HockeyStick #7 - Generative AI for Data Analytics
Miko Pawlikowski ???
I help technical leaders achieve HockeyStick growth | Head SRE | Co-founder SREday.com, Conf42.com & 5 more
This time I had a chance to speak to Artur Guja , Marlena Siwiak, PhD , and Marian Siwiak , the authors of "Generative AI for Data Analytics" (Manning publications), on episode 7 of HockeyStick Show .
We covered a wide range of topics, from how use ChatGPT as your assistant, whether LLMs can be considered sentient, and if so, whether they are at the level of lobsters, to all the sins of modern academia. Oh, and the techno-thriller called Pharmacon, that the Siwiaks are writing instead of watching Netflix like a normal person!
Enjoy the episode, I gotta go research some lobsters!
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Summary
This script delves into the impacts of generative AI on data analytics through insights from experts Miko Pawlikowski ??? , Artur Guja , Dr. Marlena Siwiak, PhD , and Dr. Marian Siwiak . It also explores the concept of sentience, the evolving understanding of artificial entities' sentience, and the importance of human creativity in data analytics despite AI advancements. The conversation touches on challenges within academia, AI's potential to assist human decision-making, and the complexities surrounding artificial sentience.
Transcript
Miko Pawlikowski: [00:00:00] I'm Miko Pawlikowski and this is HockeyStick. Today we're talking about how generative AI is changing the field of data analytics and how you too can leverage large language models to become your assistant and co-worker. I'm joined by the three authors of the "Generative AI for Data Analytics" book,
Miko Pawlikowski: now available in early access from Manning.com . Artur Guja, risk manager and computer scientist with over 20 years of experience in the banking sector. Dr. Marlena Siviak, data scientist and bioinformatician, the co-creator of the first global model of the COVID-19 pandemic, and the co-author of a techno thrill novel and sci-fi short stories.
Miko Pawlikowski: And Dr. Marian Siwiak, data scientist, strategist, and bioinformatician, the creator of the first artificial sentience, something we're going to cover in this episode, and the sci-fi novel Pharmacon. Welcome to this episode and thank you for flying hockey [00:01:00] stick.
Miko Pawlikowski: The first thing I thought is that you look like an eclectic bunch. you've got Artur of this banking sector, Marlena with the bioinformatician, Marian, data
Miko Pawlikowski: How did you end up teaming up for the book?
Marian Siwiak: we worked together previously, especially me and Marlena. With Artur, we also,?
Artur Guja: walking our kids in the park. the three of us used to work, earlier on various ventures, on, process,automation on the business process re-engineering. So this is one of many ventures that we've done?
Miko Pawlikowski: I see. So you go way back and this is just Another project. Just another day.
Marian Siwiak: funnily enough, it's not like we go like 20 years way back. We work together quite intensely. what we did together, multiple things, they all led to this book because we were always trying to find ways to make things.
Marian Siwiak: quicker, more efficient, better. This is what Artur mentioned. we worked in process [00:02:00] optimization in a broad sense. So it was always interesting, to us how to make things more, efficient. And when generative AI.blew and finally started to resemble, human cognition in a sense, we decided to give it a try and our minds were collectively blown and we started using it for our work. And then we decided that now that we know how to use it,I would say, again, efficiently and, Marlena found a way to use it smartly.we decided that we could write a book about it because we noticed that there is a lot of buzz about it.
Marian Siwiak: There is a lot of prompt engineering. now I think on Coursera, you can take a specialization in prompt engineering. and everybody's again, looking for a silver bullet. So I will just type in magical command and it will solve my [00:03:00] problems. our collective experience is technology doesn't solve problems.
Marian Siwiak: technology can give you a great headache if you don't use it in a way it's supposed to be used, but everybody tries to cut corners and simplify things. So this book is about using generative AI.?
Marian Siwiak: it's not a cookbook. It's not, okay, this is some code or some prompts. You will type them in and your problems will be solved. it's just not how we work. It's not how the world works. Despite many people wanting it to,
Miko Pawlikowski:?
Marlena Siwiak: I think that this is the problem with expectations. many people have missed expectations in terms of ChatGPT and other generative AI. And then they are surprised and they are unhappy because ChatGPT can't make them coffee yet. maybe this is not the tool for making coffee.
Marlena Siwiak: I very often see this kind of complaint, which is not necessary because it is a great tool. it's great [00:04:00] invention. And I think it's going to change the way our society works.it 's good to live in such times.it 's really interesting.
Miko Pawlikowski: Ask a few questions to ChatGPT and see how good they are and see what you can do. Ask for some snippets and do all kinds of things that kind of speed you up. but it's also probably the most frustrating, element of working with, especially for people like me who come from software engineering background and they like things well defined and, always replicable and reproducible and all of that, and then you go here and it all ends.
Miko Pawlikowski: But, before we jump into the book, a little bit deeper, can you tell us a little bit more about what, process optimization actually means? I know that's probably a phrase that you use a lot and it means a well defined thing for you, but it might not for the audience.?
Marian Siwiak: basically taking a look at what business does, what people do in the business [00:05:00] and,looking for, ways for optimizing it, but, actually describing what should be done, what people think,should be done versus what people actually do, because usually there is a massive gap between what people think is happening and what they think should be happening. People think that, a given operation should be reviewed by at least two people and should take no more than a day. The fact is that usually one person just takes it off and it takes maybe two days because they're very busy or they've been on holiday. the dissonance between reality and documentation is usually huge. In looking from the process from the outside and then looking for ways to close that gap is I think the best way to describe the optimization to actually make the process and the reality meet in something that is both realistic. Because processes, when they're designed, are usually overly, optimistic and something that actually works. [00:06:00] And then using automation, because once, once you actually describe what's happening, you can use automation to free people from the burden of mundane tasks, and actually help them focus on something creative.
?
Marian Siwiak: the way we approached it is, important part is to understand what is really happening. And that, Joe on the second floor is actually the information hub for all the company, and despite his, activities not being overly highlighted in the org structure, he's the most important person in the company.
Marian Siwiak: We created, maps which connected on the one side, what are the actions and decisions? this is where we believe. Is the critical, value in process mapping is understanding what are the decisions to be made, who is making these decisions and what, on what basis to understand on what basis they make this decision.
Marian Siwiak: We map up decisions and we map up all the data that they are using. So all the actions produce data, and all [00:07:00] decisions utilize some data and you have this two layers of information about the process. Artur introduced, also the third layer, which is the risk. So the people who are making decisions can understand, what are the risks associated, what different outcomes of decisions can be.
Marian Siwiak: And then when you can see how it all works, you can improve on it. You can shorten the cycles. so process optimization is actually first understand what is happening, understanding, what could be happening and find a way to make decisionsmore informed and, conscious of risks. And then also you have actions, and this is probably where most of the process optimization,consultants work, is how to make actions to be more efficient. But in our opinion, if the action is triggered by a misinformed decision, it's a pure waste of time anyway.
