S1: Welcome to Unsupervised Learning, a security, AI and meaning focused
S1: podcast that looks at how best to thrive as humans
S1: in a post AI world. It combines original ideas, analysis,
S1: and mental models to bring not just the news, but
S1: why it matters and how to respond.
S2: So hello everyone, welcome to this AI at Ypo session.
S2: We're delighted to have Daniel Miessler here with us today
S2: to speak on the skills and mental frames required to
S2: thrive in an AI world. Daniel Miessler is the founder
S2: of Unsupervised Learning, which is a company that focuses on
S2: building products that help companies, organizations, and people identify, articulate,
S2: and execute on their purpose in the world. Daniel has
S2: over 20 years experience in cybersecurity, and has spent the
S2: last several years focused on applying AI to business and
S2: human problems. Daniel has held senior positions at Apple, Robinhood, Ioactive, HPE,
S2: and many other companies, as well as consulted for or
S2: been embedded in hundreds of others in the fortune 500
S2: and global 1000. So thank you very much for being
S2: here with us today, Daniel. The floor is now yours.
S1: All right. Yeah. Thank you for having me. So what
S1: I want to talk about today is what I believe
S1: is coming for us with AI, and what we can
S1: do to get ready for it. So I want to
S1: start with some background, some analysis and predictions, and then
S1: give some recommendations on how to get ready for this
S1: thing that I believe is coming. So I don't normally
S1: do intro slides because I feel like I would rather
S1: just get into the ideas, but in this case, I'm
S1: going to make a little bit of an exception, because
S1: I think it's important to see like the background of
S1: where this is coming from. So I won't be sharing
S1: any CV stuff, but just want to talk about some
S1: stuff that I think is relevant. So I've been thinking
S1: and writing online about things in the future since around 1999,
S1: and started a podcast about security called Unsupervised Learning in 2015,
S1: and I wrote a short book on what I saw
S1: as being the future in around 2016. And in that book,
S1: I basically talked about a future of digital assistants, basically
S1: helping us as humans, a world made up of APIs,
S1: and basically having those digital assistants mediate the world for us,
S1: be like a broker in between us and that API world.
S1: And I think this is already starting to happen now.
S1: So I studied ML pretty deeply in 2017, especially Andrew
S1: Ng's full course, which I still recommend. I believe he
S1: has a Coursera version now as well, but the the
S1: YouTube version is quite good. I then worked at Apple
S1: in 2018 for a few years, starting off in a
S1: machine learning group there, and ended up building another product
S1: there in the security group, and I've been continuing on
S1: this path since like the middle of 22. I basically
S1: went full time into this. So, uh, I basically see
S1: two problems in the world, which is humans losing a
S1: sense of meaning and AI making that much, much worse.
S1: So what I'm essentially doing now is trying to help
S1: solve that. So the point of all this is not
S1: to say that I'm right about everything that I'm about
S1: to share, but rather that I've been thinking about it
S1: for quite some time. Um, another good thing to mention
S1: here is, uh, anything about predictions is like, it's, uh,
S1: you shouldn't really believe them because it's really hard to
S1: predict the future. Especially, yeah, when you're talking about a
S1: thing that hasn't happened yet. Um, so I'll talk with
S1: a decent amount of confidence here, but, um, it's really
S1: because if you hedge all the time, it's not really compelling.
S1: And I do add caveats a lot. So a couple
S1: of things I won't be talking about. I have other
S1: presentations on existential risk from AI, and lots to say
S1: about AGI and superintelligence specifically, as well as some thoughts
S1: on which countries might do what or whatever, but those
S1: are big topics on their own, so I won't be
S1: talking about those here. With that out of the way,
S1: I want to jump in. So what I want to
S1: talk about is, um, how I see AI affecting companies, uh, startups,
S1: the reduction of creative friction, hiring, broadcasting oneself, and an
S1: unfortunate separation between groups of people as a result of this.
S1: And for each of these, I'm going to start with
S1: an observation and then give a prediction. So the first
S1: major concept is a new way of thinking about companies.
