Latest / Luke's English Podcast / Where Genome Editing and Artificial Intelligence meet New Species Lie(M4A_128K)

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Where Genome Editing and Artificial Intelligence meet New Species Lie(M4A_128K) · Luke's English Podcast

• 0:07
  Speaker 1: Hi Simon, so how are you doing?
• 0:09
  Speaker 2: I'm good thanks, Tim, how are you?
• 0:11
  Speaker 1: Yeah, not too bad. So today's guest is kind of
• 0:15
  Speaker 1: a little bit like genetic, chemical engineering, sort of protein-y
• 0:20
  Speaker 1: stuff that you do, but also like evolution and like
• 0:24
  Speaker 1: how evolution works and he envisages a bit of a
• 0:30
  Speaker 1: brave new world to be honest.
• 0:33
  Speaker 2: Yeah, absolutely. Sort of exciting, a little bit frightening, a
• 0:37
  Speaker 2: little bit, you know, a little bit of everything.
• 0:39
  Speaker 1: He's, yeah, so what, for our listeners, what he thinks
• 0:42
  Speaker 1: we're just, you know, we've started, you know, we've gone
• 0:47
  Speaker 1: into a transition where people have now designed on the
• 0:50
  Speaker 1: computer a virus. They've built it and it kind of works.
• 0:53
  Speaker 1: It goes into cells and makes copies of itself. So
• 0:55
  Speaker 1: they've done that from scratch. And he thinks this is
• 0:58
  Speaker 1: kind of signaling an age where we can author life.
• 1:01
  Speaker 1: We can author DNA and we can build things. So
• 1:03
  Speaker 1: we could, you know, we could, whatever we could envision,
• 1:07
  Speaker 1: we might be able to get biology to do it.
• 1:08
  Speaker 1: And it's, I mean, you know, he admits we're not
• 1:13
  Speaker 1: there yet, but he thinks we're there. And he talks
• 1:16
  Speaker 1: about various uses for it, whether it's in medicine and
• 1:19
  Speaker 1: what have you. But obviously the kind of headline eye-catching
• 1:23
  Speaker 1: stuff is we might be able to sort of build Yeah,
• 1:29
  Speaker 1: I mean.
• 1:31
  Speaker 2: That's the really exciting and novel thing, right? So not
• 1:37
  Speaker 2: just sort of tinkering with what's there already, but starting
• 1:40
  Speaker 2: from a blank slate.
• 1:43
  Speaker 1: Yeah, so, you know, flying hippopotamus, maybe. Who knows? Something
• 1:47
  Speaker 1: like that.
• 1:48
  Speaker 2: Is that the first thing your brain goes to?
• 1:50
  Speaker 1: That's always the first thing my brain goes to, flying hippopotamus.
• 1:53
  Speaker 1: I think, you know, terrifying. That's what would really terrify me.
• 1:58
  Speaker 2: Shall we get him on?
• 1:59
  Speaker 1: Yeah, let's call him in.
• 2:01
  Speaker 3: Yes, thank you for having me on the show. My
• 2:04
  Speaker 3: name is Adrian Wolfson. As you can tell from my voice,
• 2:09
  Speaker 3: I'm originally from England, but I've been living over here
• 2:13
  Speaker 3: in the US for 15 years now. Most recently in
• 2:19
  Speaker 3: San Francisco, my background is in medicine and molecular genetics.
• 2:23
  Speaker 3: I trained in medicine in the UK, did about seven
• 2:27
  Speaker 3: or eight years in the laboratory of molecular biology with
• 2:30
  Speaker 3: Cesar Milstein, the guy who made monoclonal antibodies, went into industry, BMS, Pfizer,
• 2:38
  Speaker 3: head of R & D at the gene editing company
• 2:40
  Speaker 3: called Sangmo, chief medical officer of a cancer vaccine company,
• 2:44
  Speaker 3: and then ended up forming my own genome writing company,
• 2:48
  Speaker 3: and most recently have written a book called On the
• 2:52
  Speaker 3: Future of Species.
• 2:55
  Speaker 1: Which is fantastic. I really enjoyed reading. And that's out
• 2:58
  Speaker 1: on February the 12th, I think, isn't it? It's Bloomsbury
• 3:00
  Speaker 1: that's published.
• 3:01
  Speaker 3: That's right. Yeah, that's right.
• 3:03
  Speaker 1: And available from all good independent booksellers, but also on Amazon.
• 3:08
  Speaker 1: So I thoroughly recommend it to our readers. And you
• 3:14
  Speaker 1: And the premise of the book is we're basically entering
• 3:19
  Speaker 1: or just in the process of entering a really new
• 3:22
  Speaker 1: age where, you know, we can author, we can design genomes,
• 3:29
  Speaker 1: we can edit genomes to make... super species or completely
• 3:34
  Speaker 1: new species, things that evolution hasn't discovered before? Is that
• 3:38
  Speaker 1: a fair overview of where you think the technology is
• 3:42
  Speaker 1: at or very soon to be by combining gene editing
• 3:45
  Speaker 1: and AI?
• 3:46
  Speaker 3: Yeah, I mean, basically, I think that we're at this
• 3:50
  Speaker 3: kind of critical phase transition. You know, it's a key
• 3:55
  Speaker 3: moment in the history of life. And actually, the purpose
• 3:58
  Speaker 3: of my book is to call that moment because we've
• 4:00
  Speaker 3: actually entered it. We're not about to enter it We
• 4:03
  Speaker 3: have entered it by stealth, and people need to know that.
• 4:07
  Speaker 3: We're on the nursery slopes. We're not on the double
• 4:09
  Speaker 3: diamond black runs. We're on the kind of six-year-old ski bump,
• 4:14
  Speaker 3: but we're on the slope, so to speak, right? And
• 4:17
  Speaker 3: it's just a question of time before our skills mature
• 4:20
  Speaker 3: enough to get us onto the double diamond runs, right?
• 4:24
  Speaker 3: But the key premise of all of this is that
• 4:27
  Speaker 3: for the last four billion years of life on Earth,
• 4:32
  Speaker 3: all life has evolved by a process of evolution by
• 4:36
  Speaker 3: natural selection. That process has no foresight, no intentionality, no
• 4:43
  Speaker 3: guidance from an intelligence. And science has been a process
• 4:49
  Speaker 3: of observation of received life, which is modified incrementally over
• 4:55
  Speaker 3: time by this kind of sieve, if you like, a
• 4:59
  Speaker 3: selection process, which operates by randomly changing DNA, but there's
• 5:05
  Speaker 3: no actual oversight or design. And the key transition, if
• 5:09
  Speaker 3: you like, is that all of a sudden, through the
• 5:13
  Speaker 3: use of AI and the ability to write genomes at scale,
• 5:16
  Speaker 3: suddenly we acquire the potential to write biology, to author it,
• 5:22
  Speaker 3: to design it with intentionality. So all of a sudden,
• 5:26
  Speaker 3: evolution isn't the only game in town. There are now
• 5:30
  Speaker 3: two authors. There's evolution and And there's design driven by
• 5:35
  Speaker 3: humans guiding AI, a kind of collaboration, if you like,
• 5:39
  Speaker 3: between natural and artificial intelligence.
