Cohere API Community Demos | January 2023

Key Takeaways

foreign [Music] topic which is a model that we have recently released on that model is called the nightly model and so it's similar to to the others but uh you can go over it here in in models I'll show you in a little bit and you can pick command extra large nightly and command medium nightly and so uh extra large is for better performance it takes it takes a little bit to run and medium is for past response so it depends on what you want you want to chat about that replies immediately or do you want to really elaborate answer I'm going to explain all the words here so why is it called nightly well nightly is because it's trained continuously nightly means really every night but the name is given to any model that is that is some training uh all the time and so this gathers every every so often every interval of time it gathers all the feedback from users and it retrains the model so in particular if you have something you know with a lot of users coming in this is definitely super super useful that it that it trains and learns from the particular that you know it uses that you're that you're giving it uh and it and it uses to improve the model and why is it called command well if you've seen uh previous previous model sometimes you had to write uh some examples right so you want to write uh the title of a blog post so you give it three or four titles a blog posts with with titles and let it sort of learn from there that's called prompt engineering this one you don't need any prompt engineering it's just a command model you tell it do something and it it sits say something in it and it says it so that's why it's called command extra large nightly and I've been playing with it uh quite a bit the the way to do it is here at uh the cohere dashboard and you just have to make sure over here that and parameters you pick command uh either medium or extra large nightclub and why am I telling you this because we're going to have a little little contest of prompt in a bit so I tried myself to do some prompts and I tried this one called write an ad selling I use time machine in a newspaper from the 30s and it says looking for a used time machine look no further uh blah blah blah it sort of makes it sound like that uh then I tried something a little more creative and I said can you make a song about machine learning but make it please sound like baby shark and so it went baby shark oh do you know how it works machine learning machine learning or do you know what it does it learns from data and makes predictions and it's so much fun and then it repeats many times exactly like baby shark so I think this can go viral I think if somebody makes a song about this I I make a a bet that it may get like uh many views in uh in YouTube um but of course uh I want I'm only so creative you want to rely on on your creativity as well so we have the following we have a a uh prompt prompted contest and what you do is please sign up to the playground using this QR code over here and generate some cool could be it could be funny Cutting Edge interesting anything uh prompt and make sure it uses the nightly model place on the uh and then uh screenshot it and add it on Discord to the share your prompt Channel and then you know take a look and vote for the ones you like and the three with the most number of votes gets uh swag and 300 cohere credits so make sure make some fun prompts it's closest close on Monday so write any any cool prompts that that you can and and put them in the in the channel and vote for for the ones you like and we're gonna announce the winners on Monday and I repeat that here's the um the QR code to to sign up for the playground and yeah that's all I have to say so I'll pass it back to Roy all right thank you Luis yep as he mentioned you guys here to heard it first you know we are running a mini challenge over the weekend so all you need to do is sign up to our playground and then head on over to our Discord um under share your prompt and vote be as creative as you want super ex super so excited to see what our models can our model can come up with also what you can come up with as well I'm just going to drop the link these two links so stop sharing so you can drop you can drop the link in the um yep I have got the link so if you haven't signed up top playground you can do so in the first link and the Discord Community would be asked me all right so um is going to be one of our mentors today alongside some of our newly appointed community Champions tangita and Arjun now sakita is an NLP engineer who has written many insightful blog posts like the recent one on um implicit versus explicit knowledge models I'm just going to share the link here you can check it out say Sangeeta where you say hi hi Roy yeah it's been a great uh time like in community Champions like learning so much from the community and contributing at school yeah awesome to see you and also we see Sonam here as well hey Sonam our community other community hi everyone and we also have Arjun he's a data scientist who won one of our hackathons with his project called perfect prom a prompt engineering assistant for image generation I'm also going to share a link so you can check it out it's really cool um and he went on to speak at several workshops and even Mentor future hackers so we're so happy to have you here all here today and continue inspiring our community so hey Arjun any any few words do you want to say hi to everyone yeah absolutely thanks so much for sharing um about myself and my project by the way if you want to use my projects probably on ice so just wait a few minutes and it'll start back up and you'll be able to mess with it um I'm really excited to see everyone's demos there are so many interesting ways you can apply these large language models to do like really fun things and I I got my popcorn and I'm ready to see what we're going to learn about today awesome awesome so excited and um all right I think should not hold anyone back any further and it will kick off our first presentation from by Andre gromof he's going to be presenting