Show notes
Dr Chris Smith and the Naked Scientist team look back at some of 2024's top science stories, and in part 2 hand over to our Naked Gamers… Chris Berrow and Leigh Milner for a gaming special, featuring: A world record... the game that took 22 years to make has finally been released! Video games without the video… we’ll check out an audio-only release. And what does 2025 have in store for gamers?
Highlighted moments
“What this mutation is essentially doing is sending the dog's body a starvation signal and the body is responding very sensibly by increasing food intake and also by dialing down how much energy they're burning off to keep the body running.”
“the mutation was more common in guide dogs than it was in pet dogs. And that leads to this very kind of tantalising, very tempting hypothesis that maybe we have inadvertently bred our high-performing dogs to be a bit foodier because if you're foodier, you'll do anything for a biscuit and it makes you a bit more trainable.”
Transcript
Introduction
0:00Hello and happy almost Christmas from Ed Vornier at 5 Live Science. I'm Will Tingle. In this episode, we're taking a look back at the year that was, 2024, and reliving some of the outstanding scientific stories that came out of it. Everything from greedy dogs to AI to robo-fish. And a bit later on, which video games will make the perfect stocking stuffers? The Naked Scientists on 5 Live.
Greedy Dogs
0:30We begin our look back at 2024 with man's best friend. But anyone with a Labrador specifically will probably describe their dog using one word. Greedy. The breed is notorious for being a bottomless food pit. And in March, we found out why. Cambridge researchers discovered a genetic change or mutation in a gene called POMC carried by many members of the breed. It not only makes them hungrier, but it also makes them burn 25% fewer calories, so they're more likely to put on weight even with compensatory exercise. To find out more, Chris Smith went walkies to meet Cambridge University's Eleanor Raffan,
1:03Black Labrador Eddie, who was hungrily eyeing up his microphone, and his owner, Rona Edmondson. So we get up in the morning, he gets fed, and then a little bit later we'll go for a walk. Much of the day, he will sleep. If we are eating, he'll come and look hopefully, but he knows there's not much hope. A few obvious questions as a co-owner, as an owner in common with you. There's never any food left in the bowl, correct, true or false? True. The other dog's bowl gets licked clean too.
1:33The bowl would be emptied multiple times if it were refilled. It would, yes. He will never leave any food. Hello, Eddie.
1:43He liked the smell of the microphone. The thing quite quickly realised he was not going to be able to eat it.
Genetic Study
1:48What was the background to the study, Eleanor? Well, we knew this gene was important in how the brain controls appetite and eating behaviour and possibly energy expenditure, but we needed to test whether that was true and whether that really was what was driving the obesity we saw in affected dogs. So you put out a call for people with hungry dogs? Well, very specifically we didn't, actually. What we did was put out a call for people with Labradors and we made no reference at all to whether they were hungry or overweight. In fact, what we did was recruit dogs like Eddie who've been slim throughout their lives.
2:21And how did you test dogs like Eddie? They all came into our research area in Cambridge and we tested their eating behaviour in a couple of different ways and also their energy expenditure, what their metabolic rate was doing and how many calories they were burning up during their day-to-day lives. And then married that up to their DNA profile? Exactly. And we took slobber samples to get DNA from them all. How did you appraise dogs like Eddie's eating behaviours then? We tested what we call satiety, the point at which you feel full after a meal
2:53by offering these guys cans of dog food every 20 minutes until they stopped eating. And it turns out that Labradors will stop eating in the end but usually not until around the 2kg mark, which is an awful lot of food. It is an awful lot of food, but that presumably tells you that it's not that they can just accommodate massive great meals that makes them hungry all the time. They will stop, so therefore it has to be a sort of a fullness. Exactly that. And so many people came in and said, oh, my dog's never going to stop eating. But, you know, it turns out they do.
