Nuclear power, AI weather forecasts, and Do crabs feel pain?

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New AI Weather Forecasting

0:44Hello, welcome to this week's 5 Live Science. I'm Chris Smith from The Naked Scientist. Coming up, the new AI system set to transform weather forecasting, we hope. A Cambridge student rules out a wet, young Venus. Apparently, it's always been a horrible place to exist. Can crabs and lobsters feel pain? Scientists show this week they'll take aspirin to ease discomfort, so maybe we shouldn't be dumping them in pans of boiling water. And a bit later on... The great thing about nuclear energy from a propulsion perspective is it doesn't need air.

1:19It doesn't need to breathe in real simple terms. So what we're able to do and have been able to do since the late 50s is harness the power of nuclear fission in a relatively small package, put it in a submarine, and what that actually ends up with is a fighting vessel that is only limited by its ability to feed the crew.

Nuclear Energy Propulsion

1:41Are policymakers warming, finally, to the benefits of nuclear energy? The Naked Scientist on 5 Live. First this week, a new AI model called Gencast has outperformed the best traditional medium-range weather forecast and is also able to better predict extreme weather. Now, unlike our existing weather forecasting systems that operate on supercomputers running massive simulations of the atmosphere, burning in the process through megawatt-hours of energy

2:13and undoubtedly contributing to the process of climate change, ironically, the new approach uses machine learning that can spot patterns in historical weather data, basically when conditions look like this, this is the outcome, to predict future weather patterns. And it does it with just a fraction of the energy and in a fraction of the time. The findings have been published in the journal Nature, and Elam Price and Remy Lam from Google's DeepMind

Gencast Development

2:39have been telling me all about what they've done. The traditional way of making weather forecasts is to use physical equations to describe how the weather in the atmosphere evolves over time. What this means is that you have to use a very large supercomputer. It's costly and time-consuming. It's also error-prone. Oh yeah, we know about the errors. Anyone who's been a victim of the weather forecast knows all about that. So yeah, what we've been trying to do at Google DeepMind is try to uncover the potential of using all of that historical weather data that we are sitting on

3:09to improve weather forecasts. And by doing so, we believe we can make better weather forecasts and faster weather forecasts. How do you do it, Elam? We do it by training a machine learning model on about four decades of historical weather data. The model learns weather patterns and weather dynamics directly by looking at that data, and that's what it uses to make predictions going forward. When you say you train it, what is it actually looking at? What do you feed in? What's the input from which it's trying to learn?

3:41What it's looking at is historical estimates of the state of the weather in the past. And basically during training, the model is shown, okay, this is the weather state at time X. Make a prediction of time, you know, in 12 hours time. And then it gets shown what should it have predicted. And it kind of learns from its mistakes by showing it many, many, many examples of this. What are the patterns that it should learn to pick up? And did you focus on just one geography? Or were you feeding this global data? No, this is a global model.

4:13And that's important because if you want to be able to predict the weather at medium range, so that's out to about 15 days in our model's case, you really need to be able to model the global atmospheric dynamics. I was going to say, because obviously there's this old kind of joke, isn't there? The butterfly flaps its wings and then there's a hurricane on the other side of the Atlantic. But it really is all interconnected, isn't it? So you've got to be able to consider everything. But that has previously been such an intractable problem

Venus Climate Study

4:38because of scale that it hasn't been done. Yeah, absolutely. And in fact, that chaos is the name of, you know, that phenomenon, that very small things can have very large consequences. It's one of the reasons why it's important, why the weather really is inherently uncertain. We actually know that we can't predict the weather exactly. And one of the big important features of our new model is that it's an ensemble forecast. We don't try and do the impossible and predict exactly one prediction of what will happen.