S1: And I think AI is going to magnify this effect
S1: quite a bit. So one of the major uses of
S1: AI will be to optimize processes within businesses. And a
S1: company I would argue is really just components of action
S1: combined with process. So what's going to happen is consultancies
S1: are about to come in and basically turn your business
S1: into a diagram similar to like what you see over
S1: here on the right. And I've got a full posting
S1: on this which you can go read. It's called companies
S1: are just a graph of algorithms. And when a company
S1: does this, when they make your company visible to you
S1: in this way, AI thrives off of that, right? Once
S1: I can understand what your business can do, it can
S1: then start making some really powerful predictions. Um, and that's
S1: that's exactly what I think is going to happen. And really,
S1: really crazy stat here. 40% of McKinsey's business in 2024
S1: is already AI essentially doing this kind of thing. So
S1: the prediction here is that AI will find massive waste
S1: in most companies, middle management and bureaucracies will come under
S1: extreme pressure and companies will get smaller and far more efficient.
S1: And the good news is how this will affect startups
S1: and moving on to startups. A lot of people are
S1: talking about the concept of the first one person billion
S1: dollar company, which I think is a really crazy concept.
S1: Just as we saw the efficiency that you can get
S1: in a very large company, we're going to see that
S1: in tiny companies as well. In fact, it's going to
S1: be more magnified in tiny companies because you're not dealing
S1: with the the previous technical debt. So there will soon
S1: be many, many AI based services for doing every aspect
S1: of a business which will allow a single person to
S1: essentially launch the idea. The paperwork can be done, the
S1: customer service can be done, sales, marketing, analytics, optimization, all
S1: these different things can just be like AI services that
S1: they add on, and that's going to allow very small
S1: teams to do really powerful things. So that's the prediction
S1: here is the friction to creating a business falls dramatically.
S1: So there'll be a lot more 1 to 10 person
S1: companies that don't really have to grow larger than that.
S1: And I think the result of this is going to
S1: be an extraordinary multiplication of global productivity. Hard to know
S1: how much exactly that will be, but I think five
S1: X is probably safe in like the next ten years.
S1: So one of the most exciting things to me is
S1: the reduction of creative friction. So if you think about
S1: like how many Steven Spielberg's there are in the world
S1: right now, the number is tiny, right? Very, very few.
S1: But if you only factor in the combination of the
S1: genius with the fact that they're in LA and they
S1: know the people in Hollywood and they have access to
S1: all this money and they have access to all the connections.
S1: That's the reason the number is low. So the question
S1: is how many more Spielbergs are there on the entire
S1: planet with as good or better ideas who simply aren't
S1: in LA or don't have access to these resources? Or
S1: maybe they don't know how to draw, or they don't
S1: know how to write a script or something like that.
S1: And I think there's a lot of opportunity there. So
S1: what I see happening, starting very soon is basically an
S1: explosion of new Spielberg's. So I will be able to
S1: help the people with the ideas, but they have one
S1: or more bottlenecks. You know, maybe they're in sub-Saharan Africa,
S1: maybe they're in, you know, some apartment in Scotland, and
S1: they just don't have access to this stuff. And they
S1: didn't really get formally trained in these different skills. So
S1: we're talking about an explosion of books, film, poetry. Artists
S1: will just be able to talk to their AI and say, hey,
S1: you know, I have this thing, here's the story and
S1: go off into extreme detail about the story, and the
S1: AI is basically taking notes, and you combine that with
S1: the ability to generate art, the ability to generate video
S1: like we're seeing. And pretty soon the AI is going
S1: to be able to come back and say, oh, do
S1: you mean like this? And they're like, no, no, no
S1: more of this influence, more of that influence, and basically
S1: have this interactive conversation with an AI which results in
S1: an art output that I think is going to rival
S1: the best people in the world. And just imagine that
S1: for 8 billion people on the planet. So the next
S1: one here is hiring. I think hiring is going to
S1: change significantly. And there's a number of pitfalls here that
S1: we need to watch out for. But we've known for
S1: a long time that interviews are not super predictive of performance.
S1: So I think it's absolutely ripe for disruption. And what
S1: I see happening here, and I've already seen multiple companies
S1: working on this, is basically AI goes out and does
S1: full collection of everything about this person, this candidate, and
S1: it's basically looking at all their blog posts. It's looking
S1: at the papers that they've written, it's looking at their GitHub,
S1: it's looking at commentary, it's looking at basically anything they've
S1: put anywhere that's available to the eye, basically in public
S1: to read. And it's basically turning that into like a,
S1: a profile or a, a psychological profile or a work
S1: history profile. And then it can also do interviews with
S1: the person or will be able to very soon. So
S1: it first, it knows everything you've ever written online that's public.
