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4 minutes ago, villakram said:

Further analysis appears to show that the Phosphine detection in Venus is bunk or at a minimum is much less significant than the claimed detection (somewhere close to 2 sigma vs the claimed >10 sigma). Science!

Yes that's how science works.

Another win for the method.

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external-content.duckduckgo-5.jpg.3cd55e7edf41cf109edb879e0ec7f50f.jpg    Ray Harryhausen

external-content.duckduckgo-4.jpg.88783b620256a47294496a8ae6eea4b7.jpg

A mock up of the most amazing fossil ever? Triceratops and Tyrannosaurus in a death scrap. 

North Carolina Museum of Natural History hopes to have the real thing on display in 2022.

 

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Long read but interesting stuff:

DeepMind’s superhuman intelligence

Quote

The solving of a 50-year scientific problem takes computers a step closer to 'true creativity'

BY Tom Chivers
 

I am obsessed with DeepMind. I have been following their work for years now. It is DeepMind that makes me think the more grandiose claims about AI – it will reach human intelligence in my lifetime; it will transform life in ever more dramatic ways – could be true.

DeepMind, for those of you who don’t know, is a British AI company. It was founded by the endearingly nerdy Demis Hassabis, child chess prodigy and co-creator, as a teenager, of Theme Park, the classic, genre-defining, millions-selling BullFrog game. In 2014, DeepMind was bought by Google for half a billion dollars, and became world-famous two years later, when its game-playing AI AlphaGo defeated the world Go champion, Lee Sedol, four games to one.

Now its latest program, AlphaFold, has made a huge breakthrough in one of the great outstanding challenges of biology, the protein-folding problem. It is a huge deal from a biological point of view; but it is, perhaps, an even bigger deal from the point of view of how the science of the future gets done. And it is also another reminder that although DeepMind’s professed goals are ambitious to the point of being fantastical, it would be a brave punter who bet against them.

...

Hassabis, in the SMC briefing, compared the AlphaGo and AlphaFold breakthroughs by saying that the two both relied on something like human insight. With chess, there are something like 35 possible moves per turn, so to look ahead two moves you need to examine 35 x 35 moves (1,225); to look ahead five moves, it’s 52 million. With a powerful computer, you can do this kind of “brute-force” computing for a few moves, although chess programs still need to be intelligent as well as powerful.

But with Go, there are something like 200 possible moves per turn, and brute force is much less useful. Human Go players rely much more on intuition than human chess players do – this board position feels strong, in some wordless and ill-defined way; this board position feels weak. AlphaGo developed some sort of equivalent to this insight; it worked out what board positions and moves felt strong, with some kind of high-level pattern recognition, from playing hundreds of millions of games against itself.

It seems to have done something similar with protein folding. Again, computationally, it’s impossible to calculate every possibility; it’s too complex. But humans have turned out to be quite good at using their intuition to determine how proteins fold: some people became extremely good at the online computer game FoldIt, in which players tried to work out the shape of a protein from its sequence. There seem to be deep patterns that humans can pick up on, and AlphaFold can pick up on rather better. It learned this intuition by training on 170,000 known proteins and their sequences, in the same way that AlphaGo learnt from millions of games.

...

They haven’t achieved general AI with AlphaGo. But given that their system, with what seem (to my inexpert understanding) to be relatively minor changes, can become massively superhuman at chess, Go, StarCraft II, Atari games and now at the protein-folding problem, it is becoming increasingly inaccurate to refer to it as “narrow”, as well.

It’ll be interesting, now, to see how DeepMind use this technology — presumably they’ll want to monetise it, and drug discoveries are (as we’ve seen recently) lucrative things. But it’ll be even more interesting to see whether this is just the start of an era of computers doing science. I’m obsessed with DeepMind because they might actually bring about the AI future they promise. The technological singularity and Theme Park: it’s quite the legacy to leave the world.

...rest on link

 

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I mentioned early in this thread how science is a blindspot for me. I can’t stress this enough so please be gentle with my first year of junior school question. 
 

Planets. Am I right that planets move in a circle round the sun? 
 

If that is right, are all the planets in the same line, so they revolve as if going around the Sun’s, for want of a better word, equator. 

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20 minutes ago, Seat68 said:

I mentioned early in this thread how science is a blindspot for me. I can’t stress this enough so please be gentle with my first year of junior school question. 
 

Planets. Am I right that planets move in a circle round the sun? 
 

If that is right, are all the planets in the same line, so they revolve as if going around the Sun’s, for want of a better word, equator. 

The planets don't quite orbit on the same level plane, but they are close. Mercury has the most tilted orbit but even that is only slightly off the basic level of the others. They also don't orbit in a perfect circle. They also all orbit in the same direction, anti clockwise, and rotate in the same direction except for Venus and Uranus.

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5 minutes ago, Chindie said:

The planets don't quite orbit on the same level plane, but they are close. Mercury has the most tilted orbit but even that is only slightly off the basic level of the others. They also don't orbit in a perfect circle. They also all orbit in the same direction, anti clockwise, and rotate in the same direction except for Venus and Uranus.

Oh thats a can of worms. I think I will head to youtube to see if there is a basic orbit video. It might be kids youtube. Thanks Chindie. 

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