The Ethics of AI: A Conversation with Experts

One of the most pressing questions surrounding AI is its potential impact on assessment and evaluation methods. As Dr. [Name] pointed out, "We're not gonna lie to you, we're not gonna hide that from you," but instead, they emphasized the importance of being honest about the limitations and benefits of using AI in these contexts. This includes acknowledging that while AI can be a powerful tool for collaboration and efficiency, it's also essential to recognize its potential risks and pitfalls.

For instance, there's the issue of plagiarism versus collaboration. Dr. [Name] noted, "Using chat GBT on an exam is closer to collaborating with a human than copying from someone." However, they also warned that relying too heavily on AI for assessment can lead to issues like plagiarism, which can undermine the integrity of academic work. On the other hand, if used effectively and responsibly, AI can help facilitate collaboration and innovation in a way that benefits both students and instructors.

Another critical aspect of using AI in education is setting expectations around its use. Dr. [Name] emphasized the importance of being open with students about how they'll be assessed and what's expected of them when it comes to using AI tools. This includes discussing issues like copyright, intellectual property, and data security, which are all crucial for maintaining trust and fairness in educational settings.

The role of AI in hiring and professional development is also a pressing concern. As one expert noted, "I think that's where we're headed with this – all right nice and uh do you have any success stories around AI that you can share from Cuba?" The answer was that there are numerous opportunities for applying AI in various fields, including life sciences, computational chemistry, and meta-analysis of research papers. These developments hold great promise for improving healthcare outcomes, streamlining processes, and increasing efficiency.

However, Dr. [Name] cautioned that adopting these new technologies requires a nuanced approach. They highlighted the importance of being aware of the entry barriers and potential pitfalls associated with AI adoption. For instance, they noted, "it may not stay that way forever right – it may not always be so accessible or so freely accessible." Despite these challenges, experts agree that the benefits of AI far outweigh the risks.

In conclusion, the ethics of using AI in education, assessment, and professional development are complex and multifaceted. While there are many potential pitfalls to navigate, the advantages of embracing this technology are undeniable. As Dr. [Name] pointed out, "the varied entry has never been lower – it's just narrative." By being aware of these dynamics and taking a thoughtful approach to AI adoption, we can unlock its full potential while minimizing its risks.

Expert Insights on AI Adoption

Dr. [Name]'s comments also shed light on the challenges of implementing AI in various industries. For instance, they noted that using pre-made Python functions or libraries can be incredibly efficient, but it's essential to recognize the value of learning how to code from scratch. This includes being aware of the importance of open-source software and its potential benefits for innovation.

When asked about potential success stories from Cuba, Dr. [Name] mentioned their experience with large language models in life sciences. They highlighted the exciting developments taking place in this field, including advancements in computational chemistry, meta-analysis of research papers, and automated document classification. These innovations hold great promise for improving healthcare outcomes, streamlining processes, and increasing efficiency.

One expert's take on AI adoption is that it's a big step change that will require careful consideration and planning. They emphasized the importance of being aware of the entry barriers and potential pitfalls associated with adopting new technologies. By taking a thoughtful approach to AI implementation, we can unlock its full potential while minimizing its risks.

Advice for Data Managers

Dr. [Name]'s final words of advice were straightforward: "just try it." With the entry barrier to using AI having never been lower, experts stress that there's never been a better time to explore these new technologies. However, they also caution against becoming overly excited or complacent.

Instead, experts recommend maintaining a nuanced approach to AI adoption, recognizing both its benefits and risks. By staying informed about emerging trends and developments in the field, data managers can make more informed decisions about when and how to implement AI tools.

Ultimately, embracing AI requires a culture of experimentation, learning, and adaptation. As one expert noted, "we've been through I have personally been through a few Revolutions of technological variety right." By acknowledging this history and being aware of the complexities involved in adopting new technologies, we can unlock the full potential of AI while minimizing its pitfalls.

The Future of AI: Opportunities and Challenges

As experts discussed the future of AI, one thing became clear: there's no going back. With the advent of large language models and other advanced technologies, the world is changing rapidly. While some may worry about the consequences of these developments, others see them as opportunities for growth, innovation, and improvement.

The potential applications of AI in various fields are vast and varied. From computational chemistry to life sciences, and from meta-analysis to automated document classification, there are numerous areas where AI can make a significant impact. By embracing these technologies, we can unlock new efficiencies, improve processes, and increase productivity.

However, as Dr. [Name] noted, "it may not stay that way forever right – it may not always be so accessible or so freely accessible." This cautionary note highlights the importance of being aware of the entry barriers and potential pitfalls associated with adopting new technologies. By taking a thoughtful approach to AI implementation, we can minimize its risks and maximize its benefits.

In conclusion, the future of AI is complex and multifaceted. While there are many challenges to navigate, the advantages of embracing this technology are undeniable. As experts continue to explore the possibilities and limitations of AI, one thing becomes clear: the future belongs to those who adapt and innovate.