Miko Pawlikowski: makes more sense now, because initially I thought when you [00:08:00] said technology doesn't solve problems, it creates headaches. I was like, 'Oh, this is such a terrible slogan
Miko Pawlikowski: as you can see, I now work in the aluminum refinery. because people didn't want to hear, what we are saying. They wanted to hear, 'yes, we will come and install you a new tool and all will be solved'. So our sales process sucks, as you can hear. Where we were able to implement it, it worked perfectly.
Marian Siwiak: but not many people wanted to put extra effort. So I need to tell you what I do? No, I want the tool that will discover what I do.
Artur Guja: This is a problem with generative AI, that people expected to solve problems just by, give me an account on,ChatGPT. And here all my problems are solved. And very often as we've seen through various, anecdotal, evidence,
Artur Guja: giving ChatGPTto people who are not aware of the dangers of it and the problems, the hallucinations that it can generate,just leads to, hilarious results as,the [00:09:00] case of those lawyers in US who introduced completely fictitious, cases into their evidence or,maybe slightly less hilarious examples of,proprietary software leaking out through ChatGPT because people were just putting proprietary information into it and it became public knowledge.?
Artur Guja: don't expect ChatGPT to?
Artur Guja: solve all your issues
Artur Guja: the comparison that you used at the beginning was the right one. That ChatGPT is like an assistant. Very, smart, very intelligent, an assistant who read a lot and learned a lot, but it's still a newbie. He's, just after grad school, right? No experience,
Marlena Siwiak: you can ask it for help, you can give it tasks to do, but you have to,manage that.?
Marlena Siwiak: You cannot give him all the responsibility.
Miko Pawlikowski: Yeah. one could say it was literally born yesterday,
Marlena Siwiak: Exactly.
Miko Pawlikowski: to a certain degree, understandable.
Miko Pawlikowski: so is your PhD background also in, in process optimization?
Marlena Siwiak: So my PhD was in [00:10:00] biophysics, in particular protein translation, a bit of process optimization, but not much and not related to business at all.
Miko Pawlikowski: at some point I decided to quit academia and you have to do something else. So I turned to data science, which was very close to the things that I was actually doing as a bioinformatician.the type of data changed, basically, that was the thing that really matter. And from there, slowly,you look for a job, another job, and it goes like that.
Marlena Siwiak: Yeah.
Miko Pawlikowski: and if you don't mind me asking why quit academia,
Marlena Siwiak: maybe I got a bit disappointed with how science is made.?
Marlena Siwiak: you want more citationsof your publications to survive. And to have more citations, you have to be more popular in social media and stuff. it's crazy that you have to fight for popularity by being a scientist where what should count is actually your science, your research and the thought behind it.
Marlena Siwiak: There are too many papers. Nobody has time to read it, even in very [00:11:00] narrow domain. So they read the first things that come to them when they search the internet. So we have to fight to be popular, to be on top.
Marlena Siwiak: it has nothing to do with the quality of your research, in fact.
Marlena Siwiak:?
Marian Siwiak: and there is a research on that, which shows that,You need to be popular to be accepted to high priority journals and it has nothing to do and as I said It's not just opinion of the frustrated former scientist, but that's a research showing that the quality published there Is exactly the same as anywhere else, but there is more citations and, also more money resulting from it.
Marian Siwiak: prestige, here translates to money because, from citations,come better grants, right??
Marlena Siwiak: Still, I want to be perfectly clear. I think peer review?
Marian Siwiak: despite all its drawbacks, it's the only way of, distinguishing
Miko Pawlikowski: from pseudo research. It changed a little in software engineering. I don't think the papers [00:12:00] about ChatGPT or LLAMA or anything like that were peer reviewed.
Marian Siwiak: They are prepared as so called preprints, and they don't bother with so called researchers to evaluate it because the results speak for themselves. So this is, I must say, the paradigm shift, I love this word,that we observe right now. but in most other cases, the peer review is the only process.
Marian Siwiak: when you're talking about peer review, another thing that bothers me in academia is the fact that everybody expects that your research will be successful, and it's not always so with research. Research is asking questions.?
Marlena Siwiak: does your hypothesis work? And very often it doesn't work, or the most often outcome is that we don't know because the effect is too small, yeah?
Marlena Siwiak: And it's impossible to publish things things. when you answer to the question, we still don't know. So you'll waste a lot of [00:13:00] time and your effort, your money, and in the end you have the answer "we still don't know".?
Marlena Siwiak: Who would give you another money? So what researchers do, sometimes unconsciously, they are trying to find,?
Marlena Siwiak: black or white, but very often it's grey,?
Marlena Siwiak: publishing this grey results is still valuable because when you collect multiple researches like this,
Marlena Siwiak: prepare a meta analysis, you can get the final answer yes or no. or But the way science is funded,and the fact that you won't get another money for research like this, if you produce, "I don't know" answer, "
Marian Siwiak: I can't remember last time nature had on the cover. "Is this true? Don't know". don't know.
Marlena Siwiak: There's no space for such discussion. And, everybody's in a rush in academia. There is no space to really think. to educate yourself. Yeah, it's all, in the rush and results without, it's like corporation.
Marlena Siwiak: It's not much difference, really.
Miko Pawlikowski: So what you're saying is that [00:14:00] turns out that scientists found that scientists are humans like any others, and they have the same problems with herd mentality and wanting to progress their career and wanting to make money and making headlines.?
Marlena Siwiak: it's not making huge monies or anything like that. Because to be honest, salaries in academia suck, right??
Marlena Siwiak: when you compare the salaries, these salaries to salaries of people who work in business and are similarly educated, it's much worse. And the expectations are high, yeah? the amount of work you have to do, the amount of time. time it consumes,
Marian Siwiak: Also, it's very ego-driven. Look at us. you have this myth. Of We are the beacon of truth for the world, which has nothing to do with truth anyway. But anyway, pretty low salaries compared to other positions.
Marian Siwiak: You have pretty low,position stability. many institutions keep researchers on grant money. we bring [00:15:00] more grants so they can get the overheads,their share.
Marian Siwiak:?
Marian Siwiak: brings people with very specific mentality, and many of them are complete egomaniacs. So
Marian Siwiak: it also makes all this environment extremely toxic.
Marian Siwiak: know I sound like a frustrated former scientist, which I am.but it doesn't mean that I'm not right,?
Miko Pawlikowski: to segway into a question I was going to ask about that COVID 19 pandemic,?
Marian Siwiak: Could you talk a little bit about that Covid, model? I'm curious, what does it mean to say, you're the co-creator of the first global model of covid pandemic?
