Lifeboat Foundation

The rise of artificial general intelligence — now seen as inevitable in Silicon Valley — will bring change that is “orders of magnitude” greater than anything the world has yet seen, observers say. But are we ready?

AGI — defined as artificial intelligence with human cognitive abilities, as opposed to more narrow artificial intelligence, such as the headline-grabbing ChatGPT — could free people from menial tasks and usher in a new era of creativity.

But such a historic paradigm shift could also threaten jobs and raise insurmountable social issues, experts warn.

Animal ‘personalities’ are forcing scientists to rethink basic research.

There is increasing demand for synthetic DNA. However, our ability to make, or write, DNA lags behind our ability to sequence, or read, it. This Review discusses commercialized DNA synthesis technologies in the pursuit of closing the DNA writing gap.

Using living organisms to ensure water safety.

There’s a lot of articles written about how tap water in Warsaw is constantly tested by a small team of clams. It felt like a hoax to me: so I went to find out. ▪ Thanks to MPWiK Warsaw: https://www.mpwik.com.pl/

Continue reading “Is Poland’s tap water really protected by clams?” »

Recently biologists discovered how to generate new neurons in the adult brain. This is an incredible breakthrough that has enormous potential to revolutionize neurodegenerative disease research. By generating genetically-mutated mice with a unique gene that activates dormant neural stem cells, scientists were able to generate new neurons in the brain. For years, scientists have been searching for ways to promote the growth of new neurons in the brain, especially in individuals with neurodegenerative diseases such as Alzheimer’s and Parkinson’s. This new discovery could lead to new treatments and therapies that could help to restore brain function and improve the quality of life for millions of people around the world.

Leslie Samuel, founder of Interactive Biology, gives some context for the importance of genetic trading between organisms for scientific research, and notes how the loss of nerve cells in the brain is one of the hallmarks of neurodegenerative diseases. The ability to generate new neurons in the adult brain could be a game-changer in the field of neurology.

Leslie’s Thoughts

LoRA: Low-Rank Adaptation of Large Language Model🚀 Introducing ChatLLaMA: Your Personal AI Assistant Powered by LoRA! 🤖 🌟 We’re excited to announce that you can now create custom personal assistants that run directly on your GPUs! ChatLLaMA utilizes LoRA, trained on Anthropic’s HH dataset, to model seamless convos between an AI assistant & users. Plus, the RLHF version of LoRA is coming soon! 🔥 📚 Know any high-quality dialogue-style datasets? Share them with us, and we’ll train ChatLLaMA on them! 🌐 ChatLLaMA is currently available for 30B and 13B models, with the 7B version coming soon. 🤔 Have questions or need help setting up ChatLLaMA? Join our Discord group & ask! Let’s revolutionize AI-assisted conversations together! 🌟 Disclaimer: — trained for research, — no foundation model weights, — the post was ran through gpt4 to make it more coherent.

Language models (LMs) have been extensively utilized for various aided writing activities, including text summarization, code completion, and paraphrasing. LMs are effective tools for creating both natural and programming languages. Most LMs must be able to develop the next token from the sequence of earlier tokens to be useful in a wide range of applications. Due to the significance of this operation, pretraining has concentrated on improving the model’s perplexity in predicting the next token given the last tokens. However, they do have extra information that they are not using during pretraining.

For instance, they entirely disregard the following tokens while training the model to predict one token and only condition on the prefix (prior tokens) (suffix). There are alternative approaches to include the suffix in pretraining that have yet to be discussed in the literature, even though it cannot be utilized as an input to the model. They want to increase the pretraining data’s usefulness while maintaining the underlying LM’s autoregressive properties. Their strategy calls for more modeling, which at first glance could appear useless. After all, an autoregressive left-to-right LM is a primary artifact created during pretraining, and the pretraining aim closely resembles how the LM is used.

Yet, there are two reasons to explore different training objectives. Data efficiency is discussed in the first. The LM is trained using a sparse, inexpensive signal that generates a probability distribution over all potential next-token selections. However, it is only supervised using the actual next token from the training set. What if a more intense kind of supervision was used during training, where the probability distribution for the next tokens was compared to a different probability distribution? The second justification relates to other connected responsibilities. For instance, the user may prefer to fill in or edit an existing sequence of tokens in many real-world settings rather than creating text entirely from scratch.

Hundreds of books created by artificial intelligence (AI) tool ChatGPT are flooding Amazon, showing the way the technology can be adopted to produce books at scale.

Nearly 300 titles that claim to be written solely by or in collaboration with ChatGPT are listed on the online bookseller’s website, across a range of genres including non-fiction, fantasy and self-help.

Many of the books appear to be published using Amazon’s Kindle Direct Publishing tool, which allows users to quickly create, publish and promote their work using a modern-day equivalent of the self-publishing model.

Everything, everywhere, all at once? Forget it – there are ideas about time far more outrageous than simply the multiverse “Who knows where the time goes?” asked Sandy Denny, though as good a question is where it came from in the first place.