slator.com How GPT-4 and Text-to-Speech Make Visual Content More Accessible
GPT-4 image processing and conversational AI combine with TTS to make visual content accessible for the blind and low-vision community.
www.engadget.com Google is testing its medical AI chatbot at the Mayo Clinic | Engadget
Google is already testing its Med-PaLM 2 AI chat technology at at the Mayo Clinic and other hospitals..
Curious Replay for Model-based Adaptation
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
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
www.nature.com The imperative for regulatory oversight of large language models (or generative AI) in healthcare - npj Digital Medicine
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, accountabilit
George Hotz: Sam Altman won't tell you that GPT-4 has 220B parameters and is 16-way mixture model with 8 sets of weights
Why the Senate may be accidentally giving carte blanche to potentially unethical AI
thehill.com Why the Senate may be accidentally giving carte blanche to unethical AI?
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…
Andy Jassy dismisses Microsoft and Google A.I. ‘hype cycle’ and says Amazon is starting a ‘substance cycle’
Grantham Warns AI Boom Won’t Prevent Market Bubble From Bursting
Grammys boss Harvey Mason Junior clarifies AI policy and says 'music with AI-created elements is absolutely eligible for entry'
news.sky.com Grammys boss Harvey Mason Junior says music 'with AI-created elements' is 'absolutely eligible for entry'
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's updated privacy policy states it can use public data to train its AI models
www.engadget.com Google's updated privacy policy states it can use public data to train its AI models | Engadget
Google has updated its privacy policy to state that it can use publicly available data to help train its AI models..
US AI Chip Controls Don’t Deter BlackRock From Going All In: The Week in AI
Japan Dreams of AI, Overtaking Nvidia and Universal Basic Income
US to Curb China Access to Cloud Services Like Amazon, WSJ Says
GPT + MJ + Photoshop, “invent new emotions” by @jphilipp
Meta's "Threads", their twitter competitor app, shows up on the Apple app store
Meta's "Threads" aka their Twitter competitor, appears in Apple app store store with a release date of 7/7
Artificial Intelligence Companies Hunt for San Francisco Offices
Will AI Take My Job? What People Are Discussing Over The Holiday
Bridgewater’s Greg Jensen Explains How the World’s Biggest Hedge Fund Is Investing in AI
Best Prompt Generating/Refinement Prompt
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.
yea he does lol