The Future of Generative AI: Exploring the Possibilities and Implications

As we navigate the rapidly evolving landscape of artificial intelligence, one area that holds significant promise is generative AI. This technology has the potential to revolutionize various industries, including design, writing, and art. In this article, we'll delve into the world of generative AI, exploring its current state, applications, and implications.

Generative AI: A Technology Demo

One of the most exciting aspects of generative AI is its ability to model behavior that can change the future career path of individuals in a particular role. Take Andrew, a PR person on our team, who is using GPT3A to generate text. By doing so, he's modeling a specific behavior that will impact his work and ultimately, his identity and career development. This is just one example of how generative AI can be used to augment human capabilities.

Designers and Generative AI

Another area where generative AI is making waves is in design. Designers like Noah, who is not afraid to explore new technologies, are embracing GPT3A and Stable Diffusion to create images and designs that were previously unimaginable. They're using these tools as a creative brainstorming workflow, iterating through ideas and refining them with ease. As we move forward, it's likely that generative AI will become an integral part of production workflows, allowing designers to focus on high-level creative decisions rather than tedious tasks.

From Imagery to Art: The Power of Generative Images

One of the most exciting applications of generative AI is in the realm of imagery. By using tools like Dolly and Stable Diffusion, we can create images that were previously impossible to produce by hand. This technology has the potential to revolutionize industries such as advertising, fashion, and art. For instance, a designer can generate an image of a product or a scene, which can then be used for marketing purposes. The possibilities are endless, and it's exciting to think about how this technology will shape our creative processes.

Developing Text Style Guides: A Key to Reproducible Results

To unlock the full potential of generative AI, we need to develop text style guides that produce predictable result sets. This is crucial for industries such as publishing, advertising, and design, where consistency is key. By creating a standard way of communicating with these powerful models, we can ensure that outputs are in alignment with our desired style. This requires a deep understanding of the technology and its limitations, but the payoff is well worth the effort.

The Rise of Generative AI Experts

As generative AI becomes more prevalent, we'll see a new class of experts emerge. These individuals will be virtuosos at generating images, designs, and text using these tools. They'll have an intimate understanding of how to craft effective prompts, optimize results, and troubleshoot common issues. It's likely that widespread adoption of these tools will lead to the emergence of a new profession: generative AI specialist.

Chatting with ChatGPT: Feeding Style Guides into Stable Diffusion

Imagine being able to generate style guides for chatbots like ChatGPT, which can then be fed into stable diffusion to produce consistent results. This is an exciting prospect that holds significant potential for industries such as advertising and publishing. By leveraging the power of generative AI, we can create highly customized content that resonates with our audience.

Final Advice: Don't Be Scared – Explore with a Beginner's Mind

As we embark on this journey into generative AI, it's essential to approach this technology with an open mind. Don't be intimidated by prevailing narratives or discussions of AGI; instead, focus on the exciting possibilities and applications. Start playing around with these tools, experimenting with different prompts and outputs. Remember, generative AI is a powerful tool that can help your business thrive – don't be afraid to explore its potential.

