Okay, I'm going to start. Welcome everybody. Thank you for coming back. This is our last session, it's the closing session. And we are very happy to see you. It's been a very long three days. And we've been all over the world and through all the topics. And it's been quite intense. And I'd really like to thank you for staying through these conversations and being part of them as much as your schedule permitted you. We greatly appreciated your inputs. I'm just going to do my first slide that I do before each one. And I'm going to put myself in French and I'm going to speak into the microphone because I'll be recording the French version. Okay, so I think by now you know how the system works. You know that the chat doesn't really work. We don't need the chat for the question and answer. We'd like you to raise your hand if you'd like the floor. There's interpretation in English and French. If you don't turn it on, you just have to read it. You'll hear what's the language of the person who's speaking no matter what language it is. And, well, this is the last session, but the same code in principle and in theory works for all the sessions. But sometimes the one code works for that's become a very complicated manner. But there's one code for each one of the whole workshop. Go on to the next thing. I'll come back again and you'll understand. Can I go to the next slide, please? Thank you. Again, I'd like to go back to the objectives because I think it's really important for us to look at these objectives and see how far we've come. We came into the workshop three days ago, what seems like a lifetime ago, but it was really only three days ago or two days ago. And it had two objectives. The first objective was to clarify the priority areas of action per working group. So this was to drill down to what can we start working on that's really important and that we can work together on based on the outputs of two previous consultations, the dynamic coalition launch and the survey. The second point that we wanted to look at, and this was done in each of the workshops, and it is transversal actually, was what are the parameters of an electronic tool that could be developed to support collaboration within public information about and basically activities of the dynamic coalition. So we've looked at these questions in each of the workshops. And I think we've advanced quite a bit. We've had over 10 presentations by the working group chairs and we've had hundreds of interventions by the participants and you, the participants, you represent the dynamic coalition. You are a very diverse group and we're very fortunate that we had such a large participation and this made the debate much richer. It is particularly happy that it's this group represented, of course, very, I think, equitable level. The participants came from all UNESCO regions and I think the fact that we had a discussion in the morning and the afternoon, we brought together different groups from different time zones too. And our discussion was truly global. I recognize there were technical challenges. We had them, we had, everybody had those, as far as I can tell. And we'd like to thank all the participants who experienced these challenges for their patience. We have tried to fix everything as quickly as we can and we have signaled it to our technical people and we hope that this will be, the big problems will be solved next time we do such an event. But we have tried our best to overcome these difficulties. Of course, this has been a long way forward since we started this discussion on the second of March. I think some of you remember we started the discussion on a technical platform that didn't exist at all. So this is quite a leap forward. The format of this discussion, however, suggests it's more inclusive than it would have been under circumstances where we would have been face to face. Had we been face to face, we would have limited the participation and voices to those that could travel. It comes down to visa issues, passport issues, costs, personal circumstances, and it wouldn't have been open to all in spite of our best efforts because of these restrictions that I've mentioned. And this would have been unacceptable, actually, because the voices of all our participants are very, very important. And it's important that we are able to have a form in which we can really exchange globally. We have, as you can see, the slide that's up there, I don't know if anyone's done the math, but all of this, the UNESCO we are recommendation was adopted eight months ago in November at the three month point, which is a very critical point in most gestation. We had the launch of the dynamic coalition. And it was also the beginning of the COVID crisis. And the pandemic globally, it was I think just the global pandemic was declared a couple of about a week before our meeting was scheduled and we kept going and we held it. And I think it gave us a momentum to focus on the work and we were able to come up with a report that was very comprehensive on what are the areas that can be addressed by a group, an international coalition to support UNESCO member states in the implementation of this recommendation. In July at the beginning of this month for 10 days, we had an online survey. And the survey asked you to look again at the conclusions and to give your opinion on what would be the areas that we should focus on to start with. And during this time, so we're now at the eight month period. At the end of the seven month beginning of the eight month, basically, if you like, the third trimester, and the survey was done. It was during the holiday period, we had about three inputs. We had over a hundred participants in the dynamic call of Chamon. She went off the month. We had some 30. We haven't done the math yet for the online consultation because we have to sort through the the lists of participants and also to deal with the fact that many people have had the same name. So we have screenshots of the participants and we also have the links that we have to make some sort of effective work to understand who is actually here. But we have had at least 30 to 40 people. This one we have 18, but the normal amount we had about 30 people in each of the discussions. And they were different people. And during this time, over the five, six sessions that we had, we went through and we drilled down further on the two points that I mentioned. So we're at the end of the eight. And we're going forward. And the next steps will be to launch activities from September, taking into account the inputs from the consultation. And we will keep you posted on that after the coming months. Now, can I go to the next slide, please? There is an evaluation form for this. I'll send it through the link to you for the evaluation. There's a Google form. We'd like to have your feedback on this by Monday, Monday, actually by Sunday, if possible, so that we can have we can incorporate it into the report. I will send this out by email. And to the list that I've been sending information out about it, if this consultation started, if by chance you have not received messages about the following day ever since this started, could you please send a message to either me? Could you please change this? Could go to the next slide there. If you don't receive my emails, and you haven't received any emails from me, could you kindly send a message? I can't, you can't change the slide now, but you can perhaps also send a message to Neil to say that you have not received messages and I would get it. The videos of the consultations are available at the address on the screen on the side, which was also sent last night to you by email. And this OER Africa has kindly agreed to put the videos of the different sessions online so that you can listen to them if you are unable to participate for whatever reason, and or if you want to hear it one more time. They will have a draft report that's circulating for comments between the 3rd and 5th of August and a final report by the 7th of August.  That's right. We'd like you to send your comments to the videos that you may have by Monday night, basically midnight CET. And if you could send it to Neil at the address there and put in the subject of your message, the working group to which your comments pertain. And so with that, I give you the last steps and I'd like to give the floor to our transversal chairs. So to first to Mecha, I'm sorry, first to Gasper, who represents the National Commissions. And so I give the floor to you, Gasper. Thank you, Zeynep. Yes, we hear you. Okay, thank you. Thank you very much for your for your words and this opening of the closing plenary. However, myself, I was not able to follow the consultations throughout the week. But as I have understood, I'm also in my personal capacity, very happy because of the, because of the rich participation from all regions in the world and from different stakeholder groups. And I wanted to just from the point of view, from a from from my position in the in the working group on, it's called National Commissions for UNESCO. But I wanted to stress to all of you a little issue because I think for my in my view, it's of utmost importance that we keep in mind what the purpose and the framework of this dynamic coalition is. I mean, from the first point of view is, I mean, the recommendation was adopted by an intergovernmental organization by UNESCO. So it was adopted by UNESCO member states. This is governments. Of course, as you most of you were involved in the in the overall four year process and consultation process for the preparation of the of the recommendation. And we have seen how it did work and how UNESCO itself opened up its its way of work in order to include in order to include all parts of stakeholders from all fields of interest. So from governments to institutions from single experts, etc. And as we know, the objective, the objective of of the recommendation, and then of course, of the dynamic coalition is to somehow support governments because the recommendation itself, it addresses governments and to support governments to implement because it's on the side of the governments to implement the recommendation itself. And this is this is very important to stress. So we have here partners in this dynamic coalition that are at the same level. So it's from governments to institutions to single experts and to chairs, etc. etc. And I think it's important to to to tell to to say that. And so that we can see that this is a I mean, the coalition itself is a somehow, I don't know, a partnership among partners which are at the same level. So the dynamic coalition should not be, I don't know, somehow a service point for, I mean, governments or the other way around. But as I will try to to to present also during the work of my transversal working group on national commissions, because national commissions for UNESCO are in a very premier position in order to connect and to contact the respective ministries which are responsible in the respective countries and member states for the implementation of UNESCO's programs and policies in general, but of course, also for the implementation of the OER recommendation. So here, I will try not to gather only those countries and national commissions who who are who who proved to be the most active and interested in the implementation in the overall process. But I will try also to promote this to to invite more, more national commissions and more governments to be part of of the of the dynamic coalition. I think that that for this reason, all activities that we do in the framework of the dynamic coalition need to somehow ensure that they foresee supporting governments as main stakeholders, and at the same time as partners in the implementation process. I think this is important to stress and so much from from my point of view, I will try to stay here with you now until the end, if if we could probably still have a discussion on this. Thank you very much. Thank you very much. Gershper, with that, I'd like to give the floor to me, who is the chair for AI and new technologies. So thank you, Zainab. Hi, also I was listening here and they're not all the time, but I was present in some of the some of the sessions and I've heard some of the comments that that there were raised in relation to to frontier technologies. It's not of course just AI when we say frontier technologies, but certainly what there was listening to you was more or less talking about the two issues, two important issues. So one is automatic translation so AI can fit