Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo multiple design or algorithmic changes during their development cycle and use ad hoc approaches for incorporating these changes without re-training from scratch, which would have been prohibitively expensive. Additionally, the inefficiency of deep RL typically excludes researchers without access to industrial-scale resources from tackling computationally-demanding problems. To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e.g., learned policies) is reused or transferred between design iterations of an RL agent, or from one RL agent to another.

Elon Musk has claimed'AI will be smarter than any human by the end of 2025' - and while that is just one year away, an expert said the prediction may still come true. Nell Watson, an AI expert and ethicist, has shared a detailed timeline of how the tech could transform from chatbots to super intelligent agents over the next 12 months. The path would start with a massive 100 billion investment in new computing infrastructure, then AI would learn how to self-improve until it becomes'conscious.' 'Although one year is a short time frame, remember that only 15 months have passed since ChatGPT's breakthrough, which thrust AI into the public consciousness, she told DailyMail.com. 'Developments continue at a frenetic pace since, and even appear to be rapidly accelerating.' Elon Musk has claimed'AI will be smarter than any human by the end of 2025' - and while that is just one year away, an expert said the prediction may still come true Watson, who is the author of'Taming the Machine: Ethically harness the power of AI,' described superhuman AI as systems that far exceed human capabilities across the board.

MUCH is made of intergenerational conflicts, with boomers pitted against millennials or Gen Zers. But however these competing needs are resolved today, in the future, younger people will become an increasingly prized resource, because there will be fewer of them. Populations are slowly being skewed older than ever before by two seemingly unstoppable demographic forces. One is that, as countries become more prosperous, there is a decline in the number of children that people have. When that figure drops below the population replacement level of 2.1 children per woman – unless offset by immigration – the head count shrinks, as…

Since 2016, when we partnered with the AI community to create the PyTorch framework for AI research, open collaboration has been essential to its success. With thousands of contributors who have built more than 150,000 projects on it, PyTorch has become one of the leading platforms for research and production across the AI community. Today, Mark Zuckerberg announced that the project will transition to a newly launched PyTorch Foundation, which will be part of the nonprofit Linux Foundation, a technology consortium whose core mission is the collaborative development of open-source software. The creation of the PyTorch Foundation ensures that decisions will be made in a transparent and open manner by a diverse group of board members for many years to come. The governing body will be composed of representatives from AMD, Amazon Web Services, Google Cloud, Meta, Microsoft Azure and Nvidia, with the intention to expand further over time.

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and getting started requires a significant engineering investment. What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field. We introduce CompilerGym, a set of environments for real world compiler optimization tasks, and a toolkit for exposing new optimization tasks to compiler researchers. CompilerGym enables anyone to experiment on production compiler optimization problems through an easy-to-use package, regardless of their experience with compilers.

We are excited to announce our partnership with arXiv to support links to code on arXiv. Authors can add official code to their arXiv papers by going to arxiv.org/user and clicking on the "Link to code" Papers with Code icon (see below). From there they will be directed to Papers with Code where they can add their code. Once they add an official implementation, the official code section will show up on the arXiv article page. All data on Papers with Code is freely available and is licensed under CC-BY-SA (same as Wikipedia).

Countries looking to harness the power of disruptive technologies need to ensure that citizens, and particularly the poor and those left behind, benefit from the opportunities created by disruptive technologies. It is not enough that disruptive technologies lower costs of goods and services or provide other efficiencies. Jobs, education, social safety nets, and investments that give opportunities to the poor matter just as much. At this year's Annual Meetings of the International Monetary Fund and World Bank Group, stakeholders in the development community will, among many different topics, discuss how best to leverage the potential of disruptive technologies in order to address world's most pressing development challenges. Disruptive technologies include innovations that "lead to a step change in the cost or access to products and services with potential to disrupt traditional pathways of economic development."

The 4th Industrial Revolution continues to demonstrate what I call exponential "A Triple C": Perhaps nowhere is this "A Triple C" dynamic more on display than in the realm of artificial intelligence (AI), which is expected to underpin many of the key emerging technologies and power business growth across industries. It's clear that AI is becoming the new electricity, and its rapid proliferation has happened in a very short span of three years – with a large leap in the past 12 months. But what's really exciting is AI's potential to improve lives at a pace and scale not seen before. Aiding this potential is a significant business and investment shift toward a greater focus on social good. Taken together, these dynamics are now resulting in a rising number of use cases for the application of AI to accelerate progress on the United Nations' Sustainable Development Goals (SDGs).