The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

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Daily Crunch: Meet the new CEO of Google Cloud

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 9am Pacific, you can subscribe here.

1. Google Cloud’s new CEO on gaining customers, startups, supporting open source and more

Thomas Kurian, who came to Google Cloud after 22 years at Oracle, said the team is rolling out new contracts and plans to simplify pricing.

Most importantly, though, Google will go on a hiring spree: “A number of customers told us ‘we just need more people from you to help us.’ So that’s what we’ll do.”

2. Walmart to expand in-store tech, including Pickup Towers for online orders and robots

Walmart is doubling down on technology in its brick-and-mortar stores in an effort to better compete with Amazon. The retailer says it will add to its U.S. stores 1,500 new autonomous floor cleaners, 300 more shelf scanners, 1,200 more FAST Unloaders and 900 new Pickup Towers.

3. Udacity restructures operations, lays off 20 percent of its workforce

The objective is to do more than simply keep the company afloat, according to co-founder Sebastian Thrun. Instead, Thrun says these measures will allow Udacity to move from a money-losing operation to a “break-even or profitable company by next quarter and then moving forward.”

Photo By Bill Clark/CQ Roll Call via Getty Images

4. The government is about to permanently bar the IRS from creating a free electronic filing system

That’s right, members of Congress are working to prohibit a branch of the federal government from providing a much-needed service that would make the lives of all of their constituents much easier.

5. Here’s the first image of a black hole

Say hello to the black hole deep inside the Messier 87, a galaxy located in the Virgo cluster some 55 million light years away.

6. Movo grabs $22.5M to get more cities in LatAm scooting

The Spanish startup targets cities in its home market and in markets across Latin America, offering last-mile mobility via rentable electric scooters.

7. Uber, Lyft and the challenge of transportation startup profits

An article arguing that everything you know about the cost of transportation is wrong. (Extra Crunch membership required.)

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Innovation Endeavors debuts Deep Life, an incubator focused on the intersection of life science and computer science

Innovation Endeavors, the fund backed by Google’s Eric Schmidt, has for years now been taking a novel approach to working on difficult and still-evolving problems, like cybersecurity and food shortages: it sets up incubators that bring together different stakeholders to identify, develop and fund ways of tackling these issues. Today, Innovation unveiled the latest of these: a new project called Deep Life, which aims to identify tricky problems in the world of life sciences, and figure out how to use computer science — specifically innovations in areas like machine learning — to help fix them.

Target areas will include therapeutics, diagnostics and industrial life sciences in biology, chemistry and other fields; and Deep Life will provide startups with “investment capital across all stages of growth; access to experts, including scientists and decision-makers; proprietary data sets; early feedback on product; identification of market needs; initial customers and potential partners. In exchange for their startup support, Deep Life member organizations gain access to emerging technologies and hard-to-find talent,” according to a blog post introducing the new project penned by Innovation Endeavors’ co-founder Dror Berman.

Deep Life will unveil the first fruits of its efforts during a pitch day on May 30, and it’s accepting applications for places as of right now.

Alternately called an “ecosystem” and “collective,” Deep Life — in the words of Berman — is “taking inspiration” from Farm2050 and Team8, the two other incubators that the firm helped create in past years. The model is to bring in a number of big names and then — in addition to building on ideas — fund startups to productise them. Eventually, they are spun out as independent companies that get acquired (here and here) or continue to operate independently.

As with these two other incubators, Deep Life is harnessing collective knowledge from a number of existing stakeholders in the life sciences ecosystem. The list includes LEO Pharma, a Danish pharmaceutical company focusing on pioneering dermatology; Mount Sinai Hospital; Novozymes, a producer of industrial enzymes and microorganisms for a broad range of industries; Schmidt Futures; Clalit Health Services & Research Institute in Israel; and academics, entrepreneurs and others, including Aviv Regev, a computational biologist and faculty chair of the Broad Institute of MIT and Harvard.

Additionally, Innovation Endeavors says that it will be tapping learnings from startups it has already backed, including Bolt Threads, Color, Freenome, GRO Biosciences, Karius, Vicarious Surgical, Viz.ai and Zymergen.

In all, it’s not clear how much funding is going into Deep Life, and whether the two lists above also become financial backers of the project. Separately, Innovation Endeavors last summer announced a $333 million fund and plans to contribute a sizeable amount of backing itself to Deep Life, from what I understand.

The intersection between tech and life sciences is, of course, not a completely new area. Tech has been a cornerstone of how science has developed and how applications of it are delivered, for example in medicine.

What’s a little different here is the much closer focus on the role that tech is playing in the very germination of ideas and building knowledge, rather than just enabling the efficient operation of a service.

Among the areas that Deep Life tells me it hopes to cover are the use of experimental design (active learning) to uncover biological knowledge; generating data sets that are more effective than current approaches; the application of simple measurements to generate complex, high-content readouts through generative models; combining data modalities (e.g. molecular and imaging-based complex phenotypes); simplifying data readouts to help predict outcomes (using AI); adding to the molecular vocabulary of cells, host organisms and their subcomponents; and building data platforms for biology growing from distributed, asynchronous efforts into an open source whole.

