Riding the AI Wave

Although Hammond says he barely remembers his life before computers and coding, there was indeed a time when his world was much more analog. Hammond grew up on the East Coast and spent his high school years in Salt Lake City, where his mother was a social worker and his father was a professor of archaeology at the University of Utah. Over the course of 50 years, Philip C. Hammond excavated several sites in the Middle East and made dozens of trips to Jordan, earning him the nickname Lion of Petra. Kris joined these expeditions for three summers, working as his father’s surveyor and draftsman.

“Now, once a week, I ask ChatGPT for a biography of my father, as an experiment,” Hammond says, bemused. “Sometimes, it gives me a beautifully inaccurate bio that makes him sound like Indiana Jones. Other times, it says he is a tech entrepreneur and that I have followed in his footsteps.”

While those biographical tidbits are more AI-generated falsehoods, Hammond and his father have both traced intelligence from different worlds — one etched in stone and another in silicon. Wanting a deeper understanding of the meaning of intelligence and thought, Hammond studied philosophy as an undergraduate at Yale University and planned to go law school after graduation. But his trail diverged when a fellow member of a local sci-fi club suggested that Hammond, who had taken one computer science class, try working as a programmer.

“After nine months as a programmer, I decided that’s what I wanted to do for a living,” Hammond says.

That sci-fi club guy was Chris Riesbeck, who is also now a professor of computer science at McCormick. Hammond earned his doctorate in computer science from Yale in 1986. But he didn’t abandon philosophy entirely. Instead, he applied those abstract frameworks — consciousness, knowledge, creativity, logic and the nature of reason — to the pursuit of intelligent systems.

“The structure of thought always fascinated me,” Hammond says. “Looking at it from the perspective of how humans think and how machines ‘think’ — and how we can ‘think’ together — became a driver for me.”

But the word “think” is tenuous in this context, he says. There’s a fundamental and important distinction between true human cognition and what current AI can do — namely, sophisticated mimicry. AI isn’t trying to critically assess data to devise correct answers, says Hammond. Instead, it’s a probabilistic engine, sifting through language likelihoods to finish a sentence — like the predictive text you might see on your phone while composing a message. It is seeking the most likely conclusion to any given string of words.

“These are responsive systems,” he says. “They aren’t reasoning. They just hold words together. That’s why they have problems answering questions about recent events.”

Eager to explore ways users and developers can work together to prevent potential threats from AI, Hammond launched Northwestern’s Center for Advancing Safety of Machine Intelligence (CASMI) in 2022 in partnership with the Underwriters Laboratories (UL) Research Institutes’ Digital Safety Research Institute. One of the first centers of its kind, CASMI aims to establish best practices for evaluating and developing AI that is safe, equitable and beneficial. (CASMI won a 2024 Chicago Innovation Award for its commitment to AI safety.)

“Before we can prevent harm or implement safety measures, we have to understand and articulate what issues exist,” Hammond says. To that end, CASMI has connected researchers across the country to identify those potential harms. “CASMI is funding a dozen projects, which look at the effects of AI on mental health, children, law, medicine and journalism,” says Hammond. Part of this work is aimed at the development of the AI Incident Database, an open-source project that keeps a historical record of AI’s failures and negative effects on users and society.

Beyond the potential erosion of critical thinking skills, Hammond worries about AI’s flaws invisibly seeping into various aspects of society.

Thus far, the collective imagination has fixated on dramatic potentialities — AI stealing human jobs or sentient machines turning rogue. Although autonomous lethal devices do exist — Hammond even provided consultation on the ethical dilemmas of such machines during his time as part of the United Nations Institute for Disarmament Research — AI’s everyday threats are much more subtle. And they aren’t a distant fear in an imagined future. They are already here.

The examples are nearly endless. Physicians have started using speech-to-text software for medical notetaking, but these systems can get words wrong or might guess what’s next based on past context, adding false bits of information or introducing errors into clinical records. Police departments use AI to transcribe audio from bodycam footage to create incident reports — but if the AI is trained on historical incident reports that reflect existing societal biases (such as disproportionately associating certain demographics with criminal activity), that practice can end up amplifying biases. Recently developed chatbots are prone to unsettling “hallucinations.” Unlike simple inaccuracies, these conjured realities are entirely fabricated and nonsensical — and presented as fact. And then there are deepfakes — digitally manipulated images and video, which exacerbate the existential crisis of misinformation and disinformation.