[Automatic captions by Autotekst using OpenAI Whisper V3. May contain recognition errors.]
Yes, GPT-UO is now about a year and a half old.
So we will look a little back in time, but mostly in the future.
As far as we are aware, we are the first university in Europe that offers a person-friendly and GDBARE compliant GPT service to all students and employees.
So in our Nordic neighbouring countries, they are still a bit confused and wondering how the mountain monkeys got this.
The timing of the launch was reasonable, so a little story from the backstage.
On September 7, 2023, Jonas Gahr Støre and Sandra Bork were on campus to announce that
The government is investing 1 billion kroner in Norwegian KF research.
Our rector saw his average to launch our beta version.
Note that this was three weeks before we were actually launching.
So he could report that VU had its own ongoing GPT solution.
He had access, but he was a bit confused about the beta stamp.
But yes, the world.
This worked fine.
We have a solution.
I may not have hit the dialect, but anyway, we had a solution.
We kept our heads up until the launch.
It went very well.
It was well received.
At UiO, everyone got access to a user-friendly generative K-A with universal education, which was around the first of October.
And there are probably no web applications at the University of Oslo that have been as fast growing as this one.
It was from 0 to 10,000 users in a few weeks.
So now we are up to about 26,000 users total, including external users.
The number of tokens that have flown through the service since the launch was, at the beginning of May, 994.5 billion.
This means that the number has now exceeded 995 billion.
So it is not insignificant.
Millions?
Wow!
Almost billions.
And students are the largest user.
It is also a good chunk of employees.
So what we have done with GPT-VO is a good start, but now we think the time is right to look forward.
The commercial solutions from, among other things, OpenAI, they are starting to become alarmingly good.
So this is a challenge.
We must remain relevant.
And one way to do that is to focus on functionality that protects higher education and research.
The roadmap for further development of GBT-UO.
There are also some big, hairy goals here.
But it gives a reasonably good indication of the most essential functions that we plan to work further with.
One thing is the possibility of simply being able to share instructions, i.e.
custom instructions, for example with a group of students at a seminar.
File loading for summary and analyses, i.e.
to be able to choose alternative models that run on local hardware with up to red data.
Support for vision or machine vision.
It can for example be used to interpret and analyze data that is presented in graphs, diagrams or other types of visualizations.
Vision will also be able to read and interpret handwritten text.
Another thing is the ability to use GPT-4 Omni, which is the latest model from OpenAI.
This is a multi-modal model, if you can call it that, which means that it can generate content across different forms of input.
The ability to resonate with a combination of sound, machine sense and text.
The Agile product team that develops GPT-VO takes the lead in some user stories.
This is a form of work hypothesis that mostly has an anchor in reality.
It is up to the product team how to solve these.
The first has its origin in an informal collaboration with a professor called Thomas Espesset.
He is a professor in cognitive and neuropsychology.
The idea is to provide one-to-one guidance to students on a subject in the first round of psychology.
It is desirable to implement best practice learning techniques that are empirically validated and well-tested.
That is, to offer a kind of personalized and interactive learning experience that reflects the advantages of one-to-one learning.
So by using large language models and a set of specific knowledge and so-called strategic algorithmic prompting, the AI leader should be able to adapt to individual learning needs for students.
And some of this can be done today with newly developed system prompts.
We will look at an example.
This is an example of a system prompt for a KI guide.
Note that this can only be done with the appropriate instructions.
In the future, you can think that the subject matter manager can load up knowledge in the form of literature and pensum to a kind of guide bot.
That is, fine-tuning for a specific subject.
So this is on 1768 signs and gives the GPT a clearly defined goal.
It should be positive, encouraging, and help students understand concepts and ask questions again.
So an effective and good system framework for GPT-4 or any language model must be reasonably specific and include a certain context.
So the model must be controlled in the right direction.
Give it enough background information and clear instructions for the tasks it will perform.
It also requires an iterative approach.
You have to refine the prompts based on output and fine-tune them.
Let's move on to examples.
This is always exciting.
Then we will try this guide bot.
I choose an instruction that I have already put in.
It's called PI Tutorial.
And then we can start asking it.
Can you help me?
It can definitely help me with what I need.
That's fine.
What do you want to learn about today?
You start asking questions.
What is the subject?
What do you want to learn about development psychology?
Interesting.
Very positive.
Then you want to ask about my level.
Try to adapt to my level of knowledge.
No, I want to say lower grade, actually.
Lower grade.
