How to Screen Record on a Mac

There are many times when recording your Mac screen can come in handy. Maybe you want to create a screencast tutorial. Perhaps you’re creating a business presentation. Or maybe you’re interested in making video notes for yourself.

Whatever the reason, it’s simple, and you have a few different ways to do it. So here’s how to record your screen on a Mac with several methods.

How to Record on Mac With QuickTime

QuickTime comes preinstalled on your Mac and you can do a lot with it, like rotating video files for instance. So using this tool to record your screen makes perfect sense. Open QuickTime Player, then select File > New Screen Recording from the menu bar.

Click the arrow next to the red button to set up the recording. Here, select from None or Internal Microphone for your audio and check or uncheck Show Mouse Clicks in Recording.

Now hit the red button, then simply click to record your entire screen, or drag to choose a specific part of it, and start recording. This will place the QuickTime Player icon in your menu bar. When you finish recording, just click that button.

Your recording will pop right open for you to view. To save it, select File > Save from the menu bar, give your recording a name, and choose its location. Click Save and you’re done.

Benefits

• The app is installed on your Mac by default, so there are no extra costs or installations.
• QuickTime Player offers additional features such as movie and audio recordings.
• You can use AirPlay or sharing options immediately from your recorded video.

How to Record on Mac With the Screenshot Utility

One of the new features that comes with macOS Mojave is the screenshot utility. This cool tool lets you capture screen recordings in addition to screenshots.

To open the utility, press Cmd + Shift + 5 on your keyboard. At the bottom of the window that displays, you’ll see two options to Record Entire Screen and Record Selected Portion.

If you choose Record Entire Screen, a camera icon will appear. This is handy if you use more than one monitor. Just move the camera to the screen you want to record and click for the recording to begin.

If you choose Record Selected Portion, drag the corners of the box you see to adjust the size. You can also move the box to a different area on your screen. Click Record to start recording.

Benefits

• The screenshot utility is a new feature of macOS Mojave, so it’s free and required no software installing.
• Features include a built-in microphone for audio, a timer for timed recordings, and the ability to show mouse clicks for tutorials.
• Like QuickTime, you can use AirPlay or sharing options immediately from your recorded video.

How to Record on Mac With Third-Party Apps

With the above two options, there’s no reason to seek a third-party app for recording your Mac screen unless you want or need more features. Here are a couple of options that offer a little extra.

Viking Recorder Lite

Once you install Viking Recorder Lite, a handy icon will pop into your menu bar, letting you start a recording in a snap. You can capture your entire screen or just part of it, include audio along with mouse clicks, and adjust the codec and frame settings.

To begin, select Start Screen Recording from the menu bar dropdown box and make your setting adjustments in the popup window. Select Stop Recording from the dropdown when you finish and then follow the prompt to save your recording.

Standout Features

• Viking Recorder Lite comes with both a built-in movie editor and YouTube downloader.
• You can use hotkeys, tweak the user interface, view help balloons, have the menu bar icon blink during recording, and use the Notification Center.
• As you move the New Recording window, you can see a quick preview.

If these standout features interest you, then give Viking Recorder Lite a try for free. If you like it and want more features, including unlimited video lengths, you can look at the paid version.

Download: Viking Recorder Lite (Free) | Viking Recorder ($3)

Smart Recorder Lite

Smart Recorder Lite is another good screen recording app with a super-simple interface. Open the app and select your options for the capture device, full or partial screen, screen quality, audio source, and saved path.

To begin, open the app, make your settings adjustments, and then click Start Recording. An icon will pop into your menu bar where you can see the elapsed time as you record. Click that icon when you finish, and your recording will open immediately for you to view. It will also save to the location you specified in the settings.

Standout Features

• Smart Recorder Lite lets you record your screen or use the FaceTime HD camera.
• You can record audio from additional sources (even more than one at a time), including the built-in microphone, computer sound card, or input device.
• Screen quality options range from low to high, and frame rate options go from 1-30FPS.

If you like this, you can grab Smart Recorder Lite for free. You can also take a look at the paid version, which offers additional features like recording times beyond 300 seconds.

Download: Smart Recorder Lite (Free) | Smart Recorder ($5)

Next: How to Take Screenshots on a Mac

Recording your computer screen used to be more complicated. But as you can see, it gets easier all the time. Hopefully, one of these methods is exactly what you need to screen record on your Mac.

And if you’re interested in learning more about Mac screenshots or how to use your Mac to capture your Android screen, we’ve covered those too.

Read the full article: How to Screen Record on a Mac

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8 Must-Have Smartphone Apps for Electric Car Owners

Amazon Fire Stick vs. Roku: Which One Is Better?

The popularity of cord cutting continues to gather pace. Collectively, cable TV companies are losing millions of subscribers every year.

If you’ve ditched your TV subscription, there’s a good chance you’re trying to decide between an Amazon Fire TV stick and one of the many Roku devices as your new entertainment platform.

Keep reading to find out which gadget you should buy.

A Complicated Comparison

Unfortunately, it’s impossible to make a like-for-like comparison between Amazon Fire TV devices and Roku streaming sticks.

We need to consider two Amazon products: The Fire TV Stick and the Fire TV Stick 4K. On the Roku side, there are six devices which can be thought of as Fire TV competitors: Roku Express, Roku Express +, Roku Premiere, Roku Premiere +, Roku Streaming Stick, and Roku Streaming Stick +.

We’ll cover all of these devices in this article.

Amazon Fire Stick vs. Roku: Cost

Before we get into the features and the technical specifications, let’s deal with the elephant in the room—the cost of the devices.

