3. Ideas on teaching EFL and a kindergarten When teaching young learners, one must be prepared to face the challenge with an open heart and respect. Children deserve our respect as much as adults do, and if we manage as educators to offer them our respect, and moreover our trust, they will progress beautifully. 1. Possible challenges To fully explain some of the difficulties and the ways of dealing with them, when teaching kindergarteners I will start by taking a look at the most challenging part. In my opinion, sparking their interest in keeping them focused for a longer period, is one of the most difficult tasks. A good teacher has to adapt to every situation, and children will require different skills and a higher level of energy. Even though the attention span in kindergarteners is relatively small compared to that of adults, there are a few methods that prove deficient in the classroom. This century has blessed us with the gift of technology, and wisely used, it can become of great help and therefore ease the teacher's task. A variety of songs, virtual stories, or even cartoons can be incorporated in the lesson. 2. Lesson duration From my own experience teaching English to Japanese kindergarteners, I noticed that the time allocated for the lesson has to be limited. A child cannot have a productive one-hour lesson or more. So where possible, it would be wise to speak with the principal, or the person in charge to reduce the time allocated for English learning. If this is not possible, try to use the last 20 or 30 minutes to play games, and therefore not allow the children to get bored. In my opinion, an ideal lesson for a kindergartener would take around 30 minutes, and would be based on the following plan. 3. Greetings, how's the weather? How are you? Quick review of the previous vocabulary using big flashcards, choral repetition as well as individual practice. Introduce the new vocabulary, three to four words, trying to spark their interest by showing only half of the picture, and letting them guess. The choral repetition can be conducted by the teacher, and to make it interesting, low voice, medium and high voice can be used, asking the same from the students. Hand gestures and funny faces will help a lot. Song based on the new vocabulary. Activity that requires them to move around the classroom. A good example would be touch the card race or motaro, the kids walk in a circle listening to a song. When the song stops, they have to go to the card indicating the word said by the teacher. The cards are displayed on the floor, and the children have to pay attention to them, and memorize them while walking past them. Thank you, goodbye. 3. Shorter attention spans. Another difficulty when teaching young learners, is that they might easily start fighting or refuse to cooperate. Having a teacher assistant who knows their first language would be of great help, and while most of the kindergartens and schools offer this, there might be some situation when the teacher has to deal with the problem by himself. I think that in this situations, it is necessary for the teacher to try and calm down the child. The other children can be kept busy for a couple of minutes with an activity, coloring a picture representing the new word taught, tracing letters, or watching a short cartoon. Another option would be to ask the child to come next to you, while continuing to teach. He will eventually calm down and return to his seat. If this is not the case, asking for help would be the best solution. Are you ready to teach English abroad? Take a Tefal course. My personal opinion is that working with children is the most rewarding experience. Being able to see them grow and help them acquire a new skill, adds meaning to our life as teachers. I think that we learn from them as many things as they learn from us, and being able to help them grow beautifully, to gain confidence in their ability, to keep their curiosity and wonder, and not be afraid of making mistakes, are things that we as adults, should learn once again.


I would love just to say we did it and to have a big enough organization that in some way shape or form, we could block YRF. Today we're back to do a YRF video.  I'm Rick. Can you call us on Instagram, Twitter, more juicy content. It's so juicy. If you get to the right signal, you can hear the juice percolating. Big word source on Patreon, I'm a fellow official Twitter account. Really? Thank you everybody so much. It's on Patreon. The reason we're coming to India. And ring the bell so you don't miss out on the notification squad. Bell, bell. It's Ringelitris. What are we doing, Rick? We are going to react to something, Corbin. Right you are, Kevin. A trailer? No. Boo. A song? No. Ugh. A travel video. Now we are reacting to the seven most emotional and thought provoking ads from India. Oh, so it's hopefully... I was close.  I don't cry. You guys know me. I never cry at anything. But... Here we go. We're going to get like seven straight ads here? Yeah. Do I need tissue? You might. I don't know. I don't see what happens. I'm feeling particularly strong right now.  Listen. Didi was crying again. Why? Didi was saying that she is going to school. So my brother told me that I could go to school. Brother would go to school. That's why I didn't think that I would dress like my brother. And then everyone would think that I'm a boy. And then I would also go to school. Oh my god. Pizza? Pizza? Pizza. Pizza is good. Yeah. So he was saying that... Pizza? Is it good? No idea. Where did he come from? From where did you come from? I'm from a city, it's the bus that doesn't. Why is it the bus that doesn't? It's easy. It's easy. It's easy. It's the distances. It's easy. It's easy. It's easy. It's easy. It's the distances. It's easy. It's easy. It's the distances. It's easy. It's easy. He cut and then he was like, ah, buy you a chocolate. What a beautiful voice. I don't understand. Okay. It's possible that he might die because of my Saina. But I've also been watching your movie since then. Why do we live with someone else's life? Y'all don't give away for ambulances? Seriously?  Let's go and take Priya for shopping at 7 o'clock.  I gave you a simple report. There are mistakes on that. Sorry, sir. I think I made a mistake hiring you. Did they give each other gifts? It looks like that. Hold on. It's a credit card coming. What did they have to do with a credit card? Oh. He didn't answer.  Straight up a poo. I think I lost some translation for us. Translation with the name changes name. You put something in his name and communicated something. We rightfully heard. The illness becomes impotence and strengthens when you improve. In the healthcare insurance of Signa TT, like your own, it's a little more for your health. A little more for your health. In the medical insurance, it's a little more for your health. Proacted loving programs. The health insurance of Signa TT. Health is life. I want to give a Theme, sir. Was that it? Yeah, that was kind of, I wasn't, yeah, not what I was expecting. Yeah, I was expecting more because we've seen some powerful commercials like the Samsung one. Yeah, we've seen some pretty, it was the, some of them made me just flat out cry. Yeah. But maybe these are actual TV ads. Nose weren't played on TV as we know, they were more internet. Right, that's true. Promotion. That's true. It helps a lot when you can hash out a story for three to five minutes. Yeah, absolutely. It does. It helps you. Yeah, that's one of the things I'm really impressed by artistically is when there's like a 60-second spot or a 30-second spot that actually captures me emotionally, always. There's some commercials that within five seconds they've got me and that just boggles my mind that the creative team behind those ads can do that. Which ones? I can't think of anything on the top of my head. Like the Budweiser horses? Oh yeah. The Budweiser horses running, the minute I see those Cladd's Dales? Well, if they're friends with a dog. That's true. Yeah. A dog? The way they get at least in America how they capture you quite easily is they put a dog, military maybe, something like that, stuff that everyone is emotionally invested in already because you have a dog. But that's a very tricky thing with the commercials here because then you get to that line of are you just trying to pull the heart strings versus actually doing something that has a message behind it that means something. So yeah, it's not always that good. The ambulance one is, it happens here. You're supposed to do here in the US, follow behind the ambulance, chase the ambulance. In the US because we obviously drive on the right side of the street, the rule is if you hear and see the siren if it's coming from any direction what you're really supposed to do is you're supposed to pull over to the right and get out of the way. A lot of people just stop right in the middle of the road, which doesn't help at all. And some people just don't stop. But what's it like in like Mumbai, how do the ambulances get around? Probably just like every other car just goes straight through. I mean, I guess that's the point is it's probably much more difficult in cities like Mumbai who the congestion is so insane and there's nowhere to go unless everyone collectively decides let's all move this way. So I don't know if you have a law there like we do where you have to pull over to the left or not. And I would hope no one would be sitting in their car thinking, hmm, should I choose the life of the person in the ambulance behind me or make it to my movie on time? That's a tough one. Gotta make it to the movie. It depends on the film. If it's doom three, definitely gonna let them die. Yeah, there was some guy, I mean, the military one was pretty good and then the two were lost in translation. Yeah, there was a couple, a couple of things that I didn't understand. Yeah, like the one with where they started off and he said pizza and they said no, it's pronounced pizza. That whole thing, there's something culturally language or something that went way over my head. I think it's certain people will make fun of different people's accents because they can't say English words correctly, maybe. I didn't understand the one where the guy cut in line and he was like, I'm sorry, let me buy you a taco. Here you go. I'd like to know why so many girls don't get educated. Like what's that about the rule? Like the ones that are kind of in the not in the metropolis areas and yeah, they like the ones that they're just marrying off their dogs guys when we saw it was a film. It's okay. I'm sorry. Sweet. Sweet. Yeah, when he wouldn't let his daughter come learn, right, because she has to help and she has to get. So I guess that must be still a thing in rural areas that the most where it's like girls are not allowed, seriously not allowed to get educated.  There's a lot of people in India. Yeah, I know it's 20.  So yeah. But yeah, I was expecting my heartstrings to be pulled on like some of those other commercials. Yeah. Maybe there's a better compilation. You can let us know. There are some like funniest ads, but I thought that I don't have subtitles. Oh, and so I'm obviously comedy is a hard enough thing to get if it's a different language. Unless it's Akshay coming out of a bubble bath. That's just straight up good comedy. Unobjectively hilarious.


