--the classic quip is, we're really good at doing AI, but we just can't do AV.
But we seem to have got it. OK. Look, so I'm just going to give a brief talk about the recent report that we put out at the beginning of May, "Artificial Intelligence-- Shaping a Future New Zealand." And so this is a report where we looked across the whole of the landscape in New Zealand of who's doing what with AI and then looking at around the world from an economic and a social society point of view and, really, just seeing how New Zealand stacks up and really to identify what are the opportunities and some of the challenges and get a better understanding of AI altogether.
That's me. So I head up the AI Forum. We're an independent organisation, not-for-profit. And we're part of the New Zealand Tech Alliance. And so a number of communities including NZTech itself and FinTech and the IoT communities and HealthTech and EdTech as well. So as I said, we're a broad-based organisation.
We bring together government. So we have a great support during our first year from our MBIE's Digital Economy team and also from global technology companies from local corporates here in New Zealand and economic development agencies right through to-- and all the universities-- and then also right through to some of the exciting AI start-ups that are coming through.
So there's a big logo map there. And so I really do encourage everybody to find out a bit more about the Forum and do get in touch to get involved. So the report, it's only 108 pages, so easy bedtime reading. I won't really talk too much in depth about the whole range. I'm just going to pick out a few highlights. But there's a picture on the screen there of the highlights page. And from a economic viewpoint, we basically got a team of economists to do some modelling and look across, what are the potential upsides across various different industry sectors from applying AI and automation to the economy?
And the key number there is there's potentially up to $54 billion GDP boost by 2035. I'll go into a little bit more of how that breaks down just later on. We did look at the impact on jobs. And there's a constant narrative with points of view at both ends of the spectrum on whether AI will involve lots of job displacement or whether, actually, jobs will continue to evolve over time.
And actually, AI will free us up to do the more complex, higher-value tasks and leave the manual repetitive tasks to machines. So the analysis that we looked at there really-- it's quite subtle, but, basically, what we find is that this is no particular evidence that this is a technological revolution, a new technological paradigm unlike the paradigms that we've seen before. So when we go back and talk about when cars replaced horses and around 1900 there were lots of blacksmiths. And 20 years later-- not so many blacksmiths, but lots of car mechanics.
And then similarly, I think there's a stat in the report where, in the 1960s, there were 21,000 typists and stenographers in New Zealand. And there are next to none now. But that doesn't mean there are 21,000 job losses. It means that people have just been able to, over time, retrain and to apply their skills to new roles and new tasks enabled by new technologies.
So again, I'll talk a little bit more about that. I think the key finding from our report, when we looked around the world, is that a number of our peers in the OECD have been developing and investing in significant nationally-coordinated AI investment strategies. But to date, New Zealand hasn't. And so our real call to action-- I'm going to talk to this in a moment-- is that we really need to start working on a coordinated national strategy for AI to maximise New Zealand's opportunities from the technology.
And then also, the report is called "Shaping the Future of New Zealand." And we raised the point that these technologies are going to have a major impact on our future lives. And we're just starting to see that. And they're going to permeate through all aspects of our work, all aspects of our daily lives. And if we don't start investing now, to actually get control of some of the levers to shape our future, then, increasingly, these technologies may be shaped for us. So that was our call to action.
Given that I'm talking to a government audience as well, there was a piece of research done by the Oxford Institute, I think it was, at the back end of last year, which looked across all 35 OECD countries for government AI readiness. And New Zealand did actually stack up reasonably well there in ninth position.
However, from the work that we did, talking to people across government, we did find that AI capability and use by New Zealand government is pretty disconnected. I think the people in the room, and the work that LabPlus is doing is probably at the forefront of public sector AI capability in New Zealand. And so there's definitely a call to action, again, to work more towards a horizontal capability across government here, rather than operating in silos.
Probably the last Main point is also just to acknowledge the shortage of talent and the competition for talent worldwide for AI experts. And so, really, how we, as a small country in the South Pacific going to attract and retain and also educate through our education system enough workers, not just to develop the technology, but also to work alongside the technology in the future. So lots of questions there.