Amazon’s entry-level Fire TV Stick costs $39.99. The 4K model will set you back a further $10, coming in at $49.99.

The cheapest Roku model is the Roku Express. At $29.99, it’s more affordable than the Fire TV. At the other end of the scale, the top Roku model (Ultra excluded) is the Roku Streaming Stick +, which costs $59.99.

From a cost standpoint, the nearest Roku devices to Amazon’s two products are the Roku Premiere ($39.99), the Premiere + ($49.99), and the Streaming Stick ($49.99).

Amazon Fire Stick vs. Roku: Specifications

This is where things get confusing. Let’s try and make sense of all the different models on offer from the two companies.

First, the Amazon devices. The basic Fire TV Stick has a 1.3GHz processor, 8GB of internal memory, and support for Bluetooth 4.1. It plays videos in 720p or 1080p resolution at up to 60 frames-per-second (FPS).

The 4K model is a notable improvement. You’ll find a 1.7GHz processor, support for Bluetooth 5.0, and 2160p video resolution. The internal storage stays at 8GB.

Three Roku products—the Express, Express +, and Streaming Stick—only offer 1080p resolution, the offers offer 4K.

Amazon Fire Stick vs. Roku: Controls

All Roku and Amazon Fire TV devices ship with a dedicated remote control.

Both Amazon controls support Alexa. If you want to control your Roku with your voice, you will need to buy a Roku Premiere +, Roku Streaming Stick, or Roku Streaming Stick +.

Both devices also have an accompanying remote control smartphone app.

Lastly, if you have an Amazon Echo speaker, you can sync it with your Fire TV Stick and use it to control your content.

Amazon Fire Stick vs. Roku: Interface

Visually, the Amazon platform is more modern and feels more polished. However, critics have argued that it pushes Amazon’s own content too aggressively.

It’s a valid viewpoint. You’ll only see one row of your own apps at the top of the screen. And if you have too many installed, you’ll need to scroll all the way to the right and click on View All to see them.

The rest of the home screen real estate is taken up by content from Amazon Prime Video. Even if you don’t subscribe to the service, you will still see it.

Roku’s interface is more customizable. All your channels are displayed in a scrollable list. If you install third-party add-ons, you can even place your channels into groups for easier navigation.

On the downside, Roku’s menus and visuals are dated. They badly need a refresh.

Amazon Fire Stick vs. Roku: TV Shows and Movies

If you’re looking for a provider-agnostic device, Roku is the best streaming stick on the market. It’s not only better than Amazon Fire TV sticks; it’s also better than Android TV, Apple TV, and Chromecast dongles.

You’ll find apps for just about every on-demand video and music streaming app, including Netflix, Hulu, YouTube, Google Play Movies, Spotify, and TuneIn Radio.

Roku also offers a vast library of private channels. You need to enter a code in the Roku web portal to install them on your device. Be warned—many of the private channels lie in a grey area of legality.

The biggest problem with the Amazon Fire Stick is the lack of a native YouTube app. Ongoing bickering between the two tech giants forced Google to pull its apps from Amazon’s platform almost a year ago, and there’s no hint that they’ll be returning any time soon.

YouTube is still available via a web browser on the device, but it’s a clumsy workaround.

Amazon Fire Stick vs. Roku: Web Browsing

Speaking of browsers, it’s worth noting that only the Amazon products let you surf the web. Two browsers are available—Amazon’s own Silk Browser and Firefox. You can control them both easily using the Fire TV remote.

You can also sideload apps on Fire TV Sticks. The process lets you install any browser from the Google Play Store. However, most other browsers are not compatible with the remote, meaning you will also need to install a mouse app on your Fire TV.

In contrast, Roku devices do not offer any facility to browse websites.

Amazon Fire Stick vs. Roku: Gaming

Roku devices and Amazon Fire TV Sticks both offer games on their platform.

However, hardcore gamers might find Fire TV devices are more suitable for their needs. Generally speaking, Roku games are a bit “cutesy”. Sure, they’ll keep you entertained for half an hour, but they don’t offer longevity.

The games on Amazon’s devices are beefier. You’ll find titles such as Minecraft, Badland, and Star Wars.

Of course, if the ability to game on your streaming device is high on your list of priorities, neither a Roku or a Fire TV Stick can hold a candle to the Nvidia Shield. You can stream titles from your PC using Nvidia GameStream, download a host of local games from Nvidia and Google Play, and install emulators for classic consoles.

We have written about the best games on Amazon Fire TV and the best games on Roku if you’d like more information before making a purchase.

Amazon Fire Stick vs. Roku: Screen Mirroring

Roku devices have Miracast technology built-in. If you’re not aware, Miracast is like a wireless version of an HDMI cable. Most Android and Windows devices are Miracast-compatible. Apple devices are not.

Some older Amazon Fire TV models also support screen mirroring. Oddly, it’s not available on the third-generation Amazon Fire Stick nor the 4K model.

Which Is Right for You?

It’s really difficult to choose a clear winner. Much depends on how you plan to use your device, which gadgets you already own, and which streaming service you subscribe to.

All else being equal, we’d recommend either the Amazon Fire TV 4K or the Roku Streaming Stick +. And remember, you could even buy a Chromecast. We have written a comparison of Roku and Chromecast if you would like to learn more.

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15 Mac Apps That Enable Mojave’s Dark Theme Everywhere

One of the best new features of macOS Mojave is the dark mode. Not only does this look way cooler, but it’s also easier on your eyes at night.