in. And the other one was about the cybersecurity. So those are certainly two points where AI is making quite a large progress. But it's not the only thing. So what I wanted to stress at the beginning is that if you look at the complete ecosystem of OER is not just about the presentation. It's about a lot of things that AI can help on one side the creators and on the other side the users of OER. So so let's say teachers and on the other side students. So what I was what we are we are we see we think that one of the major problem is essentially the the this disparity of this version of different OER across the globe. So there is no there is no single point of entry right now that you could find that you could get to to a large collection of everything what is going on in the globe on OER. This is number one and number two. Of course there are not there are no tools available yet that this would be made simple. So if you are the creator of a teacher of let's say the the applicant that we are having on the in the others program which is open occasion for a better world we see is this is the issue the issue of how to how to get proper content how to get the proper services and how to use them then later on to prepare something which would be a valuable a valuable service. So it's you know one of the most important thing actually is using AI to collect this information to collect all this content to collect OER and services across the globe making them in present them in a structured way because as it is today you would need to spend quite a lot of time in order to find out the proper content now and of course this includes as well the automatic illustration but this is just one point of of this processing of OER. So it's important so AI is not just just about those two things that there was discussion about. So it's a little bit more and I think that putting AI in in a in a service to support on one side the ones who are creating OER and on the other side to the ones who are using OER for learning those are the two main things that AI can bring in. Now about the cybersecurity cybersecurity is somehow inherent in the services already because you know if there is a if you have a repository of of any type of let's say learning service and this repository sits on a well developed well developed cloud service then cybersecurity is somehow inherent in that. Of course the other completely different issue is when you are dealing with something which would be a home main service and a home main application and and I like so cybersecurity is a is a national issue as well. So a national issue would mean that even the the most developed countries are not really dealing well with the cybersecurity. So the issue about using then the AI to solve this solve this the problem of the cybersecurity is more about on the national level which goes not only into the education but as well to the other services which are supported on on the on the globe. So here I think that it is an issue it is an important issue. I was answering one of the questions at the at the at the beginning which was about the cybersecurity saying that if you look at the OER as itself and you don't you just look at the content and services so without learning analytics without gathering information about the usage and the users then the cybersecurity for OER goes into the level of let's say fake news or fake content or the content which is not properly properly presented. Those are the things then that can fit into the several quality criterias that we are trying to develop in order to provide to a user at the end of the day quality content. So there are several levels several aspects of cybersecurity as well but what I wanted to say is again the how to make the use reuse creation of OER simplified easy to use so in order in some kind of a let's say semi-automatic way of how to construct a the learning course for a particular subject for a particular day in close to real time. Those are the things that AI can certainly help. I was telling you I was showing you already the tools that are on disposal just from the ones we are aware of there are many others as well so this is not something which is a researcher development is something which is a commodity already so it's just about picking these things and putting that into practice to solve one of several of the issues which are very relevant now for the for the OER adoption. So this is the end of just briefly my observation. Thank you very much Nietzsche. Could I now give the floor to Lexi? I think you have a PPT also. I say no thank you very much. Okay so I just have a few slides here and I'll speak fairly briefly about monitoring. So I raised this in the opening plenary as well. We're still in the very early stages as Zaynab noted as well in terms of the objectives of these sessions. We're really trying to drill down and identify priority activities. So at this stage there were a few points that did emerge that although not explicitly related to monitoring I think have implications moving forward so I will speak to those. Perfect. Yeah so first of all one of the things that I noted was discussions were beginning to look at these potential outputs. As I mentioned in the opening plenary session around monitoring you know it's really important moving forward that we think through the results chain of these activities. So what is the objective the overall objective or the outcome how do the activities and outputs lead towards that. So that was really starting to emerge in some of these discussions that linkage between the possible outputs as well as the higher level outcomes. And while certain discussions did center around specific outputs such as tools or policies or capacity building programs I think there was also discussion that was beginning to emerge about how these outputs would lead to the mainstreaming of OER in a more sustainable way and the impact of this on key stakeholders. So that results chain which is starting to emerge I think will be very important as we move forward and begin to think through monitoring in a more specific way. The second point that I think again not explicitly but indirectly is linked to monitoring were the discussions around research. So in the opening plenary session again I mentioned you know three of the essential purposes of monitoring and the third was that it can inform research. So I participated in two out of the four thematic working group sessions and in those sessions as well as in the videos and materials that I reviewed from the other two they all touched on research. So research was framed in these discussions as being you know very essential for our advocacy efforts and in terms of creating demand for OER or demand for support for OER by providing evidence for sustainability cost effectiveness impact on teaching and learning. So in general research was framed in that way in these discussions as being very important to the efforts going forward. The participants in the sessions at least that I was involved in generated numerous ideas for research topics related to the themes. So there was a lot of different ideas about some of these areas that we might need to delve into. There was also some discussion about how previous research and monitoring had actually influenced continuous improvement and led to implementation of further recommendations and interventions. So I think the the linkage between these research topics that are starting to emerge and the output and outcome indicators that had been generated in earlier consultations was quite evident. So a lot of the topics that were discussed in terms of research are really related to outcomes and impact. What is the impact of OER? So I think moving forward it will be really helpful to think of monitoring activities in terms of action research and this was raised actually in the working group for this idea of action research so that we're not just monitoring progress for reporting function but it's also you know collecting this data that can inform more in-depth research that can be used for advocacy efforts moving forward. And then lastly one of the topics I think common to the working group discussions which as Zenef noted is a transversal question across those working groups was on the requirements for a tool or platform for information sharing which was and collaboration sorry which was mentioned earlier. So again all of the groups touched on this and not directly related to monitoring but it brought up some ideas of how this tool could be used for that purpose as well. So you know we've noted that the the main purpose would be to facilitate communication as well as foster collaboration. But I think also this is going to be a very rich resource of data in terms of what is happening and it was also mentioned to that in a few of the working groups that this tool could really help to share good practice challenges so it would provide really rich qualitative data as well in terms of not just what's working but also what's not working and that feeds into that monitoring evaluation and learning cycle so how do we take the information about what we're doing and really use that to inform and improve our interventions. So I think this is something to keep in mind as we we begin to develop this tool in more concrete terms just to think through how it could be leveraged or how we can draw information from it not only just for communication and information sharing but also as a source of data for tracking our progress. Yeah so I think those are my main observations about the the implications of these recent discussions in terms of monitoring and I just want to say overall it was a very engaging and productive series of consultations, a really diverse group and a lot of interesting ideas and examples were shared so it was a you know enjoyable experience and looking forward to continue working with everyone for this. Thank you. Thank you very much Alexi we greatly appreciate your inputs and your your clarifications on how the important role that monitoring will have will be voted. It's interesting that you bring up the point of action reach search which is an interesting point that came up in the discussions. With that I'd like to open the floor to any questions you may have or comments and we have some time so please feel free. There are no so far there's nothing in the question answer box. Are there any points any last statements anybody would like to make? I'm looking for raised hands. I think maybe everyone's very tired after a very long week. Three days it's only been three days it's not a big question response okay find okay well with that I would like to invite you to please fill out the evaluation form and let us know how you what you found worked what you think we could work on to improve the next time and this is the first time that we have done a consultation of three days with six sessions in two languages in multiple time zones and we'd like to this consultation nothing would have been possible without the support of the entire advisory group. Everybody was mobilized and we worked very hard and thanks to everyone's efforts we made all this together. I'd like to particularly thank Neil for stepping in with OER Africa to support the the work in a very intense manner and greatly appreciate that I'm able to use the videos quickly on OER Africa and the work that he and Movini have done in participating in and really in moderating a number of the sessions if not all almost and we I think we have had a chance to have some very rich discussions which I have not seen often in the past. I'd just like to mention one thing that I was thinking of recently when I was thinking about this week I think some of you would hear but in about 2009 I was working with the common on a project that was called Baking OER out of the OER community and basically at the time we were talking and we were going basically the world tour in which we spoke about it was with Sir John Daniel and we spoke about what OER is and what's happening and how it can be used in different communities in different contexts and it was the beginning of a discussion. Now we're more than 10 years past there's a recommendation there is a global community and there's a very active global community with a very diverse voice and we're having this discussion today online with interpretation over the time zones and we're able to move forward even more so this would have been unimaginable maybe so this was unimaginable more than 10 years ago I can't even imagine what is possible even with