Conclusion

Generative AI holds immense promise for industries across the board. By exploring its current state, applications, and implications, we can unlock new possibilities for creative expression, design, and writing. As we navigate this rapidly evolving landscape, remember to approach generative AI with a beginner's mind and a playful attitude. Don't be scared – explore with confidence, and let these powerful tools help you shape the future of your industry.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign this is Richie breakthroughs in generating images and text have been the big story for artificial intelligence in the last year gpt3 and its derivatives like chat gbt as well as Dali and stable diffusion I've already had a huge impact in just a few months since their launch today we're going to talk about how businesses and data professionals can make use of these AI Technologies as well as how Ai and humans can work together joining me is Scott Downes the CTO at invisible Technologies is that engineering product design and marketing teams at multiple growth stage startups he's got a really deep technological knowledge but also a great sense of how technology applies to businesses and The Wider world let's hear what he has to say hi Scott thank you for joining us today so to begin with just going to give us a little bit of context to tell us about what does invisible do sure invisible Technologies is the full name of the company and I mentioned that because we're a technology company but we firmly believe the technology is best when it's invisible so what does that mean it means that when we think about what successful execution of a process is for a client what we want to do is focus on outcome and results more than on the particular tools or Tech that we used so what do we actually do our business is focused on mapping processes for our clients and executing them at large scale so examples of the types of work that we do but it's pretty broad because we really are stubbornly horizontal in the way that we've built our platform our belief is that any significant business problem that you're looking at can probably be better handled if you have a clear map of how it should be executed and that every process of significant scale is going to involve some element of human labor and some element of Technology automation even Ai and ml techniques so the way that I often think of it is if you're a scientist on an Arctic exhibition and you took a core sample you would see all these interesting things in the core sample that you took if you take a core sample of any highly functioning organization and you pull out a process like say lead generation for a sales team with data enrichment what you'll find is that there's a combination of Integrations with third-party platforms like Salesforce they're smart intelligent High judgment individuals making decisions about what tools in Tech we should be using serving as like approvers and people who are looking to guarantee quality but there's also a full set of third-party tools that might come into place or custom automations that enable success so just like a normal lead generation process might involve integration with a Salesforce platform third-party data enrichment through a tool like Zoom info custom personal review I mean when you put all those pieces together the problem space that we think about with invisible is orchestration so what's the right balance of people and Tech that's what we do well brilliant so I find this interaction between technology and people processes it's really fascinating stuff and I'd love to get into that in more depth in this episode before we get to that can you tell us a little bit about what you do as Chief technology officer I have had a number of different roles in my career and I think that like one of the reasons why I love being a CTO for a scaling startup is that it lets me explore all these different areas of my own personality and my own interests that I've had over time so once upon a time I was a English major in college I've tried to make a living as a musician I've worked as a designer but one of the things that was constant for me from an early age was writing code I loved programming from elementary school so as I got older I didn't have the same enthusiasm over software engineering as a career prospect that some folks have these days a little older so growing up in the 80s and 90s we didn't see programming as cool until the.com era sort of hit and all of a sudden there were a lot of folks like me who had diverse interests and skills who saw technology like software development as a way to scratch all those itches or deal with all those different kind of passions in a centralized way so if you were a programmer who you know could write and communicate but also who had an interest in design a passion for how that should work and understood business and wanted to solve interesting problems all of a sudden you became a really valuable person and I've just been on that path ever since so some of the reasons why I love my job is that in a given day I might touch you know eight different areas of focus so I might be working with the design team a design review I might be looking at an ETL with the data engineering team or talking to the react developers about a front-end application that we're building I might be talking with a product team about strategy or an executive team about corporate strategy in our business model so I'm just kind of addicted to that diversity of Interest so that's why I do it but I guess to answer your actual question more practically I'm responsible for engineering product and design I'm also running marketing for the moment we're going to pass that off I've maintained some stewardship of marketing in multiple companies over the years I'm really passionate about building great software products that's cool certainly having that balance of doing technical things and doing creative things where you're interacting with humans that's something that appeals to me as well all right so let's go back to you what you're talking about before about invisible Technologies doing this mix of things with technology and with processes and the technology side of course is built on gpt3 so I know there's been very hyped recently but for those people who haven't heard about gpt3 can you just give a little overview of what it involves sure well first of all I'll say we're fans of all cool technology we're heavy users of all sorts of platforms RPA tools that we use I don't want to start listing names or companies or tools that we work with because the list is very long and I don't want to forget anybody but when we think about how to solve problems we think about what the right technology is for the job not everything's running on gpt3 but open AI is doing some amazing work and we are very enthusiastic advocates for gpt3 as a practical tool in your toolbox so gpt3 I feel like even people who are outside of the technology world are starting to feel the ripple effect or the impact of large language models and GPT in particular so I think that a lot of folks will already know what I'm talking about but what gpt3 is for those folks who haven't heard is what we call a large language model so machine learning models are trained on data sets in order to solve specific problems well the problem is set for GPT and the data that it's fed is incredibly broad so one of the ways that I like to explain it to folks who are hearing about it for the first time is like what if you took the entire contents of the Library of Congress or all of the web every single word that has ever been seen before and then you trained a computer to kind of make sense out of all those words and sentences and paragraphs in context and you assume that you've created this we call that a model we call that a large language model and then what you'd like to do is give some text as input or a prompt to that large language model and see what's likely to come next so I think that most folks can have some intuition for the idea that like if you've read every book by Edgar Allan Poe every story by Edgar Allan Poe and you see the first paragraph of a new story you can imagine where it might go and basically what gpt3 is and what any large language model is aspiring to do is to be effectively predictive of what might come next given all the knowledge that it's consumed how does that work for you does that make sense it does make sense yeah so it's about predicting the next bit of text given a text input and so maybe you can talk a little bit about how invisible Technologies is using gpt3 in this context working with text sure I'll take a step back and say a little bit