• 5:42
  Speaker 2: So it's a bizarre kind of, I mean, I'm not
• 5:45
  Speaker 2: now religious, but I come from a religious background, and
• 5:48
  Speaker 2: it's almost like a reversal, right? It's of the kind
• 5:52
  Speaker 2: of traditional religious way of thinking where there's a designer,
• 5:56
  Speaker 2: and then this is where we've had evolution comes first,
• 6:00
  Speaker 2: and then the sort of designers are coming along secondaries.
• 6:04
  Speaker 3: Yeah, I mean, obviously, if you're able to design life,
• 6:08
  Speaker 3: and as I said, we're on the nursery slopes. A
• 6:10
  Speaker 3: chap called Brian He at the Art Institute in Stanford
• 6:14
  Speaker 3: has technically used an AI-driven process to design the first
• 6:20
  Speaker 3: ever entirely new and synthetic species. It's actually a biological
• 6:25
  Speaker 3: entity because it's a virus, not a more complex organism.
• 6:29
  Speaker 3: But nevertheless... It is a designed, AI-driven, designed genome, right?
• 6:34
  Speaker 3: So that's a kind of proof of concept. We can't
• 6:37
  Speaker 3: write new types of zebras or giraffes or whales just yet, right?
• 6:42
  Speaker 3: But what it shows is that the design process underlying
• 6:47
  Speaker 3: all life is rules-based. And if we can unpick those
• 6:51
  Speaker 3: rules one by one, we will eventually have the ability
• 6:56
  Speaker 3: to build life as we desire, right? Now, Is that,
• 7:00
  Speaker 3: I mean, I think you're alluding to the question, are
• 7:02
  Speaker 3: we playing God? Have we acquired some kind of divine
• 7:07
  Speaker 3: potential or capability? And my answer to that is what
• 7:13
  Speaker 3: we've acquired is responsibility, right? Because before all of this,
• 7:17
  Speaker 3: you know, we just passively tinkered with what was out there.
• 7:21
  Speaker 3: You know, gene editing, for example, is a bit like
• 7:24
  Speaker 3: redlining a text. You know, you can tinker with genomes,
• 7:27
  Speaker 3: but you're fundamentally constrained by what nature has handed you, right?
• 7:31
  Speaker 3: You can't really deviate from the tree of life very far, right?
• 7:37
  Speaker 1: Sorry, go on.
• 7:39
  Speaker 3: I was just going to say, with the ability to
• 7:40
  Speaker 3: write life and design it, we can actually explore potentially
• 7:44
  Speaker 3: the forest of life, which is everything which has never been,
• 7:48
  Speaker 3: which is far, far greater than what has been. But
• 7:51
  Speaker 3: also we can re-examine what exists and think about how
• 7:55
  Speaker 3: we might wish to change it for the benefit of society.
• 7:59
  Speaker 3: And medicine.
• 8:00
  Speaker 1: So the nursery slopes, I'm just sort of thinking what
• 8:03
  Speaker 1: they look like at the moment, where we are. And
• 8:05
  Speaker 1: we had one guest on the podcast a few months ago,
• 8:10
  Speaker 1: a guy called David Baker, who won the Nobel Prize
• 8:13
  Speaker 1: for Chemistry. And what he did was he basically designed
• 8:18
  Speaker 1: a new protein which would bind to snake antivenom. He
• 8:23
  Speaker 1: then took the genetic code to produce that new protein,
• 8:29
  Speaker 1: put it into bacteria, produce the protein. So it was
• 8:32
  Speaker 1: a bacteria that he was modifying. But then when he
• 8:35
  Speaker 1: injected that protein into mice, he discovered that it did
• 8:38
  Speaker 1: actually work and it neutralized the, you know, it neutralized
• 8:42
  Speaker 1: the venom. Is that, is that the sort of, when
• 8:44
  Speaker 1: you talk about the nursery slopes, is that the sort
• 8:46
  Speaker 1: of thing you're thinking about?
• 8:48
  Speaker 3: Yeah, that, yeah, that's sort of extremely consequential and kind of,
• 8:56
  Speaker 3: on the ski lift, if you like it, because I mean,
• 8:58
  Speaker 3: that's not to demean what David's done. David's a genius,
• 9:02
  Speaker 3: as is John Jumper, Demis Hassibis, all of those guys,
• 9:05
  Speaker 3: because what they've done is they've shown that in the
• 9:09
  Speaker 3: same way that mathematics was the language of physics, it
• 9:13
  Speaker 3: seems as if the language of biology is best interpreted
• 9:17
  Speaker 3: not using mathematics, but using AI. And what they showed
• 9:22
  Speaker 3: is that if you use AI simply to look at
• 9:25
  Speaker 3: the simplest kind of functional units of biology, which are genes,
• 9:30
  Speaker 3: protein encoding genes, you can actually, by interrogating huge data sets,
• 9:36
  Speaker 3: pull out principles which we don't actually even understand, which is,
• 9:41
  Speaker 3: I didn't really finish the question about why we're not gods,
• 9:44
  Speaker 3: but we don't understand the rules, right? And we don't
• 9:46
  Speaker 3: make universes, by the way, either, right? So we're very,
• 9:49
  Speaker 3: very far from any type of metaphysical power. But as
• 9:54
  Speaker 3: I said, the key is we've acquired responsibility. But what
• 9:57
  Speaker 3: David has done, David Baker and David is a genius,
• 10:01
  Speaker 3: as is Dennis DeSebius, as is John Jumper. What they
• 10:04
  Speaker 3: did is they've kind of used AI to enable us
• 10:07
  Speaker 3: to predict the structure of proteins and to redesign proteins.
• 10:11
  Speaker 3: And proteins are kind of atomic units from which organisms
• 10:15
  Speaker 3: are built. What they haven't done, what they haven't done,
• 10:18
  Speaker 3: is go beyond that and actually kind of generalize those
• 10:22
  Speaker 3: principles to whole genomes. And the person who's leading that
• 10:28
  Speaker 3: kind of particular stream of scientific endeavor, which is, to me,
• 10:32
  Speaker 3: what it means to be on the slopes. That's why
• 10:34
  Speaker 3: I said they're on the ski lift. I mean, that's
• 10:37
  Speaker 3: in absolutely no way to trivialize what they've done, what
• 10:42
  Speaker 3: they did. laid the foundations for everything else that goes
• 10:46
  Speaker 3: beyond that. And it was absolutely of similar importance. And
• 10:49
  Speaker 3: they all were very, you know, worthy of their Nobel Prizes.
• 10:54
  Speaker 1: Actually, you know.
• 10:55
  Speaker 3: But actually, in my view, the next step is the
• 10:59
  Speaker 3: one that's really kind of takes it to the next level,
• 11:01
  Speaker 3: which is to take those same kind of approaches of
• 11:04
  Speaker 3: interrogating huge databases using AI and then technologies for writing
• 11:10
  Speaker 3: DNA to actually build genomes, right? And so Brian He,
• 11:16
  Speaker 3: as I mentioned before, who I think is really the
• 11:19
  Speaker 3: pioneer of this field, recently showed that he could use
• 11:22
  Speaker 3: AI interrogating huge databases of viral genomes and bacterial genomes
• 11:28
  Speaker 3: to design a new viral species by the formal criteria.