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Google’s managed database service to support Microsoft SQL Server

It’s not every day that you read a headline with both Google and Microsoft. Google is announcing today that its managed database service Cloud SQL will soon support Microsoft SQL Server. The company showed a sneak preview at its Google Cloud Next conference.

The message is clear — if your company uses Microsoft SQL Server database, you don’t have to use Microsoft Azure. Your database will work just fine on Google’s Cloud SQL.

Google already supported Microsoft SQL Server in traditional virtual machines — so you had to manage it yourself. If you have a license and you want Google to manage your database for you, Cloud SQL will be able to do it. No backup, no manual replication, no patch, etc.

Many enterprise customers still rely heavily on a traditional on-prem server infrastructure. Google is trying to remove all the obstacles you could find when moving to the cloud.

In other Cloud SQL news, customers who use PostgreSQL can now use version 11 of PostgreSQL. Amazon RDS also supports version 11.

Finally, Google’s managed NoSQL database service Cloud Bigtable now supports multi-region replication. That feature was already available in beta. You can now safely read and write your NoSQL data from multiple regions at the same time.

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DroneBase raises capital and partners with FLIR Systems to train pilots on thermal imaging tech

Publicly traded sensor technology developer FLIR Systems is investing in a strategic round of funding for the outsourced drone imaging company, DroneBase.

The two companies are also partnering to provide FLIR’s thermal imaging technology and training services to DroneBase’s stable of pilots.

Terms of the investment were not disclosed.

“Our investment in DroneBase helps expand the adoption of FLIR thermal imaging technology by putting it in the hands of more pilots who fly drones every day,” said Jim Cannon, the president and chief executive of FLIR, in a statement. “DroneBase’s enterprise pilot network will receive training by professional thermographers, enabling DroneBase to offer specialized thermal inspection services for customers on a wider scale, and creating an opportunity for FLIR to incorporate additional service offerings through DroneBase in the future.”

Los Angeles-based DroneBase has contracted pilots to complete more than 100,000 commercial missions in 70-plus countries for residential and commercial real estate, insurance, telecommunications, construction and media companies, according to a statement.

Through FLIR’s Infrared Training Center, FLIR and DroneBase will develop a specialized training program that will be certified exclusively by DroneBase.

“Through FLIR’s strategic investment in DroneBase, we are now able to offer scalable thermal solutions to enterprises of any size,” said Dan Burton, founder and chief executive of DroneBase, in a statement. “This access to valuable data will allow stakeholders to make better decisions about their most critical assets. Like myself, many DroneBase pilots relied on FLIR products when they served in the military. This integration will offer military veterans a chance to work with FLIR again and leverage their training in their civilian lives.”

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Google Cloud Data Fusion lets you see all your data sets before ingesting them in BigQuery

Google is announcing several updates to its data analytics products at its Google Cloud Next developer conference today. The company wants to make it easier to move data to Google’s cloud, manipulate data and turn this data into insights.

First, Google wants to make it easier to access all your data from Google’s platform. The company is launching Cloud Data Fusion in beta. As the name suggests, this service lets you aggregate all your data sets in a single interface.

Even if your data is hosted on other cloud services, you can view it from the Cloud Data Fusion interface. You can then figure out which data set you’ll need to process in BigQuery for instance — BigQuery is Google’s cloud-based service for analyzing large amounts of data using SQL queries.

After selecting your data sets, you need to make sure BigQuery can ingest all this data. You might need to transfer some data to Google’s cloud.

Even if you already store data on Google Cloud Storage, chances are your data is spread out across multiple systems and data formats. For instance, you could already be uploading terabytes of raw data to Google’s servers, but it’s no use if you can’t process it with data in Salesforce or Workday.

The company already lets you transfer data automatically using the BigQuery Data Transfer Service. And Google says that this service now supports over 100 SaaS applications, including Salesforce, Marketo, Workday and Stripe.

This service already lets you transfer your data warehouse data. So if you want to switch from Amazon Redshift or Teradata, you can migrate everything.

Now that all your data is available, you need to visualize it. Google already lets you create interactive dashboards and reports in Google Data Studio. That service is powered by BigQuery BI engine. Google says that many complex queries now take less than a second to process.

Soon, Google will open up BigQuery BI Engine to other data visualization services, such as Looker and Tableau. BigQuery BI Engine will run behind the scene to process your data.

If you don’t want to learn SQL queries, chances are you use Microsoft Excel or Google Sheets to sort and process data. Google already lets you connect BigQuery with Google Sheets. The company goes one step further by creating a new type of infinite spreadsheet — connected sheets. Even if you have billions of rows of data, Google Sheets can now display your BigQuery-powered spreadsheet.