S1: And then it can have a conversation with you about
S1: the pertinent skills of what that company is looking for.
S1: And it also is going to be doing ML magic
S1: to say, okay, here are the things we actually know
S1: that are predictive of success in this in this job.
S1: So this is going to be really powerful because it's
S1: then going to result in a score and a recommendation
S1: of like, yes, we should hire this person or yes
S1: or no, we shouldn't. Now one thing to mention here
S1: is that anytime you have ML involved and AI involved,
S1: it's a little bit scary to have a thumbs up
S1: or thumbs down or a score when you're not exactly
S1: sure what the criteria are. And we absolutely need to
S1: protect against bias in this case. Um, so transparent AI
S1: is going to really help here. The next thing I
S1: see happening is a surging requirement to be visible in
S1: the world, to be resilient in this in this world
S1: that's coming up. And authenticity essentially becomes signal. So ideas,
S1: ideas and personality become the things that people care about.
S1: And it becomes more important than medium. It becomes more
S1: important than, you know, being being on a TV show.
S1: As we're already seeing like CNN is not really competing
S1: with YouTube anymore. So ideas and personality become the things
S1: that people care about. And it's more important than medium,
S1: and it's becoming more important because the creator content is
S1: going to become more direct. It's going to be like
S1: an influencer, create something and it goes directly to the consumer,
S1: and they're not even really going to care what medium
S1: it was on. And that's going to be even more
S1: true when it's actually a person's eye that's going to
S1: collect the stuff and bring it back to the principle.
S1: So the prediction here is that I will work for
S1: people to find the best content for them to read. So,
S1: for example, my eye will be going out while I'm
S1: sleeping to collect the coolest new ideas, the coolest new stories,
S1: the coolest new opinions. And in order to find those
S1: for a given person like, say, somebody who's listening to this,
S1: you will have had to have put that out there.
S1: So you have to get your ideas out there into
S1: the world. So basically, if you're not visible, you're vulnerable.
S1: And this brings me to something I'm quite concerned with
S1: about AI, which is we already have a major divide
S1: in terms of like overall success in life around being
S1: a voracious reader or not. And there's there's a decent
S1: amount of data on this which I don't have here,
S1: but I can include later. But essentially there's lots of
S1: anecdote anecdote around this as well. So Charlie Munger has
S1: this great quote. In my whole life I have known
S1: no wise people who didn't read all the time, none, zero.
S1: And I think that really resonates. I think it's a
S1: powerful concept, and I'm worried that AI is going to
S1: be a massive magnifier of this. It's going to be
S1: just like reading, but much, much worse. And here's the problem.
S1: The the problem is we're basically going to have two groups, right?
S1: One group that has say, 10,000 AI assistants working for them.
S1: So they learn the AI, they learn the programming, they
S1: deep dive into this whole world, and they hire all
S1: these very, very cheap AIS to do all these different
S1: things for them. So they're constantly modifying their their finances.
S1: They're constantly finding better books. They're constantly summarizing books for
S1: them and basically turning this person that they're working for into,
S1: like a super knowledgeable, like almost like a superhuman brain.
S1: And some top percentage of the world is going to
S1: function in this way, whether that's 1% or 5% or 10%,
S1: whatever you want to call that. But another group is
S1: going to essentially use it to find entertainment, or they're
S1: going to be influenced by, uh, potentially malicious actors through
S1: that medium, but they're not going to be doing this
S1: thing that the first group is doing, and this is
S1: going to separate groups more than ever. So with that,
S1: I want to talk about what I think we can
S1: do to make ourselves resilient to this. So I want
S1: to talk about a concept called human 3.0. And my
S1: first recommendation here is that you essentially want to combine
S1: all this together to understand your full authentic self, to
S1: know what you want and to share it with the world.
S1: We used to essentially be our resumes or we kind
S1: of still are now. I consider what we're in now
S1: to be human 2.0, where the value of your you
S1: as a human is what you can put in a
S1: CV or a resume. And I think the value of
S1: this human 3.0 concept is that you are your full
S1: spectrum self. You know, your your authenticity. Your personality is
S1: now not a thing that you never include in your CV.
S1: It's now the thing that is able to cut through
S1: and make people want to listen. So if you don't
S1: broadcast your full self, you won't be able to differentiate.