• 11:33
  Speaker 3: So that, to me, is the proof of concept that
• 11:37
  Speaker 3: AI can design new life. To me, it's like a
• 11:41
  Speaker 3: Wright Brothers moment. The plane has kind of bumped off
• 11:44
  Speaker 3: the ground. It hasn't quite taken off, but we know
• 11:48
  Speaker 3: that we can fly in principle. And it's just a
• 11:52
  Speaker 3: matter of time before we build a 747. And I
• 11:56
  Speaker 3: won't try and guess how long that will be. But
• 12:00
  Speaker 3: we have taken off. This is happening. People need to
• 12:03
  Speaker 3: know about it. There are obviously huge societal consequences. We
• 12:07
  Speaker 3: need to ensure that there are guardrails in place, that
• 12:10
  Speaker 3: there's an alignment internationally about what we should and shouldn't do.
• 12:14
  Speaker 3: We need to do this wisely, safely, responsibly, in an
• 12:18
  Speaker 3: equitable manner that benefits all humankind and that benefits society
• 12:22
  Speaker 3: in a meaningful and safe manner.
• 12:26
  Speaker 2: But Adrian, isn't the big impediment to doing that still
• 12:32
  Speaker 2: this big gap we have in that in the genotype-phenotype relationship.
• 12:38
  Speaker 3: Yes, you're absolutely right, Seema. And that's where we believe,
• 12:43
  Speaker 3: you know, AI will help us. And AI, you know, remember,
• 12:48
  Speaker 3: it's still in its infancy. Really, the type of AI
• 12:51
  Speaker 3: that's enabled us to, you know, people like David Baker
• 12:55
  Speaker 3: to do what they have done is, you know, it's
• 12:57
  Speaker 3: only a couple of years old. And that was limited
• 13:00
  Speaker 3: by technology. It needed these computing architectures called GPUs, right,
• 13:06
  Speaker 3: which were developed for the gaming industry, actually, driven by
• 13:10
  Speaker 3: arcade games like Space Invaders. I remember, I hope none
• 13:16
  Speaker 3: of my old school teachers are listening, or they may
• 13:18
  Speaker 3: not even be alive, but I confess on this program
• 13:23
  Speaker 3: to missing the odd day of school to go and
• 13:25
  Speaker 3: play Space Invaders in a cafe in Hampstead. It In fact,
• 13:29
  Speaker 3: the graphics enabled, which at the time was a cutting
• 13:34
  Speaker 3: edge video game, were exactly the same graphics that had
• 13:37
  Speaker 3: been used for AI. And it just turned out that
• 13:41
  Speaker 3: they were useful for AI. And that's what's enabling this
• 13:45
  Speaker 3: new revolution. Now, we're not going to be able to
• 13:49
  Speaker 3: understand the rules of life in a day, right? We're
• 13:52
  Speaker 3: doing it incrementally. And that's why I said, you know,
• 13:54
  Speaker 3: we're on the nursery. So whereas the rules... And they're
• 13:58
  Speaker 3: grammatical rules, because biology is a language. We're talking about
• 14:02
  Speaker 3: the language of life. But instead of trying to learn
• 14:05
  Speaker 3: German on day one, one of the most complex languages grammatically,
• 14:11
  Speaker 3: we're starting off with Pidgin English, which is a viral genome.
• 14:14
  Speaker 3: Or if you want to think of it in terms
• 14:16
  Speaker 3: of authorship, one of the most memorable books that my
• 14:21
  Speaker 3: child did was Topsy and Tim Go Fishing, which is
• 14:24
  Speaker 3: the most intellectually taxing book today. for us, right, but
• 14:29
  Speaker 3: it was at that age, right, but it's not Dostoevsky, right,
• 14:32
  Speaker 3: or War and Peace, right, but it is on the road,
• 14:35
  Speaker 3: you know, once you can write Toxie and Tim go fishing, then,
• 14:39
  Speaker 3: you know, you're on the road to reading and writing Dostoevsky.
• 14:43
  Speaker 2: Perhaps for our listeners, we should just explain what we
• 14:46
  Speaker 2: mean by that gap between genotype, just thinking between genotype
• 14:50
  Speaker 2: and phenotype, so what we're saying here is just because
• 14:53
  Speaker 2: we know what a genome is, we don't know what
• 14:56
  Speaker 2: the sort of consequences of those genes are, how they
• 15:00
  Speaker 2: manifest in an organism, how that organism interacts.
• 15:03
  Speaker 3: One other important point there, Seema, and this actually goes back,
• 15:07
  Speaker 3: I've just been reading, I shouldn't be plugging someone else's
• 15:09
  Speaker 3: book when I'm doing, but I just read a brilliant
• 15:12
  Speaker 3: book by Gregory Raddatt, which is about the sort of
• 15:15
  Speaker 3: history of Mendelism and how actually, you know, very few
• 15:19
  Speaker 3: genes are Mendelian actually at the end of the day.
• 15:21
  Speaker 3: And that's because in reality, you there are actually multiple
• 15:26
  Speaker 3: layers of information that contribute to what used to be
• 15:30
  Speaker 3: called traits or phenotypes in the modern parlance, right? And
• 15:34
  Speaker 3: actually in my book, so I'll go back to my book,
• 15:37
  Speaker 3: I actually use a term which I think is very useful.
• 15:40
  Speaker 3: Nobody else has used it. And I refer to the informium, right?
• 15:45
  Speaker 3: And the informium is the information that constructs a phenotype.
• 15:50
  Speaker 3: And that information, it's true, is grounded in DNA. And
• 15:54
  Speaker 3: I'm not saying genes only, because genes, protein-encoding genes, are
• 15:58
  Speaker 3: just a small part of the story. Most of the
• 16:01
  Speaker 3: genome transcribes RNA molecules, and they've been overlooked historically, right?
• 16:06
  Speaker 3: And we've talked about there being 20,000 genes. If you
• 16:11
  Speaker 3: account for all the RNA-producing genes, there.
• 16:13
  Speaker 1: Are many, many more, right?
• 16:14
  Speaker 3: However, it's not just genetics which controls phenotypic development, learning, culture,
• 16:21
  Speaker 3: the bugs that live within you, you know, the bacterial
• 16:24
  Speaker 3: genes in your body far outnumber our own. And it's
• 16:27
  Speaker 3: now been clearly shown that having one species more or
• 16:32
  Speaker 3: less of bacteria in your body can influence your response
• 16:36
  Speaker 3: to immunotherapy. It can switch other genes on and off, right?
• 16:40
  Speaker 3: So the genome will never be the whole story. And
• 16:44
  Speaker 3: therein may lie the fundamental problem that we may not
• 16:47
  Speaker 3: ever be fully computable even in principle, right? Because in
• 16:52
  Speaker 3: order to fully describe a human being or any other organism,
• 16:56
  Speaker 3: you would need a complete description of all of these
• 16:59
  Speaker 3: different levels of information, which may not be possible even
• 17:03
  Speaker 3: in principle, although a genomic description may be the closest
• 17:06
  Speaker 3: we get.
