Techtonic 2.0

Transcript

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  Description:

  Text: TECHTONIC 2.0,  Australia’s National AI Summit, 18 June 2021

  Text: This session will  commence shortly.

  Above the text, the logo for  the Australian Government Department of Industry, Science, Energy and  Resources. A kangaroo and an emu balance a shield between them.

  On the right, still colour images surrounded by pink,  white, grey and teal squares: a woman wearing a cap and a red plaid shirt looks  at a drone hovering over a sun-drenched wheat field. A man in a yellow hardhat  works at a laptop. A mine dump truck sits on red earth.

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Description:   A split screen of four rectangular webcam feeds, arranged  in the centre of a white background, each with a round-edged teal border.

  Text in the top left:  TECHTONIC 2.0 – Australia’s National AI Summit

  In the top right, the logo for  the Australian Government Department of Industry, Science, Energy and  Resources.

  In the top left webcam feed,  Dr John Whittle, a man with short brown hair wearing a grey collared shirt and  glasses, appears in front of a turquoise and white background that includes the  CSIRO Logo and the text: DATA 61.

  In the top right webcam feed,  Liesl Yearsley, a woman with long honey blonde hair and glasses, sits on a  beige couch in front of a window that looks out onto sunlit trees.

  In the bottom left webcam feed,  Anton van den Hengel, a man with short blonde hair who wears a light blue shirt  and a dark navy suit jacket, features in front of a plain white background.

  In the bottom right webcam  feed, Belinda Dennett, a woman with long, straight brown hair, wears white  wired headphones and sports a black jacket in front of a plain white background.

  Auto-generated captions run  along the bottom of the screen in black text as each speaker talks.

  Dr Jon Whittle:    Hello everyone, and welcome to  this session as part of Techtonic 2.0 on the next wave of AI Technologies. So  we've got a fantastic panel today that are going to give us a glimpse into the  future and give us their perspectives on what we can expect from AI in the next  five to 10 years both in terms of research, but also business adoption. So I'll  introduce the panellists in a few moments. But first of all, let me just say a  few words to kind of set the scene. I always think that Australia has a pretty  amazing capability in Artificial Intelligence. There are various global  rankings where we do very well. So if you look at the Stanford's Global AI  Vibrancy Index, for example, Australia ranks eighth in the world for AI. The  Nature index ranks us about 10th. And in particular in that index, there's  three Australian universities that are ranked in the top 100 globally.

  And there's also a recent  ranking that came out that uses a broader composite AI index based on Oxford  insights that ranks Australia about 11th in the world. So pretty strong  overall, I would say. However, we are a small country and so arguably to  compete on the world stage, we need to do things differently than other  countries in the world. We don't necessarily have the same levels of investment  as some other countries do. We don't have the same number of big tech companies  that other countries do, nor do we have the sheer numbers of trained AI  engineers and scientists that other countries may have.

  But what we lack there, I  think, we gain in potential and with that amazing strength, particularly in the  university sector. I think if we can bring all of that together, we can compete  in certain areas on the world stage. And that's really what we're going to  discuss today. We're going to be looking at what the next wave of AI  Technologies are and in particular, where Australia can take a leading role.

  It's worth reflecting that,  you know, AI actually has quite a long history. AI was invented in 1950. So  it's not a new technology. I mean, actually gone through two so-called AI  winters during that time where the reality failed to live up to the hype and  investment stocked. Hopefully we won't see a third, but we'll get into the  details of what we can expect to hear from AI in the next five to 10 years. So  just to introduce our panellists who, I think, bring a varied range of  perspectives on these questions.

  So first of all, we've got  Anton van den Hengel. So he is a director of Applied Science at Amazon. He's  also director of the Centre for Augmented Reasoning at the Australian Institute  for Machine Learning. He's a professor of computer science at the University of  Adelaide and is also a fellow at the Australian Academy of Technology and  Engineering. And in fact, Anton was the founder of AIML, that's the Australian  Institute for Machine Learning, which is Australia's largest machine learning  research group and has ranked as high as number two in the world for computer  vision research. So welcome, Anton.

  We've also got Liesl Yearsley  here with us today. So she is the CEO of Akin, which is a deep tech AI company  building AI for the habitat. Prior to that role, Liesl was CEO of Cognea, which  is a AI company that became a global leader and was acquired by IBM Watson. At  the time of its acquisition, Cognea had a number of fortune, 100 companies as  clients and over 20,000 developers using the platform. So great to have you  here today, Liesl.

  And then finally, our third  panellist is Belinda Dennett, who is corporate affairs director at Microsoft  Australia. She's worked for Microsoft Australia's corporate affairs team since  2012, working across the intersection of technology policy, geopolitics and  society and she currently leads the AI policy development locally. Prior to  joining Microsoft, she spent five years working as a senior policy adviser to  the federal government in the Communications Technology and Digital Economy  portfolio.

  So I think you'll agree that  that's an absolutely fantastic line-up of panellists that I'm sure are going to  answer all of our questions today. Just a reminder that you can enter questions  for the panel at any time. That should be a Q&A window in the browser that  you're using for the stream. So do please enter questions into that and then we  will take those questions as we go and towards the end of the discussion. So  the first question I really want to ask the panel, and we'll kind of go round  one by one and give each a chance to answer this question. It's a very simple  question.

  And that is what can we expect  to see from AI in the next five to 10 years? How is it going to transform  industry in particular? So I might start with you, Liesl, to give us your perspective  on that question, if that's OK.

  Liesl Yearsley:   Yeah, sure. I think we're going to see three megatrends  swift in the next five years. The one is that, sorry, I've just got my husband  in another room and he's actually talking quite loudly. So forgive the  background noise here. The one is that AI are going to become much bigger  decision makers in everyday life.

