06 May 2019

Google refreshes Android Auto with new features and a darker look


Android Auto — the in-car platform that brings the look and functions of a smartphone to the vehicle’s central screen — is getting a new look and improved navigation and communication features that will roll out this summer.

The improvements and new look were revealed Monday during Google I/O 2019, the annual developer conference.

The most noticeable change might be the overall look of Android Auto. It now has a dark theme, new fonts and color accents designed to make it easier for drivers to quickly and more easily see the content on the car’s central screen.

The new version of Android Auto has also improved its notifications. Drivers can choose to view, listen and respond to messages and calls more easily.

Engineers have updated the software to make it more seamless. The system, if properly enabled, would pop up on the car’s screen once the vehicle was turned on. However, the user would still have to restart their media or navigation option. Now, Android Auto will continue playing the media and navigation app of the driver’s choice. Drivers can tap on a suggested location or say “Hey Google” to navigate to a new place.

The navigation bar on Android Auto has changed, as well. Drivers will be able to see their turn-by-turn directions and control apps and phone on the same screen.

Finally, the platform has been adjusted so it will fit various sized-screens. Android Auto now maximizes the in-car display to show more information, like next-turn directions, playback controls and ongoing calls.

Android Auto is not an operating system. It’s a secondary interface — or HMI layer — that sits on top of an operating system. Google released Android Auto in 2015. Rival Apple introduced its own in-car platform, Apple CarPlay, that same year.

Automakers that wanted to give consumers a better in-car experience without giving Google or Apple total access quickly adopted the platform. Even some holdouts, such as Toyota, have come around. Today, Android Auto is available in more than 500 car models from 50 different brands, according to Android Auto product manager Rod Lopez.

Google has since developed an operating system called Android Automotive OS that’s modeled after its open-source mobile operating system that runs on Linux. Instead of running smartphones and tablets, Google modified it so it could be used in cars. Polestar, Volvo’s standalone performance electric car brand, is going to produce a new vehicle, the Polestar 2, that has an infotainment system powered by Android Automotive OS.


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Where top VCs are investing in media, entertainment & gaming


Most of the strategy discussions and news coverage in the media & entertainment industry is concerned with the unfolding corporate mega-mergers and the political implications of social media platforms.

These are important conversations, but they’re largely a story of twentieth-century media (and broader society) finally responding to the dominance Web 2.0 companies have achieved.

To entrepreneurs and VCs, the more pressing focus is on what the next generation of companies to transform entertainment will look like. Like other sectors, the underlying force is advances in artificial intelligence and computer power.

In this context, that results in a merging of gaming and linear storytelling into new interactive media. To highlight the opportunities here, I asked nine top VCs to share where they are putting their money.

Here are the media investment theses of: Cyan Banister (Founders Fund), Alex Taussig (Lightspeed), Matt Hartman (betaworks), Stephanie Zhan (Sequoia), Jordan Fudge (Sinai), Christian Dorffer (Sweet Capital), Charles Hudson (Precursor), MG Siegler (GV), and Eric Hippeau (Lerer Hippeau).

Cyan Banister, Partner at Founders Fund

In 2018 I was obsessed with the idea of how you can bring AI and entertainment together. Having made early investments in Brud, A.I. Foundation, Artie and Fable, it became clear that the missing piece behind most AR experiences was a lack of memory.


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Week-in-Review: The iPhone fades and SpaceX confirms an explosion


After a dozen years of riding high, the iPhone is showing signs of weakness in a struggling smartphone market where Apple is still managing to be the biggest loser.

Here’s a snapshot of where things are…

Apple hasn’t been broadcasting its quarterly unit sales the past few quarters, so we’ll have to lean on external researchers, but even the rosiest portrayal from Canalys suggests that the Cupertino giant saw a 23% drop in year-over-year iPhone unit sales, selling 40.2 million iPhones in Q2 of this year compared to 52.2 million iPhones a year ago.

That egregious drop takes Apple to its lowest Q2 unit sales since 2013, though the company has been solidly bumping up the average selling price in a move that has largely been working, though iPhone revenue was down 15% year-over-year, as well.