• 17:08
  Speaker 1: So, I mean, given that, what do you envisage the
• 17:11
  Speaker 1: starting point being? So do you imagine a, so, you know,
• 17:15
  Speaker 1: are you starting with a completely You want to make
• 17:17
  Speaker 1: a new species. Are you starting with a new species
• 17:20
  Speaker 1: of bacteria, let's say? So still something reasonably simple compared to, say,
• 17:26
  Speaker 1: a multicellular organism like you and me with 30 trillion
• 17:29
  Speaker 1: plus cells. Are you envisaging we start with the blank slate?
• 17:35
  Speaker 1: Or are you saying, okay, well, we know that there
• 17:38
  Speaker 1: are some highly conserved genes that all of life uses.
• 17:41
  Speaker 1: We'll throw a few of those in. And then we're
• 17:44
  Speaker 1: going to start sort of saying, okay, I want it
• 17:47
  Speaker 1: to have green hair, you know, the dashing good looks
• 17:52
  Speaker 1: of Tim, you know, whatever. How are you envisaging this?
• 17:58
  Speaker 1: You know, is it the back stage?
• 18:00
  Speaker 2: Yeah.
• 18:00
  Speaker 3: I think the first thing to say is, you know,
• 18:02
  Speaker 3: why might we wish to do this, right? My prediction,
• 18:06
  Speaker 3: and I think I'm definitely right about this, is that
• 18:08
  Speaker 3: we're entering the age of biology or what I call
• 18:13
  Speaker 3: generative biology, right? what people in the UK generally call
• 18:17
  Speaker 3: engineering biology, right? But basically, biology is set to become infrastructure.
• 18:22
  Speaker 3: That's really important. So everything that's done through destructive methods
• 18:27
  Speaker 3: like chemistry could in principle be done using biology, which
• 18:31
  Speaker 3: is sustainable. And in order to do that, we need
• 18:35
  Speaker 3: to turn biology into a programmable material like computer code, right?
• 18:41
  Speaker 3: and into a predictive engineering material. So let me give
• 18:44
  Speaker 3: you some examples. You know, for example, you could store
• 18:47
  Speaker 3: most of the world's information and storing information, as you know,
• 18:51
  Speaker 3: is incredibly energy consuming. You could store most of the
• 18:56
  Speaker 3: world's information in relatively small amounts of DNA, right? So
• 19:01
  Speaker 3: DNA could be used to archive information really efficiently. You
• 19:05
  Speaker 3: can use DNA to make biomaterials. I went to the
• 19:08
  Speaker 3: Ben Ali and In Venice, the year before last, they
• 19:11
  Speaker 3: were building bricks from mushroom proteins, for example. People are
• 19:15
  Speaker 3: making car seats out of biomaterials. You know, spider silk
• 19:22
  Speaker 3: has the tensile strength of steel, right? People are making bioconcrete,
• 19:27
  Speaker 3: which repairs itself, right? You know, bioenergy, biocomputing, right? And
• 19:33
  Speaker 3: then another huge, so it's not just, I mean, making
• 19:37
  Speaker 3: new species is part of the story, but the biggest story,
• 19:41
  Speaker 3: if you like, is just making biology programmable and also
• 19:46
  Speaker 3: enabling us to really leverage AI because at the moment,
• 19:50
  Speaker 3: there's a huge disconnect between our ability to design things
• 19:54
  Speaker 3: on computers and our ability to then build those things
• 19:57
  Speaker 3: and then feed back to the AI. And in order
• 20:01
  Speaker 3: to do that, you need to be able to build
• 20:04
  Speaker 3: DNA at scale, something that I've actually been working on
• 20:09
  Speaker 3: with my colleague at Caltech. And you might see something
• 20:13
  Speaker 3: in the news fairly soon about that because it was
• 20:16
  Speaker 3: the bottleneck. You need to be able to design DNA
• 20:19
  Speaker 3: using AI, but you also need to be able to
• 20:21
  Speaker 3: build DNA at scale rapidly, efficiently, accurately at scale and
• 20:27
  Speaker 3: at really low cost, right? So to go back to
• 20:30
  Speaker 3: your question, I just wanted to sort of contextualize that, right?
• 20:33
  Speaker 3: You know, I think what, what, what, really makes this
• 20:38
  Speaker 3: difference to the, I mean, CRISPR was a huge revolution,
• 20:42
  Speaker 3: let's face it. Jennifer Doudna, all of those guys, George Kirchner.
• 20:46
  Speaker 3: CRISPR allowed us for the first time in history to
• 20:49
  Speaker 3: deliberately and specifically change genomes in particular positions in a
• 20:55
  Speaker 3: way that breeding could never do. Breeding was a sort
• 20:58
  Speaker 3: of black box. We could kind of crudely guide phenotypes
• 21:03
  Speaker 3: traits in certain directions. And in fact, corn, is a
• 21:07
  Speaker 3: good example of the result of that, because corn started off,
• 21:11
  Speaker 3: as you know, as tisanite, which is a wild kind
• 21:14
  Speaker 3: of weed, actually. And through breeding, selective breeding, we created
• 21:18
  Speaker 3: a global cash crop, right? And so it can be done,
• 21:21
  Speaker 3: but it takes, I think that took 30,000 years, right?
• 21:24
  Speaker 3: Something that could have been achieved today in a couple
• 21:27
  Speaker 3: of days. So gene editing is great, and it was
• 21:32
  Speaker 3: an unprecedented skill. And I spent several years of my
• 21:35
  Speaker 3: life working on gene editing. However, Gene editing, as I
• 21:39
  Speaker 3: said earlier, is like redlining a text. You are an editor, right?
• 21:44
  Speaker 3: And Tim, you've written a book. I've written a book, right?
• 21:48
  Speaker 3: We know that we pride ourselves because we are authors, right?
• 21:52
  Speaker 3: Authors don't edit. So when you wrote your book, Tim,
• 21:57
  Speaker 3: you didn't go and get Macbeth and start turning it
• 22:02
  Speaker 3: into a book on evolutionary biology or the history of
• 22:05
  Speaker 3: the universe, right? You just sat down with a blank
• 22:08
  Speaker 3: page and wrote out the contents of your mind in
• 22:12
  Speaker 3: a totally unconstrained manner. And you allowed your creativity to
• 22:17
  Speaker 3: run in any direction that you wanted it to do.
• 22:21
  Speaker 3: And that's the difference between editing life, either through selection,
• 22:27
  Speaker 3: which is one way of editing, or natural selection, or
• 22:30
  Speaker 3: through literally gene editing. When you author life, and design
• 22:34
  Speaker 3: life using AI, using rules, which you may not fully understand.
• 22:38
  Speaker 3: In fact, we don't understand them at all, but we
• 22:41
  Speaker 3: can discern them. Then that's a totally different ballgame, because
• 22:44
  Speaker 3: now we don't need to be constrained by history. In fact,
• 22:49
  Speaker 3: quite the opposite. Life can break off from the tree
• 22:53
  Speaker 3: of life and become ahistorical, And in fact, we don't
• 22:59
  Speaker 3: need to reference existing life in any shape or form.