Basic, right?
I'm a lower grade student.
So now we start at a basic level.
You can tell me what you already know about subjects, right?
Now we have to reflect a little.
I can tell you about stages.
Of course, he also begins to deliver relevant literature and thoughts about cognitive development psychology, and so on.
So we have them going.
And what would have been really cool was if she had already read the paper on my subject.
Then I could start asking, okay, but can you give me an summary of the most relevant theories and some tips to practice on the exam, for example.
So I think we have a future for this.
Let's see, then we'll have another user story.
Next user story.
These are advanced things.
I know that developers get a little nervous when I show things like this.
But we take it as a challenge.
To undertake an exploratory data analysis with GPT.
This is super relevant for those who work with data treatment analysis.
I have an example dataset today.
Some glucose measurements.
from a population that also includes a number of lifestyle variables.
Let's look at an example in the Opportunity Room.
I will now use OpenAI's Advanced Data Analytics.
We know that many of our users want this.
It's a powerful tool to gain insight into data.
You can load up different file types.
In this example, it's a CSV table.
So it may seem to you, but not to GPT-1, that there are already some patterns in the dataset.
There are some correlations and correlations.
Let's see if GPT-1 finds them.
Let's see.
Not exactly.
Let's get started.
I've done some research, so I've written a prompt at the beginning.
I don't need to spend much time on that.
It's always important to give context.
I'm a researcher and data manager.
I want to do an explorative data analysis, preferably with described statistics.
I also need a box plot, and I'd like to verify this in the SPS I set up.
Answer in Norwegian.
Thank you.
Think a little bit.
Yes, we like data.
Fair enough.
And what is also very nice is that it actually shows what it does
Python.
So it's transparent.
You can try this out directly, right?
Okay, we have to give it something to eat on.
So then I load a CSV file with a dataset.
Here is the data.
Then you have to see the data matrix, see which variables there are.
Then it starts to do something with them.
Load in, very nice, it chooses panda, pandas.
Starts to explore what this is, okay, it finds the variables, great.
Write statistics.
Visualizations.
It also gives me the SPSS syntax, and it also looks like a concept.
It also makes a boxplot.
What is very impressive is that it finds
that there is no correct format for the variables.
So it does some data processing, and converts some string variables to numeric, in order to be able to make a book plot.
And those who have worked with this know that this particular part of data analysis, i.e.
preparing the dataset for analysis and data management, takes time.
This takes about half a minute.
We also get a box plate.
It looks very nice.
It has glucose levels.
BMI and weight.
Waste.
Do you have more questions?
Do you need more analysis?
Then it's just in the message.
And I would love to.
Because now I wonder if there are any patterns in the dataset.
And then it's magic.
Is there anything interesting in the data?
This is highly relevant.
It starts to look at the correlation between variables.
It puts up a correlation analysis.
A scatterplot matrix.
And boxplot for categorical variables.
Let's see what it comes up with.
It's incredibly cool that it always comments what it does, so you can understand what's going on.
Oh, oh, oh, look at this.
A nice lib, okay.
BMI and weight have a strong positive correlation.
It's as expected, isn't it?
It's nice to see moderate positive correlation with BMI.
It's also nice.
Let's sum this up.
Smoking.
Strong correlation between BMI, weight and mid-term goals.
People with diabetes have higher glucose levels than those without diabetes, which is also very relevant.
Here we should have been able to show the actual results from the analyses that have been done.
If you run these several times, you sometimes get different conclusions, but they give very good indications as to what is actually in this dataset.
So it's very exciting to be able to do this type of thing.
Yes, yes.
You can do it.
Yes, it shows in the Python code.
We know that it is the relevant code that is actually running.
That is probably a assumption.
I don't really know that.
But we assume that.
That it shows what it thinks.
Yes, it does.
Is it actually the Python code that runs?
It should be.
But this is also for us a bit of an unpublished market, something we want to look closer at in further development.
I just thought I'd say kudos to the development team and all the good helpers who work with GPT-VO.
We look forward to more sprints with development and new functions that will increase the value for our users.
Then we can take some questions.
Yes, there are several things we have to dig a little deeper into.
I saw that you didn't use GPT-UIO in your last demo.
I did, that's true.
Why can't we do this in GPT-UIO?
Every day you don't have the option to load files and run code in the service.
We know that many want that, so that's something we want to look at in future development.
That's absolutely something we think will come.
Whether it happens through GPT-IO's existing models, or that you get to drive something locally from other models.
