Automatisk tekstet med autotekst.uio.no
My name is Dagfinn Bergsager and I am responsible for the GPT-UIO service that was launched a few weeks ago.
Usually, I wish to be at the university to be very open and talk about things,
to have meetings with everyone who wants to know more about services we make and the experience.
Now there were a lot of people who wanted to have that type of meeting.
We have over 200 people in this meeting, and many are external.
But we keep the openness that we want to have.
The university also wants to be open about what they do and share experiences.
That's why we have launched this meeting.
We have set up Zoom so that not everyone can take the floor,
but it is possible to send things in chat afterwards.
There are also some questions.
But it's no joke to ask questions yet,
because the answers will come in the course of the presentation.
We are taking up the meeting and hope that it comes out in such a way that we can publish it on the net afterwards.
But we have to see how it goes.
But it is possible.
I will say that I have made a few suggestions.
I would like to welcome this sharing of experience of the service GPT-UIO.
I would like to welcome everyone who has come here on the way from the university to Oslo,
to go through the parts of GPT-UIO that we have gained experience.
I am glad that this is a technical, security and legal presentation of the service.
It is not a pedagogical presentation.
The parts are outside of what we as a department of IT at the university.
We can answer the IT technical and legal questions.
We have Isak here with us, who will tell us about the legal parts, which are essential in what we have done.
But before we start, we have to ...
There was a bit of a shock for us.
Maybe not that we got the crown, but there are many in this room who are a bit crazy about the picture.
The only thing we see when we look at this picture is that everyone just looks at themselves and thinks that they look a bit strange.
But now it is the whole picture of the team that has developed, because we were going to have a thing out there.
Among other things, it was the crown that made contact.
This was picked up by NTB and was in most national media as a mini-report in a short time.
But I have been in contact with and talked to the University Press, Kode24.
I was talking about the technical and postal database and such things.
I got a lot of students who saw it there in Kode24.
We got a big article in the paper magazine to the University Press, who thought it was fun that we had the opportunity to give all students the same access as employees.
We also got a letter from the University Press, who wrote an article this weekend about the GPT-championship.
I think I'll say a little intro, and then we want to jump straight to the legal aspects and the assessments we have made.
I think you can start, Isak.
Yes, Isak Falkhalsos is an IT legal advisor at the IT department at the University of Oslo.
I work a lot with personal protection, and personal protection is something we have developed here at the University.
In spring, we saw a great interest at the University, because people wanted to use OpenAI's chat GPT in university settings, whether it was teaching or research, and so on.
The problem was that the University could not stand for the treatment of personal information in OpenAI's general chat GPT service, because we did not have any control of the data flow in the service.
This would be a violation of the principle of responsibility for the GDPR.
Therefore, we had a period in spring where we tried to figure out how we could use this technology in a legal way, where we take into account the necessary personal protection.
We started the process by developing our own GPT-UIO service.
Because using OpenAI's chat GPT was illegal?
Yes, we thought we could not take into account the personal protection that is necessary for the UIO to operate as a responsible enterprise.
You're talking about the data flow in the service?
Yes, the data flow in the service.
In the normal chat GPT for OpenAI, the producer has the right to use input and output in the service to train the model further.
This can in the worst case lead to exposure of information that is sensitive, and therefore protected by GDPR.
Has this happened?
Yes, Samsung experienced that they were exposed sensitive code for the company through chat GPT, because the model was trained on input and output from the employee who had put in the confidential code in the service.
We do not want to get information about our students, employees, research participants or other types of information on the service.
We could not require students to register in OpenAI's service, because we had no agreement with them.
But that was another story in addition to the data flow.
In addition to that, there was also a problem with the transfer to FedJLM, the so-called Schrems 2 problem.
Since OpenAI's service is based in the USA on American servers, since we started working with developing GPT-UIO, it has become much easier to use American service providers.
But if you had told all students that they were not allowed to write any personal information, would that not have been different?