Last year, Google introduced BigQuery ML. That service lets customers build models on top of their data warehouse using SQL. Google is adding AutoML tables in beta. It lets you build machine learning models in a few clicks instead of a few lines of code.

Finally, Google is launching a metadata management service called Data Catalog. I’m sure telecom companies are going to love this. Customers will be able to restrict access to sensitive data assets using Google’s Cloud IAM interface.

And that’s it for Google’s data analytics news at Google Cloud Next.

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Google launches new security tools for G Suite users

Google today launched a number of security updates to G Suite, its online productivity and collaboration platform. The focus of these updates is on protecting a company’s data inside G Suite, both through controlling who can access it and through providing new tools for prevening phishing and malware attacks.

To do this, Google is announcing the beta launch of its advanced phishing and malware protection, for example. This is meant to help admins protect users from malicious attachment and inbound email spoofing, among other things.

The most interesting feature here, though, is the new security sandbox, another beta feature for G Suite enterprise users. The sandbox allows admins to add an extra layer of protection on top of the standard attachment scans for known viruses and malware. Those existing tools can’t fully protect you against zero-day ransomware or sophisticated malware, though. So instead of just letting you open the attachment, this tool executes the attachment in a sandbox environment to check if there are any security issues.

With today’s launch, Google is announcing the beta launch of its new security and alert center for admins. These tools are meant to create a single services that features best practice recommendations, but also a unified notifications center and tools to triage and take actions against threats, all with focus on collaboration among admins. Also new is a security investigation tool that mostly focuses on allowing admins to create automated workflows for sending notifications or assigning ownership to security investigations.

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Google Cloud adds a managed service for Microsoft’s Active Directory

Love it or hate it, Microsoft’s Active Directory remains one of the most-used identity services in the enterprise. Google’s Cloud Platform has long allowed you to manually set up an Active Directory deployment, but today, Google is taking this a step further by announcing the beta of a managed service. As the name implies, Google will manage this service for you and automate everything from server maintenance to security configurations.

Unsurprisingly, given Google’s recent focus on hybrid-cloud deployments, you also can use this service to extend your existing on-premises Active Directory domains to the cloud.

As Google notes, the number of apps and servers that rely on Active Directory and that are moving to the cloud continues to increase. Many of these are legacy applications, but plenty of new apps also rely on it because it’s simply the standard in a given company. This also introduces new challenges for IT teams, which now have to manage additional latency and security requirements, for example. The new managed service is meant to make all of this easier and, as Google says, allow “the IT and security teams to focus on higher-value projects.”

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Google launches an end-to-end AI platform

As expected, Google used the second day of its annual Cloud Next conference to shine a spotlight on its AI tools. The company made a dizzying number of announcements today, but at the core of all of these new tools and services is the company’s plan to democratize AI and machine learning with pre-built models and easier to use services, while also giving more advanced developers the tools to build their own custom models.

The highlight of today’s announcements is the beta launch of the company’s AI Platform. The idea here is to offer developers and data scientists an end-to-end service for building, testing and deploying their own models. To do this, the service brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labeling service) and then either use existing classification, object recognition or entity extraction models, or use existing tools like AutoML or the Cloud Machine Learning engine to train and deploy custom models.

“The AI Platform is this place where, if you are taking this terrifying journey from a journeyman idea of how you can use AI in your enterprise, all the way through launch and a safe, reliable deployment, the AI Platform help you move between each of these stages in a safe way so that you can start with exploratory data analysis, start to build models using your data scientists, decide that you want to use this specific model, and then with essentially one click be able to deploy it,” a Google spokesperson said during a press conference ahead of today’s official announcement.

But there is plenty more AI news, too, mostly courtesy of Cloud AutoML, Google’s tool for automating the model training process for developers with limited machine learning expertise.

One of these new features is AutoML Tables, which takes existing tabular data that may sit in Google’s BigQuery database or in a storage service and automatically creates a model that will predict the value of a given column.

Also new is AutoML Video Intelligence (now in beta), which can automatically annotate and tag video, using object recognition to classify video content and make it searchable. For detecting objects in photos, Google also today launched the beta of AutoML Vision and for applications that run at the edge, Google launched the beta AutoML Vision Edge, which includes the ability to then deploy these models to edge devices.

A lot of enterprise data comes in the form of straightforward, unstructured text, though. For these use cases, Google today launched the betas of its custom entity extraction service and a custom sentiment analysis service. Both of these tools can be customized to fit the needs of a given organization. It’s one thing to use a generic entity extraction service to understand documents, but for most businesses, the real value here is to be able to pull out information that may be very specific to their needs and processes.

Talking about documents, Google also today announced the beta of its Document Understanding API. This is a new platform that can automatically analyze scanned or digital documents. The service basically combines the ability to turn a scanned page into machine-readable text and then use Google’s other machine learning services to extract data from it.