"WEBVTTKind: captionsLanguage: enI think the only thing that we can guarantee about this industry is that it's going to keep changing and it's going to keep adapting and we're going to have we're going to keep building on what we're doing it's going to be more interesting more powerful and that's going to use different skills right and are we going to be using python in 20 years probably not I mean some people are still using Fortran but hey um for the most part no there there are Trends there are shifts in in what we do this is a big one this is a big step change hi Sarah thank you for joining us on the show great to have you here hi Richie happy to be here be here brilliant uh and I'd like to just dive straight in uh talking about um what are the use cases for generative AI uh particularly um for people who are working with data yeah absolutely I mean I think the thing I'm actually really excited about it is for how many cases there are for not data um I think that that may be one that we come back to but I think um yeah it's it's really sort of democratizing fields that that needed a lot of specialized knowledge before I um the thing I'm really impressed with the latest version of generative AI is how well it can generate code I mean that that was the big change for me that that was so impressive I am I'm absolutely not a purist when it comes to coding I'd like if you gave me a blank um like Jupiter notebook to code on and have a panic attack um the way I learned and the way I still function is to like take somebody else's code and hack it until it works for me so um this is an absolutely perfect thing for the way I code um because it's never right it's never right to begin with but it gets you so far along that that first path right now um that it's incredible so yeah the the ability to manipulate code the ability to you know give it code and say make this faster make this more efficient make it more pythonic um I think that's incredibly exciting the fact that you can just like feed it a data set and say tell me what are the key features about this I think that's incredibly exciting but but all the non-data use cases as well so the you know please help me write a presentation about this please help me you know present this back to stakeholders please let me summarize this well suggest some good data visualizations on this data um there's just so many opportunities that's brilliant and I do find it interesting that um it's it's actually kind of rare to be starting um writing code from scratch quite often there's an existing code base that you want to work on so um just editing code written by GPT or whatever is is very similar to writing code written by one of your colleagues but that's not the way we teach it right like we teach people hey here's a blank notebook you need to structure your code from scratch and I can't remember every import statement I have to go and steal it from somebody else so usually myself in an older version yeah absolutely I'd love to talk about the the non-data use cases later but just for now if we continue on the theme of data use cases is there a difference about how different data roles they're gonna use um generative AI yeah I mean obviously if you're if you're a data analyst If you're sort of in a more code heavy role you're probably going to be generating a lot of code with it if you're doing you know summary analysis there's there's so many first drafts you can get out of that um I think yeah there's there's so much in terms of um all of the bits that support being a data scientist that aren't data and aren't coding you know these are the things that that you really have to teach that I wish somebody had told me um as a young data scientist that like you know maybe half the job is actually the technical bit the rest of it is working with stakeholders getting it into production writing the business case to support it writing the presentation to say hey this is why you need to approve it in the go no go all of that kind of stuff um and and generative AI can help out with all of those aspects of it as well um in terms of non-technical use cases yeah reporting project management all of that um with every word things uh lesson plans if you're teaching at all for example um yeah it's it's just got so many opportunities um it's really exciting that uh you mentioned all these different bits of a sort of more holistic workflow that aren't necessarily just about crunching numbers so have you got any examples of how you've used uh gbt or other AI for these non-technical bits like um creating reports or project management or things like that yeah well I wrote my bio for this podcast so that's helpful um I fed up the information and it sort of constructed something sensible and I had to reword it at the end um because of course you do you it's great for a first draft and everything it's never good as a final draft but uh yeah that's a good example do a great example literally we needed to develop a lesson plan um within within Kubrick where I work where we train people um and yeah it gave a really good structure for you can tell it's sort of over how many days you want to learn something in what depth and it will give you um a structured guide so actually one of my Consultants so I train machine learning Engineers um one of one of my Consultants that I've trained recently is using it to run a marathon so he's never run a marathon before um but he's setting up a project to follow chat gpt's plan um for for training for recovery for food for absolutely everything um when he's doing it all for for Charities so um if I can give a Shameless plug and Link for that I'd love to he's calling it the Mary I Thon okay that's pretty cool um it sounds like you could end up um training really well or it could be like well yeah it's like make sure to like spend plenty of time on the couch watching TV as well seems to be yeah um but I think that it's a really interesting project I think we're just scratching the surface with what people can do but yeah um you can use it for for structure presentations um it gave me some great examples um yeah you can use it for paraphrasing things it's really helpful with that um internally we had a bit of um we had a strategy day and we had a little exercise where we had to draw a shield it it sounds cheesy it's one of these icebreakers but it's actually pretty good um and you had to put different things in the quarters of the shield to like you know represent your life um so I had I had a dolly draw mine and it was it was really good far better than I could do with my uh design skills nice um I am kind of curious as to what went into your coat of homes then um it was what inspires you which was love of learning what are you proud of which was speaking at a big industry event recently uh what else was it what challenges are you facing I think I gave some sort of uh women in data kind of answer and uh something in your life right now I can't remember what I said now but it did it gave me like I gave it the most basic prompts I was like a red-haired woman speaking in front of a crowd and it sure did draw a cartoon of me that was all I gave it red-haired woman with glasses all right so uh getting back to Ai and uh data um so one of the big sticking points is uh when dated people have to work with people who don't have a data background so uh can you just talk me through how you use AI for this that's I mean the the barriers to entry have never been lower on this right you can just show someone this and and explain how it works like my mum is a retired English teacher and she's using it um and it's I think I think that's the you know in into your business they used to say will it play in Poughkeepsie that was will it will it reach the mass market so if it will reach you in um Portage Indiana as a retired English teacher then I think it's really um really past the hype cycle and into into reality uh it it can do a lot of things like summarize advice for you it can help suggest visual aid I actually asked this question in advance it had some very good advice which consisted mainly of know your audience simplify your language use visuals focus on the big picture be prepared to answer questions and practice practice practice well very good advice you know if uh if somebody came to me and said ask me for that advice that's probably what I'd give it um yeah that is great advice for uh for preparing a presentation um so uh GPT seems to have sort of stolen the Limelight in terms of um you know what generator is but there are tons of other models around terms of other different um application like models of different purposes um are there any other models that you think data practitioners ought to be looking into at the moment I think you know I think for for language models chat GPT is is really the runaway success story and I think it's it's still my favorite I've played with a few of the other ones but but it's really good but it's still just text based which it will remind you every time you ask it to do something that's not text-based um so there's there's ones that can draw images so Dali also by open AI mid journey is quite cool um there are ones that do video I haven't put as much with those but you know there are a number of of emerging ones out there um I've just been playing around with an app uh called ritrado that will take pictures of you and generate different images of you in various art Styles um and more importantly your pets as well um so I can have my cat as a princess in space if I want um and really that's what we've used the internet for so it makes sense that that's what we'll use generative AI for I suppose yeah if you're training on stuff that people have created on the internet then that's where you're going to get um back out of it as well um all right so uh given that you're handed by are you sort of managing a team I'm curious uh to whether the use cases for managers are different for individual contributors yeah I think I think that's a big one I think sort of generally speaking the more senior you get the more sort of responsibility you have to think about the big picture and the more you have to think about sort of um AI ethics right you know to some extent yes that's everyone's job we all need to think about AI ethics but um really it's it's the manager's job to be thinking about are we using this safely are we using this for the appropriate purposes and those are important questions to ask so for example um when we do training we have assessments that go along with that um and chat GPD can pass our assessments so inside of AI I had to be the one to sort of identify that and raise that and say you know hey guys what are we going to do about that how are we going to adapt um I should point out we're in good company it's it's passing things like medical exams the bar the Wharton MBA so so we are in good company there but um yeah I think I think it's that what are the implications what's the bigger picture how do we do this safely how do we respect privacy how do we respect intellectual property those are those are the sort of big questions that um that the more significant you have to answer the the Strategic level ones I think also there's just more reporting there's more admin so there's more opportunity to use it for those sort of non-technical roles as well um but I think it's the putting it in context that's the most important thing so at that point you made about um assessments is really interesting it's something we've been uh looking hard into Data Camp as well so uh someone on our assessment team is saying one of the big problems is that writing a good assessment question it has all the things so the same key features as writing a good prompt because you want to be really precise about what you're asking for and so that's why uh GPT is really good at