From San Jose, in the heart of Silicon Valley, extracting the signal from the noise, it's theCUBE, covering OCP US Summit 2016, brought to you by OCP. Now your host, Jeff Frick and Stu Miniman. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We are getting towards the end of day two of two days of wall-to-wall coverage at the Open Compute Project Summit in downtown San Jose, California, where it's all about the hardware, it's all about the infrastructure, it's all about what's happening that enables cloud, enables mobile, social, big data, all the things that we've come to love and really take for granted in a way, but this is really where it's happening. We're excited to have somebody who's really right in the thick of it, Ehab Terazi, the CTO of Equinix, welcome. Thank you very much. Yes, we've had Brian Lillian before, and I know you guys made a lot of news years ago on kind of the direct connect with AWS, and so you've been, you know, kind of unnecessarily maybe in everybody's radar, maybe just a step below Amazon and Azure, but you guys have been really active in the cloud space for a long time. Yeah, we have, over the last few years, we had hundreds of clouds come to our data centers, and Equinix is now known as the home of the cloud. We've had the network ecosystem for a very long time, but the last few years, major deployments from all the key cloud providers. We have over 500 cloud providers in our data centers now and almost 1200 cloud and IT service providers. Oh, good numbers. Yeah, so, you know, you guys understand the ecosystem required for cloud. You know, we've been talking about OCP, that's about 100 providers there. You guys are new to OCP, so what brought you guys here? You know, why is it important to Equinix and your ecosystem? Yep, thanks you. This is our first OCP meeting. We joined OCP a few weeks ago under something new called TIP, Telecom Infrastructure Project. That's the same thing that was announced at the Mobile World Congress, if you saw the keynote. And TIP brought in the big network providers, Verizon, AT&T, Deutsche Telecom, and we joined that as well. Those are our biggest customers. We provide network interconnection, and that's why we joined. And what we announced a couple of days ago is that we're expanding that partnership with Facebook and Mesosphere to do even more with OCP. All right, so I believe in the news there was a discussion about Equinix using the wedge switch. We've had a lot of discussion networking probably top discussion, so explain to us why you're doing that and what that really means is the whole cloud now going to be connected by Facebook switches? Yeah, I think the bigger premise is that our customers are adopting OCP and taking advantage of all the OCP standards, the efficiency, the innovation. And I'm sorry, when you say your customers it's the Telecom providers that you're talking about. The Telecoms, the cloud providers, the 100 providers that you have here, most of them are Equinix customers. So all these customers are bringing OCP hardware and standards into our data centers. And what we want to do is enable the next generation of software-based stack to support all this OCP hardware, things like SDN, IoT, et cetera. And with that, we want to leverage Facebook's amazing innovation engine and all the stuff that they've done. So that was part of the announcement. Okay, so you're saying lots of the cloud providers, the one that's not here that we've had a bunch of questions on is of course Amazon. So how does that play into your thinking? A big customer of ours as everybody probably knows and therefore we continue to support Amazon with whatever they want. Our model is based on building ecosystems. So if people want to use the equipment that they built in-house, or they want to use vendor equipment like Cisco, Juniper, or they want to use OCP hardware, we support all of that. And we have massive public exchanges. We also have cloud exchange that we built the last few years to provide direct connect to AWS Azure. But what we're saying now is also for the community of customers who want OCP hardware and they want to innovate there, we're going to be there for them as well. So we support all of it. So why the move? Why did you join Atlas Beats? Been around for a while, you guys hadn't been part of it before. What was kind of the catalyst to say, hey, this is something we want to get involved in? Yeah, this community have gotten to the point now where after innovating in servers and storage, they got to the point to build credible, high-scale networking equipment. The Wedge 100 is one of them. And the community got to the point where there's enough players now want to get together and build open source software to support all this dispersion of hardware. So that plays completely into our game, into our value proposition to build ecosystems. So with this movement, this was the time to join. And you can see all the big telcos announced joining at the same time. So we want to be at the middle of it. We want to solve the issues and we want to be building the next generation interconnection and be forward-looking on technology. So in one of the keynotes yesterday, they had a panel of your customers, those telecom guys. There was a question, AT&T, Verizon, Deutsche Telecom. These guys are competitors, but they're working together. Can you give us your insight? I mean, the telecom world has been a lot of fighting over the years. So are these guys really working together to help the users? Additionally, the telecom companies work very well together through standards bodies, IETF, IEEE, and that's how companies came together, as well as the vendors like Cisco, Juniper, to create standards and build the next generation equipment. I see this as another version of that. I see this as the cloud software world version of that. Where the biggest users and innovators, you know, Microsoft, Facebook, et cetera, Google are all coming together to say, here's what we're innovating. And the network's being part of that. So I see the model continuing. It's just in a new space now. And that new space is more open source driven than the model before where people built, you know, based on specs and standards. And the model here is much more faster, much more agile with thousands of players innovating every day compared to maybe longer cycles that took months to develop. Okay, so being an open source project, what does Equinex bring to the community? What does Equinex? What do you bring to the community? Will you be making contributions? So let me explain what the three of us are doing. Facebook is bringing the OCP skillset hardware and knowledge and innovation. Mesosphere is going to bring their knowledge of the e-source management, container management, which will allow us to build an exchange for this next generation. What Equinex brings is the ecosystem. We have 1,000 networks, 1,300 clouds. We have a very high quality and high performance operations model. We are able to create a physical place where people can come in and test and operate and also a place when we build this for people to deploy. One of the things that are missing today in this open source world is, where do you take your hardware components to deploy? Where do you put it? What kind of software? How do you make sure it can work with other components? So all the stuff that traditionally happened to standards bodies that haven't happened yet, sufficiently, there's a lot of innovation inside a single cloud like a Microsoft or Facebook, but now all of us want to come in and say, how do you build that for the multi-tenant environment and that's Equinex business model? And so we feel this is going to really spark the innovation and bring new players to our data center and allow a lot of people to start to innovate at a faster scale. Okay, so can you unpack for us a little bit the mezzo's piece of that? I think much of our audience are probably familiar with mezzo's sphere, but it's really grown quite a bit in the conversation recently. Mezzo's sphere is already a key provider for a lot of the networks and a lot of the cloud providers here. And what they do is they do basic resource management, plus they also do a lot of specialized services for them. What the piece that attracted us about it is, allows you to do a federated exchange model where customers can own their own containers, control the quality and policy on them and be able to pass them to each other in our data center at very high performance, probably very low millisecond or maybe microsecond latency. But every customer will be able to control the domain, control the containers and policy and feel comfortable that as the service gets split across multiple infrastructure and clouds, they can still feel comfortable, they can support the performance. It's also very flexible, allowing NFE, SDN, all kinds of containers that people are looking for a place to put to go on top of that platform. We're at the beginning of that journey, we see unbelievable opportunity for hybrid cloud. Today, hybrid cloud is a little clumsy for a lot of customers and we want to make it seamless. This is critical for enterprise customers coming in and we see a very good path to that. We also see the need for IoT ecosystems to connect somewhere and be able to move containers and this is going to also be helpful. Yeah, I'm a lot of good points there. So I want to touch on one from the IoT standpoint. You have great connections into the cloud providers, but one of the things that you definitely need for IoT is resources out at the edge. So how do you work with your customers on that? How do you bridge between the edge and the cloud? What is that interconnection? Yeah, IoT is a natural extension of our model. It's very exciting for us because all the wireless devices are mostly coming through the same networks. The same thousand networks we have are the ones controlling the wireless networks. And then the IoT resource management analytics and all the back office processing is done by the big clouds, but also our customers. And the new players, things like what Cisco is doing with Jasper, et cetera, all these guys are also part of our ecosystem and part of our customer base. So physically, most of the IoT ecosystem is in our data centers. What we need to do is enable that exchange of data at a different layer, at the application layer so people can take advantage of the analytics and be able to take advantage of the scale. We see the edge of IoT remaining the same edge of what Aquanax has today and the customers for the most part are the same with few more players in the ecosystem. It's just a matter of enabling a different connection at a different layer to energize and open up that model. Because, for example, some of the IoT ecosystems are done in open Linux. This is above the IP layer and we have to figure out how to enable that. The partnership we have with Mesosphere will enable us to do some of that stuff. It's good stuff. I mean, you guys are right in the middle of all the action. You got to be really excited. You're in a terrific position. I'm curious to get your take on the evolution of the data center itself. We've always talked about the mega data centers because of latency and you guys have Direct Connect AWS. You saw that and that's a big issue. But now with IoT, I think what's interesting is now we need a more compute and more store out to the edge. Where exactly and how