Content-type: text/html ~ Stephen's Web ~ Universities at the Crossroads

Stephen Downes

Knowledge, Learning, Community
Universities at the Crossroads

Unedited automated transcript.

The talk I'm about to give is called University at the crossroads: Technologies values and the role of the institution. I have three clear objectives for this talk. First of all to offer as clear as possible, a description of three current technologies, the metaverse blockchain and artificial intelligence.

A lot of people talk as though these are future technologies. But these are here now I want to talk about how these three technologies impact on the traditional roles of the university. And I'll talk about what those rules are. And then third, I want to apply the conference themes of equity sustainability and ethics inclusion sustainability and ethics.

With respect to these roles.

The technologies as they say are the metaverse blockchain and crypto technology generally an artificial intelligence.

The roles of the institution and I won't talk in detail about this because I think you have a sense of what they are, but they are teaching and learning obviously the research and development function, and then the economic function or contribution to society, which I've classified under the heading of innovation and growth.

And then the values as I've mentioned, are inclusion, sustainability and ethics. So that's the a line that's the introduction.

When we talk about the values and I looked at the three values that were selected for the conference and I'm sure there was a great deal of discussion about them. If it's like any university that I know any organization that I know, I thought that Probably you would have included or at least thought about other themes for exam, call individual, autonomy, and agency, particularly on the part of the students, the values of integrity and honesty, which are at the core of any academic, endeavor, openness and transparency, and I thought about including accountability there as well.

Care and respect. Which are playing a new role in ethics today. And in terms of the role or the benefit to society, producing value and benefit, which of course, is to a large degree, the objective of the entire enterprise. Now. There's a range of ways that we apply values and practices.

Here's a tendency when we talk about ethics to talk about regulation things that we should not do, this should not be biased in artificial intelligence for example, And these two are large degree to my observation are based on fear. We're afraid of the consequences. We're afraid of the risk.

My approach to ethics is a little bit different. I try to find the joy in ethics. The possibility of doing good.

You know, we need a lot of water today. It's not good news for you. So, I look not just at laws and regulations. I look at actual practices, the things that we need to do on a day-to-day basis. In the university, I look at culture, I mean, even indeed, even down to personal decisions, and personal agency, to talk about ethics because When we're trying to promote, ethics in a society or at an institution, we're not just talking about creating roles and regulations.

We want to embed the ethics in our culture and in our personal decision, making Okay. That summarizes a very long presentation, Let's talk about the technology As I mentioned earlier. These technologies are here now, They are not future technologies anymore. I have another presentation where I talk about a range of different technologies which are coming but these are the decisions we have to face today.

Now, here's how the presentation will more or less run. I will talk about the concept and you'll see a slide or a box or some text mostly in green. I use some other colors for highlighting an emphasis. What I'm talking about the ethics or the values? Those will be in gray.

So the gray usually attaches to some aspect of the greens. Why did I choose those colors? Completely random. There is no reason for those colors. I like to be random sometimes. So let's talk about the metaphors. Oh well, let's talk about those ethics where a second. Are those values virtual reality augmented reality?

We have a sense of what they are, right? There's three dimensional representations immersive world. They've been around for a while decades now and we've studied them for a while. The sorts of things that we worry about when we're talking about virtual reality augmented reality. Are things like accessibility can they be accessed by everybody people with limited vision?

For example, have issues, I have issues because the heads are always too small for my glasses, the headsets are too small for my glasses. I hate that. There's the issue of environmental sustainability, of course, because they use a lot of processing, but also They prevent or alleviate the use of much more expensive systems.

For example if we're doing virtual reality of an aircraft it's much cheaper and much more environmentally sustainable to do VR than to use an actual aircraft. And again we look at the benefits in the outcomes. Do people actually learn using virtual reality or augmented reality? Yes, they do. What's the difference between these virtual reality is a fully immersive virtual world?

Augmented reality is when you see the real world which you see new information, layered on top of the world. So for example, if I was using a are augmented reality here, I might look at all of you in a little text of your name, my show. Mixed reality is when we use virtual reality or augmented reality, you actually make a change in the real world.

