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The Top Companies Working on Education in Virtual Reality and Augmented Reality

Virtual Reality and Augmented Reality are new technologies which have been making all kinds of waves recently; but VR and AR are not only going to be used for gaming, several companies are exploring applications for VR and AR to be used in education. Around 90% of what we see and do is retained, which is something that has always been missing from education; students do not retain as much information if they are just reading a textbook compared to having more real applications of what they’re learning. Which is why VR and AR in education are so beneficial; these technologies engage students in a completely new way which is more fun and engaging for them, and it increases retention. Several companies are currently working on innovations in this space; and the work we’ve seen so far is surely a good sign of things to come.

Immersive VR Education
Lecture VR is a VR app by Immersive VR Education which simulates a lecture hall in virtual reality, while adding special effects which can’t be utilized in a traditional classroom setting. Lectures are accompanied by images, videos, and immersive experiences which enhance the lesson. Imagine learning about Apollo 11 and while the instructor is lecturing, they can transform the classroom to be inside the space shuttle which they’re lecturing about; adding much more to the lecture than would be traditionally possible. Another major asset of this type of learning is that students and professors can remote in from anywhere in the world, which makes education more accessible on a global level.

Unimersiv
Unimersiv is a VR learning platform which releases educational content on a monthly basis. The content in Unimersiv’s app is more individualized and immersive, and at the moment the 3 educational experiences available on the app are: Explore the International Space Station, Anatomy VR, and Learn about Stonehenge in Virtual Reality. The diversity in this content shows the true potential in the range of things we can learn by using VR in education; and it also shows that Unimersiv is dedicated to creating educational content on a wide range of topics, which makes their potential truly unlimited.

Google Expeditions Pioneer Program
Google is also making waves in the space of VR education with their exciting Expeditions Pioneer Program. The purpose of the program is for Expeditions teams from Google to visit schools around the world and provide everything teachers need to take their students on a journey anywhere; the team will also assist the teachers in setting up and utilizing this technology. The VR experiences are meant to be like a really cool field trip which teachers would ordinarily never be able to take their students on; whether it’s to an underwater coral reef, or into a city like Barcelona, the potential is truly limitless here. The way the app works is that the students and the teacher will see the same things and be in the same session, but the teacher will be able to lecture and highlight certain things which are relevant to the lesson.

Alchemy VR
Alchemy VR is creating immersive educational experiences on an impressive scale. The experiences on Alchemy VR are like a narrative being told to the user where they will get to see and experience a myriad of different things; one such example is exploring the Great Barrier Reef. What makes Alchemy VR standout in this space is their partnerships which contribute to the level of content they produce. Alchemy VR is partnered with Samsung, Google Expeditions, Sony, HTC, the Natural History Museum in London, and the Australian Museum in Sydney. Alchemy VR has actually made several projects for Google Expeditions and is set to release experiences on pyramids and the human body soon.

Discovery VR
Discovery has been telling stories like no one else for over 30 years; and now they are entering the modern era with their Discovery VR app. Some of your favorite content is available on the Discovery VR app, so you can experience some of your favorite Discovery shows in a whole new way. Discovery brining content to virtual reality is great for the space of education in virtual reality; Discovery has been creating educational content for years, they’re a household name and when people hear you can watch Deadliest Catch or Mythbusters in Virtual Reality it might just get more people interested in the potential applications of virtual reality for education.

zSpace
zSpace is unique in the space of education in VR because of the technology they’re using. zSpace feels that VR technology should not necessarily be so antisocial and one sided as everyone putting on their own headset. zSpace has monitors which are similar to the way 3D movies work, where a group of people use glasses which are similar in feel to 3D glasses; these glasses make the content come off the screen. Additionally zSpace users have a pen which they use in order to manipulate the stimulus as you can see below. zSpace currently has content available for STEM education, medical training, and more general math and science experiences.

Curiscope
Curiscope is a startup company focusing on education in VR, and their Virtual Tee is already turning heads. The way it works is that one person wears a t-shirt while another person with a smartphone launches the app and is able to learn about the human body in a unique way. This is a unique way to utilize AR in education and it is surely a sign of good things to come from Curiscope.