After introducing it in preview last year, the company also today launched the beta of its Contact Center AI. This service, which was built with partners like Twilio, Vonage, Cisco, Five9, Genesys and Mitel, offers a full contact center AI solution that uses tools like Dialogflow and Google’s text-to-speech capabilities to allow its users to build a virtual agent system (and when things go awry, it can pass the customer to a human agent).

It’s no secret that many enterprises struggle to combine all of these tools and services into a coherent platform for their own needs. Maybe it’s no surprise then that Google also today launched it first AI solution for a specific vertical: Google Cloud Retail. This service combines the company’s Vision Product Search, Recommendations AI and AutoML Tables into a single solution for tackling retail use cases. Chances are, we will see more of the packages for other verticals in the near future.

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Google launches its coldest storage service yet

At its Cloud Next conference, Google today launched a new archival cold storage service. This new service, which doesn’t seem to have a fancy name, will complement the company’s existing Nearline and Coldline services for storing vast amounts of infrequently used data at an affordable low cost.

The new archive class takes this one step further, though. It’s cheap, with prices starting at $0.0012 per gigabyte and month. That’s $1.23 per terabyte and month.

The new service will become available later this year.

What makes Google cold storage different from the likes of AWS S3 Glacier, for example, is that the data is immediately available, without millisecond latency. Glacier and similar service typically make you wait a significant amount of time before the data can be used. Indeed, in a thinly veiled swipe at AWS, Google directors of product management Dominic Preuss and Dave Nettleton note that “unlike tape and other glacially slow equivalents, we have taken an approach that eliminates the need for a separate retrieval process and provides immediate, low-latency access to your content.”

To put that into context, a gigabyte stored in AWS Glacier will set you back $0.004 per month. AWS, however, has also pre-announced a Deep Archive storage class, too, though the pricing for that service hasn’t been announced yet and the promised retrival time here is “within 12 hours.”

Gogole’s new object storage service uses the same APIs as Google’s other storage classes and Google promises that the data is always redundantly stored across availability zones, with eleven 9’s of annual durability.

In a press conference ahead of today’s official announcement, Preuss noted that this service mostly a replacement for on-premise tape backups, but now that many enterprises try to keep as much data as they can to then later train their machine learning models, for example, the amounts of fresh data that needs to be stored for the long term continues to increase rapidly, too.

With low latency and the promise of high availability, there obviously has to be a drawback here, otherwise Google wouldn’t (and couldn’t) offer this service at this price. “Just like when you’re going from our standard [storage] class to Nearline or Coldline, there’s a committed amount of time that you have to remain in that class,” Preuss explained. “So basically, to get a lower price you are committing to keep the data in the Google Cloud Storage bucket for a period of time.”

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Salesforce and Google want to build a smarter customer service experience

Anyone who has dealt with bad customer service has felt frustration with the lack of basic understanding of who you are as a customer and what you need. Google and Salesforce feel your pain, and today the two companies expanded their partnership to try and create a smarter customer service experience.

The goal is to combine Salesforce’s customer knowledge with Google’s customer service-related AI products and build on the strengths of the combined solution to produce a better customer service experience, whether that’s with an agent or a chatbot..

Bill Patterson, executive vice president for Salesforce Service Cloud, gets that bad customer service is a source of vexation for many consumers, but his goal is to change that. Patterson points out that Google and Salesforce have been working together since 2017, but mostly on sales- and marketing-related projects. Today’s announcement marks the first time they are working on a customer service solution together.

For starters, the partnership is looking at the human customer service agent experience.”The combination of Google Contact Center AI, which highlights the language and the stream of intelligence that comes through that interaction, combined with the customer data and the business process information that that Salesforce has, really makes that an incredibly enriching experience for agents,” Patterson explained.

The Google software will understand voice and intent, and have access to a set of external information like weather or news events that might be having an impact on the customers, while Salesforce looks at the hard data it stores about the customer such as who they are, their buying history and previous interactions.

The companies believe that by bringing these two types of data together, they can surface relevant information in real time to help the agent give the best answer. It may be the best article or it could be just suggesting that a shipment might be late because of bad weather in the area.

Customer service agent screen showing information surfaced by intelligent layers in Google and Salesforce

The second part of the announcement involves improving the chatbot experience. We’ve all dealt with rigid chatbots, who can’t understand your request. Sure, it can sometimes channel your call to the right person, but if you have any question outside the most basic ones, it tends to get stuck, while you scream “Operator! I said OPERATOR!” (Or at least I do.)

Google and Salesforce are hoping to change that by bringing together Einstein, Salesforce’s artificial intelligence layer and Google Natural Language Understanding (NLU) in its Google Dialogflow product to better understand the request, monitor the sentiment and direct you to a human operator before you get frustrated.

Patterson’s department, which is on a $3.8 billion run rate, is poised to become the largest revenue producer in the Salesforce family by the end of the year. The company itself is on a run rate over $14 billion.

“So many organizations just struggle with primitives of great customer service and experience. We have a lot of passion for making everyday interaction better with agents,” he said. Maybe this partnership will bring some much needed improvement.