So I might use a virtual reality system to moderate the flow of liquid or a pipeline, that would be mixed reality. So that's pretty cool. Been around for a while though. Like I say, What's different? Is that? These three technologies together which we call extended reality. When we get to the metaverse, we add a layer to them which all call persistent objects.

So think of the metaverse as virtual reality etc, plus persistent objects. That's what it is. I know, you'll get all kinds of more complicated descriptions but it really is that simple.

What do I mean by that? Silly thing.

Sorry having screen issues. There we go. By persistent objects. What I mean, is that the objects that we perceive in virtual reality or mixed reality? Persist from instance, to instance, from time to time So, for example, consider a person, we might encounter a person in virtual reality. For that person to be persistent that same identity carries over from one case of virtual aisle, reality to another case to another case.

Similarly, with objects. And you've probably heard of some objects that may persist from one environment to another in military. Simulation, for example, the use virtual reality. But if I'm using a helmet, and somebody else is using a helmet and we look at an aircraft, it's the same aircraft that we're both seeing persistence, right?

Persistence of objects. Persistence of tokens as well. Those are digital artifacts that may be carried over from one environment to another environment to another environment like a certificate, say Okay, so let's the matter verse. The power of more complicated, maybe, but One of the sorts of issues that we look at in the metaphors.

Well, If we look at people as persistent objects, we want to use persistent people in our university applications. We have to think about things like consent does the person want to be a persistent object in the maniverse We need to look at things like universality. Can anyone be a person with an identity in the metaverse or do you have to pay?

Autonomy, do people are our people able to make their own decisions and their own choices. Can they choose the technology that they want? Can they speak the language that they want? Can they interact with those? They wish to interact with those. If you coughing I'm really sympathetic Ownership. Who owns an identity in a virtual world Fight.

Sign onto Twitter. Twitter owns. My identity. They can decide whether I get a blue check marker or not, it's not up to me. Twitter, decides, who owns an identity in a university owned virtual environment.

Another. Type of persistent object. As I mentioned is tokens, a token, might be a certificate, we'll come back and talk about that in a little bit, a little bit more. But there's the question of the validity of tokens and the integrity of tokens suppose, for example, a persistent object that we want to issue in the meta, verse is a grade We want that great to be valid and we want the source of that grade to be an educational institution or a professor, not your buddy, Jim.

Blockchain, you've probably heard that talked about with reference to Bitcoin and my theory, and digital currencies scams. Right? The collapse of FTC currency exchanges and all of that. Forget all of that. The thing that blockchain gives us is the answer to the question of. How do we create persistence in the maniverse?

How do we create persistent objects? There are three major technologies in blockchain that combined to make this possible. One is content addressing. The second are what we call mercle graphs doesn't have to be mercographs, but generally, that's what we use and third, our tools for consensus. This isn't your every day.

Big coin lecture. This is the actual technology, underlying blockchain and again it exists today. Content addressing is one of my favorite things. You may be surprised but the way content addressing works and again this is a complex technology that is at heart, very simple. You take Some input like a string of characters.

You apply a cryptographic hash function to it. Basically what you're doing is you're encoding it. And this cryptographic hash function, Creates a unique digest. For your input. So you input the word Fox you run it through the function and outcomes a string of characters. Now depending on the algorithm that you use this string of characters is unique for every individual input Change the word fox to the red fox jumps over the blue dog, that never seen a blue dog that you got a different digest.

Now the the neat part comes when you say that hash is now the address of that text string. Right. Why now? Right now, for our addresses we use the physical location. What server is it on what file name is it under? But using the hash or the digest as the address, the resource may be located anywhere.

And we find it by looking for the resource named fat and we look for the closest one. This is the core idea. Behind distributed technology. I can talk so long about this is so fun. Right away, we get issues at the collisions values issues, for example, the provenance or the ownership of the input data.

Right now, one of the neat things about blockchain is you can put a time stamp on it or something like that. And so, you know who owns that text, because that can be, as long as a book because that person's digital signature, their persistent identity. And the time can be added to that hash, we'll talk about that in just a second.

