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Google's new AI can predict flash floods 24 hours before they strike. How it works: > Uses Gemini to extract confirmed flood locations and times from global news > Builds a dataset of past events that never formally existed. > That dataset feeds a neural network > The neural...

23,274 views • 4 months ago •via X (Twitter)

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Climate change caused the catastrophic floods that tore through Central Texas over the last few days, killing at least 27 people, including nine children, and turning calm rivers into violent torrents, according to the media. At Camp Mystic in Kerr County, the Guadalupe River surged from about three feet to nearly 29 feet in just 90 minutes, sweeping away cabins, vehicles, and people with little or no warning. Climate change caused a warmer atmosphere, which holds more moisture, and unleashed it in increasingly intense bursts. Volumes of precipitation were extreme. They had less than a 0.1% chance of occurring in any given year, according to the New York Times. Texas climatologists warned that the frequency and severity of such events have already increased and could intensify by another 10 percent by 2036. In East Texas, “the number of days per year with at least two inches of rain or snow has increased by 20 percent since 1900,” noted the Times. But that tiny increase in precipitation doesn’t explain the floods or the deaths in Texas. Over the past century, global flood deaths declined by more than 80 percent. That happened not because nations reduced rainfall but because they have learned how to live with it. They built levees, dams, and drainage systems. They developed early warning systems and evacuated people before the water arrived. Kerr County in Texas failed to do any of these things. Despite its location in one of the most flood-prone regions in the United States, the county had no formal flood warning system in place. There were no sirens, no automated text alerts, no rapid evacuation protocol. The river rose, and families had no idea it was coming... Please subscribe now to watch the full video, read the full article, and support Public's award-winning reporting!

Michael Shellenberger

69,644 views • 1 year ago

Someone just stole from 37,000 $TAO holders and walked away clean. Not because they broke a rule. Because no rule existed to stop them. That changes today. Here is what happened. Covenant AI ran one of the most watched subnets on Bittensor. On April 10, the founder sold their entire position and disappeared. No warning. No announcement. No on-chain signal. By the time holders found out, the price had already moved against them. This was not a hack. This was not a bug. This was a founder legally exiting into their own community with zero accountability. Bittensor just closed that door permanently. The Conviction upgrade is live on mainnet today. Every emission a subnet owner earns now locks automatically the moment it arrives. They cannot touch it immediately. If they want to exit they must submit a public unlock transaction on-chain. Visible to every single person on the network the second it is submitted. Then the clock starts. 30 days before 63% of their position becomes spendable. 90 days before 95% is accessible. You now have a month of warning before the first dollar of sell pressure hits. A silent exit is no longer possible. The founder has to tell you they are leaving before they can leave. And it goes further. Any holder can now lock their tokens toward a different address they believe would run the subnet better. The address with the most locked support behind it becomes eligible to take over entirely. Bad owners can be replaced by the community before they do damage. Before today, subnet investing had one risk nobody could price. The person running it could vanish overnight, and you would never see it coming. That risk has been removed from the equation. Skin in the game used to be a promise. Now, it is a number on a block explorer that every holder can verify in real time. The people who understand what accountable infrastructure means for the value of $TAO will not need to explain themselves later. This is still early.

2xnmore

19,528 views • 3 months ago

Chamath: “Private equity in general is totally hosed.” 🏢🚨 “I think the history of this is important.” “There was a long standing belief that the best way to generate the best risk adjusted return was to have what's called a 60/40 allocation. 60% to bonds and 40% to equities.” “Over many years, especially when we artificially suppressed rates at zero, a lot of people started to move their allocations away from 60/40 and they started to make more and more investments further out on the risk curve.” “The biggest beneficiaries of that were venture capital, private equity, and hedge funds.” “The thing with private equity is that because rates were zero, they had an infinite amount of borrowing capacity at very little downside to them, and so they were able to manufacture returns much faster than venture capital and hedge funds could.” “So as a result, you had an initial group of people that were defining the asset class, making a ton of money, and then you had all these fast followers that said, ‘Well, if they're doing it, I can do it too.’” “But then always what happens is then you have this flood of laggards that just flood the zone.” “And it's these laggards that make it very difficult to generate returns because they start overpaying for assets, they start mismanaging and under managing the assets that they do own.” “That created a lot of competition, and so that's why you see this hockey stick graph.” “And when you see that kind of graph, it doesn't matter what asset class it is. The returns go to zero.” “And so we've seen this in venture capital. We've seen this in hedge funds. And we're now going to see this in private equity.”

The All-In Podcast

800,205 views • 10 months ago

*New Paper on AI & Democracy* Imagine two approaches to democracy. The one we have today, where citizens choose a professional politician to represent them and others. Or an augmented form of democracy, where each citizen controls a personalized AI that helps them participate in thousands of nuanced decisions. This second approach is the idea of Augmented Democracy I introduced six years ago at TED. In our latest paper we explore a simplified version of Augmented Democracy by combining off-the-shelf LLMs, such as ChatGPT, with data collected using a collaborative government program builder. This was an online game where people build a personalized government program using proposals extracted from the programs of the candidates of the 2022 presidential election in Brazil. So how accurate are these augmented forms of democracy? Imagine a user who gave us 40 answers. We can use the first 20 to fine-tune a model that we can test using the 20 answers the model didn’t see. We can then compare the accuracy of these predictions with the ones obtained by a “bundle” rule, which assumes that users that self-reported to be from the left or right always chose the proposals from the candidate that shares their political identity. This showed us that LLMs were more accurate at predicting policy preferences than the bundle rule, meaning that the preferences captured in the participation data were more nuanced than a left-right axis, and that the LLMs can capture some of that nuance. Also, the LLMs can choose among policies coming from the same candidate, which is something that we cannot do using a bundle rule. But can these LLMs help us complete the aggregate preferences of the population? Direct or unbundled forms of participation can result in incomplete data when people answer only a fraction of all questions. In our paper, we simulate this incompleteness by sampling the full dataset. We ask how close we can get to the full dataset by using a random sample, or a random sample augmented by predictions made by these LLMs. Overall, we find that LLM-augmented data gets much closer to the full dataset than a pure random sample. These results do not mean that augmented democracy technology is ready, but they means we are in a much better place to continue exploring this idea than six years ago. This paper was a collaborative effort with Jairo Gudino, PhD student at CCL at the University of Toulouse Capitole and Umberto Grandi from IRIT also at the University of Toulouse Capitole. We hope you find these results insightful!

César A. Hidalgo

26,915 views • 1 year ago