fairy tales it's a fantasy AI generator that uses cohere for text generation and stable diffusion for images it allows you to create the main text of a fairy tale from a short line and generate illustrations for C very cool Andre he is a full stack developer and participated in the assembly AI hackathon Andre [Music] [Music] because we're here and let me show my screen um so uh I want to talk about my project for fairy tale generating kind of first of all let me introduce myself I also said I worked as backend developer and experienced a python and a little bit golang and typescript and started learning Ai and ml less than a year ago just played with simple Google Tasks using number and pandas and and so in December November just found out hackathon and decided to implement my my first production project and why I decided to do it because of my daughter I have a daughter yeah and often she asked if she asked me to tell some stories like hey there tell me please a story about you and your friend and when he was young blah blah blah and I said okay I came up with one story with another and third and after return story my imagination ran out and I decided to optimize this process and here helped me with this so I a little bit started the tour of creating stories and full details and it tried to main approaches uh first approach it's based on Joseph Campbell book like hero with Thousand Faces it's a left image and second one it's uh morphology approach based on Vladimir Pro book and I decided to use second approach uh it's similar to the first one but it uses a functionals it's mean each Hero has a function and each plot has function and volume uh extracted main functions for hero and go to one functions for plus so how what's my project uh you should have like example line and in this slide you can see like a funny tell about two girls blah blah blah and I have like a plan to generate characters based on hero functions plots and storytellects and last step is gain rate images used uh stability stable diffusion uh First Step it's generally tractors as I said before uh Vladimir probe extracted seven or eight main functions hero sorry mind hero with their functions and like hero Villa and honor purple princess and father and I base it on two very famous Tales like a Red Riding coat and a Little Mermaid and based on this summary of these Tales I extracted a description for her characters and can you rate uh send this into kagiro and generate my own characters in the descriptions it's her step uh oh sorry second step sorry yeah second step is to generate plots extracted 31 main function for plots abstention and Direction relation Etc and I also created a prompt for cohere based on the same uh tails and their summary extracted mind function for Little Mermaid and retrying food and whether it is this example I generate plots for mind story about two girls you can see uh eight main functions but um yeah sometimes it's not unique and uh right now I don't have any approach to delete note unique plots maybe I will Import in the future so in the last step it generates full story of the text um I pass as example into the computer API [Music] um characters descriptions and uh first four plots from my example and you write like first paragraph second paragraph Etc and you can see like a screenshot of UI of my story and you will see like full text of uh fairy tale Sasha and the Magic Forest and last step you can generate uh image for character or plot just by one click and I send a call to stable division API and that's it I think so about my future plans and expectation you know with this project uh I would like to finish my last step because right now I just have only Simple Text for story without like formatting and demonstration and also I should update my prompts because I didn't didn't generate correctly character functions just on the description and also I would like to implement a building of plot interactor in UI so just you can remove or add missing plots Etc and about my experience work with a few API I was pleasant work with this because it's a good document to have good documentation with a lot of parameters to customize it but I use only one method to generate but in the future I would like to create my own model but is it not only two stories like like for example but a marked marks several stories about Little Mermaid which Hood I don't know yeah yeah so it's my point of finalize this project and I think it was so fast so make friends pay attention and uh yeah sorry about link you can play with with my demo UI and tails.ie.life awesome thank you so much Andre Jay just commented great work really exciting story AI will truly change the media industry he's so right about that I am going to head over the floor to our mentors um comments mentors this is really awesome yes yes I put the link in the chat my Tales don't sign with life yeah it works like a human but uh then you can already imagine Mike you can get like some error because a stable diffusion so but uh stable AI um reject some some strange uh prawns more like little girls maybe yeah you can be careful about your requests into a stable AI because like little girl playing defaults it's looks strange for stable diffusion and they reject to this sequestered um I think you did a really wonderful job with this project a lot of a fairy tale I've seen a lot of projects that are kind of along this vein where you generate stories and do things but I really like your approach where you kind of broke up the different parts of the story and created separate prompts and data sets for those parts in order to create a more cohesive explanation I think one possible like path in the future you could take is adding some sort of interactivity children love when you're kind of like giving them opportunities to contribute to the stories that you're kind of making so it would be neat if there were a way for someone to kind of build upon a story in progress so that every night uh I know some kids they like have like really favorite plot lines or really favorite characters so it'd be neat if someone could like have a completely different