Recommending Actions for Your NLP Bot

At first glance, the machine learning methods typically used in NLP applications (such as chatbots) and those used in recommender systems (for recommending products) are not often leveraged together in the same applications.

NLP is the machine learning domain that makes your virtual assistant capable of engaging in human language based conversation and recommender systems, as the name suggests, recommend products/services you will hopefully like (thus saving you the trouble of discovering them on your own); but it is not often you see NLP and recommender systems together.

Where Conversational UI Meets Recommendations

But let's think about that for a moment. Is there a solution space where NLP and recommender systems intersect and why would anyone want to do such a thing? I will make the case that every so called AI powered virtual assistant (aka chatbot and their kin) needs context and part of that context can be provided by a personalized recommender system that helps guide the conversation and streamline the conversational user experience.

A Messaging First World

We are in the midst of a messaging application revolution. A new generation of users are making messaging based applications their preferred medium for communicating with people, places and things around them, especially when it comes to the digital world (and contextual world). And there is no lack of applications from fintech to social applications leveraging and rediscovering the command line interface as the new mode (or not so new mode for many command-line geeks) of communication between humans and computers.

Your Next Question or Answer is a Recommendation Away

Ok, so where do recommender systems fit into the world of NLP and conversational driven user interfaces? Well, conversational applications are not without their own challenges. Typing and speaking takes effort on both the user and virtual assistant, in order to engage in timely and efficient interaction. But what if your virtual assistant new what you wanted to do next (or what you might/should like to do next)? What if your NLP powered bot could suggest to you actions you might want to take and save you the trouble of verbalizing it - maybe give you a quick one click shortcut (can be voice powered as well) to driving and continue the conversation?

This is where recommender systems can play a vital role in making your virtual assistant not just clever at understanding intent and named entity recognition from voice or text, but also present a sense of intelligence in remembering your past behavior or predicting what you might do next (or should do next) by relating your behavior to what others in similar roles and situations have done next. So even with no prior knowledge of "you", the virtual assistant might prescribe next actions based on what others in similar roles and situations have done. Does that not sound like a recommendation system?

Prescribing vs Predicting

Recommender systems are inherently about prescribing things (which can include actions not just items) applicable to your context at a given point in time (time based being a critical context as well). I foresee a future where both business and consumer application oriented virtual assistants and NLP bots will leverage highly personalized recommender systems to take the human-to-computer interaction to the next logical evolution (as promised by many sci-fi books and movies :)

Matrix Factorization and LSTMs are Your Friend

So for all you NLP bot developers, make things like matrix factorization and collaborative filtering your friend. Hybrid recommender systems based on collaborative filtering and content filtering (product and customer meta data) have been the state of the art for the past few years (since the Netfix contest). However the future of recommender systems will be powered by deep learning and concepts like LSTM and product and item embeddings. Research in this space is evolving fast. A mix of shallow and deep learning techniques are racing to enable this world of intelligent NLP bots and efficient conversational user interfaces.

AI and Machine Learning Madness

The hype around machine learning and AI is just off the charts these days. This reminds me of several years back when just about every software product started putting the term "Big Data" in front (or at the end) of all of their product literature and company tagline.

AI is the Art of Using ML at the Right Time and in the Right Context

Today, startups are claiming to change every industry and the world just because they put some "machine learning" inside their products. Machine learning has the potential to give software super powers, but alone it is does not create great software.

Listen, I am just as excited as the next geek about the potential of deep learning and the democratization of data science, but please stop putting machine learning in your company's slogan, like it was the secret ingredient that you were looking for. Sorry, this is becoming my new pet peeve.

Machine Learning, at the end of the day, is just one more tool in our arsenal of tools and skills needed to build more efficient and more intuitive solutions and services for our customers and for humanity as a whole. Infusing machine learning into your products is more than technology. It takes a new way of thinking and new of running engineering teams and engineering processes. 

A Great Product is Much More than AI

Please talk about the benefits of using AI/ML/DL, don't just say "because you are using machine learning" you are changing the world or have a better product. There is that little thing called building a product that delivers value to the end user that you still have to get right. And believe it or not, this still requires human creativity and execution. Claiming your infusing your product with ML will not necessarily make it a better product or the world a better place.

AI is in the Product - It Should not be the Product

I do believe that machine learning related technology will be in every future product, just like we have transistors in every computer today. Machine learning will allow us to build better and more efficient products. How you use the machine learning to build a great product will matter much more than the fact that you are using machine learning. Keep that in mind when you come up with your next startup tagline and company slogan!

