'Oppenheimer' swept away the competition at the 2024 Oscars, receiving seven awards including earning renowned director Christopher Nolan his first golden man statuette. While this is the filmmaker's first major award-winning film, he has been producing movies since 1998 when he made Following - and has made 10 more since. We asked ChatGPT to rank his other 11 films dating back to 26 years to the'Following' and 2010 film'Inception' up through his his 2012 film'The Dark Knight Rises' and his 2020 film'Tenet.' Renowned director Christopher Nolan took home his first Oscar for his critically acclaimed film, ' Oppenheimer.' The historic film starred Cillian Murphy as J Robert Oppenheimer, the director of the Los Alamos lab that designed and built the world's first atomic bomb during World War II - he is often known as the'father of the atomic bomb' Oppenheimer swept the box office when it was released on July 21, 2023, reeling in a whopping 82.4 million in opening weekend, winning Nolan Best Picture and Best Director during Sunday's award show.

Currently, the biggest shareholders include media company Advance Magazine Publishers, Chinese tech firm Tencent, US investment firm Fidelity, and Sam Altman, the chief executive of ChatGPT makers OpenAI.

The news: In the summer of 2021, OpenAI quietly shuttered its mulrobotics team, announcing that progress was being stifled by a lack of data necessary to train robots in how to move and reason using artificial intelligence. Now three of OpenAI's early research scientists say the startup they spun off in 2017, called Covariant, has solved that problem. They've unveiled a system that combines the reasoning skills of large language models with the physical dexterity of an advanced robot. How it works: The new model, called RFM-1, was trained on years of data collected from Covariant's small fleet of item-picking robots, as well as words and videos from the internet. Users can prompt the model using five different types of input: text, images, video, robot instructions, and measurements.

The U.S. government must move "quickly and decisively" to avert substantial national security risks stemming from artificial intelligence (AI) which could, in the worst case, cause an "extinction-level threat to the human species," says a report commissioned by the U.S. government published on Monday. "Current frontier AI development poses urgent and growing risks to national security," the report, which TIME obtained ahead of its publication, says. "The rise of advanced AI and AGI [artificial general intelligence] has the potential to destabilize global security in ways reminiscent of the introduction of nuclear weapons." AGI is a hypothetical technology that could perform most tasks at or above the level of a human. Such systems do not currently exist, but the leading AI labs are working toward them and many expect AGI to arrive within the next five years or less.

Workers at some of the world's leading AI companies harbor significant concerns about the safety of their work and the incentives driving their leadership, a report published on Monday claimed. The report, commissioned by the State Department and written by employees of the company Gladstone AI, makes several recommendations for how the U.S. should respond to what it argues are significant national security risks posed by advanced AI. Read More: Exclusive: U.S. Must Move'Decisively' To Avert'Extinction-Level' Threat from AI, Government-Commissioned Report Says The report's authors spoke with more than 200 experts for the report, including employees at OpenAI, Google DeepMind, Meta and Anthropic--leading AI labs that are all working towards "artificial general intelligence," a hypothetical technology that could perform most tasks at or above the level of a human. The authors shared excerpts of concerns that employees from some of these labs shared with them privately, without naming the individuals or the specific company that they work for. OpenAI, Google, Meta and Anthropic did not immediately respond to requests for comment. "We have served, through this project, as a de-facto clearing house for the concerns of frontier researchers who are not convinced that the default trajectory of their organizations would avoid catastrophic outcomes," Jeremie Harris, the CEO of Gladstone and one of the authors of the report, tells TIME. One individual at an unspecified AI lab shared worries with the report's authors that the lab has what the report characterized as a "lax approach to safety" stemming from a desire to not slow down the lab's work to build more powerful systems.