story every single night with the similar characters or similar person taking on the same Adventure or something like that other than that I was pretty impressed this is a really neat project either foreign question like how did you maintain the same visual appearance of products bonus I do not maintain this I just generate but I know there's a stable diffusion allow me like to set um in an image in in the prom pass in the image in the problem but I didn't determine this this way in this yeah this manner so maybe in the future but I'm not sure maybe I need to like use my custom model for stable diffusion tool but is Justin Bieber awesome thanks for the question uh any more questions for Andre um if not we will proceed to our thank you so much Andre well done this is amazing and you can really test out like the demo in the link and provide more feedback if you have any questions for Andre all right so next up we have Theodore Theodore is going to be presenting Architects use it uses coheirs language models to generate functional designs uh generated layouts can be saved to all popular design formats and uh Theodore is a senior researcher in the city intelligence lab of Austrian Institute of Technology where he led the development of infrared the first AI driven Performance Based Urban Design tool all right Theodore hello hi everyone it's very nice to see you uh it's nice to meet some people from the community finally I've I've been around this course for a while uh thank you for having me thank you for the introduction things have changed a bit I'm I'm I'm currently working in in Oregon uh as a advanced design lead and Architects this what I'm about to present this is kind of uh a fruit of Labor of of about two years I think like the first time that was uh shown to the public was about a year and a half ago and it's yeah it's it's it's been something very important to me so I don't really have a presentation I have a sort of like puzzle of different things but I can tell you the story how it started and actually uh I think it's an interesting story for me because I'm not an AI engineer I'm not a machine learning engineer right apple is about the site is a bit blinding I know it's been a while since since it was made uh but I I was an environmental design engineer so not really AF and about six years ago I I quit and I decided that I need to find ways to do a better job because the world around us we can see that it's not it wasn't being built very well it wasn't being designed very well or operated very well and I thought the AI could help and Architects was just an intuition uh I was I was playing around with Dali and trying to see whether you know text to image generation and layout generation would be easy would be doable and it was kind of doable but it wasn't very useful not very practical because in design we want like you said functional designs we want things that we can utilize Downstream in different tasks and images are really good and very nice for ideation but you can't really extrude them you can make a 3D model out of them you can create two simulations or things like that so the intuition was that language models can do design right so so I have a a kind of it's not really a presentation but I will try I will try to to show what I mean by that and and Architects was kind of designed as language so that was the idea you go from design with language which is the semantic generation models like Dali and stable diffusion to design as language and it's really a simple a very simple idea this is all it is right you really ask the model nicely in a way where you I didn't know it at the time but but it is sort of a command model like command sort of structure right you have a user prompt always that asks the the model to create something so in this case a house with four bedrooms and three bathrooms and then you have an output that the model needs to create that is always a layout and then the simple intuition was that the model can learn to generate geometry literally a geometric representation of something right and I created a lot of synthetic models uh to do this and I'm trying to find yeah and to do this I created a lot of synthetic models again this is the the picture let's say uh you create synthetic data this was my domain expertise right I used something called grasshopper it's a parametric generation tool you create annotations you find in a language model and then once you have a model you can sample it and create more synthetic data so so actually we did some I did some alternative training back then but but the whole idea is this like you have Samsung domain knowledge you deploy it and and it worked it worked and you know you can get things like this this is something that Architects might generate and the beauty is that this intermediate representation this sort of linguistic expression of design is really efficient it's really nice because the models the two things that surprised me one they learned completely how to design in this domain so they create about 99.9 valid desires right they might not give you what you want when you ask and probably they won't but they always give a design at this sort of useful or usable and second they have an incredible diversity I couldn't explain the diversity so they created much more diverse designs than what I showed them was very limited training set and it still is a very limited domain but on their own they had incredible diversity so when I analyze the results and I thought this was because they were pre-trained in language that's my intuition I can't really prove it but then so yeah and then of course you can do some cool things for example you know very quickly like I hope this will play you can you know sort of deploy them this is right now rhinocerosity is a design software The Architects typically use and you can get the design and we made this a while ago it's unfortunately I wish I could share it I think the app doesn't work you have a 3D 3D model with with some weird Windows there and you know deploy it there and you can do things with it right you can you can make it you can run simulations