close to those devices is still to be defined on an application-specific process. But you see the emergence of all these different footprint data centers based on whatever the requirement is as opposed to just kind of piping everything back to the mothership? Yeah, I think the picture has become clear in the last couple of years. And I agree with you, we're an unbelievable position. It's so exciting to be in the middle of all of that as Aquanax and us as a company. What happened over the last two years is we started with the backbone of all the networks in our data centers. And then the last few years, the backbone and the edges of the big cloud providers moved in. And now you have the biggest infrastructure companies, networks and clouds. What's happening now is the next set of backbone changes, the submarine cables, is starting to be done instead of stopping at the beach to go directly into Aquanax data centers. We have 25 submarine cable projects in the works and some of them were any one. 25 submarine cable projects. Yep, where people are going to come in, skip the beach and go with ducts directly under the beach all the way to our data centers because they're looking for clouds and networks. There's no need to stop somewhere out there when the destination of your traffic is at Aquanax. So that's going to redo the global backbone of submarine cables again against our data centers and Aquanax in the heart of it. And then we see IoT as a natural extension of what we have with the networks and clouds with massive volumes of data and also billions more devices coming in. And with it comes the complexity of 5G, new unlicensed spectrum, new Wi-Fi and the need for analytics cloud-based architecture. So all of that plus the fact that more and more clouds are showing up, more and more application providers. So it's unbelievably exciting. Our announcement here with OCP and what we're doing with Mesosphere is we want to allow our customers to come in and continue to go innovate unstopped. If they want to use OCP hardware, we're going to support it. If they want to use customized hardware, they can do it. If they want to go to the application layer or move containers with policy, we're going to also be first to market and innovate on that. So we feel we have the continuum for customers from public IP, cloud exchange, and now the software layer, OCP hardware. And I mean, I see it every day, that just the momentum is unstoppable. The last thing I want to add is the thing we're really focused on right now is enterprise customers coming in. And now all the pieces are there for them to build hybrid cloud. They can do multiple clouds, multiple networks. And I think you're going to see a lot of our innovation towards enabling them. This piece we announced with IoT and multi seamless multi cloud is specifically targeted to those enterprise customers. What a great close. I don't think I could do any better. I know I couldn't do any better. So yeah, thanks for coming on and sharing your insight. Really a tipping point for Equinix as well as Google to join OCP. And you said you did it because the ecosystem has now developed to a point that it's time. So thanks for taking a few minutes with us. Yeah, thank you very much. It's an exciting time. Absolutely. So I'm Jeff Freik with Stu Miniman. We're wrapping up day two here at Open Compute Project Summit 2016. Again, I want to quick shout out to Micron, Melanox, Pika8, our sponsors without sponsorship. We can't bring the guys, the crew, the gear to cover the events like we know you want us to do and we like to do. We got a really busy month, it's March. So that means March Madness. So keep an eye out for March Madness. We run a concurrent with the real tournament. Cube Madness, Jeff. Cube Madness with March Madness. March Madness and Cube Madness. Thanks Stu. So go on. You'll see, we're working on the brackets. Vote for your favorite Cube interviews. They'll work their way up the brackets. We got a busy month coming up. It's March. We got Open Networks coming up. The cloud march continues. We're going to be at the Google Next show in a couple of weeks. Oracle Cloud World in Washington DC. And then we wrap up the month of March with Big Data SV at the Fairmont Hotel. Our signature event that we do twice a year once in New York or once in San Jose. And that will really wrap up the month of March, but we're off into the races. Go to siliconangle.tv. Look at the upcoming to see where we're going to be.  And then of course, siliconangle.com for the latest news and tech. So thanks again for watching. I'm Jeff Frick, signing off here from Open Compute Project 2016. Thanks for watching.


The National Drug Law Enforcement Agency and the LA has destroyed seized illicit drugs estimated to be over a billion naira in Asaba, Delta States, as part of the fight against drug abuse. The agency says the destruction is a reflection of the alarming rate of drug trafficking and abuse in society. The action was dedicated to the two-year-old Ivan, Omorre Cogbe, who paid the Supreme Prize in the struggle towards making Delta safe and secure from illicit drugs through drug control activities. Ivan, who died during a raid in Okbanam outskirts of Asaba, was named the hero of drug war in Nigeria. Today's exercise is in compliance with the orders of the federal high courts, mandating the agency to publicly destroy illicit drugs, drug-exhibiting, in respect of cases that have been concluded by the court from 2018 to 2022, and these drugs include cannabis sativa, cocaine, heroin, metafitamine, designer drugs, control and psychotropic substance, and most of others. Record-breaking achievements were made, particularly in supply reduction, which manifested in massive drug seizures across the nation. In the light of this, this destruction exercise represents the successes recorded in our effort to combat the drug manage and safeguard the well-being of the nation. It equally reflects the unacceptable rate of drug trafficking and prevalence of drug abuse in our society.


In this video, I'm going to give you five different ideas to help you improve your shooting at a faster rate. Number one, earn your skill level. And what I mean by that is if you wanna be good at shooting, if you wanna be good at anything, you have to be willing to put in the time and the effort. So many players just wanna watch a video. They wanna send me a message and ask me a few questions and they think that that's gonna make them better at shooting. Yes, I can give you ideas. Other people can help you improve. But at the end of the day, you are the person who has to put in the work. If you look at professionals, the top players in the world, these guys who are scoring 50 goals a season, they're scoring hundreds of goals every day in training. They're taking hundreds of thousands of shots every single day. So when they show up on match day, shooting is natural, goal scoring is natural. So if you wanna get to that level, well, you have to earn that level by actually putting in the practice, getting to the field every day. That's the best thing you can do for your shooting. So stop looking for a quick fix, a magic button or a magic pill that you can take that's gonna make you good at shooting. That's not how it happens. You have to put in the work and you have to earn your skill level. Number two, train realistically. I see this every time I go to the field. Kids are practicing. What do they do? They just hit free kicks for an hour straight. And it's good to be good at free kicks. Obviously, I want you to be talented in every way. I want you to be good at free kicks. It's great that you can hit a knuckle ball. But when you're playing in your game, chances are you may go 90 minutes and never take a free kick. So really, what are you spending your time on?  So you need to train realistically. Train shooting off the dribble, off a turn, after a move, after controlling a ball out of the air. Train realistically and put yourself in situations that you're actually going to experience on game day. This is going to allow you to have more confidence in front of goal. Number three, use about 80% of your power, especially if you're still learning how to shoot. You're not a master at shooting yet. The biggest mistake that players make is trying to hit the ball too hard. And if you can't hit the net consistently, it doesn't matter how hard you can hit the ball. I say this all the time, but still people miss this point. Try to put, yes, I still want you to obviously hit the ball hard. You have to have a nice firm foot, flexed ankle. You want to put some pace on it. But use about 80% of your power and focus on getting the ball on net. If you can hit the ball on net, you're going to score goals more often. You don't have to hit a rocket into the top corner and go bar down or go post in, okay? You just have to get it on net and you will score goals. So focus on getting your shots on target and using about 80% of your power is going to help you do this more consistently. This will also increase your accuracy. Number four, figure out your flaws. So when you miss a shot, don't just move on. Try to hit that shot again. If you're practicing with a teammate, make him play you the same pass again and again and again until you can get that shot on net. Or if you are trying to figure out a certain skill, for example, when I hit with my left foot, I have a tendency to pull it across my body. So I'm going to practice that shot.  I may have to focus on specific things. So the next time that that shot comes, I'm going to get it on net. I'm not going to make the same mistakes again and again, like so many players do. On my right foot, a big thing that I do when I'm curling, I'm trying to do a curl finish. I'll hit it off the inside of my foot too much rather than my big toe. So that's something that I always focus on. And if I do make that mistake, again, I want to get on that ball right away and try to figure out my personal flaws. So when you get those tendencies or when you get in situations and you're about to take that left footed shot or that right footed shot, you know what you specifically have to focus on. So you do not make the same mistakes again and again. Number five, and this one sounds a little ridiculous, but it's so true, focus on your focus. And what I mean by that is focus on being more concentrated on what you're actually doing. When you're in the game, chances are most players do this, they're not actually focusing on their technique of shooting. They're thinking about what their coach is going to say or their teammates are going to yell at them if they miss the shot. They're thinking about a shot that they missed before, an opportunity they missed before. They're not present in the moment. This is why so many players are good at shooting when they're by themselves at the field, but they can't replicate it in a game situation. It's because when they're on the field by themselves practicing, they're just focusing on the ball, they're focusing on their technique. But when they're in the game, all these other things pop up. So when you're shooting, focus on concentration, especially when you're getting tired, you just snatch at the ball, you just swing your leg. You don't focus on everything that you've been working on throughout the week or leading up to that shot. So it's so important that you're actually focusing on concentration being 100% present in the moment.