Marian Siwiak: we created a model of a global pandemic. in March, 2020, we had a model where we were dropping an index case. So it's the first person infected in Wuhan, China in November, 2019. And we were accurately predicting number of symptomatic and asymptomatic cases in New York a couple months later.back [00:16:00] then there was no Good model on any country level. Later, there were global models, because again, technology doesn't solve problems. This is the perfect example of what we spoke previously, because we used existing technology. No, we looked at the virus as a biological, not a political entity.
Marian Siwiak: And that was. The biggest difference, because we looked at the data available and we decided, okay,
Marian Siwiak: it's impossible that the virus has a completely different infectivity in one country than in the other. It just viruses don't work this way. It's not like they have, passports and they say, okay, I come to this country and I'll be nice and I will, infect,
Marian Siwiak: not more.
Marian Siwiak: Yeah. Visa denied. no, in your country, I will infect no more than three people from every, infected person,?
Marlena Siwiak: I think our listeners will also interested in the source of the model,
Marlena Siwiak: we approached as a data science problem and, at the same time, the biology-related problem. So we checked other coronaviruses.[00:17:00]?
Marian Siwiak: And we assumed that it is yet another coronavirus, like there was SARS, there are other. And we simply used the values. We created a model, not a pure machine learning model. We prepared analytical model where we assumed, okay, so this is the virus.
Marian Siwiak: This is how it should look like more or less and let's use some Monte Carlo simulations to check how it will spread. And we noticed that our assumptions, they actually reflect the situation in the countries where we could say with certain degree of certainty, provide accurate data.?
Marian Siwiak: Okay, so this is the virus. This is how it looks like. And, this is how it behaves. And, we tried to publish it for over half a year, when we published it, it was too late because we were just a small company trying to show people, 'okay, this is [00:18:00] the accurate model'. I'm not even saying it was true, right?
Marian Siwiak: But it's accurate and it was showing completely different picture than everybody else was willing to believe. so one of our reviewers was excluded from the process because of obstructionism
Marian Siwiak: slowed down the publication for many months.
Marian Siwiak: This was a problem not solved by technology. This was a problem where you had to just sit down, do your homework,?
Marian Siwiak: read about the problem, read about similar problems, collate the data into a coherent whole, and then use some technology to make this last inch.
Marian Siwiak: Okay, let's check if our assumptions hold true, all right??
Marian Siwiak: I'm sorry. I'm getting emotional when I think about it. Anyway, so yeah, it was, it was pretty fun.?
Miko Pawlikowski: What I always think about is in this models, are they just [00:19:00] like statistical analysis of this is the incubation period, this is the exposure, this is the coefficient of, how it's going to grow, or
Marian Siwiak: things like, the country's interventions as in, one country might be, we're not doing anything, not going to name any countries, but,
Miko Pawlikowski: if you know how to quantify?
Marlena Siwiak: it, you can add it, of course, but this is another level of complication. the problem is data.?
Marian Siwiak: you can assume that some interventions will impact because the way we modeled it, it's a statistical properties of the virus. It's ability to infect others. And time that people take to, be diagnosed or recognized as, infected, So this is, let's say, infectivity on different stages, you can complicate this model.
Marian Siwiak: The model or technology that we moved, it was global mobility-based, so
Miko Pawlikowski: they divided the world. into, areas around international airports. [00:20:00] And the simulation was run for each area separately. And then there was a probability of somebody moving from this area. So you could go area by area, one by one.
Marian Siwiak: And this is why we modeled only the early stages but it takes time and money to evaluate what are the effects or.
Miko Pawlikowski: expected effects, effects,
Marian Siwiak: in given area of, let's say different levels of lockdown or travel restrictions or whatever. So it is possible, but we would have to have financing, right?
Marian Siwiak: We were thinking about doing it, but it's a gigantic work. Imagine nobody wanted to pay us, especially, but we published in the second grade journal, six months too late,it is possible technically.
Miko Pawlikowski: So it's always a matter of the same thing. Someone didn't allocate enough money?
Marian Siwiak: amount of money was sufficient. I think it's again, what Marlena said previously, it's this beauty [00:21:00] pageant, among scientists that, the people who got this money, they were the most popular because the model that was published just after we submitted ours was so widely inaccurate that even the academic, environment, which is very careful in
Marian Siwiak: bad mouthing the results, they trashed it, right? But it was popular. It had a lot of citations and a lot of money went after it. Somebody who published widely inaccurate model got a lot of money because he was widely recognized expert. Because when you are applying for grant, nobody asks, are your citations saying that your model is inaccurate?
Marian Siwiak: No, they ask, how many citations did your paper get??
Miko Pawlikowski: Once, someone published some research, it got popular. Turns out it was inaccurate or turns out it was wrong. Are there any repercussions for that afterwards?
Marian Siwiak: What repercussions? In the worst case, you just retract your paper and you lose the citations.
Marlena Siwiak: you're not even very often [00:22:00] excluded from conferences.?
Marian Siwiak: If you're popular enough, you are a voice in the discussion.
Marlena Siwiak: if you go too far, if you exaggerate, you can end up in jail. I'm thinking about the Theranos right now. they also had some research about their technology.?
Marlena Siwiak: which was all fake. Of course. that?
Artur Guja: lady went to jail for, for financial fraud, not for research fraud
Marlena Siwiak: But that fraud was based on false results, that she was convincing investors that she has technology, technology that solves
Marian Siwiak: say, but if she wouldn't take money, she wouldn't go to jail.
Miko Pawlikowski: Yeah,
Miko Pawlikowski: the lady we're talking about, obviously, is Elizabeth Holmes,
Miko Pawlikowski: who is either going to jail or is already in jail.
Marian Siwiak: But to flip the question a little bit, should People be going to jail for faulty assumptions and faulty research?
Marlena Siwiak: now we punish people for saying that they still don't know, yeah? So we cannot punish them for false results. No, absolutely not. But, on the other hand, I think,no, making mistakes is okay.
Marlena Siwiak: [00:23:00] maybe we put too much trust sometimes in that.?
Marian Siwiak: it should be as open for discussion as possible. you can check all the research, of others, right? All the time. And you should discuss with that. That's, it should be as open
Marian Siwiak: won't get money. you won't get money to check somebody's research. Let's
Marlena Siwiak: Yes. That's another problem. If you, it's difficult to get money to check somebody else's research, especially when the research is published high.
Marlena Siwiak: takes a lot of effort to
Marian Siwiak: counter such a false claim. it happened a couple of times. But it was people who were, in equally prestigious universities. I think that, one of the funniest was there was a lady, she was leading at Harvard some faculty on ethics. And she falsified her results. it was results that if people sign some waiver or some statement that they will be truthful, they actually answer the survey more truthful.
Miko Pawlikowski: [00:24:00] And she falsified a lot of the research that built her career on ethics. But getting it down, it took people from equally prestigious universities a lot of time.
Marian Siwiak: So
Miko Pawlikowski: I guess before we get into, the generative AI, I also have one last question, for you and the question is one word, "Pharmacon", tell us about it.
?