All right, so without any further ado, we have Frido presenting Thought Healing Python Applications. Take it away. Thank you. So hello, everyone, welcome to this presentation that will be a set about Healing Python Applications. And in this talk, we will talk about things that are developed in Thought. So let's get started. Before we start, let me introduce myself. My name is Frido Lim. You can find me as under acronym Fridox or on Twitter or on GitHub. And I am a Thought member since 2019. So that's basically from the beginning of the project. I like Python. Actually, I love Python, and I do road cycling. Now, when it comes to Thought, that was a research project that started in AICOV team in the office of the CTO. And one of the main offerings we do is cloud-based Python resolver. You can visit us on Thought Station Ninja web page where you can browse documentation, but also links to YouTube channel where you can find our demos or Twitter where we post periodic updates. So as stated, one of the main offerings we have is a Python resolver that is run in the cloud. Basically, it's not just a resolver, but it's a recommendation engine for Python applications. And we will talk about main features and why the recommendation engine can be suitable for developers. The recommendation engine is publicly available to the community, so you can use it. And the core of this recommendation engine is a stochastic resolver that is implementing gradient-free reinforcement learning methods. In production, we run temporal difference learning and that's something we will talk about more in this presentation. If you are interested in details how the core mechanics and how the resolver is designed, you can browse documentation that is available online. OK, so if you've ever used Python, you probably know that's the Python installer that can install dependencies into your environment. So if you have a dependency, you simply install it and it's downloaded the resolution process, also results, dependencies that are needed to install the library and then you can use the library. Now, what we do, we offer an alternative that is a CLI tool called Tamos. And you can install Tamos using PIP. And once you have Tamos in your environment, then Tamos can generate a configuration file for you that is subsequently used in the cloud resolver. So if you want to give Todd a try, you simply can issue PIP install Tamos, Tamos config that will generate a configuration file and then you can do Tamos advice that will take the configuration file and together with requirements of your application, we'll send them to the cloud resolver. Now, why the configuration file is needed? As the resolution process is not done locally, as in case of PIP, the backend resolver needs to know how to behave in this resolution. So the configurations file states information such as operating system that you use, Python interpreter version that you use, but also other aspects that are not considered in PIP such as CUDA version that you have available, but also base container image that you are running. So the resolution process is really specific to your runtime environment. Besides software information that is captured in Tamos config, Tamos also captures information about hardware that is available on your host. So it captures information about CPU and GPU and this is valuable to the resolver because if the resolver is targeting performance applications that we will talk about later, then the resolver can take into account also hardware information that is available on the host. Besides this information, the Tamos config also can optionally gather information about source code for which the recommendation is computed. So Tamos config, sorry, Tamos advice under the hood performance static source code analysis and extracts information such as what libraries are used in your sources as well as what symbols from these Python libraries are used. Then there's one additional option that is recommendation type and this recommendation type basically says intention with the application that you have. So if you are developing an application that should be secure, should not have any security vulnerabilities, then the resolution process can look differently in opposites to resolution process that is targeting very well performing software used for training machine learning models, for example. So this is all inputs to the cloud-based resolver and the cloud-based resolver uses this input together with knowledge that is aggregated about Python packages but also about runtime environments and can resolve a software packages so that it provides log file together with some justification that says why the given log file with set of packages should be used. As stated before, the recommendation engine uses reinforcement learning. This feature or this implementation of reinforcement learning was brought to the resolver to come up with the best or the most suitable set of libraries that should be installed into your environment. If you consider all these inputs that are going to the resolver and all the possible versions that are available out there, versions of Python libraries, but versions of CUDA, versions of operating systems and stuff like that, then you can imagine that there's a wide range of possible resolutions and all depend on requirements on your software environment and on your hardware. So there's possibly infinite state space of all the possible resolutions. So the resolver needs to somehow know how to navigate in this state space and how to resolve the most and the best possible software libraries that you can use. On each request, we train a model. So the resolution, each time you submit the request to resolver, the resolution trains a model and the resolver first performs exploration phase where when the resolver checks what are the possibilities, how it can resolve software packages and subsequently resolver performs exploitation phase. So based on knowledge that it aggregated in the exploration phase, then the resolver can come up with the resolved software that is considered the most suitable for your runtime environment where your Python application runs in. The whole resolution process is then treated as a resolution pipeline and this pipeline is made out of units of different type. There are core or base pipeline unit types that are registered in the resolution process and then you can also extend the resolution process by implementing your own pipeline units that are of specific type and the type distinguishes when these pipeline units are called and also semantics behind these pipeline units. The implementation of the recommendation engine can provide a way to extend the resolution process by implementing these pipeline units and this extension can be done error in Python code or you can declaratively provide the ammo files that are automatically taken into account in deployment loaded by the recommendation engine and can adjust the resolution process in a way that the most suitable packages are resolved. The pipeline is again constructed dynamically so on each request pipeline is constructed based on inputs to the resolver and also based on the knowledge that is aggregated in the Python ecosystem. Now let's focus on the declarative interface for the resolver and let's check these ammo files how they are written and how they can be used. These ammo files are called prescriptions and they prescribe some rules how to heal Python applications. So these prescriptions form a declarative interface to the cloud resolver. If you are familiar with OpenShift or Kubernetes you have manifest files that you apply to the cluster and these manifest files basically desired state of the cluster. Similarly these prescriptions can be applied to the resolver but in this case they somehow adjust the resolution process in a way that resolver comes with the most suitable software packages that you can use in your application. As stated before these ammo files are automatically consumed by resolver in a deployment and our versions so we do releases of these ammo files as well. If you are interested in prescription concept and you would like to write your prescriptions feel free to browse the documentation that is available on our homepage. Now let's take a look at some examples. So these prescriptions were designed to help resolving high quality software. That means that sometimes some releases create some runtime errors or undesired behavior that library maintainers did not test it or simply there is some misbehavior in libraries. So here you can see the very first example in which case pillow inversion 830 does not work with any NumPy. So if you install pillow together with NumPy pillow in the specific version you will always get this type error that is raised on runtime. The issue was reported on upstream and newer releases of pillow do not have this issue. However, if you need to stick with older version of pillow for some reason this issue can arise. Note also that even if this issue was seen in older versions of pillow that doesn't mean that the resolver always comes with the newest pillow. The version that is resolved depends also on other dependencies. So if there are shared subgraphs in dependency graph dependency resolvers can come up with pillow 830 that will cause these runtime errors in your applications. So to bypass this we can create a prescription in a tot that will avoid installing pillow together with NumPy. So here you can see a YAML file that states the prescription. As you can see it is created out of multiple sections. So the very first section states units that are declared in the specific prescription. In this case we are using pipeline unit that is of type step. So this is specific to a step that is performed during the resolution process. And the name of this pipeline unit is pillow 830 type error step. This name is used in the resolution process to uniquely identify this pipeline unit in logs or when checking prescription schema. Now the next section says should include which has just one directive and it is advisor pipeline true which basically says that this prescription is used during recommendations. In the upcoming examples we will see other declaratives that can be stated in the should include section and they additionally configure when the given pipeline unit should be present in the resolution. The next part of this step pipeline unit says when the pipeline unit run should be triggered. This is the match section that says the pipeline unit should be run when pillow in the specific version coming from by PI org is about to be resolved and resolver has already one dependency that is resolved and that is numpy. This is not specific to any numpy version as can be seen and also not specific to any numpy coming from different index than by PI org. Now the final section that is run states how the pipeline unit should behave when the run is actually triggered. So here you can see that pipeline unit does not accept the step that is performed by the resolver. So there you can see not acceptable pillow inversion 830 does not work with the given numpy. And then there is also provided some information to the user that this pipeline unit discarded the given step. So users of talk can see this in the