Since I installed Mojave, I’ve been on a quest to set a dark mode for everything. This includes all the third-party apps I use and frequently visited websites. I also found a utility that automatically enables dark mode at a given time, making it smarter.

If you like dark apps and you’ve updated to macOS Mojave, use the following apps, themes, settings, and utilities to join the dark side.

How to Enable Dark Mode on Mac

First, make sure you’re running macOS Mojave. If not, head to the App Store to download the update.

Then open System Preferences > General > Appearance and select Dark.

You’ll notice that macOS’s theme changes to black, and this isn’t just for the menu bar and Dock. Once enabled, every default app like Safari, Finder, and even Notes gets a black or gray background with white text.

Dark Mode for the Web

You probably spend most of your time in a browser. So first, let’s add a dark mode for your browser.

1. Dark Mode Themes for Chrome

If you’re using Chrome, install the Mojave Dark Theme to get a dark-gray look for the Chrome window. The colors are very close to what you’ll find in Finder.

But if you want to go completely black, use the Morpheon Dark theme instead.

Download: Mojave Dark Theme (Free) | Morpheon Dark Theme (Free)

2. Dark Reader

Dark Reader is an awesome extension that automatically adds a dark mode to every website. And it has a surprisingly good hit rate. Most of the websites that I visit regularly work well. The background turns dark gray with white text. And you can adjust the brightness and contrast to make the background completely black.

In my experience, Dark Reader works better on Chrome than on Safari. And while it’s free on Chrome, the Safari extension costs $2.

The best part of Dark Reader is how customizable it is. You can create themes or turn off dark mode for individual websites. This is useful for websites that already have a stellar dark mode, like Gmail, YouTube, and others.

Download: Dark Reader for Chrome (Free) | Dark Reader for Safari ($2)

3. Black Theme for Gmail

The new Gmail update comes with a great new dark theme. Click the Settings icon, select Themes, scroll down, and select the Dark theme. We’ve covered how to customize Gmail in other ways, too.

4. Night Mode for Reddit

The new Reddit UI also has a built-in dark mode. After logging in, click on your Profile button and toggle the Night mode.

5. Trello Night for Chrome

Trello is one of the few productivity apps that’s mostly used on the web. Chrome users can now enable a dark mode using the Trello Night extension.

Download: Trello Night for Chrome (Free)

6. Night Mode for Twitter

Twitter’s default Night mode is more blue than black, but it does the job. After logging in, click the Profile button and then select Night mode.

Dark Mode Mac Apps

There’s an amazing collection of dark mode Mac apps that pair well with the stock apps. The best part is that most apps on the list can synchronize their theme based on your system preference. So when you switch from light mode to dark mode in your system preferences, all supported apps will instantly switch to dark mode too!

7. Caprine for Facebook Messenger

If you use Facebook Messenger regularly, you might benefit from a dedicated Messenger app like Caprine. It supports native notifications and comes with a sweet dark mode.

Download: Caprine (Free)

8. ChatMate for WhatsApp

ChatMate for WhatsApp is a better version of the WhatsApp desktop app. It supports Do Not Disturb, has a privacy mode, and more importantly, packs a great dark theme.

Download: ChatMate for WhatsApp ($3, free trial available)

9. Ulysses

Ulysses is the best writing app for macOS and the recent update for macOS Mojave brings a new dark theme with an improved contrast ratio. The syntax and links are in blue, which makes them much easier to read.

Download: Ulysses ($5/month, free trial available)

10. Bear

If Apple Notes is not your cup of tea, take a look at Bear. It has great organization features and supports Markdown. Plus, the dark theme looks gorgeous on a Mac’s Retina display.

Download: Bear (Free, subscription available)

11. Fantastical 2

Fantastical 2 is the preferred calendar solution for those who don’t like the stock Calendar app. It’s featur- rich, comes with natural language processing, and looks great on Mojave. Its built-in dark theme activates whenever you turn on macOS’s dark mode.

Download: Fantastical 2 ($25, free trial available)

12. Things 3

Things is a simple yet gorgeous task management app for Apple devices. It takes the complexity of the Getting Things Done system and distills it into a simple UI that’s a pleasure to use and look at. Things 3 for macOS has been updated with an equally gorgeous dark theme.

Download: Things 3 ($50, free trial available)

13. Tweetbot 3

Tweetbot is still the best Twitter client for Mac. The recent Tweetbot 3 update brings a new design and a dark theme. Like many other professional apps on this list, Tweetbot can automatically change the theme based on your macOS theme preference.

Download: Tweetbot 3 ($10)

14. Spark

Spark’s recent update makes it one of the most innovative email apps on macOS. You can now chat with your team members from inside an email and collaborate on emails before sending them out. The new dark mode looks great too.

Download: Spark (Free)

15. ReadKit

ReadKit is one of the best RSS readers for macOS. It supports a ton of syncing services and offers great reading customization. Switch to the dark theme and make your RSS reading easy on your eyes. ReadKit can match your system theme as well.

Download: ReadKit ($5, free trial available)

Make Dark Mode Easier With NightOwl

If you like to toggle between the light and dark modes frequently, going to System Preferences every time will get old quickly.

Install the free NightOwl app and you’ll have a quick switcher right in the menu bar. Plus, you can schedule dark mode to automatically kick in at a certain time or right after sunset.

Download: NightOwl (Free)

Dark Mode Isn’t All Mojave Offers

Many professional independent apps on macOS have been updated with a dark theme. If you use an app like iA Writer, AirMail, Todoist, OmniFocus, Sublime Text, Day One, and other favorites, just go to the app’s preferences and look for theme options.

