Welcome to the Highline College CTC Link video series. In this session, we'll show you how to go to the CTC Link mobile web view and enroll for classes. First, go to a web browser and type in mobile.highline.edu. This will take you to the CTC Link mobile web page. From here, click on the tile called CTC Link Login. Then enter your CTC Link ID and password. You are now on the Highline dashboard. From the dashboard, you can enroll for classes by going to the left navigation panel and clicking the Enrollment menu. From the Enrollment menu, you can view your enrollment dates, view and adjust your planner, which is where you can add classes for future terms. Use your shopping cart, drop classes, swap classes, and view what waitlist you may be on. To register for classes, let's click on the shopping cart. We are now on the shopping cart page. Use the shopping cart to temporarily save classes until it's time to enroll for your selected term. Our shopping cart is currently empty, but let's add some classes. Let's start by selecting our term from the drop-down at the top of the page. If you're not sure when you can start enrolling for the available term, use the Enrollment Dates link to get that information. From the Enrollment Dates page, it's really easy to return to your shopping cart to start adding classes. To add classes, click on the Add Classes button. You can add classes from your planner or if you know the specific class section by entering a class number. But let's go ahead and search for classes by selecting the Class Search. This will open the Class Search page. The Class Search page has many different criteria and filters that you can select to find classes you would like to enroll in. First, make sure you've selected the right term to enroll from the term drop-down. To view all filter options, go ahead and click the More Filters button. Students will have various criteria and filter options to choose from. For example, students can search for all classes in an academic department by selecting that department from the Academic Organization drop-down. This allows you to search for all English classes regardless of their subject heading. You can also use other filters like Instruction Mode, Days and Times, Instructor Name, Units, and several others. When you are ready to search for your classes, go ahead and click the Search button. To view the detailed information about those sections, click on the arrow for that section. This expands information for that section, and you can read class details, textbook materials, and class availability. When you locate the appropriate section, you can enroll, add to cart, add to planner, or even share the class with a friend by clicking on the various Action buttons. When we go ahead and select to add to our cart, we are given a couple of options. Permission numbers are required for some sections if the class requires instructor consent or if you've received instructor permission to enroll without a prerequisite. All sections do not require a permission number, but this box will still display. If a class section is full, you can be placed on the wait list. By selecting the wait list if class is full box, this will add you to the wait list when you enroll. Go ahead and select the Save button to add the class to our cart, or you can click Cancel to return to your search results to choose a different section. The class has been added to your cart, and you may now search for other classes by scrolling to the top of the page and selecting new criteria. You can click Reset Filters to remove your previous search criteria and then make other searches. Again, search for the class, select the details, and make sure that's the section you would like to add to your cart. Click the Add to Cart button. If a permission number is needed, go ahead and enter that or select the wait list if class is full button. Then click Save. Once all classes have been added to your shopping cart, return to the Enrollment menu and return to your shopping cart page. The classes you selected will now display in your shopping cart. You do not have to enroll in all classes in your shopping cart at once. Select the class or classes you would like to enroll in by selecting the boxes, and then click the Enroll button. The Enrollment process will now display your results. As you can see, some classes got added to your schedule. Other classes may have an error like a prerequisite has not been met. Click OK to return to your shopping cart. Any classes that were not added to your schedule will remain in your shopping cart. If you need to change your enrollment, you can drop classes or swap classes by selecting those menus from the left-hand side. When you are finished with your enrollment transactions, you can navigate to other pages in mobile by using the left menu. Congratulations! You have enrolled for classes using the CTC Link mobile web view on your desktop.