• 23:02
  Speaker 3: We might get some clues from it as to what
• 23:05
  Speaker 3: works and what doesn't, but we're no longer constrained. And
• 23:09
  Speaker 3: remember that life as we know it, both past, present
• 23:14
  Speaker 3: and future, is but a tiny grain of sand on
• 23:19
  Speaker 3: a massive beach of possible life, you know, endless beach.
• 23:22
  Speaker 3: So we've explored the tiniest fraction of possibility. So just
• 23:26
  Speaker 3: imagine what's out there now. Just by way of example,
• 23:29
  Speaker 3: imagine that a dodo had never existed, right? We'd have
• 23:33
  Speaker 3: no idea that a dodo-like bird exists in principle. In fact,
• 23:38
  Speaker 3: a dodo has a timeless mathematical existence. It's extinct. But
• 23:42
  Speaker 3: the description of a dodo mathematically, biologically, will always been
• 23:47
  Speaker 3: there in perpetuity for the rest of time. And similarly,
• 23:52
  Speaker 3: it would be possible to build a map of all
• 23:55
  Speaker 3: possibility one day using AI And to be able to,
• 23:59
  Speaker 3: you know, it's like the map of the world, really,
• 24:01
  Speaker 3: but it's much, much bigger, obviously. As, oh, over there,
• 24:04
  Speaker 3: you've got flaminoceroses across between a flamingo and a rhinoceros.
• 24:08
  Speaker 2: But Adrian, isn't this really scary, what you're saying? In
• 24:13
  Speaker 2: the sense that the potential for disaster seems to me
• 24:18
  Speaker 2: to be enormous.
• 24:20
  Speaker 3: We could create... As I said earlier, I... I think
• 24:25
  Speaker 3: it's clearly a technology which is very consequential in the
• 24:29
  Speaker 3: wrong hands and without a clear moral kind of foundation
• 24:34
  Speaker 3: and with the kind of values that I alluded to earlier,
• 24:37
  Speaker 3: sense of responsibility, doing it wisely and really thinking deeply
• 24:43
  Speaker 3: about how everybody could benefit from it, how society can benefit. Then,
• 24:47
  Speaker 3: you know, without that kind of consensus and guardrails, yes,
• 24:51
  Speaker 3: it is dangerous. But like all technologies, This is a
• 24:54
  Speaker 3: dual use technology. So there was, you know, think of
• 24:57
  Speaker 3: the wheel. What a dangerous technology, right? You can use
• 25:00
  Speaker 3: it to make the wheelbarrow to take your fruits and
• 25:03
  Speaker 3: vegetables into market. Or you can make a chariot, a
• 25:05
  Speaker 3: war chariot. Look at fire. That's an incredibly dangerous dual
• 25:10
  Speaker 3: use technology.
• 25:11
  Speaker 2: But Adrian, but the big difference is... The big difference,
• 25:15
  Speaker 2: I'm going to push you on this a little bit.
• 25:17
  Speaker 2: The big difference here though with the wheel is I
• 25:20
  Speaker 2: can anticipate both uses.
• 25:23
  Speaker 3: Well, you've already anticipated the dangers of building an amp
• 25:29
  Speaker 3: the size of the Empire State Building, although we are
• 25:31
  Speaker 3: limited by physics and chemistry, so I'm not saying that's possible, right?
• 25:35
  Speaker 3: But look, here's the thing, right? As I said, the key,
• 25:38
  Speaker 3: the reason I wrote my book is not to proselytize
• 25:41
  Speaker 3: about this or even to advocate for it.
• 25:44
  Speaker 1: It's happened.
• 25:45
  Speaker 3: I'm calling a moment. I want everybody to know this
• 25:50
  Speaker 3: has happened, right? So it's not a question of should
• 25:53
  Speaker 3: we do it? We have done it, and it continues
• 25:55
  Speaker 3: to happen at an exponential rate. The ship has left
• 26:00
  Speaker 3: the dock, right? So what matters now is that people
• 26:03
  Speaker 3: know it's happened, know enough about it to have an opinion,
• 26:07
  Speaker 3: and that there is some kind of framework for a
• 26:10
  Speaker 3: global consensus to make sure this happens in a responsible
• 26:14
  Speaker 3: and thoughtful manner for the greater good of humankind, right?
• 26:18
  Speaker 3: And there are many, many good uses, this type of
• 26:21
  Speaker 3: information could be put to not least human health, right?
• 26:26
  Speaker 3: We haven't really talked about that. However, really in the
• 26:29
  Speaker 3: wrong hands, this is a very dangerous.
• 26:31
  Speaker 1: Technology as well. So AI is central to what you're
• 26:35
  Speaker 1: talking about, machine learning and what have you. And obviously AlphaFold,
• 26:41
  Speaker 1: which was one of the breakthrough uses of AI, it
• 26:47
  Speaker 1: was able to work out how strings of amino acids
• 26:49
  Speaker 1: would fold into proteins in many cases. It needed a
• 26:53
  Speaker 1: big data set to learn from where we have the
• 26:56
  Speaker 1: 3D structures of proteins and we had the DNA sequences
• 27:01
  Speaker 1: in order to do that. Do we have anywhere near
• 27:05
  Speaker 1: enough data at the moment to do anything beyond a virus?
• 27:12
  Speaker 1: And the reason I ask that is for our listeners,
• 27:14
  Speaker 1: viruses obviously use the host cell to replicate But, you know,
• 27:19
  Speaker 1: we've got all, if I just think about, you know,
• 27:21
  Speaker 1: building even just a simple cell, you've got potentially, you know,
• 27:26
  Speaker 1: gene cytoplasm incompatibilities that we don't understand. And we've got,
• 27:31
  Speaker 1: you know, that's just one thing.
• 27:32
  Speaker 3: There's a huge number of things. So many thoughts come
• 27:35
  Speaker 3: to mind when you say that. And I wish I
• 27:36
  Speaker 3: could give you 10 answers simultaneously. The first thing I'd
• 27:41
  Speaker 3: like to say is that, quote Peter Medawar, science is
• 27:44
  Speaker 3: the art of the soluble, right? So I've absolutely no
• 27:47
  Speaker 3: doubt that we'll be able to compute all of those things, right?
• 27:53
  Speaker 3: And the ability to write DNA in a manner that
• 27:55
  Speaker 3: matches our ability to design will enable us to get
• 27:59
  Speaker 3: that feedback as we design to learn those rules, right?
• 28:02
  Speaker 3: I just want to quickly though, before I sort of,
• 28:04
  Speaker 3: you know, expound on that, and if I just go
• 28:06
  Speaker 3: back to AlphaFold, because it's a really important thing for
• 28:09
  Speaker 3: the listeners, and some of them may not know what
• 28:11
  Speaker 3: it is, but it also illustrates a really good point, right?
