Technology Makes Walt Disney in Minimal Time with Realistic Computer Graphics

Creating a realistic computer simulation of how light suffuses a room is crucial not just for animated movies like “Toy Story” or “Cars”. Special computing methods should ensure this, but they require great effort. Computer scientists from Saarbrücken have now developed a novel approach that turned out to be so promising, that it was adopted by companies in record time—among others by Pixar, well-known in the movie industry for its computer animation, and now a subsidiary of the Walt Disney Company.

The realistic depiction of light transport in a room is important within the production of computer-generated movies. If it does not work, the three-dimensional impression is rapidly lost. Hence, the movie industry’s digital light experts use special computing methods, requiring enormous computational power and therefore raising production costs.

Not only in the film industry, but also in the automobile industry, the companies invest to make lighting conditions for a computer generated image as realistic as possible. Already during the development process, entire computing centers are used to compute and display realistic pictures of the complex car models in real time. Only in this way, designers and engineers can evaluate the design and the product features in an early stage and optimize it during the planning phase. “They build hardly any real prototypes. Hence, the designers want to make sure that the car body on the screen looks exactly as the real vehicle will appear later,” explains Philipp Slusallek, professor of computer graphics at Saarland University, Scientific Director at the German Center for Artificial Intelligence (DFKI) and Director of Research at the Intel Visual Computing Institute at Saarland University.

With current computing methods, it has not been possible to compute all illumination effects in an efficient way. The so-called Monte Carlo Path Tracing could depict very well the direct light incidence on surfaces and the indirect illumination by reflecting light from surfaces in a room. But it does not work well for illumination around transparent objects, like semi-transparent shadows from glass objects, or illumination by specular surfaces (so-called caustics). This, on the other hand, was the advantage of the so-called photon mapping. But this method again led to disappointing results for direct lighting of surfaces. But since these two approaches were mathematically incompatible (Monte Carlo integration versus density estimation), it was not possible to merge them, and therefore it was necessary to compute them separately from each other for the particular images. This raised the computation costs for computer-animated movies like “The Hobbit: An Unexpected Journey”, where up to 48 pictures per second have to be computed—for a movie whose “normal” version is 169 minutes long.

In cooperation with Ilyan Georgiev, PhD student at the Graduate School for Computer Science in Saarbrücken, Jaroslav Krivanek from the Charles University in Prague and Thomas Davidovic from the Intel Visual Computing Institute at Saarland University, Slusallek developed a mathematical approach in 2012 that combines both methods with each other in a clever way. They reformulated photon mapping as a Monte Carlo process. Hence, they could integrate it directly into the Monte Carlo Path Tracing method. For every pixel of the image the new algorithm decides automatically, via so-called multiple importance sampling, which of both strategies is suited best to compute the illumination at that spot.

The researchers from Saarbrücken also supplied mathematical proof that the results of the new computing method comply with those of the two former methods. “Our new method vastly simplifies and speeds up the whole calculating process,” says Slusallek.

The method “Vertex Connection and Merging'” abbreviated as VCM, was not only accepted at one of the most important conferences within the computer graphics research field ― SIGGRAPH ― in 2012, but was also very well received by industry. “We know of four different companies that partially integrated VCM in their commercial products only a few months after the scientific publication. The most recent example is the new version of the software Renderman developed by the company Pixar. For decades this has been the most important tool in the movie industry. We are very proud of this achievement,” Slusallek says. The Californian (US) company Pixar, famous for movies like “Toy Story,” “Up,” “Finding Nemo,” and “Monsters, Inc.” is part of the Walt Disney Company. Pixar originally got its name from Apple founder Steve Jobs. Up to now, Pixar has received twelve Oscars for its movies.

3D-Printing Microscopic Fish

Nanoengineers at the University of California, San Diego used an innovative 3D printing technology they developed to manufacture multipurpose fish-shaped microrobots—called microfish—that swim around efficiently in liquids, are chemically powered by hydrogen peroxide and magnetically controlled. These proof-of-concept synthetic microfish will inspire a new generation of “smart” microrobots that have diverse capabilities such as detoxification, sensing and directed drug delivery, researchers said.