development of the technology and the recognition that OER has had globally since that time so this is a very important work that we're part of and that you're part of and you're in your context so I do really look forward to the to the follow-up it's just the beginning we're only at the eighth month so it's the end of maybe the gestation period but it's definitely the beginning something very diverse and very exciting I'd like to thank also Olivia who's our colleague in the in the communication information sector and of course the interpreters who have had to deal with all our microphones all our different microphones are different problems of connection made it possible for us to be able to communicate in two languages and that is a very big step forward from even three months ago where we were and I'd also like to thank very much Alex Tissier who's the technician you see him on the list on the screen at Tissier he's he's been helping us trying to fix problems as it comes along and we have in the background we had Alex Alexandra Oakley and Valerie Chaunot who were also putting in place everything and making it possible there was a lot of hands behind the scene trying to make sure everything worked and so there is this would not have been possible and I have a so this is this is what's I want to make sure that we can express our thanks to all these colleagues properly thank you very much I have two comments here I'm going to read them out interpreted okay we're green congratulations to the entire team with global effort to help governments of the UNESCO OER recommendation is a historic opportunity creative comments stands ready and is eager to support these thank you great and we look forward to working together with you Igor's comment are the working groups continue engaging at help us once the final reports have been circulated how is it going to well it's going to work the way I did in my presentation you will we'll send the final report to you we're going to send you we'll ask for your inputs by Monday afternoon on Monday evening midnight which is the 27th of july I believe by email to you on the sessions if you have any and especially if you're going to go listen to them again or for the first time online we'd also like to ask for your inputs to the value we will circulate a draft report the next Monday 30th of August for inputs by the Wednesday the 5th of August and a final report will be available from the 7th of August launch activities from September taking into account the inputs from this consultation and so there's another so thank you very much and what's this are the working ones okay are there any comments or hands or anything for the moment for the month of August afterwards I hope that everybody gets a break I think July has been quite intense and we don't know what September is going to bring in terms of everything else on the planet so we do hope that people will get a chance to get a bit of a vacation in August are there any other comments with that then would anyone else in the panel like to take the floor I wanted to say if I may say that many many thanks to you and of course your team and and everybody who supported you in this efforts from my side and of course sincere thanks to everybody who attended and who is with us that's all thank you gosh thank you very much okay so um I think with that we are we're ready to end this long adventure that we've had over three three days and we will be getting back in touch with you for the next steps and first of all with the formal report and then the launch of activities in September and we'll continue posted as developed further thank you very much I'm wishing you a very pleasant weekend and a nice day or nice afternoon depending on where you are waiting there's more there is a statement input from Fatima and Housini thank you all we said the treaties thank you Zana for all of your efforts and Zana's work through it during this time not that we had expected but a little bit but incredibly valuable that Igor says thank you all stay safe and take care I think that goes that's a very important comment right I hope everyone does and Rania says thank you so much for this fruitful meeting I'm glad to be part of the tea of the people participating that's it with Zana Morales thank you and then what's that thank you all looking forward to our work together we're also looking forward to our work with all of you and we're wishing you also pleased to take very good care of yourselves and your loved ones stay safe and we look forward to continuing our collaboration together worder Zana thank you apologies for access and convenience as best wishes to all keep safe Alejandro Mexico secretary to public education will be sharing analysis of the survey results it's constituting the element for roadmap within the ministries I did assist in the results I will resend the results of the survey and they were sent the first evening but I'll resend thank you very much I will close the session now and I wish everyone a very nice evening and continue as soon as possible thank you bye bye bye thank you


in which Konstantinos Mechanetsidis will talk about QNLP in more depth and It will cover the experiments that we've realized and what we're working on at the moment This will be followed by a session an interactive session by Richie I was going to give you a demo of Lambeck our QNLP toolkit and Hopefully will make this Interactive a bit. He's already sent you the notebooks on Slack So you can already download it. I mean focus on what Konstantinos is saying But if you have a bit of time you can download it and install it All right, the floor is yours. Thank you very much Can everyone hear me? Yeah Okay, hello We will close with experiments on QNLP. I will show you how we realize in practice all of the stuff that Bob was talking about especially and specifically for for Realizing some quantum natural language processing on actual quantum computers as well as numerical simulations and I will close with the vision for the next year and the experiments we will Dazzle all of you in a year or so so At Quantinium we we are in the Oxford team as Bob has probably said a big group Focusing on experiments for QNLP and I will guide you through the Theory how we build models for QNLP and then at the end I will show you Specifically some of the results we have obtained very briefly if you want more details we can talk later and then at the very end I Will show you a specific experiment only one And basically a blueprint for an experiment, but that doesn't mean that I will be the only one that is only the first step for for large-scale QNLP with Text circuits, so let's get started. So I Have here Bob. I have John Firth and Jim Lambeck and each one has contributed a specific idea and If you get these three ideas You get quantum disco models, right quantum from Bob Distributional from John Firth who says that you shall know the meaning of a word by the company It keeps basically the guy was saying create word embeddings from large text by counting how they occur In each other's contexts and Jim Lambeck was saying I love algebra So I will make a quantum. Well, I will make a grammar model based on algebra and We will see how quantum and algebra play very well together I'm sure both talk about this briefly But here I will show you how we use this to build quantum model specifically. So John Firth's Idea for word embeddings here specifically will mean I will create my word embeddings I will stick my meanings inside Hilbert space Right and my model will manipulate these meanings so that I can make a task in NLP work And the compositional aspect is what gives us some sort of science science-based some sort of scientific handle of what's going on in language because I don't want to be Doing the mainstream thing which is stick a bunch of layers together in huge neural networks train it in a Okay, initially stupid task, but then it does impressive things like the things you have seen in the news GPT whatever however What goes out of the window is interpretability and knowing what's gone what's going on in inside this giant inscrutable matrices of real numbers so To tie things together let's start from where Bob left off But I want I won't dwell too much in this because it has been covered. I will just say that My tools will be boxes and wires as it has been throughout the whole day. I have states I have effects. I have processes. I have scalars and I also have an operation that allows me to kill wires, which is the discount and it's all boxes and wires and always has been So this is our base for for thinking about things and building models If you remember from the previous talks today, you should remember There's two ways to compose boxes one is sequential and one is parallel So one process after the other and one is at the same time right and by these two Ways of composing diagrams and Richie will show you during the demo how you can do this in software with disco pie With these two ways of composing diagrams Well boxes you can make any bigger diagram composed of small boxes, right? So from small processes you can compose them to make big processes And there's special kinds of boxes that if we open up inside we see that they're just wireings for example a swap the identity a cup and a cup Most importantly, I want to focus on what happens when you stick cups and cups together to make a snake and Bob has talked about this already. This is like in quantum teleportation, but here abstractly. It just means that wires can wiggle, right? I don't care about their shape. I just care about what they are connected like how they are connected and The big selling point of this is that I want to be build models such that the wiring actually means some Right, so that when I look inside my model when I open it up and I can see what is connected to what I can at least somehow understand something because if you for example see these Wirings that people draw in big neural network diagrams these these wiring actually don't mean anything You have to then we have to train in a big task and then go back and inspect I don't want to have to do this I want to have my model be such that its architecture actually means something and here The wiring will mean something it will basically mean how information is flowing around in quantum It's quantum information But more generally just information it depends on what type of distributional semantics I'm giving to the models today will only talk about quantum semantics So the wirings will tell us how quantum information I meaning is flowing around in my models Linguistic processes Let's go from word to sentence. I have some words in a sentence I compose them together according to grammar like Jim Lambeck was saying and then I make a sentence Right, I make the meaning of the whole. This is how pre-group grammar composes things every word gets Some types assigned to it and then the types have some algebra that says and with n to the R which is its right adjoint compose together and this composition I will show with these cups I Wirings so my grammar will only be wirings all of my meaning is inside the word states and Grammar is wiring and it just tells me how to compose things. So if I have the meanings of the words I compose stuff according to grammar I get the meaning of the whole sentence and That meaning is flowing on the s type wire The first obvious Model native nlp task you can do is check how similar to sentences are and this is how you do this You take one sentence sun melts gelato. You it's a state. It's a sentence state You take an effect, which is some other sentence upside down upside down stuff are effects And that other sentence is ego like what is it monk dissolves ego, right? Maybe melts is similar to dissolves, but the nouns are quite different. So this will thing will have some overlap But I cannot evaluate my overlap yet. This is just the shape of it. This is a blueprint for me to build my model yet Later, I haven't showed you that yet. And then if you want to go beyond sentence level if you want to go to text because There is this rock star who I follow on Twitter who says that it's the future and I believe him So if you compose sentences, you will get text. How do you compose sentences? Well with this Cossack? What does this Cossack do this? This Cossack says I don't believe it's sentence space.  