A trillion tiny robots in the cloud: The future of AI in an algorithm world

A trillion tiny robots in the cloud: The future of AI in an algorithm world Kelly Stirman (@kstirman) is the VP of Strategy at MongoDB. Kelly works closely with customers, partners and the open-source community to articulate how MongoDB is quickly becoming the world's most popular database. For over 15 years he has worked at the forefront of database technologies. Prior to MongoDB, Kelly served in executive and leadership roles at Hadapt, MarkLogic, PeopleSoft, GE, and PricewaterhouseCoopers.


It’s not words, but how we say them that speaks volumes. By analysing your tone of voice, a new computer algorithm can predict whether your relationship will last, and it arguably does so more accurately than professional therapists.

Now, I’m not here to advise you on couples therapy. What I’m interested in is how algorithms like this one are going to change the way we live and provide a massive opportunity for the cloud industry.

Data + machine learning algorithm = AI

Popular culture leads us to believe that the future of artificial intelligence (AI) will be a single, magical supercomputer. Think HAL 9000 from 2001: A Space Odyssey, or the ship’s computer on Star Trek. But we’re starting to understand now that we’ve been looking at it all wrong.

x.ai has created an AI-powered personal assistant that schedules meetings for you. In the US alone, there are 87 million knowledge workers who spend up to five hours a week scheduling meetings

The future of AI isn’t about one giant super-intelligence. Instead, it’s about many small, dedicated agents that know you intimately and work on your behalf to improve your everyday life. That could be helping you shop, get to work or, even, find a partner. Each is focused on a discrete task, and each gets better over time and adapts to your needs as they evolve.

This kind of smart software isn’t new. It’s been almost 20 years since chess Grandmaster Garry Kasparov lost to IBM’s Deep Blue in a chess match.

Amazon has had machine learning for many years too. Every time the giant retailer serves up a recommendation, AI made the decision and what would be the best option for you on that particular day.

But if Amazon can have AI working to sell you more things, shouldn’t you have your own AI working to find better deals from other vendors, looking for reliable reviews, or keeping you ahead of the latest trends?

Of course, algorithms are also nothing new, but it’s become vastly easier to write and use them in recent years. The main driver behind this has been cheap and ubiquitous computing, an abundance of data, and a platform that brings these elements together: cloud computing.

For AI to be useful it needs all three: a good algorithm, millions of relevant data points, and computing power to process it quickly so it can drive actions in real-time. Lose any one of these and it’s not nearly as useful in the modern world.

The point is this: your business is going to get disrupted by AI, but not in the way you might have thought. Rather than preparing yourself for one monolithic, all knowing consciousness, it’s going to be a trillion tiny agents all focused on specific tasks, and all of them hungry for data.

Finding the time

One example of this focused approach is x.ai. The New York City startup has created an AI-powered personal assistant that schedules meetings for you. It’s a simple enough to use. You connect your calendar to x.ai and then CC in amy@x.ai whenever a discussion starts about scheduling. Once you copy in Amy, she takes over the thread, finds a mutually agreeable time and place, and sets up the meeting for you. The person at the other end has no idea she’s not a human.

How brilliant is that? In the US alone, there are 87 million knowledge workers who spend nearly five hours per week scheduling meetings. I don’t imagine many of us enjoy the process and would be more than happy to delegate to a virtual assistant instead.

It’s not such a simple fix though. The technology that powers the virtual assistant is complex. Amy passes each email through natural language processing and supervised learning engines that understand the context of the information. The data is then enriched and stored in MongoDB where it is combined with other information such as the user’s preferred working hours and their current time zone. Based on these inputs Amy determines the appropriate course of action and crafts a response. There’s no app to install. Amy exists only in the cloud.

This is only one example of how algorithms are changing our lives. Cities are starting to automatically adjust traffic flow based on weather, construction, congestion, events, and other real-time factors. Ads you land on while browsing your favourite sites run an algorithm over your data and match it with their calculated preferences about you to serve up something that is highly relevant.

Many of the most popular cloud and data technologies are already responding to this trend. Apache Spark is full of machine learning libraries that come built into the framework. Google released TensorFlow as an open source project, which makes the machine learning technology behind Google Translate and many other products, freely available to anyone.

With these tools easily accessible by developers, it’s easy to see how many different tasks could be quickly re-imagined as algorithms that delivered as convenient services. In fact Peter Sondergaard at Gartner is predicting a whole new Algorithm Economy.

Things you can’t algorithm

Cloud computing solved the two biggest hurdles for AI: abundant, low cost computing and a way to leverage massive volumes of data. However, a number of challenges remain. Chief among those challenges is the one affecting the whole industry: skills.

Cloud computing solved the two biggest hurdles for AI – abundant, low cost computing, and a way to leverage massive volumes of data – however a number of challenges remain

While open source libraries make it easy to get started, for genuinely powerful AI you need actual data scientists. People with strong programming backgrounds, a deep understanding of mathematics and statistics, as well as business domain knowledge. Needless to say, those people are rare.

The other challenges will mainly be around the data. Most modern data is inherently unstructured – it’s geographic data, sensor data, and social data. If your stack is built on decades-old relational technology you are going to struggle to feed modern algorithms running in the cloud.

Despite the challenges, the main lesson is this: small, focused, cloud-based algorithms are going to be the AI that changes our lives over the next decade. It’s better to solve one problem really well, than it is to solve 100 problems poorly. Today’s markets reward companies that maintain their focus.

To take advantage of these trillion robots in the cloud, you’re going to need a thoroughly modern infrastructure.

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