It’s not Apple’s cross to bear alone; the broader smartphone market has been in decline, down 6.8% year-over-year, according to the same report. But the iPhone’s decline contributed to roughly half of the global market’s missing units while China’s smartphone triumvirate of Huawei, Oppo and Xiaomi managed to buoy the broader sector from diving even lower.

Huawei’s unit sales shot up more than 50%.

Apple wasn’t the only non-Chinese phone maker wallowing in misery. Google cited a rough market for smartphones after delivering disappointing earnings, while Samsung saw a 10% decline in unit sales this quarter, according to Canalys.

Shoot me tips or feedback
on Twitter @lucasmtny or email
lucas@techcrunch.com

The smartphone market has had six straight quarters of year-over-year sales declines. This was the lowest quarter of smartphone unit sales in nearly five years. Whether Apple can better perform might be a question of how they can seek to differentiate themselves in China while still managing to squeeze consistent revenues from markets where it leads.

More doom-and-gloom from my buddy Brian Heater here:

iPhone hard hit as global smartphone shipments nosedive

On to the cheerier topic of dead robots…

AMY OSBORNE/AFP/Getty Images

Trends of the week

Here are a few big news items from big companies, with green links to all the sweet, sweet added context.

  • Zuckerberg tries again
    Facebook is dead as you know it, or at least that’s what CEO Mark Zuckerberg wants you to think after his audacious relaunch of the company as a lover of privacy. The company gave its Facebook app and desktop site a major face lift and spoke at length about being better. Sitting in the audience, I couldn’t help but think that Zuckerberg was spinning for extra credit with decisions that had long been made. More from my colleague Josh Constine.
  • SpaceX cops to an explosion
    Elon Musk’s space company may have to push back its timeline for a manned launch after the company confirmed that its Dragon crew capsule exploded during testing. The disappointing development suggests SpaceX has some more work ahead of it before it’s ready to safely transport humans into space.
  • Another dead robot
    Cozmo won’t be scooting into any new homes; the startup behind the cute little robot is dead after the dissolution of a new funding round. Anki raised a staggering $182 million over the course of its life and sold 1.5 million of the curious, little wheeled bots, but now it seems to face the same lonely death as Jibo, which similarly perished a couple of months ago.
  • Palantir not so nice after all
    Peter Thiel’s Palantir has long held onto this very nefarious reputation as an evil company that’s working with government agencies and screwing over progressive ideals in the process. It wasn’t always super clear how true this was because it kind of seemed like Alex Karp and company was just scrambling to get private sector customers so it could justify its private valuation ahead of an IPO. Well, turns out the company is shitty after all.
  • Headset hullabaloo
    The VR market may be dead, but don’t tell that to the companies making VR headsets. Yes, the new headsets are still all bulky and weird but they are undoubtedly better. Oculus’ introduction of the Quest (reviewed here by me) and Rift S (review from me, again) next month might just add a little life to the dead VR dreams — and if that doesn’t work, Valve has a $1,000 option it’s now hocking.

Forward-looking statement

What’s coming up next week? Well, you can expect a bunch of Microsoft news at its Build developer conference and there will also assuredly be a lot emerging from Google I/O, where I’ll be spending a couple of days next week. Here’s what we think is coming…

What to expect from Google I/O 2019

“…It’s shaping up to be a biggie, too, if this week’s Google earnings call was any indication. Sundar Pichai teased out a number of upcoming offerings from the company that we can expect to see on full display at the show…”

HVEPhoto/Getty Images

GAFA Gaffes

How did the top tech companies screw up this week? This clearly needs its own section, in order of awfulness: (This week was admittedly a little light on the gaffes, but don’t be too disappointed, that’s good!)