5:09Instead, we make multiple predictions of what might happen. And that gives us a sense of the range of different possible scenarios in the future. And it lets us calculate, okay, how likely are some scenarios, how likely are other scenarios? How much better is it, Ilan? So if we compare what our weather forecasters thought was going to happen with what your model suggested was going to happen, how good is it? So it's hard to put an exact number, one single number on how much better Gencast is because there are, you know,

5:41lots of different things that we would like from a weather model and the improvements are, you know, different on different tasks. But overall, say on the headline metric that is averaged over all times, all weather conditions over the year, more than 97% of the evaluated targets, you know, Gencast is better. But it's also better on a lot of the specific things we care about. For example, we might care specifically about extreme weather. And we can ask, you know, how good is Gencast at predicting, say, a once in a seven-year high temperature in a given location?

6:11We evaluated that in the paper and we see Gencast improving at that. And similarly, we care about predicting the trajectories of tropical cyclones. You know, these have devastating consequences and the more advanced warning we have, the better. And we were able to show that, you know, Gencast is giving us better predictions of the tracks of these storms. It's giving us about a 12-hour advantage in accuracy over the state-of-the-art operational models at the moment. One of the things that Remy said earlier was that in order to do what we do at the moment takes a supercomputer

6:41to do the sorts of calculations and run these models that enable us to make the predictions we have. How much better in energy terms is doing it your way than running those supercomputers? I don't have a good answer to that in energy units, but I can give you a comparison that makes it quite apparent, I'd say, the difference in scale. So in comparison to hours on a supercomputer with tens or hundreds of thousands of processors, we're talking about making a 15-day prediction by Gencast in eight minutes

7:12produced by only a single TPU chip. So that's a chip just a bit bigger than a computer. So there's really kind of orders of magnitude difference in the amount of computation that it takes to generate forecasts with Gencast and machine learning models compared to these traditional physics-based models. What are the implications of that then, Remy? Apart from the fact that you can argue, well, we'll save a lot of energy because we won't have to run these supercomputers and we'll get the results, which potentially are more accurate more quickly. Apart from those,

7:42what are the implications for this? So I think this is quite a pivotal point in the way we do weather forecasting. It's much faster to make prediction and it doesn't require a supercomputer. So what it means to me is that it will be more accessible to the weather forecasting and to conduct research in weather forecastings. We're also making the model publicly available so people can do research on it. And I think this is really going to accelerate the progress within weather forecasting, both because it makes the research accessible, doesn't require a supercomputer, but also provides a new way of improving model,

8:13really pushing on the axis of the data rather than purely the compute axis. Ilan Price and Remy Lam there. Space scientists at the University of Cambridge

Venus Volcanism Study

8:22have, they think, answered a longstanding question about our near neighbour, Venus. Now, whilst it's a hothouse of a planet today with a surface temperature that's sufficient to melt lead and sulphuric acid for rain, given its similarity to Earth, was it once, people wondered, a lot wetter? Well, the answer is it wasn't once wetter like we are. And by looking at the gases that Venusian volcanoes release, Teresa Constantinou has been able to work out how much water there really is inside the planet.

8:53And the picture is not one of a once watery world. Earth and Venus are often thought of as sister planets. That's because they're very similar in mass, radius, density, and distance from the sun. But now they've ended up looking really, really different. So you've got Earth, we've got oceans, we've got a nice climate, quite comfortable. However, on Venus, the surface conditions are extreme. You've got an atmospheric pressure at 90 bar, so that is 90 times the pressure that you experience when you're standing on Earth.

9:24And that is the equivalent of being about a kilometre underwater. And I imagine the pressure you'd feel in your ears from that. That is extreme. And then you've got the toxic atmosphere made of carbon dioxide, and you've got sulfuric acid clouds. So this is vastly different from what it would be like on Earth. However, there's been this theory suggesting that Venus was once very much like Earth, so that it had oceans, another cool temperate climate. But that is really unknown. Even scientists are still debating to this day whether that was the case. So that's what we were kind of

9:54seeking out to answer, trying to see if there's any evidence in what Venus looks like today that speaks to that climate past, that tells us whether Venus ever had oceans. We struggled to do something similar for our own planet that we're on that's a lot less inhospitable. So how can you get to the bottom of those questions on Venus? Yeah, it's a great question. It has been very tricky. So far, the way it's been done for Venus is using climate modelling. So that's very much what people would use when you're checking the weather for here on Earth, but doing that for an early Venus,