Hi there, welcome back to my YouTube channel. This is Daniel Rosalier. I want to do another video today about reddit search operators So reddit search operators I did one a few months ago about a couple of them and I said I wanted to keep it short So I only did one or two and for the purposes of completion I want to do another video going through more of them because these are actually really really useful if You are using reddit for yourself or for work or you want to see what people are writing about your business on reddit Let's say and that's what I think is one of the most useful things about reddit relative to other major social networks Is that reddit is in the vast majority of its content is social discourse on the open web Sometimes called a clear web if you compare reddit to let's say Facebook where you have a lot of private Facebook groups Those are technically deep web. They'll be on the reach of search bots because they are closed Communities are not index sometimes you have other fora in which webmasters will configure a disallow bot access in the robots.txt So that's why reddit has become kind of is often used by researchers as a kind of surrogate for Social conversation on the internet because it's really really easy Compared to many other social networks at least to find telegram groups in the same same category there So there is reddit does have its own search language now One way that you can search through reddit posts I've just I started this video then I just kind of aborted it. So these are some things I was Using to demonstrate but let's let's say I was using Google You know, you can use site reddit.com and your search query that will work But you're probably gonna have better results by actually searching within reddit using the reddit search operators now Where you'll find these documented. It's reddit.com forward slash wiki forward slash search And I went through one or two of these as I said the last day and for this video I want to go through a few more before I do I just want to break down what you what you can use So firstly you have the field search operators and I'm gonna spend the majority of this video talking about these Then you have boolean operators reddit actually supports three boolean operators now boolean The most policy here to say these are named after George Boole who was a professor of mathematics as Queens college now university college cork, which is where I went for my undergraduate So boolean operators are used in Google searches, but you can also actually use them. They're supported within reddit's language So you've got and or not and reddit just points out here that they're going to be deprecating and Pipe and minus in the near future. So just as an FYI. They want you to be using these So that makes it possible to combine different things. So let's say I wanted to do author Daniel Rossell or author Daniel 123 let's say, right? So this would return any post either by this reddit user or by this reddit user now Just in case anyone watching this hasn't signed up for reddit and doesn't use reddit reddit when you create a reddit account you pick a username and then you're going to guess a URL that looks a bit like this reddit.com forward slash you forward slash whatever your username is a subreddit's going to look like this It's got R in the middle. So user has you so whatever comes after this that is your username and these field search Operators are case sensitive. That's important to you just to note as well So you want to make sure that if you use a capital D, you remember that when you're doing these etc So I'm just going to go through some of the useful ones firstly author is really useful if you're trying to track a Certain redditor, and I don't mean this in kind of a creepy way or you're trying to see what a reddit account Posted in a forum then you could easily combine those two operators. So let's say trying to think of a subreddit I that I use I use posting quite a lot of subreddits, but let's say Subreddit sys admin now we have a query ready to go there if I've ever posted in the subreddit called sys admin and I chose that because I'm going to just kind of roll with that for a couple of examples here and then I can Just put this in like this and go like this and hit return and if everything goes good, here we go So I posted two Threads over the course of my reddit career in sys admin I'm gonna actually just go into it because I want to I want to use it for just to demonstrate one other flare So it's a pretty big subreddit 56,000 members in it. So That was the author operator. So just to reiterate there It's author username and you can use and or a not in combination with that. The next one is flare So flare in reddit is used to basically organize if you have a big subreddit That's why I wanted to keep a tab open for sys admin The moderators of the reddit can configure what are called flares So flares are used to basically organize if you've got a big subreddit like sys admin and There's a lot of posts going in there And you want some way for people interested in a particular Sub-topic to search through the posts the mods or the moderators will configure flares now sometimes The flares are not publicly visible in other times They are so you might be able to see all the flares that are actually there But if we take at this subreddit sys admin, you can see one two three four five six different flares Now what what happens when I click on a flare? Let's say in this case rant. So if we look at the url structure here Something has changed after sys admin. It's added this This highlighted text here a question mark hyphen I'm forgetting all these all these all these characters f equals flare underscore name And equals rant And and double quotation marks as well Okay, so that's how we can use a flare operator. So in this case, it's flare rant So I'm just gonna keep this here as well. And because it's case sensitive I'm just making sure I'm preserving that capital R Another one is NSFW which stands for not safe for work. So kind of a racier content on reddit. It has It has one and zero and yes or no So you can use either one is yes and zero is no so I can do NSFW zero to indicate no Self for text submissions No, otherwise one and zero also supported as so self. Yes Now that's only going to bring back text submissions and I can do I'm just gonna keep these all on the clipboard here Self know and we're going to combine these with different operators site is really useful if you're doing brand monitoring on reddit So you do site and you can have either a URL or you can even actually have a URL, you know, you can either have a base domain or you can have an actual full URL. So let's just Let's pick up at Forbes comm and let's just run that through the search Subreddit, this is really really useful. So this will limit your searches to a particular subreddit and again