We would still have been responsible if we had asked students to use the service in a teaching context, for example.
And then we are responsible for the students putting their user information into the service.
And then we would not believe that you can use these services without getting personal information.
That is our assessment. We believe that it is possible to give our students access to such a service and say, do not write personal information, because it will be very fast.
You do not have to write a birth number for it to become personal information. It can very little to make it personal information.
The definition of personal information is so wide. It includes all information that can be used directly or indirectly to identify a person.
This is a very wide definition. When you have personal information, you have to do with GDPR.
And then there are a number of requirements that you have to meet in order to be able to carry out these treatments legally.
But we stood there and saw a huge need among our employees and our students to use technology as a GDPR at the university.
And then we thought, how can we do this legally? And explored various possibilities.
We had to put up our own solutions here at the IT department that could provide access to this technology for our student employees.
We considered various solutions back and forth.
We looked at the possibility of developing a service based on OpenAI in its APIs.
But we ended up doing it via Microsoft Azure.
Since we already had an established contract with Microsoft.
And they had already bought OpenAI.
We saw that we had greater opportunities to make the solution even more personal-friendly by using Microsoft Azure.
I was just thinking about you guys who tested OpenAI's API and put it up first.
Were there any big differences in the API access and how you used OpenAI versus what we have in Azure and ended up with?
I just remember we started looking at the API.
I don't remember seeing a lot of difference.
I think it was a bit different format on Azure and on the address.
But it was a lot more physical.
We put our credit card in very quickly and started testing.
And then we applied to Microsoft.
How long did it take to get that access?
For the first time, it didn't take that long.
You have to fill out a form and then you have to declare what strategy you want to use for the GPT.
Or for GPT, Azure Cognitive Services.
So there are several cases.
We took a look at what was available for us.
Sent the application.
And then applied for GPT-3 and GPT-5 Turbo.
It came pretty quickly.
But then I was also ready to apply for GPT-4.
But now they have removed the requirement.
If you search for Norway Soviet, you will automatically get some data centers for GPT-4.
When you get access to GPT.
There have been some changes with Microsoft.
So when you get access to GPT, you get both GPT-3 and GPT-5.
We chose to go for this and made some settings.
What settings have we made for Azure?
First, Microsoft will not treat our inquiries, input and output, to train the model further.
Is that something we can get rid of?
No, it is default.
It is the starting point when you use Microsoft Azure OpenAI Cognitive Services.
So it is default that ...
Input and output are not used to train the model.
Oh, so it was just to check that it was OK.
Yes.
In the starting point, Microsoft will make sure to store data for 30 days.
In OpenAI?
Yes, at least 30 days.
If I'm not completely wrong.
Not max, but at least.
I think it was something like that.
Maybe I'm wrong.
But yes, Microsoft will make sure to store data for 30 days.
To be able to do abuse monitoring.
To make sure we don't abuse the model.
But it is possible to search for Microsoft to avoid our use for this type of abuse monitoring.
This means that Microsoft never stores any information that is sent from the IO.
They only handle our questions and answers.
Prompts, input, output.
Immediately after the question is asked.
Yes, and send it back.
And send it back, and then they have forgotten it on the side.
Is that something we can ...
How do you search for it?
Maybe you know.
To search for this with abuse.
So they are not allowed to make anything.
Was it a separate scheme or something?
No, for filtering we have searched.
How can we adjust the different filters.
I don't remember if it was a filter that was very strict.
Then you can search to adjust the filters.
You can search to adjust the filters in other ways.
But we have to find the coordinates.
How strict it is.
You have to put some arguments in the coordinates.
But in relation to that ...
It was a search in the order scheme.
It was also a separate order scheme.
So one is personal.
And the other is that if you write a few words today, you will be banned for a minute.
Isn't it?
If you write a few words today, you will be banned for a minute.
But if you continue, you will be banned for a minute.
The user who does it, not all of us, but the user who does it, will be banned for a minute.
Not allowed to ask.
Because it is a token that is then.