passing assessments because it's a similar sort of optimization process um going back to uh the uh the point about managers um do you have any advice for managers who are considering adopting generative AI on their teams I mean safety and security and privacy are are the first things obviously don't put State secrets in it don't put pii personally identifiable information um you know you you don't necessarily know where it's going you don't necessarily well you do know that open AI has access to anything you put in into it and similarly anything else so just be careful use it for General things use it for things that aren't you know gonna break the world if if they get out um but don't also you know you you can't pretend it doesn't exist right when it first came out I saw sort of two really odd knee-jerk reactions to it one was one was to try to pretend it didn't exist like oh if we don't tell them about it maybe they won't use it well yeah come on it's all over the Internet it's the most successful product launch in in history chapter is um by a lot of metrics so you know you can't do that and and the other option was to just you know consider straight up Banning it just block it from the servers so you don't don't use it but that's just shooting yourself in the foot in the long run right it's yeah I I think of it as I I'm old enough to remember like back the early days of the internet and when Google first came out that kind of thing it feels very similar to that um that sort of you know almost almost Wild West nature of it at the time and okay we had a.com bust and and all of those things I expect all of that to happen too but can you imagine these days asking people not to use a search engine that'd be insane and and suddenly just stop you know stop people from using any kind of generative AI blocking it Banning It Whatever except in very specific use cases uh would similarly I think hinder productivity yeah I certainly remember like back in the early 2000s being at some jobs where the internet was like very heavily locked down and it became almost a sport to see like which sites Could you actually access you know but even then I I basically couldn't program without stack Overflow so um now now I've got Jazz every day it's all good brilliant and uh do you have any tips for like how you go about sharing best practices for using AI or um sharing prompts or anything like that like what do you do to make your team more efficiently use AI I think we're all still figuring that out that's one of the interesting things about it just like we all sort of figured out how to use the search engine well back in the day um we're all gonna have to figure out how to do prompts well but at the same time I think um I think I think this is moving into another question question but um the best qualities the best data scientists right you can teach you can teach specific skills but they're going to change all the time right what you really need are the ability to ask good questions and the ability to to critically evaluate information and with generative AI that's the most important thing still asking good questions is is another way of saying prompt engineering basically um and again like you were saying about assessments the more sort of specific and um detail you can be in asking that question the the more specific and detailed your answer is going to be and then you know chat GPT will and and all the generative AI I don't mean to pick on it specifically will very confidently give you its answer right or wrong and and being able to say ah hang on that makes sense or that makes 80 sense but I need to tweak this or that's how it should work but that's not how it works in practice um that's always been an important skill and and that will continue to be um incredibly important here now that we use these tools more um absolutely agreed and just to put yourself a little bit further uh do you have any um ways of making sure that uh people are critically thinking about the responses that they get I I like when you can tell when people have obviously used it not edited at all I do just often reply thanks to EBT to that if you see it if you see it in an online discussion um any tips I think just as you would with okay you've asked your friend who's sitting next to you who's coding with you who you know generally to be a good programmer a good data scientist sometimes they're going to be wrong um and and don't just take everything at face value if it's Code test the code if it's critical information then you know still consider looking up sources on that um don't believe it's citations it will hallucinate citations still um so it will it will assemble sort of plausible looking citations to research papers with with names and sensible uh titles but when you try and look them up on the archive they don't exist so trust but verify that seems like good advice I have to say um the data science use cases I feel like I can usually tell whether something's been written by a human or by AI but in some cases like for things like sales stuff or marketing stuff that stuff even if it's written by a human it often sounds like it was written by AI anyway so it's very it's much more difficult to tell but I mean we've always used templates for these purposes right like I I sort of believe I sort of genuinely believe if you're gonna do something twice you should probably automate it and and when it comes to like telling people stuff writing it down is how we automate that so if you I've always been a big fan of using like templates for you know routine routine communication plug in the bits that you need and and go this is just templates plus attempting to fill in the blanks for you um that's a that's a nice way of putting it um all right so I'd like to talk a little bit about jobs because I think there's a lot of fear that some jobs are going to be just completely replaced by AI um so I guess first of all um are there any tasks or roles that you think are going to be automated completely um with generative AI I think bits of jobs are going to be automated away I don't know that entire jobs are going to be automated away again just as we saw sort of in the early days of of the internet there were sort of scares about oh well all brick and mortar shops Club as well you know some did but but not all of them did I I still go shopping on on the High Street um I still like to look at stuff there are still cases where you know something physical is still better than what's online so I think we're gonna see a similar transition some stuff is going to disappear we don't program using Punch Cards anymore we don't do math using slide rules anymore but we still program we still do math we're just using different tools to do it so I think those jobs are going to shift rather than completely disappear and and hopefully it's the boring stuff that we get to automate away and we get to focus on the interesting stuff the fun stuff the stuff that's gonna um make a difference in the world okay so um in that case I'd like to talk about um cases where you're having a human an AI work together so um do you see any particular cases where that's going to grow in popularity well I mean even even the generative AI is that it's it's human in the loop reinforcement learning and and that's a pretty productive approach I think we've definitely seen there have definitely been some studies about um medicine where um you know studies that show doctors using AI do so much better than than either Alone um I think that's the way forward are there any cases where you think um you should just never use AI that it should be blocked I I don't actually think you should never use AI again I'm going to go back to the like should you never look stuff up analogy of course not if you're going to do anything you're going to do research on it and this is one of these tools to do research you know again don't put the state secrets in how can I best protect my Nuclear bunker well no that would that would be insane but um there are lots of cases where it shouldn't do everything but I think in in your toolkit you wouldn't say you can't you know there's there are no parts of math where you can't use a calculator well okay there are Parts where like a calculator is not going to help you that much and similarly there are going to be Parts where where generative AI is not going to help you that much but are you really gonna tell people no no for for very good reasons you need to go back and use a slide rule now I don't think so uh there's probably like three slide rule enthusiasts to listen to us right now you know it's my favorite way of doing math no I I think they're really cool I have a I have a lovely one and it's a lovely it's a lovely tactile thing and it's a it's a great reminder of of what we used to do um and I used to I never used Punch Cards but my my physics department used old Punch Cards as like our our scratch paper so I have a fondness for it right and there probably is somebody running some fantastic um hole punch machine well and and you can still use it for um machine knitting interestingly enough or um to card weaving they still use very much a whole bunch of Technologies where a lot of that came from but um but yeah for the most part um you know I'm I'm super into hand knitting but for the most part I I still buy my socks commercially all right so um the sort of recurring theme so far seems to be um if you've got uh privacy or security problems that's gonna be the biggest blocker to using AI do you have any uh tips on how you might sort of reason about this or how you might um get around these blockers I mean for the most Parts you if it involves anything that can identify a human think twice before you do it right personally identifiable information is is obvious things like names ID numbers things like that but especially even combined um a lot of a lot of things are identifiable so if you say that Ginger American especially in a certain tone of voice I probably know you're speaking about me so uh you know it is something really impressive like um you know 95 of a data set can be de-anonymized in like four data points it's really sort of shocking so be really careful about putting information about people into it I think that's that's the biggest thing to say and then anything um anything you wouldn't want on the front page of a newspaper I think that's that's always a good test for AI ethics if it comes out you know Sarah writes a model that you know I don't know says rude things about Ginger Americans um then just don't if it if it got out if you wouldn't want literally everybody in their next door neighbor to read it don't put it in a public facing um tool okay so it really is about just think about what are the impacts going to be um if this becomes public or if you get a wrong answer yeah and you know and obviously well you should know if you're dealing with anything that has um a security clearance or intellectual property implications Etc um I think those are those are pretty obvious don't use it cases and I'm hoping most companies and organizations have some kind of guidelines on uh what are the important or basic data that ought to be kept secure so uh maybe those are the ones you keep away from AI yeah but I think you also know um I mean I would be shocked if very many companies have updated their security policies in line of this revolution I mean this has only been out since November 30th 2022. um you know this is when it all really took off obviously bits of it existed before then but but I think that's the sort of key date um in all of this so I'd be shocked if if companies have kept up with it I know certainly regulation has never kept up with it that's always been a problem when thinking about AI ethics and data privacy and concerns like that so it it is kind of kind of up to us to