about some of the common business problems that we deal with so like I said in my initial answer like we're stubbornly horizontal what does that really mean it means that we try not to overly focus on any particular use case or on a particular vertical so our process platform can be used by marketing departments or operations departments or Finance departments or hiring departments so we're not only targeting say corporate marketing and also we don't only target one specific industry so we don't only target the energy industry as an example so when I say that we're stubbornly horizontal that's what I mean but as it turns out the period of really explosive growth for us happened during the early stages of covid where we started working with on-demand delivery companies and the problems that they were having at that particular moment were just particularly well suited for our philosophy of how to solve problems with processes specifically we worked on large-scale menu transcription because all of a sudden everyone in the world still needs to eat but going to a restaurant not as viable as it was the prior Thursday and all of a sudden you've got hundreds of thousands of restaurant menus that need to be transformed and put into the catalog of options for various delivery companies if you think about some of the prevailing approaches for how those companies were solving those problems well of course they had systems for every restaurant to go and upload information themselves but very unreliable and it turns out restaurants are not like data entry companies it's not a natural skill set necessarily to import that kind of a data or upload and Export that data into proprietary systems and some of those companies look to solve that problem through engagement with bpos or Outsourcing companies and what they would do is they would say here's a bunch of raw input now let's have a bunch of people look at these and figure out how to turn that into a menu for your local pizza restaurant notoriously complex as it turns out so many options you're not just getting a burger with or without pickles you might have thousands of different possibilities for what custom pie you want from a pizza shop so you combine that sort of a problem space with an immediate need to massively scale up what were largely human operations with some understanding that Tech might help like maybe we can use OCR tools just transcribe the contents of menus maybe we can use scrapers to read websites and extract data and and transform that in a way that can be uploaded to those systems what we found and again it's in alignment with our philosophy is that a combination of off-the-shelf tools and custom built tools that do those sorts of problems well solved by machines should be orchestrated in concert with large-scale human efforts because it turns out sometimes you need a high judgment individual to discern that you don't typically serve pork rare right you might not have temperature options for every menu item that was a transformative moment for our company to be engaged everybody was elbow deep in this problem space I was working on menus till midnight every night myself and everyone in the company really internalized the values that we were trying to present with our platform this idea that machines aren't good enough by themselves and people aren't good enough by themselves but there's some form of synthetic intelligence that happens through the right balance of humans and automation so this is all a big setup for how we use gpt3 at that point we found that we have a bit of a specialty around managing large complex inventory problems catalog problems and I think everybody who's ever used doordash or ubereats or any of those applications has probably had the experience that I've had which is I really want that particular menu item with the following options let's say I want steak strips with my particular Mexican dish instead of ground beef and I know that every time I've gone to that restaurant all I have to do is say hey can you give me the steak not the ground beef and of course they're happy to do it but if you go and you look in the app for some reason it's missing or it's confusing or maybe two pieces oh wait but it says ground beef and steak strips on the same option like how can that even be or even the standard option that you typically purchase is priced wrong or it's spelled wrong and you're not even sure what's going on and those sorts of problems with managing the accuracy and proper classification of menu items and generally inventory overall has turned out to be a space that's like rampant turns out that that's not a problem that's unique to on-demand delivery catalogs it's a problem that appears everywhere with any digital system and it's another reflection of this General picture of what do we do to create the right balance of fully automated systems and systems with human review it does seem like at first glance it's a really simple problem to try and transcribe menu items you think okay well I'll just put my data in a format like some kind of API and it's sort of fine but then actually thinking about the experience of going to like a restaurant website and for some reason the menu is always kind of buried somewhere within that site in a PDF and it's a font you can't read and you're like well how do I buy anything so yeah scaling that across millions of different restaurants to seem like really quite a difficult problem well I mean it's almost the exception when you have high quality in that space so how does that lead to where we are today well one of the things that we've found in recent work with gpt3 is that a lot of people think about gpt3 in terms of generative text problems like we use this too I'll just say transparently candidly like we actually use gpt3 to write blurbs and pieces of marketing material for our newsletters and we're excited by the novelty and extreme relevance when you ask questions or set up the right prompts to get a an interesting blurb from gpt3 it often leads to like real deep questions like we ask might use chat GPT which is a recent Innovation to say write a blurb for our company that reflects the following values and or write a blog post that's when the interesting part happens right the engagement of humans with generative text it's almost like some of us walk into a meeting without a plan or an agenda and those meetings are inevitably ineffective or you spend lot of time trying to establish ground rules in terms of meeting about a meeting Amazon I think famously has meeting protocols that require people to spend the early part of a meeting reading and then React to what was in the initial narrative that was shared I think that the existence of generative text for marketing use cases leads to higher quality conversations because you already have a framework to start from that's not the example I wanted to give that just happens to be one example gosh Jasper's doing amazing things in that space it's a great company yeah certainly I can identify with the idea that like having a machine right marketing materials is great because it's one of those things where it doesn't necessarily have to be Innovative text it's just sort of saying the right thing and the right tone of voice a lot of the time one of the things that we've found pretty cool pretty exciting is that there are specific problem spaces where given that framework of like managing complex data that requires human review goes through Transformations integrates into third parties systems in that General problem space there are several things that tend to come up like classification problems so it could be that broadly that you have options associated with steak or with a pizza that don't apply to fish or specifically that you may have items in your catalog that have been misclassified so you could imagine a situation where I'm looking to buy a laptop battery replacement and I go and I look at batteries and I see double a batteries and D cell batteries and all kinds of batteries that don't seem to relate to computer batteries and then I go and I look in the different part of the hierarchy of the taxonomy and I go oh I had to start at computer before I could get to batteries that are relevant to me batteries at the top level wasn't the batteries I was looking for and that's just a navigation problem imagine what happens when things are wrongly classified so even if I knew to go to the computer space I might find double a batteries in the computer battery space and I might find my laptop replacement battery over there with something entirely wrong so just broadly I would describe those as classification problems for inventory management or catalog management so we