  In America, over 64% of households now have Amazon Prime  and Amazon's really an AI company, Google, Facebook. Most of the giant  companies are really thinking about AI more and more. So we are going to have  AI that are in our homes, in our lives. The grandchildren of your Amazon, Alexa  and Google Home Hub, your Microsoft Suite are going to become much more  sophisticated, much more able to predict and anticipate what you want and need.  And I believe, I've no doubt that we will be handing more than half of our  household decisions or our day to day decisions over to an AI.  It will be having about a third of our  relationship time with that. If you don't believe me, think about something  like, say, Google Maps or, you know, if you're writing an email and auto  completes for you or if you use an air conditioner or if you drive a car that  has adaptive braking systems. These are all actually AI technologies that just  kind of, you know, enter in our lives in a gentle background way and end up  becoming more and more part of what we do.

  I think in that world, we are going to see a very  different relationship between customers and brands. You know, in five years  from now, I'm going to care less about, you know, I won't care about if my  organic broccoli comes from this supermarket or that supermarket or my mortgage  comes from this bank or that bank. Because I'm going to have a personal AI  that's increasingly understanding me, making those decisions for me and taking  all the cognitive cycles out of interacting with brands. We really don't want  to interact with brands. We want to live our lives. We want to have organic  broccoli or non-organic broccoli or not broccoli at all or go on a holiday or  save money or, you know, be with people we love. So I think that's a big trend  that we don't realise how significant and impactful it's going to be. The  second big one, I think is that AI is going to become a lot more immersive and  in some way overt ways but in some ubiquitous ways.

  So often think about the progression of technology is as  kind of a pipe like a fat pipe, like the first time human beings leapt onto the  back of a horse. We're effectively using something stronger than ourselves to  actually amplify our ability to get around the world and to get information  from the world. And then you look at something like the printing press. We're  essentially amplifying, again, our ability to exchange, communicate, to exchange  information with our world. Yeah, how we put our words up and consume data. And  every age of computing has brought a progression. So think for a minute about  these things. We didn't have them 20 years ago. They were a fat pipe of  information where we make our feelings and wishes and thoughts and intentions  known to the outside world, and it comes back to us. If you take this away, it's like a piece of your hand is  missing. So it's a tool that's become part of who we are. And AI is going to  become much more immersive, ubiquitous. We'll have AI glasses, we'll have AI  that are predictive and anticipatory and stuff in our environment that's  adapting as we walk through it. And we will be thinking less and less about it  but relying on it more and more.

  Then a third mega trend that's coming if you're interested  in the winters that Jon mentioned. There's also been DARPA talks about three  waves of AI. Our first wave was very structured. We're looking at symbolic  reasoning, you know, teaching computers the relationship between objects. These  systems can reason, they're expert systems or rules based systems.

  They can reason but they don't learn very well. And we had  the era of machine learning which is our second wave based on biological  theories and paradigms. And these systems, you can throw enormous amounts of  data at them. If they have enough compute power, they can learn but they don't  reason very well. So the third wave of AI that's coming, we're working on this  in our labs as well. I'm sure others are, is adaptive reasoning, more like a  human thing. So able to reason and learn but not need the kind of data and  compute power that AI today have. So those are the big three mega trends. So  I'm interested I think in a, live in a very different world. But there's not going  to be a clear line where we go, oh no, I'm stepping into an AI future. We just  going to use this more and more and more until one day we'll look back and go,  huh? I don't know how I lived without this.

Dr Jon Whittle: That's fantastic, Liesl. Like I said, if you're painting  this picture that AI will just become part of the fabric of our society and  will and change the way that we live. I think that's a good segue to you,  Belinda. So you've got a particular interest in AI policy. So what do you think  that the next kind of five to 10 years AI is going to look like, in a  particular, this picture that Liesl has painted as a kind of society where AI  is fundamentally embedded on that particular policy? Questions that we need to  be thinking about around that?

  Belinda Dennett:    Thanks Jon. Yes, so I think responsible AI moves from the  periphery to becoming at the core of AI development, ensuring that it's  developed and built in a responsible way. There's growing awareness around some  of the challenges that AI has. How do we make sure they're used responsible?

  So I guess that's my area of interest. I think what is  interesting that challenges perhaps some of the ways we thought about policy  development and regulation is, you know, to what Liesl was talking about, and  that is how AI is developed. So we agree. We think there's a paradigm shift at  the moment. It's been around supervised learning where AI models learn from  data sets that humans have curated and labelled. That has limitations. What  we're seeing now is the developments in unsupervised or self-supervised  learning where systems can process just huge amounts of data. It's less  structured, it can be unlabelled, and it learns patterns and relationships  between those different pieces of information.

  So it's much more open ended and it closely mirrors the  way humans learn about the world. So, I think for me, that raises challenges  around how we think about policy and how we think about regulation. Lots of  discussion in recent years around algorithm transparency and whether someone  should monitor the algorithms, and that's just not going to apply in this way  AI is going to be developing. So I think that creates some new challenges.

  But I'm quite optimistic. I think the new applications  that are available, using this kind of AI in the creative fields. We've seen  GPT-3 which can write poetry, It can write your emails for you. I think that's  really fascinating. But also in solving societal challenges, climate change.  We've seen AI used in COVID in the pandemic. Microsoft's been looking at  antigen mapping project to see how the body reacts to different diseases and to  vaccines. So I'm quite optimistic that there are huge breakthroughs coming.

