Lifeboat Foundation

You have probably heard of ChatGPT and DALLE-E, a new class of AI-powered software tools that can create new images or write text. The algorithm brings to life any idea you may have by putting together fragments of what it has previously seen — such as images annotated with meta-descriptions of what they represent — to generate original content from user-defined input. But now generative AI technology is revolutionizing drug discovery. Absci Corporation (Nasdaq: ABSI) is using machine learning to transform the field of antibody therapeutics: Absci has put out a press release today announcing the ability to create new antibodies with the use of generative AI.

GenerativeAI: You’ve seen it with images like DALL-E, you’ve seen it with text like ChatGPT. Now you can see it with protein design as well.

New study demonstrates the potential for machine learning to accelerate the development of innovative drug delivery technologies.

Scientists at the University of Toronto have successfully tested the use of machine learning models to guide the design of long-acting injectable drug formulations. The potential for machine learning algorithms to accelerate drug formulation could reduce the time and cost associated with drug development, making promising new medicines available faster.

The study will be published today (January 10, 2023) in the journal Nature Communications.

Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company’s AIs used to beat human players in games such as Go and chess.

The AI discovered algorithms that break decades-old records for computational efficiency, and the team’s findings, published on 5 October in Nature¹, could open up new paths to faster computing in some fields.

“It is very impressive,” says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. “This work demonstrates the potential of using machine learning for solving hard mathematical problems.”

That general question is still hard to answer, again in part because of those pesky errors. (Future quantum machines will compensate for their imperfections using a technique called quantum error correction, but that capability is still a ways off.) Is it possible to get the hoped-for runaway quantum advantage even with uncorrected errors?

Most researchers suspected the answer was no, but they couldn’t prove it for all cases. Now, in a paper posted to the preprint server arxiv.org, a team of computer scientists has taken a major step toward a comprehensive proof that error correction is necessary for a lasting quantum advantage in random circuit sampling — the bespoke problem that Google used to show quantum supremacy. They did so by developing a classical algorithm that can simulate random circuit sampling experiments when errors are present.

This post is also available in: עברית (Hebrew)

Hackers constantly improve at penetrating cyber defenses to steal valuable documents. So some researchers propose using an artificial-intelligence algorithm to hopelessly confuse them, once they break-in, by hiding the real deal amid a mountain of convincing fakes. The algorithm, called Word Embedding–based Fake Online Repository Generation Engine (WE-FORGE), generates decoys of patents under development. But someday it could “create a lot of fake versions of every document that a company feels it needs to guard,” says its developer, Dartmouth College cybersecurity researcher V. S. Subrahmanian.

If hackers were after, say, the formula for a new drug, they would have to find the relevant needle in a haystack of fakes. This could mean checking each formula in detail—and perhaps investing in a few dead-end recipes. “The name of the game here is, ‘Make it harder,’” Subrahmanian explains. “‘Inflict pain on those stealing from you.’”

A new algorithm is probably not efficient enough to crack current encryption keys — but that’s no reason for complacency, researchers say.

Great advice here. I follow much of it; my diet is good though there is a little bit of processed stuff in it. I do not drink or smoke. Interesting that Dr Stanfield has a rapamycin human trial going.

We have the tools available today to have a healthy 105-year lifespan, and I’ll summarise it all in this video. Plus at the end we’ll go through the emerging therapies in the longevity space that will push us towards a healthy 120-year lifespan.

Continue reading “How To Have A HEALTHY 105-Year Lifespan (Soon 120-Years)” »

An app developed by a Princeton University student helps determine if a text is written by a human or by the artificial intelligence tool ChatGPT.

Edward Tian, a senior cs major, stated in a tweet that his algorithm, GPTZero, can “quickly, efficiently detect whether an essay or article or any text is written by ChatGPT or human.” You can download the beta version of this app here.

ChatGPT is gaining popularity for its ability to generate coherent essays on any topic in seconds. Investors are interested in the technology, according to Wall Street Journal. OpenAI parent company could soon attract investment valued at $29 billion.

GOOGLE’S NEW SENSOR DENOISNG ALGORITHM brings yet another game changer for LOW LIGHT PHOTOGRAPHY. Within a handful of years, this will be added to other factors coming down the pipe, giving further impetus to a revolution in night vision. The video below speaks for itself. In effect, the system takes a series of images from different angles, exposures, and so on, then accurately reconstructs what is missing:

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers.

Continue reading “Google’s New AI Learned To See In The Dark! 🤖” »