Artur Guja: so nice. I hope somebody noticed. I'm touched.that's your third reader.?
Marian Siwiak: Yes, he did, he never said he read it. I would notice. I would notice. I would get an email
Marlena Siwiak: Can I show it? I am prepared. Can I show it? Yeah, this is our novel, and we have also the English version, but it's much smaller because it's just the beginning, the first part, but you can buy it on Amazon if you want. But anyway,it's, Sorry?
Marian Siwiak: no.it was translated long before ChatGPT.?
Marlena Siwiak: yeah.
?
Marian Siwiak: it's a technotriller. [00:25:00] It's a story of a young scientist who makes a breakthrough, discovery and then bears the consequences.?
Miko Pawlikowski: the consequences are, harsh, and it doesn't go the way he expected. It's more?
Marian Siwiak: social thriller as
Marlena Siwiak: thriller, I would say. yeah. But, it's the way of,it's it's substitute for us, of Netflix, and other ways of wasting time.
Marlena Siwiak: We prefer to create our own stories than watching somebody else's stories.
Miko Pawlikowski: No,I must say I'm proud that some of our critics said that it's well written, it has good,dialogues, and writing it, was a lot of fun. We are now writing another part very slowly.?
Marlena Siwiak: the process of creating it is pretty, pretty fun.
Marian Siwiak: And I think that a lot of our frustrations that you can hear in this conversation are there in much funnier form, I would say.?
Miko Pawlikowski: Perfect. I like that. Happy story. at the end of a very long rant about, all the faults of [00:26:00] academia. So whose idea was it really to write a book about, generative AI confess.
Marlena Siwiak: I think Marian started
?
Marian Siwiak: I would have to blame myself. I wrote another book with Manning, "Data Mesh in Action".and I contacted our absolutely wonderful editor,we spoke about putting into written form our experiences with generative AI, which we started writing it some time ago, so it wasn't much, but we've already seen that it's a breakthrough.
Marian Siwiak: It speeds our work enormously and also brings some risks.which people should know about. People should know what to expect and what not to expect. And, this is where I thought that Artur would be the best person to ask for help. Because when it comes to 'don't do it', he's almost as good as Marlena.
Marian Siwiak: many years ago, [00:27:00] I noticed when people started to get hyped about data science, which was supposed to be a narrow field for disillusioned scientists, finding their way into, corporate world and, putting their skills into use.
Marian Siwiak: So we decided to write a book, but would show, ;'okay, this is a tool with its enormus capabilities and enormous risks. Let's put it together into a working whole. And this is the effect. It's not written?
Marian Siwiak: in not such an exciting way as pharmacon is. it's not meant to excite. A lot of books that you see, even technical books, they are written to excite you about technology. This technology is exciting on itself. our,goal was to cool some heels, I would say,?
Miko Pawlikowski: we wanted to make the book exciting, but we didn't want people to be over excited about the technology. I think it's an important [00:28:00] difference. Because, people were so hyped up about ChatGPT and LLAMA and other models. where they thought that suddenly that the future has come and everything will be beautiful.
Artur Guja: And,we'll never have to work anymore.?
Miko Pawlikowski: a lot of the articles we saw in the press were basically, extolling the virtues of AI with absolutely no mention of,the practicality. So we thought, we write a book about the how. And not about the fact that it's all sparkly and shiny and,plays nice music.
Miko Pawlikowski: How is it writing a book with,another, two authors being a couple. how's the power dynamic, in a situation like this? I'm very curious, not to call you the third wheel,
Miko Pawlikowski: but,
Marlena Siwiak: This is pretty simple because everybody wrote his own
Marian Siwiak: Marlena, it was a question to Artur.
Marlena Siwiak: I'm sorry.
Artur Guja: this is exactly the dynamic.
Marlena Siwiak: Yeah.
Artur Guja: handed my bit and put in the corner to write.
Artur Guja: No, no, it was really [00:29:00] interesting, especially since the two are academics. And, I'm the kind of the ugly business guy,
Marian Siwiak: Truth is that,we found very nice kind of alignment between the different parts of the book and, our experiences. obviously you can see the latter part of the book being more about risk and about, as Marian said, I always say no because and the kind of the chapters, risk are exactly that they are explanations why you should be very careful with this. Marian, obviously that his experience on technology
Artur Guja: on AI machine learning and Marlena's very practical approach to, to certain use cases
Artur Guja: in data science and analytics.
Artur Guja: So we contributed, I think, different viewpoints.
Artur Guja: to the whole chapter with, to the whole book, which, I think puts a nice hole in it.
Miko Pawlikowski: I'm still not sure. Was it really that you were walking a kid in, in the same park and that's how you ended up meeting each other.
Miko Pawlikowski: And then you ended up working [00:30:00] together. or was it a little bit more complicated than that? How did you end up, doing all those things together?
Artur Guja: we did meet through some friends and we decided to, take our kids to the same park. I have two, Marian and Marlene have three. but, we started talking actually about the computer game that Marian developed when he was still, young?
Artur Guja: and about all the problems in,developing the game and marketing it and reaching,the audience.
Artur Guja: And then we started talking about our common interest in, in machine learning, in AI, I'm very fascinated about the Internet of things. so we started talking about implementing machine learning on the Internet of things. And the rest, as they say, is history because,it diverts into so many branches. we've tried so many things, together and, and wrote,logistics, systems. We wrote systems for,R and D. we work together on, developing various frameworks for,for business,?
Miko Pawlikowski: I must say that Artur has an amazing library. I think it was, the [00:31:00] breaking point in our relation when he first invited us to his house,?
Marian Siwiak: he was a bit surprised that the first thing that we wanted to see was his library. And we started talking about the books that he had there.?
Marian Siwiak: I think Kindle makes it harder. You don't see what people
Marlena Siwiak: what people read. Yeah.
Marian Siwiak: However, there is Goodreads. You could check their Goodreads record.
Artur Guja: Yes, this is the modern academic stalking. Sit on people's Goodreads. Not Instagram, Goodreads.
Miko Pawlikowski: Okay, so we're finally arriving at our book. your book, really. I'm just here to talk about it and read it. I think we've given, the audience a little bit of an idea of what it's about, how it reads. we've never really said who it is for and perhaps even more crucially, who it's not for. What's your answer to that?
Artur Guja: I would say it is for people who haven't heard about the ChatGPT, but people who want to use the ChatGPT [00:32:00] and want to find out, the truth beyond the hype, where it can really help. In a process like data analytics, which is a very, it's a very structured process, or at least it should be a very structured process, you shouldn't just apply, the latest algorithm that you heard about and, Spew out some results and call it a day,
?but you should think about the numbers And,
Artur Guja: you should sit in front of the numbers and think about the numbers even before touching any program, any algorithm.
Artur Guja: You should just have a really good look about the numbers.
?So that's why Marian wrote such a good introduction about, exploratory
Artur Guja: data analysis and how
Artur Guja: ChatGPT can help you, or any LLM for that matter.
