As someone who has built multiple AI-powered businesses in the legal community, I know firsthand the exciting potential of technology to transform the way we practice law. From predictive coding in electronic discovery, to AI-based contract analysis, legal tech has the power to make our jobs easier and more efficient. But with any new technology comes risk, uncertainty and responsibility. It's easy to get caught up in the hype of the latest buzzwords and trends, but when it comes to serving a demanding audience like lawyers and their clients, you better understand that there's a difference between'playtime' and'production.' What do I mean by that?

In his book Zero to One, Peter Thiel explores the core fundamentals that successful technology startups have in common. The takeaway: startups should embrace seven pivotal traits -- otherwise, queue red flags. This post will explore the similarities (and some of the differences) between technology startups and digital transformation, with a focus on applied AI. The intended audience includes data practitioners (DS, DE, etc.), technologists, and business executives -- as both technical and non-technical folks closely touch AI integration. TL;DR: Your applied AI project should adequately answer seven questions about its commercial viability, just like technology startups. The parallels between technology startups and digital transformation are numerous: the overarching goal is to create value through novel applications of technology.

In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word "decision" to mean a lot of things. Let's tease out some distinctions: Cassie Kozyrkov and I have realized that there's unmet need to fill the space marked "DI", above, where: We, along with a few thousand others, have realized that this is an important, yet massively under-treated, corner of the decision problem formulation space. "Decision intelligence is the discipline of turning information into better actions at any scale." "Decision intelligence answers the question, 'If I make this decision today, which leads to this action, what will be the outcome tomorrow?'--Lorien Lately, I've heard a case that "Decision Intelligence" should be expanded to include both A and B above.

Hope you are doing well. We're in December and is time to review the year that passed and evaluate how well we performed. And of course as Machine Learning Engineers we would not be able to do that without also evaluating our classification algorithms! So... Grab a cup of coffee because we are going to talk about four important metrics for evaluating our models in this article. Suppose we are a classification algorithm that is predicting whether or not is going to rain.

Your portfolio project could make or break your chances of landing a job. With a growing number of people aspiring for a career in machine learning, it's important that you're able to distinguish yourself from the other candidates. In this light, many use their portfolio project to set themselves apart and grab the attention of hiring managers. You can work on competitions from Kaggle for your project. But, realizing the full experience of an ML engineer comes from working through the entire machine learning workflow, which is best done when working on your own unique project -- Kaggle skips the early stages of the machine learning workflow.

Your Home Is Your Castle; Don't Forget The Moat In this week's lead quote, a16z's Casado and Martin claim diminishing returns to scale for more data. This is a bold proposition because it undermines a key argument for many data programs: that more data creates a defensive moat to protect the enterprise. Certainly, all organizations in competitive markets want to create defensive moats, and data can help. But it has to be done the right way. The right way to build a viable data-based defensive moat is specific data, not more data.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

There is an ongoing misconception that AI/ML are intrinsically valuable, and that therefore working in the field is bound to make you rich. A ML model is only as valuable as the problem it solves.

Let's strip away the jargon in machine learning and AI to take a look at what's easy, what's hard, how to spot opportunities, and what you need to know to avoid the two biggest threats in AI. Along the way, we'll meet an emerging discipline that focuses on using machine learning and AI to improve your business: decision intelligence engineering! Cassie Kozyrkov - Chief Decision Scientist, Google Tech Open Air is Europe's leading technology and innovation festival. Our mission is to connect, engage and inspire through transformative discourse, knowledge exchange and collaboration. Connect with us and stay up to date about our next events: http://www.toaberlin.com