The technique used to fabricate the microfish provides numerous improvements over other methods traditionally employed to create microrobots with various locomotion mechanisms, such as microjet engines, microdrillers and microrockets. Most of these microrobots are incapable of performing more sophisticated tasks because they feature simple designs—such as spherical or cylindrical structures—and are made of homogeneous inorganic materials. In this new study, researchers demonstrated a simple way to create more complex microrobots.

The research, led by Professors Shaochen Chen and Joseph Wang of the NanoEngineering Department at the UC San Diego, was published in the Aug. 12 issue of the journal Advanced Materials.

By combining Chen’s 3D printing technology with Wang’s expertise in microrobots, the team was able to custom-build microfish that can do more than simply swim around when placed in a solution containing hydrogen peroxide. Nanoengineers were able to easily add functional nanoparticles into certain parts of the microfish bodies. They installed platinum nanoparticles in the tails, which react with hydrogen peroxide to propel the microfish forward, and magnetic iron oxide nanoparticles in the heads, which allowed them to be steered with magnets.

“We have developed an entirely new method to engineer nature-inspired microscopic swimmers that have complex geometric structures and are smaller than the width of a human hair. With this method, we can easily integrate different functions inside these tiny robotic swimmers for a broad spectrum of applications,” said the co-first author Wei Zhu, a nanoengineering Ph.D. student in Chen’s research group at the Jacobs School of Engineering at UC San Diego.

As a proof-of-concept demonstration, the researchers incorporated toxin-neutralizing nanoparticles throughout the bodies of the microfish. Specifically, the researchers mixed in polydiacetylene (PDA) nanoparticles, which capture harmful pore-forming toxins such as the ones found in bee venom. The researchers noted that the powerful swimming of the microfish in solution greatly enhanced their ability to clean up toxins. When the PDA nanoparticles bind with toxin molecules, they become fluorescent and emit red-colored light. The team was able to monitor the detoxification ability of the microfish by the intensity of their red glow.

Google’s DeepMind

Google’s DeepMind brought us artificial intelligence systems that can play Atari classics and the complex game of Go as well as — no, better than — humans. Now, the artificial intelligence research firm is at it again. This time, its machines are getting really good at sounding like humans.

DeepMind unveiled WaveNet, an artificial intelligence system that the company says outperforms existing text-to-speech technologies by 50 percent. WaveNet learns from raw audio files and then produces digital sound waves that resemble those produced by the human voice, which is an entirely different approach. The result is more natural, smoother sounding speech, but that’s not all. Because WaveNet works with raw audio waveforms, it can model any voice, in any language. WaveNet can even model music.

Speaking Up

Someday, man and machine will routinely strike up conversations with each other. We’re not there yet, but natural language processing is a scorching hot area of AI research — Amazon, Apple, Google and Microsoft are all in pursuit of savvy digital assistants that can verbally help us interact with our devices. Right now, computers are pretty good listeners, because deep learning algorithms have taken speech recognition to a new level. But computers still aren’t very good speakers. Most text-to-speech systems are still based on concatenative TTS — basically, cobbling words together from a massive database of sound fragments. Other systems form a voice electronically, based on rules about how letter combinations are pronounced. Both approaches yield rather robot-y sounding voices. WaveNet is different.

Flexing Those Computing Muscles

WaveNet is an artificial neural network, that, at least on paper, resembles the architecture of the human brain. Data inputs flow through layers of interconnected nodes — the “neurons” — to produce an output. This allows computers to process mountains of data, and recognize patterns that would perhaps take humans a lifetime to uncover. To model speech, WaveNet was fed real waveforms of English and Mandarin speech. These waveforms are loaded with data points, roughly 16,000 to sample per second, and WaveNet digests them all.

To then generate speech, it assembles an audio wave sample-by-sample, using statistics to predict which sample to use next. It’s like assembling words a millisecond of sound at a time. DeepMind researchers then refine these results by adding linguistic rules and suggestions to the model. Without these rules, WaveNet produces dialogue that sounds like it’s lifted from The Sim.