Let's call them like this and Then every every one of these are distinguishable. So to every one of these wires a specific noun is corresponding Then all of these nouns they go into some text process as we had before for a sentence But now it's for a whole text. There is a grammar that we are developing in Oxford But it's for a text level grammar not only for sentence And then the nouns after they get modified by that text process they exit modified It's a very dynamic thing. Now. Let us see how these text process is. What is it composed of? The most basic thing is let's make some noun states, right? I have this meaning vacuum and then there is a noun preparation Box this prepares noun states. So basically you have the the meaning vacuum You have a noun preparation box and this prepares a noun state For this now sorry for the squeaks Okay, this is how I make noun states They are initial states. They enter a text So now I have order one processes these these are boxes. They are not states. They are processes, right? If you remember before at the sentence level I Had every word be only states everything was zero order and then grammar was telling you how to compose here I'm making distinctions. I said nouns are first-class citizens that makes them states zero order if I go to other type parts of speech like adjectives or verbs They will be processes that modify the lower order stuff, which is nouns an adjective modifies a noun, right? So if I have for example an adjective, you will ask. Oh my god. So let's see if this helps I will have some noun entering and This will give me out a modified now For example car This is the state of red car The intuition here. It's basically the point is that we are following our intuition to build models and it's not just our intuition It's not just loose things. All of this stuff is based on formal linguistics and there's a lot of Formal language theory behind it that that Formulates it in a way that it generalizes Now, let's see how a verb would look like say you have a verb like Loves standard examples You have dog of human dog and human enter loves Dog loves human right? So this is the combined state for the phrase dog loves human right So this this thing this loves box Is coupling them together and then they are not separable anymore? They entered as two independent things. They got Interacted by some verb and then now they are in combined state that state that if it was a quantum state I would tell you it's entangled that's exactly what I'm gonna tell you later, but now The pictures tell you exactly the same story without me having to tell you anything about quantum So you say it's a feature of the model for language in the abstract Okay, this is order one stuff order two stuff. Yes, the order matters, of course It matters if dog is here and human is here or the other way around Of course, we also love our dogs, but sometimes love is not two-way street and you have drama, right? Okay, no, I'm going to Yes, that's right But the places in which I have inputs and outputs are distinguishable.  They're not like the spiders of ZX right this Is different from this? This this box is different. I will go only one one order higher, which is to order second order Let's let's think of an example here an example would be an adverb, right? An adverb is a modifier of a modifier, right? An adverb modifies a verb and verb modify now so for example, I can have quickly and I can have runs This is quickly runs The higher-order thing modifies the lower-order thing This is the point of going order two. There are also order three things sometimes, but I'm not gonna get there today. Ask me later And now if you have a text like a story This is my simplified version of the matrix best movie ever This is the script of the movie, right? You have the nouns that go in neo morphiostrinity matrix kung fu Right, and this is this is how it goes like the verbs become boxes that modify nouns you have You see neo What do I have morphiost finds neo I have Then neo goes into quickly exits matrix then I have trinity loves neo and so on right We have the characters with their initial states There is a story that happens to them and then they exit modified by the way our team is developing a tool That does this in an automatic manner for a large fragment of English It's basically all based on CCG parsers they exist. They are trained on huge CCG three banks and then our team has figured out a way to To use co-reference resolution to see what nouns are the same between different sentences and then you string them all together into a big Text diagram. So this is not just drawings I mean they are my drawings, but you can you can see how it works also in software and richly can show you some of this later, but I Don't think we can make it available yet, but Coming soon So you see the bigger the story The bigger the text diagram So I'm not gonna draw you huge text diagrams But I really want you to to to remember that this is how it's gonna be my scaling parameter for my problem size here Is the text diagram? This is gonna be important later for when I talk about advantage and and why we want to go quantum even so The biggest the story the bigger the text so the the wider means more nouns and the deeper means more stuff in The story happens right nouns adjectives verbs and stuff like that One model native task text similarity you take two texts. You want to see how similar the stories are There are text that belong there are nouns that belong to that that are common to the two stories There are nouns that belong only to the one text and they're now that belongs only to the other texts All of these sets of nouns create a noun universe Okay, I put all of the nouns side-by-side in tensor product The nouns relevant to text one enter that text the nouns relevant to text to enter the other text from the bottom Text to has become upside down because that's an effect, right? And then if you take this overlap this should have the meaning of text text similarity in the same way as Sentence sentence similarity similarity worked. It's just the same generalized. It's a generalization of the same idea So compositionality allows us to take small ideas and generalize them to larger context without thinking more. That's the point Another model native task that is not global It's not taking all of the nouns in the universe and doing something with them. This is a local task I discard all of the other nouns that exit from a text. I don't care about them I just care for example to check How much the noun at the position I? What is its similarity with its past self after it exited so this is like Okay, I gave it a cute name quantifying the character arc, right and if you take this idea of Throwing out all of the nouns you don't care about and doing something local with One or some of the nouns you can do something which is actually done in the real world There exists real-world data out there for this task. This is called question answering So what do you do? You take a context text that says a bunch of facts That that's text t1 One there shouldn't be there just t okay You discard all of the nouns that are not relevant to the question you take the question you make it into affirmative statement You stick it as an effect So basically you do text similarity between the text and The question but the question acts locally as an effect so you can throw away all of the other nouns Now imagine my text my context text scales my question will not also scale most likely usually questions are Small knowledge and Context can scale but usually you ask questions of some finite amount So or like finite support here to be to be precise So I have local question and then you should imagine that the text grows in size if my context text grows So now let's finally go quantum all of this was abstract for model building. I started half past, right? Okay When we go quantum we want to respect the tensor products If I want to ask a question about an attribute how does this work? So if I want to for example, I have a red car whatever second-order thing Right, and if I say give me the color of the car. That's quite tough because it's not alone Yes, you can say car is red. It's a statement stick it as an effect to red and car So red is an adjective Well, let me think Yes, car is red will be a legit text diagram. You can flip it upside down Dagger it now. It's an effect So car will exit the text that says whatever the car is You stick the car is red as an affirmative as an effect And if you have a high overlap and you're confident that it's red If the context text said Car is blue and then you stick car is red. You should have lower. This is the idea Any other questions before I go to model building? Yes, I need the microphone. Otherwise, it's not going to be recorded so when you mentioned the overlap within the entry in state and the effect is it about I Mean you have to define it quantitatively, right? So what is the overlap between red and blue or happy and sad? Because otherwise everything is or to normal if it's not equal. So there should be some metric I guess This is the point of going to model building. So I haven't said anything about how you make anything quantitative right, this has been all abstract and It has nothing to do it's only the compositional part I have been only talking so far about the compositional part but This framework is called this call distributional compositional the distributional aspect is exactly what you're saying is making everything quantitative Right now we can give distributional semantics We can decide what are the spaces on which the states are defined? What are the state spaces that the process is modify? Right, we will decide that they that we want them to be quantum Hilbert space and quantum processes Right states will be defined in quantum Hilbert space processor will be quantum maps and so You can get quantitative results by doing But like exactly the same way that you get quantitative results if you do anything in quantum you take overlaps of states You measure operators stuff like that Okay Now why do I have a big picture of the tensor product here is because quantum theory inherently is Is is built on the tensor product sisters compose according to the tensor product and I want that because my grammar theory my my boxes and wires that describe my grammar model the compositional part are Also behaving as if they compose with tensor products because Because basically I have non separability when I compose things This is what Bob was calling shredding your compositionality or quantum compositionality. I'll just call it composition so now Right tensor product before with the diagrams means just things happen in parallel When you when you give the model semantics tensor product will be chronic your product the usual So if you are simulating these things with linear algebra Because linear algebra is how you simulate quantum theory on your laptop or by hand or on a blackboard It's the chronic your product the usual outer product the chronic your product of two matrices or vectors or tensors or whatever Quantum theory if you have a quantum computer and you put quantum systems together in a controlled setup. They will just compose By themselves with a tensor product, right? So yes, the usual tensor product here. I have I'm really beating the dead horse to to show you that tensor product is flowing around And so this F is is what I'm calling my semantic functor jargon aside, it's basically a map that Does there's a mistake here? I'm sorry about this. They but okay. It's a map that does box wise substitution of The boxes and the wires, right? So every wire gets Assigned some qubits. So for example, we had before a sentence level that every word gets a type The noun type will get Q and qubits the sentence type will get Q s qubits. So every wire Carries some number of qubits and it's up to us to decide how many they are the number of qubits decides How big does Hilbert space dimension is that flows are along this wire? and If I have Q Q bits on a wire the Hilbert space is two to the Q dimensional, right? Everyone knows them My mistake in this picture is that I have bent the wire on the right To make it an effect, but I didn't do the same on the on the on the right-hand side Negligence on my part But you will understand from context in the next slide what's going on Now I want you to focus on sun that goes into melts, right? Sun becomes a state preparation circuit Unitary so it's a circuit quantum circuit u that is parameterized by a control parameter set theta and I have subscript s so theta s is theta for sun. Okay, so sun gets its own parameters that go into a quantum circuit and They prepare a quantum state For the word sun The same