It's case sensitive. So let's go for sysadmin here. It is capitalized in the title and not in the actual subreddit. So be Cognizant and make sure you're using the right things And that is basically it Search results are limited to 1000 results search terms may be stemmed So a search for dogs may return results with the word dog in them and the three bullying operators are and or not And you can also use parentheses to group parts of a search together The example reddit gives here is hedgehogs or porcupines not sonic. So Let's now combine these into a few different Practices so let's say I wanted to only find the sysadmin post on reddit. So Reddit is going to be subreddit is going to be sysadmin and for flair We're going to do rant. Sorry. This is only looking for rents, right? So I'm just going to copy that here Put this in here and from anywhere on reddit. I've just jumped to Only the post in sysadmin and that have rant as the flair and I can sort by Relevance hot top new just as if I was using reddit regularly of the same search options. Um, so that's really really cool So let's do Another combination, let's say for instance, let's just use this one site forbs.com And now what you're going to see is it's going to be all the posts that have referenced forbs.com and reddit So because that's a pretty big website. That's why I chose it if I click on new we should start to see pretty Significant Traffics we can see 48 minutes ago one hour ago one hour ago There's a lot of redditors sharing Forbes links and if I did like medium.com or something like that It would be quick as well. Now if I go into sysadmin, that's subreddit So let's just take the last guy you posted here And I'm going to just wrap up with this just to show one of the boolean operators So the world of goods so if I wanted to do something like this Subreddit is going to be sysadmin and we can use parentheses for this part of the query Author is going to be me or it's going to be the world of Good and we don't want not say for work post so NSFW sets a zero now What I can do is just grab this put it into the There we go. So now what we're seeing is results posts that are either by This reddit user of the world of good my posts are going to be in there here So you can see this is me at the bottom. This is the world of good here And we can search by new and I'm not sure there's a limit to how many times How much you can batch queries into one into one query if that made any sense Like if I could use a bunch of different user names, so this is just another as another example here If I wanted to see who was sharing my medium posts on reddit I could just type my medium handle like this into the search bar and it will return That and I could do again, you know not author to exclude Post I was sharing and actually see what other people were sharing my posts on reddit And that is those are the majority of the useful search operators So again, if you want to find them, it's reddit.com forward slash wiki Forward slash search and whether you are doing brand monitoring or you're a reddit power user Or you just want to narrow down a search query ie, you know You want to see what some redditor or reddit tours Posted in a certain subreddit that were text posts that were not not safe for work Then using one of those queries you can easily build out a query put that into reddit and get the results Hope that video is useful. Thank you for watching and feel free to subscribe to this YouTube channel to get more videos


YouTube team keep it clean. What's going on the same graven here with another video and I was literally in the middle of recording These big huge questions from subscribers videos They may end up being like an hour long because we got like 50 million questions. No exaggeration. But anyway We've been hearing all this stuff about the Titans and all these players catching cases and whatnot And it's like Titans. Well, what do y'all have going on to where y'all just trying to mess stuff up for everybody? What's the problem? but no, seriously, we do hope that everybody ends up being okay from everything that's happening, but It was just frustrating to think about like what what what is happening there? But anyway as they continue to get themselves together as they continue to get things cleared up as they continue to Get well There have been some schedule changes because of course the Titans were supposed to play the Steelers and take care of Business against them Pittsburgh Steelers this weekend But that game will no longer be happening right now and that game will be postponed Until later, but let's look at the the new updates because at first there were some rumors that it might affect the Ravens and have an Impact on the Ravens, but we had not gotten anything official yet, but now it's official That the game the Pittsburgh Steelers at Tennessee Titans game that was originally scheduled for this Sunday October 4th has been rescheduled to week 7 on October 25th So that game got moved to week 7. They will no longer play it today and this will now be there by week So they got to try to get healthy and whatnot try to get everything cleared up But now the Ravens in week 7 they were supposed to play the Steelers They were supposed to play the Steelers in week 7 So, you know the the Steelers in the Titans, I mean, yeah, they just write the Ravens in the Titans can't play the Steelers on the same day So what happened the Pittsburgh Steelers at Baltimore Ravens game will move from week 7 October 25th to week 8 November 1st at 1 p.m. So I mean I Was like I was happy my wife was she was even happier because She was like, oh man, that's two yet Well, not two years in a row because last year the game happened on our anniversary But this year she was like, oh man, that's crazy. The Ravens by week is on our anniversary And I was like, oh, yeah, we about to be married for eight years and yeah, the Ravens by we okay Let's go Ravens. Okay. We appreciate y'all. We see y'all but them Titans Titans they they ruined our playoff hopes last year and now they overheard Trying to interfere with anniversary plans That's okay It's okay. We're gonna figure that thing out. Anyway, but Titans man. We got to get this thing together, man Anyway, I just ah It is what it is. So now the Ravens by week is going to be on week seven right right So it moves the Ravens by week it moves their by week a week up and moves the game the Steelers game a week back So that's how it impacted the Ravens I know I was talking to Ivy because I know she was questioning She's a Ravens fan by the way She was questioning like what's gonna happen cuz she had heard the rumors too, but now it's officially been cleared up So now the Ravens who originally had that by week November 1st no more by week then They will play the Steelers then but their by week will be the previous week and then the Titans and Steelers they