behave ethically when using it and and make sure that we're not doing anything for evil and to not make sure you know to make sure that we're protecting ourselves and and our company's rights when we're using it absolutely so just moving on from um the impact on jobs and tasks to upskilling I think uh generative AI has some pretty big implications for education so uh yeah so uh to begin with um how do you think uh it helps people learn new skills I mean you can just you can just chat with it you can just ask it it's incredible um you know explain time series to me and you can have a chat with it and you can say oh tell me more about that second paragraph and it will and you know is it going to be right about absolutely everything in detail but probably not so so do be careful about that and and do verify that but um it it can basically be like having a private tutor now which is incredible um I'm a big fan of Duolingo for language learning I'm I'm on it all the time I'm learning far too many languages and what they've done with it um I think is is incredibly impressive you can do really sort of tailored learning I think this is this is the thing we know about education in general right is is that um really focused practice on the areas that you're getting wrong is what helps you upskill rapidly and this is where um AI powered learning has such great potential because you know as I'm doing my French lessons it can say oh you know she always gets a subjunctive wrong and it can give me you know loads more review about that um and help me with that and similarly I assume if I'm uh learning programming languages for example on data Camp um I'm going to be able to get sort of uh you know she always forgets a Define the arguments there or something I don't know what common mistakes I make but um usually just typos you can help me catch that too so the idea of personalization so being able to figure out what is this particular learner doing wrong seems really powerful absolutely um are there any other sort of areas where you think okay this is this is a game changer but this is going to really improve people's learning performance yeah I hope so I mean have if you've ever sat through like really standardized boring corporate learning um you you really hope that it gives you something other than than that PowerPoint presentation with um a bad quiz at the end we've all done that like you know there are things that we have to do regularly for for very good reasons you know things like antibiary and Corruption training um but that could certainly be be more engaging it could be um stickier you know in terms of it it sticks in your brain better um I think yeah that that level of personalization interactivity adaptability and that's something that I think in the past for the most part you would have had to work with someone really directly in a small group to get um and there's so much opportunity to to yeah get the get the generative AI to quiz you um so it's great for practicing job interviews you can get it to role play um a job interviewer and and ask you all those sorts of questions and then it can even evaluate the response so if you pass that back in um so I think there's there's so much opportunity to to quiz you you know when I when I was a kid when I was studying for my exams I'd always get my mum to quiz me um and that was that was the best practice um when you go in and Ace it based on that so yeah now you've got your your own private tutor potentially uh yeah uh AI tutors that's feature coming soon to data Camp so uh I do like that example uh yeah uh that that's very nice the idea of uh being able to converse with someone practice your conversations had to seem a very powerful way of learning um so just on a related note um just the fact that you can use AI does that change the skill set that you need particularly for data analysis and data scientists I mean it does but again I still think those most important skills are always asking the right questions and critically evaluating information and so that just makes those even more important because asking good questions is is kind of another way to say prompt engineering so in terms of technical skills um like if AI can generate your code does that change your relationship with how you learn about coding I think there's just less sort of rote memorization that has to happen but again I in my career I have seen this progression that has happened right it used to be back in the day that if you were writing some complicated thing in in C or C you know C plus plus whatever like like during my PhD um you had to code in those equations by yourself right you had to you had to have that math you had to code that in specifically and okay that made the code run way faster um potentially there were good reasons to do that nowadays we don't do that anymore we use Python packages if we're programming in Python right somebody's done that function for me I don't need to write a neural net from scratch I'll go off and use tensorflow or Pi torch or whatever right so it's just the next level of abstraction we were we were already sort of getting there with I think you know I thought the next step up from from um packages was going to be sort of pre-trained models and does that make sense this is that because it's trained on the entire internet but I I think it's just that it's just that next level of abstraction up and why wouldn't we win appropriate use the more powerful tool to do that I definitely agree with that um I'm wondering because um this is also built on deep learning and um the whole area seems like really important like you need a few people to understand what's going on um do you think uh this sort of natural language processing skills or deep learning skills are going to become more important or is it just well this is all done for you we don't need that it depends on what you want to do if you just want to use the outputs of these things then then no I don't need to know how a search engine works to to draw the internet but if you want to work with it if you want to be on The Cutting Edge of Technology if you want to be adapting it if you want to be you know making your own tools with it then I think yeah absolutely natural language processing and um and deep learning are are some of the most important skills those are especially thinking about just text-based models um if you're thinking about you know do you want to do image generation do you want to do video generation you know then you need obviously on top of that um computer vision skills but you do still need some NLP because it has to be able to interpret prompts um so so yeah it will be there but again how many of us are really writing our own python packages these days rather than using that output to to write the code I think there's a parallel there as well okay uh that seems like sensible advice um and so I'm wondering um in general how does it how does the existence of generative AI um make change your decisions when you are trying to hire uh data professionals that's interesting um I mean if they didn't know what it is they've clearly been living under a rock um so I think I I think that's genuinely true that because you do want people to be able to you know be aware of Trends in in the industry and and have ideas and opinions about where things are going so I think um you know people having an awareness of it and people having interesting insights on on how to use it I think those are probably great interview questions um but again I'm still always hiring for can you ask good questions can you critically evaluate the responses are you creative can you learn quickly can you adapt quickly um all of that all of that's in practice now you you need to you need to do all of that you need to do it to um just successfully use the tool full stop but also to be able to adapt to a changing workplace that's going to integrate tools like this more and more excellent um has it changed the profile you might look for or are there any new skills that you think are more important now I do think NLP and deep learning are going to become more and more important I think I think that's that's absolutely the right shout on that um but I think individual skills and Technologies change a lot and you can learn them and you need to be constantly learning I I think the only thing that we can guarantee about this industry is that that it's going to keep changing and it's going to keep adapting and we're going to have we're going to keep building on what we're doing it's going to be more interesting more powerful and that's going to use different skills right and are we going to be using python in 20 years probably not I mean some people are still using Fortran but hey um for the most part no there there are Trends there are shifts in in what we do this is a big one this is a big step change but the core skills are you logical are you creative are you pragmatic can you you know make things make sense um those those are the sort of more fundamental skills rather than specific Technologies because I can I can go to a data Camp course tomorrow and refresh myself on time series if I need to um that's not the point it's it's new I know how to find that information do I even know how to ask questions about this like hey what kind of a problem am I talking about that's that's a more important question to ask than what specific piece of technology or how do I write these five lines of code about it um framing framing the problem thinking about how to solve it how to how to communicate the results at the end of it that's always going to be more important than a specific skill one thing you've mentioned a few times is the idea of um prompt engineering and it seems that this might be a real job I'm never quite sure if it's like if it's just a task or whether it's like a whole job in itself uh can you just tell me a bit about what it involves yeah I mean prompt engineering is is another way of saying asking the right question I think there are there are tricks to getting it to to do what you want so giving it context again but a lot of these things are just good advice for how to tell a human to do stuff as well so give it context tell it why so in this situation I want you to be a data scientist using python so give it that prompt ask specifically what you want if you're looking for a format of an answer you know give it an example say hey give me an output like this again really great advice for dealing with humans as well um so so yeah I think it's going to be a super important skill but quite a lot of jobs have I think in very limited circumstances that is going to be a specific job but I think that's going to be part of like a mega pipeline in in certain areas I think like you do see you know search engine optimization I think is is again a good comparison is that the job title in a lot of cases probably not right but if you have a marketing rule that's probably an essential skill and in a big enough specialized enough organization you might have someone with their only job is that but I think it's more and more a skill we all need but it's a skill we can still all use because again for dealing with humans giving context saying explicitly and clearly what you need giving it an example of what good looks like still all really great advice excellent so maybe a job in big organizations but um otherwise it's just a skill everyone needs well you know how to search stuff on the internet right now is a skill that everyone needs right how to how to phrase that question properly how to sort through all the crap how to ignore the ads how to how to do all of that we don't even think of those as skills anymore um but but 20 odd years ago we were all figuring this out for the first time uh and it's what we're doing again I think internet search it does sound like