The Alignment Problem: Machine Learning and Human Values

“They realize that a neuron with a low-enough threshold, such that it would fire if any of its inputs did, functioned like a physical embodiment of the logical or. A neuron with a high-enough threshold, such that it would only fire if all of its inputs did, was a physical embodiment of the logical and. There was nothing, then, that could be done with logic—they start to realize—that such a ‘neural network,’ so long as it was wired appropriately, could not do.”

Christian presents the foundational concept in neural network design of neurons emulating basic logical operations. The realization of early researchers in the field that these networks could potentially replicate any logical function opens up a wide range of research questions, with implications for both artificial intelligence development and biological neural processing.

“As machine-learning systems grow not just increasingly pervasive but increasingly powerful, we will find ourselves more and more often in the position of the ‘sorcerer’s apprentice’: we conjure a force, autonomous but totally compliant, give it a set of instructions, then scramble like mad to stop it once we realize our instructions are imprecise or incomplete—lest we get, in some clever, horrible way, precisely what we asked for. How to prevent such a catastrophic divergence—how to ensure that these models capture our norms and values, understand what we mean or intend, and, above all, do what we want—has emerged as one of the most central and most urgent scientific questions in the field of computer science. It has a name: the alignment problem.”

Christian’s definition of his book’s title centers the Ethical Implications of AI Usage, emphasizing the potential risks and challenges associated with the rapid advancement and integration of machine learning systems into various aspects of society. The “sorcerer's apprentice” is a symbol that stands for the unintended consequences that can arise from AI systems executing commands too literally. It underlines the need for developing mechanisms to ensure that these systems adhere to human ethical standards and intentions, capturing this challenge within the concept known as the “alignment problem.”

“We often hear about the lack of diversity in film and television—among casts and directors alike—but we don’t often consider that this problem exists not only in front of the camera, not only behind the camera, but in many cases inside the camera itself. As Concordia University communications professor Lorna Roth notes, ‘Though the available academic literature is wide-ranging, it is surprising that relatively few of these scholars have focused their research on the skin-tone biases within the actual apparatuses of visual reproduction.’”

The Alignment Problem highlights an underexplored area of study that affects how skin tones are captured and represented by cameras. Lorna Roth’s statement calls for a broader examination of the tools and technologies used in filmmaking, emphasizing the need for research and development to correct these ingrained disparities.