WoofbertVR
Woofbert VR is focusing on bringing art to VR technology, they are looking to reshape the landscape of storytelling and revolutionize the way we look at art and culture. WoofbertVR is a unique way to visit an art gallery, either individually or in a classroom setting giving students the opportunity to take a field trip which they would never normally get to experience. Exploring a gallery using WoffbertVR’s app is nearly identical to how you would explore it in person; you can follow any path you choose, zoom in on paintings you are interested in, and listen to audio segments on certain exhibits. The only difference here is that you do not need to take an expensive trip in order to visit a gallery which has some of your favorite paintings or pieces, now it can be done from the comfort of your own home.

Nearpod
Nearpod is an organization which combines VR and AR technology with traditional lesson plans in a classroom for a more immersive technology driven approach to learning. Nearpod utilizes 360 degree photos and videos in lesson plans, and also has something akin to a PowerPoint for students to use alongside the 360 photos and videos. There are also options to have students answer questions by typing into their laptop or tablet. Nearpod VR is giving us an idea of what a classroom of the future is going to look like, with lesson plans being enhanced with VR and AR technology and students being more engaged by using this new technology.

EON Reality
EON Reality is looking to change how teachers utilize technology in the classroom. Students and teachers can create blended learning environments with the EON Creator, which is an interactive tool that allows users to combine 3D content with videos, sound effects, notes, Powerpoint, and more. EON also has an EON Experience Portal where teachers and students can upload their work and also explore the creations of other teachers and students. There is also the EON Colliseum where students and teachers can meet virtually, which makes collaboration possible at any time and from nearly any location.

Schell Games
Schell Games is one of the largest independent game development companies in the United States; with their forte being in what they refer to as transformation games, or games that change people for the better. Among the countless games they have developed includes Water Bears VR, which is a VR puzzle game aimed at kids that promotes systems thinking and spatial recognition. There has always been a challenge in education with engaging students, especially kids, and making learning fun has always been a practical solution to that challenge. With companies like Schell Games producing fun VR games for kids, which have the added bonus of being educational, we find a fun solution to the challenge of getting kids interested in learning.

Gamar
Gamar is a company who is attempting to make museum visits more modern through the use of Augmented Reality. The way that Gamar’s content works is that they have interactive experiences and supplemental information which can be paired with certain locations to enhance museum visits. One of the chief rules of a museum is not to touch anything, but with Gamar’s app museum attendees will be able to point their smartphone or tablet at an exhibit at one of their paired locations and get additional information on that exhibit. Museum visits can be tedious for kids when all they want to do is pull out their smartphones and play with them. Gamar has provided a solution to this problem by finding a way to engage kids in museums and doing it in a way that’s interesting to them and on their level. AR and VR technology are great tools for education, but if we can’t get kids to use them then it’s all for not; which is why we need organizations like Gamar who find ways to get kids to utilize this technology in educational ways.

ThingLink
A unique take on 360 images has been developed for classroom use by ThingLink. The way that their technology works is that it allows teachers, or students, to annotate 360 images with various interactive tags, or links to different videos or applications or third party tools. Imagine being able to look at a 360 picture of London and click on various things throughout the picture, one tag might be a video enhancing something you are looking at, while another tag might be a writing assignment or a place to record notes. This kind of all in one learning experience will help in breakages in immersion which would typically occur in learning which utilizes VR or AR technology.  If students do not have to look away from a 360 image in order to take notes or do an assignment then the immersion will never truly be broken. ThingLink’s technology encourages students and teachers to utilize AR in the classroom for a unique one stop experience, and it has the potential to shape adoption of VR and AR technology in the classroom on a broad scale due to its appeal to students and teachers alike, not just one or the other.