As part of going out around New Zealand to look at the AI activity that's happening at the moment, we sort of went out and discovered this Cambrian explosion, if you like, of the new firms experimenting and organisations working with them and thinking about AI. And so there's a bit of a logo map there that we've put out. I think we've done three iterations of this now. There's another one, coming with about another 20 logos soon.
And so you can see, there's a wide variety of organisations right through in the private sector, in the research and education sector-- so lots of universities are starting to think deeper-- well, have been for a long time. And this is actually one of the things with AI is many of the theories and the technologies that we're using today are based on academic work that was done back in the '80s and '90s, and a lot of it, actually, in Canada, interestingly.
But it's really only now, that we've seen the growth in readily available cloud computing, the ability do massively parallel computations using GPUs, Graphics Processing Units, and that also the increasing amount and the ability to store and move around large quantities of data. These have all been the technological divers, if you like, of the capabilities we're starting to see come through now.
And then, also, you can see, in the public sector section there-- so again, a small number of organisations working with and thinking about AI. And we've obviously come across more since the report has been out. So I would encourage you, if your organisation is working with AI, then please do let us know. We can include you on the map. This has been a really good exercise to get a benchmark. There's about 140 logos on this map. And it's actually quite a good exercise just to connect people together and for people to understand who is doing what and drill a bit deeper.
As I said, we did look around the world, and found other countries around the world investing in national AI strategies. In particular, obviously, the US has a lead with its Silicon Valley ecosystem. Just before the Trump government came in, the Obama government administration put out a paper calling for a national AI strategy. But that was promptly pulled from the White House website. And I think you can find it on the archives one now. That was a really good piece of work.
Interestingly, in the absence of that, China has moved ahead significantly, in terms of its investment. And they put out a national strategy last year with the stated aim of creating the world's leading AI industry in the world by 2030. And they're talking about a $150 billion AI industry by 2030 as well. Parts of their strategy involve teaching AI in schools. And China has a very different attitude to data and data privacy than we see here in the West.
And so some of the challenges that we're seeing in the West here, with regards to data privacy, have not, basically, constrained some of the technology companies and businesses that are operating in China. The main example, I'm sure everyone has seen, the stories of what you can do with mass surveillance and facial recognition in China right now.
Another anecdote, again, coming out of China is that Microsoft is hiring 5,000 machine-learning engineers and researchers into their research centre in China. So when you look at the scale of New Zealand, relative to the amount of the investment that's going on in China and in other parts of the world, European Union came out with a $20 billion investment pool about a month ago.
And so we're really seeing other regions around the world starting to recognise the importance of these technologies for the future. And part of our report is really just to acknowledge this and ask ourselves the question, do we see this as important for New Zealand's future as well? And if so, what do we do?
So there is research happening in New Zealand. And we talked to a number of universities there. I was actually in talking to Professor Meng Zhang at Victoria yesterday. And he's got a team of-- I think they've got 12 staff and six post-docs and 30 PhD's currently studying in the AI department at Victoria. So there's a significant amount of AI research happening up and down the country.
We did notice a challenge getting that research out of the lab and commercialised. And it's a classic challenge that we have in New Zealand of commercialising university IP. But other ecosystems around the world do seem do do it better than us. And so just, look at ways to maximise the capabilities that are coming out of the universities here and turn that into benefits-- and not just in the commercial sector, but potentially applied across government as well.
One example that's come out of the University of Auckland is the company Soul Machines. And so you may have seen there talking heads, what they call, digital humans and really talking about customer service use cases to automate customer service processes and personalised as well. We looked out across what's happening in AI, around the world and in New Zealand right now. And I think the key takeaway is that these are extremely horizontal technologies. So they are applicable across pretty much any domain, certainly, any domain where you have data.
And so the key business drivers for them will be, if you're in the commercial sector, to make more revenue, to make money and, across all organisations, is to be more efficient and to save cost. And then third real driver, I think, is also to improve customer experience. So you see the talking head down on the bottom left.