I have a question for this.
We know that it lives willingly away.
That's a fact.
That's something to refer to the journalist who asked about Harald VI.
There is a lot of royal information about him.
But if we are going to be serious about using the tools we should, then we should verify that the Python code actually gives the right answer.
And that you get what it proposes, and verify that it actually works, and use this to
Learn Python, run Python, and take this seriously, and verify what you get.
Because it is possible that what comes out is not a big deal.
It's a statement from me, but we know that the chat GPT life will go away, so we must always verify what comes out.
Also the Python code, run it yourself.
Absolutely.
And you're right about that.
copy the codes over and replicate the answers.
I wondered about GPT-UIO.
Is it like this that when you log in, you get a unique identifier that is sent over to Microsoft when you use the API for OpenAI there?
Yes, the developers can maybe help me a little with that.
I think that when Dagfinn demonstrated this after the launch, it was like, oh, my token has expired, so I have to log in again.
So the API didn't work right then and there.
And what I'm wondering about specifically is whether the information that is sent over to connect to the API is personal information or not.
For example, the user name, the user name, it becomes valuable.
Talking about what is sent over, the token that is sent to the API, is that personal information or not?
As we have interpreted it, it is not a person identifier.
Yes, now I am a developer and not a lawyer, but we have had more lawyers on this.
You just hit the definition of what a person ID is.
Can I refer to that right now?
No, but I think it can be defined as a person ID, and that's why we have an agreement with Microsoft, and that's why we can only use that GPT solution.
Because we have a lot of databases.
We send personal information to Microsoft, and they have agreements.
And we only send it to Microsoft's data centers in Europe.
An important detail on that.
By hiding the name of the user, for the model we interact with,
The reduced risk, we think, that personal information will come along.
I'll just give a comment to what was asked of me about the running of the code.
As far as I know, the new GPT models will show it, since the code is first generated and then run in a real Python interpreter.
Because it has the ability to do that, and it will indicate, here I have guessed the code and the result, or here I have made the code, run it, and this is the result.
You would be able to see the difference between the two.
That's what Dagfin pointed out as a data machine emoji in the starter.
This little blue terminal icon-like thing.
First of all, thank you.
I'm very excited to hear from you.
You are doing a great job in putting this into the hearing education.
And then there are always men.
The CPT, whether it has a lot or little literature on developmental psychology, it actually answers in fragments, and partly wrong, and is not really interested in research on developmental psychology.
And that is actually a problem you should correct for students, because then they sometimes get the wrong answer,
In fact, the literature in the 1500, 2000 and 4000 pages they have about development is actually correct.
The fact that they represent in the way they do in these windows is a significant learning problem.
You have to work together to see how you can minimize this problem, and possibly also check this out with other authoritative resources on updated development psychology, so that you illustrate both how well it can be used, and the elementary problems that actually remain when you do it domain-specific.
But a lot of good work.
Thank you.
Thank you for that.
We agree that this can also be a problem for the students, if used in the wrong way.
I wonder how a model like this, a tuned model, will weigh the additional knowledge, versus what is in the foundation model.
And that's one of the things we're going to look at in the future, how we can fine-tune up to Pensum, where we have more concrete projects, where we're going to weigh concrete articles and the like.
So we're very excited about the outcome of that.
Yes, and we also see that when we use custom instructions on GPT-4 Turbo, it responds much, much better.
What I noticed there was 3.5 Turbo.
It is not as political in terms of its knowledge base as Model 4.
Do we have any plans to set up local language models, GPT models in EduCloud?
Local language models?
That is something we think can be useful and relevant.
To be able to choose different models from GPT.
We will probably test out some of them in the summer.
We already have some who use GPT models in EduCloud, but it's not a general service.
But it's possible to spin up language models in EduCloud.
We haven't done it in TSD in the past, but we shouldn't forget that we can get that as well, just by upgrading Colossus.
We will see about that.
Next data at UiOS.
So I think that will be a big thing that will happen next year.
That we will have single models spun up, and that we can have split language models, both in TSD and EduCloud, which are the two places we want to have these models running.
And since the ones with open source code are getting better, it might be interesting to offer some of the best.
For example, some who are good at good things and niche use as well.
That was the stupid thing you did now, that you used the bad stuff in developmental psychology.
Yes, yes.
But it was fast, at least.
You just didn't know that there was a professor in psychology or pedagogy who was ready to reveal all the mistakes.
Yes.
Thank you very much.