Hunger Experiment
3:23And that reflects the fact that there are pathways in the body that say, no, no, that's enough. But the third thing we looked at was hunger. And we did that using something we call the sausage-in-a-box test. And we showed these guys a sausage and put it in a plastic box that was clear so they could see through it and they could smell it because it had holes in the lid. And then we videoed what they did. And when we did that, we found that dogs like Eddie, who had the mutation we were studying, were far more attentive to the sausage-in-a-box and paid so much more attention to it and got so much more excited
3:54compared to the dogs without the mutation. And what about the point you made about burning more energy off? Are they really hungry all the time because they're just getting through huge numbers of calories or are they just greedy? The other part of our study was to look not just at the amount of energy they might be getting in from their food, but also how much energy they were burning off in maintaining their body's functions. And we were astonished by the results, which says that dogs like Eddie, who have two copies of the mutation, burn off about a quarter fewer calories every day.
POMC Gene
4:24Tell us about the gene then, what it does and how it's doing that to dogs like Eddie. So POMC is a gene that we've long studied as having a role in how the brain controls body weight. It makes sense to have some energy reserves in the body in case there's a period where there's nothing to eat. And so the body has a system to get an appropriate amount of fat mass and POMC is integral to that system by acting in the brain to dial up and down hunger and energy expenditure. What this mutation is essentially doing
4:56is sending the dog's body a starvation signal and the body is responding very sensibly by increasing food intake and also by dialing down how much energy they're burning off to keep the body running. If they really were starving, that would be a very sensible thing to do. But because it's due to a mutation and actually they've got plenty of food, it will tend to make them put on weight if they're given a chance. And is that because we've bred them to have a certain set of traits, we've inadvertently bred that in? Possibly. I'm slightly reluctant to answer this question. In our original study,
5:26we had this really intriguing finding that the mutation was more common in guide dogs than it was in pet dogs. And that leads to this very kind of tantalising, very tempting hypothesis that maybe we have inadvertently bred our high-performing dogs to be a bit foodier because if you're foodier, you'll do anything for a biscuit and it makes you a bit more trainable. I have tried to test that scientifically and I can't prove it yet. So I'm a little bit cautious about talking too much about it but I think that may still have legs
5:59as a hypothesis and may still be true. And when you tested Eddie, we've heard that he likes his food. What did you find genetically? Where does he sit on this spectrum? So Eddie has two copies of our mutation. So he's kind of at the worst end. He's got a double dose of this. Of course, it matters what's in his other genes as well and we have by no means explained why all Labradors are greedy. You know, we've just got a bit of it in a quarter of them and we're working very hard on what the other things that are important are too.
6:29Were you pleased, Rowena, to have an answer to why Eddie is a slave to food? Yes, it's nice to have an explanation but it didn't really change my behaviour towards him. Eleanor Raffan, Rona Edmondson and, of course, Eddie.
AI and Neural Networks
6:43In June, our summer series of Titans of Science returned. We heard from the godfather of AI, Geoff Hinton, who would go on to win the Nobel Prize in Physics later in the year. And I can only assume we played no small part in that win. Anyway, whilst AI is not so slowly creeping into every facet of our technological lives, the way in which AI actually makes its so-called decisions is still not clear to a lot of people. AI was built to function as a neural network, a model heavily influenced by the structure and makeup of our own brains. And so by simulating neural networks,
7:15you can learn a lot about how brains learn and how AI might learn too.