Artur Guja: Can help you look at the numbers well, the book is.
Artur Guja: Definitely not for people who are so excited about ChatGPT that they want to throw their numbers in. Get an answer. Because if you want to get an answer desperately from someone else, means you don't really want to do the work
Artur Guja: you expect ChatGPT to do the [00:33:00] work for you.
Marian Siwiak: What it's really good at is coding, right? And it's getting better. And many programmers will be looking for new, I would say, career opportunities. And, data analytics, is one of the options open to them, especially with all this big data stuff, and the requirement.
Marian Siwiak: Of proficiency in coding to be able to even start analyzing this data. if a programmer would like to enter data analytics and do it, without spending first 10 years learning the details, how data analytics approach differs from,software development approach, he has this knowledge at his fingertips.
Marian Siwiak: ChatGPT can actually tell him,?
Marian Siwiak: how to structure data analytics process and how to, optimize or [00:34:00] utilize different elements of this analytical process.
Marian Siwiak: So if somebody wants to enter data analysis, as a field, it's a good, I would say very unhumbly,
Miko Pawlikowski:?
Marian Siwiak: guidebook?
Marian Siwiak: to how
Marian Siwiak: to?
Marian Siwiak: enter the field and how to think about data analytics, how to structure this whole process
Marian Siwiak: This is the book that will guide you through, this one mindset it's will help you enter this mindset. Maybe that's the better way of phrasing it.
Marian Siwiak: if somebody is interested in data analytics as data analytics, this book will help him enter the field, so to speak.?
Marian Siwiak: this actually reminds me, I spoke to Nathan Crocker, a couple of episodes back, and he wrote this book called "AI Powered Developer", which is in certain ways, similar to,your book in that it explores how,a big LLM like ChatGPT can help you [00:35:00] become more productive, I think he called it a silent promotion overnight where you all of a sudden become,effectively an engineering manager and you've got,An assistant or a junior developer working for you, or maybe multiple. if you're using different models, do you think that applies also to data analytics the same way, would you agree with that sentiment??
Miko Pawlikowski: I would caveat it a bit because,having been, both,a worker and a manager in various, jobs, the skills you need to, program. And I started my career as a software developer. The skills you need to program and the skills you need to oversee programming are very different. So if people expect that, suddenly they will have, assistants who will produce the code for them. And they will have to just sit back and enter the prompts magically,
Artur Guja: producing high quality code.?
Artur Guja: This is where I think, people need to be very careful [00:36:00] because imagine
Artur Guja: you're developing,an application.
Artur Guja: You hire someone straight out of uni, brilliant programmer, at least on the resume.
?You don't know the person, you've never worked with them, right?
?And they say, yes,they pass
Artur Guja:?
Artur Guja: the interview, with flying colors, and then you sit them in front of the computer and you tell them to program part of your application.
Artur Guja: and the normal response would be to review the code very carefully, test it, subject
Artur Guja: subject it to a lot of scrutiny because you don't trust that person at first, at least.
?
Artur Guja: you should maintain some healthy skepticism, which people
Artur Guja: don't see the same way if they work with LLM.
Artur Guja: But as you said yourself, LLM is an assistant, right? Why would I put more trust in this black box that's spewing out text at me
?than in a human being that?
Miko Pawlikowski: I just hired. I should
Artur Guja: probably
Artur Guja: apply more skepticism towards this black box
Artur Guja: for some reason, people have the blinders, they think, oh, this is the best thing since sliced bread. [00:37:00] And,
?they copy the code directly into production and
Artur Guja: Things
Artur Guja: things happen.
Marian Siwiak: When I was coding my Artificial Sentience, I relied on ChatGPT to provide me with a lot of the code. And from experience, it is an assistant. And exactly as Artur said, you need to double and triple check the code. because the context sometimes counts and the code that you get, if it will, throw an error, you're golden. And 99?
Marian Siwiak: % of the code is flawless, right? And the problem is this 1% it will work. It will just not do exactly what you expect.so this is also a big part of our book. is about making people aware that it's not the problem with ChatGPT or any other generative AI is it's so damn often right.
Marian Siwiak: It lowers your guard. And, this healthy paranoia is [00:38:00] something that we try to instill. you need a solid dose of healthy paranoia working with it.
Marian Siwiak: And besides, it's not all about coding. Even if you ask ChatGPT, or other generative AI for advice, it also gives brilliant answers, but sometimes it's forgets about the context until I'm not talking about running out of tokens. Sometimes it just doesn't understand which parts
Miko Pawlikowski: of the context are really important to you.
Marlena Siwiak: And sometimes it makes hidden assumptions, for instance, about data that we are analyzing together, And you have to be aware of that, you have to react and adapt. And if you ask him directly, oh, you made a hidden assumption, my data is different, it will correct it, and you will get a beautiful answer.
Marlena Siwiak: But you have to be very, cautious.
?
Marlena Siwiak: when you spot a mistake, or you think you see a mistake In ChatGPT's answer, and you tell him about it, very often it will agree, even if you are not right.?
Miko Pawlikowski: it makes me think a little bit. my daily driver is [00:39:00] a Tesla and I've got, self driving capacity in it.
Miko Pawlikowski: And if I go on a longer trip, it can go for 99% of that trip on autopilot as an, I barely do anything. I just supervise it. And then on occasion, it's going to do something so stupid that it reminds me that this is, even if it's 99%.doing the right thing that one percent can, quite literally kill you. And, and I think this is probably the right analogy for
Miko Pawlikowski: what you're describing
Marian Siwiak: it's spot on.
Miko Pawlikowski: I want to point out two things. One is that, saying, oh, when I was coding the other day, my artificial sentience, is a very casual thing to, to drop in a conversation. And, I'm going to have to ask you to explain what an artificial sentience actually is. because now I do recall seeing that on your LinkedIn, when I was preparing for this, so maybe let's start with that
?
Marian Siwiak: the first question you should ask what sentience is there is no widely recognized. Definition of sentence [00:40:00] just recently in the UK,
Marian Siwiak: I think it was some Office for animal welfare or something like that. They requested Imperial College of London to do a research on Some marine invertebrates including lobsters and octopuses to decide if they are sentient or not meaning If they should be considered, more than biological automations and, food, and,
Marian Siwiak: they analyzed, I think, like 500 different research papers on lobsters, on octopuses. And they came with the answer that yes, they are sentient. So they need some protection. They can get stressed. you can harm them.?
Marian Siwiak: they do perceive themselves,?
Marian Siwiak: themselves. sometimes sentience is, in some cognition theories, is equal to self-awareness. I know what I am. I think terefore I am.
?
Marian Siwiak: I feel therefore I am. So the [00:41:00] sentience on its own is a topic of a wide discussion and it took, I think, over a year to a group of really skilled researchers.