happens for melts. There is a theta melts that goes in a parameterized quantum circuit and Prepares a state for melts and now after you post-select all of these Okay, let me let me show you here So the cup the cup becomes a C not and then post-selections this you can prove with the the rules of ZX and It's it's it's trivial. You just do this You have a cup like like in Bob's lectures. You say I'm gonna put a An identity spider here. It doesn't mean anything. Oh, by the way, I can grow a One-legged spider from either with unfusion and then oh look. This is a C not gate Right. So a cup is basically a C not followed by post-selection on the plus state and the zero state, right? So now that you are experts on ZX, this should be obvious to you. So the cups become C not and post-selection if you bend stuff around Which you can but you have to be careful that you're bending with 180 rotation. So that is not dagger. That is transposition and it's easy to make this mistake when you when you write the code but You have to automate it once and then forget about it But this is this is crucial. This is transposition when you bend states to effects in In disco cut, right at the sentence level And now if you have two sentence states to Two sentence quantum states you can of course take their overlap this this comes back to the question before How do you quantify this you can take the overlap of two quantum states, right? You can use whatever protocol you like. You can use the swap test or something. There are standard things to do this So you have the quantum state of one sentence the dagger of the quantum state of the other sentence And this is a valid quantum operation. You can evaluate it on on any quantum computer You like I haven't told you how what these fetas are that I'm bad my meanings. I will tell you about it later and Then when we go to text level which is which is the most interesting thing Again, let us see how we replace my how I make my circuit components for all of the All of the parts of speech according to their order, right? I said I have zero order one order one order two things So the order one things are the nouns. So what happens to the nouns? Well, I will choose a convention for my meaning vacuum. It's going to be the zero state, right? All of the wires in this co-circ carry noun types, right? We said nouns are first-class citizens nouns flow around and stuff happens to them So all of the wires will have the same number of cubits, right because they're of the same type So Qn I choose it once. It's the dimension of the wire of the Hebrew space flowing around my wires And you see a noun preparation Circuit will be this u theta n and this theta n depends on the noun n, right? so Dog will have its own parameter set House with have its own parameter set will be different and so on and This this thing prepares a state, right? So for example you have Theta of car This state is prepared by u theta hitting the Vacuum which I chose by convention to be the zero state and it that's without without loss of generality, right? so If you go to the one order things, they will be well The order one things are easy. I'll just make everything be unity. This is this is a choice. I can make them be a Generic quantum maps by adding ancillas and discarding them, but let's not go there It's everything I will say from now on Will still hold if I make these more general quantum up. So let's stay with everything being unitary So whenever I see a box, I will make it a unitary box and And you see whenever I have adjectives or verbs, I will always respect You never have the situation where you have more wires that come in a box than exit a box There's a conservation of nouns right as many nouns enter a text as many nouns Exit the text so it's fine to have everything unitary because Unities need to have the same input and output Dimensions, right? So if I have some adjective, for example, I'll give it a unitary and it will be parameterized by some parameter set specific to that Now the higher-order thing is a bit strange One choice is to say I'll break it like this like a sandwich. I have this this comb thing that is a higher-order Higher-order box like like for an adverb. I will I will break it into unitary is that sandwich something? So let me show you what I mean so if I have They quickly runs was our example before I will have you beta Quickly some parameter set with index one and there will be some other second parameter set for quickly so quickly has two parameters sets and Whenever I have something like runs so you theta run Is just the sandwich by these two guys right so these two guys for quickly sandwiching runs will return to me a Circuit that is supposed to be for quickly runs the whole thing. Of course, I can simplify my life I can say from order to and above I Don't want things to be quantum Processes I don't want everything to be quantum processes all the way to highest orders, but also, you know You won't have infinite order stuff. This is Usually in language everything stops to order three or four, right? I mean how often do you have modifier of modifier of modifier of modifier? It's it's kind of insane. So What I can do is say I don't want this to be quantum maps another choice is to have say so I have here for for runs I have Runs and I can have a classical control for quickly that modifies the parameters of runs, right? So this can be a classical function like a neural network parameterized by its own parameters for quickly and here its input is theta runs So theta runs enters quickly and is modified and enters here So that thing is total is together quickly runs. I mean also this makes sense, right quickly. It just modifies Modifies the the runs, right? It's It's like it's like a knob Or like very very will also be higher order very red I can have like a classical control on the circuit that is very and just modifies the parameters of red and just have very red or like Less red stuff like that But all of these are just design choices and they're all valid Okay similar idea And now to come back to the the point of how you do something interesting with Quantum text circuits quantum tech circuits for question answering for example As I said before I have a context text. It's a big quantum circuit now the context text this and this theta t is all of the parameters it's it's the it's the Coordination of all the parameters sets all of all of the words that involve that are involved in that text Up there in the t-text Theta q the same theta q is the set of all parameter sets of all of the words that appear in the question Q So I have a big quantum circuit all of the nouns go in Yeah, so zero states are initialized on on qn qubits For each wire that you see here Every wire is for a noun. They enter a big text circuit like my my matrix story before then The nouns that are irrelevant The the quantum wires that are irrelevant to the question are discarded. I never measure them, right? That's what discarding means and then I do the dagger circuit of the Question text which has been turned affirmative It's an effect and then basically I can measure everything. I can do a bunch of Measurements I have deliberately drawn these with greens because they are the so-called bastard spiders from the from the Dodo book that That Alex and Bob wrote picture in quantum processes If you want to see how quantum and classical wires interact First of all, you need to draw two types of wires The whites are the the quantum the greens are the classical and then there are extra rules rules of how classical and quantum wires interact and The the green dot is basically a z-spider and And And Very conveniently it models the coherence I measurement in the Z basis So basically this thing tells me measure all of the cubits there if you measure all of the cubits there You will get a probability distribution for all of the bit strings. I in the computational basis states that That are defined by all of the green wires, right? If you go and see the probability of all zeros, this is quantifying the this overlap Right because zeros zero comes in when you prepare a noun When you are prepared a noun You want to see how much zero comes out? Right because this theta q it basically includes the Unpreparation of the nouns that are included in theta q So if I see how much zero is coming out how much how often I measure the zero states there This gives me the overlap of this whole question answering thing. This is very important questions about this because This is basically the whole Setup you can take this and generalize it to other things and since I set up the whole thing Motivated by wanting my wiring to mean something. I set it up so that it has tensor structure I could have said well forget quantum or I don't know about quantum. Maybe I wanted everything to just be tensors If I ended up because the model says that's how you should do question answering, right? It's this is this how this is how the task looks like in the model This is the native way of doing question answering or text text similarity, right? local text text similarity if I had tensor semantics generic tensor semantics Evaluating this thing Grow it becomes exponentially hard if the text context text scales, right because Contracting tensor networks at least exactly and also approximately is hard. You need exponential resources However, I set everything up so that it is Valid as a quantum operation, right? Of course simulating these quantum operations you would use tensor networks and And linear algebra on a computer basically which is exponentially hard quantum theory quantum mechanics is hard to simulate But if you have a quantum computer This is how you do it, right? Now if this beats GPT, whatever It's besides the point here because we started by a motivation We started by motivation to build a model Then you make it work It's it's backwards. It's not Make a brute force all-powerful God and throw it at tasks It's it's it's being more of a scientist about things even if they don't perform Amazingly at first you have a question. Yes So this type of circuit for verify the statement, no you dug you dug Is it's it's it's the docked product of two state So if it's like the fidelity, so if all zero enter and all zero I'm sure I measure in output it means that the segment is true, but there is As far I know if the system is too large This process don't work Don't work because there is the Catastrophic Catastrophe to go on or to go on a little catastrophe. So I think what you're saying is that the bigger the circuits the more random they will look see the more the more it will scramble and Getting the the zero state. It's exponentially unlikely. No, there is Also for a noiseless circuit If the system I'm also talking about noiseless Noiseless, okay. Yes. If the system is larger enough. Yeah, the orthogonality Catastrophe make Turns out to Falsify falsified the statement But the reality is true because the all the state Appears orthogonal for if the system is large enough I don't know if I express correct myself Maybe I can ask this question My understanding about the authentic catastrophe is that you When you solve the generalizing by problem You end up with lots of new dependencies and then your Matrix inversion becomes unstable unstable or is here. You're just solving a variational problem The basis is naturally orthogonal, I think here Because you're using qubits Well before we say anything about variational I need to go and explain two experimental approaches because the word variational is important there This I think this this conversation has has some depth. Let's have it later. I think it's it's basically about How likely it is to even to measure zero? If the Hilbert space grows if the Hilbert spaces grow too much for generic circuits you and UT and and you you right, it's true that if these circuits are random Like generic typical, right? And I'm growing The number of green wires on average. Yes, it will be exponentially unlikely in the size of the green wires to have to ever measure zeros I think this is similar to what you're saying But remember what I said before I don't want to be growing the the The number of green wires my the my questions are always local and The scaling parameter of my problem is the context text the only text that grows here is the t not the q I mean if if what you were saying was was a problem then the whole bqp thing would be a problem the bqp setup The whole of how we define quantum Turing machines and decision problems with quantum circuits Is like this you just throw all of