10:25seeing if it was ever cool and temperate, cold enough essentially, to have liquid water at its surface. But we wanted to kind of approach the problem a slightly different way. We wanted a more direct test, something to do with what Venus is like today that is a very clear clue as to whether the past of Venus ever had oceans. So it's almost like, you know where we are today, you're going to wind the clock back using some kind of model of how things evolve and change to work out what it would have been like back in the day. So that's essentially what has been done so far,

10:56but we wanted to do something else. We wanted to see if there was any clue within Venus as it is today that was a signature of this past. Essentially, we wanted to see if Venus in its present day has any clues about it ever having oceans. So for example, on Mars, just by looking at the surface, you can see where water has shaped surface features. So much like flowing water through soil or sand would shape little channels and valleys. We see that on Mars. However, Venus's surface is really young in geological terms, even though it's a few hundred million years old. But essentially, there was a large volcanic event

11:27that released lots of lava covering most of the planet's surface. So if there was any evidence of flowing water, so like valleys, it is now covered, so completely erased. So we've had to come up with entirely different ways to figure this out. So different clues to look for. And what are they? How do you do it? The way we did it is we looked into volcanism on Venus to find out how much water is inside the planet. Now, this is a very key clue to whether Venus ever had any oceans in it past. So setting the composition of the gases being released by volcanoes on Venus, we can see how much water is inside the planet.

11:59Now, imagine a volcanic eruption here on Earth, if you've ever seen any photos of them. You essentially see these large billowing clouds coming out. Now, most of that is water and this is directly coming from the inside of the planet where the lava comes from. We wanted to do the same for Venus by studying the composition of the gases coming out of Venus's clouds. We wanted to see if there was any water or rather how much water there is inside the planets. And this was a very key signature about the climate's past. How were you actually making those measurements, though? It'd be great if we considered a probe now

12:29and study Venus's clouds. And there are plans in the future. There are probes being sent to Venus to do that. But obviously, we haven't done that yet. In fact, any probes that they sent in the past kind of died when they reached the surface due to the extreme pressures and temperatures. So we did it slightly differently. Most of the data that we have about Venus or the thing we understand the most about Venus is its atmospheric composition. So we modeled that atmospheric composition. We studied the chemistry of the atmosphere and we looked at different gases being destroyed in the atmosphere through chemistry, which must be restored

13:00by volcanism to maintain atmospheric stability. So we use that to kind of study what volcanic gas you would need to be added into the atmosphere to sustain what it is like today. So you can basically see the atmosphere from far away. You don't need a probe to do that and you can work out what's in it and therefore you can work out what must be going into it and what's coming out of it. So therefore you get that sort of dynamic of what the volcanoes must be doing and therefore the amount of water can be inferred indirectly. Yes, exactly. So we've been observing Venus for decades now.

13:30We've sent missions there that took some measurements of what atmospheric composition is and we've also used telescopes on the ground here on Earth to observe Venus and again study the composition of the atmosphere and that's exactly what we've used. What does that reveal then? Is it much wetter like the Earth is or were we wrong?

Weight Loss Research

13:47It is really, really dry. There is so little water coming out of the volcanoes on Venus and this is vastly different to Earth where you've got lots of water. Most of the gases released by volcanoes are water but that's not what we found for Venus. In fact, there was very little water meaning the planet itself has very little water as a whole. So Venus was never a potential home from home for us. Fascinating. Teresa Constantinou there. She's at the University of Cambridge and she published that work this week in the journal Nature Astronomy. This is 5 Live Science

14:19with me, Chris Smith. Still to come, we'll hear from the scientists who's discovered that crabs do appear to feel pain and we'll be asking whether nuclear technology is essential if we're to meet our future energy needs. Before that though, weight loss jabs like Zempick and Manjaro have been in the news a lot lately. They work by simulating the action of one of the body's signals called GLP-1 which shoots up when we eat. But there's also another important signal in the form of a hormone

14:49called leptin and this is released by fat tissue in increasing amounts as we gain weight and the brain uses it to regulate our eating behaviour except when we start to put on too much weight at which point the brain becomes deaf to the signal. Well now the pharmaceutical company Nova Nordisk have developed a new molecule one end of which looks like leptin and the other end resembles GLP-1. This seems to be able to trigger a specific population of appetite regulating