Backpropagation Algorithm
7:19This is what he told Chris. So we now have computer models of neural networks, things that run on a computer but pretend they're networks of brain cells that work really well. You see that in these large language models and in the fact that your cell phone can recognise objects now. So we understand how to make things like that work and we understand that the brain's quite like many of those things. We're not quite sure exactly how the brain learns but we have a much better idea of what it is that it learns. It learns to behave like one of these
7:50big neural networks. If it's down to the fact that we've got brain cells talking to brain cells and they're just big populations of connections, is that not relatively easy to model with a computer? What's the hold up? Why is it hard to do this? Well, the tricky thing is coming up with the rule about how the strength of a connection should change as a result of the experience the network gets. So for example, very early on in the 1940s or maybe early 1950s a psychologist called Hebb
8:21had the idea that if two neurons, two brain cells fire at the same time then the connection between them will get stronger. If you try and simulate that on a computer you discover that all the connections get much too strong and the whole thing blows up. You have to have some way of making them weaker too. I love that line what fires together wires together. It's never left me because I remember reading Hebb's book when I was at University College London. So how did you try and address that then? Was it sort of just a damping problem? You make it so that
8:52the nerve cells get bored more easily as it were so that doesn't overheat in the way that the computer would otherwise have them do? Well that's kind of the first thing you think of when you try that and it still doesn't work very well. So the problem is can you get it to work well enough so that it can do complicated things like recognise an object in an image or in the old days recognise something like a handwritten digit? So you take lots of examples of 2s and 3s and so on and you see if you can make it recognise which is a 2 and which is a 3 and it turns out that's quite tricky
9:23and you try various different learning rules to discover which ones work and then you learn a lot more about what works and what doesn't work. What doesn't doesn't work and why? OK I'll tell you something that does work because that's obviously more interesting. You have a layer of neurons that pretend to be the pixels so an image consists of a whole bunch of pixels and the pixels have different brightnesses and that's what an image is it's just numbers that say how bright each pixel is and so that's
9:53the input neurons they're telling you the brightness of pixels and then you have output neurons if you're recognising digits you might have 10 output neurons and they're telling you which digit it is and typically the network at least to begin with wouldn't be sure so it'd hedge its bets and it'd say it's probably a 2 it might just be a 3 it's certainly not a 4 and it would represent that by the output unit for a 2 would be fairly active the output unit for a 3 would be a little bit active and the output unit
10:24for a 4 would be completely silent and now the question is how do you get those pixels as inputs to cause those activities in the outputs and here's a way to do it that all the big neural networks now use so this is the same algorithm as is used to train big chatbots like GPT-4 it's used to train the things that recognise objects and images and it's called backpropagation and it works like this you have some layers of neurons between the inputs and the outputs so the neurons
10:54that represent the pixel intensities have connections to the first hidden layer and then the second hidden layer and then the third hidden layer and finally to the outputs so they're called hidden layers because you don't know to begin with what they should be doing and you start off with just random connections in these networks so the network obviously doesn't do anything sensible and when you put in an image of a digit it will typically hedge its bets across all the possible ten digits and say they're all more or less equally likely because it hasn't got a clue what's going on and then you ask the following question
11:25how could I change one of the strengths of the connections between a neuron in one layer and a neuron in another layer so that it gets a little bit better at getting the right answer so suppose you're just trying to tell the difference between twos and threes to begin with you give it a two and it says with a probability 0.5 it's a two with a probability 0.5 it's a three it's hedging its bets and you ask well how could I change the connection strength so that it would say 51% two and 49% three and you can imagine
11:55doing that by just tinkering with the connections you could choose one of the connection strengths in the network and you can make it a little bit stronger and see if that makes the network work better or work worse if it makes it work worse obviously you make that connection a little bit weaker and that's sort of a bit like evolution you're taking one of these underlying variables a connection strength and you're saying if I change it a little bit how can I change it to make things work better and save those changes and you could do that and it's obvious that in the end that will work but it would take
12:26huge amounts of time so in the early days we would use networks that had thousands of connections now these big chatbots have trillions of connections and it would just take forever to train it that way but you can achieve pretty much the same thing by this algorithm called backpropagation so what you do is you put in an image let's say it's a 2 the weights are initially random weights on the connections so information will flow forward through the network and it'll say 50% is a 2 and 50% is a 3