resolution block. Okay, so now let's take a look at another example that is adjusting requirements in GPU enabled environments. If you are not familiar with TensorFlow, TensorFlow is a machine learning library and its dependency graph is quite interesting. So that's why I will use TensorFlow as an example in these slides. So here you can see that we have TensorFlow GPU so the name and the pipeline unit that we will see shortly considers TensorFlow GPU as a pseudonym to TensorFlow package. If the environment has GPU. So let's take a look at prescription. All the examples that I will show onwards live in dot station prescriptions repository and that's the publicly available repository of prescriptions that the public instance of thought the cloud based resolver is using during the resolution. So here you can see the prescription. Now the pipeline unit has different type and that's pseudonym. Here you can see that the pipeline unit is registered if the given runtime environment has CUDA or CUDNN configured, not all, but both are configured in the runtime environment. And then we are matching packages such as TensorFlow Intel TensorFlow Intel CPU coming from pipi.org. And if the resolution process finds or encounters these packages, then the recommendation engine resolves or considers TensorFlow GPU as an alternative to these packages stated. So here you can see prescription, the version that is considered as an alternative is the same as the matched version of these packages. And then you can see again some log message and information provided to viewers. Then another example considers versions of CUDA. So that TensorFlow GPU is the version of TensorFlow GPU is correctly resolved. TensorFlow has support metrics when it comes to CUDA and CUDNN. It's sometimes it causes headaches to match these versions, but this cloud based resolver knows how to resolve TensorFlow GPU in specific versions so that the CUDA is utilized and the right TensorFlow GPU is used with the support it CUDA. The same applies to CUDNN that is basically another library used by TensorFlow. So here you can see CUDNN version. As you can see, these pipeline units can be very atomic pieces and can form complex resolution process that is respecting rules or prescriptions that are applied. Another example is fixing library overpinning issues. So in Python ecosystem, you can spot that libraries provide two broad range of supported version that are claimed to work. So here TensorFlow in version 2.1.0 and compatible overpinned version H5Py. So this prescription unit that is of type step makes sure that H5Py is resolved in a correct version that works together with TensorFlow. Let's move on to the next example and that is using specific Python package index that provides optimized wheel builds. Again, we have example with TensorFlow. So here you can see that when users use AVX2 enabled processors, then there is considered or sorry, prioritized builds that is available on the AICOE TensorFlow index. So in this case, TensorFlow in any version coming from this index will be scored positively and will take precedence in the resolution process over other builds of TensorFlow. You can see that this recommendation, that this pipeline unit is applied when people ask for performance, secure or stable software stack. Let's move on to example number five. In this example, we will see static source code analysis in action. So if you are a Python coder and you know this MKTemp function that is provided by temp file module, you probably know that this function is deprecated due to security vulnerability in race or is vulnerable to race conditions. It's also, this is also stated in the official Python documentation. So this prescription warns users about this fact when they use latest performance or stable or testing stacks. On the other hand, when people or users of TOT want to resolve secure software stacks, then the prescription errors and brings this error to users. So if this library function is called in sources, this prescription makes sure that users of TOT are notified about vulnerability in this function and the severity of the notification is based on recommendation type that users ask by the resolver. Example number six, checks runtime environment. So if people use environment where they have GPU, so GPU is not now and they do not have CUDA version properly set up. So the client too does not detect CUDA version. Then they are notified that there is no CUDA available and eventually other pipelining needs will make sure that, for example, TensorFlow GPU is not the result because there is no GPU to be utilized. That was example number six. And let's move on to our last example that is resolving Python packages considering RPM packages available in the runtime environment. In this example, we expect that Git, the RPM is present in the runtime environment. If it is not present, then this pipeline unit of type C filters out packages or any versions of package Git-Pyton. That means that the resolver will try to find another resolution path without Git-Pyton because Git-Pyton will not work on the host because of missing native dependency in the runtime environment. Then you can see this information is printed to logs. So if the resolution fails to find another resolution path, people will still see or users will still see that Git-Pyton is causing troubles when resolving software packages as Git is not present in the runtime environment. If you are interested in this prescription concept feel free to browse documentation and also feel free to browse the repository that holds these prescriptions for open source packages. We have bots that automatically update these prescriptions but also some user input will be available as if this database grows. That means that open source Python ecosystem will gain quality and the resolver can resolve better software considering knowledge about Python ecosystem. So this way we welcome you in contributing to the station prescriptions repository directly opening pull requests and implementing prescriptions if you're interested in this technology but feel free to also just report any issues that you spot in Python applications. The documentation for the declarative interface for the resolver is available also on totstation.ninja and I've already mentioned we have a homepage that is totstation.ninja which links to our YouTube channel so feel free to subscribe and you'll get also linked to our Twitter. We have totstation, Twitter handle without dash and here you can find updates and information about progress when it comes to development. Feel free to also reach out to us on this media and this way I would like to thank you and see you next time. Thank you. Yeah, thank you so much Frida for the talk. Folks, if you have any questions please feel free to put them in the chat and we have Frida here for another two, three minutes to answer them. Okay, so if you have questions I will be in the chat and this way I would like to thank you and hopefully you find tot valuable. Thank you. Right, thanks Frida.


OK, welcome, welcome, everybody. Hi, I'm Daza Greenwood from MIT Media Lab and also executive director of law.mit.edu, which is the convener of today's workshop, the eighth annual MIT Computational Law Workshop. I just want to start by saying, having done these things since actually the late 90s at MIT on this topic of law and technology, I honestly believe this is the best program yet. And that's owed largely because of a breakthrough with widely accessible, generative artificial intelligence and its applications for law and its impact on law and legal processes. So with that, let's get right into it, shall we? Jesse, are you with us and can you come off video mute? I am. Thank you for handing over the stage and for setting this up, Daza. Very excited to share with you guys what I've been building. I think you'll find it very helpful for some of the legal use cases that you've been considering. If you will allow me to share my screen, I'll jump right into a demo. Absolutely. Go ahead and hit screen share and let us know if you have a problem. Great. And Jesse, by way of introduction, am I correct in saying that you previously worked at OpenAI and were involved with ChatGBT? Yes. So I'm the co-founder and CEO of Multi, which is the startup that is responsible for this tool, which I'm about to demo to you. This was started back in May. Prior to that, I was working at OpenAI. I worked on large language model infrastructure, did some early work on ChatGBT dialogue systems and grounded question answering. I also achieved a new state of the art in machine translation using large language models and was a major contributor to OpenAI's theorem proving release, where they applied large language models to mathematical theorem proving inside of proof assistance. So I wanted to echo a sentiment that I heard in the last presentation, which is that we need systems that are going to be able to deal with the unprecedented amount of scale that these large language models are going to enable. And that's not scale in terms of parameters, but scale in terms of the volume of text processing that has to be done. So I think that that same consideration applies equally well to all knowledge work in general. The kind of intelligence that we see in large language models right now is kind of like an alien sort of intelligence, which is a pretty good approximation to a somewhat unreliable teenager. And as these large language models become more and more commonplace, we will begin to see large amounts of knowledge work, not just knowledge work inside of the legal field, but knowledge work elsewhere, which involve processing large amounts of text, which involve operating software tools, begin to be more and more automated. And so the problem becomes, how do you orchestrate that kind of knowledge work automation at that kind of scale? And that is the problem that Multi is trying to solve. We are building a software platform so which anticipates this future where there is this massive abundance of near human level intelligence and automation. And our first product, MultiFlow, which we released back in November, provides a visual, intuitive, and low-code interface for people to assemble AI-first workflows. And let me give you an idea of what I mean. So I'll give you guys a bit more background on myself. And this kind of showcases some of the features that we brought online in MultiFlow recently that might be especially relevant for people working in the legal field. So we recently added a way for our users to upload PDFs. So this contains an uploaded version of an unupdated resume of mine. So this is a pretty long document. And with resumes, it's kind of hit or miss whether or not they'll actually fit inside the context window of a large language model. But we've gone to the trouble of doing the kind of plumbing that Daza mentioned during his talk. And we can actually handle question answering over documents of arbitrary length. So we recently added a document Q&A block which takes in a document and a query and provides a bullet-pointed list of answers which were extracted from this document. So we can ask, what schools did I go to? It kind of says here. But let's pretend it's further down in the document. We