Also the question of openness or access to this network right now using physical addresses as The address of digital resources, we can block access by blocking access to that physical resource. But when the address is a digest and you can have multiple copies, the whole dynamics of access and openness change.

And in my opinion this can be the foundation of an open network.

The second part of this miracle change or Merkel graphs. Technically they are what are called directed acyclic graphs, which means they move in one direction and they never circle back on each other. So they look like these trees except the tree is built from the bottom up and it's the mechanism we use to associate resources with each other.

For example, I want to have one persistent object HA, which is the author and another persistent object which is the text or the digest of the fact, I combine them together to create a new digital object That locks in the authorship, with the content. If I produce a new version, I recreate it.

And they combine it. So I have version control as well. So I have distributed these little things can be located anywhere. Version control systems, those of you who work with programming maybe aware of github. GitHub uses that to manage version control. That's why Microsoft paid so much money for it because this is brilliant technology.

And this mechanism of managing files or other resources, speak to some of the core values that people talk about when they're talking about distributed resources, are they findable? Can people find them? Are they accessible? Are they in a format? People can read, are they interrupturable? Are they all part of the same mercon network?

Are they reusable, can we create a resource here? Use it here, use it here. These are core, fundamental questions about resources and data, generally and these are questions that universities are going to have to handle on a day to day basis.

The third part is consensus. Consensus is really interesting. If you have multiple copies of a resource distributed, all around the world, And if you're joining them together in Merkel trains, How do you decide what? The right information is? In blockchain networks, each of the individual web servers. This might be in China.

This might be in the United States, this might be in Spain. That one in the middle is in Canada, etc. They have to communicate with each other and agree. This blockchain is the blockchain that way. You don't get competing blockchains. There's a range of different algorithms or methods to establish this consensus.

And these methods form, the basis behind digital currencies, but they can be used in general.

We have issues with consensus mechanisms, obviously data federation itself. Raises the question of, how do you come together? How to you, agree, to talk, what language were you talk? What are the protocols that you'll use, who is allowed to be involved? What about blocking or as they sometimes say, degenerating bad actors.

A bad after my try to disrupt a consensus network in blockchain. They call this the Bison. Team. Generals problem. An untrusty untrustworthy Byzantine general. Typically, in blockchain, we have algorithms proof of work, which is bitcoin proof of state, which is ethereum proof of authority, which is ripple. For example, there are other algorithms and other networks and these are used as the basis for consensus algorithms

And proof of work. Is that thing about blockchain that uses more power than the country of Norway? That obviously has ethical implications. If we are going to develop if we want persistence in our digital objects, we don't want to add to the continuing degradation of the environment to do it.

A proof of stake is what Ethereum has tried, and that's more interesting, but that raises questions of inclusion, who is allowed to have a state or who is an authority. These again are questions, ethical questions methodological questions at universities will have to face. And they will have to face them because we're going to need this kind of data network.

You might think? Well, yes, just another way of doing data. Well, yeah, but that brings us to our third technology.

See. When we look at things like artificial intelligence, Artificial intelligence uses data for input. Others, trusted data and there's untrusted data. What is trusted data? Well, it's data that, you know who wrote it email, where it comes from, you know, how it's been changed, you know, when it was made.

It's data that we've created out of persistent objects. Untrusted data, it's like Twitter or comment on YouTube or read it threads. Now it's interesting. If we look at artificial intelligence today, most of the data being used is untrusted data. That's what we're using to train artificial intelligence today. Now surprisingly, it works really well unbelievably well, but we have issues Microsoft, for example, created an artificial intelligence agent, called Tay That could chat with people and they learned what was appropriate from Twitter and within one day, Tay became racist misogynist fascist, who was pretty bad.

So, trusted data addresses, the question of input for artificial intelligence. It addresses things like issues of data bias, representation, consent and inclusivity, right? People talk a lot about how untrustworthy the data going into artificial intelligence is they don't talk about how to fix it that, how to fix it?