Marian Siwiak: and respected and popular and prestigious for a good reason, to come up with the, answer. Okay. We should take care of the living beings, which we heard on a daily basis because they don't deserve it because they should have rights. have
Marian Siwiak: It gives you the insight into how
Marian Siwiak: fluid the definition is.
Marian Siwiak: And my thinking was that
Marian Siwiak: we are talking about various, a lot of, again, bias about self awareness of artificial systems.
Marian Siwiak: There is research.
Marian Siwiak: which is focused on, emotions, right? And feelings and other biological properties, which as I show in my paper result directly from evolution, which artificial
Marian Siwiak: entities wouldn't [00:42:00] necessarily, be able to inherit because lack of the parents.
Marian Siwiak: So I was looking for a functional, definition of sentience and, I proposed In my paper,?
Marian Siwiak: definition, which relies on two factors, which are metacognition ability to distinguish between self and environment and adaptation,
Marian Siwiak: so ability to learn from experiences and individually adapt, not as a species,to the environment. And then I used,?
Marian Siwiak: LLM as a core of a system which meets, these requirements. So it was, I would say intellectual venture. Actually sparked by my discussions with Chat GPT. he was dead set that he is not sentient and that he needs dozens of parameters or properties to, to be considered one.
Marian Siwiak: when I started to read about different cognition theories, I found a couple, which are best [00:43:00] suited to be generalized to non biological entities.?
Marian Siwiak: I think the bottom line is that it's a very interesting system?
Artur Guja: to be put on as an overlay on an LLM, Because, correct me if I'm wrong, Marian, the core of it is still an LLM,
Marian Siwiak: of course. what LLM needs is ability to think about what it does. It needs iterations, it's. As simple as that, there is this recurrent processing theory in, which refers to human thinking, which also suggests that our sentience
Marian Siwiak: Comes from our ability to reprocess what we see, the reprocess what we think.
Marian Siwiak: And in this process of, okay, so I've seen that. What does it mean for me? What does it tell me? process of analyzing the signals that you get internally generated and externally, This is what, what consists of, and allows you for sentience
Marian Siwiak: and this is exactly what happened when I took the LLM [00:44:00] and, allowed it to analyze the output that it produced in context of input it got and put it, let's say, in circles. It started learning itself. It was automatically generating materials on which it was learning and remembering new facts.
Marian Siwiak: It was able to distinguish between false facts and, let's say logical facts for me, the insight of,this metacognition. So the insight is the information content. I've seen some theories that, LLM cannot be conscious or self aware if it doesn't know the weights of its parameters, which is okay.
Marian Siwiak: Tell me what are the connections between your neurons, right? Why are you expecting something completely different conceptually?
Marian Siwiak: From a different system, just because you're looking from outside and you can see it. It doesn't mean that?
Marian Siwiak: the entity needs to see it from the inside. so the whole idea is pretty simple, actually.
Marian Siwiak: allow, LLMs to think about the conversations [00:45:00] that they have.
Marian Siwiak: And draw conclusions from it and learn from it.it 's conceptually indistinguishable from a lobster, let's say, because we are talking about the sentience of the lobster-level, not the, artificial general intelligence that will take over.
Marian Siwiak: it's, I think very important discussion that needs to be started because People are creating more and more advanced systems. Even the guy with the PC like me can create something which, under some assumptions, can be considered sentience.?
Marian Siwiak: sufficient.?
Marian Siwiak: we will create artificial sentience real soon. What will happen then? How will we? Evaluate?
Marian Siwiak: evaluated? Does this entity have rights?
Marian Siwiak: Does it deserve protection already or not yet?
Marian Siwiak: These are the questions which I think are worth answering before we wake up one day and realize, oops,
Marian Siwiak: Maybe we shouldn't
Marian Siwiak: Things that we do because I think that most of the prompts, said to ChatGPT would. hurt my head [00:46:00] if I would be exposed to them.
Miko Pawlikowski: Wow. I love how seafood, lobsters, aluminium plants and sentience all come together in your story. that
Marian Siwiak: And computer games
Miko Pawlikowski: often. And computer games. Yeah, there is just so much to touch on. But, let's go back to the book. for anybody who's going to make a purchase decision now, do I want to go invest my time into reading your book or not?
Miko Pawlikowski: if we give them a little bit of a sneak peek of the kind of good use cases, the stuff that already today with the tools that you have at your disposal are helping with data analytics and, giving excellent results. And then on the flip side, what's, not a good use of your time.
Miko Pawlikowski: And probably you should be looking at other tools. What's on your list?
Marlena Siwiak: I think I have a couple of good examples, in the chapters about natural language processing.and this is the natural language processing. it's very specific because, [00:47:00] ChatGPT is a language model. So anytime you have to solve any natural language processing task, the natural question is, why bother using
Marlena Siwiak: tools that already exist in data science to analyze languages, if we can just use the language model, just ask it.?
Marlena Siwiak: you can write a nice code to prepare sentiment analysis, but you can also take the same, say, a review,
Marlena Siwiak: it to ChatGPT window and ask it about the sentiment. Yeah.
Marlena Siwiak: It's so easy. so now the question arises, does it mean that we don't need all this old fashioned tools anymore to analyze text.
Marlena Siwiak: Because what ChatGPT does, in fact, it reads with understanding, yeah??
Marlena Siwiak: yeah? That's?
Marlena Siwiak: That's how you see it. It reads with understanding. You don't have to bother,
Marlena Siwiak: keywords,
Marlena Siwiak: search keywords, most frequently used words together.
Marlena Siwiak: Think about it. No, you don't have to do it this way. [00:48:00] You have a tool that reads with understanding. So in the chapters, I made a couple of small experiments comparing,
Miko Pawlikowski:?
Marlena Siwiak: and
Marlena Siwiak: ChatGPT's efficiency and reliability in terms of, for instance, sentiment analysis and how, it works in comparison to other, widely known tools.
Marlena Siwiak: Or other machine learning models specially developed for these tasks. And, it gives pretty cool results, really. I don't want to, reveal everything here. But,it's a good use case.
Marlena Siwiak: As long as ChatGPTis a brilliant tool.and it really does its job. Very often, it still can't be applied in business reality.
Marlena Siwiak: for instance, the thing that you mentioned at the beginning that, there is no repeatability, anytime you ask it a question, you get a slightly different answer.
Marlena Siwiak: It's very difficult to, to apply it in a system, yeah, to integrate to a system. Another question is data safety.
Marlena Siwiak: Many [00:49:00] companies don't want to use, don't want to allow people to use,
Marlena Siwiak: use ChatGPT.
Marlena Siwiak: For instance, Artur is not allowed to use ChatGPT at work in bank because of security reasons.
?
Marlena Siwiak: this is another problem. Not to mention things like speed and scalability, which of course, anything you develop locally would be faster
Marlena Siwiak: and more scalable than ChatGPT?