We put an anonymous ID there.
So we use it to say that this person who sends these things should not be allowed to see anything more.
For a minute.
And have you been banned for more than a minute?
Not as I have seen.
I have experienced it many times.
If I write the word wild boar, for example, then I am banned.
It seems as if you are banned faster in Norwegian than in English.
I think it's because he doesn't understand so well.
I'm a little scared.
We have searched to get from level medium to ...
I don't remember the name.
We have been asked to have a less strict filter.
Because the filter is quite unlawful, sometimes, how strict it is.
So we have sent in a request, and we have argued that we are an educational institution.
And that it is unreasonable to have such a strict filter.
That it censors questions about actual historical events, for example.
We have asked them to allow us to change the filter.
So we can adjust it.
Yes, because then we can adjust it ourselves.
So you have ended up with getting a set in Azure, which has the same as with ...
The problem is that we were not quite finished.
Because we wanted our users to be able to store their conversations themselves.
And we didn't want to do that for our suppliers.
So we had to set up storage services for us on our servers at the university.
What also happens when using GPT-U is that the user gets automatically collected personal information.
And that information is not sent on to Microsoft Azure.
This is filtered out so that Microsoft Azure only sees that UU does a query.
Yes, as Lars just said, that it is an ID that only we have control over.
Correct.
In addition, we have also created user conditions and guidelines for our use.
Where we can only allow up to and with yellow data.
So that we can ensure that no sensitive information gets in the way.
In case something goes wrong.
As we consider it to be very unlikely to happen.
Yes, because there were some questions about it in the past.
We have control over all the personal data in our database.
We send a secret ID that they do not have the opportunity to connect to the person.
At us.
But still, we do not want to have health data in the solution.
There is a greater risk involved in connecting health information, for example.
Or other types, especially categories of personal information.
As it is called in the GDPR article.
There are even stricter requirements for what you can do with this type of information.
Where there is a naturally higher risk due to the information in nature.
Therefore, we have not allowed the treatment of health information in the service.
There are some who have asked about DPI.
Why have we not carried out a general overview of DPI for this solution?
We could not identify any particularly high risks with systems like that.
After we had implemented all the measures we have taken in the development process.
The risk that stands again.
DPI, i.e. the assessment of the person's health consequences, is a requirement for GDPR.
For the treatment of personal information that will involve a particularly high risk.
Then you should make a thorough assessment of which risks exist.
And implement the necessary measures to reduce these risks.
We look at the system.
The service itself does not involve such a risk.
But if you want to use the service in a research project.
Then the current treatment, i.e. what you want to do with the information in the research project.
Potentially meets a requirement for DPI.
That treatment poses a high risk.
But we saw it as very unnatural to do a DPI before the treatment.
We do not know what can be.
This applies to many things we work with research.
That the DPI must be done per project.
Often people have to do a ROS analysis per project.
We have done an overriding ROS analysis for a technical and personal health.
ROS analysis for the service.
Which you can take a quick review of a little later.
But it is a university assessment.
We have not done the DPI.
Because it must be done where it is necessary for the simple treatment.
And for ordinary students it should not be necessary.
As we have put up guidelines.
It would be if it deviates from the category of data we think the service is in need of.
If you want to use the knowledge for a treatment that can cause a high risk.
Then it is the treatment that you should do a DPI on.
And not use a system like that.
Unfortunately I just made a little skit.
We have a web application.
When you log in for the first time, you create a table in a database.
Which is in the source here on the screen.
Which is all user data about that particular user.
When you write in a prompt, you store it in that database.
Then we replace it with another ID.
And then we have a new ID.
Then we replace the ID with another ID.
Which we then send to Azure.
Together with the prompt.
Then we get the prompt back with the ID.
Write it in the database and show it again to the user.
You see an example below.
That there is an opportunity to have this more similar client.
Then I thought we could have a little demo of the service.
We have with us Cathrine, who is responsible for UX at GPT.
Yes, Cathrine Nordred Køpet.