Virtual Reality and Augmented Reality are the next big step forward in education, and these technologies are good for educators and students alike. Educators will be able to capture the attention of their students like never before and get them more actively involved in the classroom. Students are always trying to do what’s cool and what’s new; utilizing VR and AR technology in classrooms will appeal to students because it’s the latest and greatest thing and it’s very cool to use. Using VR and AR technology will not only make learning more interesting and exciting to students but it will also increase retention because it engages them in a way which textbooks simply cannot compete with. These technologies give us a window into the future of education, and it probably won’t be long until it is standard for classrooms to come equipped with some form of VR or AR technology.

( Source: Touch Stone Research)

Why Data Is The New Oil

Artificial intelligence is only as good as the data it crunches.

The field of artificial intelligence is red hot thanks in part to big companies like Google, Facebook, and Microsoft using AI-related techniques to train computers to recognize objects in photos and understand human language.

But these companies were only able to train their computers to perform these difficult feats because they have the enormous quantities of data that’s required. Companies that want to use artificial intelligence techniques like machine learning algorithms to improve their businesses will need to either have or acquire copious amounts of data, which will only become more valuable as the field of AI advances.

That’s one of the takeaways from a panel on artificial intelligence Monday in Aspen, Colo. during Fortune’s annual Brainstorm Tech conference.

“Data is the new oil,” Shivon Zilis, a partner with the venture capital firm Bloomberg Beta, said about data’s increasing value.

Although companies like Google GOOG 0.57% , Facebook FB 1.09% , and Amazon AMZN 0.78% have open sourced their own artificial intelligence software so any programmer can access the code and use it to build their own apps, they are not making the necessary data available, Zilis explained. These companies are releasing these AI software toolkits for free so they can recruit more engineers to their companies, she said.

When it comes to competition, it’s the data these companies posses that’s more important than the actual AI software tools they use and release to the public.

David Kenny, the general manager of IBM’s IBM 1.12% Watson data crunching service, agreed with Zilis and said “the value of data goes up every day AI advances.”

“Data will become a currency,” Kenny said. He also explained that only 20% of the world’s information is stored on the Internet, with the other 80% being privately held within companies and organizations.

Additionally, Zilis discussed the history of so-called “AI winters” in which the hype of AI failed to meet the general public’s expectations, and led to periods of years of declining research into AI during the mid-1970’s and the late 1980’s to early 1990’s.

Today, however, the rise of the cloud and the ability to access tremendous amounts of computing power to analyze huge amounts of data has the potential to strengthen the existing AI-related algorithms and lead to new more powerful algorithms. Because of these technological developments, Zilis does not believe companies will lose interest in AI.

“Are we going to hit a winter?” asked Zilis. “Absolutely not.”

( Source: Fortune Magazine, written by Jonathan Vanian)

Using deep learning to analyze genetic mutations

Full article written by David Beyer can be found here: Deep learning meets genome biology

  • The application of deep learning to genomic medicine is off to a promising start; it could impact diagnostics, intensive care, pharmaceuticals and insurance.
  • The “genotype-phenotype divide”—our inability to connect genetics to disease phenotypes—is preventing genomics from advancing medicine to its potential.
  • Deep learning can bridge the genotype-phenotype divide, by incorporating an exponentially growing amount of data, and accounting for the multiple layers of complex biological processes that relate the genotype to the phenotype.
  • Deep learning has been successful in applications where humans are naturally adept, such as image, text, and speech understanding. The human mind, however, isn’t intrinsically designed to understand the genome. This gap necessitates the application of “super-human intelligence” to the problem.
  • Efforts in this space must account for underlying biological mechanisms; overly simplistic, “black box” approaches will drive only limited value.

(Source: Deep Genomics)

Liberating Machine Vision From the Machines

Until recently, computer vision — used most widely in manufacturing — and mainstream computing technology have existed in parallel worlds. Along with other factory floor technologies, computer vision tends to be machine-specific, hardware driven, and makes little if any use of the Internet. Many the advances we take for granted in modern computing — ubiquitous connectivity, unlimited data storage in the cloud, insights drawn from massive unstructured data sets — have yet to be applied systematically to the factory floor in general and to computer vision specifically.