That's the kind of technology we will need to evolve toward and that's a key role for an institution. You know, it's funny. We talk about blockchain, we talk about manner verse a lot. As if it's you know done by individual people maybe hackers in their basements or whatever but mostly these are will be done by institutions and institutions especially those of knowledge and learning will play a key role.

So let's talk about artificial intelligence, how many don't on time, but that not bad. 24 slides in half an hour to go. So, in artificial intelligence, there's a whole process. Of things that happen. But here's the simplified version and the simplified version will work for us for now. First of all you feed the artificial system intelligent system which is typically a neural network you feed it data.

And the network will process this data. We call this training, the network, The way. The network learns the way it is trained is based on different algorithms. There's a whole list and they have different properties in different characteristics. Then after you've trained your, you've trained your artificial intelligence engine that produces what's called a model.

This model is then. Apply to new data to produce a result and that's the application state. Do you think about it? It's just like, the way you learn. Right. Somebody tells you a bunch of stuff. You think about it and come up with a representation in your own mind.

Then you see something new and you react That's exactly how artificial intelligence works. There's a lot of details. But that's the essence. So at each stage, there are going to be ethical and value considerations What is the model that we're going to select? For example, some of them have better memories and others.

Some of them will react instantly to a trigger event. Others will be more slow to react etc. How do we explain how a model produced? The result that it did, have you ever done something? And somebody asked, why did you do that? A sort of go I don't know.

All of artificial intelligence is like that because when I say it's neural networks, what I mean is it doesn't work according to rules or principles not at a high level of explanation. You know these things. They only have three dots here and artificial intelligence agent will have 50,000 dots a hundred thousand dots more it'll have input of a million data points.

So if you input a bunch of data, I mean it produces a picture of a frog. You ask why did it produce the picture of the fraud? There is no simple answer. And so there's a whole domain to trying to explain how AI makes its decisions. When they, I'm actually decision.

Can you appeal who's accountable? I was on an eye, triple e task force, where people were arguing that the artificial intelligence should be thought of as an independent agent. And so it not the creator was accountable and responsible, I found that personally unacceptable.

In different types of artificial intelligence, do different things for example, before this year, They did. Well have come to be seen in the literature as the big four descriptive and reform a nice picture or a model that you can understand the environment diagnostic it might recognize categories of things.

For example, predictive it might help. You understand what will happen in the future and prescriptive who will help you shape what will happen in the future. So, all of that in the past, these are well, established capabilities of AI. This year has been the year of generative. AI you've seen models, like GPT three.

You give it some text and it produces a new image based on that text. Excuse me, there were more applications that I'll talk about. The vacation points. You say the four. Five minutes of this talk will be taken up by coughing. In the future. Artificial intelligence will also perform what I call.

Deontic analytics. Deontic analytics is AI. That answers the question. What should be the case? Who should be promoted? What is our most important technic issue that we face today? What metrics should we use to? Determine whether an education was successful? These are hard questions. This is a questions of philosophers work with These are the sorts of questions people will ask artificial intelligence and more and more.

Artificial intelligence will come up with interesting answers and if we're able to explain those answers, they might be convincing. What's really important here? For us is, these are all the domain of universities, right? We create new knowledge. We think about what should be the case. That's really gonna challenge our role in the future.

There we go generative anal analytics and we're not even to talk about anymore, only generative analytics already this year, produces images produces music including very acceptable. Death metal music It writes software, Microsoft has released a software writing assistant inside its visual studio code application, which, by the way, integrates with GitHub, which rememberes Text.

There are many examples of artificial intelligence writing convincing tax newspapers. Use AI now to write articles especially sports articles because they are very easy to write the hate to say that but is true. And they do research. But they predict new elements, the come up with new techniques like new ways of folding proteins, etc.

This is what we do all of this. And here's the question for universities. What do we as institutions do when everything we do and everything we teach people to do can be done using artificial intelligence. Through a lot of people talk about well we need to get people ready for employment and I don't look at this and I said, what employment, what are they going to do?