were doing some recent testing for one of those use cases where we had a classification problem and we started with the assumption that we might have to work with a specially trained model to address this particular classification issue but we actually found that like not even a fine-tuned model and I'm sure we can talk about that a bit more if we need to but just plain old stock gpt3 was able to help us meaningfully to solve classification problems there was a specific example we looked at where gpt3 beat our human testers at identifying a particular item I believe it was like a women's makeup like distinguishing between eyeliner and mascara which I don't think I know the difference now I'm embarrassed but I need to go look it up so subject for a different podcast perhaps eyeliner versus mascara maybe my makeup expertise is is poor but yeah that was an example of a situation where when you think about the problem space that I've posed of how do we have accurately classified items in a taxonomy whether that be for an accurate catalog or for an accurate diagnostic assessment for a radiologist classification problems are a significant problem in Tech and we're finding that open AI is amazing work in this space has made the cost and barrier to entry for solving some of those problems much lower and that's just one example I don't want to brag too much on ourselves I do want to say like I think notion is an example of a company that has released some AI features in their platform that address some of the same issues that we see in that kind of inventory space as well like you might want to clean up misspellings you might want to clean up grammar and we've lived in a world for a long time where you think that you might need dedicated tools for that you know and I think the grammarly still exists right like I think there are companies that exist to Target spell check and grammar but increasingly those sorts of tools become table stakes in mature platforms because of the capabilities provided by like open ai's API for gpt3 so if it's super simple for me to say like here's an input can you clean up the grammar and it does and it's effective then every product gets better and those kind of things like for those who haven't seen what notion is doing in that space they have some just really nice little generative text thing a little grammar and spell check thing like those kind of immediately super practical things kind of run at odds with what you hear sometimes in the media which is more obsessed with robots taking over the universe or Skynet or something like the reality is that the AI Tech that's coming out every week these days is really impressive at solving simple practical problems and if you can solve small simple practical problems in a scalable process that sound that you hear is a cash register right you're saving money on every single run and if you're trying to process 10 000 menus or update a hundred thousand inventory items then the ability to take a little bit of the load off is 20 if it's eighty percent these are totally real world viable scenarios for us where we find that problems that used to require humans now can be solved to a reasonable level of accuracy not 100 to a reasonable level of accuracy with commonly available not a lot of customization required off-the-shelf models with human in the loop review well that's like a dream come true for a company like us because it just validates the thesis that technology is best when it's invisible humans are a key part of every meaningful bit of work and for us to put together AI That's going into spaces that we never could imagine it going in before with well-trained highly capable human agents that's magic okay so there's a lot to impact that but it seems like the central message is just that if you can make these writing tasks a little bit easier then it's going to help you scale and so the other businesses who are kind of interested in making use of gpt3 or other text generation tools where do you think is a good place to get started well I think Chad the GPT playground apparently if you look on Twitter everybody's gone crazy over in the last few weeks I even think about like how when you and I first spoke and the time when we're recording a podcast and the time that this is published there's so much innovation in this space that the story will change materially in each of those windows if this is published in a month I fear that people will be going oh I already know about chat of course but Twitter is blowing up right now with examples of people see seeing these kind of transformative interactions they can have by effectively it feels like a conversation where you maintain context instead of it being a single prompt with a single response you send a message get a message in response you send another message so I just would encourage people to Google Chat GPT and go have some fun I think that if you ask me for an example how GPT is changing the day-to-day lives of software Engineers including Engineers on my team if you asked me a few months ago I would talk about kodaks and co-pilot and GitHub and how we're now at a place where Engineers on my team are already using those tools to figure out how to do tasks that are you know machines are good at remembering minutia and people aren't always and I would rather have Engineers who are thinking about architecture and business problems and how to solve those rather than remembering the exact syntax to call a particularly idiosyncratic API that maybe doesn't look exactly the way that other apis look or that there might be some Nuance to how to invoke these other Frameworks or libraries through tools like copilot GPT powered you could a few months ago say I need to write a method that does the following or I need to write a function that does the following and have it pre-populated well the technology has moved so quickly that now you're seeing people have those sorts of conversations with chat GPT that have context and maintain it across questions so there's an example I'm going to bring up like with Elon pushing so hard on the Twitter culture and executing a bunch of layoffs and kind of being a harsh task master in that space some joking tweets popped up with one person providing an example of how they would respond if they had to produce a document for Elon Musk describing what they've worked on this week and they asked a question like chat GPT I work at Twitter and I would like to come up with 10 good ideas that might be worth exploring that would show that I'm providing value as a software engineer at this company and it responds with 10 numbered and relevant results oh cool that's amazing well let's take number nine can you go ahead and generate a document for me that describes how I would use that and include some statistics okay now generate some sample code and then after the sample code was generated you know what that sample code looks too simple can you add some enums and some extra variables and then it does that literally does that right so that kind of interaction wasn't possible at least it wasn't broadly available a month ago I know some people it's very easy to get attached to this Narrative of like fear about jobs being lost but historically you know I don't go too far on a limb here but I'll say that seems like the Industrial Revolution did some good stuff for us and the ability to connect through the internet has done some good things for us and I think that there's always a little bit of twinge of fear associated with rapid technological advancement there also is an excitement and they're really kind of the same feeling it's just how you interpret it so if you feel your heart race a little bit when you think about these kind of things coming into existence well good you're paying attention it is amazing and the implications are complex but what I personally find is that by having at our philosophical Center as a company and even me as a person that knowing that these are tools that enable humans to do amazing things that's what motivates me and excites me at the end of the day we all have Iron Man suits we're all Iron Man absolutely it just seemed like it's a really sort of transformative time and generative AI is really sort of come of age maybe in the last month but I suppose in terms of using it you've talked about a few of these use cases around like automated code we talked earlier about marketing things like that I suppose to the things on a lot of business Minds at the moment are things around productivity increases and cost savings so are there any specific use cases that you think are going to be important in those areas it's hard to say I know that the ones that I see that are particularly