S1: The following is a C and a podcast. This is
S1: the climate conversations, I'm Jamie Hill today. I'm talking about
S1: climate change and artificial intelligence. As far as a climate
S1: crisis goes, we're all looking for solutions, whether it's the
S1: pursuit of carbon neutrality or simply trying to push the
S1: boundaries of energy efficiency. So what can I add? Well,
S1: on human interventions right now face limitations from monitoring and
S1: forecasting to optimizing energy use in datacenters. Buildings and power
S1: grids could be a tool in fighting climate change. Yes,
S1: advances in deep learning and in computational power have made A.I.
S1: less science fiction, more non-fiction and here and now. But
S1: are there also downsides of using A.I. to deal with
S1: a problem as complex as climate change? With me today
S1: is Marcus Croft, professor of chemical engineering and director of
S1: Cambridge Care's the University of Cambridge's Research Centre based in Singapore.
S1: Professor Craft is an expert in using computational models to
S1: reduce carbon emissions. And his new book, Intelligent Decarbonisation, explains
S1: how I might be able to end climate change. Marcus, welcome.
S2: Hello, everyone. Thank you for giving me a chance to
S2: be on the podcast.
S1: Great to have you. I'm going to jump straight into
S1: the first question for the average man in the street.
S1: I'll go out on a limb, and I'll say that
S1: maybe artificial intelligence wouldn't be completely understood fully right? In general,
S1: we know it helps us analyze data and make predictions,
S1: optimize systems, perhaps before we get into climate change. Give
S1: us a quick sort of overview of how EIA is
S1: already being used today as part of our daily lives.
S1: Let me not you wouldn't be entirely aware of
S2: the use of the AI that impacts on everyone. Bailey
S2: is in the search engines and in customer feeds and
S2: web pages like Amazon or similar companies. This is where
S2: we benefit most of the AI right now. In particular,
S2: machine learning has been enormous for about three years ago,
S2: there has been a breakthrough in natural language processing using
S2: the Transformer machine learning networks. We have also increased our
S2: ability to do automatic translation, something I enjoy using almost
S2: every day and all to try to understand what we
S2: are looking for when we type a search request into Google,
S2: for example.
S1: So I'm going to jump on the term that you use, obviously,
S1: is something that we are quite aware of here in
S1: our newsroom. And as machine learning to sort of Segway
S1: into climate change, I'm sure there are machines out there
S1: which are learning about man's involvement and attempts to manage
S1: climate change. Give us a broad sense, then, how AI
S1: and technology in general has already supported our plans various
S1: forms to get to net zero mitigating the impacts of
S1: climate change. What kind of EIA is this and how
S1: might it apply to a specific area? See our water
S1: usage or electricity generation?
S2: This is a very good question, and it's hard to
S2: respond to. One of the problems with the response is
S2: that what do you mean when you see different people
S2: mean different things when they use the word on the
S2: and sort of thing, something magic happens and you get
S2: a fantastic solution out of nowhere. And I don't think
S2: this is actually the case if you look at today's technology.
S2: Of course, there is a trivial way to reduce CO2 emissions,
S2: and that is just to stop using fossil fuels. But
S2: that would have enormous impact on our society and is
S2: generally regarded as not acceptable for staff. Typekit very difficult
S2: to go back to Stone Age. So, OK, what can
S2: we do instead? It is clear that we have to
S2: replace the fossil fuels with something else, and depending on
S2: which sector you're looking at, different solutions are coming out.
S2: The more simple one was actually in the electrical power
S2: networks here. Many people may have heard of the term
S2: smart grid, so this is the first example where digitalisation,
S2: not just A.I., is changing the way we operate. This
S2: is partly necessary because if we use different energy sources. So,
S2: for example, the sun or wind power, then we have
S2: much more fluctuations. The on the grid does not necessarily
S2: match the input of energy that we have. So we
S2: have to look into, for example, clever storage technology in
S2: all these aspects, which would be handled by mathematical algorithms.
S2: And here we go. What do these algorithms look like today?
