What Will It Take to Get A.I. Out of Schools? The tech world assumes that A.I.-aided education is necessary and inevitable. A growing number of parents, educators, and cognitive scientists say the opposite. I don't like A.I., and I am raising my children not to like it. I've been telling them for years now that chatbots are manipulative and dangerous, that A.I.-image generators are loosening our collective grip on reality, that large language models are built atop industrial-scale intellectual-property theft. At times, I find myself speaking with my kids about A.I. in the same terms that we might discuss a creepy neighbor who lives down the block: avoid eye contact, cross the street when you walk past his house, and, when in doubt, call on a trusted adult. Yes, I, too, have suspected that the creepy neighbor walks on cloven hooves inside his Yeezy Boosts, but he probably isn't going anywhere--in fact, he keeps buying up properties around town--so just try your best not to engage. Somehow, I was not prepared for the creepy neighbor to start hanging around my kids' schools; somehow, I thought we had until high school.

We introduce and study the problem of calibrating conditional risk, which involves estimating the expected loss of a prediction model conditional on input features. We analyze this problem in both classification and regression settings and show that it is fundamentally equivalent to a standard regression task. For classification settings, we further establish a connection between conditional risk calibration and individual/conditional probability calibration, and develop theoretical insights for the performance metric. This reveals that while conditional risk calibration is related to existing uncertainty quantification problems, it remains a distinct and standalone machine learning problem. Empirically, we validate our theoretical findings and demonstrate the practical implications of conditional risk calibration in the learning to defer (L2D) framework. Our systematic experiments provide both qualitative and quantitative assessments, offering guidance for future research in uncertainty-aware decision-making.

The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in GPSSMs has not been satisfactorily addressed yet: system identification, i.e., learning the model. To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network. Inference with this structure allows us to recover a posterior smoothed over sequences of data. We provide a practical algorithm for efficiently computing a lower bound on the marginal likelihood using the reparameterisation trick. This further allows for the use of arbitrary kernels within the GPSSM. We demonstrate that the learnt GPSSM can efficiently generate plausible future trajectories of the identified system after only observing a small number of episodes from the true system.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network.

What He's Doing Now Boggles the Mind. My partner, a 36-year-old man, is being let go from his job. He was informed that his company would be cutting him and his entire department. The only person staying is his boss, who will be overseeing the new AI customer service client they are replacing the real life people with. But that's not what this is about, even if AI is going to be the death of humanity.

We study the problem of completing a binary matrix in an online learning setting. On each trial we predict a matrix entry and then receive the true entry. We propose a Matrix Exponentiated Gradient algorithm [1] to solve this problem. We provide a mistake bound for the algorithm, which scales with the margin complexity [2, 3] of the underlying matrix. The bound suggests an interpretation where each row of the matrix is a prediction task over a finite set of objects, the columns. Using this we show that the algorithm makes a number of mistakes which is comparable up to a logarithmic factor to the number of mistakes made by the Kernel Perceptron with an optimal kernel in hindsight. We discuss applications of the algorithm to predicting as well as the best biclustering and to the problem of predicting the labeling of a graph without knowing the graph in advance.