can also ask something like, well, what was Jesse's SAT score? And we can also ask, what are Jesse's notable publications? So what's happening here is that I am building a program in a visual programming language for which multi-flow provides both a user-friendly front end and also a runtime that keeps track of all the state. And we can step through the execution of this program by clicking Run. And this is all powered by large language models underneath. And the amount of abstraction which is inserted between the user and the actual large language model API calls is entirely up to the user. I'm operating a fairly high level here. But later on, we'll see an example where we actually get a bit closer to the metal in terms of how much we're actually micromanaging those large language model calls. So let's take a look here. So it correctly extracted the fact that I went to UCLA. I got a bachelor's in mathematics in 2015. I got a master's in math in 2018. This resume is not up to date. I actually got my PhD a month ago. So it also correctly sees that my SAT score is 2280. And it correctly extracts the fact that some of my major work has been in formal theorem proving and the applications of machine learning to automated theorem proving in mathematics. So I'll pause here for any questions. Thank you. Yeah, let's go. I actually use multi-flow to write the PhD. That's awesome. So if we wanted to, say, generate some copy. Right. So here's another example where a visual front end for prompt chaining and for being able to recursively assemble prompts and then feed them into language models and use them to produce a complex result might be useful. Suppose that you need to write a short blog post about some arbitrary topic. So in this case, I've asked it to create a blog post about the fact that clowns across the world have decided to go on strike. So what we've done here is we've combined few shot prompting where we show the language model a bunch of examples with the kind of structure of saying, well, now we're going to few shot prompt an introduction. We're going to few shot prompts some body paragraphs. We're going to few shot prompt a conclusion. And we use the topics which are extracted from the language model call as prompts for actually producing the actual body paragraphs, which will be inserted inside the blog post. And so you can see that once this entire thing is run, in fact, we can just re-execute the entire thing. So we see that it regenerates this blog post and it also creates a bunch of images. And these images can be prompted so that they arise in different styles. And this is something which is possible through the string formatting that we have through our text boxes. So as you can see here, the way that we manipulate text inside of this interface is that we use template variables, which are denoted by these double brackets.  And you can pipe input into these. And so all we're doing here is we're concatenating the copy about clowns with some styling prompts. And then this becomes the input to a call to a stable diffusion model. So now let's maybe jump over to a use case that might be particularly interesting for people working in the legal field. So here's an example of a flow which performs dialectic reasoning on some topic. So suppose that you wanted to analyze the pros and cons of some controversial topic and then synthesize them. And you wanted a highly interpretable trace or audit trail of what the language model was thinking throughout that entire time. So what you can do in this case is you can just ask it to create bullet-pointed lists of pros and cons and then ask it to argue against itself. So I'm going to try something different here. So I'm going to try building more housing in San Francisco, my favorite topic. So you can see here that what's happening here is that we've created the instruction to create a bullet-pointed list of pros or potential benefits of the following topic. And this is input to a language model API call. This is our text generation block. There are some settings here where you can control various parts of the OpenAI API. And you can see that here it generates a list of pros. We can then ask it to expand each of these short bullet points into a full-fledged paragraph. So you can see that this is piped into this text generation block. And this provides a paragraph which expands on each of these points. Finally, you can ask it to produce a point-by-point rebuttal for each of the claims, which are made inside of this essay.  So this kind of structure-chained reasoning is especially important for high-touch applications or particularly high-stakes applications, which involve lots of language, like legal, because this provides a clear audit trail of the model's reasoning, what the ingredients were that went into each of its choices. And it also gives you the ability, or at least the interface that we have here, gives you the ability to quickly rewire the way that the model is prompted in order to achieve the outcome that you want. And what's happening at the bottom of this flow here is that we've just gone and reversed the chirality of this. So we've asked it to come up with a list of cons instead to expand that into an essay and then produce a rebuttal. And then from these, we can actually So one thing that we could do here is we could ask the model to take these two things and synthesize them. So this is actually something that we can do right now. So OK, so let's pipe in the text above and let's format it so that the model knows that these are two different pieces of text. So this is rebuttal 1, and this is rebuttal 2. And let's separate these with some kind of demarcation. So above, you have been given. So this kind of prompting works with the instruct models on the Open API. Above, you have been given a rebuttal of the pros and cons of topic, even a rebuttal, the pros and cons of topic, and a rebuttal of the pros and benefits, cons and drawbacks of topic. Synthesize these into an essay with multiple paragraphs. And then let's do a little bit of prompt engineering and tell it to write an eloquent essay, an eloquent, well-articulated essay. OK, so with this in place, we see that due to the formatting, which I mentioned earlier, there are three inputs. So we need to pipe in the first rebuttal, and then we need to pipe in the second rebuttal, and then we have to pipe in the topic, which is all the way back here. And finally, we can run this through the text generator to get a final output. And now let's rerun this thing again. So this gives you guys an example of the kinds of rapidly and increasingly sophisticated use cases that you can achieve with complex prompting and a tool like this. And what our front end gives you is the ability to inspect the intermediate outputs to rapidly change the prompting style on the fly and also to deploy these to an API. So if you're a developer who wants to integrate this kind of technology into your own application, we are actually constructing a function in a visual DSL for programming large language models. That's why you see these input and output text. And so what we have here is a function that takes in a single input, which is a topic, and produces an output, which is this well-argued, articulated essay, considering very carefully all the pros and cons of this topic. And this can be deployed to an API that you can call from inside of your own application or also to a web app that you can just create and share and which hides all of the intermediate outputs. We're standing. And so I hope you can all see why it is. I wanted Jesse to share this. So a lot of us have got at best a concept of a very flat interface. I mean, the great thing about ChatGPT is that it provided very wide, almost population scale, immediate access to the technology. But it's hardly the ceiling of how this technology can be configured and composed and integrated into other systems. And so thank you so much for showing that, Jesse. I forgot to mention that I'm bending our rule a little bit here on product demos. Obviously, MIT doesn't endorse this product. This is for educational purposes to see what's possible. And let me just ask, let me start with, we're going to have to move to the next session pretty soon. But Dan or Michael, if you have any reactions, questions, or comments, I invite you to jump right in. Go ahead. I would just say this is what, when we talk about why you want to use an LLM and what the results that we've showed mean, this is what we're envisioning their actual use case, right? Like not a human directly asking GPT for answers, but something like this, let's say the Illinois Legal Aid Online, which Dan works with in his capacity at Chicago Kent, instead of building a rules-based triage system, which is what the ILIO does today to answer questions about, let's say, landlord-tenant disputes for tenants in the state of Illinois, would replace the rules-based system or parts of that rules-based triage system for people who are trying to deal with their legal problems with something like this. It would be inside of this larger ecosystem or, if we're being honest, a real product. And this is just a component in a product, not a product itself. Yeah, absolutely. I think that's the future that we're heading towards. And eventually, systems like this are going to become more and more widespread. And we're going to be living in a world that is going to be orchestrated by language model programs like this. No, I just thought this is great. Congratulations on this. This, I think, is a great follow-up from what we had shown, because this kind of shows you where you can take all of this, particularly I like this idea. I mean, just for an education perspective, teaching my students about, when we do legal composition, we think about the relative weights or merits of arguments in law. This is a way to enable them to see those kind of, I don't know, the battle royale between these arguments or what have you. I can imagine just in teaching legal writing. You asked this question earlier about what's the future of the bar. I mean, how about we have a measure of people's performance, but you can use the best tools available to then solve some theoretic client problem and that's your demonstration. And so if this is the tool you have, then you get to use it, and you get to use any tool. And that's the world I'd like to head to, which is you use the best in class tool to solve the people's problems. Anyway, I don't want to turn this into a revival, but it'll happen very quickly, otherwise. Thank you. Yeah, and I didn't get a chance to show you, but the document Q&A feature, which I started the presentation with is actually very useful for analyzing contracts. It nails most questions like for a purchase agreement, what are the obligations to the buyer and seller and so on. So there are lots of use cases. I'm going to drop a link to the website that I was using in the chat. And I encourage you guys to sign up for the wait list and come check it out. Thank you so much for giving everybody an introduction to what this technology is and what's possible. And not only are tools evolving, but the technology itself, just put your seatbelt on for Claude from Anthropic and for GPT-4. So this truly is the beginning.