Salesforce at 20 offers lessons for startup success

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Google launches Cloud Code to make cloud-native development easier

Google today launched a set of plugins for popular development environments like IntelliJ and Visual Studio Code that promise to make building cloud-native applications significantly easier. You can’t say ‘cloud-native’ without saying Kubernetes, so it’s no surprise that the focus here is on helping developers build, debug and deploy their code to a Kubernetes cluster right from their IDE.

Typically, Google argues, developers edit, compile and debug their code locally. That’s often just fine, but it can also create issues when the production environment doesn’t quite match the local one. But building containers comes with its own challenges — and nobody really enjoys writing YAML files by hand just to test code. Indeed, the promise here is that the developer doesn’t have to write a single line of YAML.

The promise then, is that you can continue to develop your code just like you used to, while Cloud Code handles all of the work of turning it into a cloud-native application. The tools are also integrated with Google’s DevOps tools like Cloud Build and Stackdriver.

Cloud Code combines a number of existing open-source tools, including Kubectl, the command-line tools for working with the Kubernetes API, Jib for building containers for Java applications, and Skaffold for setting up the continuous deploying pipeline for Kubernetes applications.

The service will works virtually all popular programming languages and Google says that support for .NET is also in the works.

“This essentially gives you turbocharged, cloud-native app development, right in your IDE,” Google Cloud VP of product and design Pali Bhat told me. “It brings remote app development right into your developer loop right in the IDE. This unlocks the power of all of these developers and lets them build for Kubernetes, build for cloud-native, without having to worry about all fo the different pieces that they had to learn.”

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The Google Assistant can now finally manage your G Suite Calendar

As part of its Cloud Next conference, Google today announced a small but welcome new Google Assistant feature that allows Google’s virtual assistant to finally help you manage your work calendar in G Suite.

Traditionally, as Google rightly notes, the Assistant has mostly been about helping you keep track of your personal life. Now, however, you’ll also be able to sign in with your G Suite account and ask the Assistant for information about your work day. This integration should work across all Google Assistant surfaces, including the car and Assistant displays like the Google Home Hub.

Right now, this feature mostly focuses on asking for calendar events, though. As far as we can tell, you won’t be able to create an event through the Assistant just yet. Google does note, though, that you can use this feature to, for example, ask about an upcoming event and then send an email to the other attendees.

What’s interesting here is that so far, Google has never positioned the Assistant as a productivity tool in the workplace. Names like ‘Google Home’ and ‘Home Hub’ pretty much make that clear. Sometimes, though, work and home life overlap and at its core, the same technology that allows you to turn on your kitchen lights with your voice could also be used to pull interesting data out of a spreadsheet.

It’ll be interesting to see if Google plans to expand on this theme in the future or if this is a one-off integration.

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Google makes the power of BigQuery available in Sheets

Google today announced a new services that makes the power of BigQuery, its analytics data warehouse, available in Sheets, its web-based spreadsheet tool. These so-called ‘connected sheets’ face none of the usual limitations of Google’s regular spreadsheets, meaning there are no row limits, for example. Instead, users can take a massive dataset from BigQuery, with potentially billions of rows, and turn those into a pivot table.

The idea here, is to enable virtually anybody to make use of all of the data that is stored in BigQuery. That’s because from the user’s perspective, this new kind of table is simply a spreadsheet, with all of the usual functionality you’d expect from a spreadsheet. With this, Sheets becomes a frontend for BigQuery — and virtually any business user knows how to use a spreadsheet.

This also means that you can use all of usual visualization tools in Sheets and share your data with others in your organization.

“Connected sheets are helping us democratize data,” says Nikunj Shanti, Chief Product Officer at AirAsia. “Analysts and business users are able to create pivots or charts, leveraging their existing skills on massive datasets, without needing SQL. This direct access to the underlying data in BigQuery provides access to the most granular data available for analysis. It’s a game changer for AirAsia.”

The beta of connected sheets should go live within the next few months.

In this context, it’s worth mentioning that Google also today announced the beta launch of BigQuery BI Engine, a new service for business users that connects BigQuery with Google Data Studio for building interactive dashboards and reports. This service, too, is available in Google Data Studio today and will also become available through third-party services like Tableau and Looker in the next few months.

“With BigQuery BI Engine behind the scenes, we’re able to gain deep insights very quickly in Data Studio,” says Rolf Seegelken, Senior ​Data Analyst, Zalando. “The performance of even our most computationally intensive dashboards has sped up to the point where response times are now less than a second. Nothing beats ‘instant’ in today’s age, to keep our teams engaged in the data!”

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What’s left of Google+ is now called Currents

Google+ for consumers is officially dead, but it’s still alive for enterprise users. Only a few days after completely shutting down the public version of Google+, Google today announced that it is giving the enterprise version a new name. It’s now called Currents.

If that name sounds familiar, it’s because Google once offered another service called Currents, a social magazine app with Google+ integrations that was later replaced by Google Play Newsstand. That history clearly bodes well for the new Currents.