S1: And this starts with knowing really who you are. What
S1: is your mission? What are your goals? Really, really importantly,
S1: what do you think are the most important problems in
S1: the world and what are your metrics? How do you
S1: know if you're doing well as a as a person
S1: or not? And if you can't define yourself in this way,
S1: you're going to be more replaceable from I. So the
S1: next piece of this is getting in the habit. And
S1: this goes back to the the broadcasting of self. This
S1: is essentially how you do it. You got to get
S1: in the habit of being able to articulate yourself clearly.
S1: So one way to think about this is a great
S1: quote that I found from Paul Graham, but it's actually
S1: from Leslie Lamport. If you think without writing, you only
S1: think you're thinking. If you're thinking without writing, you only
S1: think that you're thinking, I really, really love this. And
S1: it essentially the way I encapsulate this is you've got
S1: to get good at thinking, writing and presenting. Um, because
S1: it's no longer for special people, for authors, this is
S1: the only way that you're going to be able to
S1: become visible and stay visible to the world, especially when
S1: I is the one crawling and looking for signal and content.
S1: So you've got to get really good at articulating what
S1: you're about, what you're working on, and why you're working
S1: on those things. And these are the primary skills that
S1: I recommend that you cultivate. So this is really an
S1: encapsulation of the whole thing. It's clear articulation of self.
S1: It's being insatiably curious. This is another big thing that
S1: Charlie Munger talks about. And that's the reason he reads
S1: so much. It's continuous learning, deep integration of AI to
S1: accelerate that learning, discipline and passion towards chasing your curiosity,
S1: focusing on useful work, and staying focused on the problems
S1: that you're solving, and using those problems as your your
S1: guiding light. And in terms of practical skills being good
S1: at programming, you don't have to be an expert programmer.
S1: You just have to know the concepts and be able
S1: to interact with AI, because AI does a lot of programming. Uh,
S1: but you still have to be able to tell it
S1: and understand the concepts. So I would say programming is
S1: important AI tool integration. You want to learn AI and
S1: most importantly, thinking, writing and presenting and all those really
S1: are writing and presenting really are just ways to ensure
S1: that your thinking is clear. And so bringing it all together,
S1: this is what I recommend we do tactically and immediately.
S1: So that's capturing yourself in terms of purpose, goals, metrics, mission,
S1: that sort of thing. Get really good at explaining your
S1: encapsulation of that to other people. So that's writing, speaking,
S1: presenting that sort of thing. Learn to program and learn
S1: to use. I probably read 2 to 4 hours a day.
S1: High quality content. Build your presence online. So get your
S1: website going. Essentially talk about it doesn't matter. A lot
S1: of people say, look, I have nothing to say because
S1: I'm still learning. Be visible, learn in public. And that
S1: will allow you to do the next piece, which is
S1: to connect with others who are doing the same. Because
S1: you can have ten different people who are talking about
S1: the same stuff, but if they are being their full selves,
S1: those ten people will look and sound very different and
S1: they will find different audiences. And most importantly, you want
S1: to do this authentically as your full self and share
S1: this with the world. Thanks for your time.
S2: Well, thank you so much, Daniel. That was incredibly profound
S2: and a lot deeper than I was expecting, which was amazing. Yeah,
S2: I have a few questions myself, but I think I'll
S2: start by handing it over to my colleague Daniel, who
S2: is Wipo's information security engineer. And yeah, he will cover
S2: some questions that were pre-submitted from yeah, people who initially
S2: registered for this talk. So Daniel Jeremiah, the talk is
S2: now yours. Sorry. The floor is now yours.
S3: All right. Thank you so much for the eye opening presentation.
S3: I don't know about you, Olivia, but I know what
S3: I'm going to be busy with for the next couple
S3: of years.
S4: Yeah.
S3: We have received many, many questions. Colleagues, thank you for
S3: your interest. And I believe that several of them could
S3: probably be grouped under different headings, different buckets related to
S3: I and our jobs. The integration at Waco and to
S3: what level we can trust. I. So here's the first one.
S3: Will I replace human labor in the near future?