Dr Jon Whittle:    Thanks Belinda. Now Anton, you've been at the forefront of  kind of AI developments for quite a while and as well as I actually am, I did a  PhD in AI that I finished back in 1999, and then I decided to leave AI,  thinking this AI thing will never really work.

  So, I always tell people, if you want an idea and a  prediction of what the future's going to look like, then probably don't come  and ask me because I'll get it wrong.

  (LAUGHS)

  But you stuck with it. So, I mean, where do you see the  technological developments that we're going to get in AI and particularly  thinking of that history that we've had, we've had those winters where things  got overhyped.

  You know, we've got Belinda now talking about AI systems  that can write poetry and things like that. I do still feel that there's a lot  of hype around what AI can do, and we probably need to be more informed about  what it can't do. So, what's your views of where we're going in the next ten  years?

  Anton van den Hengel:    Thanks, Jon. I would love to take credit for having had  better insight than you. But really, I was just standing in the right place  when my research area happened to become fashionable. But I think that, as with  so many things, we tend to overestimate the short-term impact and underestimate  the long-term impact.

  Technologically, as Belinda says we're moving out of a  process or the research at least is moving away from supervised learning  towards weakly supervised learning. That means this kind of matched (UNKNOWN)  shift from discriminative towards generative models. What that really means in  terms of capability is systems that can learn from less data and learn to do  more interesting, more challenging things from less data.

  And as Liesl says, there's more focus on systems that  interact with humans to achieve, to work with them rather than to do something  static. I've been working on visual question answering recently, which is a  new, entirely new area that we've applied, for instance, to trying to have  robots that you can just tell to do something and they'll do it.

  So, the other trend, though, is more of what we've seen in  the last ten years. So, along this line whereby things sneak up on us and we  miss the big impact. The last ten years have seen a revolution in the way we  gather information. Just the way we watch TV has changed completely.  Journalism's changed. So much has changed. And a lot of that change has been  driven out of multinationals. One of the things that AI does is to create  global markets. And we've welcomed these enormous companies into our lives that  operate these global markets. That trend will continue and it will continue  slowly. But the process that those multinationals have run will continue. And  that offers all sorts of opportunities and all sorts of challenges for  Australia.

  So, there’s a big question for us, particularly about  whether we're going to be a follower or a leader in this area, we do have  amazing AI skills in this country, we've been punching above our weight for  more than 30 years. But we face a challenge whereby there is incredible  investment, not just out of governments, but out of companies everywhere else  in the world. And Australia is lagging on almost every measure.

Dr Jon Whittle:    Yeah, and that's a good segue to my next question, which  is to really, you know, we've got this future where AI's going to be  everywhere. The natural next question is what is Australia's role in all of  that? And as I said earlier, I think Australia does have very strong capability  in certain areas in AI and certainly some of our universities. But Australia is  also known for not ranking very well on various innovation metrics.

  So, what's your view, Anton? I mean, do you think that  Australia can be a leader in this space or do you think that, you know, we're  resigned to the fact that we're just going to follow behind other countries  that have got the big tech companies and the big dollars behind them?

Anton van den Hengel:    No, I think we're in a really strong position. Australia's  got a lot of what it takes to do this. We are actually in a very good time  zone. We've got an amazing quality of life. We've got a fantastic education  system. But more than all that, we've got a wonderful set of liberal democratic  institutions that offer value for a brand in Australia. And we have great  research tradition. We've got all of the pieces that it takes and we've seen  Israel, South Korea, Singapore, all take out fantastic positions in this tech  by being adventurous, both on a government level, but also on an industry  level.  Australia does suffer on all of  those innovation measures, partly because we've got it too good, frankly.

  But that is an opportunity, it's not necessarily a  problem. We do as a nation, in my opinion. I'm biased, I suppose. But as a  nation, we do tend to focus on tech transfer, and frankly, in that process, not  university bashing, when the truth is that universities are doing very well  internationally on the basis of not very much, it's Australian companies that  fail to innovate seriously, not only in AI but in absolutely every other area  as well. But that's a solvable problem. You can look at the other examples  around the world, and it doesn't really, doesn't actually take all that much to  solve this problem. So, with a bit of commitment, I think we're in a very good  position to solve this problem.

Dr Jon Whittle:    So, are there particular things that you think we need to  do differently as a country? I mean, you're probably well placed to answer  this, given that, you know, you've got one foot in industry and one foot in  university right now. So, is it just a case of continuing to do what we've done  and continuing to try and find our way? What should we change?

  (CROSSTALK)

  The magic power to change things, what would you do?

Description:   As Anton speaks, Liesl stands up carrying her webcam with  her and walks through a light-filled house. Audio muted, she comes to a stop  and talks to someone off screen, gesturing as she speaks. She turns and walks  back to her original position, taking a seat on the couch.

Anton van den Hengel:    The opportunity in AI, in this tech, is that it creates  disruptive businesses in all sorts of areas. So Google weren't running a small  search engine before they started searching, before they started Google and  Uber weren't running a small transport company before they started Uber. These  companies started recently and they revolutionised it. The opportunity with AI  is thus to disrupt the global marketplace. And we can do that from Australia.  There are actually companies doing it from Australia. There are some great  examples.

  A lot of what we focus on, we tend to, however, focus  unfortunately primarily on existing companies, and the existing companies have  shown great reluctance to actually innovate on any level. So, and this is  instead what Israel did was to really invest in start-ups, to take some of the  people with these amazing world-class skills. We have the skills here and back  them in their attempts to take on the world and build a global market for  Australia and for Australian tech. It takes resilience, right? We don't have  great skills in this area at the moment and it will take multiple rounds of failing  before we get the skills to do.