S2: In many cases, because we have so much data, we
S2: actually don't need to sit down and do physics. But
S2: we could use just the data and sometimes and very
S2: often the a combination of two. And that helps us
S2: control the smart grid or make the smart grid smart,
S2: which is then helping to secure the electricity issue for
S2: the population and industry. I will come into the control
S2: of smart grids,
S1: so I'm going to use the example of dual island
S1: here in Singapore to drill down a little bit into
S1: how grids are smart, how energy systems are smart, and
S1: maybe I will help them get even smarter, right? In
S1: your book, you've written about use cases that are associated
S1: with your island. What did your research? Find on how
S1: iOS could do things from reducing costs to emissions in
S1: a major petrochemical hub like Drone Island and what other
S1: applications are being tested here and what holds the most
S1: promising in your view?
S2: I have to say that if you look at your
S2: own island, that is potentially the most difficult spot. So
S2: for variety of reasons, I personally believe that you will
S2: try to do things in other areas, in particular in
S2: the smart city projects much earlier. So you have basically
S2: two problems. The first problem is, of course, to supply
S2: the energy in a carbon footprint free way. At the moment,
S2: the energy is by meaning a gas power stations, so
S2: you would have to selectively, but which the company owned.
S2: You're allowing to their own proprietary power stations. And we
S2: have a lot of oil generators just for safety. So
S2: and backup replacing the energy will not be straightforward, although
S2: you could argue that hydrogen may be the way forward.
S2: The second problem you have with the chemical industry is
S2: that the product itself have a high carbon footprint in
S2: terms of logistics transporting them around, and b they are
S2: made out of fossil fuels, you know, so that the
S2: refineries produce the commodity chemicals that are then going to
S2: our product that needs to be replaced. We have a
S2: double challenge, so to say. So if you can solve
S2: the problem with two or island, you're basically on top
S2: of things, which is very exciting. And that's partly why
S2: I thought we should look at that. But clearly, I
S2: would be lying if I said, we have a solution
S2: next Monday. Okay, this is long term research with particular
S2: things you can do now. For example, you use overall
S2: network of companies under alignment. So, for example, Company one
S2: may produce waste heat, which then can be used by
S2: another company. You see cost energy resources by doing so,
S2: and we do indeed have the study where we looked
S2: into this. We also did a little study just to
S2: highlight how difficult the problem is where we say if
S2: you want to be brought to the extreme place nuclear
S2: reactors under a. There are many reasons why you wouldn't
S2: want to do that. But if you were just looking
S2: at the CO2 footprint, then this could be a way for.
S2: But it was more an academic study in order to
S2: highlight the consequences if you really want to follow through
S2: with zero carbon footprint challenge.
S1: I would imagine, therefore you would sound as if in
S1: the short term, the very short term things that maybe
S1: can be improved on within a very unique situation like
S1: dual island would be improvements on the fringe rather than,
S1: as you see really huge structural changes. And on the fringe,
S1: you are talking about efficiencies in energy usage. How far
S1: do you think the eye has already helped there and
S1: what more is out there in terms of research that
S1: can actually push the boundaries of energy efficiency in really
S1: intensive environments like Jurong Island?
S2: I have two things to say to that. If you
S2: look at your items, you will see most of the companies.
S2: There are big international companies. So, for example, Exxon being
S2: one of them or both aid and others, they have
S2: fantastic research labs. They know very well what are the
S2: newest methodologies be on the forefront? Developing this methodology so
S2: they know about. But equally, you have to realize that
S2: the investment cycle in such big plant actually quite long.
S2: There is always a balance between sort of capex and opex,
S2: and that will lead to an additional time delay, although
S2: sometimes technology may already be known. Implementing them, making the investment,
S2: getting a return on base, that is what needs to
S2: be looked at if you want to estimate the time
S2: it takes to completely change the industry. In my view,
S2: it also becomes clear that one of the problems is
S2: actually policy making because policy making will have a direct
S2: impact on these costs. Most countries are aware of it,
S2: and if you look to Europe, they discuss carbon tax
S2: and trading. And I think over the next five to
S2: 10 years, it become increasingly the method of choice to
S2: push new technology in to make this investment cycle shorter.
S1: I'm going to switch accommodation to another large company, Google.
S1: And you make reference to them in your book Intelligent Decarbonization,
S1: where you mention an Irish system called DeepMind, helps cool
S1: Google's data centers, and they are obviously quite big users
S1: of energy. And it's apparently helped cut energy consumption by
S1: 30 percent, it said. Explain how that worked. Will this
S1: technology be game changers in a way in terms of
S1: energy efficiency?
