1 00:00:00,000 --> 00:00:04,090 PROFESSOR: OK, very briefly, EyeSpot, 2 00:00:04,090 --> 00:00:06,300 a project we are doing in the Senseable City Lab. 3 00:00:06,300 --> 00:00:09,570 But let me tell you a couple of words about Senseable City Lab. 4 00:00:09,570 --> 00:00:11,760 It's a new research group here at MIT. 5 00:00:11,760 --> 00:00:16,920 It's in between the Media Lab and Urban Studies and Planning. 6 00:00:16,920 --> 00:00:18,780 Well, what do we do there? 7 00:00:18,780 --> 00:00:20,800 What we do is look at this. 8 00:00:20,800 --> 00:00:24,240 That's yesterday's wireless. 9 00:00:24,240 --> 00:00:27,990 That's Marconi's Cape Cod Station. 10 00:00:27,990 --> 00:00:31,260 Look at all of that steel, and engine, and the energy, 11 00:00:31,260 --> 00:00:33,390 and amount of effort, just to transmit 12 00:00:33,390 --> 00:00:36,450 a few bits of information across the Atlantic. 13 00:00:36,450 --> 00:00:38,460 Now look at today's wireless. 14 00:00:38,460 --> 00:00:39,960 It's more like this. 15 00:00:39,960 --> 00:00:44,070 And you can actually transmit much more information 16 00:00:44,070 --> 00:00:44,980 using that. 17 00:00:44,980 --> 00:00:48,270 And what we're doing is rethink in a creative way 18 00:00:48,270 --> 00:00:50,950 the interface between people, mobile technology, 19 00:00:50,950 --> 00:00:52,230 and the city. 20 00:00:52,230 --> 00:00:56,130 Now concerning EyeSpot, this project 21 00:00:56,130 --> 00:00:59,740 is based on the MIT campus. 22 00:00:59,740 --> 00:01:01,230 That's what you see there. 23 00:01:01,230 --> 00:01:06,360 So most of you are familiar with Boston and MIT. 24 00:01:06,360 --> 00:01:09,660 And the interesting thing is that if you talk to anybody 25 00:01:09,660 --> 00:01:12,660 at MIT, they will tell you there is a big revolution happening. 26 00:01:12,660 --> 00:01:15,660 And this revolution is in the way people live and work. 27 00:01:15,660 --> 00:01:19,500 And it's brought by laptop computers and Wi-Fi. 28 00:01:19,500 --> 00:01:23,800 So if you look at this, this is how people used to work here. 29 00:01:23,800 --> 00:01:26,520 And this is more like today. 30 00:01:26,520 --> 00:01:28,860 Now, this is a bit biased, as you see we. 31 00:01:28,860 --> 00:01:32,490 Could find the worst possible image of a computer room-- 32 00:01:32,490 --> 00:01:34,980 dull, boring, natural light. 33 00:01:34,980 --> 00:01:36,330 And this is a sunny. 34 00:01:36,330 --> 00:01:37,860 Day it's not like today. 35 00:01:37,860 --> 00:01:41,380 But the thing is that there's a big change. 36 00:01:41,380 --> 00:01:44,610 And what we are trying to do with EyeSpot is try to define 37 00:01:44,610 --> 00:01:47,130 and see how the change is happening, 38 00:01:47,130 --> 00:01:50,820 and quantify this by allowing people to locate themselves, 39 00:01:50,820 --> 00:01:52,960 actually, with a few-meter accuracy using 40 00:01:52,960 --> 00:01:54,780 the Wi-Fi network. 41 00:01:54,780 --> 00:01:57,240 And so it's a unique case study that we 42 00:01:57,240 --> 00:02:00,090 are starting to monitor, where we got 20,000 people 43 00:02:00,090 --> 00:02:03,990 in the MIT campus, a big urban chunk, 44 00:02:03,990 --> 00:02:06,820 with 3,000 access points. 45 00:02:06,820 --> 00:02:07,890 That's quite a bit. 46 00:02:07,890 --> 00:02:10,919 If you think that cities like Philadelphia 47 00:02:10,919 --> 00:02:14,160 actually are planning to cover all of the city with Wi-Fi 48 00:02:14,160 --> 00:02:20,760 in the next couple of years, and that's less than 3,000, 49 00:02:20,760 --> 00:02:23,970 less than the number we have of access points. 50 00:02:23,970 --> 00:02:28,080 This high density means that we can locate everybody with just 51 00:02:28,080 --> 00:02:29,590 a few-meters accuracy. 52 00:02:29,590 --> 00:02:33,210 This is just a map with some of the access points. 53 00:02:33,210 --> 00:02:40,650 And actually, I'll show you the two maps for the project 54 00:02:40,650 --> 00:02:42,105 you'll see at the museum. 55 00:02:46,820 --> 00:02:47,320 Oops. 56 00:02:50,010 --> 00:02:51,990 The first type of map is a map that shows you, 57 00:02:51,990 --> 00:02:54,480 in real time, the activity going on the network, 58 00:02:54,480 --> 00:02:57,300 and how many people are working in different parts 59 00:02:57,300 --> 00:02:57,930 of the campus. 