GPT-4 image processing and conversational AI combine with TTS to make visual content accessible for the blind and low-vision community.
Google is already testing its Med-PaLM 2 AI chat technology at at the Mayo Clinic and other hospitals..
Link: https://www.nature.com/articles/s41746-023-00873-0
Title: The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare
Author(s): Bertalan Meskó & Eric J. Topol
Word count: 2,222
Estimated average read time: 10 minutes
Summary: This article emphasizes the need for regulatory oversight of large language models (LLMs) in healthcare. LLMs, such as GPT-4 and Bard, have the potential to revolutionize healthcare, but they also pose risks that must be addressed. The authors argue for differentiated regulation of LLMs in comparison to other AI-based medical technologies due to their unique characteristics and challenges.
The article discusses the scale, complexity, hardware requirements, broad applicability, real-time adaptation, societal impact, and data privacy concerns associated with LLMs. It highlights the need for a tailored regulatory approach that considers these factors. The authors also provide insights into the current regulatory landscape, particularly in the context of the United States' Food and Drug Administration (FDA), which has been adapting its framework to address AI and machine learning technologies in medical devices.
The authors propose practical recommendations for regulators, including the creation of a new regulatory category for LLMs, guidelines for deployment, consideration of future iterations with advanced capabilities, and focusing on regulating the companies developing LLMs rather than each individual model.
Evaluation for Applicability to Applications Development: This article provides valuable insights into the challenges and considerations regarding regulatory oversight of large language models in healthcare. While it specifically focuses on healthcare, the principles and recommendations discussed can be applicable to application development using large language models or generative AI systems in various domains.
Developers working on applications utilizing large language models should consider the potential risks and ethical concerns associated with these models. They should be aware of the need for regulatory compliance and the importance of transparency, fairness, data privacy, and accountability in their applications.
The proposed recommendations for regulators can also serve as a guide for developers, helping them shape their strategies for responsible and compliant development of applications using large language models. Understanding the regulatory landscape and actively addressing potential risks and challenges can lead to successful deployment and use of these models in different applications.
The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that ...
Title: The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare
Author(s): Bertalan Meskó & Eric J. Topol Word count: 2,222
Estimated average read time: 10 minutes
Summary: This article highlights the need for regulatory oversight of large language models (LLMs), such as GPT-4 and Bard, in healthcare settings. LLMs have the potential to transform healthcare by facilitating clinical documentation, summarizing research papers, and assisting with diagnoses and treatment plans. However, these models come with significant risks, including unreliable outputs, biased information, and privacy concerns.
The authors argue that LLMs should be regulated differently from other AI-based medical technologies due to their unique characteristics, including their scale, complexity, broad applicability, real-time adaptation, and potential societal impact. They emphasize the importance of addressing issues such as transparency, accountability, fairness, and data privacy in the regulatory framework.
The article also discusses the challenges of regulating LLMs, including the need for a new regulatory category, consideration of future iterations with advanced capabilities, and the integration of LLMs into already approved medical technologies.
The authors propose practical recommendations for regulators to bring this vision to reality, including creating a new regulatory category, providing guidance for deployment of LLMs, covering different types of interactions (text, sound, video), and focusing on companies developing LLMs rather than regulating each iteration individually.
Evaluation for Applicability to Applications Development: This article provides valuable insights into the regulatory challenges and considerations related to large language models in healthcare. While it primarily focuses on the medical field, the principles and recommendations discussed can be applicable to applications development using large language models or generative AI systems in various domains.
Developers working on applications that utilize large language models should be aware of the potential risks and ethical concerns associated with these models. They should also consider the need for regulatory compliance and the importance of transparency, fairness, data privacy, and accountability in their applications.
Additionally, developers may find the proposed practical recommendations for regulators helpful in shaping their own strategies for responsible and compliant development of applications using large language models. Understanding the regulatory landscape and being proactive in addressing potential risks and challenges can lead to the successful deployment and use of these models in various applications.
OpenAI’s CEO, Sam Altman, was at a Senate subcommittee hearing recently. This was a moment many were waiting for, and to their credit, the senators seemed to have done their homework and fostered a…
https://archive.is/20230707210114/https://fortune.com/2023/07/07/andy-jassy-dismisses-microsoft-google-ai-hype-cycle-amazon-starting-substance-cycle/
https://archive.is/20230701101545/https://www.wsj.com/articles/grantham-warns-ai-boom-wont-prevent-market-bubble-from-bursting-fd7f5a98
As AI continues to make its presence felt in the music world, the Grammys have updated their rulebook. Now Recording Academy CEO Harvey Mason Junior has clarified rules around human vs AI-created work.
Google has updated its privacy policy to state that it can use publicly available data to help train its AI models..
https://archive.is/20230701122132/https://www.bloomberg.com/news/newsletters/2023-07-01/us-ai-chip-controls-blackrock-inflection-raises-1-3-billion-the-week-in-ai
https://archive.is/20230704074007/https://www.bloomberg.com/news/articles/2023-07-04/japan-dreams-of-ai-overtaking-nvidia-and-universal-basic-income
https://archive.is/20230704111538/https://www.bloomberg.com/news/articles/2023-07-04/us-to-curb-china-access-to-cloud-services-like-amazon-wsj-says
https://archive.is/20230630231426/https://www.bloomberg.com/news/articles/2023-06-30/artificial-intelligence-firms-hunt-for-san-francisco-offices
https://archive.is/20230703004526/https://www.bloomberg.com/news/articles/2023-07-03/will-ai-take-my-job-it-s-a-hot-topic-for-investors-this-summer#xj4y7vzkg
https://archive.is/20230703202352/https://www.bloomberg.com/news/articles/2023-07-03/bridgewater-s-greg-jensen-explains-how-the-world-s-biggest-hedge-fund-is-investing-in-ai#selection-3781.0-3781.88
Act as a prompt generator for ChatGPT. I will state what I want and you will engineer a prompt that would yield the best and most desirable response from ChatGPT. Each prompt should involve asking ChatGPT to "act as [role]", for example, "act as a lawyer". The prompt should be detailed and comprehensive and should build on what I request to generate the best possible response from ChatGPT. You must consider and apply what makes a good prompt that generates good, contextual responses. Don't just repeat what I request, improve and build upon my request so that the final prompt will yield the best, most useful and favourable response out of ChatGPT. Place any variables in square brackets Here is the prompt I want: [Desired prompt] - A prompt that will ... Ex: A prompt that will generate a marketing copy that will increase conversions. Start by asking the user what they want the prompt to be about.