  1. Googlers aren’t happy about workplace retaliation:
    [Google employees are staging a sit-in to protest reported retaliation]
  2. Researchers studying Facebook’s ad platform aren’t getting the access they say they need:
    [Facebook accused of blocking wider efforts to study its ad platform]
  3. Apple wades into anti-competitive criticism with latest app bans:
    [Apple defends its takedown of some apps monitoring screen-time]

Horacio Villalobos//Corbis/Getty Images

Extra Crunch

Our premium subscription service roars ahead. We had a fascinating piece go up this week diving into Slack’s financial filings that discovered some discrepancies in the VC funding that was reported versus what was actually raised:

The curious case of Slack’s missing $162 million

“…Given that most of the stories covering Slack derived from the company’s own announcements, you would expect that those stories and the data in the S-1 would match. In short: they do, somewhat…”

Here are some of our other top reads this week for premium subscribers — you should catch up with our full Niantic deep-dive if you haven’t already; this list is a nice primer though…

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Google at ICLR 2019




This week, New Orleans, LA hosts the 7th International Conference on Learning Representations (ICLR 2019), a conference focused on how one can learn meaningful and useful representations of data for machine learning. ICLR offers conference and workshop tracks, both of which include invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction and issues regarding non-convex optimization.

At the forefront of innovation in neural networks and deep learning, Google focuses on on both theory and application, developing learning approaches to understand and generalize. As Platinum Sponsor of ICLR 2019, Google will have a strong presence with over 200 researchers attending, contributing to and learning from the broader academic research community by presenting papers and posters, in addition to participating on organizing committees and in workshops.

If you are attending ICLR 2019, we hope you'll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at ICLR 2019 in the list below (Googlers highlighted in blue).

Officers and Board Members
Hugo Larochelle, Samy Bengio, Tara Sainath

General Chair
Tara Sainath

Workshop Chairs
Been Kim, Graham Taylor

Program Committee includes:
Chelsea Finn, Dale Schuurmans, Dumitru Erhan, Katherine Heller, Lihong Li, Samy Bengio, Rohit Prabhavalkar, Alex Wiltschko, Slav Petrov, George Dahl

Oral Contributions
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
Jacob Menick, Nal Kalchbrenner

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
Curtis Hawthorne, Andrew Stasyuk, Adam Roberts, Ian Simon, Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck

Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Posters
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee

Diversity and Depth in Per-Example Routing Models
Prajit Ramachandran, Quoc V. Le

Eidetic 3D LSTM: A Model for Video Prediction and Beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

GANSynth: Adversarial Neural Audio Synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard

Learning to Describe Scenes with Programs
Yunchao Liu, Zheng Wu, Daniel Ritchie, William Freeman, Joshua B Tenenbaum, Jiajun Wu

Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William Freeman, Joshua B Tenenbaum, Jiajun Wu

The Singular Values of Convolutional Layers
Hanie Sedghi, Vineet Gupta, Philip M. Long

Unsupervised Discovery of Parts, Structure, and Dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William Freeman, Joshua B Tenenbaum, Jiajun Wu

Adversarial Reprogramming of Neural Networks
Gamaleldin Elsayed, Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein

Discriminator Rejection Sampling
Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein

On Self Modulation for Generative Adversarial Networks
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly

Towards GAN Benchmarks Which Require Generalization
Ishaan Gulrajani, Colin Raffel, Luke Metz

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
David Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow (no longer at Google)

A new dog learns old tricks: RL finds classic optimization algorithms
Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

Contingency-Aware Exploration in Reinforcement Learning
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson

Diversity is All You Need: Learning Skills without a Reward Function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Raphael Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur

Meta-Learning Probabilistic Inference for Prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner

Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Dengyong Zhou

Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

Optimal Completion Distillation for Sequence Learning
Sara Sabour, William Chan, Mohammad Norouzi

Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

Sample Efficient Adaptive Text-to-Speech
Yutian Chen, Yannis M Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aaron van den Oord, Oriol Vinyals, Nando de Freitas

Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song

The Laplacian in RL: Learning Representations with Efficient Approximations
Yifan Wu, George Tucker, Ofir Nachum

A Mean Field Theory of Batch Normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S Schoenholz

Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson

Predicting the Generalization Gap in Deep Networks with Margin Distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine, Yoshua Bengio

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

Complement Objective Training
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

DOM-Q-NET: Grounded RL on Structured Language
Sheng Jia, Jamie Kiros, Jimmy Ba

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Justin Fu, Anoop Korattikara Balan, Sergey Levine, Sergio Guadarrama

Harmonic Unpaired Image-to-image Translation
Rui Zhang, Tomas Pfister, Li-Jia Li

Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang

Learning Finite State Representations of Recurrent Policy Networks
Anurag Koul, Alan Fern, Samuel Greydanus

Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Patrick Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

Music Transformer: Generating Music with Long-Term Structure
Chen-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew Dai, Matthew D Hoffman, Monica Dinculescu, Douglas Eck

Universal Transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser

What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, Tom McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

How Important Is a Neuron?
Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan

Integer Networks for Data Compression with Latent-Variable Models
Johannes Ballé, Nick Johnston, David Minnen

Modeling Uncertainty with Hedged Instance Embeddings
Seong Joon Oh, Andrew Gallagher, Kevin Murphy, Florian Schroff, Jiyan Pan, Joseph Roth

Preventing Posterior Collapse with delta-VAEs
Ali Razavi, Aaron van den Oord, Ben Poole, Oriol Vinyals

Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal, David GT Barrett, Kimberly L Stachenfeld

Spreading vectors for similarity search
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou

Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy

Workshops
Learning from Limited Labeled Data
Sponsored by Google

Deep Reinforcement Learning Meets Structured Prediction
Organizing Committee includes: Chen Liang
Invited Speaker: Mohammad Norouzi

Debugging Machine Learning Models
Organizing Committee includes: D. Sculley
Invited Speaker: Dan Moldovan

Structure & Priors in Reinforcement Learning (SPiRL)
Organizing Committee includes: Chelsea Finn

Task-Agnostic Reinforcement Learning (TARL)
Sponsored by Google
Organizing Committee includes: Danijar Hafner, Marc G. Bellemare
Invited Speaker: Chelsea Finn

AI for Social Good
Program Committee includes: Ernest Mwebaze

Safe Machine Learning Specification, Robustness and Assurance
Program Committee includes: Nicholas Carlini

Representation Learning on Graphs and Manifolds
Program Committee includes: Bryan Perozzi

Marshall continues to impress with new retro portable speakers


Marshall, the headphone company and not the loudspeaker company of the same vintage, today announced two new portable speakers. Like the company’s previous offerings, these speaks ooze a retro vibe. The two new speakers, the Stockwell II and Tufton, join the Kilburn II but stand tall, literally and figuratively, apart from the rest of Marshall’s speakers as portable models with a vertical orientation, internal batteries, wireless capabilities, and a rugged casing that should survive a trip outside.

The large Tufton impresses with clear, powerful sound even when on battery. The highs carry over a solid low-end. It’s heavy. This isn’t a speaker you want to take backpacking, but, if you did, the casing has an IPX4 water-resistant rating so it’s tough enough to handle most weather. Marshall says the battery lasts up to six hours.

The smaller Stockwell II is much smaller. The little speaker is about the size of an iPad Mini though as thick as a phonebook. The internal battery is good for four hours and the casing is still tough, though sports an IPX2 rating so it’s not as durable as the Tufton. The speaker is a bit smaller and the music quality is as well. The Stockwell II is a great personal speaker, but it doesn’t produce a pounding sound like the Tufton. Use the Stockwell II for a quiet campfire and the Tufton for a backwood bonfire.

Sadly, these speakers lack Google Assistant or Amazon Alexa integration. Users either have to connect a device through a 3.5mm port or Bluetooth.

I’ve been a fan of every Marshall speaker I’ve tried. For my money, they feature a great balance of sound and classic design. Each one I’ve tried lives up to the Marshall name and these two new speakers are no different. Portability doesn’t come cheap. These speakers cost a bit more than their stationary counterparts. The small Stockwell II retails for $249 while the large Tufton is $399.


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Waiting for the But


Waiting for the But

What Is a DNS Server and Is It Responding?


whats-dns

How do you suppose your PC, smartphone, or tablet knows where to go when you type in a domain name like makeuseof.com? It’s not by magic—all internet connected devices make use of the domain name system, with DNS servers at its core.

But what is a DNS server, and how does it work to get you from A (a domain name) to B (the matching web server)? How do you know if your DNS server is responding properly or not? The system is designed to work without you even noticing, but it isn’t foolproof.

Let’s explain how DNS works, and what you should do if there’s a problem with your DNS server.

What Is a DNS Server?

There’s a reason that you, your neighbor, and your long-lost family abroad can all type a URL into your browser and see the same result. The domain name system is the foundation of the web, acting as a database of every single public-facing website.

A DNS server resolves an IP address for a web server and matches it to a domain name and host name (for www.google.com, the hostname would be www). The information is held in its entirety on 13 DNS root name server addresses, run by companies, government agencies, and universities. There are hundreds of matching root servers, hosted by these 13 organizations and sharing the same IP addresses across the globe for speed and reliability.

The reason for DNS is simple—it’s much easier for users to remember a domain name, like google.com, than they would an IP address.

For Google, that would be 172.217.169.14. If you hosted a website on your own server, you’d have to provide your own IP address if you weren’t using a free dynamic DNS provider or had a static IP address.

You’ll usually default to using the DNS servers provided by your ISP. You can also use public DNS servers to improve your safety online, like Google’s own public DNS servers at 8.8.8.8 and 8.8.4.4.

Why Might Your DNS Server Be Unavailable?

When your PC can’t find a domain name you’ve typed in, there could be a problem with your DNS server. Your DNS server might be unavailable because of a problem with the server, or a problem with connecting to that server (such as an internet outage).

If you’re using the DNS settings provided by your ISP, and you’re getting a DNS server not responding error, restart your router in the first instance. This may restore your connectivity to your DNS servers without any extra steps.

If that doesn’t work, resetting your DNS cache could help. On Windows, hit Win + X, select Windows PowerShell (Admin), then type:

ipconfig /flushdns

On macOS, assuming you’re running El Capitan or later, open a terminal and type:

sudo dscacheutil -flushcache; sudo killall -HUP mDNSResponder

Linux-based operating systems don’t generally perform any DNS caching unless you’re using nscd. If you are, type the following:

sudo /etc/init.d/nscd restart

If all else fails, and your DNS server is unavailable for a long period of time, then it’s time to set your own.

Why You Should Set Your Own DNS Settings

In most cases, you aren’t forced to use the DNS servers that you default to using with your internet connection. You can set your device to use alternative DNS servers if you’d prefer.

As we’ve mentioned, changing your DNS server can be a good way to protect yourself online. For parents, changing your DNS servers to a provider like OpenDNS can help you with filtering against adult content and malware.

One of the biggest reasons for changing your DNS server settings, however, is for speed. A few additional seconds of loading time for each page can start to add up—you can reclaim that time by changing your DNS settings to a quicker provider. Your ISP DNS servers (depending on your provider) might be poorly maintained, resulting in a noticeable slowdown, even with a fast internet connection.

It also helps to change your DNS settings if the servers you’re using aren’t reliable and frequently go down.

If you’re thinking about using a VPN, you might also want to change your DNS server settings from those provided by your ISP. You’ll also want to set up a VPN connection in Windows to use DNS leak protection properly. If you don’t, DNS leaks might reveal your identity to snooping authorities.

The Dangers of DNS Malware

DNS spoofing (or DNS cache poisoning) can be a way for malware creators to manipulate the domain name system to benefit themselves. Rather than google.com taking you to Google’s homepage, DNS malware can set a record in your DNS cache to an alternate server. It might look like Google, the URL might match, but your PC will have taken you to another website entirely—all without you realizing.

This kind of sophisticated phishing attack can cause you to unwittingly reveal your personal data to a rogue server. To prevent this from happening, keep your antivirus and antimalware software up-to-date and run a scan of your PC on a regular basis.

If you do find malware, clear your DNS cache using the methods listed above once it’s been removed.

How to Set Your Own DNS Settings

You can change your DNS settings on modern operating systems like Windows and macOS quickly, although it’s a little trickier on Linux, depending on your distribution.

Windows

To change your DNS settings on Windows, hit Win + X and select Settings > Network & Internet > Network & Sharing Center.

In the menu on the left-hand side, select Change adapter settings. Right-click your internet connection and select Properties. Select Internet Protocol Version 4 (TCP/IPv4) and select Properties.

From here, enable Use the following DNS server addresses and fill in the preferred and alternate DNS server addresses with your chosen DNS providers. Follow the same for IPv6 addresses.

MacOS

If you’re using macOS, click the Systems Preferences icon on your dock, then click Network.