what do you do well yeah I just Google stuff all day I've got jobs like that let's talk about your work at Kubrick uh so can you tell me what you're working on at the moment sure yeah so we're we're training the next generation of wealth lots of data professionals so instead of a set of AI look I look after our machine learning engineering track um so the people who are going to be potentially using this stuff um on a full-time basis we we have other streams as well but yeah training them up in the basics of all of the business stuff around this as well because again sort of half the job is actually getting it implemented and communicating it um trying them up with the basic skills that we need and then sending them out to client sites and supporting them out there using machine learning skills so so we're spending a lot of time obviously talking about about this new Revolution in Ai and and how interesting it is um yeah lots of lots of good conversations Lots about obviously around the assessments we had some pretty serious assessments that were you know a couple of weeks after um touch EBT dropped and and we ran the exam during those days it was passing it um you know good marks rather than incredibly perfect marks but it was you know having an honest conversation with them about you know look this is you you can do this it can answer the questions We're not gonna lie to you we're not gonna hide that from you um but you're not going to get the benefit of you know really being able to challenge yourself and really being able to test yourself um if if you use it to effectively collaborate right because um rather than rather than it sort of copying from someone right the equivalent I think of using chat gbt on exam is is closer to collaborating with a human it's closer to sort of plagiarism than you know I have automated something um so so setting expectations around that um has been has been a really interesting discussion we always talk about um aisx and I think the the ethics of of this is a big one as well there's all those same questions about privacy and security but also you know where is it going they're all worried about you know is this still the right thing to train in should we are we going to have jobs at the end of this and yeah yeah I actually think it's more important than ever um yeah it's a it's a thorny issue um the idea of using AI for assessment uh because it's it's kind of cheating but um if it's something you're going to use in your job then um maybe it's something you uh you want to be able to use um when you personally assessment as well exactly because I was um I was a I was a hiring manager at a bank I worked at one of the world's largest banks and it drove me crazy when we would get um young data professionals who would join us and would insist on doing absolutely everything from scratch with no collaboration because that was that was how you're supposed to do it in school and University but that's not what I want you to do on the job site like if you can ask Bob next to you and Bob can say Yep this is how you do it in five minutes or if you can find it on stack Overflow I would much rather do that than spend two days hashing out for yourself less efficiently like this stuff exists it's been tested it's been tried I think this is always the example we used for for um you know using pre-made python functions right don't don't write a linear regression from scratch what are you doing you you I can't learn um I think that's where we're headed with this as well all right nice and uh do you have any success stories around AI that you can share from Cuba well have to be a bit careful about client confidentiality um I think there's some really interesting stuff that's happening in life sciences with large language models I went I went to an event on that um in Industry I think that's incredibly cool um there's lots of options for um for you know developing new sort of computational chemistry with it for for looking for new drug reactions for doing sort of meta-analysis on past research papers I've seen some really interesting examples of that um I think that's an incredibly interesting area that's that's going to be relevant well to everyone because we all have to deal with Healthcare at some point in our lives um I I think that's incredibly cool we've definitely seen people switch to things that are um you know a lot more efficient different versions automating things um being able to automatically classify documents for example um looking at Supply chains there's just there's there's opportunities absolutely everywhere um if you're like hey that could be done more efficiently there's probably a way to use technology to do that excellent so lots of opportunities out there uh Denver people wanting to uh adopt this stuff um do you have any final advice for uh data managers or data teams who are wanting to um try to hand it um AI yeah I mean just just try it like the varied entry has never been lower it's it's just narrative it may not stay that way forever right it may not always be so so freely accessible or so accessible um but it's it's a it's a big step change it's exciting so so on the one hand you know it's cool be excited about it right absolutely there's so much potential on the other hand like calm down it's also probably not the end of the world it's probably not Skynet um you know I think there's so much opportunity but also keeps in perspective on it as well we've been through I have personally been through a few Revolutions of of the technological variety right we got the internet we got advances in in search engines like Google they some stuff phases out but some new opportunities are made every time um we wouldn't be talking right now without a bunch of them I'm in Chicago you're in New York look at look at what we've managed to do there's some kind of automated transcript going on in the background I couldn't have imagined that um 20 years agoI think the only thing that we can guarantee about this industry is that it's going to keep changing and it's going to keep adapting and we're going to have we're going to keep building on what we're doing it's going to be more interesting more powerful and that's going to use different skills right and are we going to be using python in 20 years probably not I mean some people are still using Fortran but hey um for the most part no there there are Trends there are shifts in in what we do this is a big one this is a big step change hi Sarah thank you for joining us on the show great to have you here hi Richie happy to be here be here brilliant uh and I'd like to just dive straight in uh talking about um what are the use cases for generative AI uh particularly um for people who are working with data yeah absolutely I mean I think the thing I'm actually really excited about it is for how many cases there are for not data um I think that that may be one that we come back to but I think um yeah it's it's really sort of democratizing fields that that needed a lot of specialized knowledge before I um the thing I'm really impressed with the latest version of generative AI is how well it can generate code I mean that that was the big change for me that that was so impressive I am I'm absolutely not a purist when it comes to coding I'd like if you gave me a blank um like Jupiter notebook to code on and have a panic attack um the way I learned and the way I still function is to like take somebody else's code and hack it until it works for me so um this is an absolutely perfect thing for the way I code um because it's never right it's never right to begin with but it gets you so far along that that first path right now um that it's incredible so yeah the the ability to manipulate code the ability to you know give it code and say make this faster make this more efficient make it more pythonic um I think that's incredibly exciting the fact that you can just like feed it a data set and say tell me what are the key features about this I think that's incredibly exciting but but all the non-data use cases as well so the you know please help me write a presentation about this please help me you know present this back to stakeholders please let me summarize this well suggest some good data visualizations on this data um there's just so many opportunities that's brilliant and I do find it interesting that um it's it's actually kind of rare to be starting um writing code from scratch quite often there's an existing code base that you want to work on so um just editing code written by GPT or whatever is is very similar to writing code written by one of your colleagues but that's not the way we teach it right like we teach people hey here's a blank notebook you need to structure your code from scratch and I can't remember every import statement I have to go and steal it from somebody else so usually myself in an older version yeah absolutely I'd love to talk about the the non-data use cases later but just for now if we continue on the theme of data use cases is there a difference about how different data roles they're gonna use um generative AI yeah I mean obviously if you're if you're a data analyst If you're sort of in a more code heavy role you're probably going to be generating a lot of code with it if you're doing you know summary analysis there's there's so many first drafts you can get out of that um I think yeah there's there's so much in terms of um all of the bits that support being a data scientist that aren't data and aren't coding you know these are the things that that you really have to teach that I wish somebody had told me um as a young data scientist that like you know maybe half the job is actually the technical bit the rest of it is working with stakeholders getting it into production writing the business case to support it writing the presentation to say hey this is why you need to approve it in the go no go all of that kind of stuff um and and generative AI can help out with all of those aspects of it as well um in terms of non-technical use cases yeah reporting project management all of that um with every word things uh lesson plans if you're teaching at all for example um yeah it's it's just got so many opportunities um it's really exciting that uh you mentioned all these different bits of a sort of more holistic workflow that aren't necessarily just about crunching numbers so have you got any examples of how you've used uh gbt or other AI for these non-technical bits like um creating reports or project management or things like that yeah well I wrote my bio for this podcast so that's helpful um I fed up the information and it sort of constructed something sensible and I had to reword it at the end um because of course you do you it's great for a first draft and everything it's never good as a final draft but uh yeah that's a good example do a great example literally we needed to develop a lesson plan um within within Kubrick where I work where we train people um and yeah it gave a really good structure for you can tell it's sort of over how many days you want to learn something in what depth and it will give you um a structured guide so actually one of my Consultants so I train machine learning Engineers um one of one of my Consultants that I've trained recently is using it to run a marathon so he's never run a marathon before um but he's setting up a project to follow chat gpt's plan um for for training for recovery for food for absolutely everything um when he's doing it all for for Charities so um if I can give a Shameless plug and Link for that I'd love to he's calling it the Mary I Thon okay that's pretty cool um it sounds like you could end up um training really well or it could be like well yeah it's like make sure to like spend plenty of time on the couch watching TV as well seems to be yeah um but I think that it's a really interesting