End of rant.

AI vs Paradox of Choice

The paradox of choice is a problem we see more and more of in our modern world. It goes beyond what products Amazon should recommend or friends Facebook should suggest. In the business world and in enterprise applications this is also a challenging problem as our applications and processes grow in complexity. The potential for machine learning powered recommender systems to augment human decision making is one of the next frontier for AI in the enterprise . 

Recommender systems can do more than just suggest what articles you should read on Linkedin or what jobs are most suited for you. In the future machine learning (and more likely deep learning) powered recommender systems will guide enterprise decision making by helping business process owners take the most effective actions and decisions in a timely manner and with hyper-personalization. 

Recommender systems will move from solving B2C optimization problems (how they are typically used today in our data saturated and over marketed world) to solving problems in B2B and enterprise applications. Ultimately recommender systems are about prescribing (they are not really about predicting) an optimal decision at the right time and place/context, so they can naturally deal with a variety of B2B scenarios such as optimizing workflow paths, streamlining supply chain actions, to augmenting human decisions for common day to day business operational functions. Enterprise decision makers are in vital need of these AI super powers. Stay tuned they are coming :)

Messaging-First Applications with Slack or FBM?

Conversational UX design is evolving as more and more apps begin to incorporate conversational UI functionality. While the concept of a messaging-centric UI can seem simple, the melding of a messaging-first user experience is nothing to underestimate. Conversational user interfaces can be simple for humans to interact with (you are just chatting back and forth), however, blending in and balancing rich visualization and complex interactions is not simple to get right. Just like any other UX, it is a balance of minimalism while allowing for rich expressiveness in the UI without overwhelming the user.

Slack is one of the leading platforms for building bots, especially for enterprise applications. However Slack has a number of bot conversational UX features that are still missing relative to other platforms such as FB Messenger and FB Workplace.

To give some perspective, here is my compiled list of features I would like to see in Slack's bot framework to improve its messaging UX and bring it on par with platforms like FB Messenger:

1) Conversational Streams and UI Alignment

Slack bots (especially in direct-messaging one-on-one dialog flows) force the bot and the user to both be left justified in the messaging UI stream. This goes against UI norms found in the majority of messaging application and related best practices for messaging apps. Typically in a streaming messaging flow, your conversational stream (you being the person interacting with the bot) is on the right of the screen and the party you are talking to (in this case the bot) is on the left side of the screen (or it can be visa-versa).

This is something not supported in Slack and makes a number of things awkward and cluttered in a bot-to-human dialog, especially when it is one-on-one (as opposed to Slack group channel). In Slack the entire conversational interaction is left justified, which can make the UI look cluttered when there are visual rich elements involved and and things like "Quick Replies" in the back and forth stream.

I hope that Slack will allow for aligning the bot vs the user on different sides of the messaging stream, something more similar to how FB Messenger works. This will allow for a more natural conversational interaction.

2) Horizontal Scrolling Carousel UI Components

Slack (mobile and desktop/web) does not provide any kind of horizontal card or horizontal scrolling carousel. While some might consider this bad design (to allow for horizontal scrolling of cards), it is often necessary to minimize the vertical area needed to display information in rich messaging interactions. FB Messenger allows for limited horizontal scrolling carousel that I find to be very useful when building bots. Hopefully Slack will incorporate this. Slack already supports rich "attachments", so it would be a natural fit to allow for some limited level or horizontal scrolling.

3) WebView Integration

Slack does not have explicit support for messaging buttons that open a webview UI. Sometimes a webview is needed to show rich web content (again here this kind of feature should not be abused). FB Messenger has this ability and allows for controlling how the webview window is opened and closed. This can be mimicked in Slack by using embedding links in the "field" elements for example, but is a bit of a hack.

4) Quick Reply Buttons

One particularly nice feature I got accustomed to using in Facebook Messenger is the feature referred to as "Quick Reply". This allows the bot to display "Quick Reply" buttons that are shortcuts for the user to enter commands that they would normally have to type.

There is a away to mimic quick replies in Slack, but again it is a bit of hack. Check this open source node/slack project for an example of how this works with Slack. Quick replies are a real necessity in a rich messaging interaction. Again here, I hope that Slack adds this feature natively instead of making bot frameworks jump through hoops to emulate this feature.

Hopefully the Slack product team will address these issues as Slack is by far the best team and enterprise collaboration/messaging platform on the market today.