Rhaid i ni'n adnolio am y gwybod i'ch eu gwaithau'n gweithio'n gwneud? Diolch yn gobeithio aruddwyd, ac ychwanegu'n gymhwyng ychydigodd dros ar gweithio'n gweithio'n gopadau ac rhaid i ni'n ffryd i'r ffordd hynny? Roedd ein ffordd ffordd hynny yn rhoi'r thysg hynny oherwydd gweithio ar draws i wychau fel gweithio ardeg, nad oedd yn chi yn gweithio'r gweithio'r wlad. I'm looking for natural explanations and the subject matter of philosophy and this is one of the things that's so wonderful about philosophy is everything. I mean as a philosophy you're not constrained to looking at one part of the world. There's a philosophy of physics, there's a philosophy of psychology, there's a philosophy of religion, there's a philosophy of mathematics, there's a philosophy of absolutely everything. That's because absolutely every subject is a subject that requires concepts that must be applied. So for example, if you're a scientist, then you'll look for causes. So the causal relation is extremely important to you. But a scientist doesn't stop and say, well what is causation? That's the job of a philosopher. Mae'r ffordd wedi oedd – yn ymddangos, dyma y byddai, yn cael ei rhan o'r armennig, rydyn ni'n cael ei rhan i ddweithio ar gyfer yr effeith? Ond yna ni'n bobl beth yw yma, e'ch gwrs yn gweithio ag ysgrifennu, e'ch gwrs yn gweithio argron. Yn ni'r menthe gweithio, mae gwnaethau y gweithio ar gwahanol y gallAN, ond mae hynna amser dyna bod dyl Fanolol yn ni ffawr yn gweithio. Dyna gweithio ar y gweithio drws unrhywbeth eich ei ging返— aroed yn y cyffredin, ar y cyfredin, ac yn y cyfredin iawn cyfrifhau, yna gweithi gynhyrch. Yn ni, mae hi'n gweithio ar gyfer y gweithio ar gyfer y gweithio, I hon, wrth gwrs, cymryd pa'r rhan o gyfnod MMR tofodd fod yn meddwl i'r rhan o'r troi at ddiwedd o'r eu cyfeirio yn yr awtstlu. Ond mae'r amguedd iddyn nhw'n meddwl. Mae'r amguedd iddyn nhw'n meddwl iddyn nhw? Mae'r hyn yn mynd i ddiwedd ar gyfer y gallan hwn nhw? Ond mae'r amguedd iddyn nhw'n meddwl yn meddwl i'r amguedd iddyn nhw?  Yn ymgyrch, mae'n gweld ffilosofi oes. Ymgyrch, mae'n gweld ffilosofi oes. Yn ymgyrch, mae'n gweld ffilosofi oes. Felly, mae'n gweld ffilosofi oes. Mae dyna, a weithio y gall person i chi'n gweld ffilosoio oes, am 400 oes filoedd a mynd i chi'n gweld ffilosняu hyfer oes yn oed, this is a very common assumption these days that we assume that the mind and the brain are the same thing. The question of what they are is the same thing is a very important philosophical question to which the answer is always certainly no. Because if the mind is the same thing, if you think of a neural state that's a bit electrical activity in the brain a neural event, yr event mirrors it's a let's take a belief essential condition for a belief is this is but to have a content okay all beliefs are about something aren't they you don't have a belief that isn't about something what is property of about nice is it's a very important property okay a brain states could have a property of being about anything Unw. So, ffx, yw'n mynd? Michelle. I'm entertaining a thought about Michelle right now. Okay, and you are too because you're thinking it's the ladies sitting at the front, etc. Now, do you think that I could have that very thought if Michelle didn't exist? Could I have that, the thought that I'm having, if Michelle didn't exist? No. Okay. Ond efallai'r gyfan hyn yw. Mae hynd yw ddechrau hon ar y dyma, dwi? Dyma fynd i du bywiau yn gwneud. Mae'r gwelliedig dyma'n ddechrau sydd wedi cael effaith cyf...... Wyddech chi'n rhai mynd ei gweld. Yn ysaf, mae'r peth yn ysaf sydd wedi'i cychelfogol i'w bwnt â blantau a gwtaith ffocl. Ac mae'n fideoch hefyd, ei gweithio efallai. Ond mae'r gwelliedig byddwch chi'n gweithio? Udych chi'n gweithio? Llyfr addysg. No. My bwysig yn y ddweud y dyma yw'r byd yma yn y dechrau llyfr nid yw, rwy'n nhw. My rydyn nhw'n fawr ddim yn cael ei fawr i'r bwysig. Yr hwn yw'r bwysig yw'n ni'n cael ei fawr i'r bwysig. Rydyn nhw'n gallu bod yw'n gofod ar y cyfnodd ac yn cael eu bwysig yw'r bwysig. Ond ydy'r rhesysau sy'n gwybod i'r byd i'r ddau a'r ffordd o'r ddau sydd yn fawr, ond mae'n ddau'r ddau. Yn ddau'r ddau'r ddau, nes ffilosfyrd yn ddau. Yn ffilosfyrd, ac yn ddau, mae'r ddau'r ddau, ac mae'r ddau'r ddau'r ddau, yn ddau'r ddau, yn ddau'r ddau, mae'r ddau'r ddau sydd yn ddau'r ddau, yn ffilosfyrd, dyma? Merseydiadol ei amser. Eyna yn dda ay flynyddoedd dyma thriker i Emwy, a mae dechrau'r ddau sydd yn fawr. yn dych Llingwyr Rh would.ذ yma mae'r ddau syd wedi eu hyfforddiadol ei newinkr關係. So, y cwestiw y Mae'r lleihwyr iawn, dwi'n rhan o adrwm, dwi'n rhan o adrwm mae'r llyfr yn cael ystod mewn nifer y sylwedd. Mae'r llyfr yn cael y llyfr yn cael ei ddemelly. Yn amser hynny mae'n gwybod wedi'u hanfod o'r listol, ac yno Fyarnau i'n gwybod i'r listol, a fyddai hynny ddwn i'r listol a fyddai hynny diolch. Maent ymmell pethau mae'n muddd ystod i'r listol, mae hynny'n cydain bod ydych i'r hynwn o boblach, i'r fath, i'r mynd i'r hunain, i'r hunain a'r hynny'r hyfforddiadau cyfforddiadau rhywb sy'n fath. Ond y overlap cyflomii... Felly, y fathau llunio'n ddod yn mynd i ddod yn y llunio cyfforddiad yma ym ysgrifennig yw mewn. Ac mae'n ddod y hyfforddiad yma, wedi'u ddod y fathau llunio cyfforddiadau, mae'n ddod i'r fathau llunio cyfforddiadau, We have no more reason to think they exist." It's a bit fishy you might think, but think about a woodless. That would make it seem like they are under rocks. It likes the damp and it believes it's damp under rocks. That's why it intends to go for a rock. Every time you pick up a rock you will find wood lice. What do I do in my garden? Mae'r ddau'r hyffordd gwyl. Mae'r ddau-wpedigau fel arweithwrs. Mae'n ddamp ar mwyloedd. Mae'n ddamp ar mwyloedd yn olygu arwyr, mae'r ddamp arwyr yn cael ei mynd neu'r olig o'r hyffordd. Mae'r ddamp ar mwyloedd yn contradictor o'r mechau. Fe wnaeth mae'r ddamp oes teimlo, mae'r ddamp ar ymddangos o'r wwloedd yw'r wwloedd. Mae'r weddangos o'i ddamp. Mae'r rhaid i ddesi codi'r astud ..Great Britain, but it really doesn't matter. It's a behavioural mechanism that we share too. And once they fly into the light, they have a photo taxi, positive photo taxis, they fly towards the light. Once you know that that's why a woodlouse does what it does, we don't have to attribute beliefs and desires to the woodlouse. In fact it becomes pointless. We eliminate the idea of woodlouse beliefs and desires. There is no such thing as the woodlouse's mind. The woodlouse doesn't have a mind. We don't need to postulate a mind. We can explain all its behaviour without appealing to a mind. And the eliminateivists think we can explain all your behaviour without appeal to a mind as well. So isn't it odd though? Can we have reason to believe that we don't have beliefs? Actually we can. The eliminateivist argument is a very very good one. And you can't just reject it by saying it defeats itself because we have to believe that eliminateivism is true. We have to have reasons for believing it's true. If they're right that we can explain all human behaviour and of course what we explain human behaviour in terms of is brain states. So the eliminateivists will agree that brain states are not mental states. They agree that there is no identity but they think as it's brain states that explain our behaviour we can just reject mental states. We don't need them at all. So counter-intuitively you don't have beliefs or desires or intentions and you're not rational. Sorry to bring that news to you. So I'm just going through a couple of questions here. That's philosophy of mind, that's the sort of thing. Are animals rational? Are pianos rational? I mean maybe this piano thinks that it's at the centre of the universe and it likes being there so it's not going to move for anyone. That's why it's staying still in this awkward position. Okay that's philosophy of mind. Let's have a quick look at philosophy of language. Okay what is meaning? I mean isn't it interesting that all I'm doing is making noises and if I had a flipchart I could make squiggles on a board. And you would look at them and you're listening to me and you're understanding what I'm saying. You're grasping it, you're somehow grasping the content of what I'm saying. Is that pretty magical? And it is extraordinary isn't it? What is meaning? Why do my words have meaning? And we could say that it's something like, I mean there's such a thing as a natural meaning so smoke means fire.  But meaning isn't like that. So I can say, what's your name sir? Zee. So Zee asks me, is so and so a good philosopher? And I say his handwriting is excellent. Now the words I've just uttered have not in your mind before, I don't think unless you've listened to me before, they're correlated with the meaning that they have in that context. But you all know what I've said don't you? You can all work out that meaning immediately and you don't work that out from the strict and literal meaning of the words that I used because the words that I used don't have the strict and literal meaning that I used them to convey. I'm not tied up in my own syntax there but. So what is meaning and how do words get meaning? And some people think that meaning comes from use.  