• 28:14
  Speaker 3: So just to go back to that. So, you know,
• 28:17
  Speaker 3: alpha fold was a, was a, an AI based method
• 28:21
  Speaker 3: for predicting how a one dimensional string of amino acids
• 28:28
  Speaker 3: folds because proteins fold like a sheet bed sheet or
• 28:31
  Speaker 3: whatever in three dimensional space to form a structure. And
• 28:37
  Speaker 3: ultimately biology relies on structure, right? So proteins have to
• 28:42
  Speaker 3: form a three-dimensional structure. Now, for many, many years, led
• 28:46
  Speaker 3: by actually initially a former colleague of mine, Alan Furst
• 28:49
  Speaker 3: in Cambridge, people tried to understand the rules, the physical
• 28:54
  Speaker 3: and chemical rules that took you from a one-dimensional amino
• 28:57
  Speaker 3: acid sequence to a folded protein. There was actually a
• 28:59
  Speaker 3: competition every year to see, you know, people were given
• 29:03
  Speaker 3: one-dimensional things like, can you predict how this would fold?
• 29:06
  Speaker 3: And then they'd compare it to a structure that had
• 29:09
  Speaker 3: been derived crystallographically, which is how traditionally, you know, the
• 29:14
  Speaker 3: structures of proteins have been determined by firing x-rays at
• 29:18
  Speaker 3: them and then solving the structure, which takes about six
• 29:21
  Speaker 3: years generally, right? And really the whole field of prediction
• 29:26
  Speaker 3: was going nowhere, right? But, you know, trying to use
• 29:29
  Speaker 3: physics and chemistry. So along comes, you know, John Jumper,
• 29:33
  Speaker 3: Demis DeSevis, and so on. And people like David Baker,
• 29:36
  Speaker 3: and they start to interrogate databases matching the known database
• 29:42
  Speaker 3: of structures with sequence, and lo and behold, AI works
• 29:46
  Speaker 3: it out, right? Now, interestingly, how did it work it out?
• 29:50
  Speaker 3: We don't know, right? Because AI did not understand all
• 29:55
  Speaker 3: of the rules. All it did is it saw the
• 29:57
  Speaker 3: patterns and uses those patterns... to make their mapping, right?
• 30:01
  Speaker 1: So- And that's why I'm asking the question about, do
• 30:04
  Speaker 1: we have enough pattern out there for the way that
• 30:07
  Speaker 1: even a bacteria works?
• 30:09
  Speaker 3: Yeah, so coming back to that, the answer is right now,
• 30:12
  Speaker 3: probably not, right? We do actually for viruses and bacteria,
• 30:16
  Speaker 3: there's a huge database of viral and bacterial sequences. Fortunately,
• 30:21
  Speaker 3: as Jacob once said, that the rules for building an
• 30:27
  Speaker 3: ant The same for those of building a human or
• 30:32
  Speaker 3: something like that. You know, in other words, the rules
• 30:34
  Speaker 3: that I think I definitely got that wrong, but the
• 30:38
  Speaker 3: principle is the same, right? In other words, biology uses
• 30:43
  Speaker 3: a universal set of construction rules, a kind of universal grammar.
• 30:48
  Speaker 3: Some of you readers will remember Noam Chomsky, who talked
• 30:52
  Speaker 3: about a universal grammar which underlies all human languages, right?
• 30:57
  Speaker 3: It's the kind of potential for language, right? And I
• 31:00
  Speaker 3: think it's probably almost certainly the case that there is
• 31:04
  Speaker 3: a deeper universal grammar of biology which underlies the individual languages,
• 31:11
  Speaker 3: the individual grammars of every species. So the grammar of tortoiseese,
• 31:19
  Speaker 3: the language to build a tortoise, is not the same
• 31:22
  Speaker 3: as the languages of leopardese, the language to build a leopard,
• 31:26
  Speaker 3: but they share a lot of similarities. if you learn
• 31:30
  Speaker 3: how to write a tortoise, there's a damn good chance
• 31:33
  Speaker 3: that you'll eventually learn how to write a leopard, right?
• 31:36
  Speaker 3: But hey, you know, like I said, we don't start
• 31:39
  Speaker 3: with Dostoevsky, we start with Topsy and Tim or something
• 31:42
  Speaker 3: even simpler, right? And once you learn how to read
• 31:46
  Speaker 3: Topsy and Tim, and well, read and write, right? Once
• 31:50
  Speaker 3: you can write Topsy and Tim, we are talking about authorship.
• 31:54
  Speaker 3: And remember, just an important point I missed earlier, that
• 31:57
  Speaker 3: You know, we started off observing biology. We finally learned
• 32:02
  Speaker 3: how to read biology when Fred Sanger in my old
• 32:05
  Speaker 3: laboratory in Cambridge worked out how to sequence DNA. We
• 32:09
  Speaker 3: then learned how to edit DNA thanks to people like
• 32:12
  Speaker 3: Jennifer Doudna. But, hey, we're at a totally new moment.
• 32:17
  Speaker 3: This is a phase change. We now are learning how
• 32:20
  Speaker 3: to author life, like literally write life creatively with intention.
• 32:26
  Speaker 3: In other words... we've transitioned from being monks in a scriptorium,
• 32:32
  Speaker 3: blindly copying ancient texts, making the odd mistake every 2000 letters,
• 32:38
  Speaker 3: I think the monks made, right? To more like a
• 32:42
  Speaker 3: kind of Gutenberg situation. Gutenberg published, printed the first Bible
• 32:48
  Speaker 3: in 1455 or something like that, right? And this was
• 32:52
  Speaker 3: the sort of industrialization of of the books really, right?
• 32:58
  Speaker 3: And suddenly this industrialization enabled the everyman to start being
• 33:05
  Speaker 3: creative and writing their own books. You didn't have to
• 33:08
  Speaker 3: just copy the Greek masters. You know, you could now,
• 33:12
  Speaker 3: it was cheap enough and easy enough that anybody could
• 33:14
  Speaker 3: have an idea and create a book, right? And that
• 33:17
  Speaker 3: was basically the basis for both the European and specifically
• 33:22
  Speaker 3: the Italian Renaissance, right? I think we're approaching this renaissance
• 33:27
  Speaker 3: moment in biology where fairly soon we might find that
• 33:33
  Speaker 3: your eight-year-old kid is designing a new organism on their computer.
• 33:39
  Speaker 3: And I think we've all got views on whether that's
• 33:42
  Speaker 3: a good idea or not. But actually, I think biology
• 33:47
  Speaker 3: will become programmable, just like kids sit at home programming
• 33:54
  Speaker 3: video games, they're going to be programming life pretty soon. And,
• 33:58
  Speaker 3: you know, I remember when I was a graduate student
• 34:01
  Speaker 3: with Cesar, my old boss, who invented monoclonal antibodies, won
• 34:06
  Speaker 3: the Nobel Prize. And, you know, 10 years later, there
• 34:10
  Speaker 3: was still only one approved monoclonal antibody, right? And I
• 34:13
  Speaker 3: remember there were all these really mean articles in the
• 34:16
  Speaker 3: press that monoclonal antibodies were a failure. They weren't going anywhere.
• 34:21
  Speaker 3: It was hype. just took a little bit longer than
• 34:24
  Speaker 3: people thought, right? It's going to happen. It may take
• 34:28
  Speaker 3: two years. It may take five. We don't really know.