Like before, Google+/Currents is meant to give employees a place to share knowledge and provide them with a place for internal discussions.

Google is probably doing the right thing by completely eliminating the Google+ moniker. The fact that there was still a version of Google+ for the enterprise created a bit of confusion when it announced the shutdown of the consumer version. Maybe this move will also allow the remaining developers on the project to leave the failed legacy of Google+ behind and try something new. Since the only focus is now on business users, that should be fairly easy, even though the code base surely still reflect a time when Google’s leadership thought that social search was the future.

RIP Google+

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Hangouts Chat is coming to Gmail for G Suite users

The least said about Google’s messaging strategy, the better. But for better or worse, Hangouts Chat and Meet for G Suite, Google’s work-focused text and video chat tools are here to stay given that the one area where Google’s messaging strategy is clear is in the enterprise. With the end of the old Hangouts experience drawing nearer, the company today announced that it is now essentially replacing classic Hangouts with its business-focused Hangouts Chat tool in Gmail.

That’s a pretty sensible move and doesn’t come as a major surprise, but this marks the first time that Google has clearly laid out its strategy for how it will replace Hangouts in Gmail for its business users.

The experience, as far as we can tell, will be very similar to the current Hangouts one. Unsurprisingly, Hangouts Meet in Gmail will not just feature people, but also rooms and bots, two of the key differentiators between the old and new Hangouts. One difference worth mentioning, though, is that rooms will open into a full-screen experience with threads, which will make for a slightly different experience compared to what you’re probably used to from the classic Hangouts.

For now, this new feature isn’t quite ready to launch yet, though. Google is asking businesses that want to participate in the beta to register their interest here.

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Google extends its BeyondCorp security model to G Suite

BeyondCorp is Google’s model for securing networks not just through VPNs and other endpoint security techniques, but through a model that focus on context-aware access policies that focus on the user’s identity, hardware and the context of the request. That has been Google’s internal security policy for a while now and over the last few months, it started brining it to its own customers, too, starting with its Cloud Identity-Aware Proxy, which is now generally available, and its VPC Service Controls.

Today, the company is extending these context-aware access capabilities to its Cloud Identity user and device management service, as well as G Suite, its productivity suite. So while earlier implementation centered around protecting a company’s technical cloud infrastructure, this release focuses on devices and cloud-based apps like Gmail, Drive, Docs, Sheets and Calendar.

In this context, some devices, for example, may be more highly trusted because they have been enrolled in the Cloud Identity service and because a number of security policies are in place for it. That’s a different kind of security posture than a system that simply trusts users because they come through a specific VPN.

Context-aware access for G Suite apps is now in beta, but only for customers who subscribe to Cloud Identity Premium, G Suite Enterprise and G Suite Enterprise for Education.

With today’s release, Google also announced the BeyondCorp Alliance, which brings together a number of security and management partners. These include Check Point, Lookout, Palo Alto Networks, Symantec and VMware. According to Google, these companies are all working to bring device posture data to Google’s context-aware access engine.

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Oppo’s new flagship has a bit of everything

It shares a name with the biggest little city in the world, and fittingly, the Oppo Reno appears to have a lot going for it. Top level, there’s a nutty pop up camera wedge for selfies, 10x zoom, a 48 megapixel camera, in-display fingerprint reader and an optional 5G version. It’s got a whole lot of everything.

The handset makes its debut in Zurich today, but the company’s offered up just about all of the insight you’ll need earlier this morning There are going to be a few different versions of the handset, including the 6.4 inch standard and the 6.6 inch version, which sports the aforementioned 10x Zoom, along with a Snapdragon 855.

We are excited to announce #OPPOReno will be one of the first commercially available 5G phones to hit the European market, and we are proud to partner with @Swisscom to achieve this milestone through our 5G Landing Project! #FirstTo5 #GetReadyForReno pic.twitter.com/WAglcDGnVF

— OPPO (@oppo) April 10, 2019

More info on the 5G version is still forthcoming, but Oppo says it will be “one of the first commercially available 5G phones to hit the European market,” using Swisscom’s network. On that note, I would be surprised to see the handset available in the States, as Oppo doesn’t have much of a footprint in this part of the world. More info on availability in places like Europe and India is coming later this month.

As for pricing, the base level model starts at around $450, with the zoom starting at around $600. Pricing on the non-5G versions go up to just over $700.

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For true transparency around political advertising, U.S. tech companies must collaborate

In October 2017 online giants Twitter, Facebook, and Google announced plans to voluntarily increase transparency for political advertising on their platforms. The three plans to tackle disinformation had roughly the same structure: funder disclaimers on political ads, stricter verification measures to prevent foreign entities from posting such ads, and varying formats of ad archives.

All three announcements came just before representatives from the companies were due to testify before Congress about Russian interference in the 2016 election and reflected fears of forthcoming regulation, as well as concessions to consumer pressure.

Since then, the companies have continued to attempt to address the issue of digital deception occurring on their platforms.