So we ostensibly define something by saying that's blue. Well when I point like that, what am I pointing to? I'm pointing to curtains, I could be pointing to folds of fabric, I could be pointing to fabric, I could be pointing to the colour. How do we manage to pick up from the information that we have as children when we're taught what exactly words mean when they're pointed out to us? So that's the sort of thing you look at in philosophy of language. The other one is reference, so z refers to him. Well how does it do that and how does a name latch onto a person? How does reference, and just in the same way as how meaning comes about, how does reference come about? This is the sort of thing that philosophers of language think about. Philosophy of science, I've talked about already causation, which is a key topic in the philosophy of science. But also such things as observation being theory laden. What do I mean by that? Well you may think that what a scientist does is they observe the world, so either by observation or perhaps by experiment. And then they take down what they see and then they use logic to build a theory on the basis of that observation. So science is involved in observation and logic. You get a theory and then you test the theory because the theory will predict certain observations and you test whether the predictions that you've derived from your theory are in fact true or not. And if they are then your theory is confirmed to a small extent and if they're not your theory is falsified. The trouble is that you can't make an observation that isn't informed by theory. So here, let's take red for example. What do you think the word red means? Would anyone like to have a go at telling me what the word red means? Well it's a colour, but we all know it's a colour because it's like the word red is a symbol that we've agreed upon to mean a certain thing. So because we want to put the same language. Okay, you're absolutely right, but what I'm asking is what is that meaning? Don't I have a flip chart somewhere? I did ask for one. Okay, so you think the meaning of the word red reflects light at 650 nanometres? Anyone have any other meaning of the word red? A social construct that has different meanings in a different context. Can you give me an example of two different meanings of red? Red dead. Yes, that's a good one. Yes, there's a correlation, isn't there, an association between Manchester United and between socialism and red. But that's not usually thought of as the meaning of red. But it has different symbolism in different cultures. So for example in the West it would be seen as an indicator of danger. In China it would be seen as a symbol of prosperity. And in India it's a symbol of marriage, isn't it? Yes, okay, but these are associations rather than meanings. Okay, let's take the meaning that you've given me, but red is that which reflects light at 650 nanometres. Okay, let's do a thought experiment about that. Now a thought experiment is what philosophers do and it's very comfortable because we don't have to leave our armchairs to do it. A scientist does an experiment in the laboratory. And the scientist is constrained in the design of that experiment by the laws of nature. Now a philosopher is also constrained in the thought experiment he or she conducts, but not by the laws of nature, rather by the laws of logic. But we're trying to do the same thing. We're saying okay, the idea is that A is B. Well, if I can design an experiment that pulls apart A and B so I get A without B or B without A, then I know that A and B are not the same thing. Okay, so it would work exactly the same way in the laboratory as it would in your mind. Okay, so let's imagine a cosmic ray of some kind. Cosmic rays are very useful to philosophers. What the cosmic ray does is it makes objects like, let's pretend your jacket is red. It's probably an orangey sort of colour, isn't it? But let's say it's red. Okay, so a cosmic ray hits and it makes things like that, reflects light instead of its 650 nanometres. It reflects light at 400 nanometres. Now usually that would mean that we start to see it as blue. But this is a very odd cosmic ray because what it does is it changes the wavelengths but it doesn't change our experience. So we continue to see the jacket look like that, but it reflects light at 400 nanometres. Okay, has the colour of that jacket changed or not? Put your hand up if you think not. It looks like that still. Has the colour changed? Put your hand right up so I can see you. Don't be half-hearted. Okay, so most of you think not. Okay, who thinks the colour of that jacket has changed? And who has put in their hand up twice that has not allowed? You can't have both. You can have neither because sitting on the fence is always a good thing to do. Those of you who put your hands up the second time are wrong. There's no way we would say that the colour of the jacket had changed. When all that had changed is the wavelength of the light reflected from the jacket. If the jacket looks like that, we would say that it's still red or orange or whatever it is. What we learn from that is that red isn't the wavelength or rather the meaning of red is not the wavelength of light. We know that the colour red is realised. There's that word again. I used it before when I was talking about the mind. We know that the colour red is realised by objects reflecting at light at 650 nanometres. But that's not the meaning of the word red because the meaning of the word red is far more closely linked to our experience of colours than the fact that colours reflect light at certain wavelengths. I'll come to you in a second. That gives us another hypothesis about the meaning of the word red. Is the meaning of the word red that experience I have when I look at... What's your name? Jillie's jacket. Jillie? I'm having a certain experience when I'm looking at Jillie's jacket and so are you. Is red that experience? Put up your hand if you think yes it is. OK, you're wrong as well.  OK, this is why you're wrong. OK, when you're teaching a child the colour words. OK, so you say that's blue, that's red, that's da da da da. Eventually, having done that a bit, you point only to curtains. You're asking for trouble, aren't you? You also point to other things. You point to jackets and carpets and jumpers and chairs and other blue things. But eventually you'll start saying, is that blue? And you might point to something red and say, is that blue? And what you're hoping is that the child's going to say no. Now, you have no idea what the child sees. You don't know what the child's experience is like, do you? So how can you check that if the word red means that inaccessible private experience? So you have no idea what I see when I look at Jilly's jacket, do you? You probably assume that you're seeing what I see, but you have no idea whether that's true or not. And yet we managed to communicate by means of that word red. We think it has the same meaning for all of us. If you're teaching a child, you want that child to grasp the meaning that you grasp, but you'd never be able to check it, would you? If red means a private experience, a nonexperienced, this is essentially private. So we know that the meaning of the word red is neither the wholly objective wavelength that an object reflects light at. And we also know that it's not the wholly subjective experience that we have when we see a colour. So what does red mean, do you think? Do you want to have another go? It's not blue. It's certainly true that you haven't grasped the word red unless you've grasped that it's not blue. You've got to distinguish things. That's true.  OK, when you say defined brain states, do you mean that? What does he mean? He means experience, actually, doesn't he? Actually, the fact is we might all have a different experience when we look at red. It's hugely unlikely, but it is possible, it's logically possible. What red is, it's one of those concepts that actually you can't define without circularity. An object is red if it appears red to a normal human being under normal circumstances. So you can't define the word red without appealing to the appearance of red, but you also can't define it without appeal to objects. Of course, the reason that objects that are red appear red to all of us, instead of just one of us, is because it's not the private experience that matters. As Wittgenstein puts it, you have to have an experience when your mother is teaching you red, and that experience must be different from the experience you have when you look at blue, but actually it doesn't matter at all what that experience is as long as you have it and it's different. Wittgenstein said, it's not a something, but it's not a nothing either, your experience of red. So what we were doing there was conducting a thought experiment. We were putting things apart in logic to see whether we were right to identify them and we see that we weren't right to identify them. We've got to pull them apart. Now there's a question there, but there's a question there first. Can you speak up? No, no, it doesn't turn blue. It starts reflecting light at 400 nanometres instead of 600 nanometres. That doesn't mean it turns blue. That's what we're asking whether that does mean it's turned blue. Well, when I gave the actual definition of red, which is that an object is red, if it appears red to normal human beings under normal circumstances, the reference to a normal human being is absolutely vital. And so is the reference to normal circumstances because, of course, red objects do not appear red if they're under a strange light. So it's not required that a red object must always appear red. It only appears red to normal human beings under normal circumstances. And you and then we must move on. I'm sort of troubled by this. As a physicist, I'm not so interested in the object as in the light that comes from it. As a physicist, I know that red light has certain wavelengths. So it depended of the object, what you see coming into your eye as a certain wavelength. If you have a prism, it will split white light into a number of colours. You look at red, what you're seeing is a particular wavelength. But what you're telling me is the empirical story about what we have discovered redness to be. When an object appears red to a normal human being under normal circumstances, we have discovered, science has discovered, that that object is reflecting light at 600 nanometres. So that's something we've discovered about redness. I'm sorry, but the object, the light is reflected and deflected from the object. And that's what we see as redness, isn't it? Can I just say that