S2: It will definitely help. It is not a Magic Machine
S2: learning hospital using controlled strategies to depend on the reaches
S2: of your data. And I'm sure this could be rolled
S2: out of the areas where digitalisation has been developed fine. So,
S2: for example, last year I was trapped in my German
S2: hometown in London, and that's not Wow. Okay, what am
S2: I going to do? So I decided to contact the
S2: mayor and ask them thanks to a smart city. And
S2: you have to know that my little hometown, just 40000
S2: people there are really poor and they don't have money.
S2: And I said, Let's do smart city together. And as
S2: a consequence, they were actually quite open. And we have
S2: a project which basically optimised the distribution network and we
S2: could be using a variety of methodologies that will reduce
S2: the cost for them by 20 percent. And we could
S2: use the CO2 impact into what I'm trying to say
S2: here is what happened with Google. It's not an exemption,
S2: something that we see no doubt in many more areas.
S2: You said API is terribly expensive, and if you look
S2: at the big data centers, you have the carbon footprint,
S2: which would be high. And this is true. However, it's
S2: a bit like the electric car. If you look at
S2: the electric car, they think that solves every problem. But
S2: it's not all me if and only if the electricity
S2: that goes into either electric cars or in the beauty
S2: sector is carbon footprint free. So it's either from solar
S2: or from wind or from fusion within. So it's not
S2: a problem. And in fact, one can say, you know,
S2: we don't have the energy from such as plenty of
S2: energy and our haven't you know what we haven't really
S2: managed to do is to get our head around how
S2: to use solar in the right manner. And this is
S2: a few that has developed enormously quickly over the last
S2: few years are now talking about perovskite cells with very
S2: high efficiencies and here. This is also very important feed
S2: for air. So one of our activities is to automate
S2: chemical laboratory, but not just the laboratory or to automate
S2: the site. Right. So you have robots that perform experiments
S2: to find more sustainable synthesis rules for better materials, and
S2: we can use machine learning to pick the right materials
S2: that will have, in my view, enormous impact on the
S2: development and the efficiency of solar cells, which then in
S2: turn will be because everything will be connected, can then
S2: make computing center just the ones that will just carbon
S2: footprint free. And then off we go.
S1: That's sort of a related point that I would have
S1: then is related to the cost of the machine learning, right?
S1: Using DeepMind, as my example is, showed that just training this,
S1: the AI system is also energy intensive in and of itself.
S1: And there's been studies that find that training a huge
S1: system like that could consume significant amounts of energy and
S1: emissions as well. Is this sort of something that you
S1: keep in mind as you look at research in terms
S1: of how and whether there is a trade off between
S1: his own energy consumption and his purpose in optimizing energy use?
S2: I have three things to say to that. The first
S2: thing is that you are right when you say the
S2: training of an artificial neural network and thinking of particular
S2: about GBP three, look to whether you use a natural
S2: language network. It's also based on this principle technology that
S2: basically has used unprecedented amounts of data. In China, there
S2: is a similar network being trained, but you don't have
S2: to do it training every time. So you train such
S2: a network and then you have to do all minor
S2: modification to use it so that the benefit multiply. Eventually,
S2: if you distributed over all machine learning algorithms that are
S2: then based on something that could be or transform the network,
S2: then the energy use is actually not that dramatic. Second is,
S2: if you look at A.I., it would be a mistake
S2: to just. Weighted to the kind of structure of the
S2: neural network and the way we trained, this field is
S2: rapidly evolving, in my view. We are just about to
S2: ship a new revolution in the way we do this API.
S2: So far, these networks are based on data and data,
S2: and though there is no understanding, they're going to use it.
S2: But if you look at our brains, we have similar
S2: performance and use only a fraction of the energy and
S2: we can learn much, much faster, which is why and
S2: probability that we will get to grips with it is
S2: very high. So, for example, we spend a lot of
S2: effort to develop policies to develop the work model. In
S2: our book, I call it the word avatar, which is
S2: basically nothing else but a representation of the world in
S2: terms of knowledge in this type of space. Now, of course,
S2: you could not just learn on the data as such,
S2: but you learn on stuff that is already known. And that,
S2: of course, will have enormous impact. If you ask me,
S2: will the training for networks that can do similar things
S2: like geometry be always as expensive as it is now?