powerful relate to some of the examples that I already described of managing complex data sets that require human review and that's a very Broad and general way of saying something that applies to almost every company so clearly there are a lot of e-commerce use cases and clearly there are use cases that relate to supply chain in general I think that one of the things that I've found exciting in the ml space really from like my first experience with doing bioinformatics use cases I really think that there's a lot of opportunity for us to remove inefficiencies and reduce costs and health care and it's an area that's of enough interest for us as a company that we're very mindful of HIPAA and we do work with some clients that have sensitivities related to that I think think that one of the more compelling use cases that I saw recently was related to increased accuracy on classification of medical scans and I spoke to a man who's running a company who one of their primary focuses is on doing analysis of medical scans where there's an AI component and a human review component and the outcomes are fairly measurable and the classification problems are pretty complex so let me unpack that a lot of folks may still have a mental frame of classification problems being really simple like hot dog or not hot dog the Silicon Valley example or color identification or if you've ever done one of those captions where it's like pick the horse that's smiling or whatever those kind of classification problems seem fairly simple and trivial and it's great like we're getting to a place where you're providing training data to make sure that those things are more accurate over time by in engaging with the captcha but I think that the possibilities associated with more accurate medical testing are lower costs and saved lives and that's something that I think it's useful for me to remind myself that cost Savings in certain spaces like it's not just to increase wealth it's actually to enable opportunity and to extend lives so when I heard this example and I walked through it in great detail with the CEO of this company that they were seeing no offense to Radiologists out there but they were able to demonstrably produce more accurate results from analyzing scans through a combination of complex classification with hundreds of potential outcomes and then human review with humans and I know we're supposed to be talking about technology here but the humans piece is just amazing to me that if you think about the work that the work associated with human in the loop processes those types of people they're a different breed they're very smart very retrainable very capable people whose job is to pay a lot of attention very detail-oriented they're kind of like my brethren in the engineering space they're a different breed they think differently they're able to focus on a skill set that still surpasses even the greatest capabilities of AI models which is they're the ultimate Arbiter of whether this scan means this or this scan means that and what I find really fascinating about that is that the psychographic profile the way that those people operate is more important than their existing training so the real heroes in this particular kind of scanning use case it's not the ml scientists or the engineers who built the model although God bless them you know they did something really important but the people who are performing that final review are intelligent High judgment high impact individuals who are saving lives and they're able to do it at a level of oversight and quality that's hard for a radiologist to do because that radiologist has a lot of jobs he's having to deal with the insurance company or bedside manner or Praxis like running the scans whereas this person who sits at a machine looks at the data looks at it very carefully those people who are close to my heart and we have thousands of them in our company we call them agents I think sometimes people confuse when we say agent they think they mean a machine technology but we have thousands of Agents all around the world who log into our platform invisible's platform every day and they solve complex problems and they're very retrainable malleable intelligent people they're The Secret Sauce the Technologies we just kind of take commoditized Tech off the shelf and orchestrate it in the right way you got to have the right people to make sure that we're making the right decisions that's really interesting and exists of healthcare is really is a sort of life or death decision my intuition would have been that the radiologist or the doctor or because they're the kind of the most senior person is going to be like the real expert in interpreting these medical images or test results but actually having someone who's really just dedicated to that one task turns out even if they haven't got like years and years of University experience and sort of Medical Training it's like just doing that one task is going to make them more efficient at that job so one thing that seems to have cropped up a few times here is the idea that you do the machine side first so you've got AI first and then it's a human reviewing what the machine's done is it always that way round or do you ever have human first then AI as the review step it's more often for us in our business that it's human first but let me walk through that a little bit one of the key value props of our platform and our business from the start I think this is just reflects the entrepreneurial ethos we've always been very excited to tell our clients we can deliver results within the first 48 hours and we know we have really bright people I mean we have a platform that enables those people to maximize their talents so it's a very common pattern for us in alignment with our business model to show early impact so typically what happens is that a company comes to us with a problem and they say this is how we're solving it right now this is the frame in which we're looking at this and that problem probably being solved by a combination of overworked employees of a particular company and some third-party system they bought maybe and what we do is we first start with the idea that like we're going to take your process if it's well documented we can encode it into our system very quickly like in an afternoon and at that point what we've done is we've taken what warts and all what your approach is to solving a problem and we're putting it into a system that enables it to scale so if you can imagine like a magical shrinking Ray or an exploding Ray like shine our Ray on on your process and make it scale up to run at orders of magnitude larger but obviously that's not the real path to efficiency what we want to do is apply our expertise so our more typical model is to start with we'll just kind of like air lift your process out of human beings encoded into our platform execute it with our agents and then find Opportunities to optimize so by having a process canvas in front of you where you can literally see every block of work that's being performed the time that it takes to handle each of those requests the cost associated with it it enables us to make very rational decisions about what's in the best interest of our clients there's a key factor and like I told you I'm enthusiastic about code I'm enthusiastic about design but I'm also enthusiastic about business models and I think that one of our secret weapons and invisible is clearly that we charge based on results we charge based on outcomes so a company that whose job it is to bring in an army of people and who's paid by hours like person hours is only going to be motivated to increase that number it's the contractor problem if I have someone come out to my house and I'm paying them you know 20 an hour to fix my deck they're not going to finish early they're going to take every minute it's just in their self-interest but for us because we are charging based on an agreed upon rate for the output we are always incentivized to optimize an incentive to optimize usually comes in the form of a kind of a second wave of automation after we've encoded your process so another thing that's implicit there is that we aren't a satin for Gat company we're a relationship company and the term that we use sometimes is work sharing so when we take on work for a client you don't think of it as Outsourcing some task to some room full of people what we're doing is we're sharing the work we're going to mature it over time we're going to enable it to scale to levels that you're having trouble achieving on your own and while we're doing that we're going to find optimizations that are going to reduce the cost because that's good for us and we have a