If I am to tell you that I like Decathlon and ask you to guess what else I like, would you be able to guess? You can guess but you will guess everything under the sun. But if I tell you I like Decathlon, I read Times of India, I shop at Fab India and I listen to Ed Sheeran and I am a fan of RCB, you get a better personality of me, isn't it? You will be able to guess more correctly what I really like. That's exactly what cookies contain. Cookies contain deterministic data of what you are and cookies essentially are personalities of people. So what do we do at Affinity Answers? At Affinity Answers what we do is to enrich these cookies at scale by looking at the deterministic attributes and enriching them with probabilistic attributes. And this is done at scale at around 1.5 to 2 billion cookies every day. Given attributes A, B and C of a cookie, we add X, Y and Z which are the most likely. How do we do this? Unrelated to the cookies, we have a social recommender system built on social data and we have a catalog of around 75,000 entities across brands, celebrities, movies, sports stars and we use that to create a recommender system and that recommender system fuels this cookie enrichment. Actually it just happens to be that cookie is the example that it took today but it could enrich any data for that matter. This is something which we call as an act-alike model instead of a look-alike model. Where is this, how is this useful? This helps in advertisers to find mid-funnel consumers at scale which is very difficult otherwise. And what about people like you and me who are consumers? We start seeing advertisements which are more discovery in nature rather than a lot of retargeting and look-alike modeling. If I like Decathlon, I start seeing ads of Nike. That may not really reflect my personality. So at the end, what we really do is use social data and transfer that learning to completely different set of different type of data sets. Can you talk a little bit about how you do it as opposed to just what is done? I think that would be more useful for the audience. What exactly do you do which is different from look-alike or the recommendation system? Take a minute to explain that. What we do is collect engagements of brand-initiated contents, engagement of consumers to brand-initiated contents and apply collaborative filtering to get a recommendation model out of that. So that recommendation model can be applied to different phases like the way I explained to you, the cookie enrichment. Okay. Questions? Okay. One question in the back there. Just trying to understand the business model better.  No, it doesn't work like that. So what we enrich is the third-party cookies. Cookies that belong to various websites are called first-party cookies. We do not enrich them. What we do is to enrich cookies through something known as data management platforms which for example, Oracle Data Cloud, LiveRamp, and they have cookies, aggregated cookies when we do not even know it is completely masked. As far as we are concerned, it's just a data with a set of attributes. Got it. And how does somebody leverage this insight and how do they tie it to their data? And advertisers through DSPs are able to buy audiences that we put on the data store at the audience store at these DMPs. And once they get bought, a small fraction of the money percolates to affinity answers. Okay. I think these are very good questions. Third-party cookies and some of the data privacy things that we were talking about are also linked to it. I mean, and that's like a much broader question. Maybe Vivek is probably not inclined to answer right now. But thank you Vivek so much for sharing what you're doing.


another session of Hacks for Hybrid Working. My name is Lauren Hanks. I'm joined here with Marin Diepwell and we are super stoked for the second session of this flex course called Digital Tools in the Virtual Workplace. So thank you everyone for that has tuned in last week and is coming back today. And if you're just joining for the first time, that's also awesome. There's plenty of blog posts and links that we'll add in Discord to help you catch up and get started. And so with that, it might make sense to actually recap you know what we covered last week and where we're where we're headed. So Marin, do you want to take it away? Thanks Lauren and hello everyone. Really excited to start seeing those contributions come in. Last week we focused on unpacking hybrid working. And we particularly enjoyed seeing all your contributions to the Splot site. If you haven't discovered it yet, just head back to the welcome post or the week one post and do have a look. It's fantastic to see pets, coffee, commutes, desks and so on appearing there. The Splot will give us a look, a little insight throughout the entire course into how everybody's reality of hybrid working is shaping up. And we also discussed a lot of readings for week one and reflected on some of our kind of key high points, low points. So if you did miss week one, fear not as Lauren says, just head back to the blog post and you can find all the important things there. Now in a minute, I want to recap on the practical exercise that we focused on during our session and Q&A in week one, which was our hybrid working wheel and a reflective exercise to help share what's working and what's not working so well. So in a moment, we'll bring up the visual for the roadmap. And I wanted to start explaining how this is all going to fit together. So Lauren, if you're ready, should we go and jump in? So welcome to your hacks for hybrid working roadmap. This is going to be at your side for the duration of the course and also really important tool for you to achieve our course aims. It's kind of a bit a mixture between a signpost and a support tool and a kind of visual notebook where you can add reflections and pictures and other thoughts that you've had. The idea is that you can download this template and take it away either printed or digital and can complete it, you know, you can scribble with pen and paper, you can put post-it notes on it, you can download it and edit it digitally, you can add pages. This is your roadmap, your template. So please feel free to use that as you need to. Now, we're going to just have a quick look at the four different parts of the roadmap, one of which we already started on last week, which is the hybrid working impact.  And then in later weeks of the course, we're going to have them think about hacks for hybrid, which I'm very excited about. I'm looking forward to that. And also charting your course, which is our final week of the course. Now, Lauren, last week we looked at the hybrid working wheel. We explored this example of the map. So just to recap, everyone had a look at these different categories from work-life balance to commuting, physical and mental health and so forth. And it was also a space where you could add your own blank category to reflect on. And then we looked at the difference for us from one to 10 that you could all assign. And this week, we're going to take this particular exercise and start populating our roadmap with our reflections from that exercise. So there is a dedicated section of the roadmap, which you can now start to complete if you haven't already had a look at this in the email. And there are three reflective prompts. And Lauren, in a minute, I'm going to come over to you to see if you can share some of your own reflections. So we're going to talk about areas that you score high, which are positive, areas that you score low, which are challenging or require attention. And then also think about which one area you most want to focus on during the course. So, Lauren, what are your thoughts on that? Yeah, thanks, Marin. I really love how these questions are laid out. I just have to say because I think it's really important that while, you know, the purpose of this course is to figure out kind of what's not working and how we can improve and make things better and what we can be focusing on going forward. I still think it's also really important that we are celebrating what is working. And so I love that the first question is really talking about, you know, areas where you've scored highly or, you know, that are positive, the positive parts of your working wheel. And so I think for me, you know, looking at my own hybrid working wheel right now, I have to say I think socializing with colleagues is something where I scored rather highly. You know, that's always a moving target in virtual settings, of course, but I think Reclaim Hosting does a really great job of making our Slack space very interactive. And I see all of my colleagues on a weekly, if not daily basis, it seems like with just our various team meetings, but also our water cooler channel and the different ways that we try to connect, you know, with work, but also outside of work with, you know, virtual working hours and Music Mondays and things like that. So we can talk more about, you know, those strategies maybe in a future conversation, but I feel really proud of how I'm able to socialize with my colleagues even in a virtual setting. I also think that my home working environment, I feel very lucky to have a dedicated office space where I can come to work and then be able to leave that space when work ends. And I have, you know, really kind of curated my desk space, which is on the spot, if you haven't seen it already. But I, you know, feel really proud of kind of the environment that I've set up physically for my work. So those are maybe two areas where I have scored highly. What about you? Oh, sorry. Oh, no, I was just going to ask how you felt about your, your working meal. Similar to you, I think I have a lot of positives. So again, home working environment, I think during the pandemic I really worked at that because I spent so much time in front of the computer. And also my physical and mental health, like I just did some sort of desk based exercises today. And that's a good example of the kind of strategies that I use to kind of break up the long hours. Commuting I love up the stairs down the stairs. And definitely, you know, home office pets, I'm scoring very highly in that one. One of the areas which I thought was challenging for me is collaboration. Because I think I've worked with folk who are for many different skill levels and confidence levels. And sometimes that can easily lead to this communication. So are there any areas that you identified which you think are challenging for you at the moment? Yeah, you know, and I can definitely relate to what you're saying just around collaboration and virtual spaces. I think, again, that sometimes can be challenging depending on the project at hand or the task at hand and making sure that you're not, you know, working on top of each other and that everyone's communicating in the best way possible. And that can sometimes be a challenge for sure. I think for me, and this is sort of what I mentioned last week too. It seems funny, but I do have commutes scored rather lowly for me. And that I think is because simply because I need to start waking up earlier. It's just a fact. And maybe others in discord can relate to that. But you know, when you don't have to get ready and get in the car and go somewhere and be at somewhere at a specific time or your commute is the 10 second walk to your desk, you know, you can really kind of wait to the last minute sometimes. And I think it's, I would be a much healthier individual if I had more time for myself before my working day started. So that's something I really want to prioritize in the coming weeks. And I think that will also, that is very related for my physical and mental health as well, you know, making sure that you, you know, my entire day, especially as it starts to get dark with the seasons changing, making sure that I'm seeing sunshine, you know, and getting outside, getting fresh air, I think is going to be really important.  