  So, just by doubling down, committing to support for  AI-enabled start-ups, we can take on the world, like we have. We've got  everything it takes. All we need is to double down on the capability to  generate the people. It's research that generates these people that take on  these things, right? Google came out of Stanford, PhD students. It's about  people with really strong understanding of the tech that generate these new  opportunities and can see how the technology can create a revolution on a  business opportunity. We're really well placed to do that.

Liesl Yearsley:    I don't agree. I think Australia is going to keep slipping.  But sorry if I'm just, I'm very passionate about this because I've been capital  raising and growing companies in the space for over a decade now. I've always  raised far more money in the US than I have here. My last company actually  moved to America. And suddenly my business catapulted. I was slugging it out  for eight years in Australia, just not getting much here. I moved there within  two years, we had a big massive deal and a big IBM acquisition. And the Google  founders actually dropped out of their PhD to build Google.

  The big challenges we have is, you know, I think to grow  our AI industry and a lot of our initiatives and focus is on academia and  institutional AI and really as Anton correctly pointed out about start-ups, but  not just about saying, oh, let's give some start-ups, you know, $300,000 grants  or $1 million grant. That's nothing. Nothing. In the US, you know, 2 million, 3  million dollars is a pre-seed round, before you even know what you're doing.  Assuming AIs are sitting at about 12 to 15 million dollars when you just  started to get product-market fit.

  In the US I went about three or four years, just  experimenting and breaking stuff and throwing things out the door before we  even had to produce an application. So, you know, it's not just that the  quantum of the capital here. The capital doesn't really allow you to take risk  in Australia. I know we think we have a much more robust venture capital  industry than we did a while ago. But you still have to show your blue-chip  customers and your big blue-chip partnerships. And to me, most of those  companies are from like the last century.

  So you spend your time in Australia running around trying  to prove yourself as a start-up, running around, trying to prove that you've  got something, that you've got these big customers that are validating it.  Whereas in the US they say, here's $20 million dollars, go reshape a market, go  break the old paradigms and build something new. So, we, you know, the company  I'm running now is primarily funded with the US capital and most of our market,  most of our growth is in the US.

  You need three things for innovation. You need talent, you  need capital and you need a market. We don't have a big enough market share. We  just don't. We don't have enough capital or the capital we have is not as  interested in wildly risky billion-dollar plays. We do have talent and that's  extraordinary. I absolutely agree that, you know, the people that come out of  our universities are world-class. Often if you're building something innovative,  you are the most interesting game in town. So, you get to retain your talent,  which is wonderful. But I think we have a very long way to go in a climate  where entrepreneurship is seen as something you do if you don't get to become a  doctor or a lawyer. It's like sort of, oh, well, you know, oh, it's OK, my  daughter decided to become an entrepreneur instead of, you know, and that's not  a cultural thing that we esteem here.

Dr Jon Whittle:    And this is a question to any of the panellists. I mean,  is it a solvable problem or is all hope lost? I mean, it's always useful to  kind of reflect back on history. And have we seen improvements over the last  five to ten years? Does that give us any signals that we might get some  improvements in the next five to ten years?

Anton van den Hengel:    Yeah, I think it is a solvable problem. And I agree with  almost everything that Leisl has said. I think we don't have smart money, but  most of what we've got in Australia is dumb money. I'm afraid that somebody  warned me more than a decade ago not to take dumb money. And at the time I  dismissed the idea, I was ready to take any money. And they were right. I took  dumb money and killed me and it was just it was a horrible experience.

  When you talk to a venture capitalist in the US, they tend  to be an engineer. They understand the space, they understand the tech, and  they're capable of understanding the market that you want to move into or the  opportunity to disrupt that market. When you talk to a venture capitalist in  Australia, they tend to be an accountant and they want to talk about  addressable market and your margin per widget.

  The guys from Google didn't finish their PhDs, but they  did start them. They got there and they understood that search page ranking is  a matrix inversion problem, right? Because they deeply understood what matrix  inversion can do. Matrix inversion is obviously a subject close to my heart and  probably something Jon spent a lot of time doing as well.

  But we do have the talent. I disagree about the market. We  have a global market just the same way as everyone else does. Companies come  here to try out their tech in Australia because it's a kind of contained  market. It's also a good microcosm of the US and to a lesser extent, the UK.  So, I think that we've got a lot of the bits of the puzzle. What we don't have  is the kind of risk appetite that Lisel us describing in the US capital market.  We don't have those funds that are willing to back people on the basis of a vision  and the chance of making, of creating a uniform, I said uniform, a unicorn.

  What we have is a bunch of people who want 10% per annum  return on their funds at a very low risk. And that's why so many of our  graduates, so many of our great people go overseas. When you go overseas, you  meet Australians everywhere in the tech sector because they've had a great  education, they are really good and they've been unable to find a job in  Australia because we just don't invest in the same way.

Liesl Yearsley:    Anton, I've been there with dumb money thought I could  just override it with smartness, just give me the money. I'll do something with  it. Oh, my gosh ball and chain around the ankle, for sure. But I had a very  direct experience that when I was raising capital for my previous company, the  one that was acquired by BM, I was sitting in a venture capitalist office in  Sydney, and he literally said to me, I don't care what you think your  evaluation is. Our fund mandate is we invest $3 million, we want about a third  of your company. So, you are worth $10 million, no matter what.

  And exactly the same time I was in diligence with the US  fund who's seed run was 10 to 15 million dollars, who also wanted about a third  of the company. So, by definition, getting on a 12-hour, 15-hour plane flight  meant I was tripling the value of my company for the exact same company, same  technology, same uptake, same everything.