the cubits in a Circuit that grows and you just measure one And here I'm not measuring one. I'm measuring five But I'm not measuring some function I'm not measuring a number of cubits that is a function of the t-text I'm always measuring say five and that's it So so the number of shots you will need is finite for a specific additive error to to this probability p of all zeros independently of how the other things case so I Never said before what is the u of theta is right u of theta is some unitary that I said is parametrized In practice, how do you do this you pick some circuits that? people have studied in quantum machine learning and That they like because they are expressive right expressive means that for That they explore the Hilbert space on which they are defined quite effectively as As if they were random for random Choices of their parameters their control parameters Here the parametrized gates are the rotation gates and the control rotation gates The fetas are not shown, but it should be implied Hadamard of course and CZ are not parametrized But Rx or control Rx and Ry are parametrized. They have the fetas inside One choice of u theta is this thing on the left, right? I've drawn them to go down to be in the spirit of all of this Language circuits because everything is read from top to bottom, but Usually people write in their papers stuff, you know quantum process that go from left to right. So here I've rotated a bit Sorry about this, but these are actually the circuits that we do use in our experiments Another choice is the thing on the right. It's three layers of that block Which is a layer of Hadamard and a layer of control Z and a layer of Rx's and then I repeat this three times How many layers of this block I choose? It's my choice. It's a hyper parameter How thick these things are is my Qn. It's it's a hyper parameter again I choose how many qubits I want to assign to every wire, right? So I said I'm gonna talk about some approaches of how we we do experiments one approach is train everything in task Build my text circuit leave all the fitas free Pick a task my task defines a cost function. No, I'm doing quantum machine learning. However, I Didn't pick some random ones that I found somewhere My my text circuit is informed by the problem. The problem here is language the the structure that The circuit inherits from the problem is syntactic structure, right the whole structure of the diagram It's not says some random circuit black box You make a cost function for your task you evaluate performance One of the thing we do one of the things we do is is binary classification The easiest task you can think of you have a bunch of sentences its sentence gets its own sentence circuit right some of the the words I Mean most of the words will appear in more than one sentence which allows the thing to even train And then you you can do supervised learning you have a test Train set and you have a test set you keep the test set aside and you train the fitas Such that the quantum circuits predict their label every every Every sentence will have a label right? Zero or one. This is a pregroup diagram like before right? If you measure the the qubit there At the sentence wire you will get some probability that it's zero and some probability that it's one Zero for one class one label one for the other you train the fitas such that You measure the correct labels I either labels that the train set says that these sentences have After you have trained you evaluate with these parameters for the words after they have been trained in task You you execute the circuits in the test set and you see your accuracy like how well the model generalizes in unseen data Because if they're unseen because a test set you kept it aside and this is all this variational loop here right the train set sentences become Quantum circuits as I showed you They go to some quantum processor you measure out the class label their probabilities if you're not happy with With with them matching the the train set labels you Iterate you update your parameters with some optimizer The fitas and then you loop around until you're happy right and then you just evaluate on the test set again on a quantum computer You can do this with with disco cut diagrams at sentence level I'm gonna do a parenthesis and show you what we are working on now because before with disco cut We have these these two experiments published in these two papers, but They're toy experiments as in the data is artificial. It's 100 or 300 sentences not not that big it's it's toy right for NLP standards vocabulary is very small But it works. It was a proof concept now if you changed it and you don't use lambic pre-groups if you use just CCG trees You don't need to post select anymore. You can just have three structures. They're easier to train on quantum computer There are no bar in plateaus. I'm not gonna stay too much on this, but it's working progress and it's very exciting So what we do is we can tackle thousands and ten thousands of sentences they can be movie reviews They can be clickbait news titles or even DNA sequences if you don't have syntactic information you can just use this middle thing which is a Binary tree with descent anglers Which is inspired by condensed matter theory for critical systems and then if you combine these two ideas syntactic information with this entanglement filtering thing from condensed matter from If you have heard of them, they're called Mera tensor networks or convolutional quantum convolutional neural networks We can combine these two and make syntactic convolutional neural networks and so on and all of these things work very very nicely And soon we'll run them on actual machines as well And and then and then there will be a paper. So stay tuned and Now when we go to disco Cirque, this is the the interesting thing that I want to close on because it's it's it's something that We have also seen working In task right So I Will tell you exactly what I mean now so Instead of training in task what you can do is instead of doing quantum machine learning like I said Instead of doing supervised learning what you can do is say I want to do question answering Okay, but I don't want to train my fetus in task Okay, I will pre-train my fetus to pre-train your fetus You need to do basically what classical NLP does which is make some word embeddings that are pre-trained task agnostic, right? The usual methods you might have heard of are called word to back or glove things like that We can exactly use these methods specifically. I can talk about glove You don't need to know too much now. I can show you what the cost function looks like we can ask me later The point is that you can pre-train stuff You can pre-train the component circuits that go in a big text and then your trusty compositionality Such that when you stick them together in a big text diagram, which is hard to evaluate classically the meanings compose In a meaningful way like they they don't generate gibberish. That's why I'm I say trusting compositionality you trust in the model But of course, this is not blind trust what we are doing is we pre-train on some corpus like There is a kids book corpus that we found and It's very nice because it has a huge vocabulary, but the sentences are small At the text are small you can take paragraphs and you can pre-train in co-care as matrices And what you can do then is take a question answering task Which your pre-training had nothing to do with and use this as a test of compositionality So this is something we are working on now But the most important thing I want to say now is that when we pre-train We can do this classically because we train small components and I can simulate evaluating small components of a big thing But if I compose them together to make a big quantum circuit, then I cannot evaluate it anymore Then I do need a quantum computer. This is the globe cost function Basically, what you do is the important parts of this of this cost function is this dij for so for word i and word j I have a similarity measure dij. Okay This dij I can evaluate classically It's it's the overlap for example of two known States or the overlap between two adjective Processes or the two overlap between of the overlap between an object an adverb and another adverb Or I can even compare things that are dissimilar an adverb with an adjective or an adjective with a noun I can take all of these overlaps By just taking them together compositionally and then replacing their quantum circuits, but these overlaps are small I mean, I can simulate 20 qubits on my laptop, right? It's fine. I can train these And I want to train such that their overlaps match the other important quantity in this equation Which is this minus log x ij this x ij is a huge matrix A co-occurrence matrix that you gather from a huge corpus Like I said this kids book data set or even you can do it from wikipedia You can take all of wikipedia and you can count x ij is basically the frequency That word I appears in the context of word j That's it. You you want to make this is basically realizing john furth's quote inside Hilbert space, right? I'm making my quantum processes have overlap such that their overlap is proportional to their Minus log likelihood For for for them being in the same context in in the in the corpus Hey, you pick a window. Yeah, it doesn't matter. Yeah, these are details of how you train So people usually fiddle about with these hyper parameters And they just find something that works. This is this black magic machine learning, right? And here we also use that black magic approach You just fiddle about and see what works And then you don't care as long as it works because you just care that these overlaps Are representative of their co-occurrence Now that I trained my quantum states and processes To to actually be quantum word embeddings now what I can do with them Is stick them in a question answering task There are datasets for question answering out there We take them we take the context text that says a bunch of facts We get a text diagram that builds us a text circuit I just replace my pre-trained word embeddings now have a big text circuit I cannot evaluate it. I need a quantum computer for it The same I do for my question And then I just measure on a quantum computer as a test About whether compositionality actually works Right So this is what I what I mean. It's not trained in task If however, the whole thing is small because there exists very simplified toy Question answering tasks. You can also train in task everything Don't do any pre-training create your Circles like this for every different question you get a different Circuit to run you can train train all of these in tasks such that the correct answer comes out Right, you can do this we have done this it works It works even with one qubit per wire It was like a WTF moment. It was pretty cool So what what what you get here is You you you need exponential resources to do this classically if the thing is big On a quantum computer, you can do it in polynomial time I will be bold enough to say that this is a exponential advantage against simulating the model classically, right? And then on top of that, let me go back Let me go back and remind you that This thing When you take sentence sentence overlap, there is a very nice paper by will zang and bob Which started this whole qnlp business and there they said If I have two vectors to compare and I want to see which which effect has the highest overlap with my vector I can invoke closest vector The closest vector algorithm quantum algorithm, which basically is grover under the hood You grover search over the possible effects And you get a quadratic speedup. Okay. It was cool in 2016 Today not many people care about quadratic speedups because they believe that the error correction overhead and all of this stuff will be quadratic So they will they will kind of kill each other off however In my very bold statement that I have a exponential speedup When I do question answering a text level with quantum disco search I can also have my extra bonus quadratic speedup on top Because I can also grover over the answers And this is something that we are putting down in in a theory paper now and stay tuned Yeah, now I think uh richie will will give you a very nice demo of lambek Which is about sentence level qnlp that automates all of the experiments I talked about And you can reproduce all of our papers And he will show you also how you compose diagrams to make text circuits And uh, yes, have you joined the zoom?