• 34:31
  Speaker 3: It's going to happen, right? Better that we're prepared for it.
• 34:34
  Speaker 3: My book was meant to be a kind of, I
• 34:36
  Speaker 3: don't know if you ever came across the Bidikas. They're
• 34:40
  Speaker 3: this sort of 19th century travel guides. They were the
• 34:43
  Speaker 3: first kind of travel guides for independent travelers, which had
• 34:48
  Speaker 3: tips on you know, what hotels to stay in and
• 34:51
  Speaker 3: the best sites to visit.
• 34:53
  Speaker 1: And all of that.
• 34:54
  Speaker 3: And I kind of, when I was writing my book,
• 34:55
  Speaker 3: I saw my book as a kind of BDCA, you know,
• 34:58
  Speaker 3: kind of a travel guide, but also a kind of
• 35:00
  Speaker 3: trusted companion that you could put in your coat pocket
• 35:04
  Speaker 3: as you navigated the future of life and use it
• 35:07
  Speaker 3: as a kind of book to guide you and inform you.
• 35:09
  Speaker 3: And that's how I hope my book will be perceived.
• 35:12
  Speaker 3: It's definitely not proselytizing. It's definitely not advocating. It's simply saying,
• 35:17
  Speaker 3: here it is. This has happened.
• 35:20
  Speaker 1: And you do that very well. We're running out of time. Simon,
• 35:25
  Speaker 1: a question from you, and then I've got one last question,
• 35:27
  Speaker 1: and then we probably should wrap it up.
• 35:30
  Speaker 2: I've got quite a sort of out there bonkers question. Okay,
• 35:35
  Speaker 2: you get to design a tiny little organism. What's your
• 35:39
  Speaker 2: organism going to be?
• 35:42
  Speaker 3: What do you mean, what's my pet organism?
• 35:44
  Speaker 2: Yeah.
• 35:45
  Speaker 1: What will it do?
• 35:46
  Speaker 3: Listen, we've already, I mean, we are already doing that.
• 35:50
  Speaker 3: So let me tell you what we're actively working on
• 35:53
  Speaker 3: because I've written about this, but I'm also doing it
• 35:57
  Speaker 3: on a day-to-day basis. And you'll both be aware of this.
• 36:01
  Speaker 3: You may even have had somebody on your program talking
• 36:03
  Speaker 3: about it, but it turns out that bacteriophage, which are
• 36:08
  Speaker 3: the viruses that prey on bacteria, turn out to be
• 36:12
  Speaker 3: actually an alternative form of antibiotic, right? And these have
• 36:15
  Speaker 3: been used actually quite extensively in Ukraine, in the Ukraine war,
• 36:20
  Speaker 3: because a lot of the soldiers there have got multiply
• 36:22
  Speaker 3: resistant Staph aureus infections, and they've shown actually they can
• 36:26
  Speaker 3: be treated pretty effectively with bacteriophage, right? So, you know,
• 36:31
  Speaker 3: one can imagine a situation, particularly in this age of
• 36:34
  Speaker 3: global antibiotic resistance, multiply resistant organisms, which is really becoming
• 36:39
  Speaker 3: a major global problem. And as you know, very few
• 36:42
  Speaker 3: pharmaceutical companies see much commercial incentive for working on antibiotics
• 36:47
  Speaker 3: compared to, say, anti-cancer drugs. And, you know, we can
• 36:51
  Speaker 3: easily imagine a situation where we can determine what infection
• 36:57
  Speaker 3: somebody has and then, you know, press a button and
• 37:00
  Speaker 3: in a bespoke manner spit out a bacteriophage sequence that
• 37:05
  Speaker 3: will kill that bacterium.
• 37:06
  Speaker 1: That's a genome, right?
• 37:08
  Speaker 3: And bacteriophage do not affect human cells at all. So
• 37:13
  Speaker 3: totally safe. Right. So that's a really simple, practical example
• 37:18
  Speaker 3: of today of how we can use this technology. Let
• 37:21
  Speaker 3: me give you another example of something.
• 37:22
  Speaker 1: That we're kind of running out of time. So give
• 37:25
  Speaker 1: us an example of something a bit more complicated than
• 37:28
  Speaker 1: a virus.
• 37:29
  Speaker 3: Yeah, I'll do that. So we're working with a group
• 37:31
  Speaker 3: in Cambridge right now. to build what we call a neochromosome,
• 37:36
  Speaker 3: which is an artificial chromosome, which can be used to
• 37:40
  Speaker 3: reprogram the genomes of crops, for example, to make them
• 37:44
  Speaker 3: drought resistant or pest resistant or require different low nutrient,
• 37:50
  Speaker 3: they can survive in low nutrient conditions or whatever. In
• 37:53
  Speaker 3: other words, moving from editing, which is very incremental to
• 37:57
  Speaker 3: actually reprogramming the genomes. This could hopefully form the basis
• 38:03
  Speaker 3: of a second green revolution because we're going to need
• 38:06
  Speaker 3: to be able to sustain all of these different, all
• 38:09
  Speaker 3: of the new humans coming into the world at a
• 38:11
  Speaker 3: kind of exponential rate and in a way that doesn't
• 38:15
  Speaker 3: destroy the last remaining wilderness and pollute the world with
• 38:19
  Speaker 3: chemicals and so on and so forth, right? So this
• 38:22
  Speaker 3: is a very immediate utility of this technology.
• 38:26
  Speaker 1: So do futurists tend to get it right, people who
• 38:29
  Speaker 1: are predicting the future, do you think?
• 38:31
  Speaker 3: You know, I've always thought, Tim, right, that the human
• 38:36
  Speaker 3: imagination has this incredible ability to predict the future, right? And,
• 38:41
  Speaker 3: you know, we've always talked about things like time travel,
• 38:44
  Speaker 3: and who knows if that's possible. There may be some
• 38:46
  Speaker 3: things which aren't possible. So I'm not saying that anything
• 38:49
  Speaker 3: you can imagine is possible, but I definitely think that
• 38:53
  Speaker 3: many of the things that we can predict are possible.
• 38:56
  Speaker 3: The best example of this was obviously Leonardo da Vinci,
• 38:58
  Speaker 3: as we all know, predicting the existence of helicopters and
• 39:02
  Speaker 3: so on and so forth. Francis Bacon anticipated synthetic biology many,
• 39:08
  Speaker 3: many years ago. He died in Highgate in London when
• 39:11
  Speaker 3: he got out of his carriage in the cold to
• 39:14
  Speaker 3: do an adult experiment on a chicken. But he actually
• 39:18
  Speaker 3: wrote a book after being released from the Tower of
• 39:20
  Speaker 3: London in which he actually imagined that nature could be engineered.
• 39:24
  Speaker 3: This was years before we even knew what a gene was.
• 39:27
  Speaker 3: So his imagination was able to fly into the future and,
• 39:31
  Speaker 3: you know, imagine different things. Are those alternative worlds better?
• 39:36
  Speaker 3: Are they natural? Are they unnatural? Who knows, right? You know,
• 39:39
  Speaker 3: I don't think that the one defining thing about humanity
• 39:43
  Speaker 3: and about nature is that it's in a state of
• 39:45
  Speaker 3: perpetual change.