will it's just This thing has been a mess. Anyway, team keep it clean. I appreciate y'all. I love y'all Titans they they they got to figure that thing out man One thing I was wondering like all these Titans players are coming up with stuff But no they said all the Vikings players have been team keep it clean So just a little strange just a little head scratcher there. Anyway, I love y'all I appreciate y'all team keep it clean make sure you finish watching the episode of questions from subscribers that we just put out a couple hours ago on the title question is should the Ravens bring back a familiar face and want to rail sucks to help out with That pass rush. I will see you all in the comment section I love you all make sure you join the Ravens group chat on flick that is down below in the description Make sure you hit up the coldest water and you know they gonna keep you cold man They gonna keep you cold and you can keep your drink clean too Even if you take it to the pool you could you could throw your drink in a pool and it'll float It'll float like it'll float better than me cuz I can't even swim really but anyway Team keep it clean. I love y'all appreciate y'all and just like the Steelers and the Titans are this week. I'm out


Three people were hurt by shards of broken glass on June 9, when a drone struck a residential building in the southern Russian city of Voronezh, regional governor Alexander Guzov said. Video showed damage and scorch marks to the facade of the apartment block. Guzov said the three people had received medical attention on the spot and declined hospital treatment, suggesting their injuries were light. Drone strikes inside Russian territory have become a frequent occurrence, but they more typically happen in areas such as Belgorod that lie closer to Ukraine. Voronezh is about 180 kilometers from the Ukrainian border. Ukraine does not comment on alleged cross-border attacks into Russia. Russia accused Ukraine last month of launching two drones at the Kremlin in what it said was an attempt to kill President Vladimir Putin. Kiev denied involvement in that incident.


Our farm was homesteaded by my great-great-grandfather on my mother's side. So I'm the fourth generation and my son and daughter are the fifth generation to be operating here. My dad started farming with my grandfather. They had some beef cows, had milk cows. They had a few acres for farming. Today our operation consists of diversified grain operation and cow calf. So we have basically a four crop rotation, corn, soybeans, wheat, and some off-elfa. And then we do a lot of stuff with cover crops to help kind of diversify everything. And that makes it a lot easier on the cattle operation too for grazing purposes. We retain our calves back for replacement efforts and then also run a feedlot. As far as the livestock side, we've really tried to integrate them into our grazing and our crop production. The last couple of years we've actually seeded full-season cover crops just for some grazing. You know, the cows are a great recycler. Those deep-roaded brassicas pull up nutrients and then run them through the cows. What better way to recycle some of that stuff? We've probably have tried to utilize some fields that are closer to some water supplies. You know, we're not afraid to put up electric fence. And we can't haul water too if we need to. We do that quite a bit in the fall for a fall grazing. It's cheaper to haul water than it is to haul feed. So that's why we really like winter grazing as long as winter will allow. I've heard the term in the medical field with COVID recently, they say, do no harm. And I think what's kind of my philosophy with the grazing is do less harm. Let's do something better than continuous grazing, you know. And so we began to understand a little bit about use and rest and do some cross-fencing so you could manage the livestock impact and those kinds of things. And it just started out of trying to do a better job. Yeah, it's hard to change that mentality that a grass blade standing there is a waste or less than optimal utilization. But you learn that old adage about take half and leave half. When we're going out and we're moving cattle from one pasture to the next, you can just see how it benefits both the grass and the cattle because you can see how much better nutritionally we can offer different grasses to our cattle by letting that grass rest. So you see the cattle get excited, they want to come, they want that new grass. They are easy to move, they're easy to work with in that aspect. And so I love just going up to our big pasture and going from section to section and looking to see what plants they ate, what plants they didn't eat, where are some improvements we can make here, where are some issues of certain species of grasses. I just find enjoyment in that and seeing what the cattle will do with it. It's probably been about two years since I was first on their rangelands and I just remember being immediately impressed with the quality of the range, their approach to management, the ease in which their cattle moved through the system. And certainly they're not the only ones that have achieved that but they're a great example of low stress, basic understanding and good range management and profitable range management. The other thing about the operation as I got to know the family a little bit is that the dualistic role of their farm and ranch with their crop side and the progressive nature of their cropping with the no-till, the covers, the forage crops, livestock integration. Mark mentioned that it's what they've always done but truly it's leadership in the landscape and the way that they've meshed those two things and now coming down to passing it on to the fifth generation is really going to be an interesting story to continue to see unfold. My family, dad, uncle, grandpa and so forth have given us one of the greatest opportunities. I mean they've set this farm up for us to continue to succeed. They've been looking at future generations. The generations that have gone before me really put a focus on conservation and health of our soils and our range management and our grasslands and so just stepping from that I'd like to just keep that vision of how can we make this better for the next generation. There's always different ways that we can be improving whether our soils or our cattle, whatever it is, there's always different aspects that we can continue learning so it's been really great to have things like South Dakota Grazing School and stuff like that where you can learn more innovation in different ways that other people have gone before us and have either made the mistakes or learned the different ways that we can always be improving. On today's economy it's really hard right now to keep going so having all this knowledge and how to better your farm is really going to help succeed in the future.


