Toward a Smarter Software Future

on July 11, 2016

The more I think about the recent breakthroughs in machine learning and deep learning algorithms, the more I think we are finally heading toward a smarter software future.

For years I had been writing about the need for better predictive intelligence in our software. It seems ridiculous that my smartphone does not know more about my context and take relevant actions on my behalf. If I’m in a meeting, send all calls to VM or send a text message. If I’m running late to a meeting, offer to send an email or text to those I’m meeting with to let them know I’m running late and an ETA of when I’ll be there (since it knows where I am on the road, the traffic situation, and my time to destination). Our smartphones are really not that smart when it comes to the intelligence equation. That is about to change.

Most modern software platforms like iOS and Android are increasingly adding elements of machine learning and beginning to extend more of the core platform intelligence to developers. The software community can now, in more ways, take advantage of platform APIs and start taking advantage of many of these advancements in deep learning to provide new and more valuable customer experiences.

For a while now, my friend Benedict Evans has been referencing a saying from Eric Raymond that, “A computer should never ask the user for any information that it can auto-detect, copy, or deduce”. Yet, for the longest time, that is exactly what computers have been doing when it comes to machine learning and AI. My smartphone need never to ask where I am. It has that data via the built-in GPS. My smartphone also has my full calendar so it should be able to deduce what I am doing and where I am. The key point is, with the advancements in machine/deep learning, we are on the cusp of computers having to ask us significantly fewer questions going forward as they will increasingly be able to auto-detect, copy, and deduce more relevant information for us on their own. This is what has changed and will open the door to a much more intelligence based era of computing.

Communal vs. Personal Intelligence
One area of this discussion I’m very interested in is the difference between “communal machine learning” and “individual machine learning”. Most people discussing AI/machine/deep learning have not made a division between the two. Admittedly, communal machine learning, or big data being collected from a massive number of users for things like maps, visual recognition of general image knowledge, etc., has been the primary ways machine learning has been taking place. We have yet to crack the AI/machine/deep learning that will take place as a computer begins to study its user in more intimate ways.

The companies who will focus on communal vs. personal AI/machine/deep learning seem clear. Google wants to be the deepest domain expert in communal knowledge. They seem less focused on going deep on the specific and intimate details of the user and trying to gather just enough information to be able to relevantly answer any query. This is one reason why I believe Google is not building something with a name like Siri/Cortana/Alexa which would encourage a user to build a relationship. Rather Google seems content to let others focus on the personal AI and simply let their big communal data sets feed the personal AI chosen/hired by the consumer to be their personal assistant.

On the other hand, Apple, Amazon, and Microsoft seem to be looking to go deeper with the individual user and build tools that let them have deeper relationships and thus, reveal more intimate details about themselves to their personal agents. Trust about privacy is the key here and it’s something the companies I mentioned seem to have a better “trust-centered relationship” than Google. Again, perhaps Google knows this and that is why their focus is elsewhere, at least for the moment.

As I talk with those in the industry thinking and building products in this area, I encourage people to understand this is likely not a one size fits all solution. Nor is it a winner take all market. Just because I may hire Siri as my personal assistant and allow Apple’s AI to learn more intimate details of my life, it does not mean I can’t use Google’s, Amazon’s, Microsoft’s, or a host of other “bots” or “agents”. In fact, it seems unwise of any company to wall off their agent from the rest of them. Ideally, some kind of generic standard will be created so all these agents can talk to each other. But at the end of the day, I do believe consumers will cling to one as their primary agent. That is a battle only a few companies can realistically engage in.

I’m currently of the mind that companies in the AI/machine/deep learning space need to focus on being domain experts. For example, Amazon may ultimately be the commerce domain expert, Google for queries, Microsoft for business, and Apple/Siri the expert for my personal life. Similarly in China, Baidu would be the search domain expert, Alibaba for commerce, etc. All these AI engines need to work together to be able to ask the user fewer questions and return more value as a result.

We don’t want to spend increasingly longer portions of our day with machines. The less we have to tell them what we want, the better. This allows us to have more time to be better at the things humans will be better at than machines. That’s where we are headed.