Make sure your connection is selected, then click Advanced > DNS. Remove any existing DNS servers with the – icon, then hit the + icon to enter your new addresses. Once you’re done, click OK.

Linux

If you’re running Linux, changing your DNS settings will depend on the distribution you’re using. It’ll also depend on the network manager that distribution uses.

If you’re an Ubuntu user, you’ll need to manage your IP address settings on Ubuntu using either the GUI or by using a terminal editor to manually edit the relevant configuration files.

Don’t Let a Bad DNS Server Slow You Down

Under normal circumstances, you shouldn’t need to think about your DNS servers. They operate in the background, working to get you from A to B as you use the web without any issues.

If you do have issues with your DNS settings, it could be a sign of DNS malware or a problem with your internet connection.  If that’s the case, check your PC for malware and, if you’re still having problems, try changing your DNS server settings to another provider.

Read the full article: What Is a DNS Server and Is It Responding?


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How to Filter Toxic Comments on Social Media


filter-comments-social

At times it seems the internet is swarming with strangers filled with hate. Especially on social media. And while that’s probably an exaggeration, for the sake of your mental health you should try to filter toxic comments.

Such comments can adversely impact your online experience or make you want to quit social media. However, thankfully, you can easily filter toxic comments on social media.

How to Filter Toxic Comments on Instagram

Mute words comments Instagram

Instagram allows you to mute a specific set of keywords. Alternatively, you can enable an option which automatically hides commonly reported words. Comments which include these words will be eliminated from your posts and won’t be visible to anyone. Plus, there’s an algorithm-powered setting which tries to detect and block offensive comments.

To access these tools on your phone, you will need to head into the settings on the Instagram app. There, go to Privacy and Settings > Comment Controls. Here you will find all the manual and automatic filters.

Under Manual Filter, you can add as many as words or phrases as you’d want. To enter more than one, you will have to just separate them by commas.

You can also tweak who is allowed to comment on your posts and restrict it to only the people you follow. If there’s a user who regularly leaves toxic comments, you can specifically block that account as well.

For doing the same on your computer, launch the Instagram web app and open Settings. Click Privacy and Settings and then hit Edit Comment Settings.

If you’d like to have an even more private experience on Instagram, here ‘s how to make your Instagram more private.

How to Filter Toxic Comments on Twitter

Mute words from timeline Twitter

Twitter lets you mute certain words from your replies as well. However, it doesn’t remove associated comments from your posts for everyone.

On Twitter, you can mask tweets from appearing on your timeline or in your notifications when they’re found with a particular phrase, word, or even hashtag.

In addition, you have the ability to define an expiry period, after which the string will be automatically removed from your muted list. This can come in handy, for instance, if you’re trying to avoid tweets about an event you’re not fond of or spoilers for a TV show.

To access Twitter’s mute settings in its mobile apps, tap the Settings and Privacy option from the left navigation drawer. Then, enter Privacy and Safety > Muted Words. To add a new entry, tap the Plus icon, type in the word or phrase, select the areas from where you’d like it to disappear, configure the duration, and you’re all set.

On your computer, fire up Twitter’s website. Go to Settings and Privacy from your profile > Content Preferences > Muted > Muted Words and add the filter.

How to Filter Toxic Comments on Facebook

Mute comments with certain words on facebook

Facebook offers some simple comment filtering options as well. You will have to manually set up the list of words you’d like to blocklist. As with Twitter, Facebook will hide matching comments entirely from your timeline instead of just from your posts.

However, Facebook’s implementation is largely barebones and you can’t access the settings from its mobile apps either. At the time of writing, it’s limited to the website.

To mute certain words on Facebook, log into your account. Tap the little downwards arrow in the top-right corner, and hit Settings. Enter the Timeline and Tagging page, and click the Edit button beside the “Hide comments containing certain words from your timeline” option.

Now, add the words, phrases, or even emojis you want to hide from the comments section of every post. Facebook also allows you to upload a CSV file too in case you have a lengthy list. When you’re done, click the Save Changes button.

How to Filter Toxic Comments Across Social Media

Google Tune Chrome extension

There’s a limit to the number of words you can mute, and it’s difficult to anticipate the kinds of toxic comments you’re going to see on the internet. So, when the official tools of sites like Instagram, Twitter, and Facebook fail, you need Google Tune.