S1: Yeah. Um, I think there's a couple of interesting, um,
S1: caveats or sort of pivot points inside of the question. Uh,
S1: replace implies a one or a zero that it's just yes,
S1: it's replaced or no, it's not replaced. That's going to
S1: be gradual. And when near future is also like that
S1: as well. It's like that. What does that mean. Does
S1: it mean one year? Does it mean three years? The
S1: the way I would answer that is to say that
S1: will AI replace human jobs? The answer is yes. The
S1: question is how much and how quickly and what types
S1: of jobs. And I would say that in general, what
S1: it's going to replace first is things that can be
S1: done easily that don't have human authenticity and human Uniqueness
S1: inside of the work. And this is why this whole
S1: presentation is oriented around getting that into what you do,
S1: so that it is harder to replace. So I think
S1: there are millions of jobs that will be replaced in
S1: the next 5 to 10 years and many, many more
S1: over the course of ten years versus five. The good
S1: news is it's also going as we talked about in
S1: the presentation. It's also going to create lots more jobs.
S1: In fact, I believe that it's going to kind of
S1: shut down the human 2.0 economy where we're basically doing
S1: raw tasks. And it's going to move us towards this
S1: human 3.0 economy, which is more human to human interaction.
S1: So the answer is yes, it will replace a lot
S1: of jobs, but I don't think that's all bad news.
S3: Well, let's hope for the enhancement of the.
S1: Yes.
S3: Thanks. All right. Thank you. The next one would be, uh,
S3: if you have any advice, what advice would you have
S3: for Ypo other organizations looking to go looking to integrate
S3: AI in our current work? Because we know, you know,
S3: we have several initiatives and ideas and explorations, but actually
S3: the the substance of the work is not yet touched. Yes.
S1: Yeah. So really good question. I think the answer is
S1: to do what I was talking about. The AI consultancies
S1: are going to do to your company. So you essentially
S1: want to go to a very large whiteboard and write,
S1: document your company, capture it as a giant graph, and
S1: break out every single little piece. Okay. The customer sends
S1: this thing in it does this following thing is done
S1: with Sarah's team that sends over to Chris's team. And
S1: here's what the teams look like. And here's exactly what
S1: they do. And here's how many people are doing that.
S1: You basically want to break that out and understand your
S1: business perfectly the way that it is today before I.
S1: And then you want to say, okay, now what parts
S1: of this look inefficient? What parts of this should we
S1: not be doing at all? That's the first question I
S1: is going to ask is what can we cut? What
S1: are the extra layers. So you want to think of
S1: the I as basically just a very harsh lens that's
S1: going to look at everything you're doing. So the very
S1: first step is understanding exactly what you are doing. And
S1: that's going to make it clear where you should apply.
S3: I think it's uh, well it's concerning, but a lot
S3: of challenges ahead. Yeah. Um, so, so the for the
S3: next we have a package is, uh, regarding the security. Well,
S3: it's close to hard. To what level can we trust AI?
S3: Is cybersecurity really possible in the face of artificial intelligence?
S3: And mainly for me at least, how do we ensure
S3: that the information we receive has come from a good source?
S3: And how can we best validate that information?
S1: Yeah, it's a great, great question. I am not too
S1: concerned about it, actually. Not because I trust the raw
S1: content coming out of an LLM that is not a
S1: good idea, but essentially what's going to happen and is
S1: already happening is that content being created from an AI
S1: will go through a pipeline of steps depending on the
S1: importance of the task. So if the task is to
S1: say no, you do not have this disease. That is
S1: such a powerful thing to say. It must be checked
S1: in multiple places. So what you're going to have is
S1: you're going to have multiple little eyes which are doing
S1: fact checking. So they're going to be checking Google. They're
S1: going to be checking official medicine publications. It's going to
S1: be maybe consulting with a human expert for a final check.
S1: But imagine a a result coming out of an AI
S1: being evaluated by 15 other AIS. And they all have
S1: to have a green check mark before the answer is
S1: actually returned to the user. So I don't think we
S1: have to worry about a world in which we just
S1: randomly trust. I think that might happen for something that
S1: doesn't matter. Like tell me a funny joke, but it
S1: won't necessarily it won't happen for the things that really matter. Like,
S1: should we turn on this, um, nuclear control system or
S1: should we turn it off? Should we raise the firewall,
S1: lower the firewall? Those sorts of things are going to
S1: have lots of extra scrutiny added on.
S3: Right. So that's that's the most interesting application of democracy
S3: I heard recently if if we have agents voting on
S3: the answers, that's, uh.
S1: Yes, absolutely. And different kinds. This is really important. You'll
S1: have someone who's like an academic, and they have this
S1: one type of scrutiny, and you can have this other
S1: industry expert kind of type of scrutiny, but they can
S1: have different backgrounds, but all of them must say yes
S1: for this kid to get an actual s o.
