SXSW 2016: Messaging Bots

My most recent post on LinkedIn cross-posted here.

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This past weekend, I had the pleasure of moderating the “Testing Your (Aritificial) Intelligence” panel at SXSW. On the panel, we had Dror Oren from Kasisto (vertical messaging assistant for banking), Alex Lebrun from Facebook M (horizontal messaging assistant) and Dimitra Vergryi from SRI (runs the speech research lab). Much of our early discussion was regarding the future of assistants in-part drawing on some of my experiences from SRI and Tempo AI but we quickly moved to the hot topic of messaging bots and the role of AI.

If you’re not familiar with messaging bots, I encourage you to read “The Bot Paradigm” from The Information as well as Jonathan Libov’s super-aggregation of messaging UX design. In short, messaging (communication) represents our primary workflow (day-to-day) and as exemplified by applications like WeChat, messaging can be used to facilitate other experiences (eg shopping, sending money, customer support).

This new modality has the disruptive potential of replacing all app experiences and if so, would represent an opportunity as significant as the iPhone App Store was to publishers.

Some panel takeaways:

  1. Horizontal (general purpose) assistants are hard! Users do not know what to ask nor remember what primary use case to use the assistant for. In addition, the technical complexity of the system is exponentially greater in that you have to deal with out of context and extreme queries. For example, Alex mentioned they no longer intend to support “pet delivery” in FB M since that’s a use case out of their machine automation wheelhouse.
  2. Standalone messaging apps don’t stand a chance. I found this feedback interesting in that they are advocating that messaging bot experiences need to be built upon the existing large players (eg WhatsApp, Slack, Facebook Messenger, Google Hangouts etc). This intuitively makes sense since the App Store is no longer a great growth channel.
  3. App bot discovery and the ultimate app store for messaging bots is still unclear. Having played with the app store within FB Messenger, I found the workflow to be sub-par. Alex and Dror suggested displaying related app bots as you type within Messenger (in-context). This certainly would be an improvement at  smaller scale but will remain a challenge if we have 100s of K of messaging bots. The ultimate design of the APIs that the messaging apps provide will be critical.
  4. Personas are necessary for messaging bots to. I found this interesting in that personas can certainly lighten the mood and set expectations but can also be tiring when you want expedience. See slides I presented on mobile AI/UX design at SXSW a previous year (wish I had a video of the presentation with the voice-over).
  5. Machine-powered conversational NLP is a very long-way out. Dror felt you must go vertical and thus their focus on the banking sector. Alex said that the FB research team described conversational NLP as level 10 technical difficulty and AlphaGo as level 1 (my FB post on the AlphaGo AI milestone). All of the panelists also acknowledged that building a data corpus (collection of queries/conversation) for training is the real challenge in any AI application.
  6. Key initial industries to be disrupted by messaging bots were financial services, customer service, commerce and travel. Not surprisingly, some of these categories are identical to what we saw with early vertical Siri-clones.

In summary, messaging bots represent the next growth channel and will spawn new billion dollar opportunities; excited to see it happen!

IOT: The Competition for Attention

My most recent post on LinkedIn cross-posted here.

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The Internet of Things (IOT) is everywhere. It was the only news at CES and it’s presently at the top of the hype curve with press attention on Apple’s new secret car,Homekit becoming available with iOS 8.1.3 and the Apple Watch launching soon!

But with IOT comes a whole new set of problems, and I (selfishly) believe that calendar and AI will be a key pillars in enabling the future IOT software platform.

Why? IOT devices are not meant to collect dust but to be actionable. Being actionable means being used and when your Amazon Echo sits on your counter and collects dust, it’s not actionable. To mitigate this, every IOT device needs to speak-up – they are competing for your attention.

To compete, they send notifications (email, SMS etc): “Cheerios are on sale,” “You’re running low on milk,” “You’re laundry machine will be done in 35 minutes,” “You haven’t walked enough steps today” and so forth.

As I alluded to in a previous post, your email is transforming from a collection of human communication to machine generated messages and tasks. And your calendar will follow because every great IOT notification is an actionable task which needs to be scheduled back into your calendar (eg “Oil change needed soon”).

So How Can We Fix This?

Well, first off, it’d be great if just 10 percent of the notifications I get on my phone were useful. And to do that would not be hard:

  1. Let me configure my notifications – Facebook and others give me too many notifications and IOT is going to fall into the same trap. Yes, I don’t want to be notified by my smart sink each morning that the water quality has negligibly gone up or down. It’s a novel concept at first but it gets old quickly — and even faster if you are sending it to me on every device!
  2. Learn which notifications I read and more importantly respond to – Email marketers are experts at this, they know when you open and click a link in an email. Notifications are the same thing, they are just another form of CRM. See Kahuna for example.
  3. Use the signals you have – More signals are not necessarily better, but the right signals can make a huge difference. Dear Nest, if you can tap into my calendar to better know when I’m home or not, and subsequently save me money on my heating bill, that’d be awesome!
  4. Be smart about when to bother me – Imagine a real-world assistant receiving a call while you were in a meeting. He wouldn’t interrupt your meeting unless he thought the call was important enough. This is hard and this is where AI and using the user’s own data (calendar, email, Facebook, Linkedin, location etc) to understand intent can make a huge difference. I do want to know “Cheerios are on sale” but only when I go shopping.

Next, distinguish between content and tasks. Most notifications are content and this can get overwhelming real fast. Two years ago, maybe one app told me whose birthday it was today but now I receive this notification from multiple apps on multiple channels each day this is annoying. Be smart about firing content notifications and focus on the unique, not the obvious.

Even better though are task-based notifications; telling me I need to buy milk is better but I don’t want that to clutter my email. Instead, map that task to my calendar, add it to my todo list, and present it to me at the right time.

The competition for attention is real – it’s happening and IOT will take it to a new level. Leveraging simple AI smarts, providing configuration and mapping those task notifications to your todo list and calendar will help. The winning IOT devices are those that are smart enough to keep my attention over the long run.