One reason: VR can make users feel sick. Nausea and eye strain can result because VR creates an illusion of 3D viewing although the user is in fact staring at a fixed-distance 2D display. The solution for better 3D visualization could lie in a 60-year-old technology remade for the digital world: holograms.
Holograms deliver an exceptional representation of 3D world around us. Plus, they're beautiful. (Go ahead — check out the holographic dove on your Visa card.) Holograms offer a shifting perspective based on the viewer's position, and they allow the eye to adjust focal depth to alternately focus on foreground and background.
Researchers have long sought to make computer-generated holograms, but the process has traditionally required a supercomputer to churn through physics simulations, which is time-consuming and can yield less-than-photorealistic results. Now, MIT researchers have developed a new way to produce holograms almost instantly — and the deep learning-based method is so efficient that it can run on a laptop in the blink of an eye, the researchers say.
"People previously thought that with existing consumer-grade hardware, it was impossible to do real-time 3D holography computations," says Liang Shi, the study's lead author and a PhD student in MIT's Department of Electrical Engineering and Computer Science (EECS). "It's often been said that commercially available holographic displays will be around in 10 years, yet this statement has been around for decades."
Shi believes the new approach, which the team calls "tensor holography," will finally bring that elusive 10-year goal within reach. The advance could fuel a spillover of holography into fields like VR and 3D printing.
Shi worked on the study, published today in Nature, with his advisor and co-author Wojciech Matusik. Other co-authors include Beichen Li of EECS and the Computer Science and Artificial Intelligence Laboratory at MIT, as well as former MIT researchers Changil Kim (now at Facebook) and Petr Kellnhofer (now at Stanford University).
The quest for better 3D
Courtesy of the researchers
A typical lens-based photograph encodes the brightness of each light wave — a photo can faithfully reproduce a scene's colors, but it ultimately yields a flat image.
In contrast, a hologram encodes both the brightness and phase of each light wave. That combination delivers a truer depiction of a scene's parallax and depth. So, while a photograph of Monet's "Water Lilies" can highlight the paintings' color palate, a hologram can bring the work to life, rendering the unique 3D texture of each brush stroke. But despite their realism, holograms are a challenge to make and share.
First developed in the mid-1900s, early holograms were recorded optically. That required splitting a laser beam, with half the beam used to illuminate the subject and the other half used as a reference for the light waves' phase. This reference generates a hologram's unique sense of depth. The resulting images were static, so they couldn't capture motion. And they were hard copy only, making them difficult to reproduce and share.
Computer-generated holography sidesteps these challenges by simulating the optical setup. But the process can be a computational slog. "Because each point in the scene has a different depth, you can't apply the same operations for all of them," says Shi. "That increases the complexity significantly." Directing a clustered supercomputer to run these physics-based simulations could take seconds or minutes for a single holographic image. Plus, existing algorithms don't model occlusion with photorealistic precision. So Shi's team took a different approach: letting the computer teach physics to itself.
They used deep learning to accelerate computer-generated holography, allowing for real-time hologram generation. The team designed a convolutional neural network — a processing technique that uses a chain of trainable tensors to roughly mimic how humans process visual information. Training a neural network typically requires a large, high-quality dataset, which didn't previously exist for 3D holograms.
The team built a custom database of 4,000 pairs of computer-generated images. Each pair matched a picture — including color and depth information for each pixel — with its corresponding hologram. To create the holograms in the new database, the researchers used scenes with complex and variable shapes and colors, with the depth of pixels distributed evenly from the background to the foreground, and with a new set of physics-based calculations to handle occlusion. That approach resulted in photorealistic training data. Next, the algorithm got to work.
By learning from each image pair, the tensor network tweaked the parameters of its own calculations, successively enhancing its ability to create holograms. The fully optimized network operated orders of magnitude faster than physics-based calculations. That efficiency surprised the team themselves.
"We are amazed at how well it performs," says Matusik. In mere milliseconds, tensor holography can craft holograms from images with depth information — which is provided by typical computer-generated images and can be calculated from a multicamera setup or LiDAR sensor (both are standard on some new smartphones). This advance paves the way for real-time 3D holography. What's more, the compact tensor network requires less than 1 MB of memory. "It's negligible, considering the tens and hundreds of gigabytes available on the latest cell phone," he says.
The research "shows that true 3D holographic displays are practical with only moderate computational requirements," says Joel Kollin, a principal optical architect at Microsoft who was not involved with the research. He adds that "this paper shows marked improvement in image quality over previous work," which will "add realism and comfort for the viewer." Kollin also hints at the possibility that holographic displays like this could even be customized to a viewer's ophthalmic prescription. "Holographic displays can correct for aberrations in the eye. This makes it possible for a display image sharper than what the user could see with contacts or glasses, which only correct for low order aberrations like focus and astigmatism."
"A considerable leap"
Real-time 3D holography would enhance a slew of systems, from VR to 3D printing. The team says the new system could help immerse VR viewers in more realistic scenery, while eliminating eye strain and other side effects of long-term VR use. The technology could be easily deployed on displays that modulate the phase of light waves. Currently, most affordable consumer-grade displays modulate only brightness, though the cost of phase-modulating displays would fall if widely adopted.
Three-dimensional holography could also boost the development of volumetric 3D printing, the researchers say. This technology could prove faster and more precise than traditional layer-by-layer 3D printing, since volumetric 3D printing allows for the simultaneous projection of the entire 3D pattern. Other applications include microscopy, visualization of medical data, and the design of surfaces with unique optical properties.