Dark modes look great and they make your Mac easier to use at night. With all these options available, why not give it a try?

Don’t forget that dark mode is just one of macOS Mojave’s awesome new features. After updating, you should also try out the new stacks feature, dynamic wallpapers, and the refreshed screenshot utility.

Read the full article: 15 Mac Apps That Enable Mojave’s Dark Theme Everywhere

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Tumblr will delete all porn from the platform

Tumblr, a microblogging service that’s impact on internet culture has been massive and unique, is preparing for a massive change that’s sure to upset many of its millions of users.

On December 17, Tumblr will be banning porn, errr “adult content,” from its site and encouraging users to flag that content for removal. Existing adult content will be set to a “private mode” viewable only to the original poster.

What does “adult content” even mean? Well, according to Tumblr, the ban means the removal of any media that depicts “real-life human genitals or female-presenting nipples, and any content—including photos, videos, GIFs and illustrations—that depicts sex acts.”

This is a lot more complicated than just deleting some hardcore porn from the site; over the past several years Tumblr has become a hub for communities and artists with more adult themes. This has largely been born out of the fact that adult content has been disallowed from other multimedia-focused social platforms. There are bans on nudity and sexual content on Instagram and Facebook, though Twitter has more relaxed standards.

Why now? The Tumblr app was removed from the iOS app store several weeks ago due to an issue with its content filtering that led the company to issue a statement. “We’re committed to helping build a safe online environment for all users, and we have a zero tolerance policy when it comes to media featuring child sexual exploitation and abuse,” the company had detailed. “We’re continuously assessing further steps we can take to improve and there is no higher priority for our team.”

We’ve reached out to Tumblr for further comment.

Update: In a blog post titled “A better, more positive Tumblr,” the company’s CEO Jeff D’Onofrio minimized claims that the content ban was related to recent issues surrounding child porn, and is instead intended to make the platform one “where more people feel comfortable expressing themselves.”

“As Tumblr continues to grow and evolve, and our understanding of our impact on our world becomes clearer, we have a responsibility to consider that impact across different age groups, demographics, cultures, and mindsets,” the post reads. “Bottom line: There are no shortage of sites on the internet that feature adult content. We will leave it to them and focus our efforts on creating the most welcoming environment possible for our community.”

The imminent “adult content” ban will not apply to media connected with breastfeeding, birth or more general “health-related situations” like surgery, according to the company.

Tumblr is attempting to make aims to minimize the impact on the site’s artistic community as well, but this level of nuance is going to be incredibly difficult for them to enforce uniformly and will more than likely lead to a lot of frustrated users being told that their content does not qualify as “art.”

Tumblr is also looking to minimize impact on the more artistic storytelling, “such as erotica, nudity related to political or newsworthy speech, and nudity found in art, such as sculptures and illustrations, are also stuff that can be freely posted on Tumblr.”

I don’t know how much it needs to be reiterated that child porn is a major issue plaguing the web, but a blanket ban on adult content on a platform that has gathered so many creatives working with NSFW themes is undoubtedly going to be a pretty controversial decision for the company.

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TrackR rebrands to Adero, pivots to finding whereabouts of groups of items

Earlier this year, we reported that TrackR, a startup that makes a small Bluetooth device that you attach to items like keys to locate them when lost, would be rebranding as Adero and pivoting the business amid an increasingly commoditized market for its basic hardware product.

Now, that is just what is happening.

Today, Adero is officially making its debut, with a product that builds and expands on the TrackR, by providing a system to organise multiple groups of items — not just so that you can find them when they’ve been misplaced, but also so that you can be proactively alerted when you have forgotten something behind in a grouped set of items when on the move.

A starter kit — containing three smart tags, three smaller item “taglets,” a tag charger, lanyards, cases and key rings — is going on sale today for $119.99. A “deluxe” case with five tags and nine taglets will be coming soon, costing $199.99. The Adero is compatible with Android 5.1 and higher, and will be soon adding iOS 11, too.

To be clear, while Adero is discontinuing the TrackR, the company has confirmed that it will continue to support those that are in the market (for the time being).

If you think that the Adero sounds like a glorified version of what it is replacing, the company says that, in addition to offering a way of grouping items that you are tracking (“smart containers”), and chirping at you when you leave something behind, it has other features that make it more useful.

These include creating time-based reminders to collect your items; a rechargeable smart tag battery; and water resistance.

The move is a significant, and necessary, leap that the startup had to take.

In a market full of competition, even the largest of the bunch, Tile, has worked on refocusing its own remit under a new CEO, taking strategic investment from Comcast to develop products with the broadband giant as it tries to tap into more connected home opportunities and the promises of IoT (before other tech players like Amazon, Google and Apple once again eat up what could have been an obvious carrier opportunity, as they have done in other areas).

For its part, Adero had been struggling to find the right margins and business model for TrackR, and attempts to expand the original product — such as a Beacon device called the Atlas — also met dead ends (after a debut at CES, the Atlas never made it to market).

Weathering layoffs and natural disaster, the low point of sorts may have been when the company quietly raised $10 million in July, at a $40 million valuation, according to PitchBook.

It was a clear downround: TrackR was valued at $150 million when it raised $50 million as recently as August 2017. Investors were not disclosed in the most recent funding, but previous backers of the company, in addition to Amazon, include Foundry Group, NTT and Revolution.

“Foundry and Revolution were hoping that they would put this money in and I could fix and scale things, similar to how I’d scaled Sonos and so on. But within six weeks, it became evident that we didn’t need to scale but figure out what the future was and where this is going,” said Nathan Kelly, who was promoted to CEO from COO in December 2017, in an interview ahead of today.