12:57and now you send a message back through the network and the message you send back is really saying I'd like you to make it more likely to be a 2 and less likely to be a 3 so I'd like you to raise the percentages on 2 and lower the percentages on 3 and if you send the message back in the right way you can figure out for all the connections at the same time how to change them a little bit so the answer is a little bit more correct that's called backpropagation it uses calculus but it's essentially doing this tinkering
13:28with connection strengths that evolution would do by just changing one at a time but the backpropagation algorithm can figure out for all of them at the same time how to change each one a tiny bit to make things work better and so if you have a trillion connections that's a trillion times more efficient than just changing one and seeing what happens but how does the layer at the bottom know what's going to be changed above it to make sure that the input that it then gets is the right one so that the change it's just made to it
13:58and its probability ends up being even better so that you don't end up changing yourself then that feeds forward back up the network changes something else but then it becomes less optimal for you if you get what I'm saying I get just what you're saying it's a very good question and essentially what's happening is if you take a connection early in the network it's kind of making an assumption it's saying suppose all the other connections stayed the same so if you change the connection strengths by a lot things could actually get worse
14:28because you could choose a way to change each connection strength that if you did that change alone would make things better but when you do all the changes at the same time it makes things worse but it turns out if you make the changes very small that problem goes away if you make the changes very small then I figure out how to change one connection strength and because the changes in all the other connection strengths are very small it's very unlikely they'll turn for example a change that helps into a change that hurts Nobel Prize winning
14:59Jeff Hinton there 2024 was also an Olympic year with athletes around the world converging in Paris to duke it out at the pinnacle of sporting competition and so we delved
Cold Therapy
15:09into the science of keeping athletes in tip-top condition one aspect is cold therapy taking a dunk in a cold body of water or ice bath with some proponents claiming better mood faster muscle recovery time and even stronger immune systems to find out more but also to avoid taking a dip into an icy lake James Titko took a trip to the light blue clinic in Cambridge which contains a cryo chamber capable of cooling participants to minus 120 degrees celsius and met up with Gosia Bieniak who took him through the treatment
15:39Hi Gosia Hi, hello I'm sorry I'm a bit late I'm James Don't worry Hi, it's really nice to meet you While my cold therapy experience was to be facilitated by modern means in a high-tech cryo chamber the healing benefits of exposing your body to extremely low temperatures has been posited for centuries That's not to say it's for everyone though and when I booked myself in at the clinic I was asked to fill out a medical questionnaire online making sure I had no underlying health conditions
16:10that might make me unsuitable for the treatment On arrival my blood pressure was taken I was given protective clothing to shield my extremities including thick socks hat and gloves and then it was time The lovely people here at the Light Blue Clinic have made sure I'm fit and firing healthy ready to take on the cryo chamber I'm faced with this enormous fridge I'm being told there's a pre-chamber which is going to go down to what was it again minus 60
16:40that sounds quite cold enough if you ask me but then I'll move on to the main chamber which is looking at minus 120 degrees Celsius and I'd be lying if I said I wasn't a little nervous but I've got a friend Gary's here with me I am indeed yeah I've been into the cryo chamber before so you're in safe hands and we're going to enjoy this experience and you're going to benefit from all the things that you get out of the cryo chamber today Brilliant well I can't take the recorder in with me it will explode probably but here goes
17:11I'll see you on the other side setting foot inside the atmospheric main chamber it took a while for the coldness to really make its mark distracted as I was by the liquid nitrogen fog dancing over my body a combination of nervous energy and the shiveringly low temperature caused me to turn into a bit of a chatterbox which I was assured by cryo enthusiast Gary
17:41was a normal reaction thanks for putting up with me in there Gary it also meant that before I knew it my three minutes was up gosh yeah I've just about come back round from my cryo chamber experience breathing's back to normal that was quite something I'm no elite athlete but athletes are using this technique to aid with their recovery
18:12how does it work so cryotherapy puts your body into shock which means we pretty much induce fight and flight response so with that with the cardiovascular system what happens is that the blood goes back essentially to the heart to protect internal organs and there isn't really much circulation in the peripheries so the idea is to reduce inflammation am I right in saying yes yes you're absolutely correct when you come out you get vasodilation so you get redistribution of blood
18:42so you get more nutrients in more oxygen in so the pain threshold shifts as well because the inflammation goes down so if there is any muscular pain for the next 24 hours it shouldn't be that noticeable from endocrine system point of view what happens is that we get endorphins in as well so it helps with regulating your mood in the most natural way that you can actually imagine the only thing is that it only lasts up to 24 hours so you kind of have to reset your system on a more regular basis but we have
