Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to help the human reach that goal. This approach requires inferring intentions, which can be difficult in high-dimensional settings. We build upon prior work that studies assistance through the lens of empowerment: an assistive agent aims to maximize the influence of the human's actions such that they exert a greater control over the environmental outcomes and can solve tasks in fewer steps. We lift the major limitation of prior work in this area--scalability to high-dimensional settings--with contrastive successor representations.

News that Dutch publishing house Veen Bosch & Keuning (VBK) has confirmed plans to experiment using AI to translate fiction has stirred up a thought-provoking debate. Some believe it marks the beginning of the end for human translators, while others see this as the opening up of a new world of possibilities to bring more literature to even more people. These arguments are becoming increasingly vocal as the advance of AI accelerates at an ever-increasing rate. This debate interests me as my work examines the intersections of art, ethics, technology and culture, and I have published research in areas of emerging technologies, particularly in relation to human enhancement. Across every new technology, debate centres on what we stand to lose by embracing change and, with AI, this echoes the developments in the recent history of genetic science.

When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones. To assist humans in repairing them, we propose to automatically decompose complex solutions into multiple simpler pieces that correspond to specific subtasks. We introduce a novel objective for learning task decomposition, termed assistive value (AssistV), which measures the feasibility and speed for humans to repair the decomposed solution. We collect a dataset of human repair experiences on different decomposed solutions. Utilizing the collected data as in-context examples, we then learn to critique, refine, and rank decomposed solutions to improve AssistV. We validate our method under competitive programming problems: under 177 hours of human study, our method enables non-experts to solve 33.3\% more problems, speeds them up by 3.3x, and empowers them to match unassisted experts.

Our approach to aligning AGI is empirical and iterative. We are improving our AI systems' ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems. Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to align highly capable AI systems, we can learn what works and what doesn't, thus refining our ability to make AI systems safer and more aligned.

"This (AI) is about augmenting human intelligence. When you step back and look at AI, yes there is hype, it will replace all jobs. We say look that's not the right way to approach it. The correct way to say is that this set of artificial intelligence technologies will augment and assist humans to do their work better," Ashvin Vellody, Partner, Deloitte India told indianexpress.com during an interaction. According to a new report by Deloitte and the Confederation of Indian Industry (CII), AI could prove to be most beneficial in areas like agriculture, manufacturing, education and health care services in India.

Artificial intelligence (AI) should be adopted to help human employees create a better customer experience, rather than remove people from the process altogether, according to BT's head of customer insight and futures, Nicola Millard. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

The potential impact of Artificial Intelligence (AI) has never been greater--but we'll only be successful if AI can deliver smarter and more intuitive answers. A key barrier to AI today is that natural data fed to a computer is largely unstructured and "noisy." It's easy for humans to sort through natural data. For example: If you are driving a car on a residential street and see a ball roll in front of you, you would stop, assuming there is a small child not far behind that ball. Computers today don't do this.