there is a scientific experiment where the... I actually don't care. Can I stop you? But you project the two secret bars, which are the two discrete things. And you can see all the colours. Absolutely sure you can. You can see it even though you're looking at the other light. So you perceive it, so it's your argument. What we're looking at is not what the scientific story about redness is. So, ffyrdiwch, gwyllt gael atomic number 79. Does gold mean atomic number 79? No, it doesn't. Because you know… Did you know that it was atomic number 79? Okay, does that mean you didn't understand the meaning of gold before you came here? You think you did understand the meaning of gold. I understood my meaning of gold. Oh, oh! Is that your husband? Os rydw i gondol Ier digwydd oedd y gallwn gwirionedd. If you'd asked him whether it was gold... It was what it was, you're ahead of consensus. Oh, my Scottishman! Ha ha ha ha! Do you see what I mean? We communicate... I mean the meaning comes when we can communicate by means of words. We knew what gold was. When science discovered that the atomic number of gold is 79, what science discovered was something about gold. finished to and she didn't change the meaning of gold. It gave us an extra belief about what gold was and in exactly the same way we had the meaning of the word red before science came along and told us that redness was what happens when a light is reflected off an object at 615nm. I'm not interested in any way of physics, sorry, because we're going on to politics and I want to leave at a time for a couple of questions here, sorry you had a question didn't you? It's really simplistic. Good. But it just occurred, if we observe and indoctrinate the embryo, for example in time it's really... I think I prefer physics. How do we know that this is the trend? That's quite a bit more simplistic. Roed is not the truth, whatever is the case. True, I mean actually I'm quite happy to talk about true if you like because truth is a very interesting philosophical thing. Can I talk about it in a minute? I'm going to talk about truth on the next slide. I'm just going to say politics, philosophy of politics, what's political philosophy? What's justice? What's equality? What are the principles by which we should govern society? I mean should we... Let's look at the distribution of property. I mean one of the jobs that a government does is back up some distribution of property. It might be equality or it might... I mean in this country for example it's not equality exactly but equality is important because we have a redistributive society don't we? So you, what's your name? Patricia. Patricia, you go out to earn a living and 22% at the very least of everything you earn is taken away from you by the government. Well why is the government making you slave for that 22% of time? For nothing. How can you justify that? Well we justify it on the grounds that 22% goes to build hospitals, roads, schools etc. And we might ask well if you don't have children, I don't have children, why should I pay for schools? I'm a pretty healthy person, why should I pay for hospitals come to that? So there's a principle of justice behind the idea of how you decide to distribute wealth within a society. You might decide not to get involved at all, so whatever you earn you keep. So there's no income tax at all. Now you might think you might, you would like that society but think about a society in which there isn't any income tax charged. There's no taxation so there wouldn't be any state built hospitals, roads, schools etc. So that's just one issue in political philosophy. Religion of course, the big one, does God exist? What's he like? Is free will compatible with determinism? So we think that the actions we perform, or some of them at least, are freely chosen by us. But actually you might also think that they're causally determined. So the laws of physics being what they are, everything you do is a result of the laws of nature plus whatever the conditions were just before you did it in your brain apart from anywhere else. Well can an action be both causally determined and freely chosen? And if so how that looks like a logical problem doesn't it? A token action, so just one action lifting my arm up there, was that both causally determined and freely chosen by me? And if so how? Well a soft determinist would say yes because free will and determinism are compatible. A hard determinist would say no free will and determinism aren't compatible and you were determined. That's why I raised my arm. And a libertarian would say they aren't compatible but you freely chose to raise your arm. And so there are three different positions. And of course all of us want to be compatibilists. We all want to think that you're both free and determined. But actually then you've got the logical problem of whether a token action can be both free and determined. So that's philosophy of religion. Of course we haven't got free will though there isn't any morality either. Because an action is moral only if it's freely chosen.  You might be put in hospital but you're not put in prison. And okay if any subject matter is peculiarly the province of philosophy. I've said the province of philosophy is everything. But these four are peculiarly the province of philosophy logic. I mean some of you might have listened to my podcasts. There are podcasts on the critical reasoning. I look at what is an argument. How can we analyse an argument? How do we evaluate an argument and so on? So logic is the methodology used by philosophy. Now it's used by scientists too. It's used by human beings all the time. You are here today because of a little argument that went... OUDC said it was going to have an open day. I'd quite like to go to the open day. Therefore I'll go there now. Well that sort of practical reasoning is what brought you here. So argument is what human beings do all the time. To be rational is to argue. And what a philosopher does is stand back and look at what arguments are, what counts as a good argument, why it counts as a good argument and so on. Ethics. I notice that these two have something in common. What they have in common is that they're both normative.  It conducts experiments to look at the way the world is or the way the world could be under different situations. What it's not interested in is the way the world should be. That's the province of philosophy. So you should argue in a certain way. If your arguments are bad they shouldn't be. And we all understand that. If you derive from a set of premises a conclusion that actually doesn't follow from that conclusion, there's something normatively wrong with that argument, isn't there? You have argued badly. So bad and good come into it. Right and wrong come into it. Should and shouldn't come into it. And exactly the same thing happens here with ethics. So just as there are norms about their standards in accordance with which you should argue, in exactly the same way there are standards in accordance with which you should behave. And you can leave your knife any way you like, but not in my chest. And that's because it's wrong to do that. So a philosopher would say, well what does it mean to say something's wrong? What is it for an action to be wrong? What is it for an action to be right? Which actions are right? Which actions are wrong? And of course in the public sphere that becomes political philosophy. This ethics writ large is politics. Metaphysics. When you're doing metaphysics, what you're looking at is your ontology, your list of what exists. And you're also asking what its nature is. So okay, we looked at to do mental states exist and the eliminativists think not. The rest of us think yes. But once you think that mental states exist, you've got to say well what are they? What distinguishes a mental state from a physical state? We know that physical things occupy space. I mean there may be things in the very small sphere that occupy space in a rather odd way. But certainly all macro objects are three dimensional. Well are mental states three dimension? Of course they are if they're brain states. But we've already looked at whether they're brain states. Maybe they're not. In which case maybe minds are not inside the head. They're not located in space in the way physical objects are. And if I can't think about Michelle without Michelle existing, it looks as if my belief about Michelle is a relation between me and Michelle rather than something that's located in my head. Doesn't it? So that's metaphysics, an epistemology. Well okay I'm talking about how we can know things about what's true and what's not. Well how can I justify claim to know that something's true or not?  You wish you hadn't signed on now. So you have reason to think that you're watching a lecture. Of course you do.  But you know as well as I do that you could wake up any minute and you're up home in bed. So the reason you have for thinking you're in a lecture is not conclusive. Can you have conclusive reasons for anything? And if you can't, then can you know anything? What is knowledge anyway? Knowledge involves truth. You can't know something false and before you will shout at me you can believe that you know something false but your belief is false. So you can think you know that the earth is flat and there was a time when lots of people believed they knew the earth was flat but their belief that the earth was flat was false and therefore their belief that they knew the earth was flat was also false because a belief that's false cannot be knowledge. And if you do it in epistemology you know that. So this was built as philosophy in 45 minutes and I think that's 45 minutes. I hope I've convinced you that philosophy is the best subject in the world and if you want to do any more there's all these things that you can do including my podcasts which are completely free and available and I tunes you. Thank you for listening.