Kelly’s resume includes years at Sonos, Tesla and Facebook’s Building 8, so he has had a spectrum of experiences of what it means to build and sell hardware. To his credit, he talked to me frankly, even after we scooped their launch.

“We realised that in the 10 years that TrackR had been out there, there wasn’t anything new,” he said. It was a call that maybe only a non-founder could make (neither Christian Smith nor Chris Herbert, the two founders, are with the company anymore). “There wasn’t enough innovation going on. We had to figure out what is next, not just rinsing and repeating in a spec war when the original idea was not that interesting to begin with.”

Going forward, he says there is another, bigger funding round in the works — likely dependent on how this effort will go, I’m guessing — and there are plans to add more intelligence into the product.

“Adero will soon be smart enough to know what you are leaving behind [when you need it]. It will say you are leaving without your wallet in the morning,” or perhaps your passport before a trip abroad.

Kelly added the company wasn’t able to get that rolled out in time for this launch — which it hoped to have out in time for holiday shopping — but an early version of the predictive feature should be out “within a few weeks.” He also noted that the company filed a number of large patents in the last year to this end.

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Google at NeurIPS 2018

Posted by Slav Petrov, Principal Scientist, Google

This week, Montréal hosts the 32^nd annual Conference on Neural Information Processing Systems (NeurIPS 2018), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Google will have a strong presence at NeurIPS 2018, with more than 400 Googlers attending in order to contribute to, and learn from, the broader academic research community via talks, posters, workshops, competitions and tutorials. We will be presenting work that pushes the boundaries of what is possible in language understanding, translation, speech recognition and visual & audio perception, with Googlers co-authoring nearly 100 accepted papers (see below).

At the forefront of machine learning, Google is actively exploring virtually all aspects of the field spanning both theory and applications. This research is often inspired by real product needs but increasingly more often driven by scientific curiosity. Given the range of research projects that we pursue, we have found it useful to define a new framework which helps crystalize the goals of projects and allows us to measure progress and success in appropriate ways. Our contributions to NeurIPS and to the broader research community in general are integral to our research mission.

If you are attending NeurIPS 2018, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving the world's most challenging research problems, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of NeurIPS 2018.

NeurIPS Foundation Board
Corinna Cortes, John C. Platt, Fernando Pereira

NeurIPS Organizing Committee
General Chair: Samy Bengio
Program Co-Chair: Hugo Larochelle
Party Chair: Douglas Eck
Diversity and Inclusion Co-Chair: Katherine A. Heller

NeurIPS Program Committee
Senior Area Chairs include:Angela Yu, Claudio Gentile, Cordelia Schmid, Corinna Cortes, Csaba Szepesvari, Dale Schuurmans, Elad Hazan, Mehryar Mohri, Raia Hadsell, Satyen Kale, Yishay Mansour, Afshin Rostamizadeh, Alex Kulesza

Area Chairs include: Amin Karbasi, Amir Globerson, Amit Daniely, Andras Gyorgy, Andriy Mnih, Been Kim, Branislav Kveton, Ce Liu, D Sculley, Danilo Rezende, Danny Tarlow, David Balduzzi, Denny Zhou, Dilan Gorur, Dumitru Erhan, George Dahl, Graham Taylor, Ian Goodfellow, Jasper Snoek, Jean-Philippe Vert, Jia Deng, Jon Shlens, Karen Simonyan, Kevin Swersky, Kun Zhang, Lihong Li, Marc G. Bellemare, Marco Cuturi, Maya Gupta, Michael Bowling, Michalis Titsias, Mohammad Norouzi, Mouhamadou Moustapha Cisse, Nicolas Le Roux, Remi Munos, Sanjiv Kumar, Sanmi Koyejo, Sergey Levine, Silvia Chiappa, Slav Petrov, Surya Ganguli, Timnit Gebru, Timothy Lillicrap, Viren Jain, Vitaly Feldman, Vitaly Kuznetsov

Workshops Program Committee includes: Mehryar Mohri, Sergey Levine

Accepted Papers
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T. Freeman, Joshua B. Tenenbaum

A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Collaborative Learning for Deep Neural Networks
Guocong Song, Wei Chai

Completing State Representations using Spectral Learning
Nan Jiang, Alex Kulesza, Santinder Singh

Content Preserving Text Generation with Attribute Controls
Lajanugen Logeswaran, Honglak Lee, Samy Bengio

Context-aware Synthesis and Placement of Object Instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz

Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerlo Feris, William T. Freeman, Gregory Wornell

cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, H. Brendan Mcmahan

Data Center Cooling Using Model-Predictive Control
Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu, Greg Imwalle

Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang

Generalizing Point Embeddings Using the Wasserstein Space of Elliptical Distributions
Boris Muzellec, Marco Cuturi

GLoMo: Unsupervised Learning of Transferable Relational Graphs
Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun

GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang, Armando Solar-Lezama, Rishabh Singh

Learning Hierarchical Semantic Image Manipulation through Structured Representations
Seunghoon Hong, Xinchen Yan, Thomas Huang, Honglak Lee

Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Learning Towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens

SplineNets: Continuous Neural Decision Graphs
Cem Keskin, Shahram Izadi

Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

To Trust or Not to Trust a Classifier
Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu

Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman, Mehryar Mohri, Ningshan Zhang

A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar

Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

A Loss Framework for Calibrated Anomaly Detection
Aditya Menon, Robert Williamson

Blockwise Parallel Decoding for Deep Autoregressive Models
Mitchell Stern, Noam Shazeer, Jakob Uszkoreit

