they were trying to. I didn’t see anyone buy them, and if anyone did, they didn’t tell me about it.

The glasses were supposed to augment a very engrossing 3-D Batman game Nvidia had nakedly set up to lure passers-by. Apparently they created a deeper z-space by giving each lens a different refresh rate. Something like that. I put on the glasses and played for a while. It’s telling either of my unsophistication with games or of how unimpressive these glasses were that I failed to notice that you had to actually turn them on—when someone out pointed my mistake, and I flipped the on switch, the only difference I noticed was a pretty blue LED light.

But enough: let’s make with the swooning.

First, Huang took the audience back to February of 1993, when he'd just finished his master’s in electrical engineering at Stanford, and Nvidia was just a gleam in a venture capitalist's eye. For perspective, 1993 is so long ago that there was no need to have a PC on your desktop even if you were trying to get people to invest in your computer company. “If we had told our investors at the time that we’d be using the same hardware to play games and try to cure cancer," he said, "I am sure we would not have been funded."

“The GPU will likely be parallel processor for the future,” he told the crowd. Computers are being driven to parallel computing because people can do magical things with them.

Nvidia’s Teraflop-capable GPUs can, in fact, do some things that would have literally appeared to be magic to a person in 1993: Augmented reality in Monday Night Football, where it’s possible for the football players to stand on top of the 3-D rendered line of scrimmage projected onto the field but under the players. The flags rendered under the ice at Olympic hockey games; Ann Curry’s set during the 2008 election coverage. But you know all this stuff.

The point is this: The GPU has evolved faster than any other tech component, their complexity increasing from a few million transistors in 1994 to billions in 2009. That’s a thousand-fold increase in complexity in only 15 years.

What did they do with all that complexity? Shaders. Shaders and programmable pipelines made it possible for computer game designers to be artists. Let’s take an extreme example. Pacman and his attendant ghosts are lovable, clunkety and pixelated.

Pacman

Let's leave aside the fact that these were animated with pixels instead of polygons and that GPUs barely existed when Pacman was born. With the obscene amount of processing power GPUs now command, a programmer can now create a specific mood for his or her game by automatically shading all scenes and objects with a hypercolor style or a sepia tint, you name it. The result can be anything from the eye-poppingly surreal textures of Super Mario Galaxy...

...to the otherworldly, overexposed dreamscape of Riven or Myst.

 

Shading is great but Nvidia wanted to take it to the next level: articulate the surfaces, but also the physics underlying what you see on the surface. Now you’re getting into computational visualization.

This is where ray tracing comes in. With ray tracing, an image is generated by tracing a path through each pixel in a virtual screen, and calculating the color of the object visible through it. Huang showed us what exactly ray tracing can do by way of a Bugatti Veyron, rendered with 2 million polygons worth of luscious, mouth-watering detail.

[This image was from the 2008 SIGGRAPH conference-- the image from Hot Chips isn't online yet but it's even prettier!]

Because ray tracing constructs the entire image using information from the computed trajectory of rays of light bouncing from surface to surface, you can light the scene, place your object into the scene, and then do a “walk through”, panning inside the car, where it’s possible to see details—there is no independent lighting inside the car—provided exclusively by ambient “light” rays diffracting and reflecting off the environment and streaming in through the windows. The lighting was so complex and subtle you begin to understand how the GPU could harness physics simulations as impossibly complex as molecular dynamics.

This animation was running on three GeForce GPUs, each with almost 1 Tflop of processing horsepower. That’s about 2.7 Tflops to sustain animation that was very close to photorealistic. (1500-2000 instructions per component, all in HD. 100 shader instructions per component, 4 components per pixel [R G B alpha], 1.5 Flops per instruction on average, 60 frames per second, etc—that adds up to 500 shader Gflps: and if this sentence makes you want to die, read "Data Monster," the tutorial on GPUs and graphics processing in the September issue of Spectrum.) But that only represents, Huang said, about 10 percent of the total math capability of a GPU.

