Hey-one Lim

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How the F*ck Do You Get Started with AI—A Lesson From the Fight Club

Google "AI" and you get over a billion results. Seriously.
Countless tech publications list "AI" as its own separate category, and according to Statista, there are thousands of "AI companies" in the world, with over 2,000 in the US alone. Statista lists the current AI market size at around $10 billion USD. While we've all heard of the applications like self-driving cars and recommendation engines like on Amazon and Netflix, there are also many creative applications of AI.
The study and practice of AI has made its way into virtually every industry vertical and social ecosystem, including branch-offs of #WomenInTech like #WomenInAI. With this apparent omnipresence of AI, you'd think that more laypeople would know how to implement it. However, AI is still largely left in the domain of AI experts like machine learning engineers and data scientists.
It's widely known that, in business, you either innovate or die. Every time a game-changing technology comes out, whether it's the Internet, blockchain, drones, or robotics, many businesses and business models become obsolete. The same is true with AI, which means that if you're not paying attention to the AI landscape, then the clock is ticking fast on your business' lifespan.

So, if your business doesn't have any AI Engineers, you still need to find a way to innovate. Here's how.

The biggest lesson I got from Fight Club was to step outside of norms and think outside of the box. We're often put in rigid mental boxes of what to do and how to think. In business, this means that if something has been working so far, you're probably not going to look for ways to change it. But then a disruption like AI will come in and force you to change. Rather than wait for that moment, it's better to be pro-active.
One way to get started if you have minimal experience in AI is with AutoML. Google Cloud's AutoML allows you to "train high-quality custom machine learning models with minimal effort and machine learning expertise." This is incredibly powerful, because you get to use many of the same models powering Google services that are used by billions of people, like Google Search and YouTube.
AutoML is split into three categories: Sight, language, and structured data. Depending on the data you have and your needs, you can apply algorithms including AutoML Vision, AutoML Video Intelligence, and AutoML Tables.
If you have data that can be publicly shared, you can consider launching an open-source project on your data, or at least sharing the data for enthusiasts to work on and potentially find useful insights.
Finally, you don't have to (and shouldn't) start with immediately hiring an AI Engineer if you don't already have data talent in-house. You can get started with a Data Engineer or other suitable position for acquiring and cleaning data to be analyzed later.
Ultimately, there are a lot of ways to stay on the leading-edge without immediately getting AI experts on-board, so get started now!

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