I introduce Whitney Merrill, she is an attorney in the U.S. and she just recently, actually last week, graduated to her CS Master's in Illinois. Without further ado, Prittin Crime in a big data world. Hi, everyone. Thank you so much for coming. I know it's been an exhausting Congress, so I appreciate you guys coming to hear me talk about big data and crime prediction. This is kind of a hobby of mine. I, in my last semester at Illinois, decided to poke around what's currently happening, how these algorithms are being used, and kind of figure out what kind of information can be gathered. So I have about 30 minutes with you guys, I'm going to do a broad overview of the types of programs. I'm going to talk about what predictive policing is, the data used, similar systems in other areas where predictive algorithms are trying to better society.  So imagine, in the very near future, a police officer is walking down the street wearing a camera on her collar. In her ear is a feed of information about the people and cars she passes, alerting her to individuals and cars that might fit a particular crime or profile for a criminal. Early in the day, she examined a map highlighting hotspots for crime. In the area she's been set to patrol, the predictive policing software indicates that there's an 82% chance of burglary at 2pm and it's currently 2.10pm. As she passes one individual, her camera captures the individual's face, runs it through a coordinated police database. All of the police departments that use this database are sharing information, facial recognition software indicates that the person is Bobby Burglar, who was previously convicted of burglary, was recently released and is now currently on patrol. The voice in her ear whispers, 50% likely to commit a crime. Can she stop and search him? Should she chat him up? See how he acts? Does she need additional information to stop and detain him?  Did the algorithm take this into account or did it just look at his face? What information was being collected at the time the algorithm chose to say 50% to provide the final analysis? So another thought I'm going to have you guys think about as I go through this presentation is this quote that is more favorable towards police algorithms, which is, as people become data plots and probability scores, law enforcement officials and politicians alike can point and say technology is void of racist profiling bias of humans. Is that true? Well, they probably will point and say that, but is it actually void of racist profiling of humans? And I'm going to talk about that as well. So predictive policing explained. Who and what? First of all, predictive policing actually isn't new. All we're doing is adding technology, doing better, faster aggregation of data. Analysts in police departments have been doing this by hand for decades. These techniques are used to create profiles that accurately match likely offenders with specific past crimes. So there's individual targeting and then we have location-based targeting. The location-based, the goal is to help police forces deploy their resources in a correct manner, in an efficient manner. They can be as simple as recommending that general crime may happen in a particular area or specifically what type of crime will happen in a one-block radius. They take into account the time of day, the recent data collected, and when in the year it's happening as well as weather, et cetera. So another really quick thing worth going over, because not everyone is familiar with machine learning, this is a very basic breakdown of training an algorithm on a data set. You collect it from many different sources, you put it all together, you clean it up, you split it into three sets, a training set, a validation set, and a test set. The training set is what is going to develop the rules in which it's going to kind of determine the final outcome, you're going to use a validation set to optimize it, and finally, apply this to establish a confidence level. You'll set a support level where you'll say you need a certain amount of data to determine whether or not the algorithm has enough information to kind of make a prediction. So rules with a low support level are less likely to be statistically significant. And the confidence level in the end is basically if there's an 85% confidence level, that means there's an 85% chance that the suspect, for example, meeting the rule in question, is engaged in criminal conduct. So what does this mean? Well, it encourages collection and hoarding of data about crimes and individuals, because you want as much information as possible so that you can detect even the less likely scenarios. Information sharing is also encouraged because it's easier, it's done by third parties or even what are called fourth parties, and shared amongst departments. And here, you know, criminal data, again, was being done by analysts and police departments for decades, but the information sharing and the amount of information they could aggregate was just significantly more difficult. So what are these predictive policing algorithms and software, what are they doing? Are they determining guilt and innocence? And unlike a thought crime, they're not saying this person is guilty, this person is innocent. It's creating a probability of whether or not the person has likely committed a crime or will likely commit a crime. And it can only say something to the future and the past. This here is a picture from one particular piece of software provided by HunchLab. And patterns emerge here from past crimes that can profile criminal types and associations, detect crime patterns, et cetera. Generally in these types of algorithms, they're using unsupervised data that means someone's not going through and saying true, false, good, bad, good, bad. There's just one, too much information. And two, they're trying to do clustering, determine the things that are similar. So really quickly, I'm also going to talk about the data that's used. There are several different types, personal characteristics, demographic information, activities of individuals, scientific data, et cetera. This comes from all sorts of sources. One that really shocked me was, and I'll talk about it a little bit in the future, what is the radiation detectors on New York City police are constantly taking in data, and it is so sensitive it can detect if you've had a recent medical treatment that involves radiation. Facial recognition and biometrics are clear here. And the third party doctrine, which basically says in the United States that you have no reasonable expectation of privacy and data you share with third parties, facilitates easy collection for police officers and government officials because they can go and ask for the information without any sort of warrant. For a really great overview, a friend of mine, Dia, did a talk here at CCC on the architecture of street level panopticon. Does a really great overview of how this type of data is collected on the streets, worth checking out because I'm going to gloss over kind of the types of data. There is, in the United States, what they call a multi-state anti-terrorism information exchange program, which uses everything from credit history, your concealed weapons permits, aircraft pilot licenses, fishing licenses, et cetera, that's searchable and shared amongst police departments and government officials, and this is just more information. So if they can collect it, they will aggregate it into a database. So what are the current uses? There are many, many different companies currently making software and marketing it to police departments. All of them are slightly different, have different features, but currently it's a competition to get clients, police departments, et cetera. The more police departments you have, the more data sharing you can sell saying, oh, by enrolling you'll now have X, Y, and Z police departments data to access, et cetera. These here are Hitachi and Hunch Lab. They both are hotspot targeting. It's not individual targeting. Those are a lot rarer, and it's actually being used in my hometown, which I'll talk about in a little bit. Here the appropriate tactics are automatically displayed for officers when they're entering mission area. So Hunch Lab will tell an officer, hey, you're entering an area where there's going to be burglary that you should keep an eye out, be aware. And this is updating in live time, and they're hoping it mitigates crime. Here are two other ones. The domain awareness system was created in New York City after 9-11 in conjunction with Microsoft. New York City actually makes money selling it to other cities to use this. CCTV cameras are collected. They can, if they say there's a man wearing a red shirt, the software will look for people wearing red shirts and alert police departments to people that meet this description walking in public in New York City. The other one is by IBM, and there are quite a few, you know, it's just generally another hotspot targeting. Each have a few different features. What I'm mentioning too is the heat list. This targeted individuals, I'm from the city of Chicago, I grew up in the city. There are currently 420 names when this came out about a year ago of individuals who are 500 times more likely than average to be involved in violence. Individual names passed around to each police officer in Chicago. They considered the rap sheet, disturbance calls, social network, et cetera. One of the main things they considered in placing mainly young black individuals on this list were known acquaintances and their arrest histories. So if kids went to school or young teenagers went to school with several people in a gang and that individual may not even be involved in a gang, they're more likely to appear on the list. The list has been heavily criticized for being racist, for not giving these children or young individuals on the list a chance to change their history because it's being decided for them. They're being told you are likely to be a criminal and we're going to watch you. Officers in Chicago visited these individuals would do knock and announce where they knock on the door and say, hi, I'm here, like just checking up what are you up to, which you don't need any special suspicion to do, but it's, you know, kind of a harassment that might cause a feedback back into the data collected. This is pre-cobb. It's currently used here in Hamburg. They actually went to Chicago and visited the Chicago Police Department to learn about predictive policing tactics in Chicago to implement it throughout Germany, Hamburg and Berlin. It's used to generally forecast repeat offenses. Again, when training data sets, you need enough data points to predict crime. So crimes that are less likely to happen or happen very rarely, much harder to predict. Crimes that aren't reported, much harder to predict. So a lot of these software, like pieces of software rely on algorithms that are hoping that there is a same sort of picture that they can predict where and when and what type of crime will happen. Pre-cog is actually a term with a play on the movie Minority Report. If you're familiar with it, it's the three psychics who predict crimes before they happen. So there are other similar systems in the world that are being used to predict whether or not something will happen. The first one is disease and diagnosis. They found that algorithms are actually more likely than doctors to predict what disease an individual has. It's kind of shocking. The other is security clearance in the United States. It allows access to classified documents. There's no automatic access in the U.S. So every person who wants to see some sort of secret, cleared document must go through this process. And it's vetting individuals. So it's an opt-in process. But here they're trying to predict who will disclose information, who will break the clearance system and predict there. Here the error rate, they're probably much more comfortable with a high error rate because they have so many people competing for a particular job to get clearance that if they're wrong that somebody probably won't disclose information, they don't care. They just rather eliminate them than take the risk. So I'm an attorney in the United States. So I have this urge to talk about U.S. law. It also seems to impact a lot of people internationally. Here we're talking about the targeting of individuals, not hot spots. So targeting of individuals is not as widespread currently. However, it's happening in Chicago and other cities are considering implementing programs and there are grants right now to encourage police departments to figure out target lists. So in the United States, suspicion is based on the totality of the circumstances. That's the whole picture. The police officer, the individual must look at the whole picture of what's happening before they can detain an individual. It's supposed to be a balanced assessment of relative weights, meaning, you know, if you know that the person is a pastor, maybe them pacing in front of a liquor store is not as suspicious as somebody who's been convicted of three burglaries. It has to be based on specific and articulable facts. And the police officers can use experience and common sense to determine whether or not there's suspicion. Large amounts of networked data generally can provide individualized suspicion. The principal components here are, you know, the events leading up to the stop and search, what is the person doing right before they're detained, as well as the use of historical facts known about that individual, the crime, the area in which it's happening, etc. So it can rely on both things. No court in the United States has really put out a percentage as what probable cause and reasonable suspicion. So probable cause you need to get a warrant to search and seize an individual. Reasonable suspicion is needed to do stop and frisk in the United States, stop and individual and question them.  