Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learning from Human Feedback (RLHF). However, a crucial shortcoming of many of these techniques is the Independence of Irrelevant Alternatives (IIA) assumption, which collapses \emph{all} human preferences to a universal underlying utility function, yielding a coarse approximation of the range of human preferences. On the other hand, statistical and computational guarantees for models avoiding this assumption are scarce. In this paper, we investigate the statistical and computational challenges of learning a \emph{correlated} probit model, a fundamental RUM that avoids the IIA assumption. First, we establish that the classical data collection paradigm of pairwise preference data is \emph{fundamentally insufficient} to learn correlational information, explaining the lack of statistical and computational guarantees in this setting. Next, we demonstrate that \emph{best-of-three} preference data provably overcomes these shortcomings, and devise a statistically and computationally efficient estimator with near-optimal performance. These results highlight the benefits of higher-order preference data in learning correlated utilities, allowing for more fine-grained modeling of human preferences. Finally, we validate these theoretical guarantees on several real-world datasets, demonstrating improved personalization of human preferences.

As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development.

Despite their incorporeal form, memories have a way of becoming a very real part of our identity, like the pattern of freckles on your face or your favorite jacket might. Remembering a childhood friend while gazing off at a field of dandelions may be pleasant, but being sucked back into a bad memory -- a difficult breakup or a traumatizing loss -- can be unbearable. But what if, a la Eternal Sunshine of the Spotless Mind, we could simply erase those memories? It's something being explored, but Philipp Kellmeyer, a neurologist and head of the Neuroethics & A.I. Ethics Lab at the University of Freiburg, has several concerns. High among them is identity.

Scientists are developing a pill that could help you forget bad memories - and they have just tested it on 60 heart broken people. Dr. Alain Brunet's memory manipulation study at the McGill University in Quebec, Canada, hopes to bring about a pioneering technique for the easing of painful memories. What had previously been a science fiction fantasy, discarding unwanted memories, may become a reality for those suffering from an'adjustment disorder' after experiencing a traumatic event. Fictional memory removal: Eternal Sunshine of the Spotless Mind, Jim Carrey and Kate Winslet - 2004. Where Jim Carrey's character receives treatment to remove memories of his ex Of the 60 people who signed up for the psychiatry study all had experienced the same emotion, the betrayal of their partner ending the romantic relationship, and all wanted to forget it.

Among the scant books in my tiny rented room in San Francisco, I've kept a spine-worn copy of Romeo and Juliet. It's the one I read in my high school English class, the pages yellowed, the margins filled with scribbled notes. Since the play was written in the 1590s, Shakespeare's portrayal of the nature of love--irrational, all-consuming--has been told and retold in countless movie adaptations. I hold onto the book to revisit those insights, and also because I'm prone to nostalgic literary tendencies like keeping old books. I am also a personal tech writer in 2018. It's my job to keep tabs on how our rapidly shifting technology is shaping not only how we communicate, but how we empathize, trust, show affection.