We're obviously seeing chatbots as well. But it's really just the ability to drive personalised experiences. And I know that Nadia, Pia and the team have been looking at the opportunity to use an AI to interface with government as an API, if you like, platform and to, basically, to provide a personalised experience and learning from your own personal data profile.
Just some other examples we came across lots of environmental use cases. Top left there, a company called Orbica in Christchurch is doing some pretty amazing stuff with geospatial mapping-- so taking satellite images, drone images, and then running machine learning algorithms across that to identify water bodies. And so Kurt who was actually on the panel yesterday, was saying that, to map out braided rivers like that takes days, if not weeks, of analyst's work, normally.
And they can do it in about 40 seconds now from a photo. then they can stream this live from a satellite, potentially, soon. And so pretty much any water body, any building, any-- counting trees, identifying wilding pines-- lots of new use cases have opened up just by opening up this area of topography data, which, generally, just sits in an archive just to provide nice-looking web maps a lot of the time.
Talking about MoleMaps there-- so in the health industry, in the health sector, huge amounts of visual data-- so pictures. And so one of the things that AI technology is really good at is finding patterns in images. And so there's a company MoleMap who are analysing photos of moles and, basically, improving skin cancer detection.
Bottom right is a great story of a New Zealand logistics firm that's replaced their spreadsheets with machine-learning algorithms. And they're basically able to predict the demand for and also optimise, basically, where the shipping containers are placed and onto which ships. So again, a key use case is to take data, a lot of data, to learn from it, to see the patterns in it, and then to use those patterns to make predictions about the future and then also to use those patterns to inform your decisions going forward.
So I said I'd talk about the economy. I'm not sure how I'm tracking for time. I'll try and speed up a bit. But just, in terms of the economic impact, we did do the analysis across sectors. What this breaks down to see is that, largely, the economic benefits from labour efficiencies-- and so this is where roles are able to be automated.
And these will often be more white-collar roles going forward. So you can see down on the bottom, mining, and agriculture, forestry, and fishing. These are industries which are actually largely automated. That's not the only factor. The other factor is what's called the absorptive capacity, which is the ability for that particular sector to innovate, to take advantage of new technological changes.
So the financial insurance services at the top there has huge potential to deploy AI and to make massive efficiencies and, similarly, manufacturing and construction. So please do-- and sorry I didn't say it at the beginning. But please do take the time to download the report. It's available for free on our website aiforum.org.nz. I'll say it at the end as well. And you can see the details of this analysis and a bit more of how these numbers are made up.
But pretty much, the biggest opportunities are in industries right now. Moving on-- so look-- we didn't just go across the business impact and the economic impact.
So look, there were major concerns, obviously, about some of the ethical issues raised by automated systems and algorithms. And so I think this reasonably well-trodden discussions about AI bias now. And certainly, the media has really woken up to some of these issues. And obviously, the Facebook and Cambridge Analytica events, recently, have really raised the dialogue, if you like, raised the ability to converse about the questions of data privacy and so on.
So this is one of the things, when we went into the report, that we were sort of seeing that people's understanding of what AI actually is is largely informed by science fiction. So people sort of triangulate off Blade Runner and Terminator and Ex Machina, all of which are humanised humanoid robots.
So one of the things we deliberately tried not to do in the report is have pictures of robots in there and trying to illustrate AI using other techniques and to try and make it more tangible. So I think, really, to be honest, I've really noticed an increase-- maybe some comments from the room, at the end-- but really nice, is an increase in the level of dialogue about some of these issues that's being had in the mainstream.
We talk about safety accountability as well. There's a picture of an autonomous-- basically, an autonomous killing machine, bottom right. So these technologies are not just used to make lives better. They can also be deployed for malicious use. And really, there are examples now of autonomous weapons. There is a moratorium that's being worked up in and around the United Nations, actually a Kiwi heading up, works for Human Rights Watch, is heading up some of the work that's happening there.