with it if you were someone like me or you can do other things so so that's the main idea and and and and yeah the uh that's that's all it is and the idea is that once you have this this is a generator in my grand vision of I don't know intelligent design this is only the first step so once you have a generator you can do a lot of cool things and these are the the future steps of Architects is to to go to those Explorations so right now we are working on on a quality diversity driven uh workflow that takes these language models which as I said are incredibly powerful and create create synthetic data for specific tasks so you can think of it as a specific generator in a way and and another project that we are trying to do and I'm hopeful I can share with the community maybe ask some help very soon is reinforcement learning with human feedback so we have a nice interface in radio that we'll be sharing and we will let people play around with the model create and give us different forms of feedback about the account changes once you have the renewable I'm sorry uh okay so I do have a great deal working this is the best idea and actually I'm very lucky because the the model is ready and warmed up I hadn't warmed it up and my apologies very early so I just want to show how how this works usually from from the of course you can prompt with anything but right now it's very limited is one of the limitations right the the kind of problems it knows are these uh so here I prompted you a house with two bedrooms or two bathrooms and I don't have the lids in here but the bedrooms are the red icons the bathrooms are the blue icons so you see the semantically was correct and it has like a living room in the kitchen so this is this is kind of how it works and you can you can also like give different uh types of uh constraints in a way as a constraint based generator that's what the model has learned you can tell it how many numbers of rooms and you know adjustances so you can give me a house with a bedroom in the north side so all this kind of things and this was really amazing to me and just the last comment to how how incredible language models are to really learn something like this just from from a few examples to really understand you know geometric relationships and then be able to generate new novel geometric relationships right so yeah that's that's the project and I can I can try to say something you know to create something in between oh yeah thank you very much this this was the project and and I hope it was interesting and I really hope that people will take it up it's been a while and there's there's been a couple of papers where people are doing this so I remember generating layouts Furniture layouts so I really hope that people at least from my domain see that it's possible to utilize these models to do incredible things okay thank you so much Theodore this is incredible and we have some questions uh can it be used for geometric models which can be 3D printed uh yeah absolutely yeah a lot of people are focusing on 3D right now and it it actually makes sense I think it's it's it's really the the Holy Grail if you if you wish if you want but 2D representations that we're creating here essentially r3d so yeah you can extrude and you yeah this one failed but you see failed back yeah sorry uh you can extrude and you can 3D print if you want yeah you can generate and I do think and I've run some tests that it doesn't just generate layout you can train language models in almost any representation in fact with some friends we made uh something that creates Game maps in unity with the same with the same approach right anything you can represent geometrically you can generate I feel that's my decision and then 3D print if you want or you know whatever we want to do downstairs seems like the sky's the limit yes yeah uh how did you incorporate grasshopper with the model and will it be like a plug-in in the future uh yeah so that video was a plug-in unfortunately when I I hasn't been developed for a while so it's it's a it's a it's a tool called pollination so it's an online banking you can sort of log in and I believe you can still use it the code is also open source I can search it right after uh so you can just take it because take it and sort of deploy it yourself but yeah it was supposed to be a plug-in and the open source it hasn't been yet but hopefully there is also a paper coming in eventually and once all that comes out we can we can send all this stuff yeah nice awesome one other question have you explored the use of text to image models for geometry applications for mathematical visualizations um yeah not much mathematical but I did try with the latest models or stability stable diffusion that lead to to see how they are because I do think we're gonna reach a point where text to image is viable and then you can use it Downstream uh they're not there yet but they do generate some interesting designs and yeah it's just maybe I'm not very good at computer vision but it takes me longer to to identify pictures from images and extract designs than actually train a language model to do it uh but yeah we are close we are closer I think we're not there yet to creating a design that is useful for designers but we are close instead of in in terms of ideation and robotic engineering nice all right we we still have a couple more questions so Theodore if you could just sort of maybe hop on there to help reply to some of them as we move on to other presenters and your time thank you so much Theodore really appreciate it thank you very awesome Round of Applause to Theodore awesome job all right so moving on to our third presenter presenters uh we have Augusto and Mustafa they're going to be presenting healthy AI assistant it combines a custom conversion Persona and grounded QA it helps to Aid in processing patients extracting relevant information to fill up forms um and providing common knowledge advice to scenarios they came in second place for Thanksgiving hackathon where we collaborated with lab Lab all right over to YouTube foreign [Music] Hospital paperwork with AI it's a very