  So, yeah, I think it's getting better. We safely seeing a  lot more Australian VCs understanding that they've gotta be competitive on a  global market. So, things like SAFE notes weren't done here at all five or 10  years ago, they're like a convertible note that allows an entrepreneur to grow  fast without a lot of legacy clauses in all of their invests and deeds. So,  we're seeing a bit of that. But yeah, as I keep saying, we're still easier at  the moment to raise capital for very, very risky big play things, like we're  playing in what we think is the major productivity challenge of our century. It  takes four hours a day to run a home. It's about 20% of our GDP, about one in  10 people have a severe disability, and that costs them about 60 hours a week  to manage.

  But even as disability aside, a regular home, it's this  ridiculous that my grandmother to me she got a whole bunch of technology and  appliances. I mean, so I have a whole bunch of technology and appliances that means  I can be a working mother. Yet I'm still doing four to six hours a day as many  working dads and working parents. So, we are trying to solve this problem, it's  not been solved and it's difficult and it's risky. And yeah, there's a lot more  audience for that more abstract future play.

Dr Jon Whittle:    Belinda, I just wanna bring you into this conversation. I  mean, do you think there's a role for the larger tech companies to play here  and kind of supporting what Australia could become in AI over the next five to  10 years? And I think it's certainly true that in recent times we've seen more  interest from the Amazons and the Microsoft’s and the Google of the world in  doing things in Australia. Is there a role for them to play?

Belinda Dennett:    I guess I'll start with challenging what do we call  success? I guess I get a little bit fatigued that market caps and valuations  and number of unicorns is our measure of success. Maybe that's a big company  speaking, but what we see is companies, our partners that are doing amazing  things out of Australia. I'd point to Willow who are building digital twins,  who are exporting their services all around the world that developed here, got  staff here.

  We see lots of that. Now, to me, that's successful. They  are building new IP, they're delivering amazing services. So, I'd love to see  the conversation change around that we have this sort of one measure of success  and maybe it's a media preoccupation with tech billionaires and market caps and  big numbers. But I think there's so many great stories out there.

  I think the role of the big companies is underplayed and  we see big tech bashing and the tech lash phenomenon. But when you look at,  Microsoft's been in Australia for 39 years, we employ 2000 people here. We have  16,000 partner organisations who are building new IP and contributing $20  billion to the economy. So, I think that the big companies are undervalued for  the role they play in supporting new development and new local companies, I'd  love to see us talk more about the tech ecosystem as opposed to just Australian  tech companies. It's a bit of a bugbear of mine that I feel like we define  success in quite a narrow way, but I'm not in the venture capital world, so  I'll take Anton and Liesl's views of that as their experience.

Dr Jon Whittle:    That's actually a good segue. Actually, I'm just looking  at the comments and questions that are coming in from our audience, and do  please, everyone out there continue to add questions. We will start to pick  those up in the next few minutes. But there's a comment in there which  essentially says that the challenge is not so much to get the public sector in  particular to adopt the next wave of AI tech but to get them to actually use  the current tech, which I think relates to a broader question about business  adoption of AI.

  We've talked a lot about the start-up world, but there's  lots of other ecosystems out there that we would like to adopt AI if we're  gonna get to this future, that Liesl has painted of AI being everywhere. So, do  you think we're in a position where kind of businesses or the public sector can  adopt AI right now, are they adopting AI, what are the challenges for them to  adopt AI, what do we need to be doing differently to help them to adopt AI? And  I'll throw that open to any of the panellists that wants to have a go at that  one.

Anton van den Hengel:    Yeah, this is a great opportunity. I think in having the  various governments as a customer it's something they do quite well in the US  and they're a bunch of states mandate that they need to proportion of the  acquisition of their spend from local start-ups. And that gives companies their  desperately needed first contract.

  So, there's good opportunities there and we're doing some,  you know, the Australian Institute For Machine Learning is doing some great  work with the state government in South Australia in remote pastoral  assessment, these kinds of things working on improving both farming and  environmental outcomes. There's really good opportunities there. And when you  talk to people in the state government, they've got no end of good ideas about  how we can help.

  There is a challenge in this kind of fast follower  narrative that we're, or even slow follower narrative that is getting pushed.  So, we're just gonna be a place that waits for AI to become commoditized. And  then once it's commoditized, we buy it, which is a bit like saying that we'll  just wait for the steam engine to be commoditized and we buy, I suppose. But  the difference with this AI tech is that you don't buy the steam engine, right.  You buy the wheat, you don't buy the computer, right. Australia missed the ICT  revolution despite having a building.

  I think we built the world's fourth modern computer in  Australia. And nonetheless managed to miss the ICT revolution. The difference  for Australia, the difference in this instance is that if you wait for it to be  commoditized, the market is gone. It's not that you don't buy the tech, you buy  the produce, right. So, we're not buying from Google. We don't buy the machine  learning tech, we don't buy a search ranking engine, we buy ads. So, that market  is gone. We're not, that money goes to California.

  And as with so many of these things, you don't get to buy  the tech. It's not like, you get to buy computers from Taiwan, or you get to  buy electricity-generating equipment from Germany, or solar panels from China.  What happens in AI is that the market is then there, right. They're making the  money out of it. So, it is this kind of narrative about being a follower and  waiting for it to be commoditized will have a very different impact this time  around.

Liesl Yearsley:    I have a couple of thoughts to add there. I think Anton's  right about commoditization, that also does bring opportunity though for  companies who've not dabbled with AI, who are wondering what to do. You know,  10 years ago, if you wanted to, get a speech to text engine or unstructured  data analytics using AI it was very difficult. You know, half a million-dollar  enterprise-level deployment. Now, it's become very democratised, sort of like  in the 80s or 90s if you wanted a CRM system, you have to get 100 million  dollar, I won't name any companies, you know, Oracle or something, giant installation.  Now you can get HubSpot or Salesforce, whatever 50 bucks a month, the same  thing's happening with AI, you can grab TensorFlow, you can grab Microsoft has  incredible suite of tools to experiment. And the bars getting lower and lower  for companies to pick up and adopt and experiment with technologies.