And on a very busy work day, it's really easy to say, Oh, you know, no, it's fine. Like, I'm just going to work through lunch or I'm, you know, not going to stand up for four hours, you know, and so, you know, making sure that I am doing that is important. But you have a standing desk, don't you? Right? I do. Yeah, I'm standing right now. So I have a standing desk next to sitting one. I have a very small standing desk, but I really appreciate it. I had an injury last year that made it impossible for me to sit at my desk and I've become a firm convert to standing up for most of the time. Although it can be very tiring. And some days I'm like, Oh, I'm really tired. Why am I tired? And I'm like, Oh, I stood up for 10 hours. So it does seem like an obvious point, but it's surprisingly easy to miss. One of the things that I identified that I wanted to focus on for myself during this course, and want to explore with along with all of you guys is thinking about collaboration in a kind of hybrid world. And let me explain what I mean. I think I'm coming across a lot of the time now, a kind of impromptu mix of in person and online. So I get invited to an event and I'm like, okay, I'll come and collaborate for half a day. And then it turns out it's like an in person event. And I thought it was online. And similarly, I think I am starting to have to juggle some commitments in person with some commitments online. And I think there is a lot of nuance to making that work, like collaborative working with kind of seamless handover between online and in person. So I'm not sure if I'm the only one who feels that this is challenging. I suspect not. And I'm hoping that you guys will leave some comments and share you a discussion on Discord about what, you know, what area you want to focus on during the course. Lauren, what's yours? Or is it commuting, getting out of bed? No, I mean, I do mention the commuting, but no, I think it's, there's more that I can use this course for besides just setting a permanent alarm. So I'm going to hold myself more accountable than just that. I think definitely one of the areas that I want to work on that seems related in my head. So just go bear with me for a second. But for focusing on physical and mental health, if that's something that, you know, is a priority for me, just in terms of how I'm working in a virtual space. I think part of that is also making sure that, you know, things are decluttered. That's really helpful for me to feel like, okay, I can separate at the end of the day and know that I've got an organized path forward for the next day. You know, knowing that I'm being efficient with my time and that I'm organized and planned. And so part of that is really thinking about you know, my relationship to the digital tools that I have in place. And that's everything from how I'm working on, you know, my phone, my device, how I'm staying connected on Slack and how I'm pulling in notifications there. And also thinking about, you know, the sites that I have in my seat panel account and keeping those cleaned and up to code as well. And so I think a lot of those, you know, areas can have small, you know, to use a quote that my family uses all times. It's like, small holes can sink a big ship, right? And I think it's really important that if we, you know, are cleaning up our digital tools a little bit and thinking about how we can make them, you know, work for us in a different way, I think it will ultimately impact, you know, my mental health, because I'll be able to just sort of separate at the end of the day. So that's kind of my way of hopefully transitioning into what we want to chat about this week, which is really, you know, thinking about those digital tools and our relationships with them and, you know, kind of starting there. So if that sounds like a plan to you, then let's do it. Yeah, let's jump right in. I think everybody's ready for week two now. We've recap week one. And do please post any questions that you have, or if you missed a Q&A in week one, just post questions, get in touch with us. So yeah, let's jump in. I'm excited week two. Awesome. So in that case, I'm going to share my screen now. And again, just kind of thinking about where we are in this roadmap. So last week, we were laying the foundation, right, for what this course is, thinking about, you know, what's working, what's not working in terms of power, you know, just thinking about our virtual spaces. And week two is really focused on those tools and platforms. So I am going to jump right in. So a couple of the things that I like to think about when we talk about the digital detox, right? Or I think it's really important, you know, the same way that you have a, you know, a spring cleaning, right? So the same way that you think about cleaning out your office space or clearing out the clutter in your closets, I think it's just as important that we are doing that for our virtual spaces too. And so I am enacting a fall digital detox. And I think that this week, everyone should be focused on, you know, different elements for cleaning out their digital spaces too. So a couple of the areas that I want to focus on, the first one being a clean desktop. I think that if you've got a clean desktop, it makes all the difference. And I'm going to call out Meredith, if you are listening right now. There was one time we were working together in person and I looked over on her laptop and the entire desktop was full of files and it just, it hurt me physically to see that. And again, everyone has a different working style and a different, if that works for you Meredith or anyone, if you're that type of, you know, if that's how you do things, awesome, then you can skip this slide. But for me, it is really helpful to make sure that I've got a clean desktop and I know where to find things. I also have the application Hazel listed here and I'll make sure to include links in Discord and there will be other supplementary links in the blog post for this week as well. But Hazel is pretty cool because it will basically rename your files and automatically send them to different storage folders as you're downloading them. So I'm thinking of, you know, I have to download a lot of files for work at right now. And so having something like that where as soon as it's downloaded Hazel is just kind of pushing it out and renaming it and putting it in a beautiful place or, you know, having, having settings. So things are downloaded somewhere else, simple stuff, but it can make a world of a difference. I also really like using background, background images that have desktop or organizers on them. I think I've logged about this at one point, but having sections of my background image, so a personal box or, you know, to do box and then I can move files to those boxes on my desktop. And it just helps me sort of situate with what I've got going on at a given time. So I encourage you to think about your own desktop. It does make a difference. Next. Are we going to do a spot check like everybody put in the commons or on discord? How many files they currently have showing on their desktop? Yeah, take a screenshot right now. I dare you. I'm 17. Oh my gosh. The second one that I encourage you to think about as a larger concept is just minimizing the spaces that you have to check. Check for things.  Are you having to message people behind the scenes and DMs?  And like, is there a way to streamline some of that? Because I think that takes up a lot of time that we don't really think about when we have to go find an answer for something internal. If that's a process or if that's just simple, simply a file, you know, do you have that concept streamlined a little bit? And, you know, it might be 10 seconds here or 20 seconds here. But if, if you know where you're looking and you're going to one place or two places as opposed to nine places, I feel like that will also help declutter and help long term as well.  So I do have that on the list as part of this as well. So the other one that I want to discuss is just notifications in general. And, Mary, I'm sure you can relate to this just or anyone can just working in virtual settings, even not even if you are in an office every day, you still receive notifications on your phone. And I think when you're talking about your relationship to digital tools in general, working aside, notifications are a part of that. And how we are taking in content is so important. So, you know, I, for instance, we just had a hurricane come through Florida and I installed all of these new sources on my phone to get the latest and the hurricane has passed, the storm is gone, but I'm still getting these banners.  And I'm realizing that every time I see something, I'm like, it's like a ping of anxiety, like what's wrong, what's going on, you know, and it kind of puts you on alert in a different way. And I think it's really important that we're kind of controlling how we're ingesting data and making sure that, again, you're creating a healthy work environment for yourself and setting boundaries, whether that means something as simple as turning off those banner notifications on your phone. Maybe it's thinking through finding a balance between, you know, positive and necessary notifications in Slack. You know, I was thinking about our Slack office space, or I have continued thinking about it for reclaim and realize one day that all of the notifications coming through were emergency level things, servers going down, escalated tickets, you know, things that require attention in a very serious way. And I think it's important, similar to how we're pulling in those notifications, we're also saying, you know, I'm awesome to myself, you know, or hearing something positive with daily affirmations or, you know, pulling in good customer satisfaction reviews or things like that. Maren, I'm not sure if that's something that you've grappled with at all recently. Oh, yeah, I would definitely do.  So like a few years ago, I took the decision, like I just couldn't cope with the amount of notifications I was getting at work. And so I've switched off everything. I don't get notified from social media, from work, from anything like the only thing that I get notified about is someone calls my mobile phone and my personal number. And I really like the amount, the volume of information that's coming at me, which I think most people have, is just so overwhelming that I really just have to set aside times, like, this is the time when I look at social media, this is the time when I look at my emails. Like if I just keep my attention on things that continuously roll over, I'm not productive. I never get anything done. Exactly. Yeah, actually, it is extremely difficult to become literate in notification management, because so many apps make it nearly impossible to, you know, switch things off completely and still retain functionality. So social media apps I'm looking at you, that is really tough to do. And so I, one of the tools, I don't want to jump too far ahead, but one of the tools we're discussing this week is the data detox kit. And I've learned a lot from this tool, like I think of myself as very digitally literate. And there were some really great tips on that in terms of, you know, just managing your personal information, managing your relationship with mobile devices or work devices, protecting yourself and just also taking a deeper dive into how invasive some of the technology that we use for hybrid working and personal communication has become. You know, and I do keep on track of those things, just like many people doing, but it is very difficult and they keep iterating. And so I love this kit. And I actually like you, I do that once a year, go on a big binge of deleting, clearing, unsubscribing, muting, sort of all of that, just to keep balance, really. Absolutely. And it's, and that's the other thing too, is like these tools are only as helpful as you make them. And so if they're cluttered with so many notifications that you, you don't read them, then what's, you know, what's the point of being there in the first place, right? Or, you know, and to your point about social media, it's strategic. You know, like, I think I was reading something around Instagram