• 39:47
  Speaker 2: Yeah.
• 39:48
  Speaker 1: Well, look, I wish we could keep talking for more.
• 39:50
  Speaker 1: It really is a fascinating subject. And, you know, I
• 39:55
  Speaker 1: really recommend your book to our readers because I enjoyed
• 39:58
  Speaker 1: reading it. And it is very thought provoking, you know,
• 40:01
  Speaker 1: and you're right. As we move from the, you know,
• 40:05
  Speaker 1: from the nursery slopes, there's probably going to be a
• 40:07
  Speaker 1: lot of falls, maybe the odd broken leg and all.
• 40:11
  Speaker 1: even the odd lost ski, I suspect. But I'd recommend it.
• 40:15
  Speaker 1: On the Future of Species, Authoring Life by Means of
• 40:18
  Speaker 1: Artificial Biological Intelligence is coming out in February, February the 12th,
• 40:23
  Speaker 1: published by Bloomsbury. It's really thought-provoking, very knowledgeable and a
• 40:29
  Speaker 1: really enjoyable read. So thank you very much for joining us, Adrian.
• 40:34
  Speaker 1: It's been fascinating.
• 40:36
  Speaker 3: And thank you, Tim and Simon. It's been a pleasure
• 40:38
  Speaker 3: talking to you and thank you for your kind words.
• 40:41
  Speaker 1: What did you make of that then, Simon?
• 40:43
  Speaker 2: It's fascinating and a lot of fun. And I think,
• 40:46
  Speaker 2: as I said at the start, I won't lie and
• 40:49
  Speaker 2: I'll say that I find it exciting, but also a
• 40:51
  Speaker 2: little bit scary.
• 40:52
  Speaker 1: No, it's true. And I mean, I think we probably,
• 40:56
  Speaker 1: we tend to think of the real extremes, which we're
• 40:59
  Speaker 1: a long way off. But I think some of his
• 41:01
  Speaker 1: points about being able to design, for example, viruses or
• 41:07
  Speaker 1: phages that will kill particular types of bacteria. So, you know,
• 41:11
  Speaker 1: a treatment for health. I think those are the first
• 41:13
  Speaker 1: things and potentially really, really positive. But of course, if
• 41:17
  Speaker 1: you design a virus, there are also bad viruses out there.
• 41:20
  Speaker 1: So that's where the kind of ethics stuff that he
• 41:22
  Speaker 1: says we need to think very carefully. He touches on
• 41:25
  Speaker 1: all of that in his book, in fairness. We didn't
• 41:27
  Speaker 1: get time to discuss it.
• 41:29
  Speaker 2: No, no, no. And I think, you know, just sort
• 41:32
  Speaker 2: of thinking about it after, you know, after we've interviewed him,
• 41:36
  Speaker 2: it's a little bit like we're going to all have
• 41:38
  Speaker 2: these sort of almost biological 3D printers, right?
• 41:42
  Speaker 1: Yeah. And I mean, I think, you know, it's interesting,
• 41:48
  Speaker 1: isn't it? Because we had on Omar Yagi, who's designed
• 41:52
  Speaker 1: these new compounds, you know, which were coming online there.
• 41:58
  Speaker 1: David Baker, who designed the protein and won the Nobel
• 42:01
  Speaker 1: Prize for it. The idea of sort of authoring complex organisms,
• 42:06
  Speaker 1: at least, beyond viruses, I suspect is some way off
• 42:09
  Speaker 1: still because it's so, so complicated. You know, you're talking,
• 42:15
  Speaker 1: you know, billions and billions of interacting molecules within each cell.
• 42:21
  Speaker 1: And actually, yes, they do follow rules. There are chemical rules,
• 42:25
  Speaker 1: there are physical rules, there are biological rules and what
• 42:27
  Speaker 1: have you. it's um and we're only scraping the surface
• 42:31
  Speaker 1: so i think it's a while off would be my suspicion.
• 42:35
  Speaker 2: Yeah i think so because these having the different components
• 42:39
  Speaker 2: and then putting them together makes it more than the
• 42:41
  Speaker 2: sum of the parts.
• 42:43
  Speaker 1: That's right and i mean i think he's true i
• 42:45
  Speaker 1: think he's right that artificial intelligence much as it has
• 42:49
  Speaker 1: you know created things like alpha fold to talk about
• 42:52
  Speaker 1: but also being able to you know, convincingly taught reply
• 42:59
  Speaker 1: to us in language that we understand. That's why it's
• 43:02
  Speaker 1: so convincing in that sense. It's come a long way, but,
• 43:05
  Speaker 1: and he's probably right that eventually we might get to
• 43:08
  Speaker 1: this point, but I think artificial intelligence itself and machine
• 43:10
  Speaker 1: learning has got to come a long way before actually there.
• 43:14
  Speaker 2: Yeah, I think so. Because, you know, when you asked
• 43:17
  Speaker 2: him about the sort of other patterns there, you know,
• 43:20
  Speaker 2: are there enough patterns for the AI to pick up on?
• 43:23
  Speaker 2: they could be there, but the AI, they have to
• 43:26
  Speaker 2: be there in a way that the AI can find
• 43:28
  Speaker 2: and use and manipulate.
• 43:30
  Speaker 1: That's right. And going from a genome, putting in a
• 43:33
  Speaker 1: genome to just the shape of an organism, how many
• 43:36
  Speaker 1: limbs it's got and what have you, isn't getting you there, right?
• 43:40
  Speaker 1: Because you've got the whole developmental processes. You've got, you know,
• 43:43
  Speaker 1: we don't even really have a, not everyone can agree
• 43:46
  Speaker 1: on the description of life, let alone, but really fascinating stuff.
• 43:52
  Speaker 1: And I mean, you know, Such enthusiasm as well. Absolutely.
• 43:56
  Speaker 2: And I think for our listeners, if you have any
• 43:58
  Speaker 2: questions or comments, as always, please write in. Please comment
• 44:03
  Speaker 2: on the various links on the different platforms you use
• 44:06
  Speaker 2: to watch or listen. We'd love to hear your feedback.
• 44:09
  Speaker 1: No. And a good book and worth reading. I recommend it.
• 44:13
  Speaker 1: So I enjoyed it.
• 44:15
  Speaker 3: Excellent.
• 44:16
  Speaker 1: Cool.
• 44:17
  Speaker 2: That's it for another week, I guess, Tim. I will
• 44:19
  Speaker 2: see you hopefully at some point soon.
• 44:22
  Speaker 1: Yeah, no, definitely. And I think we should give a
• 44:26
  Speaker 1: shout out to our producer, Rich, who's had a half
• 44:29
  Speaker 1: a knee replaced. So he's on the mend. And let's
• 44:34
  Speaker 1: wish him the best for a quick recovery.
• 44:36
  Speaker 2: As we said, he's training for Spurs soon. So, you know.
• 44:42
  Speaker 1: Ha, ha, ha. OK, listeners. OK, Saima, see you soon.
• 44:46
  Speaker 3: Bye.