15:20cells in the brain and sensitise or even resensitise them to leptin significantly boosting rates of weight loss in experimental mice. Randy Seeley is the director of the Michigan Nutrition Obesity Research Centre where he's been working on this potential new drug and he told me what he's found. Leptin is released from your fat tissue and it essentially tells your brain about how much stored calories you have in your body. Your brain uses that information to be able to

15:50maintain your body weight to figure out how much you should eat and how much you should burn. What's interesting is when it was first discovered people thought oh my gosh this is going to be a great therapy for individuals with obesity and it turned out not to be true. And the reason it wasn't true is that it turns out that individuals with obesity aren't leptin deficient that is they don't have low leptin levels rather

16:12and they seem to be resistant to the ability of that leptin to be able to give them information about whether they have sufficient stored calories. So let me give you an example right so if you took a lean mouse and you gave it leptin you're essentially tricking that animal into thinking that it has more stored calories and the animal does something pretty sensible in reply it stops eating but when you do that in a mouse that's been made obese it turns out it doesn't respond to leptin the same and so the whole trick of this is trying to understand is there a way to still use leptin from a therapeutical perspective

16:43that might be part of the armamentarium that we use to treat individuals with obesity. In overweight situations then it's as though the brain has become deaf to the signal of leptin and you're asking well can we restore hearing to that part of the brain it's like a hearing aid for the brain can we resensitise it so it does respond to the leptin signal which says you have too many calories on board you need to lose some weight. Exactly. How do we resensitise the brain? How do we turn

17:13that hearing back on in a way so that it can listen to both their own leptin and the leptin that comes in the form of this particular molecule? So you think that you've got a drug here which can do that how does it work? We identified a set of neurons in a specific part of the brain called the hypothalamus and those neurons express both the receptor for GLP-1 and the leptin receptor and we verified that that's not just true in mice but it was true in non-human primates as well that there's this set of neurons

17:44and then we used a variety of genetic tricks to either add or remove leptin receptors from that area of the brain right in these cells that also express the GLP-1 receptor and it turns out these neurons are both necessary and sufficient for the actions of this particular drug. So why should hitting the neurons with this agent that stimulates both the leptin signal and the GLP-1 signal

18:15at the same time why should that be an effective strategy to make the cells more sensitive to this fat signal leptin? You're asking a really good question. What we know is that when we add this GLP-1 component we get a response that you wouldn't get but with leptin alone and there are sort of two ways to think about this. One is hey if we sort of prime the animal to begin losing with the GLP-1 side of the molecule we now get the system cranked back up

18:45and turned back on so that it can start hearing the leptin side so that's sort of like it doesn't matter what you would do right you could do lots of different things and maybe that would all be effective at making leptin being able to hear the signal that leptin brings the other side really has to do with these particular neurons right the idea that we're hitting the GLP-1 receptor turning on that signaling cascade in these neurons and that restores their ability to be able to hear the leptin signal and that's the side that we favour but again it's pretty hard to prove between those

19:15two particular hypotheses. I suppose that if you view the weight loss that would arise from this as two phases because normally if someone loses weight then the amount of leptin would drop so they would lose whatever drive they were getting to lose more weight because they're losing leptin but if you have a molecule that seems to fool the brain into thinking the level is staying high and it stays sensitive to it you're going to reinforce the weight loss for longer so in theory you could actually have a much more effective pattern of weight loss.