principle of deflationary pricing which is that you know some people call that volume pricing but for us conceptually what we're doing is we're not just offering a discount on good faith or reducing our margins what we're doing is if you're invested in us then we're invested in you and we will find optimizations that reduce the cost for ourselves and for our clients so how does that relate to kind of the bigger picture of automation first there are certainly situations where we go to a client we understand that their pain point is they have a largely automated process that needs help they will airlift that out too we can take that and run a largely automated process from day one as well but typically the folks who come to us are having problems because they're Innovative companies they're growing rapidly and they've hit a wall with the approach that they're taking which usually is a wall that involves a decision of what tech should be engaged so take your process We'll add the right time okay we've talked a lot about different use cases around the business one area that we've sort of quickly not touched on is how these Technologies affect data teams so how does this obviously like teams are usually familiar with AI but how they make use of like other AI tools or this combination of AI plus humans together the most impactful way that our data teams can engage with the company and driving its success with our own company are in understanding what the Right Moves are to drive the right level of efficiency and quality so we want to make our clients happy and we want a lower cost so when we evaluate tools and we evaluate a lot of tools I've been sharing with you we're like kids and candy stores when it comes to all the different technological advancements whether they be in AI or with openai specifically or with other companies who are doing great work out there the key thing for us is to figure out how do you onboard internalize the right approaches in a way that moves us out of science experiment and into business outcomes that are favorable for the company so as an example like how do you decide to use gpt3 how do you decide to use any particular technology especially new technology where it's there's some element of risk you're sticking your neck out and you've got a client who's dependent on you making the right decisions so we try to be data driven in all of our decisions I think that's everyone's aspiration the details of how you achieve that are sometimes complicated so we've established a model a common process practice for how we do things that typically revolves around using notebooks or Google collab notebooks that allow us to answer business questions with literal technical Integrations embedded so as an example because openai and gpt3 are API accessible based on general principles from the start right the idea is that there's a simple engagement that we can embed in a live notebook and we can work through business problems and do tests and trials together and the same way we would decide to prioritize a specific feature in our platform based based on previous data for how we've experienced pain points so if it's like okay we find that we're spending too much time in this stage because of X Y and Z because of poor training our hypothesis is we add training to this number of people we see the outputs and we measure them it's the it's the same thing here and the key though is to kind of pull AI ml work out of the lab and into the factory floor you have to have a clear way to productionalize that I'm still hearing sometimes these scare numbers of like 50 percent of trained models never go into production and generally it to me is not a different problem than has ever existed before in the technology space it used to be there were the same scary numbers about all IT projects and large corporations oh half of them fail and they're all late in reality what we need is we need to have an agreed-upon set of measurable factors that make a difference to the business that we're all aligned on like I mean let's increase our gross margin let's increase our Revenue when you look at those specific problems and you are factual in your approach and you think about what the literal impact will be and you can measure it and observe it in the form of processes and you can a B test it against human results and you have a lot higher confidence in deploying those so for me the question is how do we make smart decisions with technology and the answer as it relates to AI is you try to do the same things you do elsewhere which is like make sure that there's a business case understand the cost understand the impact and if the cost benefit analysis is on your side you go for it you just make the right decision that seems a pretty sensible approach is like do the same as what you do elsewhere see if this is going to actually benefit you try it and if it doesn't work I guess move on or do something different you'd be surprised that how many people don't think of it that long AI is really cool I need to sprinkle some magic pixie dust on my platform let me go use this just because it would be amazing if that worked I wish all right so I know making predictions is a bit of a mugs game but gpt3 we've sort of established it's pretty much a game changer Essay with chat GPT now there are sort of rumors that gpt4 is coming out sometime in 2023 so we're sort of at a tipping point of having very useful generative AI so can you talk about like what you think your predictions are for the effects of this on businesses this sort of ever increasing power of generative AI well I'm going to make some boring predictions not because I'm scared to stick my neck out but because I think that there are some things that are pretty straightforward I do believe that there have been some people who've said that the internet hasn't made a massive difference on actual like worked hours or productivity even and what I generally tend to believe is that whatever the field the hype bubble pops and the concept that like overnight we're going to have autonomous 18-wheelers all of our interstate highways and that every truck driver in the country will be unemployed like those kind of predictions don't tend to come true and sadly like the productivity enhancements sometimes don't come true so I'll my modest prediction is simply that there will be widespread adoption of AI Technologies and it will become normalized so what that means is that people will find practical ways to improve their margins by 10 percent here and there and that they'll find that it's not so scary after all and they'll forget why we were talking so excitedly about it in the first place my other prediction which is much more idealistic and hopeful and it's probably going to be wrong is that at some point in the near future what the advancement of Technologies in this space is going to start to actually effect in the real world implementation of people's jobs is that their lives will be better and that they will be doing less grunt work and be doing more high value work and that they'll be happier in their jobs and they'll spend more time with their families and they'll feel more of a sense of peace and well-being I realize that's wildly idealistic and optimistic to say that I have to say that's not what the technology revolution has brought and some of us are ready for it you know we believe that it's about time for the fact that technology exists to help us to solve problems and do the work that we used to do or spend a bunch of time doing it's about time to take a few of those hours back for ourselves absolutely shorter working week and a happy life is all you could wish for really I truly believe it super that is a little bit General though so I'm not quite letting you off the hook so just is there anything you think that like doesn't quite work yet but it will do in the near future are there any things that are sort of that you see are just around the corner well actually I think the generative images and texts are somewhat at the level of Technology demo and one of the things that I find exciting and interesting is I'll just take a few examples that are happening around me right now so we have a PR person Andrew works on our team who's using gpt3a to generate text and what he's doing by doing that is modeling a sort of behavior that will change the future career path of other people in that role and they're going to see their identity and their career development in a different way and we have a designer on the team who's not afraid of Dolly who's not talking about how it's not real art or being defensive and scared but seeing it as a way to make it easier for him to iterate through ideas and have a scratch