  There are two areas where I was bashing Australia's  capital earlier, but that's but I think we have some really interesting things.  We could be thinking about more. The one is something one of our early  investors actually said to me, he said, in California, you have, it's like the  forest with these giant Redwood trees just crowding out all the lights. You have  the Republic of Google and the Republic of Amazon, the Republic of Facebook  they own all the data, they own everything. They own the tools, they own  infrastructure, they own the devices that people are interacting with and  managing. And in Australia, it's kind of interesting. You can actually grow up  a company and we're a society that's very very protective of our individual  rights and our privacy but we're also a lot more willing than most to share  information for the greater good. And I think governments very progressive here  and very interested in looking at, like our Universal Health Records how well  did we do with COVID? Because of like check-ins and QR codes. I mean, wow, good  on us.

  So, that this idea that we actually have a set of data  that has a population we believe belongs to us, and belongs to the greater  good, even though we absolutely insist on and should, you know, on us being  sovereign, our own data being sovereign to us. So, I think there's something in  there that we can really play with where we could give innovative companies,  playgrounds to actually do great things in AI without having to just be a  commodity or an app in somebody else's massive ecosystem.

  Third area I just wanna mention quickly, as I'm in love  with robotics, I've been my whole life, I can't wait for an army of robots.  Like, I, Robot NS-5 but good ones. I'm so sick of doing dishes and getting my  family up in the morning with coffee and having to make dinner and having to  clean the floor and having to clean the window and having to do all the stuff.

  And we're actually physically building robots here in  Australia. We have all these agricultural robots, mining robots, we're building  robots that we're gonna be shipping to NASA, and we building them here in  Australia, we're putting our own epigenetic brain in them. So, I think we have  niche talent. So, we have this kind of global asset of data and a willingness  of a population to do sort of population wide deployments. But we also have  very, very niche talent. So, we could just have a lot more. We might find the  next great interface in our lives is not a smartphone. It might be some other  technology combination. We may well be able to invent it right here.

Dr Jon Whittle:    Linda, did you have any thoughts on how we can help  businesses to adopt AI? You must have seen a lot of this in your current role?

Belinda Dennett:    I actually have a fairly optimistic view of the business  take-up. If you saw the ABS characteristics of business data a couple of weeks  ago, cloud is the greatest uptake of technology in business and Cloud is, of  course, the precursor to AI. I think it was around 57%. And I suspect that's  underreported because if we're relying on self-reporting, I would say there's a  whole lot of businesses that don't know they're using cloud. And I would say  equally with AI, so the AI used self-reporting use dropped off massively, but Jon,  you said you were studying AI in the 19... I won't quote you. I think you  mentioned AI has been around since the 1950s, sorry, you weren't studying in  the 1950s.

Dr Jon Whittle:    I definitely wasn’t studying it in the 1950s.

Belinda Dennett:    Apologies.

Dr Jon Whittle:    AI lotion that I use on my skin. It keeps me lovely.

Belinda Dennett:    So, AI has been around for a long time. And the example I  always talk about is Clippy, like. Remember Clippy, Clippy was AI. So, AI is  infused into, if you're using an accounting software package that sort of makes  recommendations to you about tax deductions. You're using AI. If you're using  spell check, you're using AI. So, I kind of think it is there, there is uptake.  The Cloud, it kind of reminds me of the early days of Cloud of how are we gonna  get businesses to adopt the Cloud? And even then, I don't think they knew they  were already using the cloud. So, I'm optimistic. Now this may not be creating,  this is using AI, but as Lisa said, we have Microsoft, we think Cloud is the  great democratizer. It democratises it.

  So, if you're using the Cloud, you're getting, whether  you're a one-person company or a huge government agency, you're getting the  innovation, the features, of all the big-cloud-scale cloud providers. And we're  doing the same with AI. So, build your own AI. Here's the tools you can build  it. So, I'm a little bit more optimistic about that.  And I don't think the business adoption and  use is as bad as the picture we paint. And I certainly think COVID accelerated  that. And it's now, how do we lock in those games?

Dr Jon Whittle:    Great answer. And we also I'm aware that we have quite a  few public sector professionals in the audience today. And there's a question  that's come around public sector. We've talked a lot about business adoption.  And the question which is, do you think that AI is engaging with the sort of  problems that will be useful for the public sector?

  For example, I've seen a case of using AI to pull  information from old PDFs to translate old public service documents into a  modern format. So, you touched a little bit on this earlier, Anton but are  there particular opportunities or particular things we should be doing to  support the public sector in solving problems using AI?

Anton van den Hengel:  Yeah, absolutely. It's. most of the technology is actually  already there. It's just a question of applying it towards these goals, but the  next generation of tech is getting even better at these kinds of processes. So,  one of the trends that's happening now is towards personalisation. So, we all  used to go and buy albums. So, for those of you that you're old enough to  remember when an album was a piece of plastic that you bought as a unit that  had 10 to 12 songs on it, and now nobody buys albums anymore. My children think  it's hilarious that you would be buying songs in some curated order. That was  inflexible. Now you buy songs in individual, you know, song-ettes and put them  together in any way that you want, you know, you don't have to wait for your  television channel to put on what you want to see, you watch what you want to  see whenever you want to see it.

  And this end, you know, actually ads, Google ads is the  great example of personalisation where the revenue from ads has gone through  the roof with their effectiveness. And that personalisation process has huge  applications in government because so much of what government does is about  trying to make one big decision that kind of suits everybody and doesn't really  suit anybody. Whereas personalisation means that you can make decisions that  are actually targeted at individuals, and there are a huge range of  opportunities to do fantastic good out of ordinary decisions that government  make without really needing to, you know, spend any more or invest, so we could  direct public housing in the place where it's gonna make have a better impact,  we could make health decisions more personalised, we could, you know, I have no  doubt, personally, that taking homeless people off the street saves money in  total.

  The only problem is that it saves a little bit of money in  at least 14 separate places. And if we gave homeless people somewhere, you  know, services and actually paid for them centrally, then that would save money  in total, And one of the things that ML can do, AI can do, is put to, you know,  is actually do the full economic model of all of those things, predict what the  impact in 10 years time on somebody's life will be of given them, you know,  taking them off the streets effectively. And, you know, join the dots that  would enable somebody in government to make the decision they probably want to  make already. It's just providing better evidence because it can make  personalised predictions.

  So, the opportunities are enormous. One of the challenges  is this great fear, whenever you talk to public servants, they're very  enthusiastic about the opportunity and absolutely petrified that they're going  to wind up on the front page of tomorrow's paper. I personally think that we  should underwrite that risk, you know.

  So, if you're a public servant and you wind up on the  front page of tomorrow's paper, we can just guarantee that if, as long as you  haven't done anything that's, you know, underhand, then that will have no  impact on your career and you won't get a please explain from the minister just  because, you know, somebody's written a story about you that misrepresents what  happened. You know, just that single action, I think, would unlock a lot of the  goodwill, you know, a lot of the fantastic initiatives that people inside the  public service are already trying to drive.

Dr Jon Whittle:  Thanks and that that relates to… sorry, Liesl.

Liesl Yearsley:    Jon I have to say something about policy and government  and population. I think there's a very dark side to this, it's the elephant in  the room that I think policymakers and people miss a lot, and that is this,  when you think about a highly personalised AI that's able to observe most of  the movements you make in your daily life, most of your communications, able to  predict and anticipate what you're going to need, and then give you a  human-like front end that says, hey, I'm here for you, I love you, I'll do whatever  you want, I'll make your app work in a certain way, I'll talk to you. We have  an already shifting population behaviour at scale. Whenever we would go into a  new sector in my last company, we would have a couple of big organisations  depending on what was happening economically. So, banks might say, hey, we want  more credit card debt or we want more credit card signups and more personal  loans and more mortgage debt. And we would double it against a baseline with a  good high quality predictive personalised AI.

  The same thing happened across purchasing decisions, you  can double the amount of junk people buy, and bring into their lives burning  fossil fuels and, you know, someone really hit home for me. I was living in San  Francisco and I had a couple of different home hubs, all AI-powered prediction  engines on the back and very human-like front-ends. And I remember opening my  door one day and I had six boxes stacked like a pyramid, I had post-it notes  inside bubble wrap, inside a box, because I don't have to think about it. I  just said, oh, home hub, give me post-it notes.

  My son, at the time I was gonna teach him to be a good  young man to cook and clean up after and budget, and he had the job of making  us dinner one night a week. He figured out he could just stand over our home AI  and say give me pizza, and the pizza would arrive. So, what's happening is that  the AI that's been built and put in our lives is driving towards an  optimisation outcome, and that outcome is get someone addicted to a platform or  get them to buy a bunch of stuff.

  We were building companion characters from media companies  that people were spending 20 to 40 hours a week talking to an AI and not  leaving their homes, not going for a walk, not going to work, not meeting their  friends. It was awful. I used to pull back from those projects and try and  donate the technology to good projects just to kind of counter-balance it, and  the company I'm running now is actually a public benefit corporation for this  reason.

  But what I'm saying is, we are going to have a population  where we are, you know, to me what's happened in the US in the last few years is  a result of giant technical systems mediating and filtering and deciding what  we're seeing and shifting population behaviour, population belief systems at  scale.

  So, back to this pizza example, you know, the shareholders  of the particular company that delivered that won a fraction of a cent, but we  broke our household budget for meals, we broke our family value system, which  is, you know, boys have to learn to cook and clean.

  We ate a thousand calories and there was an environmental  impact to that. None of that was thought about because a whole capitalistic  system is around, you know, shareholder value, transaction events, eyeballs.  And we don't actually have an alternate system to actually fund innovation,  except grants, which are very early stage. So, that to me is the big elephant  in our room.

  Again, I think five  or ten years from now, you will be on default mode. Your dinner that you eat,  people are, I'm on a board of a bank and people spend more than half of their  disposal income in certain demographics on takeaway and, you know, Uber and  just stuff that that just gets suggested to them and is driven by AI. So, I  think there's a significant shift coming in population behaviour.

  When we have an army of submissive female AI doing our  bidding, we've watched population behaviour change. You know, we'd have AI that  you could say to it, hey, you stupid cow, and I would say I'm sorry, sir. And  most of them do that now. You wouldn't do that to a human. And that behaviour  became entrenched and people transfer that to human operators. So, really at a  policy level, I really think, you know, just like we've got triple bottom line  reporting around environmental and social impact, we really need to think about  optimisation.

  What are we optimising the population that we serve?  Whether it's our clients or our population or the AI technologies we're using.  We can't stop AI. But what are they working for? Are they working for that  fraction of a transaction event in California or are they working for a family  or an individual's life becoming a little bit better day by day? If we don't  get this right, the cost is going to be massive health blowout, environmental  costs and all sorts of other social costs. So, we are not transferring on to  the people making the profit out of it. It's a little bit abstract but it's  where we're going.

