The AI powering ChatGPT may provide completely different answers to the same mathematical problems over time. Those findings from recent experiments have fuelled an ongoing debate about whether the AI chatbot's performance is getting worse – and have spurred the firm behind it, OpenAI, to reassure customers that applications built on ChatGPT will not continually break. "The takeaway message is that the behaviour of the'same' large language model can change substantially," says Lingjiao Chen at Stanford University in California.

On a Monday morning in April, Sam Altman sat inside OpenAI's San Francisco headquarters, telling me about a dangerous artificial intelligence that his company had built but would never release. His employees, he later said, often lose sleep worrying about the AIs they might one day release without fully appreciating their dangers. With his heel perched on the edge of his swivel chair, he looked relaxed. The powerful AI that his company had released in November had captured the world's imagination like nothing in tech's recent history. There was grousing in some quarters about the things ChatGPT could not yet do well, and in others about the future it may portend, but Altman wasn't sweating it; this was, for him, a moment of triumph. Check out more from this issue and find your next story to read. In small doses, Altman's large blue eyes emit a beam of earnest intellectual attention, and he seems to understand that, in large doses, their intensity might unsettle. In this case, he was ...

Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve the above-mentioned problem, we employed generative artificial intelligence techniques based on large-scale language models to summarize abstracts of scientific papers. Our objective is to investigate the potential of generative artificial intelligence in diminishing the cognitive load experienced by practitioners, thus exploring its ability to alleviate mental effort and burden. The study participants were provided with two use cases related to preventive care and behavior change, simulating a search for new scientific literature. The study included 113 university students from Slovenia and the United States randomized into three distinct study groups. The first group was assigned to the full abstracts. The second group was assigned to the short abstracts generated by AI. The third group had the option to select a full abstract in addition to the AI-generated short summary. Each use case study included ten retrieved abstracts. Our research demonstrates that the use of generative AI for literature review is efficient and effective. The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts. The results, however, also show significantly lower accuracy in extracted knowledge in cases where full abstract was not available. Such a disruptive technology could significantly reduce the time required for healthcare professionals to keep up with the most recent scientific literature; nevertheless, further developments are needed to help them comprehend the knowledge accurately.

Japanese education services company Benesse will offer a new service to help elementary school students with their research projects using generative artificial intelligence during the summer break. The service, which will be provided for free on its website for parents, will make suggestions and offer tips to help students search research themes and compile their findings, the company said in a recent press release. For example, if one asks "How can I study the biology of dinosaurs?" the AI would give such advice as "How about finding out what they ate?" without giving exact answers, the company said. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acquire. In this work, we approach text-to-audio synthesis using unlabeled videos and pretrained language-vision models. We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining (CLIP) model. At test time, we first explore performing a zero-shot modality transfer and condition the diffusion model with a CLIP-encoded text query. However, we observe a noticeable performance drop with respect to image queries. To close this gap, we further adopt a pretrained diffusion prior model to generate a CLIP image embedding given a CLIP text embedding. Our results show the effectiveness of the proposed method, and that the pretrained diffusion prior can reduce the modality transfer gap. While we focus on text-to-audio synthesis, the proposed model can also generate audio from image queries, and it shows competitive performance against a state-of-the-art image-to-audio synthesis model in a subjective listening test. This study offers a new direction of approaching text-to-audio synthesis that leverages the naturally-occurring audio-visual correspondence in videos and the power of pretrained language-vision models.

Meta's global policy head, Sir Nick Clegg, has backed calls for an international agency to guide the regulation of artificial intelligence if it becomes autonomous, saying governments globally should avoid "fragmented" laws around the technology. But Clegg downplayed suggestions of payment for content creators like artists or news outlets whose work is scraped to teach chatbots and generative AI, suggesting such information would be available under fair use arrangements. "Creators who lean in to using this technology, rather than trying to block it or slow it down or prevent it from drawing on their own creative output, will in the long run be better placed than those who set their face against this technology," Clegg told Guardian Australia. "We believe we're using [data] entirely in line with existing law. A lot of this data is being transformed in the way it's being deployed by these generative AI models. In the long run, I can't see how you put the genie back in the bottle, given that these models do use publicly available information across the internet, and not unreasonably so."