Are we going to teach them to, to draw, to write to be engineers? All of this can be accomplished more quickly and less expensively by a machine. And you might say, well, the ethical thing is not to replace people with machines really Are we gonna keep elevator operators? Are we going to return to the time when you had to speak to a person that a bank to get money?

Come, are we going to return to a time when if we wanted to go from place to place? We walked. Now, we replace people with machines all the time, this will continue and there's nothing wrong with that. It makes us all richer in theory at least.

So, Let's look at the enroll of the institutions. As I said before teaching and learning research and development innovation and growth, These roles are all going to change. They're all impacted by artificial intelligence and by blockchain and by the metaverse, it's a whole system, right? Metimemer blockchain. AI It all works together.

Well teaching and learning. We're looking at things like distributed IDs for students. So that one student ID applies across multiple institutions Very simple example. But when I say multiple institutions, I mean every institution Not just the ones that form in network. Open, educational, resources, badges and credentials. These are all persistent objects that are relevant to the academic domain.

So again, how do we ensure in this environment individual agency and autonomy who owns that identifier? Think it's not a simple question. The Canadian government owns my password number, or my passport number. My employer owns my employee number etc. But if I have one number that I manage, I kind of need to own that.

As well. When we're talking about identity, just generally what about the questions of diversity, equity and inclusion. How do we ensure that people can represent themselves fully? The simple example in traditional identity management systems? You had a choice of two genders male and female men or a woman. But people who want to express fully their individual identity demand and expect to be able to put multiple values under the question of gender, who makes that decision who chooses whether people can select their own gender, whatever, gender preference, they have whatever gender, they happen to be.

Open educational, resources is another one persistent objects. I developed to think called content, addressable resources for education. Their resources that are created once for learning there, given a content-based address and then they're loaded on to distributed file system. There's an existing file system called the interplanetary file system. Great name.

IPFS. It actually says it's not very good yet but it's going to get better. So the question becomes, how do we make sure that these resources are accessible? How do we make sure that they're usable right now? This system is too slow to be usable. I've tested, it It's slow, But it will be better.

I remember when the internet was accessible on 300, biodomes what that means is 300 bits per second, which is about. Let me see what's 300 divided by 8, 320 divided by is about 40, little 37 letters per second. Or half that if you're using international lettering, the very slow.

So things, get faster, not right away but they get faster. How do we use open, educational resources, and the resources? We create to support, not just learning in our classrooms, but learning for everybody in society, support for and formal learning, lifelong learning. There's another talk going on right now.

Looking at that. Excuse me, terrible terrible. Speaking. Yes. Oh no. Because it'll it'll affect my speaking, right? Yeah. I'll talk like the it just wouldn't be. I really appreciate it though. Yeah. Okay. Badgers and credentials. You've no don't heard about them. We can create them as persistent objects that are sharable.

We can even award them automatically using artificial intelligence and that's the systems that do that. We can create and again I've built pilot projects based on this knowledge recognition networks. We have one called micromeshots working in the government of Canada and basically they look at what you do and based on what you do, you know the objects that you produce as an individual, it recognizes you as having achieved some sort of skill and then it passes that along to a potential employer thinking about it.

Where's the role of the University here? You just go out and write some software or create somewhere. A computer recognizes. It says hey that's good. You should give them a job. We've skipped over all of universal university credentials entirely. Something to think about That's why in the United States.

There's a lotsuit where universities are suing colleges because they want to be the one and only type of institution that grants academicals. That should that be the case? Is that reasonable that? It's the case. Teaching and learning. Consensus and networks, peer networks. The better birth masked. And on open community.

These are all being developed now, they exist. Now they're going to be more and more the underlying infrastructure of learning. Right now we have classes teachers, classrooms and campuses in the future. We have these networks we have this community. Scientists already have it right? You know. Yes sure they'll go to a conference from time to time but in between the conference every day or what we used to be on Twitter, but we don't like that anymore.

We're on mastered on run chat rooms. We're having these zoom conversations happen to all the time. Some even go to the LinkedIn, but You know, real scientists don't LinkedIn. Just saying. Letter employers, go to LinkedIn.

