This is the final installment in a three-part series on artificial intelligence by DigitalOcean’s Head of R&D, Alejandro (Alex) Jaimes. Read the first post about the state of AI, and the second installment about how data and models feed computing.
So what does AI as a service mean for hobbyists, professional developers, engineering teams, the open source community, and companies today?
Starting an AI (or machine learning) project can be a daunting task at any level, and the steps should be different depending on the context. It’s important to note that sophisticated algorithms are not a requirement for AI and more often than not solutions may be simple. Even the most basic machine learning algorithm can do a decent job for some problems and once a process is set up, more sophisticated iterations are possible.
An alternative is starting with sophisticated algorithms—as long as there’s a good understanding of what those algorithms do and it’s “easy” to get them up and running. You don’t want to start your first iteration setting a large number of parameters you don’t understand.
There are some exceptions, and arguably, choices that depend on many factors, including level of expertise, but in general, it’s feasible to start small, build, and iterate quickly—you want to build an initial solution that demonstrates value. Even if it’s imperfect, setting up a process, and obtaining data, gets you off the ground. It’s imperative, however, to ask the right questions, focus on the solution, and the needs of who will be using whatever you build, and be resourceful and creative in combining data, models, and open source frameworks. Here’s how that applies to different players in the tech space:
The field is evolving extremely quickly and one could argue that most of the research being published consists mainly of experimentation, on either applying known deep learning architectures to new problems or tweaking parameters. It’s clear, however, that efforts—and progress—are being made n areas such as transfer learning, reinforcement learning, and unsupervised learning, among others. In terms of hardware, it’s too early to say, but it’s very positive to see new developments in the space.
Perhaps more important than advancements in algorithms, we can expect advances in how AI augments human abilities. There will be a much tighter integration between humans and machines than what computing has created thus far. For hobbyists, professional developers, engineering teams, the open source community and companies, this really translates to having a strong human-centered focus.
I’ve referred to AI throughout this series, but most of my examples relate to machine learning. One of the key differences between the two is that true AI applications will have an even stronger focus on user interaction and experience. At the end of the day, it’s the applications we build that will make a difference, AI or not. How “smart” the system is, or what algorithms it uses, won’t matter.
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*Alejandro (Alex) Jaimes is Head of R&D at DigitalOcean. Alex enjoys scuba diving and started coding in Assembly when he was 12. In spite of his fear of heights, he's climbed a peak or two, gone paragliding, and ridden a bull in a rodeo. He's been a startup CTO and advisor, and has held leadership positions at Yahoo, Telefonica, IDIAP, FujiXerox, and IBM TJ Watson, among others. He holds a Ph.D. from Columbia University.