60 00:02:57,930 --> 00:03:00,120 So you get this type of map changing. 61 00:03:00,120 --> 00:03:02,560 You don't see very well here, but you'll see it better 62 00:03:02,560 --> 00:03:03,780 at the museum. 63 00:03:03,780 --> 00:03:08,550 And the second map is this one. 64 00:03:08,550 --> 00:03:20,510 That's like the heartbeat of MIT. 65 00:03:20,510 --> 00:03:23,450 Now, what you see here is total activity going 66 00:03:23,450 --> 00:03:25,160 on on the campus in real time. 67 00:03:25,160 --> 00:03:26,420 And you see the past week. 68 00:03:26,420 --> 00:03:29,280 So what you see here is all of MIT. 69 00:03:29,280 --> 00:03:31,090 And that's actually a standard day. 70 00:03:31,090 --> 00:03:33,530 You see people coming in, working 9:00 to 5:00, 71 00:03:33,530 --> 00:03:35,450 and then actually, students keeping on working 72 00:03:35,450 --> 00:03:37,970 quite late, even during IAP. 73 00:03:37,970 --> 00:03:41,570 And then you've got a peak and a minimum at 6:00 AM. 74 00:03:41,570 --> 00:03:42,950 And then you've got the next day. 75 00:03:42,950 --> 00:03:45,410 It's interesting-- Friday, Saturday, and Sunday, 76 00:03:45,410 --> 00:03:48,740 when most of the 9:00 to 5:00 activity disappears, 77 00:03:48,740 --> 00:03:53,120 and actually, you've got just the remaining part of the curve 78 00:03:53,120 --> 00:03:58,460 happening, activity is slipping down on Friday evening-- 79 00:03:58,460 --> 00:04:01,022 people going out-- and on Sunday, 80 00:04:01,022 --> 00:04:02,480 when you're starting to panic again 81 00:04:02,480 --> 00:04:03,740 about Monday and the next week. 82 00:04:03,740 --> 00:04:05,000 And then there's another peak. 83 00:04:05,000 --> 00:04:06,375 And then what you see here is you 84 00:04:06,375 --> 00:04:09,140 can do this in every room on the campus. 85 00:04:09,140 --> 00:04:12,170 And what you see here, for instance, is this actual room. 86 00:04:12,170 --> 00:04:15,530 And you see most of the week, it was pretty no activity. 87 00:04:15,530 --> 00:04:16,339 And look at today. 88 00:04:16,339 --> 00:04:19,700 And today, you can get actually up to 25 users, which 89 00:04:19,700 --> 00:04:22,010 is the number of laptops-- 90 00:04:22,010 --> 00:04:23,330 I counted earlier today-- 91 00:04:23,330 --> 00:04:27,447 of people connected to the wireless internet here. 92 00:04:27,447 --> 00:04:28,280 And you see it here. 93 00:04:28,280 --> 00:04:31,055 That's today, Monday. 94 00:04:31,055 --> 00:04:32,180 It will appear in a minute. 95 00:04:36,080 --> 00:04:39,410 Anyway, you'll see the demo at the-- 96 00:04:39,410 --> 00:04:44,840 yeah, here you see the spike, today's activity. 97 00:04:44,840 --> 00:04:49,580 And you'll see the demo at the museum. 98 00:04:49,580 --> 00:04:55,460 Just a couple of things about, just to conclude-- 99 00:04:59,960 --> 00:05:06,200 another project we have is actually with a soccer team. 100 00:05:06,200 --> 00:05:09,530 Being Italian, I am very proud of this project. 101 00:05:09,530 --> 00:05:11,690 And it's with AC Milan. 102 00:05:11,690 --> 00:05:13,910 And here, we are tracking, actually, players 103 00:05:13,910 --> 00:05:17,870 with a couple of centimeter accuracy in real time, 104 00:05:17,870 --> 00:05:21,080 and then developing artificial intelligence algorithms 105 00:05:21,080 --> 00:05:24,200 in order to study their movement and optimize strategy. 106 00:05:24,200 --> 00:05:27,208 So we get this type of traces, and then analyze them. 107 00:05:27,208 --> 00:05:29,750 And then, we've got a number of other projects really dealing 108 00:05:29,750 --> 00:05:34,280 with technology and space, how technology and space interface. 109 00:05:34,280 --> 00:05:37,610 This is funded through a Senseable City Consortium 110 00:05:37,610 --> 00:05:42,500 that's sort of bringing together the different, the key actors, 111 00:05:42,500 --> 00:05:45,260 being part of this revolution from network 112 00:05:45,260 --> 00:05:48,410 operators, hardware companies, urban hardware, 113 00:05:48,410 --> 00:05:50,077 and public administration. 114 00:05:50,077 --> 00:05:50,660 But that's it. 115 00:05:50,660 --> 00:05:53,508 I think if you'd like to have a chat, it will be at the museum. 116 00:05:53,508 --> 00:05:54,050 Thanks a lot. 117 00:05:54,050 --> 00:05:55,300 AUDIENCE: Thank you very much. 118 00:05:55,300 --> 00:05:56,950 [APPLAUSE]