FB Messenger might have some superior bot-to-human interaction and UX capabilities, but it inherently lacks the team collaboration functionality and the many third-party integrations that Slack has to offer.

I do believe FB Workplace will close the gap over time, and in many ways has advantages over Slack in terms of out of the box social collaboration functionality. Slack is a bit of geeky technical tool when it comes to social collaboration and thus not as intuitive to use.

I expect both FB Workplace and Slack to evolve as head to head competitors and battle for the hearts and minds of developers much like how Netscape battled Microsoft's Internet Explorer for web domination. For enterprise owners and enterprise end user, intelligent AI endowed virtual assistants and bots will usher in a new era of innovation not seen since the dot-com days. The battle has moved from the mobile app store to the AI app store where natural language understanding and deep learning are the killer technologies in the arsenal of AI sophisticated developers.

Augmented Reality vs Conversational User Interface

Don't believe all the hype about virtual reality and its close cousin augmented reality. How many examples do we need to see before we get it that humans don't like a big gizmo sitting over their faces and eyes or attached to their body. Examples like the failed Google Glass and the failing Facebook Oculus are two examples of such failures, with more on the way if Apple is not careful.

What will win out? It's all about texting and voice stupid (not you). AI is surely coming, but it will be powered by voice-first and messaging-first applications and services. Visual augmentation with AR and VR, is cute, but it will not be what transforms our reality and changes how we interact with technology.

Humans are social animals and voice and texting is what taps into that part of our brain that drives us to connect with others/things and drives us to share ideas. So gear up and start thinking how to turn your applications and services into voice-first and messaging-first user experiences. That is where the future is going.

Conversational Bot UI Features Missing in Slack

Conversational UX design is evolving as more and more apps begin to incorporate conversational UI functionality. While the concept of a messenger-centric UI can seem simple, the melding of a messaging-first user experience is nothing to underestimate. Conversational user interfaces can be simple for humans to interact with (you are just chatting back and forth), however, blending in and balancing rich visualization and complex interactions is not simple to get right. Just like any other UX, it is a balance of minimalism while allowing for rich expressiveness in the UI without overwhelming the user.

Slack is one of the leading platforms for building bots, especially for enterprise applications. However Slack has a number of bot conversational UX features that are still missing relative to other platforms such as FB Messenger, for example. Here is my compiled list of features I would like to see in Slack's bot framework to improve its messaging UX and bring it on par with platforms like FB Messenger:

1) Conversational Streams and UI Alignment
Slack bots (especially in direct-messaging one-on-one dialog flows) force the bot and the user to both be left justified in the messaging UI stream. This goes against UI norms found in the majority of messaging application and related best practices for messaging apps. Typically in a streaming messaging flow, your conversational stream (you being the person interacting with the bot) is on the right of the screen and the party you are talking to (in this case the bot) is on the left side of the screen (or it can be visa-versa).

This is something not supported in Slack and makes a number of things awkward and cluttered in a bot-to-human dialog, especially when it is one-on-one (as opposed to Slack group channel). In Slack the entire conversational interaction is left justified, which can make the UI look cluttered when there are visual rich elements involved and and things like "Quick Replies" in the back and forth stream.

I hope that Slack will allow for aligning the bot vs the user on different sides of the messaging stream, something more similar to how FB Messenger works. This will allow for a more natural conversational interaction.

2) Horizontal Scrolling Carrousel UI Components
Slack (mobile and desktop/web) does not provide any kind of horizontal card or horizontal scrolling carousel. While some might consider this bad design (to allow for horizontal scrolling of cards), it is often necessary to minimize the vertical area needed to display information in rich messaging interactions. FB Messenger allows for limited horizontal scrolling carousel that I find to be very useful when building bots. Hopefully Slack will incorporate this. Slack already supports rich "attachments", so it would be a natural fit to allow for some limited level or horizontal scrolling.

3) WebView Integration
Slack does not have explicit support for messaging buttons that open a webview UI. Sometimes a webview is needed to show rich web content (again here this kind of feature should not be abused). FB Messenger has this ability and allows for controlling how the webview window is opened and closed. This can be mimicked in Slack by using embedding links in the "field" elements for example, but is a bit of a hack.

4) Quick Reply Buttons
One particularly nice feature I got accustomed to using in Facebook Messenger is the feature referred to as "Quick Reply". This allows the bot to display "Quick Reply" buttons that are shortcuts for the user to enter commands that they would normally have to type.