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

Contextual Pricing for Lipschitz Buyers
Jieming Mao, Renato Leme, Jon Schneider

Coupled Variational Bayes via Optimization Embedding
Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi HAO, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu

Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu

Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wei Liu, Ming-Hsuan Yang

Diminishing Returns Shape Constraints for Interpretability and Regularization
Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini

DropBlock: A Regularization Method for Convolutional Networks
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman, Jan Vondrak

Geometrically Coupled Monte Carlo Sampling
Mark Rowland, Krzysztof Choromanski, Francois Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller

GILBO: One Metric to Measure Them All
Alexander A. Alemi, Ian Fischer

Insights on Representational Similarity in Neural Networks with Canonical Correlation
Ari S. Morcos, Maithra Raghu, Samy Bengio

Improving Online Algorithms via ML Predictions
Manish Purohit, Zoya Svitkina, Ravi Kumar

Learning to Exploit Stability for 3D Scene Parsing
Yilun Du, Zhijan Liu, Hector Basevi, Ales Leonardis, William T. Freeman, Josh Tenembaum, Jiajun Wu

Maximizing Induced Cardinality Under a Determinantal Point Process
Jennifer Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda Mariet

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao

PCA of High Dimensional Random Walks with Comparison to Neural Network Training
Joseph M. Antognini, Jascha Sohl-Dickstein

Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Recurrent World Models Facilitate Policy Evolution
David Ha, Jürgen Schmidhuber

Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim

Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Bart van Merriënboer, Dan Moldovan, Alex Wiltschko

The Emergence of Multiple Retinal Cell Types Through Efficient Coding of Natural Movies
Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny

The Everlasting Database: Statistical Validity at a Fair Price
Blake Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Jeffrey Pennington, Pratik Worah

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew Johnson, Dustin Tran

A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai, Michael Mitzenmacher, Ryan Adams (no longer at Google)

Automatic Differentiation in ML: Where We are and Where We Should be Going
Bart van Merriënboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap

Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen, Eirikur Agustsson, Mario Lucic

Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber, Ofer Meshi, Alexander Schwing

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi

Transfer Learning with Neural AutoML
Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo

Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn, Junhyuk Oh, Honglak Lee

Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez

Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D Toderici

Large-Scale Computation of Means and Clusters for Persistence Diagrams Using Optimal Transport
Théo Lacombe, Steve Oudot, Marco Cuturi

Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu

Large Margin Deep Networks for Classification
Gamaleldin Fathy Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio

Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

Meta-Learning MCMC Proposals
Tongzhou Wang, YI WU, Dave Moore, Stuart Russell

Non-delusional Q-Learning and Value-Iteration
Tyler Lu, Dale Schuurmans, Craig Boutilier

Online Learning of Quantum States
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak

Online Reciprocal Recommendation with Theoretical Performance Guarantees
Fabio Vitale, Nikos Parotsidis, Claudio Gentile

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri

Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga, Stefanie Jegelka, Andreas Krause

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Visual Object Networks: Image Generation with Disentangled 3D Representations
JunYan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, William T. Freeman

Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami AlRfou, Alexander Alemi

Workshops
2nd Workshop on Machine Learning on the Phone and Other Consumer Devices
Co-Chairs include: Sujith Ravi, Wei Chai, Hrishikesh Aradhye

Bayesian Deep Learning
Workshop Organizers include: Kevin Murphy

Continual Learning
Workshop Organizers include: Marc Pickett

The Second Conversational AI Workshop – Today's Practice and Tomorrow's Potential
Workshop Organizers include: Dilek Hakkani-Tur

Visually Grounded Interaction and Language
Workshop Organizers include: Olivier Pietquin

Workshop on Ethical, Social and Governance Issues in AI
Workshop Organizers include: D. Sculley

AI for Social Good
Workshop Program Committee includes: Samuel Greydanus

Black in AI
Workshop Organizers: Mouhamadou Moustapha Cisse, Timnit Gebru
Program Committee: Irwan Bello, Samy Bengio, Ian Goodfellow, Hugo Larochelle, Margaret Mitchell

Interpretability and Robustness in Audio, Speech, and Language
Workshop Organizers include: Ehsan Variani, Bhuvana Ramabhadran

LatinX in AI
Workshop Organizers includes: Pablo Samuel Castro
Program Committee includes: Sergio Guadarrama

Machine Learning for Systems
Workshop Organizers include: Anna Goldie, Azalia Mirhoseini, Kevin Swersky, Milad Hashemi
Program Committee includes: Simon Kornblith, Nicholas Frosst, Amir Yazdanbakhsh, Azade Nazi, James Bradbury, Sharan Narang, Martin Maas, Carlos Villavieja

Queer in AI
Workshop Organizers include: Raphael Gontijo Lopes

Second Workshop on Machine Learning for Creativity and Design
Workshop Organizers include: Jesse Engel, Adam Roberts

Workshop on Security in Machine Learning
Workshop Organizers include: Nicolas Papernot

Tutorial
Visualization for Machine Learning
Fernanda Viégas, Martin Wattenberg

Cohort and Age Effects

Cohort and Age Effects

Former eBay product chief RJ Pittman takes the reins at 3D capture company Matterport

Matterport, a provider of 3D image capture technology, has named former eBay chief product officer RJ Pittman as its new chief executive.

Pittman will take the reins from former chief executive Bill Brown, who will continue to advise Matterport as the company looks to capitalize on its library of three dimensional scans.

The company currently has a library of 1.4 million three dimensional models that have been viewed at least 600 million times since the company launched.