where they will withhold notifications, even if you're on the app, and they will wait till you leave the app for a certain period of time and then push you notifications coming back. So I think it's like, I completely agree, taking control and making it an intentional choice. Okay, this is my time where I'm checking in with friends and family on social media, you know, and then I'm exiting out of that. Same thing, you know, even with email where it's like, there are sometimes where it's like, you know, I will just avoid my inbox if I know it's all spam. And it's like, okay, well then why do I have the inbox in the first place, right? So, you know, we'll continue to talk in the coming weeks just around strategies for managing some of this. But I encourage you for right now, just to take a pulse of how your notifications are coming in and how you're taking in data right now. And if there's an imbalance there between positive and negative data. So that's just something to consider. Yeah, I agree completely, Lauren. And I just wanted to say one of the things I found helpful about the tool we're looking at as well this week is that it has sort of several steps you can follow like from starting with your smartphone data to thinking about your settings and then thinking about the defaults that you're being presented with by the technology to using. So if particularly if you're feeling overwhelmed, you don't know where to start or maybe you're just looking to tweak something very specific. I think this kit provides a lot of helpful options and it also dives into some of the more trickier dimensions including kind of ecological impact and sustainability as well as kind of kind of the ethical and privacy kind of implications. So I know this goes a little bit beyond the sort of hybrid working focus, but I think it's very much related to issues we all care about. Absolutely. And so on that topic then, I think there were two other points that I wanted to cover just as you're kind of thinking through your relationship with digital tools. One of them, and you kind of already pointed to this, Marin, a little bit is thinking through your installed applications. And Marin, I think you said that, you know, once a year, whenever you go through and you just delete stuff or uninstall stuff. And I think that that's so important, you know, especially now with the danger of having things buried down in folders on your iPhone, like you get, you don't even remember what you have on your phone, or at least that's me.  Was it useful at one point and now you're no longer using it? Can it be removed and cleared out? Again, that spring cleaning idea. And to that point too, just while we're on the topic of cPanel, you know, thinking through your site and domain structures is also part of that, I think. Think about your domain real estate, you know, and if you have core sites, for instance, that you're running semester over semester, have you cloned those into separate installs and they're just becoming cluttered in your cPanel account? Could you be creating new pages each semester instead or even, I know some faculty will use a WordPress multi-site and then they have a sub-site of that for each course, you know, are there sites that maybe you used at one point but now could be flattened into static HTML to so you don't have to update them like things like that, you know, and we'll include links in this cord but I mean that can sometimes get away from you too where it's like, oh yeah, I'm going to create this whole site for a project or an event that I'm running and then the event passes and it's like, you know, sites go, you know, never to be touched again. And I think that digital footprint is something that we have to consider and talk about when we're thinking about our relationship with digital tools and Maren, I hear you, you, uh, chuckling over there. I really am, yeah, and I couldn't agree more and I think there's an interesting tension there to me between, you know, how we manage work as individuals and how, what that looks like if we're part of a larger institution and you just mentioned around, you know, how different faculty like to use different tools and like, oh, I have like dark deep skeleton secrets of, you know, all like WordPress installs and all web like project pages that no one remembered existed that need to be cleaned up and, you know, there's a whole kind of, there's so many different levels to that and I think it is, there is a tension to me between sort of being really experimental and being like, hey, I'm working on a new project. Let's just knock this up and we'll get really like somewhere exciting and it's all really creative. And then everybody kind of moves on to the next thing, but then you're stuck with the sandbox or a project page. Yeah, right. And there is, you know, sometimes there could be personal information on there that could be documents that need filing, you know, not everybody has a hazel.  And I think one that sort of long term users of technology, you know, which many of us in this room are, you know, I think I've really stuck with there are very few tidy solutions for tidy things. And if you've ever tried to close a social media account like Facebook or Instagram, like, remember once I tried to quit Instagram, but it took me like three weeks to figure out how to do that. And after that, I still hadn't quit my account. And it was like, we still retain your data. I'm like, wait, no, I want to close it. It wasn't a struggle. I tried to delete my Facebook back in the day. And I mean, I kid you not, it was probably a six month or deal where they actually deleted my data because they were like, well, you might want to come back. And are you sure you don't want to come back? And then you need to tell us like three or four times why you want to delete. So it's definitely intentional for those decisions too. But I think you also raised a good point about the longevity of projects and then where they go afterwards because I, you know, and that's something that I think a lot of our community struggles with right now is just the concept of archiving in general and what that means. And, you know, every tool handles archival differently. You know, so there are times even where I look at my old blog posts and I'm realizing that I've linked out to project sites that are no longer around. And so thinking about just, you know, am I okay with these links, links breaking now? Is it worth it to keep this site up and running in case someone looks at a blog post that I wrote six years ago? You know, so those sorts of discussion, I don't think there's a simple answer for those. It's, you know, when you're dealing with those decisions at a university scale, you know, we have a lot of schools that are thinking about just archiving thousands of sites on a WordPress multi-site, for instance. And, you know, it's a struggle for sure. So I'd be curious to know how others are handling that in Discord currently. You know, and we can also continue to chat about that in this week's Q&A session as well. So definitely. Yeah. I would love to hear more about what folks in a room are thinking about that. Definitely. And the final one that I'm mentioning. And I literally wrote on the slide, this is a black box for me. And if we're talking about things that I truly need to focus on, maybe this should be on my hybrid working roadmap as well, but photo storage and in a digital space. I mean, I, here's a question for folks. I'm curious how many photos you have on your phone right now? I will, I will look. That's not pretty.  Okay. It's still calculating. It might not be a good sign. No. I mean, I must admit, I was so pleased when I saw you put this on our topic for this week. Okay. So I have about 9,500 photos in one folder.  I mean, that's the thing is like, I've tried out Flickr. I've tried out Google photos. I've tried my storing on iPhoto on the Mac, which slows everything down, you know, I've tried drop up. Like I've just, I haven't found something that works for everything. And then I'm also scared to put all of my eggs in one basket. And then that basket goes away. And I've lost all of my history of photos, right? So yes, I, this is something that I, I struggle with still. I have, in case anyone's curious, in my recent photo album, I have 26,000 photos. Yeah, exactly. So I, and so that for me is a monumental project. So I also, I think this is a really great example of where private and professional lives in sort of join because, you know, like, I use Google Apps for Education a lot. And if you share photos with people, you know, like they've started to introduce workflows now where if you retire, a user kind of all gets unshared. But if you share it with like a different email address, you know, or private Google account or something else, then you have to manually unshare like folders and you have to check all of that.  But I do have one thing that might sound an anachronistic comment to make in the week on digital tools and platforms, but I print photo albums. Like that's my ultimate backup. I actually have books. Yeah, or like a physical hard drive that stores everything or something. No, I completely agree. It's and I don't again, I don't think there's one answer that works for everyone. Jim, if you're listening, I think, you know, like he's very into Flickr. And, you know, I think at one point he went through all of his photos. But and then it's like, do you categorize them? Do you, you know, if I wanted to go find pictures of my mom, you know, is there a way that I could do that? And so it's just it's such a huge feat. And I'd be I know there are open source storage solutions out there. But it's just I know that once I commit to one, okay, now I've got to put all of my photos in it, you know, and it's that's that's probably where I need to do a lot of work. I also I love the, you know, I love thinking a little bit further about the sort of image recognition that this is a lot behind a lot of like Google storage, for example, for photos, they're like, okay, we're training our AI on this. And it's like, okay, I don't want that, you know, and then sometimes it finds pictures like I, it like went through a phase a few years ago where it suddenly made you automatic albums of people or things. And they were not necessarily correct.  