Machine Learning changes how we approach Digital Assistants

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One of the two most interesting talks, for me of the Dublin Web Summit, have been two of the shortest. Both only lasted about 20 minutes, however both showcased the growing importance of Machine Learning and how it is being applied to change how we interact with technology and our devices.

Facebook M and Instagram’s Explore tool are two products, from the same company which are quietly looking to change how we are delivered content and how we use the web as it currently stands. Both use machine learning, combined with human intuition to develop platforms that learn and become genuinely useful to consumers as they grow and consume more information.

First, to look at Facebook M. This was announced earlier this year, and most certainly aims to take on Siri and Google Now as your go-to digital assistant. The key difference for M as it currently stands is that it doesn’t just rely on a computer supplying responses or results, these are tailored by humans who help teach the machine relevance and to better understand the query and how to respond.

The goal, is never to replace the human, but to make sure that Humans curate the machine to ensure that the personal touch remain, which can generate real usefulness for the end user. M has already been favourably reviewed, but it has been felt that Siri and certainly Google still have the edge, especially in relation to speed and utility.

Facebook believe however, that while Now and Siri are useful, they don’t provide value to you as a person. They don’t proactively engage you with reminders, or understand your behaviours or lifestyle as you ask more questions. They generally, also don’t answer intelligently. This is where Facebook looks to use Humans to add an intelligence to the information being processed and supplied.

The thinking behind this is that if you asked M to order you a Pizza, it will go an order you a pizza, rather than Now, or Siri giving you a list of Pizza delivery services. M will know the nearest one, it will have your details and it will take care of all the work, while you go about doing other things.

Facebook M goes beyond a digital assistant, it becomes a go to place for ordering, and purchasing. Facebook already has many of the world’s biggest brands and retailers seeing the benefit of advertising with them, every day. Instead of booking a flight on one app, then ordering dinner on another, before going to another to buy a travel guide, Facebook wants you to do all of this within Messenger, one app where all brands are present, which offers real utility.

Instagram take a similar approach to their explore feature, using machine learning. Extensive A/B testing using machine learning, combined with a human insight that adds common sense and intelligence to their Artificial Intelligence, have moved a feature that previously gave you images which may have been of no interest, to a section of the app which is curated by your own activity. This is something invaluable on an app as personal as Instagram.

Both of these things combined have looked at something which other services such as Siri and Now have missed. They aggregate huge amounts of data, but without much intelligence, providing information back but not much else. Facebook M looks to move the relationship beyond this to add a utility to a digital assistant as we grow to rely on them more.

The big change this helps bring is usability. More than likely, Siri and Now are not too far behind M, this will make these services really, actually useful as opposed to just handy, as they are now. Both, especially Google have brands and huge amounts of information at their disposal, already Google offers similar, but much more scattered services.

The big challenge will be how do they leave choice in the mix, people like curated content to a point, there are still some things machines, even human curated ones don’t understand. However, it’s an approach, and an insight that makes us shift our view on just how useful social networks, digital assistants and all of the data we supply actually are. Many companies have learned the hard way that walled gardens don’t pay off.

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