My name is Kate Sayonka, I'm a professor at Boston University and at the MIT IB and Watson AI lab and I'm very happy to be here today to talk about my research on data set bias. So I'm going to start with talking about the success of AI and computer vision. So computer vision is AI technology that can analyze visual scenes and you can see here an example of it applied to detecting cars and buses and pedestrians and images and it's quite good and getting better. So here's an example of computer vision for object detection in a different scene. We can also train computer vision models to classify other objects that are maybe even cartoon characters and we have quite accurate models for face recognition, emotion recognition and a lot of this is becoming a product, right? So we're seeing computer vision being used as a product, maybe in your phone, you might have a face ID that verifies your face against the database to unlock your phone. So that's very exciting. However, we also have some problems with this technology with computer vision but it also applies to machine learning in general, which is the problem of data set bias. That's what I want to talk about today. So what do I mean by data set bias? Well, suppose that you're training a model to recognize pedestrians and you collected a data set that looks something like this and you train your neural network and it seems to work really well on your held out test data from the same kind of data that you collected. And now you deploy your model on your product on a car but now this car is in New England whereas your training data was in California. So immediately you see a very different visual domain with different weather conditions like snow for example that you didn't have in training data because there's not much snow in California. But also pedestrians will look different because they're wearing heavy coats and so on. So all of a sudden the model that worked really well on your source data that you trained it on does not work so well anymore. And so we call this problem data set bias. We also call it domain shift. So the problem of data set bias is essentially this issue that the training data looks different from your test data that you're actually faced with. It's different in terms of the distribution of data. That's a more general way of putting it but you might qualify it for example as a difference between one city that you trained on and the new city that you're testing on or it could be a data set bias to images collected on the web whereas at test time you are getting images from a robot which also looks different. They have different backgrounds, different lighting and different pose. Another common data set shift that we see in machine learning is from simulation to real images. So here for example if you're simulating something for robotics and training your machine learning algorithm on the simulated data it's not going to generalize very well to real data. This could also happen with demographics for example if your training data is biased in a way that where light skinned faces are over represented in the training data but then at test time you are applying the model to darker skinned faces again you will have a data set bias issue and the model will not work as well on the test data. This could also happen with different cultures let's say you're classifying weddings and you trained on western weddings from western cultures and then at test time if you get an image of a wedding from a different culture your classifier will not generalize very well and will not be able to recognize it. So there are lots of different ways that data set bias could happen. That's my point. Now let's look at what actually this means in terms of the accuracy of the machine learning model. Here is a very simple example, very famous data set called MNIST. Everyone knows what MNIST is. It's just 10 digits that are handwritten. So if we train on this data set we know with modern deep learning we can get very high accuracy of more than 99% accuracy. However if we train for the same 10 classes of digits but our training data looks like this, this is a street view house numbers data set. Now this model tested on the MNIST data set achieves much lower performance, 67% accuracy that's really really bad for this problem. And by the way, even when the data set bias is not as extreme for example we train on the USPS digits which look to the human eye look quite similar to MNIST and yet the bias in the data leads to a similar drop in performance. And if you're curious if we swap and we train on MNIST and test on USPS we have similarly poor performance. So that's just an example of how this data set bias could affect even in a simple case of digit classification could affect accuracy. Okay, now what about real world implications of data set bias? Have we seen this in the real world? Well yes, I believe we have. This is one example that's quite famous now which is the fact that in face recognition or gender classification some researchers have actually evaluated how well existing commercial systems from Amazon, from IBM, from other companies, how well they work, what is the accuracy they achieve on different demographics. And you can see here according to one study they work a lot worse on African-American and female faces than on Caucasian and male faces. So again that's an enlarged part due to data set bias. Another very sad example of potential data bias is this accident that the self driving vehicle was involved in a while back the Uber self driving car which according to some reports did not recognize the pedestrian because it was not designed to detect pedestrians outside of a crosswalk. So if that's in case if that's your data set bias that in your data set all the pedestrians are on a crosswalk then yes your machine learning algorithm will not be able to recognize them as well if they're not in that context of a crosswalk but maybe in this case just jaywalking without a crosswalk. So you might be wondering well wait a minute can't we just fix this by collecting more data if we don't have pedestrians not in crosswalks let's just collect more data like that right. Well there are a few problems with that. The first is that some types