interesting project uh we first of all let's start by introducing the team so my name is Mustafa azazi I'm a media designer and UI ux designer also worked on multiple applications uh to develop like the interface and the experiences so I'm a designer I'm not really a uh into like coding and like NLP and everything so like being in the hackathon the this is like my first hackers want to be in is super cool I got to meet some very interesting people um and I'll pass it Mike over to Augustus to explain hi my name is Augusto um I'm a PhD student in economics so not completely related to machine learning or NLP but I have I'm very passionate on the side about the applications and the potential impact of NLP so uh we use this coherence Thanksgiving hackathon I said it was a great opportunity to try to test our skills uh with fun weekend project so today we're going to be telling you guys a little bit about it running a live demo and I hope you guys enjoy [Music] that's our project again is like healthy it's a replacing Hospital paperwork with AI so what it is is we're using a cohere AI to fill in hospital intake forms for patients uh through natural conversation and doing that was uh like at the same time like book an appointment with the corresponding doctors and answering the patient in acquiries and questions I would do that through like that the NLP models whisker here let me go on to the next slide so the problem we found is hospitals have to be able to provide care extremely efficiently and the number one step in improving Hospital efficiency is to remove paperwork that's according to the economist doctors can end up spending twice as amount of hours with their paperwork and with their patients and uh like we've tried to reduce patient wait time and as we all know like filing in medical and Hospital forms can take too long and can be an inconvenient few lies for informations and reception desks can get quite long and doctors end up with huge amount of paperwork to search through uh we'll move on to the demo and I will let you guys to like explain the demo for you so you understand it much more clearer I'll leave the Cure call for a few seconds if you guys want to scan it it should take you to our stream lead app which is hosted in extremely cloud and this first you will be directed to the home page which contains some useful information about our underlying motivation behind the project and some sample conversations that our team has had with healthy that showcases some interesting features about some emerging phenomena that llms tend to um surprisingly show and also some limitations of our model on the second job we'll um you have access to a live demo in this case I already started a conversation to see some time for for demonstration purposes um so first right we have to ask nicely and we introduce we say hi and healthy tell us that it's a nurse assistant who can help us connect to the right doctor um given our description of the action we just had this is the use case um so I just would say I just got on my I just got my finger with the paper so in the background healthy will analyze this we'll interpret this description and we'll gather relevant information in this case the category which will be an accident because of the accident which is a finger cut and injury areas the finger which doctor we should recommend it to and the urgency and if the patient is conscious one very interesting thing that it surprised me at least surprised all of us was the sensitivity to the uh slight changes in the cost of the accident pay attention to the next message it's exactly the same accident but I'm just um slightly changing what caused it from a paper cut to a chainsaw which is much more severe and healthy powered by Cochise yellow lamps can't use this increased urgency level pretty accurately a paper cut is much less severe than a Chainsaw cut potential but that's not everything we could do we can also have some conversations with healthy in the meantime while we while the patient waits to um get to the doctor and we can ask him questions like what should I do about my finger cut and using grounded QA healthy with Google the best possible response and reply with some advice and the source from where he got that information one thing I wanted to note is this data set that is being generated here dynamically this for our demo this is what the the form would look like this is just a Json file in the background and all this information is being stored during the section of State anonymously there's an ID that has been randomly generated for me and the set the star session the start time of the session if it was a question you'll reply with an answer and he'll say that answer but if it was a description it will simply say the notes of that description which will be direct will be sent to the doctor prior to meeting with this patient the model is able to recognize between the scripture or question using entity recognition in the background and finally once we are done inputting our description we can show available times you will have a few here say Saturday at 9 00 a.m works fine for me you'll update the data frame and I want to be contacted by mobile phone so that I'm ready to move my appointment congrats we have an appointment awesome so I encourage you guys to play around with it um and let us know what you guys think oops but go back to presentation and let me tell you how it works so healthy in the background follows the flow that um first is you provide decision now which is this entity recognition model in the background that recognizes if the user is inputting a description of an accident or a question um depending on which one is recognized it either triggers coheres llms or grounded QA for Googling and an accurate answer and the generated data report is stored in the session state that means that the data is anonymous and is only available during the session state so for our demos none there's no information being saved um which is something important in healthcare healthcare industry then we this Loop is going to continue until the user decides to stop chatting and