19:12clients using it twice a week we have clients using it once a week with the athletes we really want to get the timing right providing they do nutrition and sleeping and all of the basic recovery right then this can really elevate the recovery so improve muscle soreness improve mood better sleep is longer better you're talking about the athletes there and you wanting to get the time specifically right with the gold standard we're talking about three minutes 30 seconds but we also check the skin temperature before
19:43you go in so that's one of the indicators well the major indicator for us to know if that actually worked and you're going to get all of the acute responses and benefits that we talked about so we check the skin temperature on the knee and then we check it on your trapezius as well before you go in and we expect the drop to be around 10 Celsius when you come up but yeah the skin temperature is the indicator for us that tells us if the three minutes worked or if we have to try and encourage them to go in for another minute that huge chamber I was in very high tech
20:13very cool what's the advantage of a cryo chamber over an ice bath say so it's optimizing the time under which you have to be uncomfortable in order to get the full benefit that was Gosia Bieniek chilling out with James
Robotic Fish
20:30Ticko
Robotic Fish
20:30and to round off 2024 we head back around 375 million years at that time early fish species are thought to have taken one small step onto the land we've learned a lot about the skeletal structure of fish during this time including the famous specimen Tiktaalik but the process of fossilization does not preserve the soft tissues that could shed light on how the first fins acted as feet so what to do well a solution came from Cambridge's bio-inspired robotics lab why not create robotic fish to test out
21:01different muscle and tendon configurations I took a trip down to the lab myself to see Michael Ishida and hear how robots could provide some answers to some of life's earliest mysteries it sounds a little ridiculous sometimes when I say it out loud but obviously with a fossil you can't observe it moving around it's just a static piece on display and so paleontologists have all this expertise in kind of putting together these pieces making strong inferences about how it may have moved how things might have fit together but there's no
21:32real way to loop that back and kind of prove this is for sure how things happen what's missing from current fossils that you need in order to get that data there's a lot of things missing we're very lucky to get partial fossils and as I'm sure you know there's many many species that we think existed that we just haven't found fossils of yet not only are these fossils we do have incomplete but we're probably missing some in this evolutionary chain and so with robotics
22:02we can design a new robot that kind of fits in into the gaps and so all of these ways of building something to give us more information more data is something that we're very interested in so how do you go about doing that how do you go from fossil of what you think is a fish to a robot in front of us right now that can move and can provide some insight obviously there's no actual model that replicates the exact animal you can't build a robot that replicates every muscle every tendon every piece of soft material so the first
22:32job is to kind of simplify and say what is the research question we're actually asking is it about the fin so then maybe if our research question is about how this fin of the fish is able to support its body weight maybe then we build a very detailed fin with the exact bones and the soft material we think it had and then we can take more simplifications with the rest of the body we can put the motor on the middle of the body so that it doesn't affect how this fin moves because the fin is what's most important we have a long history of what's called bio-inspired
23:02robotics that's just robots inspired by animals that we can see today and so there are some species of fish today that are able to swim and walk so there's things like polypterus which is native to Africa that kind of lives in a swampy area it can swim in the water and kind of move from puddle to puddle you have mud skippers that are native to places like Japan that I'm sure you've seen all the BBC videos of this thing kind of scooting across the sand but the point is there's many species today that we can look at and get a first kind of understanding about how a fish might be able to walk the physics
23:33of fish haven't really changed from now to 500 million years ago if you understand something about walking fish today you can then kind of apply that knowledge to ancient walking fish understanding as many species as we can today will give us some insight into this ancient animal and we can see oh this strategy for walking on land in sand let's apply it to this robot fish fossil maybe it doesn't work very well in sand okay how about in mud oh maybe this also doesn't work very well well maybe rock oh okay maybe this is kind of the more of the
24:04environment we're thinking of this is a very promising and burgeoning field should this come to come good and you work out how Tiktaalik and its friends all got out of water you know 400 million years ago where would you like to go next that's a great question I think the power of robotics really is to help us explore what we call counterfactuals things that didn't happen we can see the fossil record is filled with animals that did exist they did happen and so using a robot to try other morphologies or other sizes
24:34or other designs that nature did not come up with or that we haven't observed is something that we're really interested in because then we can see oh why did