Google recently released a white paper detailing how it would deal with online disinformation campaigns across many of its products. In the run-up to the 2018 midterm elections, Facebook announced it would ban false information about voting. These efforts reflect an awareness that the public is concerned about the use of social media to manipulate their votes and is pushing for tech companies to actively address the issue.

These efforts at self-regulation are a step in the right direction — but they fall far short of providing the true transparency necessary to inform voters about who is trying to influence them. The lack of consistency in disclosure across platforms, indecision over issue ads, and inaction on wider digital deception issues including fake and automated accounts, harmful micro-targeting, and the exposure of user data are major defects of this self-governing model.

For example, individuals looking at Facebook’s ad transparency platform are currently able to see information about who viewed an ad that is not currently available on Google’s platform. However, on Google the same user can see top keywords for advertisements, or search political ads by district, which cannot be done on Facebook.

With this inconsistency in disclosure across platforms, users are not able to get a full picture of who is trying to influence them, which prevents them from being able to cast an informed vote.

One hundred cardboard cutouts of Facebook founder and CEO Mark Zuckerberg stand outside the US Capitol in Washington, DC, April 10, 2018. Advocacy group Avaaz is calling attention to what the groups says are hundreds of millions of fake accounts still spreading disinformation on Facebook. (Photo: SAUL LOEB/AFP/Getty Images)

Issue ads pose an additional problem. These are public communications that do not reference particular candidates, focusing instead on hot-button political issues such as gun control or immigration. Issue ads cannot currently be regulated in the same way that political communications that refer to a candidate can due to the Supreme Court’s interpretation of the First Amendment.

Moreover, as Bruce Flack, Twitter’s General Manager for Revenue Product, pointed out in a blog post addressing the platform’s impending transparency efforts, “there is currently no clear industry definition for issue-based ads.”

In the same post, Flack indicated a potential solution, writing, “We will work with our peer companies, other industry leaders, policy makers and ad partners to clearly define [issue ads] quickly and integrate them into the new approach mentioned above.” This post was written 18 months ago, but no definition has been established—possibly because tech companies are not collaborating to systemically confront digital deception.

This lack of collaboration damages the public’s right to be politically informed. If representatives from the platforms where digital deception occurs most often — Facebook, Twitter, and Google — were to form an independent advisory group that met regularly and worked with regulators and civil society to discuss solutions to digital deception, transparency and disclosure across the platforms would be more complete.

The platforms could look to the example set by the nuclear power industry, where national and international nonprofit advisory bodies facilitate cooperation among utilities to ensure nuclear safety. The World Association of Nuclear Operators (WANO) connects all 115 nuclear power plant operators in 34 countries in order to facilitate the exchange of experience and expertise. The Institute of Nuclear Power Operations (INPO) in the U.S. functions in a similar fashion but is able to institute tighter sanctions since it operates at the national level.

Similar to WANO and INPO, an independent advisory group for the technology sector could develop a consistent set of disclosure guidelines — based on policy regulations put in place by government — that would apply evenly across all social media platforms and search engines.

These guidelines would hopefully include a unified database of ads purchased by political groups as well as clear and uniform disclaimers of the source of each ad, how much it cost, and who it targeted. Beyond paid ads, the industry group could develop guidelines to increase transparency for all communications by organized political entities, address computational propaganda, and determine how best to safeguard users’ data.

Additionally, if the companies were working together, they could set up a consistent definition of what an issue ad is and determine what transparency guidelines should apply. This is particularly relevant given policymakers’ limited authority to regulate issue ads.

Importantly, working together regularly would allow platforms to identify technological advances that might catch policymakers by surprise. Deepfakes — fabricated images, audio, or video that purport to be authentic — represent one area where technology companies will almost certainly be ahead of lawmakers’ expertise. If digital corporations were working together as well as cooperating with government agencies, they could flag new technologies like these in advance and help regulators determine the best way to maintain transparency in the face of a rapidly changing technological landscape.

Would such collaboration ever happen? The extensive aversion to regulation shown by these companies indicates a worrying preference towards appeasing advertisers at the expense of the American public.

However, in August 2018, in advance of the midterm elections, representatives from large tech firms did meet to discuss countering manipulation on their platforms. This followed a meeting in May with U.S. intelligence officials, also to discuss the midterm elections. Additionally, Facebook, Microsoft, Twitter, and YouTube formed the Global Internet Forum to Counter Terrorism to disrupt terrorists’ ability to promote extremist viewpoints on those platforms. This shows that when they are motivated, technology companies can work together.

It’s time for Facebook, Twitter, and Google to put their obligation to the public interest first and work together to systematically address the threat to democracy posed by digital deception.

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Review: The $199 Echo Link turns the fidelity up to 11

The Echo Link takes streaming music and makes it sound better. Just wirelessly connect it to an Echo device and plug it into a set of nice speakers. It’s the missing link.