The technique requires a ton of computing power, but the results are pretty good — WaveNet even generates non-speech sounds like breaths and mouth movements. In blind tests, human English and Mandarin speakers said WaveNet sounded more natural than any of Google’s existing text-to-speech programs. However, it still trailed behind actual human speech. The DeepMind team published a paper detailing their results. Because this technique is so computationally expensive, we probably won’t see this in devices immediately, according to Bloomberg’s Jeremy Kahn. Still, the future of man-machine conversation sounds pretty good.

Chrome

Chrome, an open-source Internet browser released by Google, Inc., a major American search engine company, in 2008. The first beta version of the software was released on Sept. 2, 2008, for personal computers (PCs) running various versions of Microsoft Corporation’s Windows OS (operating system). The development of Chrome was kept a well-guarded secret until a Web-based “comic book” describing the browser was released just hours before links appeared on Google’s Web site to download the program. In its public statements the company declared that it did not expect to supplant the major browsers, such as Microsoft’s Internet Explorer and Firefox (the latter an open-source browser that Google supports with technical and monetary help). Instead, Google stated that its goal was to advance the usefulness of the Internet by including features that would work better with newer Web-based technologies, such as the company’s Google Apps (e.g., calendar, word processor, spreadsheet), that operate within a browser. This concept is often called “cloud computing,” as the user relies on programs operating “out there,” somewhere “in the cloud” (on the Internet).

Part of Chrome’s speed improvement over existing browsers is its use of a new JavaScript engine (V8). Chrome uses code from Apple Inc.’s WebKit, the open-source rendering engine used in Apple’s Safari Web browser. Chrome is the first browser to feature isolated, or protected, windows (or tabs) for each Web page or application running in it. While this means that each new tab that is opened requires as much dedicated computer memory as the first tab, it also means that if any computer code causes one of these tabs to crash, it will not bring down the entire browser. Closing a tab fully releases its allocated memory, thus solving a persistent problem of older browsers, which frequently have to be restarted in order to release the increasing amounts of memory that are requisitioned over time.

On July 7, 2009, Google announced plans to develop an open-source operating system, known as Chrome OS. The first devices to use Chrome OS were released in 2011 and were netbooks called Chromebooks. Chrome OS, which runs on top of a Linux kernel, requires fewer system resources than most operating systems because it uses cloud computing, in which the only software run on a Chrome OS device is Chrome and all other software applications are accessed through the Internet inside the Chrome browser.

Microsoft Corporation

Microsoft Corporation, leading developer of personal-computer software systems and applications. The company also publishes books and multimedia titles, offers e-mail services, and sells electronic game systems, computer peripherals (input/output devices), and portable media players. It has sales offices throughout the world. In addition to its main research and development centre at its corporate headquarters in Redmond, Washington, U.S., Microsoft has opened research labs in Cambridge, England (1997); Beijing, China (1998); Aachen, Germany (2003); Sadashivnagar, Bangalore, India (2005); Cairo, Egypt (2006); Cambridge, Massachusetts (2008); Herzliyya, Israel (2011); and New York, New York (2012).

Founding and early growth

In 1975 Bill Gates and Paul G. Allen, two boyhood friends from Seattle, converted BASIC, a popular mainframe computer programming language, for use on an early personal computer (PC), the Altair. Shortly afterward, Gates and Allen founded Microsoft, deriving the name from the words microcomputer and software. During the next few years, they refined BASIC and developed other programming languages. In 1980 International Business Machines Corporation (IBM) asked Microsoft to produce the essential software, or operating system, for its first personal computer, the IBM PC. Microsoft purchased an operating system from another company, modified it, and renamed it MS-DOS (Microsoft Disk Operating System). MS-DOS was released with the IBM PC in 1981. Thereafter, most manufacturers of personal computers licensed MS-DOS as their operating system, generating vast revenues for Microsoft; by the early 1990s it had sold more than 100 million copies of the program and defeated rival operating systems such as CP/M, which it displaced in the early 1980s, and later IBM OS/2. Microsoft deepened its position in operating systems with Windows, a graphical user interface whose third version, released in 1990, gained a wide following. By 1993, Windows 3.0 and its subsequent versions were selling at a rate of one million copies per month, and nearly 90 percent of the world’s PCs ran on a Microsoft operating system. In 1995 the company released Windows 95, which for the first time fully integrated MS-DOS with Windows and effectively matched in ease of use Apple Computer’s Mac OS. It also became the leader in productivity software such as word-processing and spreadsheet programs, outdistancing longtime rivals Lotus and WordPerfect in the process.