Meanwhile, let’s do a little side by side comparison. Intel's vaunted Nehalem CPU, trotted out earlier that day: 3 Ghz, 4 cores, and a bunch of other stuff—theoretical peak performance of 96 Gflops. That's great for general purpose computing, but two orders of magnitude short of being able to run the Bugatti animation in real time, which requires 5 Tflops. Nehalem—and the CPU in general—is designed for general-purpose computing, but not for graphics.

Animators will be making increasingly photorealistic art for games: water, fire, clouds, smoke—anything that obeys the laws of physics can be rendered to look real, provided you have the right algorithms and a monster amount of GPU muscle.  To prove that point, he showed a nice video of water gently rippling in the sunlit breeze. It was more than photorealistic. But to do all that, you’re using a 3D fluid solver that renders in agonizing detail about 262,000 individual particles to generate fluid motion. Each particle has its own shadow and motion blur. Not to mention color, alpha, etc.

 But ray tracing has a way to go, Huang said. Where it's great for photorealism, it’s not good for real-time rendering. The Bugatti for example was super-impressive in still frame; but when you moved around it, it got grainy and monochrome. Not for long—as soon as you stopped, the image filled in remarkably fast. If you're just making a movie, you can pre-bake the animation as long as you want. For games that's obviously a nonstarter.

To illustrate the true power of ray tracing, Huang showed us the directional flames Industrial Light & Magic did for the Harry Potter movie, which are apparently just unthinkable without monster processing power. Fire is amazingly complex because it’s alive, dynamic, moving and turbulent, so normally, to do fire special effects, animators use and sculpt sprites of real flames. But you can’t animate flame sprites directionally. The ILM fire simulator runs on top of CUDA, and the realistic flames shooting out of Dumbledore's hands are as good as any real-life flame thrower.

In addition, there are some things you can’t pre-animate because you don’t know how it will work at game time. For example, a really awful tackle in a football video game. Animators combine physics simulations and morph them with motion capture, because even though the motion capture is convincing to a certain extent, a brutal tackle would be really painful to motion-capture.

When a program is written taking full advantage of the GPU, obscene improvements are the norm, and not just for graphics. A certain unnamed quantum chemistry program, for example, had a 130X speedup when it was run properly on a GPU. It’s totally doable when an application is inherently parallelizeable.

The point is this: Moore’s law as applied to Intel’s CPUs can reap performance improvements of, on average, 20 percent per year.

By contrast, over the next 6 years, Huang predicted, a co-processing architecture (ganging together a CPU and one or more GPUs) would enable a performance improvement of 570X. Understandably, later blog posts that referenced this figure had people's heads exploding. But keep in mind, this is for specialized applications: graphics, oil & gas equations, seismic, molecular dynamics, quantum chemistry.

I assume ray tracing lends itself to parallel computing, and also that with a 570X performance improvement, this Bugatti will look photorealistic in real time by 2015.  But I think the real issue is whether that 570X speedup will help humanoid characters be truly photorealistic by 2015.

Huang wrapped up the talk by wowing us with all manner of Star Trek daydreams—the real-time universal translator, the smartphone app that can tell you what you’re looking if you just snap a picture of it (WANT).

But even with all those goodies, I’m still stuck on the Uncanny Valley problem. I wonder how far we’ll have to go into physics simulations before we break humanoid characters out of the Uncanny Valley. Even the most advanced animations—Beowulf and Digital Emily—are convincing until they start talking. There’s something impossible to render accurately about teeth, I think. Digital Emily was perfect until she showed her teeth, and the sad thing is, when I mentioned this to Paul Debevec, he looked crestfallen and explained that they had modeled the teeth exactly.

The upshot is this: I don’t think we’re going to get out of the Uncanny Valley until we can do essentially molecular dynamics on every part of the human face, and that includes building the teeth from the ground up.

The good news is, if Huang’s prediction proves true, and GPU performance increases by 570 over the next six years, that’s not a crazy thing to aspire to do. Whether it’s worthwhile, that’s another story.

 

 

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