"It's a considerable leap that could completely change people's attitudes toward holography," says Matusik. "We feel like neural networks were born for this task."
The work was supported, in part, by Sony.
Reprinted with permission of MIT News. Read the original article.
While the focus on music piracy dominated the media for years, an equally important (and far less discussed) phenomenon occurred during the transition from broadcast radio to streaming. People were no longer beholden to the gatekeepers known as DJs. Today, listeners have the entire history of music at their fingertips. Each person is now their own DJ.
If it's free, you are the product
Though this might appear empowering, every advancement comes at a cost. Because listeners changed how they consumed music (namely, from radio broadcasts to personalized online streams), companies had to change their monetization strategy. Now, you are the product.
When you curate a playlist, you are inadvertently sending tons of data to different companies, with Spotify, YouTube, and Apple Music leading the way. As it turns out, according to a new study from Israeli researchers — Ariel University's Dr. Ron Hirschprung and Tel Aviv University's Dr. Ori Leshman — your musical tastes reveal more about your personality than you likely ever imagined.
Musical selection is a quasi-identifier
There are different ways in which you can be identified. Identifiers, such as your social security number, are highly specific and unique to you. But then there are quasi-identifiers — things like age, gender, and occupation — that can also give away your identity. The authors claim that musical selection is a quasi-identifier, and they argue that, as with other forms of sensitive data, our playlists should be considered when constructing privacy laws.
In their paper, they write, "[T]he combination of Big-Data, together with the availability of computational power — which is notoriously known for its potential of privacy violation — introduces a privacy threat from an unexpected angle: listening to music."
To prove their point, the researchers divided undergraduate students into four groups with roughly 35 volunteers in each. Every member submitted three songs from their playlist of favorite tracks. Then, the researchers picked five members at random in each group, and the remaining volunteers were asked to vote to determine if they could match the members with their playlists.
Photo: cherryandbees / Adobe Stock
Even to the surprise of the researchers, the participants were right between 80 percent and 100 percent of the time. Incredibly, these students did not know one another well and were not aware in advance of anyone's musical preferences.
There are many outward signs that mark us in the eyes of others: what we wear, what we eat, how we style our hair, our mannerisms and posture, and even where we stand at parties. Other people pick up on these subtle clues, which in turn allows them to predict our personalities. In this study, the volunteers were able to identify the musical preferences of strangers simply by observing their outward appearances.
Of course, companies notice similar things and are able to exploit what they learn about us. In a press release, the authors stated:
"Music can become a form of characterization, and even an identifier. It provides commercial companies like Google and Spotify with additional and more in-depth information about us as users of these platforms. In the digital world we live in today, these findings have far-reaching implications on privacy violations, especially since information about people can be inferred from a completely unexpected source, which is therefore lacking in protection against such violations."
Musical preference isn't the only way in which you can be identified online. For instance, your browsing history can give away your identity. Listening to your favorite tunes while searching Google for a new recipe isn't as innocuous as you might think.--
Stay in touch with Derek on Twitter and Facebook. His most recent book is "Hero's Dose: The Case For Psychedelics in Ritual and Therapy."
An older person's cognitive health is not always obvious. Cognitive impairment and dementia manifest gradually over time, and a person may be unaware of their advance. During this subtle transition, such a person may continue living as they always have, going about their business at home and behind the wheel. But this could lead to a dangerous car accident.
So, researchers from Columbia University have announced the development of AI algorithms that can detect mild cognitive impairment and dementia in older people based on the way they drive. The authors report in the journal Geriatrics that their algorithm is 88 percent accurate.
"Driving is a complex task involving dynamic cognitive processes and requiring essential cognitive functions and perceptual motor skills," says senior author Guohua Li, professor of epidemiology. "Our study indicates that naturalistic driving behaviors can be used as comprehensive and reliable markers for mild cognitive impairment and dementia."
Random forest model
The algorithms the researchers developed were based on a common AI statistical method involving "decision trees" that form a "random forest model." The most successful algorithm, according to lead author Sharon Di, associate professor of civil engineering, was based on "variables derived from the naturalistic driving data and basic demographic characteristics, such as age, sex, race/ethnicity and education level."
Decision trees are often used in memes in which answering "yes" or "no" regarding some attribute leads you down a path to another question, which in turn ultimately leads to a final conclusion.
Credit:Big Think
Data used in the study
The algorithm was developed using data sourced by the Longitudinal Research on Aging Drivers (LongROAD) study sponsored by the AAA Foundation for Traffic Safety. It came from in-vehicle recording devices that captured the driving behaviors of 2,977 participants from August 2015 through March 2019. At the time the project began, the motorists' ages ranged from 65 to 79 years. From the raw data, the authors of the new study derived 29 behavioral variables, which they used to develop cognitive profiles of the drivers.
Credit: Zoran Zeremski/Adobe Stock
The researchers then developed a series of machine-learning models to predict cognitive issues, with differing success rates. While models based on driving variables alone were just 66 percent accurate, and demographic models less so at 29 percent, using both models together produced an accuracy rate of 88 percent.
The researchers also explored the validity of individual factors as predictors of cognitive issues. In order of most reliable to least reliable, they were: (1) age; (2) percentage of trips traveled within 15 miles of home; (3) race/ethnicity; (4) minutes per round trip; and (5) number of hard braking events.
Li is hopeful that his team's work can help keep roadways and older drivers safe. "If validated," he says, "the algorithms developed in this study could provide a novel, unobtrusive screening tool for early detection and management of mild cognitive impairment and dementia in older drivers."
