During a later visit to a Picasso exhibit in Milan, I came across a famous informational diagram by the art historian Alfred Barr, mapping how modernist movements like Cubism evolved from earlier artistic traditions. Picasso is often held up as one of modern art's most original and influential figures, but Barr's chart made plain the many artists he drew from--Goya, El Greco, Cézanne, African sculptors. This made me wonder: If a generative AI model had been fed all those inputs, might it have produced Cubism? Could it have generated the next great artistic "breakthrough"? These experiences--spread across three cities and centered on three iconic artists--coalesced into a broader reflection I'd already begun.

Given rising competition, higher customer expectations, and growing regulatory challenges, these investments are crucial. But to maximize their value, leaders must carefully consider how to balance the key factors of scope, scale, speed, and human-AI collaboration. The common refrain from data leaders across all industries--but specifically from those within data-rich life sciences organizations--is "I have vast amounts of data all over my organization, but the people who need it can't find it." And in a complex healthcare ecosystem, data can come from multiple sources including hospitals, pharmacies, insurers, and patients. "Addressing this challenge," says Sheeran, "means applying metadata to all existing data and then creating tools to find it, mimicking the ease of a search engine. Until generative AI came along, though, creating that metadata was extremely time consuming."

If you want to really hurt someone's feelings in the year 2023, just call them an AI. An all-star cast of celebrities and public figures have recently been the victim of such jokes: the NBA player Jordan Poole ("AI Steph Curry"), Raquel Leviss from the reality-TV show Vanderpump Rules ("what would happen if you asked chat GBT [sic] to create an American girl"), Transportation Secretary Pete Buttigieg ("our first A.I. cabinet member?"). That these slights span the three pillars of American life--sports, politics, Bravo--suggests that no one, or rather nothing, is safe. Such digs have popped up all over social media; on Twitter alone, insults like these have been levied against TV shows, songs, sports uniforms, commencement speeches, White House press releases, proposed legislation, and lots of news articles. That AI has become an attack is a result of the huge moment for AI we're in.

When I try to introduce the concept of AI DApps, I often find that it is particularly difficult when people lack an accurate grasp of what machine learning is. There is an overwhelming amount of information online about machine learning targeted toward audiences with different levels of technical expertise. In this series, I introduce machine learning at different technical levels, with the aim of providing a basic framework that helps you understand machine learning, regardless of your background, starting at the highest level. In traditional programming, programmers write programs, which are made of lines of code that instruct computers to perform certain tasks. For example, a programmer can write a program to detect whether the word "book" exists in a news article.