Google Inbox Smart Reply: Cognition Meets Communication

How hot is work chat in the enterprise? So trendy that it’s now being used to improve the very thing it’s supposedly killing off – email.
Google announced yesterday a new feature for its Inbox application, which is an alternate Gmail interface designed for use on mobile devices. That feature, called Smart Reply, lets Inbox users reply to an incoming email with a short message (one phrase or sentence) that has been suggested by the application.
Google’s blog post announcing the new feature highlights the natural language processing, artificial intelligence, and machine learning technologies that work behind the scenes. Inbox uses these technologies to formulate three possible short responses for each incoming message. The user taps on the most appropriate one to embed it in her response and has the option to manually type or verbally dictate additional text. (Click on the image below to enlarge it and see this in action.)

 
 
 
 
 
 
 
 

How Smart Reply Works

The three auto-generated, suggested responses are based on the content of the incoming email message and the responses that were generated and selected for previous, similar messages. Smart Reply uses separate neural networks that work in tandem; one network reads and makes sense of every word in the incoming text and the second network predicts best-fit responses and synthesizes them into grammatically correct replies. (See this post on the Google Research Blog for more technical details on Smart Reply.)
The principles and even some of the technologies behind Smart Reply are not new. The Autonomy IDOL technology that Hewlett-Packard infamously acquired four years ago is used to auto-classify digital documents based on their content. Once classified, the documents can more easily be searched for, used in workflows, and archived.
Just last week IBM announced its new intelligent data capture solution, IBM Datacap Insight Edition, which uses cognitive computing capabilities to read document-based content and auto-classify it. Like Google’s and H-P’s technologies, IBM’s software must initially be trained with a set of control documents and then continues to learn as it reviews documents in a production environment.

Smart Reply and Chatbots

Inbox’s new ability to read text and formulate multiple, viable short responses doesn’t quite turn email into real-time messaging, but it does help individuals respond more quickly to the incoming messages in their email queue. Pair that with instant notifications of new incoming email messages on a mobile device and email becomes much more like instant messaging and other forms of work chat.
Google has taken a significant first step toward creating an intelligent bot that replies to email messages for you based on their content. Contrast this with the current practice of employing prebuilt, user-defined rules to reply with a canned response depending on who sent the incoming email or based on the recipients schedule (think vacation autoreplies).
If Google were to apply its deep neural network technology in Hangouts, it would move closer to Slack, HipChat, Telegram and other work chat tools that use bots to reply to user generated queries and as intermediaries between users and integrated third-party applications. In fact, Hangouts would have a differentiated advantage – the ability to parse not only incoming messages and suggest appropriate responses, but to do the same with text-based documents that are attached to chat messages.
It is likely that Google will go beyond applying this new technology in apps like Inbox and Hangouts. Imagine the power of having Smart Reply baked into Android, so it could be deployed on watches, in cars and as part of other emerging hardware-based platforms that run on that operating system. Tap on the watch’s or car’s display and quickly choose and send a response to an incoming message.
Some more advanced variant of Smart Reply might be used to semi-automate communication between nodes in mixed networks of machines and humans – Networks of Everything. Take as an example the current generation of software (the machine) that listens to social media and discerns trending topics related to a company’s customer-facing operations. This type of software could be enhanced with cognitive capabilities so that it would be able to suggest appropriate Twitter-length responses to an individual tasked with responding to relevant incoming social content. Eventually, the software might be able to respond, without human intervention, directly to someone expressing their opinion on social media.
The possibilities are numerous and mind-boggling. For now, Google has taken an important step toward a computing future in which real-time communication at work is increasingly semi- or fully-automated.

The Internet of Things and Networks of Everything

The Internet of Things (IoT) has been a hot topic for several months now, and there are new stories about it in the business and technology press on a daily basis. While it’s easy to view these as hype at worst and vision at best, there is no denying that purveyors of hardware, software and services are dedicating and creating the resources they will use to capitalize on the IoT. Last week alone, there were three announcements that show just how quickly the IoT market is progressing and how big of a business opportunity it is.
On Monday, September 14th, IBM formally launched a distinct IoT business unit and named former Thomas Cook Group CEO Harriet Green as its leader. The new IoT unit is the first significant step by IBM toward delivering on the $3 billion commitment it made to IoT in March. IBM signaled in Monday’s press release that the unit will “soon” number about 2,000 consultants, researchers and developers, who will use IBM’s assets to help customers get up and running on the IoT. Those assets will likely include the Bluemix platform-as-a-service (PaaS), Watson and other analytics software, as well as the MQTT messaging protocol standard for machine-to-machine communication that IBM submitted to OASIS in 2013.
The next day, Salesforce.com used its annual Dreamforce conference as the grand stage on which to unveil its IoT Cloud. This offering has at its core a new “massively scalable”, real-time event processing engine named ‘Thunder’ (to complement Salesforce’s ‘Lightening’ UI framework). IoT Cloud connects IoT resources and Thunder rules-based workflow to route data between them, triggering pre-defined actions. For example, when an individual enters a retail store, a beacon can offer them discounts based on qualification criterion such as loyalty program status and in-store inventory levels. Scenarios such as this will be possible because of IoT Cloud’s integration with the Salesforce Sales, Marketing and Analytics Clouds. IoT Cloud is currently in pilot and is expected to be generally available sometime in the second half of 2016.
While these two announcements are important milestones in the respective organizations ability to help customers connect to and use the IoT, they do not enable them to do so immediately and risk being labeled as more IoT hype. The sheer magnitude of resources assembled for each of these vendors initiatives signals that they believe that the IoT will be both real and profitable in the not-so-distant future.
The final piece of related news from last week underscores that smaller, pure-play vendors are delivering tools that help their customers get on the IoT now. Build.io announced that Flow, its integration PaaS that had been beta released in March, is now generally available. Flow features a drag-and-drop interface that is used to connect IoT elements ─ sensors and other intelligent devices, backend systems, mobile applications and other software ─ into an integrated system. Connections are made at the API level. Like Salesforce’s Thunder, Flow uses rules-based event processing to trigger actions from IoT data. In essence, Build.io is delivering today a critical part of what Salesforce intends to make generally available later this year.