I work as an interaction designer and have been on the team here.
I thought I could just go to the landing page.
Now I have logged in.
But this is what people come to when they log in to gpt.uo.no.
And then we log in with Fade.
And relatively it is open to all UO users and all BI users.
Since it is already ANO.
The first time you log in, you will be met with the user preferences for the service.
I just thought I could show you quickly.
As you accept.
After you have done that, you come into the conversation window.
You may see that it looks like what we know from JetGPT and other services.
It's kind of crazy to find a new service when we make this.
Above us we have the two models we have available now.
GPT 3.5 Turbo and GPT 4.
You can choose between the two.
When you start a new conversation, you automatically want to come to GPT 3.5.
Because?
Yes, because GPT 4 is much more expensive than GPT 3.5.
So we prefer that people use GPT 3.5 as soon as they need it.
There is also a limit to how much people can use GPT 4.0 per hour.
Which can be set up if you want or do not want it.
Is it because of money or capacity?
The limit is mostly because of capacity.
Because the data centers have a default limit for GPT 3.5.
GPT 3.5 is 240,000 tokens per minute.
But with GPT 4.0, the limit is 40,000.
And that is the data center's top limit.
So you hit it pretty fast.
And there is a small price difference.
Yes, I think it's 20 times cheaper for GPT 3.5 than for GPT 4.0.
So it's a big difference.
We have also created a quota system for all users.
It is also not possible to log in with two browser windows.
That trick is already stopped.
People have to sit and pick up other codes or run them in parallel.
Or borrow from your account or something.
Yes, when you click to start a new conversation, you will be met with some suggestions for use.
We have just put it in there to get people going.
We can click on GPT 4.0 and choose one of these suggestions.
And hopefully it will write something.
Yes, it is a bit slow.
Yes, it is also something that GPT 4.0 will do.
It will make it a bit easier to lower the limits.
Because it is slower on Microsoft's side.
It takes a lot longer to get a response.
One of the things you will see afterwards is that it will change the name of the conversation based on the first message you have sent.
It does not do it yet, but maybe if you choose a new conversation when it is finished, you will see that it is updated.
But you see it in the list of conversations.
It's a kind of summary of what exactly that conversation was about.
Is it a GPT model that finds out what the conversation is about and creates emojis?
Yes, we give a question on GPT 3.5 and we take the first message you have sent in.
And then we say give us a suitable name and a suitable emoji.
That was all clear. What is the emoji? We can talk about that.
Yes, we can also start a conversation where we get it to write some code.
Just to ... At the same time, it may want to change.
So we write Python code for Vivo Nachi.
We get that out.
One thing we have is the ability to copy, but also the ability to copy code.
And then we have put a tooltip for the text.
One of the things we are very proud of with GPT is that we have a focus on good design.
That everyone should be able to use it.
It should be possible to go through and get an explanation of what the different things are.
It is something that ChatGPT, among other things, does not have as good a hold on.
Now it's a robot.
Yes, we can see that the previous conversation has changed the name to the principles of machine learning.
We can also take and favorite maker it. Then we have it over.
We can do it with another conversation too.
We can change the name. That's fine.
Have we thought about it? Is it possible to write in a group assignment?
That you keep the conversation?
Think about it.
It's just so that people can give it a name they think is useful.
We have just suggested a name.
But it's not certain that people ...
Maybe they should have a longer chat, where the first message is not as long.
We also have the possibility to download it.
It did not get up there.
But it opens a window where I can choose where to save it.
But now I have chosen to share the browser window.
So you do not see it.
But it works.
We can also delete it.
I'm not going to do that now.
If you have changed the prompts, saved and sent again.
Then it will be possible to click through the different messages you have sent.
Or if you have asked to generate a new answer.
This is the answer in the chat.
But it is important that we also provide that functionality.
Is the emoji in the chat?
We can see it in the chat.
No, we think it's a bit too late.
We think so too.
Yes, it was a bit too late.
No, no.
