In her quest to design socially assistive robots--robots that provide social, not physical, support in realms like rehabilitation, education, and therapy--she realized that personalizing interactions would boost both engagement and outcomes. Artificial Intelligence (AI) has made that easier, though as always, surprises are never far when human beings are involved. Here, Matarić shares what she's learned about meeting people where they are. Let's talk about your work on socially assistive robots. You've said that having kids inspired you to build robots that help people. How did that interest develop into the mission of supporting specific behavioral interventions in health, wellness, and education?

A year and a half into the AI boom, there's growing evidence that the hype machine is slowing down. Drastic warnings about AI posing an existential threat to humanity or taking everyone's jobs have mostly disappeared, replaced by technical conversations about how to cajole chatbots into helping summarize insurance policies or handle customer service calls. Some once-promising start-ups have already cratered and the suite of flashy products launched by the biggest players in the AI race -- OpenAI, Microsoft and Google -- have yet to upend the way people work and communicate with each other. While money keeps pouring into AI, very few companies are turning a profit on the tech, which remains hugely expensive to build and run.

Did you miss a session from the Future of Work Summit? The enterprise is quickly discovering the many ways AI can streamline and improve processes, but so far, most of these successes are happening at limited scale. Like any technology, AI functions well in controlled situations, but pushing it far and wide throughout an increasingly diversified data ecosystem is not without its perils. At scale, the enterprise is no longer a cohesive, fully integrated digital environment, but a loose collection of processes, platforms, and cultures. Of course, AI promises to change all that (or at least paper it over), but in a Catch-22, it really can't function at scale until it achieves scale -- meaning there is still a lot of work to do before organizations can push the value proposition of AI to its limits.

What might its relationship to matter be? And why are some things conscious while others apparently aren't? These sorts of questions, taken together, make up what's called the "hard problem" of consciousness, coined some years ago by the philosopher David Chalmers. There is no widely accepted solution to this. But, fortunately, we can break the problem down: If we can tackle what you might call the easy part of the hard problem, then we might make some progress in solving the remaining hard part.

Recently, I've been seeing a lot of services and products advertising automation of machine learning. Data Robot and H2O.ai offer platforms that allow the creation of machine learning algorithms in point-and-click interfaces. They'll even do the feature engineering for you! This functionality, or something like it, is slowly being built into various tools and programs. They promise to automate the creation of the whole machine learning pipeline -- from feature transformations, hyperparameter tuning, to model selection.

If, by some chance, you find yourself in Mark Cuban's bathroom, make sure to check out the reading materials. "If you go in my bathroom, there's a book, Machine Learning for Idiots," Cuban said on the latest episode of Recode Media. "Whenever I get a break, I'm reading it." That means everyone, including and especially business owners, are at risk if they don't educate themselves now. "There'll be a time when people take AI and its impact for granted, but if you don't know how to use it and you don't understand it and you can't at least at have a basic understanding of the different approaches and how the algorithms work, you can be blindsided in ways you couldn't even possibly imagine," Cuban said. "Algorithms are a function, literally, of the people who write them. Whoever they are, whatever they are, that's what you're going to get," he added. "If you don't know any better, it's like if you just had somebody who wrote software and didn't know anything about your business. There's going to be all kinds of risks involved. You have to understand it." You can listen to Recode Media wherever you get your podcasts -- including Apple Podcasts, Spotify, Google Podcasts, Pocket Casts, and Overcast. Below, we've shared a lightly edited full transcript of Peter's conversation with Mark, recorded live at Vox Media's The Deep End at South by Southwest 2019. I do a lot of these interviews now, either on a stage like this or at our own conferences or podcasts, and the thing I've learned over years is the best guest you can ever have is a billionaire who owns his or her own company because they can say whatever they want. So that's what we set up for you today. You answer your own emails. You know, I'm talented like that. Thank you for doing that. I'm not going to ask you if you are running for president. Because that's a boring ... I'm gonna get a boring answer. If you did run for president, like everyone else at South By Southwest, what would you campaign on? You put me on the spot. Let's just start by what I think is important, right, and I'm not a candidate so I don't give a shit if you like it or don't like it. First is common sense, right? Second is trying to bring people together.

Despite the ubiquity of drones nowadays, it seems to be generally accepted that learning how to control them properly is just too much work. Consumer drones are increasingly being stuffed full of obstacle-avoidance systems, based on the (likely accurate) assumption that most human pilots are to some degree incompetent. It's not that humans are entirely to blame, because controlling a drone isn't the most intuitive thing in the world, and to make it easier, roboticists have been coming up with all kinds of creative solutions. There's body control, face control, and even brain control, all of which offer various combinations of convenience and capability. The more capability you want in a drone control system, usually the less convenient it is, in that it requires more processing power or infrastructure or brain probes or whatever. Developing a system that's both easy to use and self-contained is quite a challenge, but roboticists from the University of Pennsylvania, U.S. Army Research Laboratory, and New York University are up to it--with just a pair of lightweight gaze-tracking glasses and a small computing unit, a small drone will fly wherever you look.

Apple announced stellar quarterly earnings; investors liked them; the stock rose; and Apple became the first US company to surpass $1 trillion in market value. In our love for big numbers, that made it a big story. Zachary Karabell is a WIRED contributor and president of River Twice Research. Never mind that if you adjust for inflation and go global, Apple isn't actually the first trillion-dollar company. PetroChina, the state-owned Chinese oil company, hit that number more than a decade ago, and in adjusted terms, Standard Oil did a century ago.

The way we talk about AI is a mess. It starts with the most obvious, the imagery. Just like stock photos of happy people pointing at whiteboards were a symbol of the modern workplace, wired brains and robots have now come to represent "the AI". But the visual messaging is only a small part of a much larger problem. Illustration is symbolic -- it relies on familiarity and evokes associations and expectations.

Saying you use "AI" at your company may give you bragging rights at your industry meetups and even fool the media, but actually implementing enterprise-wide transformation is much harder than just claiming you have. Before you beg management for an eye-popping AI budget, be aware: not all companies are ready for artificial intelligence. Unlike flash drives and mobile apps, enterprise-scale AI is not standalone plug-and-play technology. The quality of your data and analytics infrastructure, as well as your organization's engineering and business culture, are critical foundations for any AI initiative. Even tech-savvy companies like Google have made embarrassing faux-pas, such as mistakenly auto-labeling black people as gorillas.