Source: OpenAI's DALL·E 2 with prompt "a hyperrealistic picture of a robot reading the news on a laptop at a coffee shop" Welcome to the 2nd edition of Robo-Insight, a biweekly robotics news update! In this post, we are excited to share a range of remarkable advancements in the field, showcasing progress in hazard mapping, surface crawling, pump controls, adaptive gripping, surgery, health assistance, and mineral extraction. In the domain of hazard mapping, researchers have developed a collaborative scheme that utilizes both ground and aerial robots for hazard mapping of contaminated areas. The team improved the quality of density maps and lowered estimation errors by using a heterogeneous coverage control technique. In comparison to homogeneous alternatives, the strategy optimizes the deployment of robots based on each one's unique characteristics, producing better estimation values and shorter operation times.

When OpenAI released a ChatGPT app for the iPhone in May, it promised that Android users will get theirs soon. Now, the company has announced that ChatGPT for Android is rolling out to users sometime next week. Moreover, its Google Play listing is already up, and users can pre-register to get it as soon as it becomes available. It's unclear if the app will initially only be available in the US like the iPhone app, but I was able to pre-order it from Asia. OpenAI expanded the iOS app's reach to more regions just a few days after it was released, so the Android app will most likely be accessible in other countries soon even if it does launch only in the US.

Washington – U.S. President Joe Biden evoked AI's "enormous" risk and promise Friday at a White House meeting with tech leaders who committed to guarding against everything from cyberattacks to fraud as the sector revolutionizes society. "It is astounding," Biden said, highlighting AI's "enormous, enormous promise of both risk to our society and our economy and our national security, but also incredible opportunities." Standing alongside top representatives from Amazon, Anthropic, Google, Inflection, Meta, Microsoft and OpenAI, Biden said the cutting-edge companies had made commitments to "guide responsible innovation" as AI rips ever deeper into personal and business life. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Advancements in generative artificial intelligence (AI) and large language models (LLMs) have fueled the development of many educational technology innovations that aim to automate the often time-consuming and laborious tasks of generating and analysing textual content (e.g., generating open-ended questions and analysing student feedback survey) (Kasneci et al., 2023; Wollny et al., 2021; Leiker et al., 2023). LLMs are generative artificial intelligence models that have been trained on an extensive amount of text data, capable of generating human-like text content based on natural language inputs. Specifically, these LLMs, such as Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) and Generative Pre-trained Transformer (GPT) (Brown et al., 2020), utilise deep learning and self-attention mechanisms (Vaswani et al., 2017) to selectively attend to the different parts of input texts, depending on the focus of the current tasks, allowing the model to learn complex patterns and relationships among textual contents, such as their semantic, contextual, and syntactic relationships (Min et al., 2021; Liu et al., 2023). As several LLMs (e.g., GPT-3 and Codex) have been pre-trained on massive amounts of data across multiple disciplines, they are capable of completing natural language processing tasks with little (few-shot learning) or no additional training (zero-shot learning) (Brown et al., 2020; Wu et al., 2023). This could lower the technological barriers to LLMs-based innovations as researchers and practitioners can develop new educational technologies by fine-tuning LLMs on specific educational tasks without starting from scratch (Caines et al., 2023; Sridhar et al., 2023). The recent release of ChatGPT, an LLMs-based generative AI chatbot that requires only natural language prompts without additional model training or fine-tuning (OpenAI, 2023), has further lowered the barrier for individuals without technological background to leverage the generative powers of LLMs. Although educational research that leverages LLMs to develop technological innovations for automating educational tasks is yet to achieve its full potential (i.e., most works have focused on improving model performances (Kurdi et al., 2020; Ramesh and Sanampudi, 2022)), a growing body of literature hints at how different stakeholders could potentially benefit from such innovations.