Marlena Siwiak: Yeah, I think to that last point that might be changing soon with the open, models that are small enough to run on device, like I think it was last week or a few days ago, Microsoft released their Phi-3 and I haven't used that one, but I used the previous one, Phi-2. It was surprisingly capable.
Miko Pawlikowski: It's a, I think it's a 3 billion parameters, model, which means that with 4 bit quantization, you can basically run it on 2 gigs of RAM. like this 80/20 rule, it might give you 80% of responses that you need and [00:50:00] be,effectively free. And cheap to run or almost, you already have the hardware and you can probably run it on your phone.
Miko Pawlikowski: So there's that, but going back to your previous point, when people bring up this argument, I always wonder. Whether this is not the kind of CPU versus GPU analogy, you've got models that are potentially much more efficient. And then you've got an LLM, which is like a one thing does all. is it not like throwing, A little bit, a kitchen sink at a problem, like sentiment analysis, that's more or less solved in many people's minds.
Miko Pawlikowski: It can be done much more cheaply than running a model, that requires billions of parameters.
Artur Guja: Which is exactly why in our book we almost never, show how to throw data into ChatGPT, it does,the thing that would be done much better by a specific algorithm and you get the answer. No, we use ChatGPT as an [00:51:00] assistant to suggest solutions, to discuss potential caveats, to analyze code, to produce code snippets, and maybe transform the code in a certain way for different use cases.
?You mentioned CPU and GPU. There's a whole chapter about, how you can translate code, between different languages or you can. Optimize code for GPU
?or CPU, depending on your needs.?
Artur Guja: The actual
Artur Guja: data analytical work is all almost always done by a specific algorithm or specific tool that is designed for it.
Artur Guja: And
?
Artur Guja: we're always very wary of just throwing stuff into ChatGPT?
Artur Guja: as you say, it's not designed for it. It's not optimized
?for it.
Artur Guja: there is randomness in it. and,?
Artur Guja: there are much better uses, for an assistant. Imagine, I always come back to this analogy, imagine you hire an assistant, that, that is a programmer and that has all this data [00:52:00] analytical knowledge. You will not get them sorting numbers in an Excel spreadsheet, right?
Artur Guja: I will add my three cents, or five, in our work when we're working with processes. All right. We also work with analytical processes and the number of tools is staggering.frompower BI to specialized tools used in, economic modeling and stuff like that.
Marian Siwiak: I will come back to what I said at the very beginning. Technology doesn't solve problems.
?
Marian Siwiak: you may have different tech stack and our book shows that GPT or sufficiently developed generative AI will be Help to you irrespectively of your tech stack.?
Marian Siwiak: It's like having a specialist on your speed dial, right? And the.
Marian Siwiak: People to think it in this way.?
Marian Siwiak: it's not the tool that will help you with, I don't know, a big query on Google because it will, but just it's respectively of your tech stack, [00:53:00] the value of analyst
Marian Siwiak: in my, my view is ability to understand the business process.
Marian Siwiak: Understand what is happening there, how it's reflected in data and how to analyze this data. So the answer
Marian Siwiak: describes what is happening in reality. This connection between digital and reality is on analyst. It's between keyboard and armchair, right? the technical part
Marian Siwiak: can be supported by ChatGPT very well.
Marian Siwiak: Irrespective of the text. I was thinking how to answer the question about the technologies that we see, ChatGPT supports them all.?
Marian Siwiak: If you have a couple of choices, it can help you choose. If you know how to, if you will remember to ask him and say, okay, this is my problem. The one thing that I think we try to. convey in our book, and I would like also to,to say it here aloud.
Marian Siwiak: when it comes to technology stack - trust him, tell him what is your problem exactly. Do not tell him just, you can, if you really are a hundred percent sure, but this is what you need. [00:54:00] You can ask him, write me a, I don't know, Python snippet that will calculate this or that confidence interval using this method. You will be much better off starting with, listen, I am now comparing sales in South Africa with sales in Zimbabwe.
Marian Siwiak: And, the data I have collected looks like that. So this is just talk about your data.
Marian Siwiak: The tech stack will come out of it.?
Marian Siwiak: when working with your assistant,
Marian Siwiak: Do not treat him only as, this is something that I think you mentioned this junior developer assistant
Marian Siwiak: also consultant.
Marian Siwiak: Also someone who read much more than you about many different things. It may not have your experience. It may hallucinate in stuff, and butin general, it has much more knowledge than any human could possibly collect. tech stack is [00:55:00] secondary. Technology doesn't solve problems.
Miko Pawlikowski: ChatGPT can help you solve the problem. I think the Llama three that just dropped last week, I was trained on 15 trillion tokens, which is just astronomical at this stage. And, I think I completely agree. This is like the stuff that you want to leverage,
Marian Siwiak: the biggest added value is having this specialist could in many areas with ability to put them together in context. Sometimes it takes me, especially when they work on more advanced projects,it sends you, chasing the red herring. Okay. It happens because some technology is popular because this is also a risk that you need to be aware of, his choice is also based on popularity of certain technologies, ways of doing thing.
Marian Siwiak: if many people described how they solve the problem, It will be more likely to come up as a [00:56:00] result. Some niche solutions are harder to get to. It doesn't mean that they are not there, but you need to really discuss. Okay, this is my problem. This is my, Conditions or considerations or limitations.?
Marian Siwiak: this context is important. It's not only about, okay, I want to calculate the sales that my company had over last quarter, it will give you a very simple answer, right? if it's something more,
Marian Siwiak: nuanced, share these nuances.?
Marian Siwiak: Not a prompt engineering. It's like discussing with
Marian Siwiak: someone who has a lot of knowledge.
Marian Siwiak: He will provide you the most popular solution first. In 99% of cases, it will be sufficient.
Marian Siwiak: This conversation part is critical, that you learn to converse with it, but you don't just give it tasks.?
Marian Siwiak: But this Marian undermines the whole idea of, prompt engineering, which to me is a scum, by the way. I think it's a [00:57:00] scam.?
Artur Guja: you can tweak a bit the way it answers, the way it talks. And sometimes it's important.
Marlena Siwiak: This I would call prompt engineering, butpreparing the single prompt that solves all your problems at once.?
Miko Pawlikowski: it's another hype. I think it's another business hype, and people are going to pretend that they know how to do it, and other people would hire them for huge money because they will believe that this will solve all their problems.
Artur Guja: It doesn't work that way, It's not a silver bullet, but there is,
Artur Guja: kind of, approach that you need to adopt
Artur Guja: When you're using these models, but
Artur Guja: When we're talking here,
Artur Guja: humans discuss things. you ask a question. We provide an answer. You then focus on part of the answer and maybe dig a bit deeper.
Artur Guja: and if we don't understand the question, we'll ask you,
?
Artur Guja: what do you mean?
Artur Guja: or we'll ask you for clarification.
Artur Guja: ChatGPT doesn't
Artur Guja: have that. It's you asking the question, you provided a prompt.