It’s no surprise when you consider that until recently most computer vision software was written by computer vision hardware makers, built on embedded systems without open APIs. What comes to mind when you think of the software that came bundled with your scanner, your Wi-Fi router, your car’s navigation system? Balky, inflexible and unintuitive. The software isn’t much more than a utility to run the hardware.

But this closed world is being broken open by a convergence of emerging technologies:

  • The proliferation of cheap, high pixel-density camera sensors
  • Open implementations of vision algorithms, machine learning, and statistical tools
  • Large amounts of cheap computing power, becoming virtually limitless in the cloud

These technologies offer all the raw materials needed for a massive shift in how computer vision is practiced. It’s a shift from focusing on the raw material of visual data — the pixels and bitmaps generated by specific cameras — to extracting data from images and using statistical and data science techniques to draw insights.

This new approach to computer vision has a powerful application amid an American manufacturing renaissance emphasizing rapid product cycles and mass customization. Whereas the archetypal American factory was built around systematic, repeatable function, modern manufacturing is about flexibility, adaptability and high efficiency. We’ve gone from Henry Ford’s “any colour he wants so long as it is black” to Google’s Moto X phone — customer-configured, manufactured in the U.S. and delivered within four days.

Unrelenting Quality Demands

But that need for flexibility on the manufacturing line is in tension with unrelenting quality demands that manufacturers face across industries and down supply chains. Despite huge investments in quality control, automakers recalled nearly as many cars as they sold in the U.S. in 2012. Ford and GM made warranty payments of $5.7 billion in 2012, more than half of the $10.5 billion they reported in net income. Automakers are now paying suppliers prices based on benchmarks like defects per million, terminating those who fall below thresholds, and pushing liability for warranty claims down to their suppliers.

While automation has transformed much of manufacturing, a surprising amount of quality control is still done by hand or otherwise relies on human judgement. Many types of inspection require visual evaluation, but manfacturers’ experience with computer vision in quality control has been a frustrating one. Walk into a factory and ask the manager about computer vision, and you are likely to hear a variant of, “Oh yeah, we tried that, it didn’t work very well, we had to throw it out.”

Existing machine vision uses a 30-year-old architecture that’s capital-intensive and severely constrained in its abilities. Today’s computer vision systems operate as stand-alone islands, rarely connected to the Internet. Every time needs change, each installation has to be manually reprogrammed, unit by unit.

Worse still, little data is kept, making it difficult to spot trends or find correlations among multiple variables. Most manufacturing quality inspection by machine vision today is pass/fail. If the initial inspections of a production run pass the quality inspection, the machines are turned on and the testing data overwritten.

The New Computer Vision

The new computer vision, liberated from its hardware shackles and empowered by connectivity, unlimited data storage and Big Data-style statistical analysis, is beginning to change the role of vision in manufacturing. Instead of being a reactive tool to detect defects, computer vision is becoming a data collection tool supporting defect prevention initiatives, improving understanding of complex processes, and enabling greater collaboration across entire supply chains in real time.

With modern web services, once the data is collected it is easily aggregated into dashboards and distributed to production workers, quality engineers, and management, locally or around the globe. Manufacturers can share data with supply chain partners, making it easier to monitor their suppliers or to satisfy reporting requirements for customers.

One of our customers, a large manufacturer of high-quality bolts and other fasteners to automakers, is bringing this vision to life. Their system uses computer vision to analyze the grain pattern of bolts. If the pattern is wrong — if the grain lines end on a load-bearing surface — the bolt head can shear off when a factory worker torques it down, or worse, when it’s already holding an engine block in place.

The company is capturing images using a $100 scanner purchased at Best Buy. All the intelligence is in the software, running remotely on Amazon’s cloud computing platform. The system compares each image to thousands of other metal grain photos stored in the cloud, looking for patterns that correlate with part failure.

The bolt maker is now exploring the extension of its the computer vision system to its steel supplier, which will capture images of metal grain from each batch of steel rods it ships to the fastener maker. The fastener maker will then be able to analyze increasingly massive data sets to correlate grain patterns in the steel rods with quality measurements in the finished bolts.