then the available time goes will be shown and the doctors will be um and the appointment will be scheduled with the relevant doctor that healthy has identified as I was telling you guys in the background is healthy spyware is powered by hybrid architecture architecture that is composed of um a custom Persona called healthy that is capable of recognizing these attributes of the description of the accident or the question and also ground the QA to be able to Google answers to questions that users may have before having me before being able to meet with a doctor like what should I do with my finger cut in the meantime foreign anonymously um and all the data is only available during the session State and third an important feature of our app is that everything is being logged which can be used for continuous feedback which is very important for continuous training and it simplifies maintenance of the model in future and also allows for this nightly version probably some final thoughts we're very excited about AI in general and NLP applications I think they're we think there are a lot of uh different use cases in industry and research as well and more specifically we are excited that this kind of Technology can help developing countries expedite and increase efficiency in the healthcare industry which is notoriously inefficient some limitations that we are thinking of addressing we would have to address or hopefully Community maybe can can help us with this is uh styling The Prompt a little bit better just to simulate the conversation flow much more accurately um address issues such as bias safety and explainability which is going to be a big concern when doctors have to read this information and finally the chat memory right now healthy is limited to one message memory chat which means that it only remembers the latest message uh just for runtime issues in this case but that could be easily improved and the prompt style and also the runtime of the Google search that's it for us thank you for listening yeah we'll have to take questions awesome job thank you so much we have a comment from Arjun cervical project uh yeah Argent do you wanna yeah just really quick because there's one part of this project that's really interesting to me I think that intake forms and surveys are an area where automation with large language models could is a place that's right for Innovation there I think that doing something like when you're filling out like a 10 or 15 question survey if you have someone just dictate things about themselves and see if there are things you can extract from that in order to fill out maybe some parts of that survey I think is a really interesting use case I think what you've presented is pretty neat I think you should think about expanding the technology you have to maybe an area on which there's less inherent risk so maybe outside of like medical intake forms it could just be like surveys in general where people have this big need of like getting more user information from a demographic or just like you know companies who are trying to get information about what their users are doing that would be particularly interesting to me it's a really cool Direction really cool architecture nice job thank you thank you yeah and we've we've actually tested these similar technology on surveys survey methodology and Survey design I just work really well especially for uh services that tend to have some subjective uh attribute to them like mental health or leadership service of that kind of style where instead of having to answer very discreet questions you can now allow the user to tell a story about how he feels or about himself and then the other the natural language will the model will extract the relevant information from a much richer source of data which is a long text answer than just discrete scale super interesting I also wanted to yeah I just wanted to add like the model is able also to identify the urgency of for example the care that the person might need so that's also something that is very hard to address like unless like it's required like a physical person which we found like even as a language model again it was able to identify the severity of like an injury like for example a energy like a paper cuts not very severe versus Like a Chainsaw cut which is extremely severe and like give it this rating of like five and this is one just based on this changing of the word essentially so yeah no this is this is very cool thank you uh just on that note of the survey versus the medical one I I'm wondering if you have a a knob to turn because some time would be in in medical it would be a lot more sensitive to mistakes right whereas in a survey less what would it be a metric that you use for uh or or or or uh well definitely human editing is definitely required this is we're thinking about like for example uh hospitals in Egypt and like other countries are very understaffed uh there's for example uh one doctor for like each thousand patients and this is not mentioning also what uh like a thousand plus patients and like the rate should be like one doctor for like 200 patients so as you can see there's a lot of uh there's a lot of like you know uh like especially like in the healthcare Industries there's a lot of Need for automation uh at the same time you can have white staffs such as narcissism like other stuff uh who can deal with patients in in a different form uh so we were thinking about like uh how to at least ease their like transition of like people can get care and not and like at the same time answering questions and and getting reply to so it's a very sensitive uh topic but at the same time it will definitely require some human editing but it's like at the same time just not as much as we were thinking about like making it just a little bit easier for people who were working there in general since they're typically overworked anyway yeah I think we should we have visions in the short term we wish on something um like GB charge gbt kind of training that it's reinforced with some sort of human feedback and in this case the human should be the nurse the