Artificial intelligence (AI) is expected to add $15.7 trillion to the global economy by 2030, according to a study by PricewaterhouseCoopers. This huge figure consists of $6.6 trillion in increased productivity, as well as $9.1 trillion of increased demand for products and services because of personalization and improved quality. Those interested in investing in the AI trend should consider companies that are utilizing the technology to improve their products and services, such as DocuSign (NASDAQ:DOCU), Alteryx (NYSE:AYX), and Etsy (NASDAQ:ETSY). Let's look at these three companies, how they are using AI to grow, and why they are good investments today. DocuSign has seen a 37% compound annual growth rate over the last three years, riding the popularity of its flagship e-signature product.

This one line of code gives us over 200 features for each label in cutoff_times. Each feature is a combination of feature primitives and is built with only data from before the associated cutoff time. The features built by Featuretools are explainable in natural language because they are built up from basic operations. For example, we see the feature AVG_TIME_BETWEEN(transactions.transaction_date). This represents the average time between transactions for each customer. When we plot this colored by the label we see that customers who churned appear to have a slightly longer average time between transactions. In addition to getting hundreds of valid, relevant features, developing an automated feature engineering pipeline in Featuretools means we can use the same code for different prediction problems with our dataset. We just need to pass in the correct label times to the cutoff_times parameter and we'll be able to build features for a different prediction problem.

This is the fourth in a four-part series on how we approach machine learning at Feature Labs. These articles cover the concepts and a full implementation as applied to predicting customer churn. The project Jupyter Notebooks are all available on GitHub. All of the work documented here was completed with open-source tools and data.) The Machine Learning Modeling ProcessThe outputs of prediction and feature engineering are a set of label times, historical examples of what we want to predict, and features, predictor variables used to train a model to predict the label.

Data science is one of the fastest growing segments of the tech industry, and Alteryx, Inc. is front and center in the data revolution. The Alteryx Platform provides a collaborative, governed platform to quickly and efficiently search, analyze and use pertinent data. To continue accelerating innovation, Alteryx announced it has purchased a startup with roots in the Massachusetts Institute of Technology (MIT). Feature Labs "automates feature engineering for machine learning and artificial intelligence (AI) applications." Combining the two companies' platforms and engineering will result in faster time-to-insight and time-to-value for data scientists and analysts.

Alteryx, a publicly traded analytics company, announced this morning that it has acquired Feature Labs, a machine learning startup that launched out of MIT in 2018. The company did not reveal the terms of the deal. Co-founder and CEO Max Kanter told TechCrunch at the time of the launch the company had been based on research at MIT that looked at how to automate the creation of machine learning algorithms. "Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work," Kanter told TechCrunch in 2018. It is precisely this capability that appealed to Alteryx .

Alteryx, a publicly traded analytics company, announced this morning that it has acquired Feature Labs, a machine learning startup that launched out of MIT in 2018. The company did not reveal the terms of the deal. Co-founder and CEO Max Kanter told TechCrunch at the time of the launch the company had been based on research at MIT that looked at how to automate the creation of machine learning algorithms. "Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work," Kantor told TechCrunch in 2018. It is precisely this capability that appealed to Alteryx .

On May 9, the New England Venture Capital Association (NEVCA) is holding their annual award show, the NEVY Awards at the House of Blues in Boston. As part of the lead-up to this year's Star Wars-themed event, we're going to be posting a number of bite-sized Q&As between now and the 9th--each offering a bit more insight into what attendees can expect (here's the last one we did with Ascent Venture Partners Senior Associate Baiyin Zhou). For this installment, we spoke with First Star Ventures Founding Partner Millie Liu about the event, as well as trends across the artificial intelligence, machine learning, and Blockchain industries. This year, "Company of the Year in Artificial Intelligence, Machine Learning and Blockchain Technology" nominees include TellusLabs, Feature Labs, Inc., RapidMiner, Circle, Siacoin, and Kensho. Alex Culafi (AC): AI, ML, and Blockchain are three emerging areas of technology. Why do you think Boston is poised to become a leader in these sectors of hard tech?

Today, when an enterprise wants to use machine learning to solve a problem, they have to call in the cavalry. Even a simple problem requires multiple data scientists, machine learning experts, and domain experts to come together to agree on priorities and exchange data and information. This process is often inefficient, and it takes months to get results. It also only solves the problem immediate at hand. The next time something comes up, the enterprise has to do the same thing all over again.

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.