S2: I don't think so. I'm up set with the next
S2: few years. There will be significant progress in that respect.
S2: The final point I got already made it is our
S2: brain doesn't use that much energy, which basically tells you
S2: there is a lot of room for improvement. Frankly, we
S2: just haven't quite put our heads around it. I believe
S2: although I cannot pinpoint specific areas that will improve, I'm
S2: sure that because there is so much room for improvement
S2: that will happen.
S1: When people talk about the vast sort of potential that's
S1: still out there and then you talk about the revolution,
S1: it's still going to come in terms of artificial intelligence
S1: is that there is still a lot of uncertainty. It
S1: isn't always positive. Much of it revolves around fears around
S1: uncontrollable forces, right? In terms of what I can do.
S1: And you know, going back to your book, one of
S1: the premises is that both A.I. and climate change cause
S1: existential threats to humanity. Right? And I see this quote
S1: in front of me from your book and you ask
S1: this question and I want you to address that too.
S1: And you ask what happens if an artificial general intelligence
S1: decides that the best way to protect the Earth is
S1: to adjust the human population to sustainable numbers? And what
S1: if this number is below the current world population? Talk
S1: about that and sort of the philosophical arguments that you
S1: sort of have within yourself as to the potential and
S1: how to manage that. Those are artificial intelligence and its
S1: potential to help but also addressing the larger concerns that
S1: people may have scientist crafting this specific to climate change,
S1: for example.
S2: I personally think this is a very important question, just
S2: for the record. Okay, I want a happy, happy, happy
S2: world for everyone. So whatever we do, we have to
S2: make sure that this everybody is catered for. So the
S2: question is, what are these dangers and how can we
S2: address it? They are actually quite a bit of literature
S2: that has already done to an Austrian terms on superintelligence
S2: is a perfect example of analyzing the threats that the
S2: Superintelligence may pose. Another good book that I've read that
S2: I really love was my pick marks like 3.0 I can.
S2: We recommend this is the amazing three days of the
S2: whole artificial intelligence aspect, and the key word that is
S2: important is Google alive. So we have to make sure
S2: that the boards of technology are aligned with what we do.
S2: And this was where the problem starts, because what are
S2: our own boards? What is your Google may not be
S2: seen as mighty Google, so how do we pay for?
S2: I have decided that for our work that we start
S2: with the Sustainable Development Goals, which means basically the planet
S2: has to be sustainable. People should not be in poverty
S2: and they should have enough extreme. We should not have
S2: a lot of CO2. We should have been warned to
S2: everybody by. If you should make sure that the system
S2: tries to follow these goals, then my view is how
S2: bad can it be? Do you see the first approximation?
S2: We are sort of saying there will be an AI
S2: that starts killing people anything. I think men do that
S2: themselves enough. But we have to make sure that the
S2: right conditions for everyone, the sort of barrier that will
S2: mean that there is sort of a region, not just
S2: one point. You could imagine it would be a bit
S2: abstract that if you think about the Sustainable Development Goals
S2: at seven p.m., each of those have targets that can
S2: be quantified about and then the state of the world
S2: can be represented by one point one hundred and seventy
S2: dimensional space. Now within that space, you may have some space.
S2: The world was in that space to be good enough.
S2: But whatever happens at this point in, that space is moving. OK,
S2: so for example, if there was a massive problem, okay,
S2: we didn't have enough water for people and it would
S2: come out of it. I would say comfort zone. So
S2: you have to make sure that we constantly keep the
S2: world in this comfort zone that is found by the U.N.
S2: Development Goals. This is the way I think about too much,
S2: and maybe that was a bit too mathematical in terms
S2: of the way I described it. It is a very
S2: important question, and what we are trying to do is
S2: we not only develop the world model, we ought to
S2: develop a way to classify the work model in terms
S2: of these goal. So only then we can do forward align.
S2: The purpose of our artificial intelligence is actually to make
S2: sure that the living conditions for us are good enough
S2: to have a happy and fulfilled.