pad and I think that right now those sorts of solutions are part of a creative brainstorming workflow but I do think that they will become more part of a production workflow so right now we in our internal tools there's some places where there's imagery and artwork and in the past those were generated through a designer sitting down talking with the team maybe coming up maybe pulling some stock photos you know the standard like group of people around a conference table all pointing at something and what where we are now is that we're using generative Tech like dolly or stable diffusion to create images that then we react to and adjust and touch up and I do think that we'll get to a place where there is an understanding of I don't really like the term prompt engineering but an understanding of the way that we interact with these powerful models to get the outcomes that we want I know that sounds super vague and you're not gonna let me off the book so I'll be real specific one of the things that I think is really exciting about generating images from text is developing text style guides that produce predictable result sets so as an example for us we have started to iterate on a standard way of how we communicate in terms of constructing prompts in order to deliver outputs that are in alignment with our style I saw an artistic example of that in New York last week I saw an art opening where there was a piece of work three artworks three images that were created from different sections of a poem by William Blake and I found that tremendously inspiring to think about how poetry in William Blake's words creates these visual images that felt very blank and when I think about what we can do in the generative image space as designers it's more about honing your communication and your language rather than just honing the way that you move a mouse or a paintbrush and I think that what we will have I hope this is a concrete enough prediction is that we will create more Andrews and Noah's and folks like we have that I'm seeing grow on our team that there will be a whole class of people who have internalized the use of these tools and become experts in them they will be virtuosos at generative art generative design generative text and they will be able to do way more than old people like me can do because they won't be stuck with the old programming that does sound pretty amazing and it would be great to have like enough people like widespread adoption of these tools that there are enough people that can use them well but I really like that point you made about having a style guide for prompts in order to be able to have reproducible like images and now I'm thinking well maybe you just need to get chat gbt to generation style guide and then feed that into stable diffusion it could be fun all right so just to finish up do you have any final advice for people wanting to adopt generative AI I think don't be scared start playing the excitement of new technology is something to not to be feared like come with a beginner's mind what are the cool things we can do with this come with a playful mind and think about all the possibilities I think it's very easy to get caught up in prevailing narratives or discussions of AGI or like there's not an artificial intelligence that you're communicating with it's you with a cool tool and have fun and play with it and don't be scared of it because it's probably going to help your business it's probably going to help your life all right brilliant thank you very much and thank you for your time Scott thanks great to be here you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next timeyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign this is Richie breakthroughs in generating images and text have been the big story for artificial intelligence in the last year gpt3 and its derivatives like chat gbt as well as Dali and stable diffusion I've already had a huge impact in just a few months since their launch today we're going to talk about how businesses and data professionals can make use of these AI Technologies as well as how Ai and humans can work together joining me is Scott Downes the CTO at invisible Technologies is that engineering product design and marketing teams at multiple growth stage startups he's got a really deep technological knowledge but also a great sense of how technology applies to businesses and The Wider world let's hear what he has to say hi Scott thank you for joining us today so to begin with just going to give us a little bit of context to tell us about what does invisible do sure invisible Technologies is the full name of the company and I mentioned that because we're a technology company but we firmly believe the technology is best when it's invisible so what does that mean it means that when we think about what successful execution of a process is for a client what we want to do is focus on outcome and results more than on the particular tools or Tech that we used so what do we actually do our business is focused on mapping processes for our clients and executing them at large scale so examples of the types of work that we do but it's pretty broad because we really are stubbornly horizontal in the way that we've built our platform our belief is that any significant business problem that you're looking at can probably be better handled if you have a clear map of how it should be executed and that every process of significant scale is going to involve some element of human labor and some element of Technology automation even Ai and ml techniques so the way that I often think of it is if you're a scientist on an Arctic exhibition and you took a core sample you would see all these interesting things in the core sample that you took if you take a core sample of any highly functioning organization and you pull out a process like say lead generation for a sales team with data enrichment what you'll find is that there's a combination of Integrations with third-party platforms like Salesforce they're smart intelligent High judgment individuals making decisions about what tools in Tech we should be using serving as like approvers and people who are looking to guarantee quality but there's also a full set of third-party tools that might come into place or custom automations that enable success so just like a normal lead generation process might involve integration with a Salesforce platform third-party data enrichment through a tool like Zoom info custom personal review I mean when you put all those pieces together the problem space that we think about with invisible is orchestration so what's the right balance of people and Tech that's what we do well brilliant so I find this interaction between technology and people processes it's really fascinating stuff and I'd love to get into that in more depth in this episode before we get to that can you tell us a little bit about what you do as Chief technology officer I have had a number of different roles in my career and I think that like one of the reasons why I love being a CTO for a scaling startup is that it lets me explore all these different areas of my own personality and my own interests that I've had over time so once upon a time I was a English major in college I've tried to make a living as a musician I've worked as a designer but one of the things that was constant for me from an early age was writing code I loved programming from elementary school so as I got older I didn't have the same enthusiasm over software engineering as a career prospect that some folks have these days a little older so growing up in the 80s and 90s we didn't see programming as cool until the.com era sort of hit and all of a sudden there were a lot of folks like me who had diverse interests and skills who saw technology like software development as a way to scratch all those itches or deal with all those different kind of passions in a centralized way so if you were a programmer who you know could write and communicate but also who had an interest in design a passion for how that should work and understood business and wanted to solve interesting problems all of a sudden you became a really valuable person and I've just been on that path ever since so some of the reasons why I love my job is that in a given day I might touch you know eight different areas of focus so I might be working with the design team a design review I might be looking at an ETL with the data engineering team or talking to the react developers about a front-end application that we're building I might be talking with a product team about strategy or an executive team about corporate strategy in our business model so I'm just kind of addicted to that diversity of Interest so