of events just might be rare like jaywalking pedestrians they just might be very rare events and we may not necessarily want to force people to jaywalk so we can collect more data. So that's one problem but another really big problem is the cost of data collection so imagine that we wanted to label images from cars that an example you see here this is from the Berkeley BDD data set. Well labeling 1,000 pedestrians with per pixel segmentation labels that you see here where the label has to identify each pixel that belongs to that pedestrian it's quite expensive so it costs maybe about a thousand dollars per 1,000 pedestrians and now if you imagine the huge sheer variety of visual data that we would want to cover in our data set we want multiple poses we want multiple genders age race clothing style and so on and so on like and somewhere in there we want people who riding bicycles maybe not riding bicycles or maybe riding tricycles right so if you think about you know how many different factors of variation we would have to cover this very quickly becomes untenable and and just too expensive to collect label data that's balanced across all of these variation factors so what actually causes for performance right you might be wondering that as well well you know can't my deep learning algorithm just get better maybe I just need a better algorithm that will generalize and do better on test data so there are a couple of problems that is caused by data so bias that current models cannot handle the first problem is that the training and test data distributions are different so here you have an example of two-digit domains the blue points and the red points are from these two-digit domains and you can see that when we visualize this data we do this by extracting features from these images using the deep learning model that we trained and then plotting it in a t-snee visualization so this is what we get you can see that clearly the distribution of the training blue points is very different from the distribution of the test points and so this is a theoretical problem actually when these distributions are different we can show that theoretically they're actually bounds on how well our model will generalize another problem is that a model trained on the blue points is not as discriminative so the features it learned are not as discriminative for the target red domain and you can see that because the blue points are much better clustered into different categories than the red points right so you just may not be learning good features for these test points that the target domain so fortunately there are quite a few techniques that we can use to alleviate this I've listed a bunch here what I want to talk about here today is the technique of domain adaptation but you know there's always data augmentation there's always using sort of batch normalization some of these techniques can help in the case of data set bias but let's talk about domain adaptation so in domain adaptation we design a new machine learning approach that tries to adapt the knowledge from the labeled source data to the unlabeled target domain okay so our goal here is to learn a classifier that achieves a low expected loss under the target distribution and importantly here we assume that we have a lot of labeled data in the source domain but we also get to see unlabeled data from our target domain we just don't get to see the labels right because labels are expensive to collect so we assume that we do get to see some unlabeled data at least from the target domain so what can we do well the first technique it's very I would say fairly common now and fairly standard in the literature which is adversarial domain alignment so here we want to take a neural network which I'm showing here is this encoder convolutional neural network because we're dealing with images so we always use convolutional networks and we have some training data with labels and now so if we train this using regular classifier loss we can generate features from our encoder CNN and here I'm just showing it for two classes for clarity and then the last layer will be our classifier layer so we can visualize the decision boundary that it learns between one class and the other class now if we also get to see some unlabeled data from our target domain so let's say we put a camera on the robot and it can explore its environment and snap some photos so now it has some data is just not labeled and so if we apply the encoder CNN train on the source directly to this data we already know that we'll see a dataset shift like this so the distribution of the target points will be shifted with respect to the distribution of the source blue points and so in adversarial domain alignment our goal now is to align these two distributions the blue source distribution and the orange target distribution so how can we do this well a very standard approach is to add another piece to the neural network which we call the domain discriminator this is just a classifier that tries to assign a domain label to these input examples and if we train it with a GAN loss with a an under serial loss essentially then we iterate between the domain discriminator trying to separate the distributions and then in the next step we update the encoder in such a way that it can fool the discriminator so the discriminator's accuracy goes down and in the process the encoder learns to align the two distributions so that if everything goes well that the discriminator can no longer cannot tell the difference between the domains and these features have become domain invariant essentially so that's adversarial alignment and here's an example of it working for those two-digit domains that I showed you earlier you can see that in fact after adaptation with adversarial alignment the two distributions of the red and the blue points have now been aligned almost perfectly and in fact classification also goes up considerably and so it's not just that the distributions are aligned it actually does improve classification accuracy another technique that I want to mention is alignment in pixel space so what I mean by that is suppose again we have source data with labels and some unlabeled target data and now instead of