artificial intelligence, Semantics

To Bot, Or Not To Bot


Are Bots the Future of Computing?

Bots — simple computer-based [micro] services that you interact with conversationally — are being hailed by some as the next wave of computing, a profound platform shift and the most exciting technology since the iPhone.

Microsoft this week became the latest technology provider to strategically embrace bots and offer a framework for developing them. Microsoft joins Facebook, Slack, WeChat, Kik and many others. In Microsoft’s case, as with several others, bots are a part of a continued push into a broad set of technologies centered around artificial intelligence or AI.

But let’s forget AI for a moment. This trend towards functional capabilities delivered as well-bounded, discrete services goes back at least as far as IT concepts such as object-oriented programming and  component-based development (think of a method or web service like “CheckBalance” or “MakeDeposit”). To some degree this basic idea is also manifest in app connection frameworks such as IFTTT and associated app integration platforms like Zapier. The rapid trend towards hosting services in cloud environments and making them easily accessible through Application Programming Interfaces (APIs) has now provided much of the infrastructure or connective tissue to enable the rise of bots.

If you’re a business looking at where technology is headed as part of your product strategy, you may be asking if bots are likely to truly transform digital products and whether that’s a good or a bad thing. Personally I think they have the potential to be transformative and that they will indeed be a good thing. As with other major tech disruptions, the transformation will almost certainly not happen overnight and as it goes mainstream it won’t necessarily look like this first wave of the technology. But I’m convinced bots are coming (some are here already) and smart businesses should start now exploring the opportunities that will come from building products using a bot model.

Wait, I thought Bots Were Bad????

We’re not talking here about hijacked PCs running programs whose sole purpose is to spew SPAM, or other automated programs that ‘mindlessly’ push pre-programmed content. The bots coming onto the scene now are helpful software agents, with many of them embodying at least some form of intelligence. By that I mean they have the ability to interpret a request for a service and to respond appropriately with relevant information, content or actions. Here’s the clincher in my mind: the services these bots perform need to offer real identifiable value to the user who is interacting with the bot.

Why Is This Happening Now? You Might Ask

In a world where everything is becoming an endpoint, bots are the little engines that interpret incoming signals — in context– and provide responses or initiate outbound actions. Think about the streams of data generated by Internet of Things (IoT) based sensors and all the data traffic traveling through messaging services.  This data is not just valuable en masse for historical or predictive analytics. It can be put to more immediate, localized and personalized use. Whether pinged directly through a single trigger word or command, or more passively activated using AI to interpret a chunk of data in a message, bots are driven by actionable data [intelligently] understood within a context. The ubiquity of devices such as sensors, beacons and smartphones, along with omnipresent network connections, always-on communications channels, vast sources of content and publicly-accessible APIs have all reached a tipping point of availability and maturity to support human interaction with the world in a way that is augmented by automated software agents – bots.

While the bots may be stand-alone services, to support a critical mass of them they need to be hosted within a platform that either resides in or interfaces with our existing communications channels. Today that takes the form of smart phone OSs, SMS/text messaging services, social messaging services such as Facebook Messenger, Kik, WeChat and Line, and/or virtual personal assistants such as Google Now, Microsoft Cortana or Amazon Echo’s Alexa. Virtual personal assistants can be thought of as universal or generalized bots that orchestrate or intermediate more specialized sub-bots. Mobile payment services such as Apple Pay are also contributing to the trend. Although simple text is sufficient as a basic user interface to bots, advances in natural language interpretation and understanding, and in voice recognition and speech synthesis provide additional, richer ways for humans and bots to interact. The technology then is wholly or largely existent today. Given the sheer volume of data and APIs, we need technology just to handle that volume. Technology also offers the opportunity to both simplify and augment our personal and business communications and transactions. So arguably the demand side of the equation exists now, too.

Should My Business Dive In Now or Wait?

It’s early days and a lot remains to be ironed out, particularly in terms of bot platforms. Every technology provider seems to be doing their own platform, there are few or no standards and little if any integration across platforms. There will be successes and failures and shakeouts. So it’s too early to jump in, right? Absolutely not! Any business that isn’t venturing into this space risks missing the boat as the wave of bots washes in. There are ways to venture into this space that mitigate the risk, while providing valuable revenue and experience to businesses and real value to their customers. Here are a few tips for getting started:

  1. Start by creating bots from services that your business already has exposed as APIs or single-function apps
  2. Focus on bots that are easy for your customers to understand and use, and that provide real, identifiable value to your customers. It’s okay if the service is simple and the value is small, like providing limited sets of information or content in response to specific customer requests or focusing on a commonly performed task with a small, fixed number of options
  3. Design your bots conceptually, then implement them initially on 1 or 2 platforms within communications channels where your customers are most active and where your business has experience, presence and familiarity. That could be SMS/text messaging for a single telecom carrier or mobile OS, or an in-message bot command embedded in one of the messaging services like Kik or Skype
  4. Let your customers know they are part of something new and that you value their input and feedback. Make the experience fun for your customers, and for your product team, too
  5. Don’t get too far ahead in setting goals and expectations. Monitor your initial bot experiences, using your own tools or through the analytics that are provided as part of a bot platform or delivery channel. Don’t be hesitant to iterate, pivot or rethink. You’re not betting your business on these, at least to start with, so failures don’t have a large downside. Look for where you are getting initial success, try to understand the success drivers, and replicate those as you add to the volume and complexity of your bots.