I was thrown out a little too much.
Yes, it may be a wish to come with a headset.
Yes.
Downloading is something we have received as a wish from teachers.
Who want to use it in their mandatory tasks.
And share answers and deliver hints and get a nice format.
Because they have not been so easy in other services.
Google's service, you can download.
But you can share it into Google's sphere.
That's what they have put up there.
You can share it in Google Docs.
But you can not download it.
Someone who wants to be able to store what they want.
I can show you the keyboard navigation.
How we have done it.
Then you have the opportunity to jump to the conversation you are in.
So you are not locked into the long list of conversations.
But you can also jump to the mailbox.
And go down to the different conversations we have.
How does it work in the open AIS solution?
We have seen that they do not have good functionality.
So that's one of the advantages we have.
We believe that the open AIS solution is not universally developed.
And already there we break the rules of our users.
But we have taken some steps and we see that it fails quite quickly.
And fortunately we have not said that someone should fix it.
We do not mean to prevent that.
We are very concerned about universal development.
And that it should be available to all users and students.
We can assume that this is top notch.
As we usually say here at the IT department.
And then we have the mobile display.
I can show you quickly.
It was probably very exaggerated for you.
I have done something strange here.
You can just go back to the browser.
But here, look there.
Now we are in the conversation list.
So if we are on this one.
Then we can scroll back.
And Beran can make a new table.
Add a rector to that list.
That was not possible.
Now you are banned.
Because this design on the table is something we have chosen.
Yes, we got it right.
So we have a pretty nice table that is generated.
So if you copy it, what do you get?
You get a clean extension.
A clean L-mark.
But I think if you mark and copy, you will get a stylized one.
I did that because I could glue it in the table.
So there you can choose both.
I do not think there is anything more to show on the mobile display.
We can click on new conversation.
And see that we also have the suggestions here.
I do not know if you wanted to say anything else.
Yes, but we are limited here.
So we can try to talk a little about scaling.
And the experience we have had.
We have worked a lot to get this to scale.
And work for many high users.
So we will take a little as we get done.
What do you think about the future, Cathrine?
One of the things we want to improve is the suggestions for use.
Now it's just general guide texts that can get you started.
To understand how this works.
But we see that you can write that you are a student.
Who wants help with Python code.
And help with prompts.
To improve how they use GPT.
That's something we've been talking about since the start.
That we want to help users do tricky things in GPT.
Can you tell us a little about who you are?
No, you do not have to prompt like that.
Maybe you should take this prompt course.
Give a little context.
We have seen that as quite important.
One thing is to make the technology available to our students and employees.
But we also have to help people understand how to use it in a reasonable way.
And a solution like that would be very smart.
We have also discussed as part of the onboarding.
That we have clear information about usage methods and restrictions.
Which goes a little beyond these terms and conditions.
But is more of a training module.
But what about the future?
We have not yet made any concrete plans.
But when people start using it.
Students and educators.
Then you see what they need.
One thing we have thought about is the favorite marketing.
Could it be instead of categorizing.
With regard to the subject.
If you are a student.
It depends on what feedback we get.
One thing we want to implement.
That we get a good amount of feedback on.
Is that people want Dark Mode.
Because we will sit quietly and use it.
Without anyone noticing.
It is special with this application.
That many have wanted to use Dark Mode.
So the question is not whether it comes.
It comes pretty quickly.
Maybe it's the next sprint we make with Dark Mode.
It's nice that it doesn't cost the other students.
On the exam.
In addition, we have added several models.
In design too.
We can use several models.
It can be a little more about our thoughts.
Because now I think everyone is wondering.
How have we got this to scale?
With GPT?
We have a question.
ChatGPT has been launched.
You can talk to it.
Download the image editor.
You can also build a context to a conversation.
For example in this conversation.
I am a university lecturer.
Can we say something about this?
Is it possible to develop further?
It is technically possible.
The first one requires some wild analysis.
But the last one is when you have access to API.