Microsoft dramatically expanded its electronic publishing division, created in 1985 and already notable for the success of its multimedia encyclopaedia, Encarta. It also entered the information services and entertainment industries with a wide range of products and services, most notably the Microsoft Network and MSNBC (a joint venture with the National Broadcasting Company, a major American television network).

As a result, by the mid-1990s Microsoft, which became a publicly owned corporation in 1986, had become one of the most powerful and profitable companies in American history. It consistently earned profits of 25 cents on every sales dollar, an astonishing record. In the company’s 1996 fiscal year, it topped $2 billion in net income for the first time, and its unbroken string of profits continued, even during the Great Recession of 2008–09 (its net income had grown to more than $14 billion by fiscal year 2009). However, its rapid growth in a fiercely competitive and fast-changing industry spawned resentment and jealousy among rivals, some of whom complained that the company’s practices violated U.S. laws against unfair competition. Microsoft and its defenders countered that, far from stifling competition and technical innovation, its rise had encouraged both and that its software had consistently become less expensive and more useful. A U.S. Justice Department investigation concluded in 1994 with a settlement in which Microsoft changed some sales practices that the government contended enabled the company to unfairly discourage OS customers from trying alternative programs. The following year the Justice Department successfully challenged Microsoft’s proposed purchase of Intuit Inc., the leading maker of financial software for the PC.

Why You Should Learn Computer Programming

aNews that numerous cathedrals are offering short courses in Latin is a reminder of the long decline of the language over the years. It was a core subject in the British education system until fairly recently – and not because anyone planned to speak it, of course. It was believed to offer valuable training for intellectual composition, as well as skills and thinking that were transferable to other fields.

It may have been the right decision, but when it was ultimately decided that these advantages were outweighed by Latin being a dead language we arguably lost that intellectual training in the process. This is why we want to make the case for moving another discipline to the centre of the curriculum that offers analogous benefits – computer programming. And unlike Latin, it is anything but dead.

Noam lore. Brian Talbot, CC BY-SA

There are many computer languages for different purposes. C and C++ remain the fastest to execute and are used by the gaming industry, for instance. In the internet era, much of the page design is done with the likes of JavaScript or PHP. Meanwhile Python has been rapidly gaining a reputation as a general purpose code that is easy to learn.

There are many parallels between natural languages and programming languages like these. You must learn to express yourself within the rules of the language. There is a grammar to comprehend. And what you write must be interpretable by another human being. (Yes, it must be interpretable by a computer. But just as Noam Chomsky’s example of “colourless green ideas sleep furiously” is grammatically correct nonsense, you can write obfuscated computer code that no one else can decipher.)

People who program can communicate with computers, which is becoming more and more important now that computers have a hand in almost everything. In today’s IT-literate world, we are all expected to be fluent in word processing and spreadsheets. The next logical step is to be able to program.

The younger generation are already exposed to computers almost from the day they are born, which explains for example Barclays bank’s recent launch of Code Playground, an initiative to engage young children in the basics of programming via a colourful website.

There is a myth that only maths geniuses are suited to programming. It is more accurate to say you need a logical approach and an ability to problem solve. Just as Latin constructs reinforce communication, programming constructs reinforce problem solving. It teaches you to break a problem into achievable chunks and to think very precisely. And once you have mastered the basics, it opens up great potential for creative thinking.

Then there are specific workplace benefits, such as for businesses that are building a bespoke piece of software. Errors sometimes occur when documents outlining in English how a program should work are translated into computer code. Those who have an appreciation of a programming language can write these more clearly. Indeed, businesses usually have to employ specialist analysts as intermediaries to help with this translation process.