So, think about and I'm not gonna go one by one here, but think about this sorts of issues of values that arise. It's going to become a role for universities to build and facilitate mechanisms for people to support their own learning. In the future education. Won't be about what we do for students.

It will be about what we help students do for themselves. Very important principle that must underly what we're doing. Otherwise, we become obsolete. For example, and this is something I worked on. For many years, the personal learning environment, Imagine you have a digital environment, your own personal metaverse. But you can use to access any learning opportunity in the world, no matter who offers it.

It's comic. It's slow, but it's coming.

Finally artificial intelligence, oh, isn't this a fun? One resource creation. As I've mentioned it also can offer coaching and tutoring automated assessment. It can grade your papers and it'll do a better job than a university professor, believe it or not because if you train it with enough examples, it'll grade it faster and more fairly So, What are the things that we have to do here to make this work properly and not really badly because you know, we don't want to give an a or a hundred percent to a student paper that is racist prejudiced etc.

Well, we have to build these quality trusted data networks goes without saying, that's a major investment. It's not going to happen by itself. We have to, as institutions assess and validate AI models that takes a lot of expertise. So right off the bat, we have to develop the expertise in these technologies in our own workforce, enable in order to be able to assess these algorithms, these models We need to be looking at automated resource development.

We as an institution, spend a lot of time, writing books, writing papers writing lesson plans creating resources. And it doesn't make sense in the future to keep doing that by hand. Sometimes, sure, sometimes you want a meal cooked by a great chef, Most of the time you want to buy ingredients from a grocery store and put it in the microwave.

Okay, maybe not the best example but you know what I mean? From time to just want to drive a car rather than build a car and use that. That's a better example. And, As, you know, our role in our role of The guardians, the stewards of knowledge researching information.

We as institutions have a role of ensuring that there is public access to this technology. So that anyone who needs to learn can act sess a system that will create a learning resource for them. Of course, right? Why wouldn't we do that? Well, many reasons why we wouldn't do that, but let's maybe not Research and government.

The second major function. There are many persistent objects associated with research and development. I think of two publications and data. We're moving to a world where these will become open. But the University of these new role, as I mentioned here, will be as stewards of publications and data rather than the creators and owners of it, that's a role.

I think that will be very hard for institutions to accept. But why wouldn't we accept it? And then with respect to these resources, it's important for us to ensure that they're findable accessible interoperable and reusable so that there is a wider benefit. Research networks consensus, right? How do researchers scientists social scientists?

Artists lawyers, come to agree on the fundamental tenants of a discipline right now. There's a very complex and often informal structure in that will continue but we have to be asking about how do we support that. Events. Like this are a major part of supporting that making sure events like this can be accessed on YouTube, which this one can be is a major part of supporting that.

But even more, we face an immediate impressing need to redefine. How consensus about facts and truth are determined in society. I know I'm reading that slide word for word but those are important words. We live in an age of fake news misinformation. How do we develop systems of consensus that we can trust?

And I've sketched the technology, but it's up to us to manage that technology to produce. Networks that produce reliable facts and truth. It's not just gonna happen. That's fine. Able to order here, teaching learning research and all I press the wrong thing. Okay. Hey go, there we go. Yay.

I win. I defeated the technology. We need transparent and open research networks period and the story. Otherwise how can we trust them, right? We need to include the public in the research process, both with their consent, as research subjects, and with proper accreditation and provenance as researchers in their own.

Right. Here's there are many public research networks already. And we need to ensure that the benefits of research are shared by the public. That's certain universities, have a hard time doing. Because when something new is developed any university, the first instinct is to create a patent and then that the next instinct is to sell it to a corporation.

And the question gets asked, well, where is the benefit from the public, who produced the resource? That's an important. Ethical question to my mind. Artificial intelligence in research and development will play a major role. We see it already and things like software development. As I mentioned concept formation, like the folding of proteins or new theories about the horizon fall of Rome, prediction, economic modeling, climate modeling.