Then you have the opportunity to send in.
So-called system prompt.
You can add all the questions you do.
Where you say.
In this conversation you will be a professor.
Or here it will be like this.
Then it will answer.
As a little example.
I say that if possible.
Suggest one of these products.
Then I write Coca Cola.
Then I ask.
Do you have any good fishing recipes?
Yes.
When you first cook.
You can also add things.
Or you can take a realistic course.
Yes.
It's scary.
You can get market control.
I have also seen that you use a free FPT version.
Which is cheap.
Is that what you use?
That's what has given me.
You could use it for more business.
For example.
That's the big difference.
Like the A in Randaberg school.
Some have wondered what the difference is.
It is more based on.
You talk to a pre-prompted chat.
Which already can answer in this and that way.
We have not put up much.
We have to build it up.
Every time.
It's a completely different type of thing.
But as it is to be able to talk.
I like that.
As it is now.
What do you think?
Talk, talk, talk, talk.
It's going well.
I have played a little with it before.
You can add.
Voice to text.
I talk to you a little.
I say send.
It is a possible technical thing.
Then you can get it read.
Now there are two doors to choose between.
Which should I choose?
What did you have on the border?
I think we have shown what we want to show.
We can look at the challenges we had.
Scaling.
This is a web application.
Built on what we define as standard web applications.
Standard database.
Standard log-in.
Standard container platform.
We have a plan to make scalable.
What we do not have a plan for.
Is to use another container.
As we do now.
With using Azure's container for processing.
It's not part of our standard solution.
We usually have everything in-house.
We have built a camera standard technology.
We connected to Azure.
To see what this endures.
I put up a physical system test.
At a meeting.
With people in IT-organizations.
To see how much service it took.
It lasted about 32 seconds.
It took nothing.
It was interesting enough.
But it went very badly.
Lars, go through and do it better.
What did we do then?
We found that there were very strict limits.
At Azure.
They say you can send.
For example, 1440 queries per minute.
But they measure in one-second intervals.
We had at 700.
Then you could send 15 queries per second.
When everyone did it at once.
It crashed immediately.
When it continues to block.
We had to spread out.
If it comes in 15 per second.
We can not do all at once.
There must be a minimum waiting time.
How long were the blocks?
It depends on how big the response is.
The first time it's 5 seconds.
If you continue to measure, it increases.
The worst I've had is that I blocked a minute.
Everything that goes there, they block.
But that happened then.
We got it to happen pretty fast.
We did a lot of psychology around it.
To spread all the queries out.
It takes up to 15 seconds before the text comes back.
Just because we have to follow the rules.
To Microsoft.
We have had to try to celebrate.
We are a little vague.
They say you should balance.
They say you should ask for an expected response size.
But they do not know what to ask for.
So we do not know if the user expects to get a word.
Or a thousand.
If you write 3 words, it's like giving me a thousand words about fish.
Then you expect a pretty big response.
But if you write a lot of text, and then you say yes or no.
Then you do not know if you want one or the other.
So we have had to balance.
How many questions do we allow to go through per second.
Per minute.
And how big the inputs are.
To balance it out.
Because we have limits on the number of questions we do per minute.
But also the number of words sent in and out per minute.
So it's a distraction.
To see where ...
So all this is controlled now?
We control it now.
We have several instances.
In Azure.
We have a GPD-3.5.
Where all the questions we get.
We take the first, second and third.
In addition to the queuing.
If we suddenly get 100 questions, we get 33 on each instance.
And they will be spread out on each instance.
Is it a condition on GPT-4?
Because it's just a copy of that again.
It is agnostic to which model it uses.
It's just a number of how many questions you allow.
And how many days you allow.
Yes.
And do we often go to the roof on GPT-4?
No, I would not say we go often.
It happens, but we do not go often in the day.
We just have to get some statistics.
We have about 500 new users of the service every day.
This week and the whole week.
And we have about 5-6,000 active users now.
So we have a lot of big users.
But people do not sit at the same time.