Instead of examining only a single station, large data sets let companies trace complex interactions down the production line and across the supply chain. Upstream stations may produce parts that are technically within tolerance, but when certain ranges of acceptable variation are combined, they cause downstream defects after installation.

For our bolt-making customer, the raw material (a steel rod) and the batch of bolts made from that rod may each be well within spec, but retrospective data analysis may show that certain combinations of grain pattern in the steel rods lead to higher failure rates on bolts used for specific applications.

As automakers adapt the system it will gain even more power. Should an automaker report that the fastener-maker’s bolts are breaking and leading to warranty repairs, the parts supplier now has the analytical tools to determine the source of the problem. They can run analysis to determine whether the failed bolts came from a particular batch of steel rods, or were made on a day when their line was adjusted to a specific tolerance – or whether the problem wasn’t with the bolt itself, but rather with the worker on the left side of the assembly line who consistently overtorques the engine bolts.

Once the captured data is in the cloud, such systems can store an unlimited amount of data indefinitely, for reanalysis and retrieval anytime. They let plants run correlations over time, track trends and identify root causes, and as new variables of interest arise, go back and analyze previously acquired data.

As each plant gets smarter, the whole system gets smarter. Like Google learning more about consumers with their every search and click, we’re able to aggregate our learnings from quality issues common across industries.

Ultimately, vision can turn physical world challenges into Big Data problems. We know how to solve these Big Data problems better and better every day.

(Written by Jon Sobel, CEO and co-founder of Sight Machine Inc.)

The future of computer vision

Within 20 years, computer vision will be a commodity component within the fabric of the worldwide analytics infrastructure, similar to the telecommunications infrastructure of today, containing distributed analytics and databases services. Application-specific analytics and intelligence will be added to all devices by default within the Internet of All Things (IoAT), including visual, audio, textual, numerical and sensor analytics. A few new Neural Computing (NC) architectures will be standardized in silicon, applicable to all forms of data.

Major government and corporate initiatives are currently underway, similar to the space race, to create artificial brains which will contribute to the NC of the future. Future systems will contain application-specific mixtures of NCs, CPUs, GPUs, sensor processors, and IO. The underlying technology will be a near zero-cost commodity, and the revenue will come from services, similar to phone or cable services.

Imaging devices will be more accurate with more on-chip processing power for image processing and analytics. Image processing algorithms will be similar to those used today, with no major innovations expected. The computer vision community will standardize on a few feature descriptors and features learning architectures, enabling a generic NC platform for application-specific innovation and market growth.

Computer vision and analytics systems will be far superior to the primitive deep learning models in use today, combining deep-learning and multivariate wide-learning together, with improved feature descriptor models and comprehensive training protocols enabled by ubiquitous databases containing labeled samples of any type of image or data such as audio, textual, financial, and information about a person, place or thing. Personal privacy will virtually disappear.

Within 20 years, most mobile and hand-held devices will contain NCs connected to remote analytics services to enable personal, business, commercial, governmental, military, law enforcement and legal organizations to perform combined audio, visual, historical, and textual evaluations to enable shopping, tourism, employment interviews, banking, commerce, law enforcement or housing applications.

Neural computers will evaluate facial expression, body language and clothing style for emotions and intentions, as well as audio evaluation of the tone and rhythm of spoken words for latent intentions and assumptions, including analysis of the words from email, texts, blogs, and historical records from local governments, academic institutions, purchasing records, and other financial transactions.

The analytics will provide scenarios and what-if analysis and prediction of future behavior within a set of circumstances, for example allowing a commercial enterprise to design situations or opportunities to suit their preferences and influence purchasing behavior, or by allowing governments to develop policies and propaganda to test the reactions of a population, their preferences, intentions and personal beliefs.

Computer vision will be a central component of the future of the analytics infrastructure. Imagine government policy and business plans being designed around the predictions generated by an NC to form future programs and evaluation of each program by another NC to form recommendations, with the best recommendation being chosen by another NC to send to the final decision authority – a human…or an NC?

(Source: www.KrigResearch.com)