Lifeboat Foundation

This sure didn’t take long — a ChatGPT clone for your desktop.

In this video I discuss New AI Model developed by researchers from Stanford and how AI models train each other to get better. Exciting times!

Continue reading “AI Inception 🤯 New Groundbreaking AI from Stanford” »

Robots in their current form contribute far more to our modern day life than you may realise. They may not be the sci-fi androids many imagine, but they’re hard at work doing tasks like building cars, or learning how to control nuclear fusion (opens in new tab). Only in recent years are we starting to see robots like you might have imagined as a kid, with Boston Dynamics’ creations doing all sorts of crazy stunts (opens in new tab) like dancing (opens in new tab) or guarding Pompeii (opens in new tab).

Robotics isn’t all about metal machines it turns out, and biohybrid robots may be part of our cyberpunk future too. It’s only been a few days since I was introduced to OSCAR, an artist’s rendition of a disgustingly meaty, pulsating flesh robot (opens in new tab). As wonderful and vivid as those videos are, it’s a good time to take a palette cleanser with a look at a real-world biohybrid robot.

LiGO is a new machine-learning technique developed by MIT researchers that cuts by about 50 percent the computational cost required to train large vision and language models.

When pondering the probability of discovering technologically advanced extraterrestrial life, the question that often arises is, if they’re out there, why h…

“AlphaFold was a huge advance in protein structure prediction. We were inspired by the advances they made, which led to a whole new wave of using deep learning,” said Professor David Baker, a biochemist and computational biologist at the University of Washington.

“The advantage of ESMFold is that it is very fast, and so can be used to predict the structures of a larger set of proteins than AlphaFold, albeit with slightly lower accuracy, similar to that of RoseTTAFold,” Dr. Baker said, referring to a tool that emerged from his lab in 2021.

DeepMind open-sourced the code for AlphaFold2, making it freely available to the community. Nearly all proteins known to science—about 214 million—can be looked up in the public AlphaFold Protein Structure Database. Meta’s ESM Metagenomic Atlas includes 617 million proteins.

A leading neuroscientist claims that a pong-playing clump of about a million neurons is “sentient”. What does that mean? Why did they teach a lab-grown brain to play pong? To study biological self-organization at the root of life, intelligence, and consciousness. And, according to their website, “to see what happens.”

CORRECTIONS/Clarifications:
- The cells aren’t directly frozen in liquid nitrogen — they are put in vials and stored in liquid nitrogen: https://www.atcc.org/products/pcs-201-010
- The sentience of some invertebrates, like octopuses, is generally agreed upon. Prominent scientists affirmed non-human consciousness in the Cambridge Declaration on Consciousness: https://philiplow.foundation/consciousness/

Continue reading “Lab-Grown Brain Learns Pong — Is This Biological Neural Network ‘Sentient’?” »

Stanford’s Alpaca AI performs similarly to the astonishing ChatGPT on many tasks – but it’s built on an open-source language model and cost less than US$600 to train up. It seems these godlike AIs are already frighteningly cheap and easy to replicate.

It’s no secret that OpenAI’s ChatGPT has some incredible capabilities—for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge.

But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model. Gathering so much data is an involved process in itself. Then come the monetary and environmental costs of running many powerful computers for days or weeks to train a model that may have billions of parameters.

“It’s been estimated that training models at the scale of what ChatGPT is hypothesized to run on could take millions of dollars, just for a single training run. Can we improve the efficiency of these training methods, so we can still get good models in less time and for less money? We propose to do this by leveraging smaller language models that have previously been trained,” says Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Presented by Intel.

Every day around the world, companies leverage artificial intelligence to accelerate scientific discovery, and transform consumer and business services. Regrettably, the employment of AI is not occurring evenly. McKinsey’s ‘The State of AI in 2022’ report documents that adoption of AI by organizations has stalled at 50%. AI leaders are pulling ahead of the pack. One reason is 53% of AI projects fail to get to production. As the benefits of AI to everyone are too great and the issues with AI being in the hands of only a few are too concerning, that it is an opportune time to survey the challenges of going from concept to deployment.