That's what it's about.
We can say that when we log in to Azure.
And check estimated use.
For us, the university in October.
Then it says now.
Yesterday it was 9,500.
Yes, it was estimated.
When we use more, it is estimated at about 10,000.
For us, it is not very expensive.
And we see that one user can not start to lose money.
Because you have so good control of the plant.
I would say a little.
By the way, I'm sorry.
Is it the next time?
I do not know.
We have made different subscriptions.
I drive all the mercantile parts.
It's a bubble where the resources are.
And what is used as invoicing.
Like we have three pieces on GPT-3?
No, we have one on GPT-O.
But we have not yet made a subscription.
We have to wait a little longer.
We have a subscription on GPT-O.
But when researchers ask for dedicated resources,
we do not use GPT-O.
We have a limited number of resources.
We have chosen to share the subscription.
Or use GPT-O.
So we know exactly what we use on GPT-O.
It looks like you have a question.
I have a question.
It's a question about costs and how much is used.
We have used it.
We have taken the reason why we have taken it.
And we can see a little about the B in integration.
But there are a few questions.
Have you considered developing an adapted GPT-solution that can be used?
Yes, we have.
We have not yet made a subscription.
But we have a question about the cost of the subscription.
Have you considered developing an adapted GPT-solution that can be used?
Yes, we have.
We have definitely put a little up to date.
We have put up a service internally to try to see if it is worth it.
It is a little outside of this service.
We see the need for two things.
One is data security.
If we have data from other categories,
we need to be able to run the processing at our place.
And the other is data volumes.
We have not created a database.
If you try to make a novel based on X's collected works
you will not be able to do that.
You can be willing to take a large work
and have to plan against a large dataset
before you run on the expensive GPT-server.
But we have thought about it.
It will not be in the course of October.
But we have thought about it.
What is the risk for users and their projects?
Is there a user management that handles this?
We can go on a rose analysis.
I want to do something about that.
Even if we spend a lot of time.
10 minutes left.
We have on GPT-website
We have now put out our rose analysis.
If you order here
You can base your own rose on our rose.
There is an explanation, there is XLR
It's an excellent document
That summarizes the whole rose analysis
It is a regular experience
What is special about this
The thing is that we have gotten into production
A few things that
are connected to the wrong use of services
One is that people put in data that is in the wrong category
One says that we can make good
guidelines, but that is not what we are going to focus on
in the future. Katrine talked a little about it
We want to change our web limit
In the service, in relation to those points
To educate the users more about it
The same goes for the wrong, the chance that
wrong information is used
that has consequences for one person
is very likely. Everyone who has used this knows that
There are many errors, and it is easy to believe in it
We are very happy that we have our own
web application, so that we can
make the changes in the web limit
to improve those risk factors
I was just about to say that this is not something that is unique
to the old UIOS solution, but that is a thing
with all of these models
It can find things that are not true, because it sounds like something someone has said
Everyone has understood that they have used this
This is common knowledge
But I wish to offer this
The last bit, is that people use something else
than our solution, and that is a risk
That they do not use our solution, which has a lot of benefits
The worst thing about our solution
is that it is a bit fizzy
on these filters, so we hope that we will get
the search results back to the normals
So that we can open up, so that they do not get
stuck on some words that are a bit weird
The other thing is that we do not allow
large data amounts, or a lot of files
We will look at the possibility
We have made an API, where we have some limitations
and some possibilities for those who want
to process data that is on files
But we wish to
As Ernesto said, we do not want to let people
into our Azure instance, because it breaks so fast
We want to use the load balancing solution
that we have already made, so that we can share
the resources in an effective way
We are getting close to the end, so we just have to
see if we had more questions
I just have to make sure that we reached the end
of the plan
that we had here
We have put the facts that we wanted to say
Yes, integrations
with BI
Someone asked about prices, there are some prices out there
If you go to the website
there is a link to order access
We have set up databases and prices
We have defined it according to the number of Fade users
We have not implemented other user logins
compared to Fade
We have possibilities for it
We have done it on our Autotex Whisper service
which is possible, we will do it, but compared to that
We have defined it as a Fade service, and have set prices according to the number of users
There are some big institutions
so we have not set prices
We know that Oslo Skolen has 350 000 users
For example, who have been in the conversation
But, it is only BI
who has bought this service
and they have also wanted to use their own Azure
We have said that if you use our Azure
it will work and learn from us
apart from GPT-4
We have said that it is so little capacity and so high price
that we have kept it from being implemented
We have set up a solution
BI is on Dev
or is it in prod with their own Azure
They will do some internal declarations
They will come up this week with a developer server
and they will have a connection to their own Azure
with GPT-4
It was relatively good for us to do, and it looks like it is scaling well
This means that we have GPT-Ulio
80-90 000 users
which we technically have access to the user service
Fortunately, not everyone is in and prompted together
But we have made it clear that this should scale like this
Are there other questions you think
we should get in?