Current State of the Internet of Things and Networks of Everything

These announcements, taken together, mean that the IoT is poised for takeoff. The first sets of user-friendly tools that organizations need to connect IoT nodes, transmit their data and use it to drive business processes are available now, in some cases, or will be coming to market within a year. We are on the cusp of a rapid acceleration in the growth of the market for software underpinning the IoT, as well as the network itself.
This latest batch of IoT announcements from software vendors underscores another thing: the IoT will initially be built separately from enterprise social networks (ESNs). Many organizations, particularly large enterprises, have experimented with ESNs and a few have managed to build ones that are operating at scale and creating value. Those businesses will be turning their attention to IoT development now, if they haven’t already. They will pilot, then scale, their efforts there, just as they did with ESNs.
Eventually, organizations will realize that it is more efficient and effective to build Networks of Everything (NoE), in which humans and machines communicate and collaborate with one another using not only the Internet, but also cellular, Bluetooth, NFC, RFID and other types of networks. This construct is just beginning to enter reality, and it will take a few years before NoE get the market attention that ESNs did five years ago and the IoT is now.
At some future point, when NoE have become a fixture of networked business, we will look back at this month (Sept. 2015) and declare that it was a watershed moment in the development of the IoT. We’ll also laugh at how obvious it seems, in hindsight, that we should have just built NoE in the first place.

Real-time Messaging in the Enterprise: Here We Go Again

There was a good Wired article, published yesterday, that bemoaned the rapidly-growing plethora of communication applications centered around real-time chat. Its author lists consumer-oriented applications to demonstrate the situation:

“I bounce through a folder full of messaging apps. I talk to a few people on Hangouts, a few others on Facebook Messenger, exactly one person on WhatsApp. I Snapchat all those people, too. I use Twitter DMs, GroupMe, HipChat, Skype, even Instagram Direct a couple of times. Livetext, Yahoo’s new app, is fun; I’ve been using that. Oh, and there’s email. And iMessage. And, of course, good ol’ green-bubble text messaging.”

The same problem is beginning to develop within businesses as their employees self-adopt enterprise-first chat tools from startup vendors that have been in-market for a while, including Slack, Hipchat, Wrike, Flowdock and others. Oh, and let’s not forget that many employees use the consumer-grade applications mentioned in the Wired article to conduct business, even if it’s against company policy.
Of course, all of these newer chat tools compete with IT-approved enterprise real-time messaging offerings for employees’ attention and love. IBM Sametime, Microsoft’s Lync and Yammer, and Salesforce Chatter are just a few well-known examples of longer-lived, enterprise-grade messaging applications and services that support real-time exchanges. To further compound the clutter, we are also seeing new chat offerings, from established enterprise collaboration software vendors, that mimic their consumer-oriented cousins. Jive Chime and Microsoft Send are real-time chat apps that have been released in the last four months to support organizations’ increasingly mobile workforces.
There are a few problems created by this overwhelming collection of enterprise real-time messaging options. First, these applications are largely siloed from each other, so employees have to remember in which one a certain conversation occurred or know in which application they have the highest probability of gaining a specific coworker’s attention. Second, some can interoperate with other enterprise applications via RESTful APIs, while others require more costly, time-consuming integration efforts. Third, some messaging applications support information governance initiatives such as records retention and disposal whereas other offerings essentially assume that chats are throw-away conversations that do not need to be archived and managed.
There are so many other issues that they will be better dealt with in another post. But they are bound by one clear fact: we’ve made all of these mistakes with previous generations of enterprise messaging technology.

The BIG Problem: Why?

The biggest problem facing the newest wave of enterprise chat tools is an existential one. It is not clear why they are needed when existing real-time messaging tools satisfy the same use cases. I voiced this in the following mini-tweetstorm on the day that Microsoft Send was announced. (read from the bottom of the graphic to the top)
Larry's Enterprise Chat Tweetstorm
That’s right. You can hold my feet to the fire on that prediction. Enterprise real-time chat is destined to quickly fail as a market segment and technology with significant, positive business impact. Just like the combination of status update and activity stream features in enterprise social software failed to displace email, instant messaging and other, well-established forms of business communication.
Insufficient technology is not the cause of poor communication within organizations. We have had at our disposal more-than-adequate messaging technologies for decades now. The real reason that employees and their organizations continue to communicate poorly is human behavior. People generally don’t communicate unless they have something to gain by doing so. Power, influence, prestige, monetary value, etc.
Well-designed technology can make it easier and more pleasant for people to communicate, but it does very little to influence, much less actually change, their behaviors. So the latest enterprise real-time chat applications may offer improvements in user experience, but they won’t measurably increase communication frequency or effectiveness in most organizations unless their deployment is accompanied by change management efforts that include meaningful incentives to communicate.
I intend to track and chronicle the rise and fall of enterprise real-time chat as part of my research agenda at Gigaom Research. Stay tuned over the coming months as we watch this drama unfold.
 
 

Why can’t we just admit that journalists are human?

Yahoo fired its former Washington bureau chief on Wednesday for a joking comment he made during a video broadcast from the Republican convention. Isn’t it about time we admitted that journalists have emotions and opinions, rather than expecting them to be impartial robots?