We study online Reinforcement Learning (RL) in non-stationary input-driven environments, where a time-varying exogenous input process affects the environment dynamics. Online RL is challenging in such environments due to catastrophic forgetting (CF). The agent tends to forget prior knowledge as it trains on new experiences. Prior approaches to mitigate this issue assume task labels (which are often not available in practice) or use off-policy methods that can suffer from instability and poor performance. We present Locally Constrained Policy Optimization (LCPO), an on-policy RL approach that combats CF by anchoring policy outputs on old experiences while optimizing the return on current experiences. To perform this anchoring, LCPO locally constrains policy optimization using samples from experiences that lie outside of the current input distribution. We evaluate LCPO in two gym and computer systems environments with a variety of synthetic and real input traces, and find that it outperforms state-of-the-art on-policy and off-policy RL methods in the online setting, while achieving results on-par with an offline agent pre-trained on the whole input trace.

Spring Data JPA, part of the larger Spring Data family, makes it easy to easily implement JPA based repositories. This module deals with enhanced support for JPA based data access layers. It makes it easier to build Spring-powered applications that use data access technologies. Implementing a data access layer of an application has been cumbersome for quite a while. Too much boilerplate code has to be written to execute simple queries as well as perform pagination, and auditing. Spring Data JPA aims to significantly improve the implementation of data access layers by reducing the effort to the amount that's actually needed. As a developer you write your repository interfaces, including custom finder methods, and Spring will provide the implementation automatically.

In the classroom of the future--if there still are any--it's easy to imagine the endpoint of an arms race: an artificial intelligence that generates the day's lessons and prompts, a student-deployed A.I. that will surreptitiously do the assignment, and finally, a third-party A.I. that will determine if any of the pupils actually did the work with their own fingers and brain. Loop complete; no humans needed. If you were to take all the hype about ChatGPT at face value, this might feel inevitable. But a response to the hit software demo, released by OpenAI in November to instant fanfare, is coming. You only have to look at how schools dealt with the potential externalities of newly essential tech during the pandemic to see how a similarly paranoid reaction to chatbots like ChatGPT could go--and how it shouldn't. When schools had to shift on the fly to remote learning three years ago, there was a massive turn to what at that point was mainly enterprise software: Zoom.

Artificial intelligence will become an indispensable part of our lives day by day, and if you miss this opportunity, maybe you will be a little behind the times in the future. But you're here because you don't want it to be like that. You're lucky, so am I. This course will show you over 10 AI sites and how to use them. With these sites, you can write text, make green screen-free backgrounds, record studio quality audio, create advertisement photos for your product, create music for your video, learn to ask questions to artificial intelligence, create images and write descriptions for your social media with a few clicks with these sites, you can print keywords to AI for your Amazon products and learn how to edit your videos more easily.

This course focuses on the theme of "protection and sustainable utilization of oceans and marine resources to promote sustainable development". The course adopts a combination of theory and practice to introduce related technologies and typical applications of ocean intelligent autonomous monitoring. Typically, the course includes unmanned surface vehicle(USV),unmanned aerial vehicle(UAV), autonomous underwater vehicle(AUV), and related algorithms for data processing. Students are awarded 2cr for completing the course. Students from all study programs are welcome, and thus no formal requirements are set.

The 37th AAAI Conference on Artificial Intelligence (AAAI2021) starts on Tuesday 7 February and runs until Tuesday 14 February. Find out about some of the main events that are taking place throughout the conference, this year to be held in Washington DC. Francesca Rossi will deliver her presidential address on Thursday morning (9 February), following the official opening of the conference. AAAI have announced the following distinguished invited speakers at this year's conference. The diversity and inclusion events take place throughout the conference.

The AWS AI & ML Scholarship Program, in collaboration with Udacity, is an AI/ML-focused scholarship program providing 2,500 scholarships over 2023, as well as mentorship, to students that identify as underserved and underrepresented in technology. The program aims to make the future tech workforce more diverse by removing financial barriers, providing training for careers in tech, and offering mentorship support to individuals who are underserved or underrepresented in tech.

Bilevel optimization has been developed for many machine learning tasks with large-scale and high-dimensional data. This paper considers a constrained bilevel optimization problem, where the lower-level optimization problem is convex with equality and inequality constraints and the upper-level optimization problem is non-convex. The overall objective function is non-convex and non-differentiable. To solve the problem, we develop a gradient-based approach, called gradient approximation method, which determines the descent direction by computing several representative gradients of the objective function inside a neighborhood of the current estimate. We show that the algorithm asymptotically converges to the set of Clarke stationary points, and demonstrate the efficacy of the algorithm by the experiments on hyperparameter optimization and meta-learning.

AI constructivism as inspired by Jean Piaget, described and surveyed by Frank Guerin, and representatively implemented by Gary Drescher seeks to create algorithms and knowledge structures that enable agents to acquire, maintain, and apply a deep understanding of the environment through sensorimotor interactions. This paper aims to increase awareness of constructivist AI implementations to encourage greater progress toward enabling lifelong learning by machines. It builds on Guerin's 2008 "Learning Like a Baby: A Survey of AI approaches." After briefly recapitulating that survey, it summarizes subsequent progress by the Guerin referents, numerous works not covered by Guerin (or found in other surveys), and relevant efforts in related areas. The focus is on knowledge representations and learning algorithms that have been used in practice viewed through lenses of Piaget's schemas, adaptation processes, and staged development. The paper concludes with a preview of a simple framework for constructive AI being developed by the author that parses concepts from sensory input and stores them in a semantic memory network linked to episodic data.

In the post-pandemic era, the widespread adoption of remote work has prompted the educational sector to reassess conventional pedagogical methods. This paper is to scrutinize the underlying psychological principles of ChatGPT, delve into the factors that captivate user attention, and implicate its ramifications on the future of learning. The ultimate objective of this study is to instigate a scholarly discourse on the interplay between technological advancements in education and the evolution of human learning patterns, raising the question of whether technology is driving human evolution or vice versa. Keywords: Artificial intelligence (AI), Human-machine communication, COVID-19, Chat GPT, Learning 3.0, Critical Thinking 1.Introduction of ChatGPT ChatGPT, a chatbot developed by OpenAI, can interpret and respond to natural language input using the GPT-3 language model which has 175 billion parameters (Floridi & Chiriatti, 2020). The utilization of a word-driven dialogue system offers assistance in cross-domain problem resolution and the generation of content to answer users' inquiries.