So go and look at the campaign to stop killer robots for more information. But certainly, New Zealand hasn't got a position on autonomous lethal weapons currently. And I think it's an opportunity for us to take a lead there as well. Because I don't think there would be many New Zealanders that would be wanting to see the proliferation of, basically, weapons which do not have human control across the top of them.
And this applies also to defence as well as offence. And so for our cybersecurity strategy in New Zealand, we really need to start deploying and understanding the potential AI-driven attacks. And then, how do we use AI to defend against that?
The AI Forum's part of the International Partnership on AI. So I won't talk too much about that. But that's an international body made up of lots of the large tech firms and other universities and think tanks around the world who are working on those six themes to ensure that AI does benefit people and society and not just shareholders.
So these are the report's recommendations. And sorry, I'm expecting that you can't see them just move that down. So as I said, our key call, really, is theme one there to coordinate our AI research and development and encourage AI uptake and maximise the opportunities. And so we expect that, when the new chief technology officer is appointed, that this will form part of the work of New Zealand's wider digital strategy.
Theme two is creating awareness for discussions to continue the work that's being done by the AI Forum and others to move away from talking about Terminator, talking about superintelligence. And so it is a conversation that's worth having. Will machines take over one day? The quip from Andrew Ng, who's a professor at Stanford-- real AI guru globally, but talking about Elon Musk, talking up the existential threat from AI-- and he is saying, you're trying to land a person on Mars, but right now it's like you're worrying about overpopulation on Mars.
And so yes, these issues definitely need consideration. But they've sort of dominated, by virtue of the celebrity endorsement of those issues, they've sort of dominated discussion. And actually, economic competitiveness and those ethical and social issues that we talked about are actually probably more important and more tangibly in front of us right now.
I will Just speed things up. So theme four is increasing data accessibility. We really do need-- data is the fuel that feeds AI. And in New Zealand, we really could do better, I think, at opening data out of our public data sets but also in health as well. So I'm at a health conference today. And real challenge is taking data out of silos across the health sector to actually deliver better health outcomes.
Theme five is to grow the talent pool. So we, too, would like to teach AI in New Zealand schools, certainly, data science principles. So there's a new digital curriculum that's just being rolled out currently. And so we'd like to see, at least, some of the principles and the basic understanding of how these techniques are used. And then part of that is, we need to, basically, upskill teachers.
And the other thing is, how do you get started with AI? So there are plenty of online courses that you can take for free and plenty of others which are very reasonably priced. So a really good place to start is that Stanford AI course by Andrew Ng. And so I recommend-- it's actually something we're just going to put up on our website soon, once we get through Tech Week is just-- where can I go to teach myself and get myself skilled up. And then, a lot of the vendors-- so Google, AWS, Microsoft-- all of those have got courses online to get yourself skilled up and just start using the stuff.
And then finally, recommendations there are around adapting to the effects, the ethical concerns and really establishing in ethics and society working groups is, making sure that New Zealand's voice is heard and that we are basically ready for some of these changes as they come.
Now, I'll just move on to the next-- so talking about an AI strategy for New Zealand, what does that look like? One of the diagrams from the report is this value chain diagram, which really calls out how data basically fuels the AI applications that drive the social and economic outcomes that we want.
And that's supported by trust, regulation, research, skills, and also investment capital. And so we really do see the AI strategy as a series of investments in each of these buckets and really working out what it is that we're wanting to achieve to grow the economy and to make government more efficient and to make sure that the society is more fair as well.
So our work programme starts here. So we are working with all of our members and talking to a number of people throughout government as well. So if we're not already talking, please do get in touch-- contact details on the next slide-- we'd be really interested to understand how your organisation is approaching AI and what the AI Forum could do to help.
And also, I really encourage the work that Pia and Nadia and the team are doing to build a horizontal capability across government. So these are not skills that can really be deployed in isolation. The government chief data steward at Stats has a really big role to play here as well in ensuring that data is released and that we have the right data there and it's joined up with the right governance around it. And Yes, so please do download a digital copy of--