Google Tune employs a range of machine learning frameworks and lets you adjust the toxicity of online comments you’re willing to see. It works across a bunch of leading platforms including Disqus, YouTube, Reddit, Facebook, and Twitter. Since Google Tune supports Disqus, it’s available on the majority of websites as well.

You can turn it up to read every single comment or turn it down to block all toxic comments. Leaving it somewhere in the middle allows you to skip comments which fall into categories such as personal threats, insults, profanity, and sexual harassment.

The Google Tune extension can, for now, only be installed on Google Chrome’s desktop client. It is free, however, and despite being in an experimental phase, Google Tune delivers fairly accurate results.

To get started, you will have to download Google Tune from the Chrome Web Store. Once it’s installed, sign in with your Google account and enable it for the sites you visit. If you’re worried about privacy, Google explicitly states that Tune doesn’t store any of your personal data.

How to Switch Off Online Comments Entirely

If you’re simply looking for a quick way to switch off all online comments, try Shut Up. This is a browser extension which can disable discussions on platforms such as YouTube with a click of a button.

It remembers your preferences as well so that the next time you visit, you won’t have to set it up again. Shut Up is available as an extension for Chrome, Firefox, and Safari, and as an app for iOS.

How Safe Are You Online?

For various reasons too complex to go into here, negativity seems to be winning out on the internet. The only way to fight it—beyond not being part of the problem—is filtering out the worst comments. Hopefully, this article will help you do just that.

Looking beyond the comments, there are other aspects of the internet you need to be wary of. So we have a compiled a list of questions you need to ask yourself if you want to be sure you’re safe online.

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The EU will reportedly investigate Apple following anti-competition complaint from Spotify


The spat between Spotify and Apple is going to be the focus on a new investigation from the EU, according to a report from the FT.

The paper reported today that the European Commission (EC), the EU’s regulatory body, plans to launch a competition inquiry around Spotify’s claim that the iPhone-maker uses its position as the gatekeeper of the App Store to “deliberately disadvantage other app developers.”

In a complaint filed to the EC in March, Spotify said Apple has “tilted the playing field” by operating iOS, the platform, and the App Store for distribution, as well as its own Spotify rival, Apple Music.

In particular, Spotify CEO Daniel Ek has said that Apple “locks” developers and their platform, which includes a 30 percent cut of in-app spending. Ek also claimed Apple Music has unfair advantages over rivals like Spotify, while he expressed concern that Apple controls communication between users and app publishers, “including placing unfair restrictions on marketing and promotions that benefit consumers.”

Spotify’s announcement was unprecedented — Ek claimed many other developers feel the same way, but do not want to upset Apple by speaking up. The EU is sure to tap into that silent base if the investigation does indeed go ahead as the FT claims.

Apple bit back at Spotify’s claims, but its response was more a rebuttal — or alternative angle — on those complaints. Apple did not directly address any of the demands that Spotify put forward, and those include alternative payment options (as offered in the Google Play store) and equal treatment for Apple apps and those from third-parties like Spotify.

The EU is gaining a reputation as a tough opponent that’s reining in U.S. tech giants.

Aside from its GDPR initiative, it has a history of taking action on apparent monopolies in tech.

Google fined €1.49 billion ($1.67 billion) in March of this year over antitrust violations in search ad brokering, for example. Google was fined a record $5 billion last year over Android abuses and there have been calls to look into breaking the search company up. Inevitably, Facebook has come under the spotlight for a series of privacy concerns, particularly around elections.

Pressure from the EU has already led to the social network introduce clear terms and conditions around its use of data for advertising, while it may also change its rules limiting overseas ad spending around EU elections following concern from Brussels.

Despite what some in the U.S. may think, the EU’s competition commissioner, Margrethe Vestager, has said publicly that she is against breaking companies up. Instead, Vestager has pledged to regulate data access.

“To break up a company, to break up private property would be very far-reaching and you would need to have a very strong case that it would produce better results for consumers in the marketplace than what you could do with more mainstream tools. We’re dealing with private property. Businesses that are built and invested in and become successful because of their innovation,” she said in an interview at SXSW earlier this year.


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