Summary: Getting an AI startup to scale for an IPO is currently elusive. Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy. While AI is most certainly destined to be the next great general purpose technology, on a par with the steam engine, the automobile, and electrification, there just aren't any examples of new AI-first companies that look like they'll grow that big. OK, in the 80s it took a long time for the'computer age' to show up in the financial statistics and maybe we're at the same place. Still, a bunch of people, especially VCs are wondering how to grow an AI company all the way to IPO.

Summary: There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways. If you're going to explain or recommend AI to a non-data scientist, it's important to understand the difference. For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming. "Big Data" – totally misleading (since it incorporates velocity and variety in addition to volume). How many times have you had to correct someone on that?

Summary: Looking for your next job in an early stage company but want to make sure your startup has staying power. Follow the expert rankings by CB Insights that also show us the changing trends in how AI startups should be focusing their offerings. Let's suppose you're early in your data science career and your credentials are strong in the latest deep learning and ML techniques. Let's also suppose that working for Google, Apple, Facebook, Microsoft, and the other majors doesn't appeal. You want an opportunity to make a significant contribution in a smaller organization, but how do spot the best opportunities?

Summary: Even if you're not big enough to have a full blown data science group that shouldn't hold you back from benefiting from AI. The market has evolved so that there are now industry and process specific vertical applications available from 3rd party AI vendors that you can implement. There are just a few things to look out for. Now that we are squarely in the midst of the exploitation phase of AI, pretty much everything you read will exhort you to hire a bunch of data scientists and get busy. This is not to make light of the top level commitment and organizational effort that's necessary to establish an Advanced Analytics and AI Center of Excellence in your company.

Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources. Recently we wrote that training data (not just data in general) is the new oil. It's the difficulty and expense of acquiring labeled training data that causes many deep learning projects to be abandoned. It also matters a great deal just how good you want your new deep learning app to be.

Summary: How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost. The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary. Last week we wrote that sufficient labeled training data was the single greatest cost factor holding back wide adoption of machine learning. It may be the most expensive, but it's far from the only thing holding ML back. After examining the work of the Stanford DAWN project these researchers propose that literally all the steps in the development process from data acquisition, to feature extraction, to model training, and all the way to productionizing the model are all deeply flawed.

Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this. However, the latest trends in CNNs may make this obsolete. There are things that just don't seem to go together. Take oil and water for instance. Both valuable, but try putting them together?

Summary: Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company. Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning. Hope you've been following our latest series of articles describing and comparing the four major strategies for AI-first companies. Now that you're better equipped to pick a strategy, we offer a few thoughts on moving from strategy to implementation. To start with, we need to clarify what we mean by AI-first companies.

Summary: Now that we've detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it's time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup. In our last several articles we've taken a tour of the four major strategies for creating a successful AI-first company. So which one is best? Since we're going to offer a side-by-side comparison you may want to refer first to the foundation articles on the four strategies: There is wide agreement that controlling a unique data set is the most effective way to create a defensible moat.

Summary: The fourth and final AI strategy we'll review is Systems of Intelligence (SOI). This is getting nearly as much attention as the Vertical strategy we previously reviewed. It's appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning. In the last several articles we've been looking at different strategies for successful AI companies. We described Data Dominance, and the Vertical and Horizontal strategies.