nurse would train the AI nurse continuously everything is logged which would allow some developer to incorporate uh the feedback from the human nurse and you start picking up some details on patterns and of things that he should be able to help with that tend to be more important than others um yeah that's a answerable question foreign we have some comments nice one I love the development in chatbot like applications so incredible progress we also have Jay who said great work and honey nice work this is quite interesting to me and we'll be following up with you also keep a lookout for Honey's message congratulations well done this is amazing love to see these projects broad range yeah well done all right thank you but certainly not least uh we have the winners of um best user prompt and Engineering at GX plus Canada's largest 36 hour hackathon for female and non-binary students uh please welcome faiza Sophia Sawa and Mida to present semantics it's a comprehensive platform that allows users to edit emails store their past emails or give advice for anyone who want to learn how to communicate in a professional standard so over to you all thank you for that wonderful wonderful introduction just as a brief introduction the four of us were um we're all uh U of T students in our third years and this was all done within 36 hours but we kind of hope to elaborate on it so as you know young people just entering kind of trying to enter the professional field we get you know kind of feedback saying like so email more assertively or drop the long regard like just use regards or you know don't use exclamation marks in your emails or just you know speak a little bit harshly and you know these are very common pieces of advice that we've all had to go through as young professionals just entering this Workforce and in fact it turns out like 77 of students feel confident when they're entering but only 43 of employers actually tend to think that they're prepared so you know what happens to that like 40 something percent that's missing so we want to bridge that Gap and that's why we made semantics so as Roy introduced us semantics is a tool to improve in communication that creates positive Impressions and maintains professional relationships and our goal is to kind of find and share a middle ground between you know being able to commute professionally but also including your own sort of tone um and this is using you know positive negative and neutral markers so it's created with our own database and cohere sentiment analysis which we would recommend yay um but you might be wondering you know what's positive negative and neutral in terms of like being professional so as of our own database we Define that positive terms like the ones shown on the screen they tend to be more personable and Foster relationship neutral terms usually tend to lack the personal pronouns and get directly to the points and negative turns kind of use uh acronyms or you know very informal language that should not be used in professional sense so we have a live website uh now but we'll run through a demo together um so here you're on our landing page you know uh we were trying to go for a very clean uh look so if you go to our playground on the right you can write any body of text but for now we're actually going to use an email that we uh we sent when we were discussing with Roy being invited to stock here so thank you so much for inviting us again um and you kind of see we use terms like see you later alligator which is it's kind of it's unprofessional you know it's not aggressive but it's um not very positively taken in a professional sense but as you can see on this side uh the underlined in greens are very personable you know thank you so much or like we'd love to present here but then in the red terms it's like see you later alligator things that could be looked upon in a negative sense um and as you can see in the bottom we have 46 this score is actually the average of the neutrality scores for every single sentence so we're not saying that you want to aim for a hundred percent neutral but it kind of just gives you a sense of how you're coming across and in addition this is um in the future going to be implemented that you can choose how you want to come across so if you say that you want to aim for 75 neutrality with a more positive sense it will score you based on that so you know what's the point of editing your emails if you just get one sense of feedback so what we have is that you can actually go to your past draft and see all the emails you've ever sent and as you can see um the first email that we sent had a 46 score you know of how are you going to change that you can actually live edit it and continuously submit in the playground and what it does it gives you a lot of feedback there but you can also look back on your old emails and it changes the score so as you can see we swapped out regards uh we swapped the see you later alligator with regards and it gave us a score of 66 percent so as of right now our current steps is that we have a personal tier as you can see in our past drafts we've had a few unrelated users test out our minimal viable product already um we've also had you know some feedback saying that they they kind of want us to be more of an add-on or they do enjoy just having the playground like that so the next steps is that we're interested in doing an educational tier and a professional workplace tier but the issue that comes with this and scaling our you know our product is that how do you define professionalism as of so far professionalism was defined by the four of us the good thing about cohere is that you know we can work with different HR departments or universities to kind of give points of where it's is it positive is a negative what they think is neutral um use the classify uh and then kind of they have their own customized semantics dependent on whatever they classified so workplaces and users in the end can make their own standards and that is semantics and if you want to message us or you're interested in working with us on this project feel free to connect with us [Music]