project I think we're just scratching the surface with what people can do but yeah um you can use it for for structure presentations um it gave me some great examples um yeah you can use it for paraphrasing things it's really helpful with that um internally we had a bit of um we had a strategy day and we had a little exercise where we had to draw a shield it it sounds cheesy it's one of these icebreakers but it's actually pretty good um and you had to put different things in the quarters of the shield to like you know represent your life um so I had I had a dolly draw mine and it was it was really good far better than I could do with my uh design skills nice um I am kind of curious as to what went into your coat of homes then um it was what inspires you which was love of learning what are you proud of which was speaking at a big industry event recently uh what else was it what challenges are you facing I think I gave some sort of uh women in data kind of answer and uh something in your life right now I can't remember what I said now but it did it gave me like I gave it the most basic prompts I was like a red-haired woman speaking in front of a crowd and it sure did draw a cartoon of me that was all I gave it red-haired woman with glasses all right so uh getting back to Ai and uh data um so one of the big sticking points is uh when dated people have to work with people who don't have a data background so uh can you just talk me through how you use AI for this that's I mean the the barriers to entry have never been lower on this right you can just show someone this and and explain how it works like my mum is a retired English teacher and she's using it um and it's I think I think that's the you know in into your business they used to say will it play in Poughkeepsie that was will it will it reach the mass market so if it will reach you in um Portage Indiana as a retired English teacher then I think it's really um really past the hype cycle and into into reality uh it it can do a lot of things like summarize advice for you it can help suggest visual aid I actually asked this question in advance it had some very good advice which consisted mainly of know your audience simplify your language use visuals focus on the big picture be prepared to answer questions and practice practice practice well very good advice you know if uh if somebody came to me and said ask me for that advice that's probably what I'd give it um yeah that is great advice for uh for preparing a presentation um so uh GPT seems to have sort of stolen the Limelight in terms of um you know what generator is but there are tons of other models around terms of other different um application like models of different purposes um are there any other models that you think data practitioners ought to be looking into at the moment I think you know I think for for language models chat GPT is is really the runaway success story and I think it's it's still my favorite I've played with a few of the other ones but but it's really good but it's still just text based which it will remind you every time you ask it to do something that's not text-based um so there's there's ones that can draw images so Dali also by open AI mid journey is quite cool um there are ones that do video I haven't put as much with those but you know there are a number of of emerging ones out there um I've just been playing around with an app uh called ritrado that will take pictures of you and generate different images of you in various art Styles um and more importantly your pets as well um so I can have my cat as a princess in space if I want um and really that's what we've used the internet for so it makes sense that that's what we'll use generative AI for I suppose yeah if you're training on stuff that people have created on the internet then that's where you're going to get um back out of it as well um all right so uh given that you're handed by are you sort of managing a team I'm curious uh to whether the use cases for managers are different for individual contributors yeah I think I think that's a big one I think sort of generally speaking the more senior you get the more sort of responsibility you have to think about the big picture and the more you have to think about sort of um AI ethics right you know to some extent yes that's everyone's job we all need to think about AI ethics but um really it's it's the manager's job to be thinking about are we using this safely are we using this for the appropriate purposes and those are important questions to ask so for example um when we do training we have assessments that go along with that um and chat GPD can pass our assessments so inside of AI I had to be the one to sort of identify that and raise that and say you know hey guys what are we going to do about that how are we going to adapt um I should point out we're in good company it's it's passing things like medical exams the bar the Wharton MBA so so we are in good company there but um yeah I think I think it's that what are the implications what's the bigger picture how do we do this safely how do we respect privacy how do we respect intellectual property those are those are the sort of big questions that um that the more significant you have to answer the the Strategic level ones I think also there's just more reporting there's more admin so there's more opportunity to use it for those sort of non-technical roles as well um but I think it's the putting it in context that's the most important thing so at that point you made about um assessments is really interesting it's something we've been uh looking hard into Data Camp as well so uh someone on our assessment team is saying one of the big problems is that writing a good assessment question it has all the things so the same key features as writing a good prompt because you want to be really precise about what you're asking for and so that's why uh GPT is really good at passing assessments because it's a similar sort of optimization process um going back to uh the uh the point about managers um do you have any advice for managers who are considering adopting generative AI on their teams I mean safety and security and privacy are are the first things obviously don't put State secrets in it don't put pii personally identifiable information um you know you you don't necessarily know where it's going you don't necessarily well you do know that open AI has access to anything you put in into it and similarly anything else so just be careful use it for General things use it for things that aren't you know gonna break the world if if they get out um but don't also you know you you can't pretend it doesn't exist right when it first came out I saw sort of two really odd knee-jerk reactions to it one was one was to try to pretend it didn't exist like oh if we don't tell them about it maybe they won't use it well yeah come on it's all over the Internet it's the most successful product launch in in history chapter is um by a lot of metrics so you know you can't do that and and the other option was to just you know consider straight up Banning it just block it from the servers so you don't don't use it but that's just shooting yourself in the foot in the long run right it's yeah I I think of it as I I'm old enough to remember like back the early days of the internet and when Google first came out that kind of thing it feels very similar to that um that sort of you know almost almost Wild West nature of it at the time and okay we had a.com bust and and all of those things I expect all of that to happen too but can you imagine these days asking people not to use a search engine that'd be insane and and suddenly just stop you know stop people from using any kind of generative AI blocking it Banning It Whatever except in very specific use cases uh would similarly I think hinder productivity yeah I certainly remember like back in the early 2000s being at some jobs where the internet was like very heavily locked down and it became almost a sport to see like which sites Could you actually access you know but even then I I basically couldn't program without stack Overflow so um now now I've got Jazz every day it's all good brilliant and uh do you have any tips for like how you go about sharing best practices for using AI or um sharing prompts or anything like that like what do you do to make your team more efficiently use AI I think we're all still figuring that out that's one of the interesting things about it just like we all sort of figured out how to use the search engine well back in the day um we're all gonna have to figure out how to do prompts well but at the same time I think um I think I think this is moving into another question question but um the best qualities the best data scientists right you can teach you can teach specific skills but they're going to change all the time right what you really need are the ability to ask good questions and the ability to to critically evaluate information and with generative AI that's the most important thing still asking good questions is is another way of saying prompt engineering basically um and again like you were saying about assessments the more sort of specific and um detail you can be in asking that question the the more specific and detailed your answer is going to be and then you know chat GPT will and and all the generative AI I don't mean to pick on it specifically will very confidently give you its answer right or wrong and and being able to say ah hang on that makes sense or that makes 80 sense but I need to tweak this or that's how it should work but that's not how it works in practice um that's always been an important skill and and that will continue to be um incredibly important here now that we use these tools more um absolutely agreed and just to put yourself a little bit further uh do you have any um ways of making sure that uh people are critically thinking about the responses that they get I I like when you can tell when people have obviously used it not edited at all I do just often reply thanks to EBT to that if you see it if you see it in an online discussion um any tips I think just as you would with okay you've asked your friend who's sitting next to you who's coding with you who you know generally to be a good programmer a good data scientist sometimes they're going to be wrong um and and don't just take everything at face value if it's Code test the code if it's critical information then you know still consider looking up sources on that um don't believe it's citations it will hallucinate citations still um so it will it will assemble sort of plausible looking citations to research papers with with names and sensible uh titles but when you try and look them up on the archive they don't exist so trust but verify that seems like good advice I have to say um the data science use cases I feel like I can usually tell whether something's been written by a human or by AI but in some cases like for things like sales stuff or marketing stuff that stuff even if it's written by a human it often sounds like it was written by AI anyway so it's very it's much more difficult to tell but I mean we've always used templates for these purposes right like I I sort of believe I sort of genuinely believe if you're gonna do something twice you should probably automate it and and when it comes to like telling people stuff writing it down is how we automate that so if you I've always been a big fan of using like templates for you know routine routine communication plug in the bits that you need and and go this is just templates plus attempting to fill in the blanks for you um that's a that's a nice way of putting it um all right so I'd like to talk a little bit about jobs because I think there's a lot of fear that some jobs are going to be just completely replaced by AI um so I guess first of all um are there any tasks