Startup life: Go big, or go home

#startuplife

#machinelearning

#vision

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I’ve set out to change the world, and there are two lines to the story.

The first is my background as a developer/consultant. As of today I’ve been employed by Futurice for 8 years. That’s over twice as long as any of my previous jobs.

Working as a consultant has many upsides. You get to work with, live and feel various organizations. You get to work with a diverse set of people and skills. You can pivot every so often into a new direction, with a new customer and often also with some new technology. This is a blessing, especially for someone fresh out of school, as it really helps finding what is the most interesting tech or domain in an expanding array of choices. For me this domain has been server-side development and architectures. Database design and integration. The Cloud.

I’ve tried other domains as well. I’ve dealt with 3MB heap restrictions when writing a map rendering engine for a low-end feature phone. That might actually still be in use somewhere, some 5 years later. The other end goes to dealing with massive scale backends for that next big social media, or to building an analytics backend with a data warehouse. Most of these consulting gigs have been both challenging and rewarding in many ways.

Still, for me personally there’s always been an itch for something more. Not quantitative more, but qualitative. When working for clients, I’ve always felt the most effective after getting more intimate with the domain and the company. I feel the urge to be a true expert in the field and domain I work with. It takes time to build the knowledge and bump into enough roof and walls to really feel at home. Usually this only happens at the end of the project, when we’re about to wrap up and set sail for some new customer challenge. The other form of toil with consultancy comes from having multiple, often conflicting, sets of priorities. There is always something urgent on the client-side. At the same time there are the employer things to take care of. Recruitment, sales, on-the-side-consulting, mentoring, to list a few. They’re all interesting and rewarding as well, but often at odds with the client work. These two buckets ultimately compete for the same limited resource, i.e. my time. For some years already I’ve talked about my aim to set these priorities straight for once. The clear way to do so is to try something else than consulting, and thus I’ve been on the lookout for something of a new longer term commitment. Enter Aito.ai.

The other storyline is more tech-related. AI is hot, sizzling, at the moment. In a endless list of tech buzzwords, AI seems to be by far the most pervasive in quite a while. It does have the potential to be a tool to apply on an almost endless variety of problems in our domain. It’s much more of a Silver Bullet than say, IoT, blockchain, or containerization, all of which have also been hot in the recent few years. The appeal of AI is that it’s conceptually an easy thing to explain to any layman, as opposed to any of the aforementioned techs. And since the tech is so pervasive, also the projections for the future range from anything from the end of mankind, to immortality for everyone. Wait But Why had an insightful take on this already some years ago. Big figures like Stephen Hawking, Bill Gates and Elon Musk are also on the cautious side of things.

On the other hand, it’s clear that predicting the future is way harder than looking at things from hindsight perspective. The whole field is still in flux, and as I somewhat philosophically reasoned, the future is actually made right now. If I want to be part of what this ends up to be, I’d better saddle up and start moving in the direction of the heat. Enter Aito.ai.

Our vision and mission

Aito is at the intersection of the two storylines I described. We, the three founders, Antti, Vesku and me, come from quite different backgrounds, but share both the curiosity for the topic, and the goal to make the field more approachable. As with most technological advancement the near future is often overestimated, as people rush to make headlines with extravagant claims. IBM’s Watson actually won Jeopardy as early as in 2011. That’s really a lifetime ago when talking about computer’s and in the context of Moore’s law. People seem to be losing their patience in waiting for something more to happen. Deep Blue’s win over Kasparov goes as far back as 1997. Some of the people I work with weren’t even born back then!

On the other hand there are now almost daily news on advancements in the field of machine learning (ML) and AI. Automated cars are on the apparent verge of become reality within a few years. Natural language processing and speech interfaces are common place in quite a few homes, and in almost all mobile phones already. So, ML, AI and statistical tools combined with cheap storage and big data are powerful tools already today. More and more companies use these to run their everyday business.

Our vision and mission statement with Aito is to make AI/ML easy to approach and understand. We want to allow people to use the algorithms and tools we create to solve their own custom problems. We also want to take away the effort in doing so, and allowing people to solve a broader set of problems than with single-purpose AI-models, without having to get a PhD in the field, hire loads of people already having one, or in general by just throwing loads of money on the problem. This we plan to achieve by creating an AI-optimized database (for the lack of a better word), and our own query language for it.

Go big, or go home, I guess.

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