Lifeboat Foundation

Since the public release of OpenAI’s ChatGPT, artificial intelligence (AI) has quickly become a driving force in innovation and everyday life, sparking both excitement and concern. AI promises breakthroughs in fields like medicine, education, and energy, with the potential to solve some of society’s toughest challenges. But at the same time, fears around job displacement, privacy, and the spread of misinformation have led many to call for tighter government control.

Many are now seeking swift government intervention to regulate AI’s development in the waning “lame duck” session before the inauguration of the next Congress. These efforts have been led by tech giants, including OpenAI, Amazon, Google, and Microsoft, under the guise of securing “responsible development of advanced AI systems” from risks like misinformation and bias. Building on the Biden administration’s executive order to create the U.S. Artificial Intelligence Safety Institute (AISI) and mandate that AI “safety tests,” among other things, be reported to the government, the bipartisan negotiations would permanently authorize the AISI to act as the nation’s primary AI regulatory agency.

The problem is, the measures pushed by these lobbying campaigns favor large, entrenched corporations, sidelining smaller competitors and stifling innovation. If Congress moves forward with establishing a federal AI safety agency, even with the best of intentions, it risks cementing Big Tech’s dominance at the expense of startups. Rather than fostering competition, such regulation would likely serve the interests of the industry’s largest corporations, stifling entrepreneurship and limiting AI’s potential to transform America—and the world—for the better. The unintended consequences are serious: slower product improvement, fewer technological breakthroughs, and severe costs to the economy and consumers.

A 100+ page detailed analysis on 18 LLMs for embodied decision making.

ArXiv: https://arxiv.org/abs/2410.07166 Website: https://embodied-agent-interface.github.io.

The research focuses on evaluating how well Large Language Models (LLMs) can make decisions in environments where physical actions are…

Continue reading “Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making” »

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers.

Oasis: A Universe in a Transformer — try it out now:
https://oasis.decart.ai/welcome.

Continue reading “Crazy AI Learned Minecraft — Try It Out For Free!” »

Sotherby’s says the Ai-Da Robot work “marks a moment in the history of modern and contemporary art”

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Healthcare start-up Tennr reports a $37 million Series B fundraising round – nine months after raising an $18 million Series A funding round. The young company plans to use machine learning in order to improve patient record keeping, prevent medical error and reduce waiting times for patients. The Series B round was led by Lightspeed Ventures, together with existing investors Andreessen Horowitz and Foundation Capital, and raises the total amount of money raised by the company to $61 million.

Several US healthcare providers have already begun working with the firm, both private doctors’ practices and major clinics and hospitals. These providers receive referrals from primary care providers in different formats to register patients and document their case history. Since providers often compete with each other for patients, there is no standard format used in the industry nationwide, with many companies relying on handwritten documents, messages from private email accounts, and some even using such outdated technology as fax machines. This causes significant delays in the provision of treatment, and increases the likelihood that patients will be misdiagnosed, referred to the wrong clinic or denied access to a specialist whose expertise they require.

Tennr has made it its mission to solve these problems by automating this process: it extracts the relevant information from referrals, no matter what form they’re received in or what technology was used to generate the documents, which not only enables more rapid response times but also creates an unprecedented level of standardization in the medical field, nationally and in the future perhaps globally as well. The company has already processed tens of millions of referrals for patients in the USA, ensuring an appointment with a specialist in a few hours, instead of having to wait several weeks and at times months.

Bruce posted a really good post related to this.

An emotionally-manipulable AI in the hands of the Pentagon and CIA? This’ll surely end well.

Amazon.com Inc. is ready for another hefty investment in the San Francisco-based generative artificial intelligence startup Anthropic, one of the main rivals of OpenAI.

Yes, AI and music is here. Its free, and you don’t even have to sing anymore.

Suno AI’s new “Personas” feature, which lets you save unique vocal styles, vibes, and music elements as customizable Personas! With this game-changing tool, you can preserve the exact feel and voice of any track and reuse it in multiple songs. In this video, I’ll walk you through:
#sunoai #aimusicgenerator #sunoaimusic.
- Creating a Persona from any song in your library.
- Customizing Persona names, adding images, and setting privacy options.
- Applying Personas in different genres, from pop to heavy metal.
- Tips on using Personas for various music styles and languages, including Turkish and Hindi songs.
- How to use your own vocals by uploading an audio sample.
- Plus, I’ll give you details on the exciting Timbaland remix contest happening on Suno AI!
Don’t miss this guide if you’re ready to expand your music creation with personalized vocal styles and make the most of Suno AI’s features.
Link to my Persona : https://suno.com/persona/95b06068-6af3-407d-a7c9-f2b4c756c783

Continue reading “Suno AI’s New ‘Personas’ Feature is AMAZING! — Full walkthrough” »