The Link bridges the gap between streaming music and a nice audio system. Instead of settling for the analog connection of an Echo Dot, the Echo Link serves audio over a digital connection and it makes just enough of a difference to justify the $200 price.

I plugged the Eco Link into the audio system in my office and was pleased with the results. This is the Echo device I’ve been waiting for.

In my case the Echo Link took Spotfiy’s 320 kbps stream and opened it up. The Link creates a wider soundstage and makes the music a bit more full and expansive. The bass hits a touch harder and the highs now have a new-found crispness. Lyrics are clearer and easier to pick apart. The differences are subtle. Everything is just slightly improved over the sound quailty found when using an Echo Dot’s 3.5mm output.

Don’t have a set of nice speakers? That’s okay, Amazon also just released the Echo Link Amp, which features a built-in amplifier capable of powering a set of small speakers (read the review here).

Here’s the thing: I’m surprised Amazon is making the Echo Link. The device caters to what must be a small demographic of Echo owners looking to improve the quality of Pandora or Spotify when using an audio system. And yet, without support for local or streaming high resolution audio, it’s not good enough for audiophiles. This is for wannabe audiophiles. Hey, that’s me.

Review

There are Echo’s scattered throughout my house. The devices provide a fantastic way to access music and NPR. The tiny Echo Link is perfect for the system in my office where I have a pair of Definitive Technology bookshelf speakers powered by an Onkyo receiver and amp. I have a turntable and SACD player connected to the receiver but those are a hassle when I’m at my desk. The majority of the time I listen to Spotify through the Amazon Echo Input.

I added the Onkyo amplifier to the system last year and it made a huge difference to the quality. The music suddenly had more power. The two-channel amp pushes harder than the receiver, and resulted in audio that was more expansive and clear. And at any volume, too. I didn’t know what I was missing. That’s the trick with audio. Most of the time the audio sounds great until it suddenly sounds better. The Echo Link provided me with the same feeling of discovery.

To be clear the $200 Echo Link does not provide a night and day difference in my audio quality. It’s a slight upgrade over the audio outputted by a $20 Echo Input — and don’t forget, an Echo device (like the $20 Echo Input) is required to make the Echo Link work.

The Echo Link provides the extra juice lacking from the Echo Input or Dot. Those less expensive options output audio to an audio system, but only through an analog connection. The Echo Link offers a digital connection through Toslink or Digital Coax. It has analog outputs that’s powered by a DAC with a superior dynamic range and total harmonic distortion found in the Input or Dot. It’s an easy way to improve the quality of music from streaming services.

The Echo Link, and Echo Link Amp, also feature a headphone amp. It’s an interesting detail. With this jack, someone could have the Echo Link on their desk and use it to power a set of headphones without any loss of quality.

I set up a simple A/B test to spot the differences between a Link and a Dot. First, I connected the Echo Link with a Toslink connection to my receiver and an Echo Input. I also connected an Echo Dot through its 3.5mm analog connection to the receiver. I created a group in the Alexa app of the devices. This allowed each of the devices to play the same source simultaneously. Then, as needed, I was able to switch between the Dot and Link with just a touch of a button, providing an easy and quick way to test the differences.

I’ll leave it up to you to justify the cost. To me, as someone who has invested money into a quality audio system, the extra cost of the Echo Link is worth it. But to others an Echo Dot could be enough.

It’s important to note that the Echo Link works a bit differently than other Echo devices connected to an audio system. When, say, a Dot is connected to an audio system, the internal speakers are turned off and all of the audio is sent to the system. The Echo Link doesn’t have to override the companion Echo. When an Echo Link is connected to an Echo device, the Echo still responds through its internal speakers; only music is sent to the Echo Link. For example, when the Echo is asked about the weather, the forecast is played back through the speakers in the Echo and not the audio system connected to the Echo Link. In most cases this allows the owner to turn off the high-power speakers and still have access to voice commands on the Echo.

The Echo Link takes streaming music and instantly improves the quality. In my case the improvements were slight but noticeable. It works with all the streaming services supported by Echo devices, but it’s important to note it does not work with Tidal’s high-res Master Audio tracks. The best the Echo Link can do is 320 kbps from Spotify or Tidal. This is a limiting factor and it’s not surprising. If the Echo Link supported Tidal’s Master Tracks, I would likely sign up for that service, and that is not in the best interest of Amazon which hopes I sign up for Amazon Music Unlimited.

I spoke to Amazon about the Echo Link’s lack of support for Tidal Master Tracks and they indicated they’re interested in hearing how customers will use the device before committing to adding support.

The Link is interesting. Google doesn’t have anything similar in its Google Home Line. The Sonos Amp is similar, but with a built-in amplifier, it’s a closer competitor to the Echo Link Amp. Several high-end audio companies sell components that can stream audio over digital connections yet none are as easy to use or as inexpensive as the Echo Link. The Echo Link is the easiest way to improve the sound of streaming music services.

Review: The $299 Echo Link Amp adds Alexa to any speaker

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