S1: I'm going to start closing off our conversation and ask
S1: really large, even larger questions, maybe to get a sense
S1: of your take on it. It seems that obviously experts
S1: in the field will say that it's is not going
S1: to be a silver bullet in dealing with climate change.
S1: But there will also be optimists, the likes of Bill Gates,
S1: for example, who believe that technology will potentially evolve to
S1: help overcome such large problems. Where do you stand in
S1: the sort of the realist versus optimist sort of spectrum?
S1: How confident are you in his potential as seen in
S1: that context, for example, in 2050, if we were to
S1: look forward next? Yes. How much of the gains in
S1: our efforts at decarbonization could actually come from gains in
S1: technology and AI? Is it something that should be part
S1: of the thinking for people and governments out there?
S2: In my opinion, absolutely. But the time period that you
S2: have mentioned is so enormous that it's very hard to
S2: think about the technological changes. I have already indicated there
S2: may be potential breakthroughs in both the efficiency, but also
S2: in the performance of these systems. And it just will
S2: help us to solve the problems. And you know, they
S2: can be used for maintenance prediction and energy optimization or
S2: basically in the classical way. We don't need to use
S2: a machine learning algorithm. You can use a classical, argumentative,
S2: similar results, maybe not as good as you can do
S2: now with the AI, but there is more than that
S2: because if you look at society at large, it's a
S2: complex system. There is a lot of information going back
S2: and forth. There is a lot of information loss you
S2: see from one people to another. If you just look
S2: at how science has been done in the past, how
S2: science is done now in students from something of the
S2: life of the ordinary person to not be, nobody else
S2: knows about it. That's over very soon. Every finding is
S2: a finding that is available to other instantaneously. Imagine the
S2: acceleration of the process of finding new things and new
S2: solutions to things, and that is all about government. If
S2: I look at how governments have to do policy, they
S2: are often in the dark and they have a report here,
S2: a report there, and they can't really base their opinions
S2: on proper fact. It's very hard for them to get
S2: it started with the smart cities and smart states and
S2: completely change. Not only they will know at any point
S2: in time, you know what the state of this, but
S2: they can induce progress in the areas we basically call
S2: these parallel worlds in your system, you can work out
S2: what if scenarios and you can implement it right away
S2: very fast. So the time for implementation time for planning
S2: all that shrinks enormously and that will help us to
S2: solve the challenges that we have. It's not just global warming,
S2: it's living in peace together. It's making sure that we
S2: build our environment, and I believe that this will help
S2: to stop same.
S1: It certainly sounds really optimistic. It sounds Huebel. But as
S1: a last question, do you therefore also in your work,
S1: have some concern that in using AI, in looking forward
S1: to the potential for A.I., humans like us may also
S1: be tempted to maybe subcontract decisions using an artificial intelligence
S1: instead of, you know, I would say, hard human choices,
S1: hard human decisions that have to be made separately from
S1: what I have to deliver.
S2: Well, I subcontract decisions in my life with software.
S1: That's always the smart thing to do.
S2: Yes, they do. But what I'm trying to say is this,
S2: of course, we will subcontract decisions to our own benefit.
S2: Every decision has consequences. You have to be aware of
S2: what they are and be willing to see them through.
S2: You know, if you look at this palest time machine
S2: and you have the Eloise and the Warlocks and the
S2: Eloise living in this fantasy world, where they don't have
S2: to care for anything and sort of slowly develop into vegetables,
S2: is that going to happen with humans? I hope not.
S2: Even if there was a super intelligent, you knew everything
S2: that can, you know, it would be not relevant to
S2: us because we still enjoy each other's company. Our thoughts,
S2: of course, would be in order. You can just look
S2: things up, but that doesn't mean that you have fully
S2: understood it and you can still think about it. Or
S2: you can think about what the system was a great agency.
S2: So I don't think it'll happen impact on our meaning
S2: as a human being. And I think this is very important. Well,
S2: Marcus Kraft, thank you very much. Thank you for being
S2: with opportunity to talk to.
S1: And thanks for listening to the climate conversation, stay up
S1: to date on CNN's coverage of climate change on CNN Asia.
S1: You can also find this and other senior podcasts on
S1: our website and on iTunes and Spotify. The team behind
S1: this podcast, Christina Robert Insulating and Erin Low. I'm Jamie
S1: Hoh again to next week.