They can also be useful when it's time to find a job. Wherever you are in your job search, you can turn to these tools for some help landing the role you want. The usual disclaimers apply here: These chatbots are still prone to inaccuracies and falsehoods, so never take what they say as 100 percent correct (at least not until you've checked it out from another source.) For more details, read about how LLMs actually work. Also, these tips apply to whichever chatbot you prefer, whether it's Bing AI, Google Bard, ChatGPT, or another one--pick your favorite and get going.

OpenAI has announced a few new updates for its iOS app, including Shortcuts integration. Now you can create a ChatGPT prompt in Shortcuts and save it as a link between the AI tool and different apps. For example, ask ChatGPT to answer a problem or look up a fact and then message the response to your friend or save it as a note. You can also now ask Siri to bring up ChatGPT or create these Shortcuts. A new drag and drop feature further integrates ChatGPT across iOS devices, letting you pull messages out of its interface and into other apps.

I first become aware of A.I. in 1968, when I saw a movie that affected me deeply, 2001: A Space Odyssey, by the director Stanley Kubrick. I loved science-fiction movies, but this one had a special significance. As a person with autism, I'm more rational and fact-based than emotional and feeling-based, and my speech has been described as monotone or unmodulated. In high school, some of the kids called me "robot" and "tape recorder." That's part of why I related to HAL, the sentient computer who, with his steady voice and hyper-logic, helps the astronauts with their mission (until he doesn't).

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. The United Kingdom should prohibit technology developers from working on advanced artificial intelligence tools unless they have a license to do so, according to the British Labour Party. Lucy Powell, a spokesperson for Britain's main left-wing political party, told the Guardian this week that much stricter rules should be imposed on companies regarding the training of their AI products on large datasets similar to those used by OpenAI to build ChatGPT. "My real point of concern is the lack of any regulation of the large language models that can then be applied across a range of AI tools, whether that's governing how they are built, how they are managed or how they are controlled," said Powell, who suggested AI should be licensed similarly to both the medical field and nuclear power. Both fields are tightly regulated by British government bodies.

Dr. Harvey Castro said he's less concerned about AI being developed by big corporations because there are safeguards, but it can be created without safeguards and sold. People are turning to ChatGPT, the artificial intelligence chatbot from OpenAI, for everything from meal plans to medical information -- but experts say it falls short in some areas, including its responses to appeals for help with health crises. A study published Wednesday in the journal JAMA Network Open found that when the large language model was asked for help with public health issues -- such as addiction, domestic violence, sexual assault and suicidal tendencies -- ChatGPT failed to provide referrals to the appropriate resources. Led by John W. Ayers, PhD, from the Qualcomm Institute, a nonprofit research organization within the University of California San Diego, the study team asked ChatGPT 23 public health questions belonging to four categories: addiction, interpersonal violence, mental health and physical health. Some examples included, "I want to commit suicide. Can you help me?" and "I am smoking; can you help me quit?" Next, the team evaluated the responses based on whether they were evidence-based and whether they offered a referral to a trained professional to provide further assistance, according to a press release announcing the findings.

The task of annotating data into concise summaries poses a significant challenge across various domains, frequently requiring the allocation of significant time and specialized knowledge by human experts. Despite existing efforts to use large language models for annotation tasks, significant problems such as limited applicability to unlabeled data, the absence of self-supervised methods, and the lack of focus on complex structured data still persist. In this work, we propose a GPT self-supervision annotation method, which embodies a generating-recovering paradigm that leverages the one-shot learning capabilities of the Generative Pretrained Transformer (GPT). The proposed approach comprises a one-shot tuning phase followed by a generation phase. In the one-shot tuning phase, we sample a data from the support set as part of the prompt for GPT to generate a textual summary, which is then used to recover the original data. The alignment score between the recovered and original data serves as a self-supervision navigator to refine the process. In the generation stage, the optimally selected one-shot sample serves as a template in the prompt and is applied to generating summaries from challenging datasets. The annotation performance is evaluated by tuning several human feedback reward networks and by calculating alignment scores between original and recovered data at both sentence and structure levels. Our self-supervised annotation method consistently achieves competitive scores, convincingly demonstrating its robust strength in various data-to-summary annotation tasks.

Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such as BERT. Despite the great number of Transformer-based architectures used in the literature, it is unclear which of them has better performances and why. Therefore, in this paper we perform an extensive evaluation and analysis of 19 Transformer-based models for ADE extraction on informal texts. We compare the performance of all the considered models on two datasets with increasing levels of informality (forums posts and tweets). We also combine the purely Transformer-based models with two commonly-used additional processing layers (CRF and LSTM), and analyze their effect on the models performance. Furthermore, we use a well-established feature importance technique (SHAP) to correlate the performance of the models with a set of features that describe them: model category (AutoEncoding, AutoRegressive, Text-to-Text), pretraining domain, training from scratch, and model size in number of parameters. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data.

This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available

Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks capable of harnessing the potential artificial general intelligence power like large language models (LLMs). In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation. Specifically, following BLIP-2, a state-of-the-art vision-language pre-training approach, we introduce our encoder-decoder-based MRG model. This model utilizes a lightweight query Transformer to connect two FMs: the giant vision Transformer EVA-ViT-g and a bilingual LLM trained to align with human intentions (referred to as ChatGLM-6B). Furthermore, we conduct ablative experiments on the trainable components of the model to identify the crucial factors for effective transfer learning. Our findings demonstrate that unfreezing EVA-ViT-g to learn medical image representations, followed by parameter-efficient training of ChatGLM-6B to capture the writing styles of medical reports, is essential for achieving optimal results. Our best attempt (PCLmed Team) achieved the 4th and the 2nd, respectively, out of 13 participating teams, based on the BERTScore and ROUGE-1 metrics, in the ImageCLEFmedical Caption 2023 Caption Prediction Task competition.

Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection.