Lifeboat Foundation

Summary: AI models trained on MRI data can now distinguish brain tumors from healthy tissue with high accuracy, nearing human performance. Using convolutional neural networks and transfer learning from tasks like camouflage detection, researchers improved the models’ ability to recognize tumors.

This study emphasizes explainability, enabling AI to highlight the areas it identifies as cancerous, fostering trust among radiologists and patients. While slightly less accurate than human detection, this method demonstrates promise for AI as a transparent tool in clinical radiology.

Enterprise LLM usage.

AI spend in 2024 has gone from $2.3B to $13.8B, a 6x increase.

The enterprise AI landscape is being rewritten in real time. We surveyed 600 enterprise IT decision-makers to reveal the emerging winners and losers.

Summary: The Human Cell Atlas (HCA) consortium has published over 40 studies revealing groundbreaking insights into human biology through large-scale mapping of cells. These studies cover diverse areas such as brain development, gut inflammation, and COVID-19 lung responses, while also showcasing the power of AI in understanding cellular mechanisms.

By profiling over 100 million cells from 10,000 individuals, HCA is building a “Google Maps” for cell biology to transform diagnostics, drug discovery, and regenerative medicine. The initiative emphasizes diversity, including underrepresented populations, to ensure a globally inclusive understanding of health and disease.

In 2024, the Kavli Institute of Brain and Mind will reach its 20th anniversary. To celebrate this milestone, we hosted a special symposium on Monday, October 28, 2024 at Salk Institute-The Generative Mind: Biological and Artificial Intelligence. Please enjoy the presentation \.

AlphaQubit: an AI-based system that can more accurately identify errors inside quantum computers.

AlphaQubit is a neural-network based decoder drawing on Transformers, a deep learning architecture developed at Google that underpins many of today’s large language models. Using the consistency checks as an input, its task is to correctly predict whether the logical qubit — when measured at the end of the experiment — has flipped from how it was prepared.

We began by training our model to decode the data from a set of 49 qubits inside a Sycamore quantum processor, the central computational unit of the quantum computer. To teach AlphaQubit the general decoding problem, we used a quantum simulator to generate hundreds of millions of examples across a variety of settings and error levels. Then we finetuned AlphaQubit for a specific decoding task by giving it thousands of experimental samples from a particular Sycamore processor.

Continue reading “AlphaQubit tackles one of quantum computing’s biggest challenges” »

The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.

My Links 🔗
➡️ Subscribe: / @wesroth.
➡️ Twitter: https://twitter.com/WesRothMoney.
➡️ AI Newsletter: https://natural20.beehiiv.com/subscribe.

Continue reading “Sam Altman ‘Level 4 Innovator is MUCH closer’ | PLUS ‘Unsupervised Sentiment Neuron’ breakthrough” »

We converted the calculations in Morgan Levine and Steve Horvath’s famous research paper on phenotypic age into a free biological age calculator.

It’s a great (cheap) alternative to $400 epigenetic age tests and means you can test more frequently to see if longevity interventions are actually…

This free biological age calculator is based on a pioneering paper by longevity experts Dr. Morgan Levine and Dr. Steve Horvath.

Continue reading “Free Biological Age Calculator” »

Collaboration with Google and Qualcomm brings Samsung’s smart glasses to market.

As artificial intelligence (AI) becomes more common and sophisticated, its effects on human lives and societies raises new questions. A new paper published in The Quarterly Review of Biology posits how these new technologies might affect human evolution.

In “How Might Artificial Intelligence Influence Human Evolution?” author Rob Brooks considers the inevitable but incremental evolutionary consequences of AI’s everyday use and human-AI interactions—without “dramatic but perhaps unlikely events, including possibilities of human annihilation, assimilation, or enslavement.”

In the paper, Brooks considers (“often with considerable speculation”) some possible forms of human-AI interaction and the evolutionary implications of such interactions via natural selection, including forms of selection that resemble the inadvertent and deliberate selection that occurred when humans domesticated crops, livestock, and companion animals.