There is a away to mimic quick replies in Slack, but it again, it is a bit of hack. Check this open source node/slack project for an example of how this works with Slack. Quick replies are a real necessity in a rich messaging interaction. Again here, I hope that Slack adds this feature natively instead of making bot frameworks jump through hoops to  emulate this feature.

Hopefully the Slack product team will address these issues as Slack is by far the best team and enterprise collaboration/messaging platform on the market today.

FB Messenger might have some superior bot-to-human interaction and UX functionality, but it inherently lacks the team collaboration functionality and the many third-party integrations that Slack has to offer. Perhaps FB Workspace might have something to offer down the road, but it has a long way to go in order to catch up with Slack.

Building Slack Bots with Node.js

There are a number frameworks for building Slack bots such as botkit and beepboop. But sometimes you just want to use the native Slack APIs and build your bot without the added burden of a complex bot application framework. If this sounds like what you are looking for, then you have come to the right place.

This project demonstrates how to apply the essential Slack APIs using the keep it simple principle and a straight forward approach to extending and customizing your bot's conversational dialog and interactive UX (interactive buttons/menus).

Slack has an extensive set of APIs for building user-to-bot conversational UI and interactive messaging applications. This project implements a bare minimal bot application written in node that provides a template for integrating with the various Slack APIs. This is a starting point application for building your bot that can be thought of as a basic modular template and set of coding patterns for building a Slack bot that exercises the essential Slack APIs and bot interaction features.

Support for api.ai is included for demonstration purposes, but other NLP services can be used instead.

The focus of the project is to make it easier to implement NLP intents and conversational bot/user interactions and the associated UI interactions that might come about from the use of Slack attachments (such as Slack interactive message buttons and interactive message menus). Adding new NLP intents and handling the callbacks for interactive UI elements are demonstrated in this basic bot. Examples for using incoming webhooks and slash commands are also provided.

The hope is this project will make it easier to start building a Slack bot by providing a template on which you can extend your bot with new dialog/conversations and interactions. The coding patterns will hopefully make it easy to extend the conversational user interaction when building your bot.

By design, this bot uses the Slack Event API and avoids any use of the Slack RTM API.

Note, that this bot was not designed to work with Slack Enterprise Grid. Some minor modifications would be required to have it work in a cross team enterprise environment.

Slack APIs demonstrated in this bot application:

• Event API (subscribes to: "message.im" and "reaction_added").
• Sending Text/Attachment/Interactive Messages via channel reply.
• Sending Text/Attachment/Interactive Messages via Incoming Webhook.
• Handling Slash commands.
• Handling Interactive Message actions.
• Handling oauth.access to store and use bot access token.

In addition to the core Slack REST APIs, this app demonstrates how to use the following slack wrapper APIs:

• @slack/client
• @slack/events-api
• @slack/interactive-messages
• @aoberoi/passport-slack

Give the GitHub project a spin. Hopefully you will find it a good starting point for building Node native Slack bots.

Python Data Science and Machine Learning Stack

If you ever wondered how the whole Python Data Science and Machine Learning stack all fits and plays together (Numpy, Scipy, Pandas, scikit-learn....)

The Rise of Cloud ACID Databases

Cloud Spanner and AWS Aurora among others are promising traditional RDBMS capabilities with cloud scale-out (does not mean cheaper btw).

Here is interesting article comparing Cloud Spanner with its inspired open source offshoot called CockroachDB.

The somewhat established, niche SQL scale-out contenders, like Clustrix, MemSQL VoltDB and NuoDB are getting more competition than they can handle these days from all sides.

Stop Calling Them Chatbots

I generally agree that the chat and NLP capabilities of a chatbot should not be seen as an end unto itself. Voice and text/messaging skills are just tools for reducing the friction between the end user and the device/application.

GUIs will go the way of the Dinosaur

For the last 30+ years we have seen graphical user interfaces grow in visual richness, but this is an evolutionary dead end. The future of human-to-device interface is Streaming-UI applications. Visually fixed and structurally static GUIs are the past. The future will be dominated by applications that "stream" a conversational conscientiousness and user experience using a fluid mixture of text, voice and point and click widgets/controls.

A Future of Streaming User Interaction

We need to stop calling the new crop of intelligent applications "virtual assistants" and "chatbots" - they are Streaming-UI applications that blend voice/text/point-click with a good dash of AI, NLP and other related technology. They are not rigid or fixed, instead they are streaming across the screen and across conversational time. So stop calling them "chatbots". They are Streaming-UIs and they will need a whole new way of thinking, development/design skills and tools to build them well. The user experience and application building rules have changed.