or roles that you think are going to be automated completely um with generative AI I think bits of jobs are going to be automated away I don't know that entire jobs are going to be automated away again just as we saw sort of in the early days of of the internet there were sort of scares about oh well all brick and mortar shops Club as well you know some did but but not all of them did I I still go shopping on on the High Street um I still like to look at stuff there are still cases where you know something physical is still better than what's online so I think we're gonna see a similar transition some stuff is going to disappear we don't program using Punch Cards anymore we don't do math using slide rules anymore but we still program we still do math we're just using different tools to do it so I think those jobs are going to shift rather than completely disappear and and hopefully it's the boring stuff that we get to automate away and we get to focus on the interesting stuff the fun stuff the stuff that's gonna um make a difference in the world okay so um in that case I'd like to talk about um cases where you're having a human an AI work together so um do you see any particular cases where that's going to grow in popularity well I mean even even the generative AI is that it's it's human in the loop reinforcement learning and and that's a pretty productive approach I think we've definitely seen there have definitely been some studies about um medicine where um you know studies that show doctors using AI do so much better than than either Alone um I think that's the way forward are there any cases where you think um you should just never use AI that it should be blocked I I don't actually think you should never use AI again I'm going to go back to the like should you never look stuff up analogy of course not if you're going to do anything you're going to do research on it and this is one of these tools to do research you know again don't put the state secrets in how can I best protect my Nuclear bunker well no that would that would be insane but um there are lots of cases where it shouldn't do everything but I think in in your toolkit you wouldn't say you can't you know there's there are no parts of math where you can't use a calculator well okay there are Parts where like a calculator is not going to help you that much and similarly there are going to be Parts where where generative AI is not going to help you that much but are you really gonna tell people no no for for very good reasons you need to go back and use a slide rule now I don't think so uh there's probably like three slide rule enthusiasts to listen to us right now you know it's my favorite way of doing math no I I think they're really cool I have a I have a lovely one and it's a lovely it's a lovely tactile thing and it's a it's a great reminder of of what we used to do um and I used to I never used Punch Cards but my my physics department used old Punch Cards as like our our scratch paper so I have a fondness for it right and there probably is somebody running some fantastic um hole punch machine well and and you can still use it for um machine knitting interestingly enough or um to card weaving they still use very much a whole bunch of Technologies where a lot of that came from but um but yeah for the most part um you know I'm I'm super into hand knitting but for the most part I I still buy my socks commercially all right so um the sort of recurring theme so far seems to be um if you've got uh privacy or security problems that's gonna be the biggest blocker to using AI do you have any uh tips on how you might sort of reason about this or how you might um get around these blockers I mean for the most Parts you if it involves anything that can identify a human think twice before you do it right personally identifiable information is is obvious things like names ID numbers things like that but especially even combined um a lot of a lot of things are identifiable so if you say that Ginger American especially in a certain tone of voice I probably know you're speaking about me so uh you know it is something really impressive like um you know 95 of a data set can be de-anonymized in like four data points it's really sort of shocking so be really careful about putting information about people into it I think that's that's the biggest thing to say and then anything um anything you wouldn't want on the front page of a newspaper I think that's that's always a good test for AI ethics if it comes out you know Sarah writes a model that you know I don't know says rude things about Ginger Americans um then just don't if it if it got out if you wouldn't want literally everybody in their next door neighbor to read it don't put it in a public facing um tool okay so it really is about just think about what are the impacts going to be um if this becomes public or if you get a wrong answer yeah and you know and obviously well you should know if you're dealing with anything that has um a security clearance or intellectual property implications Etc um I think those are those are pretty obvious don't use it cases and I'm hoping most companies and organizations have some kind of guidelines on uh what are the important or basic data that ought to be kept secure so uh maybe those are the ones you keep away from AI yeah but I think you also know um I mean I would be shocked if very many companies have updated their security policies in line of this revolution I mean this has only been out since November 30th 2022. um you know this is when it all really took off obviously bits of it existed before then but but I think that's the sort of key date um in all of this so I'd be shocked if if companies have kept up with it I know certainly regulation has never kept up with it that's always been a problem when thinking about AI ethics and data privacy and concerns like that so it it is kind of kind of up to us to behave ethically when using it and and make sure that we're not doing anything for evil and to not make sure you know to make sure that we're protecting ourselves and and our company's rights when we're using it absolutely so just moving on from um the impact on jobs and tasks to upskilling I think uh generative AI has some pretty big implications for education so uh yeah so uh to begin with um how do you think uh it helps people learn new skills I mean you can just you can just chat with it you can just ask it it's incredible um you know explain time series to me and you can have a chat with it and you can say oh tell me more about that second paragraph and it will and you know is it going to be right about absolutely everything in detail but probably not so so do be careful about that and and do verify that but um it it can basically be like having a private tutor now which is incredible um I'm a big fan of Duolingo for language learning I'm I'm on it all the time I'm learning far too many languages and what they've done with it um I think is is incredibly impressive you can do really sort of tailored learning I think this is this is the thing we know about education in general right is is that um really focused practice on the areas that you're getting wrong is what helps you upskill rapidly and this is where um AI powered learning has such great potential because you know as I'm doing my French lessons it can say oh you know she always gets a subjunctive wrong and it can give me you know loads more review about that um and help me with that and similarly I assume if I'm uh learning programming languages for example on data Camp um I'm going to be able to get sort of uh you know she always forgets a Define the arguments there or something I don't know what common mistakes I make but um usually just typos you can help me catch that too so the idea of personalization so being able to figure out what is this particular learner doing wrong seems really powerful absolutely um are there any other sort of areas where you think okay this is this is a game changer but this is going to really improve people's learning performance yeah I hope so I mean have if you've ever sat through like really standardized boring corporate learning um you you really hope that it gives you something other than than that PowerPoint presentation with um a bad quiz at the end we've all done that like you know there are things that we have to do regularly for for very good reasons you know things like antibiary and Corruption training um but that could certainly be be more engaging it could be um stickier you know in terms of it it sticks in your brain better um I think yeah that that level of personalization interactivity adaptability and that's something that I think in the past for the most part you would have had to work with someone really directly in a small group to get um and there's so much opportunity to to yeah get the get the generative AI to quiz you um so it's great for practicing job interviews you can get it to role play um a job interviewer and and ask you all those sorts of questions and then it can even evaluate the response so if you pass that back in um so I think there's there's so much opportunity to to quiz you you know when I when I was a kid when I was studying for my exams I'd always get my mum to quiz me um and that was that was the best practice um when you go in and Ace it based on that so yeah now you've got your your own private tutor potentially uh yeah uh AI tutors that's feature coming soon to data Camp so uh I do like that example uh yeah uh that that's very nice the idea of uh being able to converse with someone practice your conversations had to seem a very powerful way of learning um so just on a related note um just the fact that you can use AI does that change the skill set that you need particularly for data analysis and data scientists I mean it does but again I still think those most important skills are always asking the right questions and critically evaluating information and so that just makes those even more important because asking good questions is is kind of another way to say prompt engineering so in terms of technical skills um like if AI can generate your code does that change your relationship with how you learn about coding I think there's just less sort of rote memorization that has to happen but again I in my career I have seen this progression that has happened right it used to be back in the day that if you were writing some complicated thing in in C or C you know C plus plus whatever like like during my PhD um you had to code in those equations by yourself right you had to you had to have that math you had to code that in specifically and okay that made the code run way faster um potentially there were good reasons to do that nowadays we don't do that anymore we use Python packages if we're programming in Python right somebody's done that function for me I don't need to write a neural net from scratch I'll go off and use tensorflow or Pi torch or whatever right so it's just the next level of abstraction we were we were already sort of getting there with I think you know I thought the next step up from from um packages was going to be sort of pre-trained models and does that make sense this is that because it's trained on the entire internet but I I think it's just that it's just that next level of abstraction up and why wouldn't we win appropriate use the more powerful tool to do that I definitely agree with that um I'm wondering because um this is also built on deep learning and um the whole area seems like really important like you need a few people to understand what's going on um do you think uh this sort of natural language processing skills or deep learning skills are going to become more important or is it just well this is all done for you we don't need that it depends on what you want to do if you just want to use the outputs of these things