19:45So that's what we show one of the things that Novo cleverly did was to make versions of the molecule that only had one side or the other but looked similar we were able to use those kinds of research tools to be able to ask what happens when you only push on one side or the other and the answer is you don't get as much response as if you were able to push on both of them at the same time. And how much response do you get? We know when people use these agents when people have done trials with the existing GLP-1 agonists drugs like a Zempic for example Manjaro that kind of thing we know that translates

20:16over the period of treatment to a weight loss of between 10 and 15% of the body weight so if you bring your new double acting drug to bear what sort of levels do you think you would get with this? I don't think we can know from this study we didn't do the kinds of comparisons that would allow you to be able to directly compare molecules like trizepatide or semaglutide as you can imagine it's not a trivial thing to be able to do mice are different and particularly they're different in how they metabolise the drug that is how long the drug lasts

20:47and so trying to do those kinds of comparisons turn out to be really tricky so I think it's going to take some time to tell whether a molecule like this one could be as effective or more effective we just don't know today It's a fascinating strategy that was Randy Seeley and that study has just come out in the journal Science Translational Medicine Now anyone who's ever

Crustacean Pain Perception

21:08felt a twinge of disquire when they've selected a crab or a lobster and then watched it being dropped into a pan of boiling water to cook it should perhaps in future trust their instincts because scientists have discovered that crustaceans do appear to perceive pain Elotherios Kassuras from the University of Gothenburg has found that crabs dabbed on their sensitive bits with strong vinegar will administer aspirin in the aftermath to ease the discomfort suggesting they really are feeling pain

21:38There are some criteria that need to be fulfilled so we're certain and we can say beyond an unreasonable doubt that crustaceans they experience pain Pain is a difficult one though isn't it because it's a perception I can define pain I can say I'm experiencing pain and you'll know exactly what I'm talking about but how do we know that other animals share that visceral experience or whether it's just an automatic reflex for them and they don't experience the same

22:08emotional effect that we do So we worked on shore crabs and we used different stimuli on the soft tissues of the body like the legs the claws eyes and antennae which is the little pointy things and we stimulated with acetic acid which is vinegar and it's painful to these animals and we used also mechanical pressure to see how they respond to that so from these two different stimuli

22:39and the responses that we got in the nervous system we could see that the signal transfers through the body to the brain and a response arises and we recorded these responses Right so you can demonstrate that they have the neurological capacity to detect a stimulus that we would regard as painful and it changes brain activity when you put that stimulus into the nervous system so you're two thirds of the way to showing that they are experiencing pain how do we then clinch it

23:10to get the final tick in the box that this is then registering in their brain as an unpleasant thing in a way that we would say well that looks like they're feeling pain We want to inflict pain on them and see how their behaviour will change and then if we provide drugs such as analgesics imagine aspirin if they would prefer the aspirin than the pain stimulus or they want to go to the aspirin to relieve themselves from pain that behaviour will tell us that they

23:41actually want to avoid the stimulus no matter what so they don't experience that and that's the last step of my PhD to see how the behaviour and learning come into play and that has not been answered yet but I'm working on that So crabs can take aspirin really? Yeah so far we tried it on lobsters and it seemed to work okay but more to come about that too in the next paper that I'm trying to publish

24:11So summarising then you stimulate them with something we would regard as painful they flinch effectively it's like me touching a hot plate and you can then show that they will actively seek out pain relief off the back of that with things like aspirin which would kind of suggest that it's an ongoing discomfort for them and they're alleviating it Exactly that's the next step of my experiments Well what are the implications of this then because I mean we traditionally just get crustaceans like crabs and we dump them in pots of boiling water

24:42to cook them so does this mean that we ought to be rethinking this? I think so too and the industry also needs to rethink that and we are scientists when we use them in the lab so first important is we have to find methods to kill them as fast as possible and then the distribution probably has to be dead crustaceans and not alive ones and if they're alive the restaurants or the people that they want to kill them we need

25:12to find methods to kill them fast when we catch them and probably they have to be dead when we buy them from the shops What's a good way to do that then not that there's a good way to kill anything but when it's a necessity is cold temperature you think possibly putting them in the freezer to drop the temperature down so they effectively become hypothermic and unconscious or something is that the best way? We tried ice lary because we're on the verge of finding the best method and we're conducting experiments on that

25:42and cold doesn't seem to do the trick especially on large lobsters we studied Norway lobsters languishing and they didn't die as fast even in the freezer in minus 20 so we think the best will be electroshock because it renders them unconscious really fast we are we are trying to investigate that more but I think many companies try to implement electrical stunning as a better method than chilling crabs and lobsters on aspirin you learn