S3: Great. So thank you very much. I believe that's that's
S3: all I have.
S5: Uh.
S3: Thank you. It's. It was amazing. And a lot of
S3: lessons for me, and I hope for for our colleagues.
S3: So I will hand back to, to Olivia to to
S3: wrap up. Thank you.
S2: Perfect. Thanks. Um, also, maybe Daniel. Do. Daniel, do you
S2: have any questions from your side? Because I've got 1
S2: or 2 I'd like to ask. I think we have
S2: a few more minutes. So.
S6: Uh, so.
S3: My questions were, you know, security and this thing of
S3: trustworthiness and, uh, explainability, but I think I have my answers, so.
S3: So I believe I'm good.
S2: Okay, perfect. I have a quick question about what you mentioned,
S2: about how personalities and ideas are most important and that
S2: we should be online. But what about AI influencers? What
S2: are your thoughts on those?
S1: Yeah, it's, uh, it depends how good they get. Uh,
S1: if they get really, really good, um, and they're allowed
S1: to continue, uh, because it could be that, um, some
S1: governments decide that they're too good and they're disrupting human
S1: capabilities to actually be creative. At which point they could
S1: be shut down. But if they are not shut down
S1: and they are really, really good, the bad news there
S1: is that they might quickly become much, much better than
S1: any human like. They'll just be more attractive. They will
S1: speak more attractively. The ideas will be better, they will
S1: be funnier. And it's like the human creators, first of all,
S1: they have to sleep so they won't be creating as much,
S1: and they might move down in the rankings. And the
S1: most popular influencers and the most popular shows and everything.
S1: It might be all I created. I do believe that
S1: that is the type of thing. It's like the last.
S1: It's the last stand for humanity is to be this
S1: creative thing. And if if they take that away, I'm
S1: not sure what else we have. So I could honestly
S1: see that being the place that regulation happens to say
S1: we these must be validated humans to be creators.
S2: Well, yeah. It's interesting. That's a scary thought. Um, and also, yeah,
S2: on the line of being out there and being visible online,
S2: what are your thoughts when it comes to data privacy risks?
S2: For example, if I put all this information of me
S2: out there and cybersecurity risks as well.
S1: Yeah, it's a great question. I believe the answer is
S1: it won't matter that much. Uh, unfortunately or fortunately, I think, um,
S1: the way this is going to move is people are
S1: going to give so much data to their AI, their
S1: personal AI. And because those are largely startup companies, they're
S1: all going to get hacked. Um, so these startup companies
S1: are going to get hacked. And rather than just having
S1: your financial information, it's going to be essentially like your soul.
S1: It's going to be like, I had these traumatic experiences.
S1: Here are all my relationships. It's going to know all
S1: your health data. When that gets hacked, it's going to
S1: be unbelievably traumatic. But watch this only for the first
S1: few people, only for the first year or two, because
S1: we're not used to having that level of personal exposure online.
S1: But soon we will. And then we're just going to
S1: know that we're all the same. We're all equally flawed.
S1: We all have these equally weird things about us. And
S1: there's one possible way for it to go is that
S1: it just becomes so, uh, regular for this to happen,
S1: that it becomes the new baseline and nobody cares. Yeah.
S2: Well, so, yeah, if everything's out there for everyone to see,
S2: then they can't use it as a weapon. That makes sense.
S1: Exactly.
S2: Okay, well, that was it from my side. Um, thank
S2: you very much, Daniel. This was fantastic. And thank you
S2: for your time today. Um, to everyone watching, we do
S2: have an AI at Ypo Viper Teams channel. So if
S2: you'd like to join the community, please do so. Um, yeah.
S2: You can find out about upcoming AI repo sessions and
S2: other un AI events there, as well as well as
S2: industry news, and you can join general AI discussions. And
S2: please feel free to reach out or consult the AI
S2: repo intranet page if you'd like to learn more. So
S2: thank you again, Daniel Mazia. Thank you Daniel Jeremiah and
S2: have a good day everyone.
S1: Unsupervised learning is produced and edited by Daniel Miessler on
S1: a Neumann U87 AI microphone using Hindenburg. Intro and outro
S1: music is by zombie with a Y. And to get
S1: the text and links from this episode, sign up for
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