then then no I don't need to know how a search engine works to to draw the internet but if you want to work with it if you want to be on The Cutting Edge of Technology if you want to be adapting it if you want to be you know making your own tools with it then I think yeah absolutely natural language processing and um and deep learning are are some of the most important skills those are especially thinking about just text-based models um if you're thinking about you know do you want to do image generation do you want to do video generation you know then you need obviously on top of that um computer vision skills but you do still need some NLP because it has to be able to interpret prompts um so so yeah it will be there but again how many of us are really writing our own python packages these days rather than using that output to to write the code I think there's a parallel there as well okay uh that seems like sensible advice um and so I'm wondering um in general how does it how does the existence of generative AI um make change your decisions when you are trying to hire uh data professionals that's interesting um I mean if they didn't know what it is they've clearly been living under a rock um so I think I I think that's genuinely true that because you do want people to be able to you know be aware of Trends in in the industry and and have ideas and opinions about where things are going so I think um you know people having an awareness of it and people having interesting insights on on how to use it I think those are probably great interview questions um but again I'm still always hiring for can you ask good questions can you critically evaluate the responses are you creative can you learn quickly can you adapt quickly um all of that all of that's in practice now you you need to you need to do all of that you need to do it to um just successfully use the tool full stop but also to be able to adapt to a changing workplace that's going to integrate tools like this more and more excellent um has it changed the profile you might look for or are there any new skills that you think are more important now I do think NLP and deep learning are going to become more and more important I think I think that's that's absolutely the right shout on that um but I think individual skills and Technologies change a lot and you can learn them and you need to be constantly learning I I think the only thing that we can guarantee about this industry is that that it's going to keep changing and it's going to keep adapting and we're going to have we're going to keep building on what we're doing it's going to be more interesting more powerful and that's going to use different skills right and are we going to be using python in 20 years probably not I mean some people are still using Fortran but hey um for the most part no there there are Trends there are shifts in in what we do this is a big one this is a big step change but the core skills are you logical are you creative are you pragmatic can you you know make things make sense um those those are the sort of more fundamental skills rather than specific Technologies because I can I can go to a data Camp course tomorrow and refresh myself on time series if I need to um that's not the point it's it's new I know how to find that information do I even know how to ask questions about this like hey what kind of a problem am I talking about that's that's a more important question to ask than what specific piece of technology or how do I write these five lines of code about it um framing framing the problem thinking about how to solve it how to how to communicate the results at the end of it that's always going to be more important than a specific skill one thing you've mentioned a few times is the idea of um prompt engineering and it seems that this might be a real job I'm never quite sure if it's like if it's just a task or whether it's like a whole job in itself uh can you just tell me a bit about what it involves yeah I mean prompt engineering is is another way of saying asking the right question I think there are there are tricks to getting it to to do what you want so giving it context again but a lot of these things are just good advice for how to tell a human to do stuff as well so give it context tell it why so in this situation I want you to be a data scientist using python so give it that prompt ask specifically what you want if you're looking for a format of an answer you know give it an example say hey give me an output like this again really great advice for dealing with humans as well um so so yeah I think it's going to be a super important skill but quite a lot of jobs have I think in very limited circumstances that is going to be a specific job but I think that's going to be part of like a mega pipeline in in certain areas I think like you do see you know search engine optimization I think is is again a good comparison is that the job title in a lot of cases probably not right but if you have a marketing rule that's probably an essential skill and in a big enough specialized enough organization you might have someone with their only job is that but I think it's more and more a skill we all need but it's a skill we can still all use because again for dealing with humans giving context saying explicitly and clearly what you need giving it an example of what good looks like still all really great advice excellent so maybe a job in big organizations but um otherwise it's just a skill everyone needs well you know how to search stuff on the internet right now is a skill that everyone needs right how to how to phrase that question properly how to sort through all the crap how to ignore the ads how to how to do all of that we don't even think of those as skills anymore um but but 20 odd years ago we were all figuring this out for the first time uh and it's what we're doing again I think internet search it does sound like what do you do well yeah I just Google stuff all day I've got jobs like that let's talk about your work at Kubrick uh so can you tell me what you're working on at the moment sure yeah so we're we're training the next generation of wealth lots of data professionals so instead of a set of AI look I look after our machine learning engineering track um so the people who are going to be potentially using this stuff um on a full-time basis we we have other streams as well but yeah training them up in the basics of all of the business stuff around this as well because again sort of half the job is actually getting it implemented and communicating it um trying them up with the basic skills that we need and then sending them out to client sites and supporting them out there using machine learning skills so so we're spending a lot of time obviously talking about about this new Revolution in Ai and and how interesting it is um yeah lots of lots of good conversations Lots about obviously around the assessments we had some pretty serious assessments that were you know a couple of weeks after um touch EBT dropped and and we ran the exam during those days it was passing it um you know good marks rather than incredibly perfect marks but it was you know having an honest conversation with them about you know look this is you you can do this it can answer the questions We're not gonna lie to you we're not gonna hide that from you um but you're not going to get the benefit of you know really being able to challenge yourself and really being able to test yourself um if if you use it to effectively collaborate right because um rather than rather than it sort of copying from someone right the equivalent I think of using chat gbt on exam is is closer to collaborating with a human it's closer to sort of plagiarism than you know I have automated something um so so setting expectations around that um has been has been a really interesting discussion we always talk about um aisx and I think the the ethics of of this is a big one as well there's all those same questions about privacy and security but also you know where is it going they're all worried about you know is this still the right thing to train in should we are we going to have jobs at the end of this and yeah yeah I actually think it's more important than ever um yeah it's a it's a thorny issue um the idea of using AI for assessment uh because it's it's kind of cheating but um if it's something you're going to use in your job then um maybe it's something you uh you want to be able to use um when you personally assessment as well exactly because I was um I was a I was a hiring manager at a bank I worked at one of the world's largest banks and it drove me crazy when we would get um young data professionals who would join us and would insist on doing absolutely everything from scratch with no collaboration because that was that was how you're supposed to do it in school and University but that's not what I want you to do on the job site like if you can ask Bob next to you and Bob can say Yep this is how you do it in five minutes or if you can find it on stack Overflow I would much rather do that than spend two days hashing out for yourself less efficiently like this stuff exists it's been tested it's been tried I think this is always the example we used for for um you know using pre-made python functions right don't don't write a linear regression from scratch what are you doing you you I can't learn um I think that's where we're headed with this as well all right nice and uh do you have any success stories around AI that you can share from Cuba well have to be a bit careful about client confidentiality um I think there's some really interesting stuff that's happening in life sciences with large language models I went I went to an event on that um in Industry I think that's incredibly cool um there's lots of options for um for you know developing new sort of computational chemistry with it for for looking for new drug reactions for doing sort of meta-analysis on past research papers I've seen some really interesting examples of that um I think that's an incredibly interesting area that's that's going to be relevant well to everyone because we all have to deal with Healthcare at some point in our lives um I I think that's incredibly cool we've definitely seen people switch to things that are um you know a lot more efficient different versions automating things um being able to automatically classify documents for example um looking at Supply chains there's just there's there's opportunities absolutely everywhere um if you're like hey that could be done more efficiently there's probably a way to use technology to do that excellent so lots of opportunities out there uh Denver people wanting to uh adopt this stuff um do you have any final advice for uh data managers or data teams who are wanting to um try to hand it um AI yeah I mean just just try it like the varied entry has never been lower it's it's just narrative it may not stay that way forever right it may not always be so so freely accessible or so accessible um but it's it's a it's a big step change it's exciting so so on the one hand you know it's cool be excited about it right absolutely there's so much potential on the other hand like calm down it's also probably not the end of the world it's probably not Skynet um you know I think there's so much opportunity but also keeps in perspective on it as well we've been through I have personally been through a few Revolutions of of the technological variety right we got the internet we got advances in in search engines like Google they some stuff phases out but some new opportunities are made every time um we wouldn't be talking right now without a bunch of them I'm in Chicago you're in New York look at look at what we've managed to do there's some kind of automated transcript going on in the background I couldn't have imagined that um 20 years ago\n"