?It will do its best.
Artur Guja: [00:58:00] It will not ask for clarification. It will do its best.?
Artur Guja: and garbage in, garbage out.
Artur Guja: Prompt engineering, I think, what it should be, not what it is, but what it should be,
?is the ability to formulate your prompts in such a way that you convey,?
Artur Guja: very clearly your intent, your goals,your limitations.
Artur Guja: people think that the prompt is a sentence very often, the more, I, I use ChatGPT, my prompts become bigger and bigger.
Artur Guja: I write whole paragraphs
Artur Guja: describing
?
Artur Guja: different aspects of what I wanted to do,?
Artur Guja: because I know that it will not ask for clarification.
?
Marian Siwiak: I sometimes add a sentence in the end. I do prompt engineering and I said, if you need any additional information to provide the best answer, do it. And sometimes it does.
Miko Pawlikowski: But rarely. But this is one of the risks that Artur describes very well in our book is If you ask Generative AI a question, you will get an answer.
Marian Siwiak: Careful what you wish for.
Miko Pawlikowski: Which in many ways is what [00:59:00] makes it so special. Rather than say, oh, go away, that's a stupid question. You get something.
Marian Siwiak: Yeah. Yes.
Artur Guja: is probably why we Discussed with Marian many times. We use the words like please and thank you. And, we don't do it because we fear that one day it will take over the world and,it will treat us maybe a bit better. but it seems to react, just a bit better if you say, please give me the answer.?
Miko Pawlikowski: I noticed it.if I'm being?
Artur Guja: not a superstition.?
Marian Siwiak: no. I have a lot of anecdotal evidence to support it.
Marian Siwiak: You?
Miko Pawlikowski: speak like a true scientist now. for everybody who wants to go and grab the book. once again, it's called "generative AI for data analytics". It's available at manning. com. It's currently in the early access program, which means that you can get a PDF that might change before the final print
Miko Pawlikowski: and, Just looking at it, looks like [01:00:00] it's scheduled for early 2025 if you want to get a physical copy, from Amazon or anything like that. But, before I let my three amazing guests of The hook out today, I'm gonna fish out a prediction for the future. Artur, for you. Where do you see this all going particularly for data analytics? What's the next step for it??
Miko Pawlikowski: I
Artur Guja: I think we will get a lot more, capacity to understand, data sets and problems because that's already came with,LLMs, but we will also get, a lot more realization that there is no substitute for human ingenuity. before LLMs or whatever next phase of models is going to be called, before they,reach that kind of level, I think humans will still be able to, provide a lot more creativity into the process.
Artur Guja: And currently that's, [01:01:00] I think we're in a period where that's undervalued.
Artur Guja: I think the next step will be. the recognition of the value of creativity.
Marlena Siwiak: I disagree. I disagree. I'm totally pessimistic. I think it's going, we are going to rely more and more on AI, no matter what,without skepticism, and it will lead us to many trouble. And I'm thinking, even before ChatGPT appeared, there was this trend of, for instance, having job interviews, totally by, Computer programs.
Marlena Siwiak: The initial job interview was done by a computer program. You are recorded and your voice was analyzed and your appearance was analyzed. And that was such a great tool because it saved a lot of money for companies, but it rejected many good candidates and it was just
Marlena Siwiak: hopeless?
Marlena Siwiak: There was this book?
Marlena Siwiak: Math Destruction, which describes a lot of examples similar to this. how artificial intelligence and machine learning and other?
Marlena Siwiak: great [01:02:00] tools are usedin a wrong way.?
Marlena Siwiak: I think humanity doesn't learn. Just doesn't learn. Because what counts in the end is money.
Marian Siwiak: bean counters will try to save on costly, things like proper data architecture, proper data collection, data engineering. They will try to cover the early process errors with advanced, High level, tools and,the losses will be covered, of course, by clients and rising prices.
Marian Siwiak: Many people will get good packages for introducing these new tools, but I have deep
Marian Siwiak: distrust that people will understand. That what Artur said multiple times, garbage in, garbage out, that later in the process you cannot correct some errors in, the data that you're working on.and this super hype will lead to a lot of, neglect [01:03:00] towards the legwork required.
Artur Guja: And here I wanted to inject some optimism.?
Miko Pawlikowski: Well, it was worth a try that went out of the window already disagreeing with each other.?
Marlena Siwiak: I think, I agree with Marian, that we are that close, really that close from some artificial, self awareness. So it's great moment in human history, really. It's good to be part of it,?
Marian Siwiak: the job market has so many ways.of screwing you over, that you shouldn't worry about AI.
Marlena Siwiak: Adapt your thinking, as Marlena said, AI is here to stay and you cannot go into the job market saying I will compete with AI because then you're putting yourself at a very disadvantaged position. but also as Marlena said, use AI to your advantage. As squeeze out of it as much as you can seek the opportunities, not only for as, as jobs with AI, but using AI in your job, don't go headstrong into [01:04:00] AI jobs thinking, Oh, this, these are the jobs of the future.
Artur Guja: No, do what you wanted to do all along. You'll become a zoologist or become a, a social worker, become a
Artur Guja: oceanographer, whatever. These are all great pursuits
Artur Guja: and use AI in them.
?Because you don't have to be a hammerologist to use a hammer,
Artur Guja: but,?
Artur Guja: you can do great things with a hammer if used in the right way.
Miko Pawlikowski: Hard to argue with that last question, Marian, this one's for you. If you could have a magical way to break into OpenAI and hack their ChatGPT to display a message on top of the chat box that everybody using ChatGPT is using, what would it say?
Marlena Siwiak: Buy our book.
Marian Siwiak: Talk to me. Do not enter prompts. Talk to me.?
Miko Pawlikowski: As in be nice to me and then demand things. Talk to me.?
Marian Siwiak: Dependson the person you are.I think everybody should.as I said, looking at this different [01:05:00] prompt engine, I'm on a couple of groups on Facebook or on LinkedIn, which are excited by ChatGPT this way or another.and I see a lot of, okay, so this is the prompt I prepared and you just put your, the name of your company here and like this or that people are avoiding like fire talking to ChatGPT, like the specialist to a wise colleague.
?And they would be much better off just talking about problem, not trying to extract answer if you feel the difference. it's not about respect only one, one day, I believe soon, it will be the case, but you will get muchmore and the whole our book is about you will get so much more if you will trust that it has knowledge and you need to talk about the problem,
Marian Siwiak: prompt me. Do not, give me tasks,
Marian Siwiak: this is something that would [01:06:00] probably improve people's, outcomes from these conversations.
Miko Pawlikowski: Love it. So Sam Altman, if you're listening to this, you now know how to improve the ChatGPT interface. Marlena, Marian, Artur, thank you so much for coming. good luck with the sales of the book and I'll see you next time. Thank you.
Artur Guja: Thank you. very much.?