1 00:00:00,480 --> 00:00:05,760 Speaker 1: Jonesy and Amanda in the mornings. You may remember a 2 00:00:05,800 --> 00:00:08,240 Speaker 1: couple of years ago on this free program and also 3 00:00:08,240 --> 00:00:11,600 Speaker 1: in the living room, we spoke about an Australian app 4 00:00:11,680 --> 00:00:15,880 Speaker 1: called dream Lab, and this was designed to use your 5 00:00:16,079 --> 00:00:20,639 Speaker 1: phone's computer power while you're sleeping to help crunch the 6 00:00:20,720 --> 00:00:23,400 Speaker 1: numbers to help cure cancer, which is a lot of 7 00:00:23,440 --> 00:00:24,680 Speaker 1: maths based algorithms. 8 00:00:24,760 --> 00:00:29,520 Speaker 2: Everyone's phones turns into a supercomputer, yes, feeding into one computer. 9 00:00:29,680 --> 00:00:32,000 Speaker 1: That's right. So while you're asleep, this doesn't access any 10 00:00:32,000 --> 00:00:33,680 Speaker 1: of your data or any of those other things. But 11 00:00:33,720 --> 00:00:38,000 Speaker 1: while you're asleep, all the power from your phone goes 12 00:00:38,080 --> 00:00:41,319 Speaker 1: up to join other computers, goes up. That's what I'm 13 00:00:41,360 --> 00:00:43,920 Speaker 1: imagining seeing it up in heaven, and it crunches the 14 00:00:43,960 --> 00:00:46,040 Speaker 1: numbers to help us get closer to a cancer cure. 15 00:00:46,520 --> 00:00:49,680 Speaker 1: This same technique, the same dream Lab app, is now 16 00:00:49,720 --> 00:00:54,120 Speaker 1: being used to find a cure for coronavirus. The Australian 17 00:00:54,120 --> 00:00:58,320 Speaker 1: woman who works with Vodafone Foundation has designed and set 18 00:00:58,360 --> 00:01:00,160 Speaker 1: this up. Her name is Alyssa Laye and hullo, oh well, 19 00:01:00,200 --> 00:01:01,480 Speaker 1: LIS's a nice to talk to you again. 20 00:01:01,640 --> 00:01:03,680 Speaker 3: Hi, Manesota has to talk to you again. I can't 21 00:01:03,680 --> 00:01:06,920 Speaker 3: believe it's been a couple of years. That's a thank you. 22 00:01:07,240 --> 00:01:10,320 Speaker 2: But did it work with the cancer research stuff? 23 00:01:10,920 --> 00:01:14,360 Speaker 3: We've had some great discoveries with the cancer projects. So 24 00:01:14,480 --> 00:01:17,120 Speaker 3: one of them was actually using the same approach that 25 00:01:17,160 --> 00:01:19,520 Speaker 3: we're going to use for the COVID nineteen project, and 26 00:01:19,560 --> 00:01:24,679 Speaker 3: they found some anti diabetic drugs and antimicrobial drugs that 27 00:01:24,760 --> 00:01:27,960 Speaker 3: can actually be repurposed for cancer. And they found that 28 00:01:28,000 --> 00:01:32,360 Speaker 3: there's actually some special molecules in everyday fruits and vegetables 29 00:01:32,360 --> 00:01:37,320 Speaker 3: that are also anti cancer, So things like oranges actually 30 00:01:37,400 --> 00:01:40,280 Speaker 3: have some anti cancer beating molecules. So they're just a 31 00:01:40,280 --> 00:01:42,960 Speaker 3: couple of the discoveries that were made on the cancer projects. 32 00:01:42,959 --> 00:01:45,479 Speaker 3: And now we're turning that same approach and the same 33 00:01:45,520 --> 00:01:49,640 Speaker 3: app to look for existing drugs and foods that could 34 00:01:49,720 --> 00:01:51,480 Speaker 3: treat and help prevent coronavirus. 35 00:01:51,680 --> 00:01:54,000 Speaker 1: So how exactly does that work? Because we imagine these 36 00:01:54,080 --> 00:01:56,800 Speaker 1: things get treated by someone in a lab, you know, 37 00:01:56,960 --> 00:01:59,400 Speaker 1: looking at things under a microscope. But most of this 38 00:01:59,680 --> 00:02:01,760 Speaker 1: is isn't it. 39 00:02:01,760 --> 00:02:05,920 Speaker 3: It is a lot of the researchers today are bywards meticians, 40 00:02:05,920 --> 00:02:10,080 Speaker 3: and they're dealing with maths and huge, huge hues amounts 41 00:02:10,080 --> 00:02:12,800 Speaker 3: of data. And one thing that is slowing their progress 42 00:02:12,880 --> 00:02:15,480 Speaker 3: is access to supercomputers to crunch this complex data and 43 00:02:15,480 --> 00:02:17,480 Speaker 3: make sense of it. And that's why the dream lab 44 00:02:17,480 --> 00:02:20,480 Speaker 3: app comes in because essentially our phones today are small 45 00:02:20,560 --> 00:02:22,920 Speaker 3: but powerful computers that are in our pockets all day 46 00:02:22,960 --> 00:02:24,919 Speaker 3: and then at night, when we're not using them, they're 47 00:02:24,960 --> 00:02:27,480 Speaker 3: sitting there idle. And so what dream Lab does is 48 00:02:27,520 --> 00:02:31,280 Speaker 3: put that computer processing power to work for good. It 49 00:02:31,320 --> 00:02:33,800 Speaker 3: doesn't access any of your private data. You literally just 50 00:02:33,880 --> 00:02:36,320 Speaker 3: download an app, you charge your phone, and then it 51 00:02:36,360 --> 00:02:38,680 Speaker 3: gets to work to help crunch this complex data for 52 00:02:38,720 --> 00:02:39,440 Speaker 3: the researchers. 