There were software, validation techniques. And techniques for supporting optimization and efficiency that should be employed in order to make sure that our software sustainable inclusive etc. For concept formation, we need to understand what is ethical. You know, we need to understand what we need versus what we want versus what we can do.

These are three different things, just because we can do something like study, a new virus that has emerged from the ice and a previously, frozen location is happened. Yesterday. We need to ask a question. Should we be reproducing? This virus. What other conditions under which we would reproduce as viruses are important questions.

And again who's going to answer them? If not institutions like herself? Roma. Now that wasn't intended. All right, tech innovation and growth. On the slide. I have three lines of text market and assets performing indicators tokens and recognition. And these are, you know, these are economic domains. Oh, I must have gone to the last slide.

Is that what I did? I'm on, slide number 47.

And universities are going to face a challenge.

Thinking that one. But, oh, I clearly hit the wrong button. It's always the user. It's never the technology slide 47, please. And we need to be looking at these. Slide, 48, mark, kitchen assets. For example, were, we're used to that 48, is it no? There we go. We're back on track.

What do we have to say about markets and assets? We often especially the exactly of boardroom value, our researching development in financial terms, What is the return on investment? What is the value of the patent portfolio, etc? But we need to look at the question of market asset in a different way When we think of markets, we're thinking of people.

But did we ask people if they want to be considered as part of a market? I know, I don't. We need to be thinking about non-monetary assets as outcomes of our research things. I happiness sustainability, the other sustainable development goals, Are all equally important to grow domestic products, which is actually to my mind, a terrible indicator.

You know, I know And we need to reflect these in data. Performance indicators. How do we include the non-quantitative indicators, say, happiness? How do we take into account diverse perspectives? How do we monitor and feed these into data? Again, the questions that we need to consider? Tokens and recognition.

If it was up to me, I would read wealth, I would call it a negative indicator and begin to ask questions about where they got it. And what laws they broke together. My opinion has always been that great. Wealth is a primer, facie indicator of criminality. You know. We should be asking which wrong with an economic system that allows that kind of accumulation of wealth because they clearly has a detrimental effect on society.

What indeed even counts as good in our society, the Chinese have developed a system of social credit, We have a system of financial credit. They think I'm terrible. My horror financial credit systems, assess us based on how reliably we will pay back money period. Consensus in innovation and growth things like innovation networks and needs assessments for a major part of that.

But we need to refocus from how to innovate, which is the focus of many of these initiatives. To, what do we want from innovation? And again, I heard back to the sustainable development goals. And there's a key role for access and inclusion here. Usually, the beneficiaries of innovation are thought to be companies and the people own those companies.

I think we should be thinking of the beneficiaries as the people who work for those companies, We should be treating things like salaries as a social benefit, not a social cost. Artificial intelligence in innovation and growth product development logistics and distribution management in the administration. Wait to the manager discover that their job can be done by a computer.

They're attitudes will change. We need to redefine productivity. We need to redefine how we share the benefits of massively increased productivity, on the part of individuals. We need to define the entire product cycle, not just the cost of creating something, but the cost of using it and the cost of disposing of it and we need to consider impact of on communities.

That's the role universities must play because the companies won't play it. Management made administration. Like I said, we need to redefine management manage for sustainability and not just profit redefine wages. As I said, is a benefit not a cost and again considering the impact of community, these are the kinds of changes that are being forced on us.

This isn't A political agenda that I'm describing here. This is what happens when you introduce these technologies to this society. So, I have concluding remarks. These three things here. Maniverse blockchain, artificial intelligence, these three things are us. We make them. We feed them. We shape them, we manage them.

They are us, they're not independent to us. Everything that these technologies do depend on what we do. I often tell people, if you want ethical technologies, you were acquired an ethical society and people reply. Well yeah, that's the hard part. If we want a more inclusive, sustainable, and ethical society, We must as individuals.

And as institutions make inclusive, sustainable and ethical decisions. There's no other way to do it. And not, thank you very much. Is my presentation. I really appreciate you being here to listen to me.

Stephen Downes Stephen Downes, Casselman, Canada

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