Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is expected to be crucial given the central role hyperparameters play in determining the performance of classical machine learning methods. In this work we introduce the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical datasets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values with qubit count, which is the cause behind recent observations that the performance of quantum kernel methods decreases with qubit count. We reproduce these negative results and show that if the kernel bandwidth is optimized, the performance instead improves with growing qubit count and becomes competitive with the best classical methods.

It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance Figure 1: Generated knowledge prompting involves of large-scale, state-of-the-art models (i) using few-shot demonstrations to generate questionrelated on four commonsense reasoning tasks, achieving knowledge statements from a language model; state-of-the-art results on numerical commonsense (ii) using a second language model to make predictions (NumerSense), general commonsense with each knowledge statement, then selecting the (CommonsenseQA 2.0), and scientific highest-confidence prediction.

The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, limiting the applicability of continual learning solutions to realistic scenarios. Consequently, this study aims to broaden our understanding of transfer and its driving forces in the specific case of continual reinforcement learning. We adopt SAC as the underlying RL algorithm and Continual World as a suite of continuous control tasks. We systematically study how different components of SAC (the actor and the critic, exploration, and data) affect transfer efficacy, and we provide recommendations regarding various modeling options. The best set of choices, dubbed ClonEx-SAC, is evaluated on the recent Continual World benchmark. ClonEx-SAC achieves 87% final success rate compared to 80% of PackNet, the best method in the benchmark. Moreover, the transfer grows from 0.18 to 0.54 according to the metric provided by Continual World.

One of the challenges in analyzing learning algorithms is the circular entanglement between the objective value and the stochastic noise. This is also known as the "chicken and egg" phenomenon and traditionally, there is no principled way to tackle this issue. People solve the problem by utilizing the special structure of the dynamic, and hence the analysis would be difficult to generalize. In this work, we present a streamlined three-step recipe to tackle the "chicken and egg" problem and give a general framework for analyzing stochastic dynamics in learning algorithms. Our framework composes standard techniques from probability theory, such as stopping time and martingale concentration. We demonstrate the power and flexibility of our framework by giving a unifying analysis for three very different learning problems with the last iterate and the strong uniform high probability convergence guarantee. The problems are stochastic gradient descent for strongly convex functions, streaming principal component analysis, and linear bandit with stochastic gradient descent updates. We either improve or match the state-of-the-art bounds on all three dynamics.

There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems. For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects. In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process.

"We start with this huge object of all these coupled-together differential equations; then we're using machine learning to turn it into something so small you can count it on your fingers," says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy. The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they're unleashed on more complex quantum systems.

Abstract: Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex dynamical systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build efficient, stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information, such as the equation of motion or fine-grained time measurements, with many recent proposals also relying on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach, combining the Wasserstein metric from optimal transport with diffusion maps, to discover conserved quantities that vary across trajectories sampled from a dynamical system. We test this new approach on a variety of physical systems -- including conservative Hamiltonian systems, dissipative systems, and spatiotemporal systems -- and demonstrate that our manifold learning method is able to both identify the number of conserved quantities and extract their values.

Several tech giants are betting that the metaverse could turn out to be the next hot trend in technology. The metaverse is widely believed to be the next evolution of the internet, allowing people to experience the internet in 3D. Not surprisingly, the metaverse is expected to touch several sectors ranging from online education to gaming to social interactions to commerce. This explains why Goldman Sachs sees the metaverse generating an $8 trillion revenue opportunity in the long run for its participants. Amazon (AMZN 1.20%) and Nvidia (NVDA -2.30%) are two tech giants that could win big from this massive opportunity.

Administrators aren't privy to the analysis teachers receive using the TeachFX app. "Eighty five percent teacher talk for me--even for an interview that makes me think: yikes!" Jamie Poskin was referring to the TeachFX analysis of the interview he'd just completed with Forbes. According to the app, he spent 85% of the call talking--which seems appropriate when answering a reporter's questions. But had he been teaching English to a class of ninth graders, that figure would be higher than it should be, according to decades of research on student learning. Poskin is the founder and CEO of TeachFX, an artificial intelligence-powered app that records teachers' lessons and gives them personalized feedback about what they do well and where they could improve.

An innovative chatbot designed for sharing critical information about sexual and reproductive health (SRH) with young people in India is demonstrating how artificial intelligence (AI) applications can engage vulnerable and hard-to-reach population segments. Working with the Population Foundation of India (PFI), Helen Wang, associate professor of communication, College of Arts and Sciences, examined the user-centered design and engagement of SnehAI, the first Hinglish (Hindi and English) chatbot purposefully developed for social and behavioral change. "Many AI technologies today are motivated by profit, but we must also be aware that AI can be leveraged in ways that facilitate social and behavior change," says Wang, who specializes in entertainment-education and storytelling as instruments for health promotion. "SnehAI is a powerful testimonial of the vital potential that lies in AI for good." The findings from Wang's instrumental case study appear in the Journal of Medical Internet Research.