just doing adaptation with alignment like I just showed you what if we first translate our source data in image space so we're generating new images from the originals but now these new images look like they come from the target domain so this is a similar idea of aligning the two data sets but now we're aligning them in pixel space because we're actually generating the images themselves and not just generating features so the advantage is that once we've done that if we're able to train this generative adversarial network that can translate from the source to the target domain now we have data that looks like it came from the target domain but it has labels because the original data is from the source so it's labeled with the categories that we need for training and by the way we can still add feature alignment on the feature space through this overall architecture and in fact we have experimented with that in our paper which is at the bottom so if you're interested you can take a look but if we do do both feature and pixel space alignment that can further improve our performance on the target domain okay well that's great this pixel space alignment seems pretty neat but so far we've been assuming that we have unlabeled target data in fact what I didn't tell you is that in order for that method to work it needed to see quite a lot of unlabeled data from the target domain but what if we only get one image or a couple images from our target domain well unfortunately the existing method like Cyclegan or cicada that I showed you doesn't quite work so instead what we need to do is take a source domain image which is our content essentially and we want to translate it to a new visual domain but we only have one example let's say of that domain so in this example our content is a dog and we want to preserve the pose of the dog but we want to change the style or the domain of the dog into this other breed I don't unfortunately don't know what breed of dog maybe you know what breed of dog this is but anyway just have one example of this new breed and so we actually propose a method that can do this and this is the result so you can see in the generated image we took the original source image and we added the style of the target image but preserving the pose of the dog right so the content is preserved so this method we call it Cocoa Funit and was published recently in eccv 2020 I'm not going to go through the details because they don't have time but essentially the model takes a content image and a style image encodes it using a content encoder and a style encoder and then combines these two encodings using an image decoder to generate the output image here are more examples so we have the style image on the top and then the content image below it and then the resulting generated image with our Cocoa Funit approach at the bottom we can take a look and see that we're able to even just using a few sometimes just one we've tried one or a couple images of the target domain here the domain is a breed of the animal or a breed of an animal so we can change the pose is the same from the dog but the sorry the pose is the same from the content image but the breed is you is taken from the style image so you can see how this is working quite well and if you're curious compared to the previous approach which is which is called Funit that we're building on actually we're improving on that quite a bit because as you can see Funit is not able to translate images using just a single style image it's kind of generating fairly poor results in this case and on average when we evaluate on a large data set we also see significant gain using our Cocoa Funit approach so that's another example of pixel domain translation one other example that I want to show you really quickly is using this idea for adaptation and robotics so here we have a robot that's trying to insert an object into another object let's say a peg into a hole or is trying to more generally we can apply this to other manipulation tasks and our input data is coming from the depth sensor so it looks like this so there's an RGB image but what we're using is actually the depth image you can see it in the middle here but to train so we want to train a computer vision model in your network that will control the robot arm to perform the task but to train we want to use simulated images so we simulate this kind of problem and generate fake depth images and train the neural network but the problem that we run into is of course we have a gap between the training domain of simulated data and the target domain of real depth images and so what we tried is using pixel level domain translation to solve the status of bias problem without collecting any label data in the target real domain you can see here an example real depth view image and then a similar simulated image and then the last one is we take the real and we translate it into the simulated domain and you can see that it's now looking a lot more like the simulated data so we're closing this domain gap okay great so I'm going to wrap up here just to recap what I talked about so data set bias is a pretty major problem for machine learning in general but for computer vision specifically that's mostly what I work on so that's what I focused on and I showed you a couple of ways that we can mitigate this problem using either feature space domain alignment or pixel space domain alignment I also think you know we could discuss if we have time after this some even more general ethical issues related to data sets for example recently there was a paper that's generating quite a lot of interest that looks at the dangers of large language models and points out that language models are being trained on progressively larger and larger data sets so it's almost like the opposite of the problem that I talked about where we have a huge data set that we're training on and now the problem that they're pointing out is that this data set might contain all kinds of bad data like offensive data or just you know even private data and by training the model on it we don't know what kind of biases or bad undesirable things that it's learning right so that's that's kind of a related but different ethical issue and this paper by the way is one of the co-authors is Timnit Gebru which you might have heard that actually she was forced to leave Google over this specific paper so yeah so there are quite a few ethical issues and I'm happy to discuss that or anything related to what I talked about and thank you very much for your attention


