Lifeboat Foundation

New strain survives powdery mildew, a costly disease, without side effects.

The end, when it came, came suddenly. An asteroid or comet 10 kilometres across slammed into the Gulf of Mexico, gouging a 180-kilometre crater and unleashing firestorms, eruptions and mega-tsunamis across the globe. The debris blocked out the Sun for years. The dinosaurs – and the other 75 per cent of life that went down with them – didn’t stand a chance.

The story of the demise of the dinosaurs 65 million years ago is well known. But that of their origin is less so. Dinosaurs were the dominant animals on land for at least 135 million years, the longest reign of any group. Had the impact not happened, they might still be in control. Where did these magnificent beasts come from?

Upgraded Dragonfly will study how dark matter shapes diffuse galaxies and faint tendrils of gas.

April, 2022

Sean Carroll (Caltech and Santa Fe Institute)
https://simons.berkeley.edu/events/causality-program-externa…-institute.
Causality.

Continue reading “The Arrow of Time in Causal Networks” »

Discovering a system’s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.

A research team from DeepMind, Mila – University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper Learning to Induce Causal Structure, proposing a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs. The team’s proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.

The team summarizes their main contributions as:

And an AI could generate a picture of a person from scratch if it wanted or needed to. its only a matter of time before someone puts it all together. 1. AI writes a script. 2. AI generates pictures of a cast (face/&body). 3. AI animates pictures of the cast into scenes. 4. it cant create voices from scratch yet, but 10 second audio sample of a voice is enough for it to make voices say anything; AI voices all the dialog. And, viola, you ve reduced TV and movie production costs by 99.99%. Will take place by 2030.

Google’s PHORUM AI shows how impressive 3D avatars can be created just from a single photo.

Continue reading “Google AI generates believable 3D avatars from a single photo” »

As the early universe cooled shortly after the Big Bang, bubbles formed in its hot plasma, triggering gravitational waves that could be detectable even today, a new study suggests.

For some time, physicists have speculated that a phase transition took place in the early universe shortly after the Big Bang. Phase transition is a change of form and properties of matter that usually accompanies temperature changes such as the evaporation of water into vapor or the melting of metal. In the young and fast expanding universe, something similar likely took place as the plasma, which was filling the space at that time, cooled down.

With the massive degree of progress in AI over the last decade or so, it’s natural to wonder about its future – particularly the timeline to achieving human (and superhuman) levels of general intelligence. Ajeya Cotra, a senior researcher at Open Philanthropy, recently (in 2020) put together a comprehensive report seeking to answer this question (actually, it answers the slightly different question of when transformative AI will appear, mainly because an exact definition of impact is easier than one of intelligence level), and over 169 pages she lays out a multi-step methodology to arrive at her answer. The report has generated a significant amount of discussion (for example, see this Astral Codex Ten review), and seems to have become an important anchor for many people’s views on AI timelines. On the whole, I found the report added useful structure around the AI timeline question, though I’m not sure its conclusions are particularly informative (due to the wide range of timelines across different methodologies). This post will provide a general overview of her approach (readers who are already familiar can skip the next section), and will then focus on one part of the overall methodology – specifically, the upper bound she chooses – and will seek to show that this bound may be vastly understated.

Part 1: Overview of the Report

In her report, Ajeya takes the following steps to estimate transformative AI timelines:

Tracking travel patterns of subatomic particles called muons helps reveal the inner worlds of pyramids, volcanoes and more.