Welcome to the journey. Today we're going to talk about how to increase blog traffic with 13 proven tactics. Sam, do you know? Research shows that 61% of businesses say that their biggest challenge is generating more traffic and more leads. I didn't know that. So listen, if you don't want to fall into this group, here's what you want to do. You want to learn how to publicize your blog, and this is going to help you to generate more traffic, more customers, and spread more word of mouth. So to help you get started, we have 13 tips. Starting with promoting your blog on social media.  Oftentimes businesses, they're posting on social media, and eventually they kind of hit that writer's block, like, oh, what do I post today? We'll throw this into a calendar.  And while you're promoting this on social media, reach out to your friends and family. They are the best people to encourage to like, comment, and even share your posts. Because when they do those things, you're opening up into new networks whom you may never have had the opportunity to reach out to. Totally. Speaking of new networks of folks that maybe never heard of you before, I encourage you to do guest blogging as well. I know I do it on the GoDaddy team. Whenever we do a webinar with other partners out there in the marketing world, after we do the webinar, one of the things we do is like, hey, you know, this was a great webinar. We recorded it. We're gonna put it on our blog. Do you guys have a blog? And if that other company does, we're like, let's do a guest blog. So we'll have their blog that they did and put on our site, and vice versa. It's just another great way to get in front of new customers who've maybe never heard about you before. And collaboration is so much better than competition. Don't be afraid. There are people out there that want to join forces with you to actually promote each other. And really important for your blog when you are actually posting on there, and we're talking about social media, make sure you have an easy way for that traffic to share the blog posts to social media platforms to even help you get in front of more new customers and fans. So I have an example here. GoDaddy, our blog. This is an article that my teammate wrote recently, Caroline Barker. Shout out. Shout out to Caroline. And it's about, it's for Slons and it's about Instagram and like how to rejuvenate your Instagram for 2020, a very, very top of mind article. But notice here on the left-hand side, you can share it to tweet it on Twitter, you can share it to Facebook, et cetera. So it's as simple as this. I go and I click on Twitter and right away, it not only populates the link, but even shows the title there. And then I can add a caption too, like read this article. It was rad and super helpful, hashtag Instagram. So this is just another tip that I would have for every blog post that you do, make sure it's super easy for that traffic there to share it to social. And another little tip, something else that you can do, if you have a certain aha moment in your blog that you really want people to pay attention to, this is an advanced tip. You can actually make that small section tweetable or you can share it to Facebook and add that necessary widget so that people can add that. It's a great point, Sam. Another great point, find your niche, focus in on what it is that makes you unique and that's gonna also help influence your blog. So I wanna travel over here to one of my favorite clothing brands, Aviator Nation. And what I love about the blog, The Owner Page, she has a passion for the 70s. And not just the 70s, but the surf culture and this time period, as well as the fashion then and the music then. When you go to the blog, you get that sense of her story, her vibe, she even goes into the fashion, the music, the collection and in addition to some of the partnerships they have, like even with SoulCycle and Austin City Limits, yeah. And I know many people are saying, oh, I wanna be able to reach everyone. However, when you narrow your focus, you'll be able to find very enthusiastic brand ambassadors that'll be just eating up what you have to do. So narrow that focus, find that niche and you'll have those brand loyal brand ambassadors just coming to spread the word about you. Yes, send me a shirt. I'll rep it on the journey. And also, people like it when you keep it simple. So make sure your blog is simple in that, think about your audience. They wanna know exactly what they're getting themselves into when they come to your blog. So next tip is SEO. You don't wanna just write about everything and anything. You wanna think about what is your audience gonna be looking for? What are they gonna be searching for online? There's some ways of researching these keywords to make sure that you're writing with a purpose and these things that your audience is gonna be looking for, like, for instance, the Aviator Nation, maybe things related to 70s or disco, things that exactly, things that you know your loyal fans are gonna be looking for. Southern California. Tip number five, publish consistently. You have readers, viewers, anticipating what you're putting out there. I know for the journey, we post two to three times a week and that helps with retention. So you wanna make sure you're incentivizing people to come back. You don't wanna just go and post once and then totally ghost them. And they let them know, hey, we have fresh content coming out Mondays and Wednesdays so people know when to expect it and then they get excited. They'll know to, okay, let me show up because I know every Monday and Wednesday, whatever works for you, but stick to it because you don't wanna lose those people that have already invested into being a reader or an audience member for your blog. All right, number six, turn readers into subscribers. I mean, you can't just sit back and hope they're gonna return to your blog or gosh, I really hope they see that social media post about it and they're gonna return.  And to make sure that they are on your email list, call to action, let them know, hey, subscribe here. You can add that at the bottom of your blog so they know what to do. Just a simple tell them, hey, enter in your email so you're notified when we have this coming out. And number seven, you wanna have the right cadence. We did talk about scheduling your content and having specific days for it to go out.  Two times could be good for others. Maybe once is for you, just play around with it. It's an experiment. And I mean, if you're, say, a salon, how often can you keep posting about how to dye your hair or what styles are in? By the way, Balayage, still very cool. So you wanna think about it. You don't wanna lose the quality in your posts and cadence is gonna help you to maintain quality. And plus people, they don't necessarily wanna see something from you every day. So take your time, figure out how much is enough. Just play around with it. Number eight, invite industry experts to guest blogs. So we talked about this very briefly earlier, like when we do our webinars with other industry experts. And what's really cool is they have an expertise outside of what we're doing at GoDaddy. And so they can come in, do a post, and that collaboration then allows us as a company to get in front of a new audience. And it's a great way for you to leverage their fans. They may not have known that you are offering the different types of content. So they become aware of you and then potentially become a new fan of yours. Yeah, and then you're already getting credibility, right? Because they're already fans of this guest blogger. So then you have this instilled trust. Number nine, balance your content and ads.  And also if you get hit with more ads, I just want to leave, like the credibility goes down. It feels like spam, so I abort. All right, so for number 10, monitor your load time. 40% of people say that they'll leave a page if it hasn't loaded after three seconds. And you want to keep a close eye on this because you take pride in your blog and maybe you're adding things to it. And as you add things, you don't know how it's affecting the load time. So you want to keep an eye on this because you want your people to have a positive experience. So you could use tools like Google's PageSpeed to make sure that load time is still efficient. Number 11, make it easy for journalists to contact you. So this is really a cool way, not just journalists, but influencers to learn about you, collab with you, and then once again, what is that gonna help do? Drive traffic to your blog. A great place to do this, really beef up your About Us section. So let's go back over to my friends at AviatorNation and check out their About Business section. So here's where I know I learned the story about Paige, the founder and creative, the whole vision that she got about the California-based brand, and then you go into not only the story, which is beefed up, as they say, and the unique story, but there's great coffee and images that really embrace her vibe and the brand. One thing that I might advise Paige and I were to grab a cup of coffee or catch some waves, I'd say add video. I think she's super personable. I know she plays music, it'd be cool to see that. I know they do a great job on Instagram, but this is where you tell your unique story and then also you'll notice the partnerships that they have listed in here from South by Southwest to the X Games to Lollapalooza, and that's another great place where they're sharing their story, sharing their partnerships. So if when journalists or other influencers come across, they get an idea of like, oh yeah, this makes sense for us as ACL to collab with AviatorNation because look who they've partnered with in the past. So for tip number 12, replicate your success. Now it may take some time to actually find your cadence and get everything just right, but once you do, keep it that way. You've worked hard, so you just continue to go with what works. And finally, number 13, track which marketing efforts work best. So set some milestones weekly, monthly, quarterly, and work as hard as you can until you reach those milestones. Track it, reflect on it, and stay inspired. By tackling your niche, posting consistently. And building relationships with your readers, other journalists or influencers, you'll be sure fire on your way to getting more traffic to your blog. That's a wrap. This is The Journey. You just learned how to drive more traffic to your blog site. Be sure to comment below, like this video, and subscribe to our channel and ring the bell so you're notified when we have fresh new content coming out. This is The Journey. See you next time.