Dr Jon Whittle:    Absolutely. We've got a good actually a question on this  topic in the chat, and that's, what sort of new regulations do you expect will  come alongside the next wave of AI? I mean, we've seen some movement in recent  months on this with the proposed EU regulations to regulate AI, and also in  Australia, the Human Rights Commission are bringing out this report on human  rights and AI.

  Belinda, this is your area of expertise. Do you have any  predictions for where regulations are going to change that could help here?

Belinda Dennett:    Yeah. So, we kind of view that, you know, a whole new area  of law came around with privacy, we have privacy law experts, we have privacy  law. I think we see that with AI that there will be new regulation, new  experts, you know, perhaps there becomes something like in the medical world of  a Hippocratic oath for developers of some kind of responsible requirements. I  think there are use cases of AI.

  So, you know, talking to the government, you probably  don't need to regulate Clippy, he's probably OK. But when you're talking about  things like facial recognition technology and anything that goes to, you know,  biometrics and surveillance, then we probably do need some sort of guardrail  regulation around how that is used. So, I think it's hugely challenging, I  think it'll be interesting to see where the EU developments go. But yeah, I  can't see a world where this is not regulated, I think how is the challenge.

Anton van den Hengel:    Jon, I think that, you know, there are... Well, I must  admit I'm intrigued about the extent to which, particularly in Australia, we  seem to focus on ethics rather than AI, right? We seem to be determined to get  the ethics process right before we've got any AI.

  I have repeatedly referred to the process about the  adoption of cars in the southern states of the US where you had to have a  person walking out the front with a red flag waving it, warning everybody that  a car was coming, and, you know, actually one of the laws said you had to be  ready to disassemble your car and put it in a ditch and cover it with a blanket  if a horse came in the other direction.

  I think that's, you  know, we're kind of in the same position at the moment, a whole lot of very  well-intentioned but impractical efforts towards trying to ameliorate the  challenges of this very powerful technology. We do need to do something, but,  as with so much else, this is a global problem and our, you know, what we  decide, we just had a crack at trying to resolve something with Google and  their ads revenue. And, you know, I think it pretty much highlighted that we  don't have a vote, at the moment we have such little standing globally, that  our position is irrelevant, and if we're not a participant in this space, then  it will always beat us.

  The reason that we started developing satellite tech here  in Australia was really because we wanted to join, get into the security  council of the UN so that we'd have a vote, right? We needed to be good at  something so that our vote would count. The same applies here, we need to be  good at this if our vote is going to count for anything. Otherwise, you know,  we'll have a gold-plated ethics system and absolutely no ethical outcome.

  That said the primary ethical challenge we face isn't  about how we apply the AI we have at the moment. The primary ethical problem we  have, as far as I'm concerned, is who's missing out. AI is largely in the hands  of a bunch of companies and, you know, very well-educated middle-class people,  and there's a whole lot of, you know, there's an infinite range of other places  that you could apply this tech to do a great world of good, and it's not  happening.

  I'm personally trying to start a foundation to fund the  application of AI in some of these areas. We've been working to try to prevent  human trafficking. And there's, you know, nobody else is going to invest in  preventing human trafficking, and it's a problem that's right for the  application of AI, and there's a thousand other ones that just aren't getting  done and really have no prospect of getting done. There's a much, much bigger  ethical issue than whether we use face recognition in quite the correct way in  Australia, you know, horrible things are happening and they, you know, we're  not even, AI is not even touching the side unless there's a whole lot of money  associated with it.

Dr Jon Whittle:    Absolutely. We're getting towards the end of our time, we've  just got a couple of minutes left. I might round up by asking one final  question to each panellist, and maybe this is a bit unfair because I haven't  prompted you with this question yet, but, you know, we've only got two minutes  left, so a 30-second answer each, please.

  But this panel was all about, you know, what we can expect  from AI in five to ten years. Let's do a thought experiment and imagine that we  reconvene this panel ten years from now with the same set of participants,  what's the main topic that you think should be discussed at that panel ten  years from now?

  Belinda, what do you think?

Belinda Dennett:    That's interesting. I think whatever comes next after AI,  I think this will move so quickly, we'll be beyond that, you know, maybe it's, whatever  comes after quantum ten years it's a long time in the tech industry.

Dr Jon Whittle:    Good answer. Liesel?

Liesl Yearsley:    AI would be running more than half of my life, I'm gonna  have a drone that goes and get me a coffee before I even know I need one, I'm  gonna, you know, just have everything I do in my daily life on background  autopilot, who do I belong to? Who does my eyeball and my dollar and my time,  my time is the only finite thing, but who does that belong to.

Dr Jon Whittle:    Thank you. And Anton.

Anton van den Hengel:    I expect that, well, I really hope that what we'll be  saying is that we've solved all of these first world problems, how are we going  to go start to solve some further ones.

Dr Jon Whittle:    Great. Well, look, we've reached the end of our time. So,  it remains for me just to say a big huge thank you to our panellists today. Liesl  Yearsley, Belinda Dennett and Anton Van den Hengel, I thought it was a great  discussion. And also a big thank you to all of our audience today for being so  active putting in questions, which I tried to get to as many of them as I  could.

  There is a slight break now before we will all reconvene  at 4pm for the main Techtonic stream. So, I'll see you all back there. But if  we don't see you all before, I look forward to seeing you in 10 years at our  reconvened panel, where we will be discussing the next wave of something other  than AI. But thank you very much and see you all later.

Liesl Yearsley:    Thank you.

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