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Learn MoreTechnion Prof. Danny Raz explores how quantum computing and AI are reshaping what machines can do and how future computer scientists are educated.
While students still need to master basic coding skills and algorithm design, they must also develop the ability to assess whether an AI program solves a problem correctly.”
Prof. Danny Raz
In the field of computer science, profound questions define the boundaries of what machines can do. Are there certain problems that computers cannot solve? What new possibilities do quantum computing and AI enable? How do these expanding frontiers change how we educate the next generation of computer scientists?
In a lecture delivered to the American Technion Society’s National Board of Directors in November 2024, Danny Raz, dean of the Henry and Marilyn Taub Faculty of Computer Science at the Technion, explored these questions as he discussed key issues in modern computing, offering an inside look at the field’s cutting edge.
Prof. Raz invited the audience to engage in a time-honored thought experiment known as the Halting Problem, which asks: Is it possible to create an algorithm that can determine, for any given program, whether the program will eventually stop (halt) or run indefinitely?
He asked the audience to assume that such an algorithm exists, known as a “black box.” He then presented the idea of a new program, called a “green-red box,” which is designed to behave as follows:
Now, he asked, what happens if the green-red box is fed into the black box algorithm — in other words, if the program in question is the green-red box itself?
A contradiction arises. If the black box predicts the program will halt, the green-red box runs forever, based on the green-red box’s behavioral rules. If the black box predicts the program will run forever, the green-red box halts. Reality defies the black box’s prediction.
“We find that the black box is faulty,” said Prof. Raz. “No algorithm can predict the halting behavior of all programs, which reveals real limits to what one can achieve using computers.”
While the Halting Problem defines what computers cannot do, the rise of quantum computing presents an expanding horizon of what they can achieve. Quantum computers, Prof. Raz explained, harness the strange properties of quantum mechanics to process information in ways that classical computers cannot.
Quantum computing deals with quantum bits or qubits, which differ from classical bits in that they can exist in multiple states simultaneously. Unlike classical bits, which are either 0 or 1, qubits leverage the principle of superposition, where they can exist in multiple states at once. This property allows quantum computers to perform calculations over a vast range of possibilities in parallel, enabling them to tackle problems that would take classical computers millennia to solve.
One of the most promising applications of quantum computing, explained Prof. Raz, is in cryptography. Quantum computers can, in theory, break encryption algorithms by efficiently searching through all possible keys. While this remains largely theoretical, quantum computing is already progressing toward realizing this possibility, which has the potential to revolutionize industries like cybersecurity.
“If you have a large enough quantum computer, you could break codes,” he said. “And this is very, very attractive to many of the governments and other forces in the world.”
As technology evolves, so, too, must the way we educate the next generation of computer scientists, Prof. Raz posited. In the past, computer science education focused heavily on technical skills and problem-solving through traditional algorithms. However, with the rise of AI and machine learning, new educational priorities have emerged, and the Technion is leading the way in preparing tomorrow’s computer scientists with necessary skills.
“One of the key shifts in computer science education is the need to balance practical skills with deep theoretical knowledge,” said Prof. Raz. “While students still need to master basic coding skills and algorithm design, they must also develop the ability to assess whether an AI program solves a problem correctly.”
This is particularly important in today’s tech landscape, he explained, where companies like Google rely on programmers to work closely with AI tools that generate code.
To address this emerging need, Technion educators are incorporating new skills into their curricula, teaching students how to interact with machines like AI-powered code assistants. The goal is to ensure that students understand how to verify whether a machine’s output aligns with the desired outcome.
Despite inherent limits to what computers can achieve, quantum computing and AI represent exciting new frontiers in computation. As the field of computer science evolves, so, too, will the way the Technion educates future generations of tech professionals.
“By incorporating both practical skills and theoretical concepts— particularly in the realms of AI and quantum computing — we will prepare Technion students for the future, equipping them with the tools needed to navigate an increasingly complex technological landscape,” said Prof. Raz.