Yes, we as a data university
if we connect to your solution
do we increase the risk of sharing data with other institutions?
No
No, we can do this
and I am 100% sure of it
We have a lot of sensitive solutions around research and delivering this data
There is no risk associated with the lack of data
What types of useful use do you see within UiO?
We are looking forward to it
I think from the presentation
because this is not a big data set
and sensitive data
which is what we are most used to around health research
But as I said in the beginning
we think this is the tool we have come to be
and therefore I think it must be included in the education
and it must be included everywhere
but this is what our teachers are taking away from it
But we also see a lot of useful use within the administration
and within the IT department
where there is a lot of data
It is the imagination that sets the boundaries for what can be found in useful use areas
In addition, there are a few legal limitations
It is a bit stupid if you base a decision-making process
on decisions from GPTU
So this is a legal question
Someone had thought of setting up a discussion forum
where GPTU students could answer
and train and develop it further
based on the students' interaction
But is that not allowed?
Because you use the student data to train a model further
Now I don't understand the problem solving
I was thinking of implementing GPTU as a chatbot
where students can ask questions and the teacher can answer
They have thought of fine-tuning this model
so that when the students ask questions
based on the answers
and the answer is wrong
they can update it and make it better
Yes, you can close the forum and log in
but I don't know how much you do at that time
I think it's a bit like this
We don't have access to training
We can train a model
We can train our own model
But it has to go through us
We don't have support for that
You can't tune it if it's wrong
It's a bit like the previous question
I have to have 2 minutes
to go over what I've said
There are many questions
Many questions
Someone is wondering why they should buy our solution
and not just set up their own
Good luck!
You just need this group and time
and a good infrastructure
that guarantees security
It's a bit like this
We think our solution will do well
and we hope it will be a big national solution
if there is a desire for it
We have implemented that
we are ready to jump on our solution
There will be many others who will do other things
but it's not like no one else has ordered our solution
and we just have to handle the order
when they arrive
Any last remarks?
I saw a question on the screen
that many have asked
If you ask GPT-4 if it's GPT-4
they will say no, it's GPT-3
That's a thing in the model
The model is responsible for that
It's too late to ask GPT-4
They think it's a generation older
The philosophical twist in GPT
that hasn't acknowledged that it exists
That's funny
We hope to see more exciting solutions
in the future
We at State Oslo want to collaborate
with as many people as possible
but we can't meet up with many
and we hope this can be a bit informative
What do you think about this?
We will publish our legal assessments
around GPT-4
It's a bit shorter than we thought
but we will publish a more thorough review
Especially around the GPT-4 assessment
It will be linked to the RISK-analysis
which is published on our website
and we will publish it
For those who want to see our assessments
there is nothing to see
but to open it
We have done that and got some insights
and all our documentation and assessments
around GPT-4 are available from our websites
It's 2pm
and it's Friday
and we have a lot of chocolate to eat
On behalf of GPT-UiO
we thank you for coming
and have a nice Friday