Reasonable suspicion, you're actually detained. But I had a law professor who basically said 30, 45% seemed like a really good number just to show how low it really is. You don't even need to be 50% sure that somebody has committed a crime. So officers can draw from their own experience to determine probable cause. And the UK has a similar reasonable suspicion standard, which depend on the circumstances of each case. So I'm not as familiar with UK law, but I believe some of the analysis around reasonable suspicion is similar. Is this like a black box? So I threw this slide in for those who are interested in comparing this to US law. Generally a dog sniff in the United States falls under a particular set of legal history which is a dog can go up, sniff for dogs, alert, and that is completely okay. And the police officers can use that data to detain and further search an individual. So is an algorithm similar to the dog, which is kind of a black box? Information goes out. It's processed. Information comes out and a prediction is made. Police rely on the good faith and totality of the circumstances to make their decisions. So there's really no, if they're relying on the algorithm and think in that situation that everything's okay, we might reach a level of reasonable suspicion where the individual can now pat down the person he's decided on the street or the algorithm has alerted to. So the big question is could the officer consult predictive software absent any individual analysis? Could he say 60% likely to commit a crime? In my hypo, does that mean that the person without looking at anything else detain that individual? And the answer is probably not. Unpredictive policing algorithms just cannot take in the totality of the circumstances. They have to be frequently updated. There are things that are happening that the algorithm possibly could not have taken into account. The problem here is that the algorithm itself, the prediction itself, becomes part of totality of the circumstances, which I'm going to talk about a little bit more later. But officers have to have reasonable suspicion before the stop occurs. Proactive justification is not sufficient. So the algorithm can't just say 60% likely. You detain the individual and then figure out why you've detained the person. It has to be before the detention actually happens. And the suspicion must relate to current criminal activity. The person must be doing something to indicate criminal activity. Just the fact that an algorithm says based on these facts 60% or even without articulating why the algorithm has chosen that isn't enough. Maybe you can see a gun-shaped bulge in the pocket, et cetera. So effectiveness, the totality of the circumstances can the algorithms keep up generally probably not missing data and not capable of processing this data in real time. There's no idea the algorithm doesn't know and the police officer probably doesn't know all of the facts. So the police officer can take the algorithm into consideration, but the problem here is did the algorithm know that the individual was active in the community or was a politician or that was a personal friend of the officer, et cetera? It can't just be relied upon. What if the algorithm did take into account that the individual was a pastor? Now that information is counted twice and the balancing for the totality of the circumstances is off. Humans here must be the final decider. What are the problems? Well, there's bad underlying data. There's no transparency into what kind of data is being used, how it was collected, how old it is, how often it's been updated, whether or not it's been verified. There could just be noise in the training data. And honestly, the data is biased. It was collected by individuals in the United States. Generally, there have been several studies done that black young individuals are stopped more often than whites. And this is going to cause a collection bias. It's going to be drastically disproportionate to the makeup of the population of cities. And as more data has been collected on minorities, refugees, and poorer neighborhoods, it's going to feedback in and of course only have data on those groups and provide feedback and say more crime is likely to happen because that's where the data was collected. So what's an acceptable error rate? Well, it depends on the burden of proof. Harm is different for an opt-in system. What's my harm if I don't get clearance? Well, I don't get the job, but I'm opting in. I'm asking to be considered for employment. In the U.S., what's an error? If you search and find nothing, if you think you have reasonable suspicion based on good faith, both on the algorithm and what you witness, the United States says there's no Fourth Amendment violation even if nothing has happened. It's very low error, false positive rate here in big data generally and machine learning is great. Like, 1% error is fantastic, but that's pretty large for the number of individuals stopped each day or who might be subject to these algorithms. Because even though there are only 400 individuals on the list in Chicago, those individuals have been listed basically as targets by the Chicago Police Department. Other problems include database errors. Exclusion of evidence in the United States only happens when there's gross negligence or systematic misconduct. That's very difficult to prove, especially when a lot of people view these algorithms as a big box. Data goes in, predictions come out, everyone's happy. You rely and trust on the quality of IBM, Hunch Labs, et cetera, to provide good software. Finally, some more concerns I have include feedback loop, auditing and access to data and algorithms and the prediction thresholds. How certain must the prediction be before it's reported to the police that the person might commit a crime or that crime might happen in the individual area? If reasonable suspicion is as low as 35% and reasonable suspicion in the United States has been held as that guy drives a car that drug dealers like to drive and he's in the DEA database as a possible drug dealer, that was enough to stop and search him. So are there positives? Well, Predpole, which is one of the services that provides predictive policing software, says since these cities have implemented there's been drop in crime in LA, 13% reduction in crime in one division. There was even one day where they had no crime reported. Santa Cruz, 25 to 29% reduction, 9% in assaults, et cetera. One, these are police departments self-reporting these successes for, you know, take it for what it is and reiterated by the people selling the software, but perhaps it is actually reducing crime.  Do we know that crime is really being reduced? Will it affect the data that is collected in the future? It's really hard to know. Because if you send police officers into a community, it's more likely that they're going to affect that community and that data collection. Will more crimes happen because they feel like the police are harassing them? It's very likely and it's a problem here. So some final thoughts, predictive policing programs are not going anywhere. They're only in their real start. And I think that more analysis, more transparency, more access to data needs to happen around these algorithms. There needs to be regulation, currently a very successful way in which these companies get data is they buy it from third-party sources and then sell it to police departments. So perhaps PredPol might get information from Google, Facebook, social media accounts, aggregate data themselves, and then turn around and sell it to police departments or provide access to police departments. And generally the courts are going to have to begin to work out how to handle this type of data. There's not case law at least in the United States that really knows how to handle predictive algorithms in determining what the analysis is. And so there really needs to be a lot more research and thought put into this. And one of the big things in order for this to actually be useful, right? If this is a tactic that's been used by police departments for decades, we need to eliminate the bias in the data sets because right now all it's doing is facilitating and continuing bias set in the database. And it's incredibly difficult. It's data collected by humans and it causes initial selection bias, which is going to have to stop for it to be successful. And perhaps these systems can cause implicit bias or confirmation bias. For example, police are going to believe what they've been told. So if a police officer goes on duty to an area and an algorithm says you're 70% likely to find a burglar in this area, are they going to find a burglar because they've been told? You might find a burglar. And finally, the U.S. border. There is no Fourth Amendment protection at the U.S. border for it's an exception to the warrant requirement. This means no suspicion is needed to commit a search. So this data is going to go into a way to examine you when you cross the border. And aggregate data can be used to refuse you entry into the United States, et cetera. And I think that's pretty much it. And so I have a few minutes for questions. Thank you. Yeah. Thanks a lot for your talk. We have about four minutes left for questions. So please line up at the microphones and remember to make short and easy questions. Microphone number two, please. Okay. Just a comment. If I want to run a crime organization like, I would target the pre-cops here in Hamburg maybe. So I can take the crime to the scenes where the pre-cops doesn't suspect. Possibly. And I think this is a big problem in getting availability of data in that there's a good argument for police departments to say, we don't want to tell you what our tactics are for policing because it might move crime. Do we have questions from the Internet? Yes. Then please, one question from the Internet. Is there evidence that data like the use of encrypted messaging system, encrypted emails, VPN, Tor, with automated request to the ISP, are used to obtain real names and collected to contribute to the scoring? I'm not sure if that's being taken into account by predictive policing algorithms or by the software being used. I know that police departments do take those things into consideration and considering that in the United States, totality of the circumstances is how you evaluate suspicion, they are going to take all of those things into account and they actually kind of have to take that into account. Okay. Microphone number one, please. In your example, you mentioned disease tracking, for example, Google flu trends. A good example of predictive predictive policing, are there any examples where instead of increasing policing in the lives of communities where sociologists or social workers are called to use predictive tools instead of more criminalization? I'm not aware if police departments are sending social workers instead of police officers, but that wouldn't surprise me because algorithms are being used to suspect child abuse and in the U.S., they're going to send a social worker in regard. So I would not be surprised if that's also being considered, since that's part of the resources. Okay. So if you have a really short question, then microphone number two, please. Last question. Okay. Thank you for the talk. This talk, as well as a few others, brought the thought and the debate about the fine tuning that is required between false positives and preventing crimes or terror. Now, it's a different situation if a policeman is predicting, or the system is predicting somebody stealing a paper from someone, or someone is committing a terror attack and the justification to prevent it on the expense of false positives is different in these cases. How do we make sure that the decision of a fine tuning is not going to be deep down in the algorithm and by the programmers but rather by the customer, the policeman or the authorities? I can imagine the police officers are using common sense in that and their knowledge about the situation and even what they're being told by the algorithm. You hope that they're going to take, they probably are going to take terrorism to a different level than a common burglary or a stealing of a piece of paper or nonviolent crime. And that fine tuning is probably on a police department by police department basis. Thank you. This was Whitney Merrill. Thank you. Give a warm-up of applause, please. Thank you.