53 00:02:39,720 --> 00:02:42,680 Speaker 1: And how do the numbers work in us finding a 54 00:02:42,800 --> 00:02:45,600 Speaker 1: drug that may work for COVID nineteen, how do the 55 00:02:45,680 --> 00:02:47,760 Speaker 1: numbers equate to a qure. 56 00:02:49,400 --> 00:02:53,360 Speaker 3: Well, we are launching this up globally. We're hoping to 57 00:02:53,520 --> 00:02:57,640 Speaker 3: have half a million users to crunch the COVID nineteen 58 00:02:57,680 --> 00:03:01,240 Speaker 3: project within a couple of months. Obviously, the more people 59 00:03:01,280 --> 00:03:03,119 Speaker 3: that come in and download the app and use app, 60 00:03:03,160 --> 00:03:06,600 Speaker 3: the faster we'll get there. There's never guarantees in medical research, 61 00:03:06,919 --> 00:03:09,400 Speaker 3: but given the researchers have a track record, they have 62 00:03:09,560 --> 00:03:13,680 Speaker 3: used the same approach to already identify some things are uh, 63 00:03:13,880 --> 00:03:16,040 Speaker 3: you know, really positive for cancer. We're hoping that the 64 00:03:16,080 --> 00:03:19,080 Speaker 3: same outcome will be a chief the coronavirus. So I 65 00:03:19,080 --> 00:03:22,079 Speaker 3: guess the question is the faster you know, the timelines 66 00:03:22,080 --> 00:03:23,720 Speaker 3: depend on the number of people that use the app, 67 00:03:23,800 --> 00:03:26,360 Speaker 3: So the more people that get involved as soon as 68 00:03:26,400 --> 00:03:28,040 Speaker 3: we can complete this research. 69 00:03:27,880 --> 00:03:31,000 Speaker 1: And clearly downloading the dream lab that will consume some 70 00:03:31,080 --> 00:03:31,720 Speaker 1: of your data. 71 00:03:31,720 --> 00:03:35,120 Speaker 3: Why it does, and when you download the app, it 72 00:03:35,240 --> 00:03:37,440 Speaker 3: uses a small amount of data to download the app, 73 00:03:37,520 --> 00:03:41,320 Speaker 3: like any app download would. But once the app is 74 00:03:41,360 --> 00:03:44,040 Speaker 3: on your phone, you can either use it on your 75 00:03:44,040 --> 00:03:46,640 Speaker 3: mobile data or on Wi Fi, and you can choose 76 00:03:46,680 --> 00:03:48,960 Speaker 3: how much, so you're in complete control. And if you're 77 00:03:49,000 --> 00:03:51,320 Speaker 3: with Photophone then the mobile data is actually free. There 78 00:03:51,360 --> 00:03:53,160 Speaker 3: you go, this is good. You should do that. 79 00:03:53,280 --> 00:03:54,480 Speaker 1: You should use your phone for. 80 00:03:54,480 --> 00:03:58,480 Speaker 3: Good and not. But it's exactly not of those cat 81 00:03:58,560 --> 00:03:59,960 Speaker 3: videos and they get on dream app. 82 00:04:00,160 --> 00:04:05,320 Speaker 2: Yeah, Amanda, Well, it's so interesting because this is what 83 00:04:05,360 --> 00:04:07,280 Speaker 2: they found with the cancer thing is that there are 84 00:04:07,320 --> 00:04:11,080 Speaker 2: certain types of drugs that not all cancers are the same, 85 00:04:11,120 --> 00:04:13,360 Speaker 2: not all breast cancers are the same, and so one 86 00:04:13,400 --> 00:04:15,520 Speaker 2: breast cancer might have a different DNA is this right, 87 00:04:15,560 --> 00:04:19,000 Speaker 2: a Lissa to another one, and that specific one can 88 00:04:19,040 --> 00:04:19,720 Speaker 2: be targeted with. 89 00:04:19,680 --> 00:04:22,680 Speaker 1: A different drug, and that's what crouching these numbers found out. 90 00:04:23,760 --> 00:04:26,719 Speaker 3: I think Professor Keller is well. 91 00:04:26,600 --> 00:04:29,960 Speaker 2: Exactly what up to Amanda, who's just looking at cat videos? 92 00:04:30,279 --> 00:04:32,640 Speaker 1: Anyway, Lissa, it's great to talk to you. Download the 93 00:04:32,720 --> 00:04:35,679 Speaker 1: dream lab app to help fight coronavirus in your sleep. 94 00:04:35,720 --> 00:04:36,839 Speaker 1: See we're doing something here. 95 00:04:37,000 --> 00:04:39,680 Speaker 3: Thank you a Lissa, Thanks so much. Guys appreciate it. 96 00:04:39,720 --> 00:04:40,120 Speaker 3: Thank you. 97 00:04:40,320 --> 00:04:41,200 Speaker 2: That is fascinating. 98 00:04:41,320 --> 00:04:44,120 Speaker 1: It's fascinating and that it is all data. That's what 99 00:04:44,160 --> 00:04:45,840 Speaker 1: it is, and we can help this way. It's something 100 00:04:45,839 --> 00:04:46,360 Speaker 1: we can do. 101 00:04:46,800 --> 00:04:48,520 Speaker 2: Jonesy and Amanda, good morning.