Keep in mind that the bot-driven era of computing will be like a marathon, or at least a 10K run, not a sprint. It’s not too soon to start venturing with bots. Along the way you’ll get a better sense for how they can benefit your customers and your business. And you’ll help determine the shape they take as they come into the mainstream as the next wave of computing.

artificial intelligence, Semantics

What Netflix Could Do with Its Recommendation Engine to Excite Me as a Customer

You Might Like House of Cards, But I Couldn’t Possibly Comment

My colleague Peter Sweeney, the founder of Primal, and I were talking recently about Netflix using AI, specifically deep learning algorithms, as part of efforts to further improve its recommendation engine. I’ll admit, instead of being excited at the prospect of more insights being gleaned from my viewing history, my first reaction was concern about yet-another bubble along the lines of, or even worse than, the infamous search engine filter bubble, only this time for recommendation engines.

True, we learned earlier this winter that Netflix has re-engineered Hollywood. Netflix has a very rich and extensive categorization scheme — the product of analyzing movies and TV shows from an amazing number of angles, and presumably also following the trails of relationships that customers perceive among the films and TV shows that they view. I think Netflix provides good recommendations, certainly better than they did a few years ago. But frankly, the recommendations still hit dead ends quite often and its easy to get stuck in the rut of more of the same old thing. So my fear upon reading about what Netflix is doing was basically this: deeper mining equals even deeper rut — even more of the same old thing. And that could easily be the case. Just going deeper into analyses of the content itself, as well as my past preferences for it, might well add more categories to their classification scheme, but it doesn’t endear me more to Netflix if I still just get recommendations based largely on my past preferences, only now using more specialized categories. I’m still stuck in a rut.

What I want to experience is more of what I like to call ‘designed serendipity’. If Netflix or Amazon or one of their peers are truly uncovering deeper and more nuanced patterns, particularly within the content itself, but also about my viewing preferences, then they could use that new data to make the recommendation experience more interesting and more compelling for me — giving me something I could actually get excited about. How could they do that? They could start by proposing content from adjacent categories based on walking their classification scheme. Because there would presumably be more, finer-grained categories, exploring some of the neighboring ones could add some fun while still keeping the risk low of jarring me with an off-the-mark recommendation. They could also take those lower-level elements and apply them in somewhat different contexts, preserving the elements that I like, but also mixing in some new twists. They could even try combinations of the lower-level elements, as they’ve done fairly successfully already at higher levels of their classification scheme.

Let me use some examples to illustrate the sorts of things I’d like to see. The West Wing and House of Cards are both political dramas. But at a deeper level, The West Wing is much more about the camaraderie of the White House staff as a team, with politics and political intrigue as more of a plot device for the personal interaction. House of Cards on the other hand is more of a psychological thriller set in a political context. The political maneuvering and bold back-stabbing are core to the show — and for me at least, that’s what makes it fun to watch. Those are fairly subtle, but significant differences that if deep learning can expose, would establish its customer value. Put another way, just because I like House of Cards (the Netflix version, but even more so the original BBC version), does not mean The West Wing would be a good recommendation for me, since such a recommendation is based on more superficial similarities between the shows. I’m a friendly, collaborative, team-oriented person in my real-life, so I’d rather see ego-maniacal scheming and back-stabbing as part of my diversionary viewing!

If Netflix could ‘context lift’ those elements of House of Cards that I do like and then reapply them in different contexts, that would excite me. For example, because I like House of Cards, I might like The Tudors better than The West Wing, even though The Tudors is a historical drama. The Tudors has more of that scheming and back-stabbing (or head chopping!) that I like. While it’s a political drama of sorts, it isn’t in the same sense as The West Wing and House of Cards, so it might not come up as an obvious recommendation. To make that recommendation is more deep and subtle. I also happen to like The Americans, a drama that is political only in an espionage context, but again also a thriller with lots of unexpected twists and turns. And I hate to admit it, but I also like Revenge. Revenge has almost nothing to do with politics, but shares the dark scheming and plotting of House of Cards. Would Netflix be able to recommend either of them to me based on House of Cards? If they get to that level, they’d have my customer loyalty.

At that point, the only thing still missing for me would be adding even more pleasant surprises — turning down the ‘designed’ aspect and turning up the ‘serendipity’. What if I want something that’s conceptually related to a past viewing interest, but still quite different? If I watched Planet of the Apes (particularly the original), wouldn’t Jane Goodall‘s documentary for Animal Planet, Almost Human, be an interesting recommendation? It would for me! Or what if I want to broaden my horizons and try something completely different than what I’ve been watching? Can Netflix put my past preferences in a blender and recommend something really novel and out-of-the-ordinary? Or alternatively can I just throw at Netflix some topics that I’ve been thinking about or have a point-in-time interest in and get a recommendation made-to-order at that particular moment, based on what I just provided, or perhaps subtly influenced by my viewing history? Do those things, Netflix, and I might become a loyal customer for life!


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