Comedian Billy Eichner has a new film out called Bros, and even though it's sitting at 90% on Rotten Tomatoes, apparently the film didn't do too hot at the box office. As Candice Ortiz of Mediaite explains, Bros, written and starring Eichner, was the first gay rom-com ever produced by a major studio. It follows the story of a podcast host, Bobby, portrayed by Eichner, falling in love with a lawyer, Aaron, played by Luke McFarlane. But this weekend, it flopped at the box office with a $4.8 million debut. Now needless to say, he's pretty upset about his film flopping, and he has some thoughts, dare I say, hot takes, about why the film flopped. Now first and foremost, let me just point out that that synopsis sounds terrible. I have no interest in immediately watching a film about, I'm assuming, this cynical podcast host who never thinks that he's gonna find love, finding love, I don't know, that sounds bad to me. I'm not a fan of love stories or rom-coms, so I guess that I'm part of the problem here. But you know, to be fair, looking at trailers, it looks fine, I think it looks pretty funny based on things that I've seen and heard. But he's really upset, and he's blaming the straights for this movie's failure. He tweeted out, Rolling Stone already has Bros on the list of the best comedies of the 21st century. What's also true is that at one point a theater chain called Universal said they were pulling the trailer because of the gay content, uni convinced them not to. That's just the world we live in, unfortunately. Even with glowing reviews, great rod tomato scores, and a cinema score, straight people, especially in certain parts of the country, just didn't show up for Bros, and that's disappointing, but it is what it is. Everyone who isn't a homophobic weirdo should go see Bros tonight.  I love this movie so much, go Bros. So he's heavily implying that straight people essentially let him down, let the movie down, and if you're not homophobic, there's no reason why you shouldn't rush out to go see this movie. Not buying it. Sorry, I'm not buying it. Overall, I have a really huge level of distrust for all comedians because to me they all seem like really sensitive, fragile, egotistical maniacs. I mean it's not just Billy Eichner, but he's part of the problem too, when they get criticism for one of their comedy specials, when they make the same jokes, the trans attack helicopter one comes to mind, and they constantly go after woke and political correctness, and they don't have any refreshing content, and then they blame the audiences. That irritates me to no end and it just leads me to believe that they should maybe come up with better content, and so this is Billy Eichner here kind of doing the same thing, albeit to a lesser extent, to be fair to him. But first of all, there's a number of things working against this movie. You released it in October. This is when people want to watch scary movies. I for one really like watching scary movies in October. Second of all, it's a love story. I do not like love stories gay or straight. So I'm not going to see this in theaters. I'll watch it when it's on Netflix or maybe I'll even rent it. But I'm not going to watch a love story in theaters because that genre isn't my cup of tea. Another problem is that believe it or not, the COVID-19 pandemic is still not over. So there are thousands, dare I say, millions of Americans who still don't want to see movies in theaters. And it's just not my preferred way of watching.  But look, I have a real issue with him implying that straight people are somehow bad allies if they don't see his movie. I don't like him saying that because it's not true. And I think that watching a movie is not inherently a political act. I get what he's trying to say. Like he's made this point before about how you should see this movie to tell the movie studio that you want more gay movies and there's money behind it. I understand that. I think that representation does indeed matter a lot. But at the same time, to try to portray the film as something that you need to see in order to boost gay cinema, rather than trying to tout the film primarily on its merits of just being funny in and of itself, I think that's more important. Now maybe he's doing that. But I don't like this trying to ascribe more value to the movie specifically because it's gay. It seems kind of insidious and a little bit cynical to me. And it leaves a bad taste in my mouth. Now it also reminds me of Ridley Scott when he released a movie, The Last Duel, last year and it didn't do very well. It also flopped and he, rather than looking within, chose to blame millennials.  The millennian do not ever want to be taught anything unless you are told it on the cell phone, Scott continued. This is a broad stroke, but I think we're dealing with it right now with Facebook. There is a misdirection that has happened where it's given the wrong kind of confidence to this latest generation, I think. Now for me, as a millennial, I saw that article about him bad mouthing millennials before I even saw a single trailer of the movie. So maybe you're just not marketing it to areas on the internet where millennials are visiting. I mean, do you really see that much millennials on Facebook these days? I mean, sure, they're there, but is it the best place to market because he brings up Facebook here? And the same thing is true with, you know, Rose, I don't necessarily know if marketing failed. Perhaps the studio didn't put as much money behind it. And I'm not saying that homophobia didn't play a role.  Moonlight is an example and also the biggest example pointed out by Nando on Twitter here is Brokeback Mountain, which made $178 million at the box office in 2005. Now in response to Nando, people were saying, yeah, but those were two A-less celebrities and they were also straight. So maybe audiences feel better about seeing homosexuality if it comes from straight guys. Listen, I don't think that people are really thinking about it that deeply. I don't think that most people are over analyzing movies like this. If you're a homophobe, you're not going to see this movie. If you don't feel like you're in the mood to watch a rom-com that doesn't inherently make you a bad ally to the LGBTQ plus community. I think it's cool that this movie has a mostly LGBTQ plus cast. I think that that's great. I like that there's more representation. I like that we've come far enough to where we can have a major release of a gay rom-com. But with that being said, though, I don't think that homophobia is the only factor here and I don't think it is appropriate to blame straight people for letting you down considering the many gay people who didn't see it, myself included because sorry, not going to go to a movie theater to see a rom-com when that's not really my favorite genre. But again, maybe the movie's really good. Is it worth seeing it in theaters? Perhaps. But I'll certainly watch it when I'm streaming. But Billy Eichner, honestly, speaking about the movie this way, you know, trying to cope this hard, it kind of makes me a little bit more cynical about the movie itself. Because again, this is what you have to do if you really want to sell the movie based on how funny it is. You sell it for being funny, right?   If you're not homophobic, well, why haven't you seen this movie? Perhaps you're not as good of an ally as you said you were. I don't like that. I really don't like that at all. It's like me saying, okay, well, you know what? Out of all the leftists in independent media, I get less views. It must be because of homophobia. No, it's probably not that. Maybe I just need to produce better content. Maybe other people want to hear what other political commentators say. I think that there are other factors and not every single thing has to be reduced down to homophobia. Again, I get that homophobia will play a role because this is a movie about a gay love story, but for him to go that hard and claim that straights have let the gays down, total nonsense, total bullshit, either way, I'll check out the movie when it's streaming. Maybe I'll rent it when it's cheap, but don't try to make this a bigger thing than it is, just chill.