According to Silicon Valley Business Journal, the company had revenue in 2017 of $33 million from selling its camera equipment and software services to businesses.

The company was launched when founders Matt Bell and David Gausebeck realized the commercial potential of the motion capture and sensor technology that Microsoft had unveiled with their Kinect camera back in 2010.

At the time, the company’s several thousand dollar pieces of hardware were the cutting edge for capturing images — now it can be done with software and a cell phone camera. The march of technology has put Matterport in a somewhat precarious position, but the company continues to lock in deals with companies like Donan, an investigation service for insurers and others that looks at fire damage.

The company has inked deals with a number of different enterprise customers — and even brought on State Auto Labs as a strategic investor earlier this year.

“Matterport has the opportunity to revolutionize how property risks are underwritten and claims are handled in the insurance industry,” said Kim Garland, Senior Vice President, Commercial Lines & Managing Director of State Auto Labs said in a statement at the time.

In all, Matterport has raised around $77 million from investors including State Auto Labs, Lux Capital, DCM Ventures, Qualcomm Ventures, Ericsson Ventures, AMD Ventures, AME Cloud Ventures, CBRE, Felicis Ventures, GIC, Crate and Barrel founder Gordon Segal, iGlobe Partners, Navitas Ventures, News Corp, and Sound Ventures.

Matterport’s hardware can digtially capture, document, visualize and collaborate around properties in 3D on web, mobile and in VR. And its hosted Matterport Cloud service automates the creation of state-of-the-art 3D models, high-quality 4K 2D photography, floorplans and other assets and stores them in easily accessible formats.

There’s still a lot of contested space in the collection and capture of the real world for use in augmented and virtual reality and the addition of Pittman should help Matterport as it looks at a much more crowded competitive landscape.

“RJ’s operating experience at scale, paired with his entrepreneurial DNA and deep product vision will be instrumental to unlocking the full potential of our breakthrough technology and unparalleled 3D media and data,” said company co-founder and chief technology officer David Gausebeck, in a statement.

Indeed, Pittman discussed the importance of Matterport’s library when he spoke of the opportunity he saw for the company. “Matterport Cloud is an unrivaled dataset of precision 3D environments that represents an enormous opportunity to scale the company’s data services business exponentially. This will open up new strategic partnerships and investments as we realize the full value of this data,” Pittman said in a statement.

As an entrepreneur, product developer and real estate investor, Pittman is uniquely qualified to take charte at Matterport.

He previously worked on product, design, engineering and mobile payments at eBay and held roles at Apple and Google. In addition, he had also co-founded and served as the chief executive for the search engine that created the industry’s first graphical information interface, Groxis.

Finally, Pittman worked on a number of real estate projects in the U.S. and UK, giving him insight on the role that technology can play in the new architectural landscape.

 

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DeepMind claims early progress in AI-based predictive protein modelling

Google-owned AI specialist, DeepMind, has claimed a “significant milestone” in being able to demonstrate the usefulness of artificial intelligence to help with the complex task of predicting 3D structures of proteins based solely on their genetic sequence.

Understanding protein structures is important in disease diagnosis and treatment, and could improve scientists’ understanding of the human body — as well as potentially helping to support protein design and bioengineering.

Writing in a blog post about the project to use AI to predict how proteins fold — now two years in — it writes: “The 3D models of proteins that AlphaFold [DeepMind’s AI] generates are far more accurate than any that have come before — making significant progress on one of the core challenges in biology.”

There are various scientific methods for predicting the native 3D state of protein molecules (i.e. how the protein chain folds to arrive at the native state) from residual amino acids in DNA.

But modelling the 3D structure is a highly complex task, given how many permutations there can be on account of protein folding being dependent on factors such as interactions between amino acids.

There’s even a crowdsourced game (FoldIt) that tries to leverage human intuition to predict workable protein forms.

DeepMind says its approach rests upon years of prior research in using big data to try to predict protein structures.

Specifically it’s applying deep learning approaches to genomic data.

“Fortunately, the field of genomics is quite rich in data thanks to the rapid reduction in the cost of genetic sequencing. As a result, deep learning approaches to the prediction problem that rely on genomic data have become increasingly popular in the last few years. DeepMind’s work on this problem resulted in AlphaFold, which we submitted to CASP [Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction] this year,” it writes in the blog post.

“We’re proud to be part of what the CASP organisers have called “unprecedented progress in the ability of computational methods to predict protein structure,” placing first in rankings among the teams that entered (our entry is A7D).”

“Our team focused specifically on the hard problem of modelling target shapes from scratch, without using previously solved proteins as templates. We achieved a high degree of accuracy when predicting the physical properties of a protein structure, and then used two distinct methods to construct predictions of full protein structures,” it adds.

DeepMind says the two methods it used relied on using deep neural networks trained to predict protein properties from its genetic sequence.

“The properties our networks predict are: (a) the distances between pairs of amino acids and (b) the angles between chemical bonds that connect those amino acids. The first development is an advance on commonly used techniques that estimate whether pairs of amino acids are near each other,” it explains.

“We trained a neural network to predict a separate distribution of distances between every pair of residues in a protein. These probabilities were then combined into a score that estimates how accurate a proposed protein structure is. We also trained a separate neural network that uses all distances in aggregate to estimate how close the proposed structure is to the right answer.”

It then used new methods to try to construct predictions of protein structures, searching known structures that matched its predictions.

“Our first method built on techniques commonly used in structural biology, and repeatedly replaced pieces of a protein structure with new protein fragments. We trained a generative neural network to invent new fragments, which were used to continually improve the score of the proposed protein structure,” it writes.