Night one of the Mustang Classic caps with a thrilling 15-14 win for Stevenson over Dickinson, a head coach, Paul Cantabene, a coach.  Yeah, it was a great day of lacks here, five games and everything, but it's good to get a W here, be the top 10 team. It's been a little while since we've done that, but I thought maybe in the fourth quarter, we kind of gave them a few and kind of get the lead away. We made the big plays in the end to get Jack the goal and Cam made a big boy play to help us win it there in the end, and so we actually made the plays to win the game, but a great day for us, good win. We get to play a great RIT team tomorrow, but we like what we did today, and I think that we took a big step forward about winning some of these close games and making the plays to win those games. Coach, you referenced in the fourth quarter. This was eerily similar to the situation a year ago when you were up one against this team, and they came back and took the lead. What did you do differently? What did these guys do differently? How did you avoid suffering the same thing? Well, you got to remember a year ago, Jack Scalotti was a freshman in that situation, trying to make those plays. Now we drew up a play for him. He got it. He's a sophomore. He understands the situation better. Made the tough play. Ryan Coleman got it. Did a great job getting himself together more, and it was really a factor all around, but I think the guys are learning what our team is, we're building through the process, right? So, we had a tough loss the first one. We didn't play great. We played really hard against CNU, but we made some a lot of mistakes. We got better against Gettysburg. We got a little bit better against Lynchburg. Today, and then we're starting to make those plays, those tough plays, but there's a lot of things we've got to work at. But when you're trying to win, and the team has downed itself a little bit, now you're starting to come back and make those plays. So, now we got to stack it. We got one today. We got to try to stack one tomorrow. That's what we're going to try to do with that.  Stepping into the starting role this season, he's been really good all season long. How impressed have you been? He's good. He's been solid. He makes the save he should. And I think that's the big part of it, and he makes a few he shunned. And so that's all you can ask for out of a goalkeeper. And I think Justin's doing a great job stepping up, being a leader. He's not used to that as much in the past. And I think he's doing a good job stepping up and making those saves. And he's solid with it. And so that's all you need. I think we got a good enough defense in front of him to help him. And as long as he's solid, that's awesome. Can you go to clearing practice when you only have one day to turn around? Yeah. I think this is a confidence thing. Those guys are there. We got to make some plays to help. You know what I mean? Like Josh has long play. You got to catch the pass. Good teams make those plays, right? And we got to do a better job of that, making those little plays. And I think we just get a little tense and got a lot of new guys playing for us. So sometimes they get in those situations. And games are different. Tim tells me all the time, clearing is only one part of the game. It's a big part of the game, but it's only one part of the game. But I think we're getting better there. We did some great things at times, clearing the ball, made some really great passes. Unfortunately, we just made some unforced little silly errors. I think we got to get rid of them and it will be tight. Every team is going to turn it over in the clear, whether one way or another. We just have to do a better job of making the tougher plays in the middle of the field. Describe for me out of the timeout what Cam did there as the shot clock of his expiry. Well, we just had a little play. We were going to run there. We had a little slip pick there. We tried to get at the jack behind. We're going to set a pick inside for Cam, which we ran, but they covered it pretty well. And then we tried to bang it up top, the Coleman, against the short stick to go down the alley. They did a great job getting it, but then Cam, being the leader, first came back in four games there. He didn't get a lot of looks, but he made a great play. And that's what kind of seniors do. We need them to step up, to make a play. And he made a big one and a tough play. And that's what he's all about, toughness and doing things the right way. And if you do things the right way daily in and out, you're going to get rewarded for it. And he made the tough play for us to win. But that's what you need an upperclassmen to do. That's why you have them. And that's good teams do that. And the upperclassmen lead them. And he led us today, for sure. Just some final thoughts on what this day, I mean, you got to do it again in another five games tomorrow. But a day like this, atmosphere, everybody was fired up the night, great crowd participation. What this means to the program? What this means to the sport? Well, I just think it's a great event that we're able to build up. We started, I think it was 10 years ago, with two teams. Then we went to four teams. Then we went to six teams. Then we went to eight teams. Then we went to 10 teams. And I really got to give credit to the coaches.  And it's been awesome. So today was a great thing. And we got all the media here. Can't thank the inside lacrosse guys enough for everything they do to support Division III Lacrosse. And that's awesome. And so it's just really one of the best events in lacrosse. We had a guy call us from Dickinson a minute earlier. And he's like, when's the games? We come to an annual trip to Stevenson to watch the Mustang Classic. Who would have thought, when we started that, hey, you've got people making annual trips for this to hang out with their buddies to do that. So it's just been one of the main streams of events in lacrosse nowadays. And it's awesome. And the coaches that come here make it because they're all invested. And they don't have to do that. Playing these games back-to-back are extremely strenuous on your teams.  You've got your players going day in and day out. So it's so hard on them. And it cooters to them. But I think the players love it. You're watching a game before then. Then the game's over. And you play your game. And you're watching another game, two great teams battle for it. And that's what it's all about. Guys, these D1 guys are like, oh, we take a week to play a game. We got to get ready. And oh, my guys got to get exactly 10 and 1 1⁄2 hours of sleep. And we've got to eat at four hours beforehand. And these guys here are like, let's play lacs. Let's go. Let's play some great games. Let's get after it. Let's have a lot of fun.  Let them play. Let them be fun. Let them get after it. And that's what they want. They want to play in front of great crowds. They want to see each other play, see all their teams play. That's what they want. And this is why this event is so great, because I think all the coaches that come to this understand that. And they want to be a part of this. That's what's great about it.  He was a D1 guy once upon a time, too. Hey, coach. It was for a long time. But again, they just overcoached the guys. Let them play. Let them play. Hey, coach, congratulations on a massive win. We'll see you back here tomorrow night. Thanks a lot.


Okay, so we've got the footage here and we have to track the four corners of the laptop. So that's very easy to do now. We just go here, maybe zoom in a bit, control left click to place the marker here, let go and this should track just fine. Maybe to concentrate on that, press L to lock that to this marker and then hit control T and it's already finished. So maybe go through the shot and this looks pretty nice. Now next marker right here in the corner and maybe we can also change the speed. So just to be able to follow that better while it tracks, you can go down here to the tracking settings and then here in the speed you can switch from fastest to real time or half the speed or so. So control T again and now we can follow it much better and see how it tracks. So if there are some jumps or if it slides away, we can see that better. Alright, then shift left arrow to go to frame one, add a marker here like so and if you now hit control T, go to frame one again, control T, scale it up just a little bit then move it right here in the center, control T again. Okay, so that is the four markers and we are done with tracking. So we can press L to unlock and now we can go to the reconstruction panel to turn these four tracks into empties. So start up here, link empty to track, link empty to track and little trick, shift R will repeat the last operator. So now we have three markers here, then select that one. So shift R for repeat last operator is maybe a little bit fast in this case, then always pressing on this button. So shift R again, now we have lower left, lower right, upper left, upper right. Okay, so we are finished here and we can go to the 3D viewport. So we've got the markers here in our corners and it looks great, but still we don't have a movie inside of this screen, of course. So first we have to insert a plane and this plane can then get a texture with a movie on it. So with a 3D cursor here at the origin, we shift A, add mesh plane and now we have to try to snap the corners of this plane to these empties. And that's very easy. We just select these empties and then hit shift S, then 3, cursor to selected. Now we have the cursor here and we can select the corner in edit mode of this plane, select this corner, then hit shift S2, snap selected to cursor and now it sits there and we can move on. So tap to go out of edit mode, then select this empty, shift S3, select the plane, edit mode, shift S2 and so on. So now we have the corners here in the corners of our laptop, which is fine, but we have to attach that. And in this case, we don't do that with a constraint, now we have to really somehow deform our plane by using these empties and to do that we will use something called hooks. And that works like this. First you select the hook or the object that will be the hook for the vertex, in that case it is the upper left corner. So you select that, then shift right click, select the plane, tap to go into edit mode, then select this corner and then hit control H and then choose hook to selected object. And that will attach this vertex to this empty, to this object. And if you hit ALT A now, you would see that this corner is now sticking to that empty. That is exactly what we need. So we just repeat that with these other three corners. So select this one, then shift right click, select the plane, edit mode, then control H, hook to selected object and now repeat that. Now it just sticks to the monitor. Alright, so now that we've got that, we could start compositing.