An influx of artificial intelligence (AI) startups is heating up the battle for technical talent in Europe, leaving companies like Google DeepMind to choose between paying big or losing out on the region's best minds. The runaway success of OpenAI's ChatGPT has energized investors, who have been pouring money into promising AI startups, eager to uncover the next overnight success. Riding the investment wave, a crop of foreign AI firms -- including Canada's Cohere and U.S.-based Anthropic and OpenAI -- opened offices in Europe last year, adding to pressure on tech companies already trying to attract and retain talent in the region.

Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of LLMs. Online model selection has thus garnered increasing attention due to the need to choose the best model among a diverse set while balancing task reward and exploration cost. Organizations faces decisions like whether to employ a costly API-based LLM or a locally finetuned small LLM, weighing cost against performance. Traditional selection methods often evaluate every candidate model before choosing one, which are becoming impractical given the rising costs of training and finetuning LLMs. Moreover, it is undesirable to allocate excessive resources towards exploring poor-performing models. While some recent works leverage online bandit algorithm to manage such exploration-exploitation trade-off in model selection, they tend to overlook the increasing-then-converging trend in model performances as the model is iteratively finetuned, leading to less accurate predictions and suboptimal model selections. In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection. To further capture the converging points of models, we develop a change detection mechanism by comparing consecutive increase predictions. We theoretically prove that our algorithm achieves a logarithmic regret upper bound in a typical increasing bandit setting, which implies a fast convergence rate. The advantage of our method is also empirically validated through extensive experiments on classification model selection and online selection of LLMs. Our results highlight the importance of utilizing increasing-then-converging pattern for more efficient and economic model selection in the deployment of LLMs.

This article presents a vision for the future of prosthetic devices, leveraging the advancements in large language models (LLMs) and Large Multimodal Models (LMMs) to revolutionize the interaction between humans and assistive technologies. Unlike traditional prostheses, which rely on limited and predefined commands, this approach aims to develop intelligent prostheses that understand and respond to users' needs through natural language and multimodal inputs. The realization of this vision involves developing a control system capable of understanding and translating a wide array of natural language and multimodal inputs into actionable commands for prosthetic devices. This includes the creation of models that can extract and interpret features from both textual and multimodal data, ensuring devices not only follow user commands but also respond intelligently to the environment and user intent, thus marking a significant leap forward in prosthetic technology.

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective catwalk analysis, which is equipped with several key procedures, namely, catwalk understanding, collective organization and analysis, and report generation. By posing and exploring such an open-ended, complex and domain-specific task of FashionReGen, it is able to test the general capability of LLMs in fashion domain. It also inspires the explorations of more high-level tasks with industrial significance in other domains. Video illustration and more materials of GPT-FAR can be found in https://github.com/CompFashion/FashionReGen.

Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Large language models can potentially be used as a foundation technology for supporting caregivers as educational tools or as adjunct to care. This study aimed to develop a reliable Caregiving Language Model (CaLM) by using FMs and a caregiving knowledge base, develop an accessible CaLM using a small FM that requires fewer computing resources, and evaluate the performance of the model compared to a large FM. We developed CaLM using the Retrieval Augmented Generation (RAG) framework combined with FM fine-tuning for improving the quality of FM answers by grounding the model on a caregiving knowledge base. We used two small FMs as candidates for the FM of CaLM (LLaMA-2 and Falcon with 7B parameters) and larger FM GPT-3.5 as a benchmark. We developed the caregiving knowledge base by gathering various types of documents from the Internet. In this study, we focused on caregivers of individuals with Alzheimer's Disease Related Dementias. We evaluated the models' performance using the benchmark metrics commonly used in evaluating language models and their reliability to provide accurate references with the answers. The RAG framework improved the performance of all FMs used in this study across all measures. As expected, the large FM performed better than small FMs across all metrics. The most interesting result is that small fine-tuned FMs with RAG performed significantly better than GPT 3.5 across all metrics. The fine-tuned LLaMA-2 small FM performed better than GPT 3.5 (even with RAG) in returning references with the answers. The study shows that reliable and accessible CaLM can be developed by using small FMs with a knowledge base specific to the caregiving domain.