Right. Yeah. And I think for the time being, you know, I have put most of my photos in iCloud, the photo storage just because it's integrated with the iPhone and it's all already there and backed up. But now I feel, you know, okay, I've got to pay Apple now for storage because I have so many photos and that's my own doing to be clear, you know, but I do need to even sift through and can I delete all those screenshots that I took when I was, you know, anyway, it's just, there's a lot to go through there. And obviously 26,000 is like, I don't need 26,000. But it's a fantastic segue into having a look at how you make sense out of all of this kind of, you know, complex evolving picture. So what do you say, should we take this discussion and have a look at the tool we're trying out this week? Let's do it. Okay, so for this week, we're going back to our roadmap and we've started making you a kind of blueprint to start thinking about your own tools and platforms. So in a minute, Lauren and I will kind of build on the discussion that we've had to start thinking about which tools we would put where on the spectrum. I just wanted to kick off the discussion and explain a bit how to reflect and how to use this with one of the example readings we included in this week's post. And that is a great blog post by Mahabali on students talk to me about webcams, which I really enjoyed reading. It's a great read. I mean, Maha's got a great blog. So if you haven't explored it before, do. But I think it's a great example of a tool that many of us use webcams and video conferencing and gives a student perspective and also a faculty perspective and thinks about the advice we give to faculty and advice we might follow ourselves when using that. And I think it's a really helpful reminder on, you know, how the best intentions can result in the opposite outcome that we want and how actually for someone, a tool might be on the green spectrum, the positive spectrum of this. And for someone else, using the same tool might be on the opposite end. And I think depending on how you use it and who you are in this relationship of the technology, whether you are just a user and admin, someone who's implementing something, your experience can shift radically. So Lauren, did you have any thoughts on this post and any other examples of technology that you'd want to put onto the spectrum for this week? Yeah, you know, so looking at this visual currently, I would definitely say that my experience with Slack is certainly in the green as it's showing here. But I like what you're saying about how, you know, again, tools are only as helpful as we make them. And I think something that's a positive experience for me and the work that I'm doing, you know, if someone's using it completely differently, they might have a completely different understanding of how that tool can be used. And so Slack is one of those very broad ideas even where it's a virtual office space. So you can pull in what you want, right? And so Chris, for instance, and on our infrastructure team, he pulls in a sauna notifications that come directly into Slack. I do not do that. I have that separation, but it works for him. And so, you know, and like I have organized my my channels and Slack to have to be categorized by the teams on my sidebar. But I know Jim does not do that his is in alphabetical order. And so people do have a different way of going things. So even when I go to document something for someone using a tool, I can make recommendations. You know, but it's important, I guess, to always be remembering that people do handle those tools differently. You know, I like to pull in my calendar notifications because I always have Slack open. So Slack will remind me when I've got a meeting coming up. So that works well for me. But I'm not sure if others use that, you know, so that's definitely something that I would encourage folks to think about. And I'll make sure to also, you know, at least link different ways that, you know, Slack can be integrated with some other popular tools like Google Calendar or, you know, a sauna is one that works for us and probably one that I would put on this list as well. At least for our team, a sauna has really changed through the years. And Jim just blogged about how our working modes have changed when we started the game versus, you know, where we are now. And a sauna used to be sort of our CRM for new colleges and universities. And it was our place to kind of keep track of the history of those relationships. And then we realized, you know, we need an actual CRM for that stuff. And we're going to use a sauna for what it's meant to do, which is a transparent to do list. But I think, again, it's if you're not, you know, using it for ways that make sense for you, it's hard to find a purpose for it and a place for it. You know, and I think, ultimately, we want these tools to help us be more efficient, not add another step in our workflow that, you know, takes more time out of our day. And that's our distinction sometimes. I couldn't agree more. And it might be folk in the room now who are thinking, oh, yeah, I have loads of ideas. I'm going to put this and this and this. And this is something I really hate that's definitely going into the red bit. This is something I really love. This is going all the way to the green bit. But if you're kind of looking at this and you're like, I'm not really sure where to start or what to include. Let us share some prompts with you. We can talk through just to get you started. So one way to choose where to start might be to ask yourself, which tool or platform do you use frequently? So Lauren just used a sauna as an example of a tool that she uses frequently. But think about something that you use all the time and think, how well does it work for you? And why is that? And then that will help you locate it somewhere on the spectrum. Or secondly, I think, which tool makes the biggest difference to your work? So for example, it might be a tool that you're not using very often, but it makes a huge difference to you. So I think I'm going to jump in there and mention Canva, which is a tool that I don't use that often. But when I do, it makes a huge difference to me. Like, it's really helped speed up certain types of document and content creation for me. But I think the killer function for me is that it resizes things into the right format and resolution. So when I recently had to take like a postcard and make it into a six foot banner, it was like literally resized six foot tool bang, and then printed it in real life. It looked beautiful and wasn't isolated or anything. And that previously would have been requiring more skill and software that I could easily handle between three emails and four meetings. So that has been a really great tool for me that I've started using more and more in the last few years. What about you, Lauren? Do you want to jump in here? Well, I just had to agree with everything you're saying about Canva. I love Canva. I love that you can save things with transparent backgrounds and all the templates there are really, really well done. It's a great sort of entry point into creating any sort of visual representation for whatever you need without feeling like you need a degree in Photoshop, which I took a class on Photoshop once, and I left the course feeling like I knew less about Photoshop. So, you know, I think there's that level of design, and then there's Canva, which is a really helpful entry point. I think one for me, just answering this question. And again, this is no surprise to my team, Spark, the email client. It integrates my calendar with my email inboxes. It has a beautiful smart inbox thing that separates newsletters from people emails, from, you know, other subscriptions or notifications, and it really helps me prioritize my work. I also really like that I can create public links to an email conversation, so I can share that with a colleague or even someone who wasn't a part of the conversation, but now needs to see it later down the road. And so there's just a lot of helpful features. And if you ever want to pull me aside and talk about Spark, I could go all day there. I feel there might be a Discord sub chat coming on here, which would be amazing. So I think we're coming up with lots of examples of tools that we love, and we hope that you can join in. But what about tools we like using the least and why? So I have a kind of mild example of this in that my organization isn't a native Microsoft user. And we do use Microsoft products, but not very widely. And so I'm not a native Microsoft Teams user. And instead, I join Microsoft Teams meetings for like 10 different institutions, every few within like a short timeframe. And it hates me because there isn't a native organization associated with it. Every time I log in, I'm an institution that doesn't recognize, I can't raise my hand. It's like people who are native users can't understand why I'm so annoying and I can't work it out. Why can't you use the chat? Why can't you raise your hand? And it's a real great example of enterprise level software that is not designed to be used with external people or at least not in a way that I'm experiencing it day and day out. And I would certainly say, you know, once it works, it's fine. But like getting it to work, like in my experience for me personally, in my hybrid working setup is not great. Yeah, that's a wonderful point because I was trying to think about what I how I would answer this question because I do think I've done a pretty good job of being able to say, okay, that tool serves no real purpose for us. It's clutter at this point. Let's get rid of it. And reclaim at one point did have sort of an HR file cabinet space that held a lot of the records for employee histories. And it was where we handled performance reviews and things like that. But I did feel like at one point, you know, it was just it was this extra place that no one went to unless you needed something from there and then was it updated, right? And so we just we cut it out and we pushed everything into gusto, which is where we handle all of our HR responsibilities now. So that's something that we definitely did change. But yeah, I would have to agree with you about the meeting softwares where every team that you're trying to connect with a different version. And it's the worst when you have to install an entire new app on your on your desktop in order to meet with them, right? So yeah, that's a good point. I think folk in a room might also get a lot of software packages dictated from the powers that be. So, you know, do feel free to share comments in Discord, share if you want to your week two of the road map. That's right, there can be support groups for damaged software users. But before we finish up and talk about our roundup and what's coming up next week, I just want to go back all the way to the start of this session today, when we talked about our hybrid working wheel, and we talked about reflections there. And hopefully you've picked at least one area of hybrid working that you want to focus on. And one thing that we want to encourage you to do to make the most of this road map is to spend a little bit of time to think about which digital tools or platforms relate to that particular aspect of hybrid working that you focused on. So for me, it was collaboration. And I just mentioned one of the ones that I have, which is like, for example, teams. But I think focus on those and how well they work or where in the spectrum would they be located? There might be a really positive or negative or mixture of all of that. But it might help you identify further what you want to be working on and what you want to find solutions for. So that is, I hope, what you all need to really do a deep dive. And yeah, Lauren, we've got a lot more coming up for the rest of this week with the Q&A. But I think hopefully focus everything they need to jump into this part. Absolutely. And yeah, Ditto Marin, I am super excited for the Q&A later this week, just to check in and see how others are thinking about their relationships with digital tools. Where are the challenges? Again, this week is all about just identifying that and building off the hacks for hybrid roadmap and continuing thinking through those things. And then next week, we will be jumping into more hacks for improving. Now that we've sort of diagnosed where we are in our virtual spaces, what's the plan for the future? How can we set up ourselves for success in that way? So I am also looking forward to next week and the coming weeks for this course as well. Absolutely. And so before we close this session, just a quick reminder for you, like a little checklist of what you should aim to complete this week. So we're really hoping folk can jump in to this roadmap template, do it either in paper or digitally, fill in week one. So that's where you include your wheel and your reflections. There's extra space there for extra reflection. Feel free to share some publicly in Discord, share with the group or keep them to yourself if they're more personal reflections. And then start to make a list of the tools and platform you use for hybrid working and just start adding them to your roadmap and where the spectrum they lie. So yeah, hopefully that should be a lot of fun. I'm really looking forward to seeing what folk come up with. I know. And someone please, you know, make me feel less embarrassed about the number of photos that I have in my phone, please. I cannot be the only one that has let things go that far. So anyway, thank you, Marin, as always for helping with this session. And thanks, everyone, for participating in Discord. We're looking forward to connecting over the rest of this week. And we look forward to seeing you at the Q&A later this week as well. Awesome. Thank you, Lauren. See you all. See you.