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Thank you for joining us at WSIS Forum 2018. I'm delighted to be joined by Ms. Karen Bartelsen, who's past President and Chief Executive Officer of IEEE. Thank you for joining us. I am delighted to be here. I really appreciate the opportunity. Thank you. So, Karen, the emphasis of IEEE's intervention this year at WSIS Forum was about ethics in design in AI and AS. So what can you tell us about it? IEEE has an initiative underway for the last couple of years to emphasize that when people are designing artificial intelligence and autonomous systems, we have to think about ethics first. Because if you design something like an autonomous vehicle or a robot or an artificial intelligence algorithm, you have to consider the human aspects first. Human beings are inherently biased. We can't help it. That's our makeup. But if we're writing algorithms that will decide how to analyze someone's medical situation, we need to prevent the bias from creeping in. So we have produced a document that we call ethically aligned design. It was crowdsourced. We're now working on version three, and it will become a permanent textbook if you like, a reference source for people who are entering into the field of AI and AS development. And through that document development, we have produced 13 different standards projects. These standards will help people to decide, for instance, how do you measure the well being of a society? Not using GDP, which is a traditional way of measuring the well being of society. But that measures how well the upper people are doing, they're very successful. But it doesn't tell you are people happy, how's their mental health? Is AI and AS actually making a society better? So we have a standard that will have metrics for that. We have a standard for how to prevent bias from entering into your AI algorithm. So again, there's 13 of these, plus the document. And anyone who's interested in this in the entire world, I would encourage them to go online and download the document, read it, contribute to it if they like. And certainly we want to help prevent bad things from happening, because I personally believe that AI and AS can do some dramatic things to really improve the human condition. It's very interesting this emphasis from productivity to happiness. Exactly, exactly. And a lot of that has to do with, do you necessarily become more happy when you're more productive, or do you become more stressed out, and is the only goal in what we do as human beings to make a lot of money and to have more things? There are a lot of people who think maybe not that there should be more of a value to living and experiencing each other as human beings. And there's also the aspect of how do we protect the planet, because productivity can sometimes be very, very detrimental to our beautiful earth that we live on. So there's a lot more than just productivity, in my opinion. And that's part of what the ethically aligned design is all about. So there were lots of discussions that we've informed this year about happiness, about gender equality as well. And I think it's a theme that's very dear to you, isn't it? Well, obviously. When I first came to WSIS this year, I thought there is so much more focus on gender equality this year than last year when I was here. And I thought maybe that's my imagination, because it is a topic that I look for. But in speaking with many people, they said, no, absolutely not. There is much more emphasis. It's probably a result of the whole Me Too times up that we're seeing some very bad behaviors coming to light. And what I found important at WSIS is it wasn't just women talking about gender equality, it was men also. So there's an awareness now globally that I think will not be just temporary. I think it's going to continue and be persistent. I'm looking forward to young parents teaching their sons. You don't behave this way anymore. And we will change the whole way that our society behaves. I also learned a new word this week. It's manil, which is a panel entirely of men. So what people are now trying to do when they organize panels is make sure it's not a manil indeed. So therefore, do you find that WSIS is the right platform to discuss these issues? I do. WSIS, because there's an emphasis on the SDGs, has the ability to address probably just about everything that we need to address in society, things like gender equality, which was one of the SDGs, life under the ocean in the sea, which is one of my personal favorites because of the plastic pollution that is now coming to light, that by the year 2050, there will be more plastic in our oceans than fish by weight. It's terrifying. And when you see what's happening out there with trying to harvest huge amounts of plastic that are destroying marine life and certainly our beaches, which implies tourism is at risk as well, because who wants to go to a beach that's littered with horrible plastic? So every single SDG is addressed here. The reason it's important to an organization like IEEE is we as technologists see that we fit into every single one of those 17 SDGs. There's a technology element that will help solve these and meet these goals. And that's why we're really excited to be here at WSIS. Plus, I think what's important is the action lines that say, OK, now we understand them. What are we going to do about it? And how do we have people to drive the efforts to make the SDGs come true? Yeah, because it's not just a platform for discussion. It's also a framework. Exactly. It's a framework. It's tools. I come from industry. And so we don't like to just talk. We like to talk about things and then do them and execute them and make things happen. So that's why, again, I find WSIS to be very practical and very inspiring as to how we will move forward and make the SDGs come true. Well, thank you very much, Karen. Thank you. You're welcome, Sharon. Thank you.