“The second method optimised scores through gradient descent — a mathematical technique commonly used in machine learning for making small, incremental improvements — which resulted in highly accurate structures. This technique was applied to entire protein chains rather than to pieces that must be folded separately before being assembled, reducing the complexity of the prediction process.”

DeepMind describes the results achieved thus far as “early signs of progress in protein folding” using computational methods — claiming they demonstrate “the utility of AI for scientific discovery”.

Though it also emphasizes it’s still early days for the deep learning approach having any kind of “quantifiable impact”.

“Even though there’s a lot more work to do before we’re able to have a quantifiable impact on treating diseases, managing the environment, and more, we know the potential is enormous,” it writes. “With a dedicated team focused on delving into how machine learning can advance the world of science, we’re looking forward to seeing the many ways our technology can make a difference.”

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Bright spots in the VR market

Virtual Reality is in a public relations slump. Two years ago the public’s expectations for virtual reality’s potential was at its peak. Many believed (and still continue to believe) that VR would transform the way we connect, interact, and communicate in our personal and professional lives.

Google Trends highlighting search trends related to Virtual Reality over time; the “note” refers to an improvement in Google’s data collection system that occurred in early 2016

It’s easy to understand why this excitement exists once you put on a head mounted display. While there are still a limited number of compelling experiences, after you test some of the early successes in the field, it’s hard not to extrapolate beyond the current state of affairs to a magnificent future where the utility of virtual reality technology is pervasive.

However, many problems still exist. The all-in cost for state of the art headsets is still out of reach for the mass market. Most ‘high-quality’ virtual reality experiences still require users to be tethered to their desktops. The setup experience for mass market users is lathered in friction. When it comes down to it, the holistic VR experience is a non-starter for most people. We are effectively in what Gartner refers to as the “trough of disillusionment.”

Gartner’s hype cycle for “Human-Machine Interface” in 2018 places many related VR related fields (e.g., Mixed Reality, AR, HMDs, etc.) in the “Trough of Disillusionment”

Yet, the virtual reality market has continued its slow march to mass adoption, and there are tangible indicators that suggest we could be nearing an inflection point.

A shift towards sustainable hardware growth

What you do and do not consider a virtual reality display can dramatically impact your view on the state of the VR hardware industry. Head-mounted displays (HMDs) can be categorized in three different ways:

• Screenless viewers — affordable devices that turn smartphones into a VR experience (e.g., Google Glass, Samsung Gear VR, etc.)
• Standalone HMDs — devices that are not connected to a computer and can independently run content (e.g., Oculus Go, Lenovo Mirage Solo, etc.)
• Tethered HMDs — devices that are connected to a desktop computer in order to run content (e.g., HTC Vive, Oculus Pro, etc.)

2018 has seen disappointing progress in aggregate headset growth. The overall market is forecasted to ship 8.9M headsets in 2018, up from an approximate aggregate shipment of ~8.3M in 2017, according to IDC. On the surface, those numbers hardly describe a market at its inflection point.

However, most of the decline in growth rate can be attributed to two factors. First, screenless viewers have seen a significant decline in shipments as device manufacturers have stopped shipping them alongside smartphones. In the second quarter of 2018, 409K screenless viewers were shipped compared to approximately 1M in the second quarter of 2017. Second, tethered VR headsets have also declined as manufacturers have slowed down the pricing discounts that acted as a steroid to sales growth in 2017.

Looking at the market for standalone HMDs, however, reveals a more promising figure. Standalone VR headsets grew 417% due to the global availability of the Oculus Go and Xiaomi Mi VR. Over time, these headsets are going to be the driver of the VR market as they offer significant advantages compared to tethered headsets.

The shift from tethered to standalone VR headsets is significant. It represents a paradigm shift within the immersive ecosystem, where developers have a truly mobile platform that is powerful enough to enable compelling user experiences.

IDC forecasts for AR/VR headset market share by form factor, 2018–2022

A premium market segment

There are a few names that come to mind when thinking about products that are available for purchase in the VR market: Samsung, Facebook (Oculus), HTC, and Playstation. A plethora of new products from these marquee names —  and products from new companies entering the market —  are opening the category for a new customer segment.

For the past few years, the market effectively had two segments. The first was a “mass market” segment with notorious devices such as the Google Cardboard and the Samsung Gear, which typically sold for under $100 and offered severely constrained experiences to consumers. The second segment was a “pro market” with a few notable devices, such as the HTC Vive, that required absurdly powerful computing rigs to operate, but offered consumers more compelling, immersive experiences.

It’s possible that this new emerging segment will dramatically open up the total addressable VR market. This “premium” market segment offers product alternatives that are somewhat more expensive than the mass market, but are significantly differentiated in the potential experiences that can be offered (and with much less friction than the “pro market”).

The Oculus Go, the Xiaomi Mi VR, and the Lenovo Solo are the most notable products in this segment. They are the fastest growing devices in this segment, and represent a new wave of products that will continue to roll out. This segment could be the tipping point for when we move from the early adopters to the early majority in the VR product adoption curve.

A number of other products have also been released throughout 2018 that fall into this category, such as Lenovo’s Mirage Solo and Xiaomi’s Mi VR. Even more so, Oculus recently announced that  they’ll be shipping a new headset called Quest this spring, which will sell for $399 and will be the most powerful example of a premium device to date. The all-in price range of ~$200–400 places these devices in a segment consumers are already conditioned to pay (think iPad’s, gaming consoles, etc.), and they offer differentiated experiences primarily attributed to the fact that they are standalone devices.

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