that's why I do it but I guess to answer your actual question more practically I'm responsible for engineering product and design I'm also running marketing for the moment we're going to pass that off I've maintained some stewardship of marketing in multiple companies over the years I'm really passionate about building great software products that's cool certainly having that balance of doing technical things and doing creative things where you're interacting with humans that's something that appeals to me as well all right so let's go back to you what you're talking about before about invisible Technologies doing this mix of things with technology and with processes and the technology side of course is built on gpt3 so I know there's been very hyped recently but for those people who haven't heard about gpt3 can you just give a little overview of what it involves sure well first of all I'll say we're fans of all cool technology we're heavy users of all sorts of platforms RPA tools that we use I don't want to start listing names or companies or tools that we work with because the list is very long and I don't want to forget anybody but when we think about how to solve problems we think about what the right technology is for the job not everything's running on gpt3 but open AI is doing some amazing work and we are very enthusiastic advocates for gpt3 as a practical tool in your toolbox so gpt3 I feel like even people who are outside of the technology world are starting to feel the ripple effect or the impact of large language models and GPT in particular so I think that a lot of folks will already know what I'm talking about but what gpt3 is for those folks who haven't heard is what we call a large language model so machine learning models are trained on data sets in order to solve specific problems well the problem is set for GPT and the data that it's fed is incredibly broad so one of the ways that I like to explain it to folks who are hearing about it for the first time is like what if you took the entire contents of the Library of Congress or all of the web every single word that has ever been seen before and then you trained a computer to kind of make sense out of all those words and sentences and paragraphs in context and you assume that you've created this we call that a model we call that a large language model and then what you'd like to do is give some text as input or a prompt to that large language model and see what's likely to come next so I think that most folks can have some intuition for the idea that like if you've read every book by Edgar Allan Poe every story by Edgar Allan Poe and you see the first paragraph of a new story you can imagine where it might go and basically what gpt3 is and what any large language model is aspiring to do is to be effectively predictive of what might come next given all the knowledge that it's consumed how does that work for you does that make sense it does make sense yeah so it's about predicting the next bit of text given a text input and so maybe you can talk a little bit about how invisible Technologies is using gpt3 in this context working with text sure I'll take a step back and say a little bit about some of the common business problems that we deal with so like I said in my initial answer like we're stubbornly horizontal what does that really mean it means that we try not to overly focus on any particular use case or on a particular vertical so our process platform can be used by marketing departments or operations departments or Finance departments or hiring departments so we're not only targeting say corporate marketing and also we don't only target one specific industry so we don't only target the energy industry as an example so when I say that we're stubbornly horizontal that's what I mean but as it turns out the period of really explosive growth for us happened during the early stages of covid where we started working with on-demand delivery companies and the problems that they were having at that particular moment were just particularly well suited for our philosophy of how to solve problems with processes specifically we worked on large-scale menu transcription because all of a sudden everyone in the world still needs to eat but going to a restaurant not as viable as it was the prior Thursday and all of a sudden you've got hundreds of thousands of restaurant menus that need to be transformed and put into the catalog of options for various delivery companies if you think about some of the prevailing approaches for how those companies were solving those problems well of course they had systems for every restaurant to go and upload information themselves but very unreliable and it turns out restaurants are not like data entry companies it's not a natural skill set necessarily to import that kind of a data or upload and Export that data into proprietary systems and some of those companies look to solve that problem through engagement with bpos or Outsourcing companies and what they would do is they would say here's a bunch of raw input now let's have a bunch of people look at these and figure out how to turn that into a menu for your local pizza restaurant notoriously complex as it turns out so many options you're not just getting a burger with or without pickles you might have thousands of different possibilities for what custom pie you want from a pizza shop so you combine that sort of a problem space with an immediate need to massively scale up what were largely human operations with some understanding that Tech might help like maybe we can use OCR tools just transcribe the contents of menus maybe we can use scrapers to read websites and extract data and and transform that in a way that can be uploaded to those systems what we found and again it's in alignment with our philosophy is that a combination of off-the-shelf tools and custom built tools that do those sorts of problems well solved by machines should be orchestrated in concert with large-scale human efforts because it turns out sometimes you need a high judgment individual to discern that you don't typically serve pork rare right you might not have temperature options for every menu item that was a transformative moment for our company to be engaged everybody was elbow deep in this problem space I was working on menus till midnight every night myself and everyone in the company really internalized the values that we were trying to present with our platform this idea that machines aren't good enough by themselves and people aren't good enough by themselves but there's some form of synthetic intelligence that happens through the right balance of humans and automation so this is all a big setup for how we use gpt3 at that point we found that we have a bit of a specialty around managing large complex inventory problems catalog problems and I think everybody who's ever used doordash or ubereats or any of those applications has probably had the experience that I've had which is I really want that particular menu item with the following options let's say I want steak strips with my particular Mexican dish instead of ground beef and I know that every time I've gone to that restaurant all I have to do is say hey can you give me the steak not the ground beef and of course they're happy to do it but if you go and you look in the app for some reason it's missing or it's confusing or maybe two pieces oh wait but it says ground beef and steak strips on the same option like how can that even be or even the standard option that you typically purchase is priced wrong or it's spelled wrong and you're not even sure what's going on and those sorts of problems with managing the accuracy and proper classification of menu items and generally inventory overall has turned out to be a space that's like rampant turns out that that's not a problem that's unique to on-demand delivery catalogs it's a problem that appears everywhere with any digital system and it's another reflection of this General picture of what do we do to create the right balance of fully automated systems and systems with human review it does seem like at first glance it's a really simple problem to try and transcribe menu items you think okay well I'll just put my data in a format like some kind of API and it's sort of fine but then actually thinking about the experience of going to like a restaurant website and for some reason the menu is always kind of buried somewhere within that site in a PDF and it's a font you can't read and you're like well how do I buy anything so yeah scaling that across millions of different restaurants to seem like really quite a difficult problem well I mean it's almost the exception when you have high quality in that space so how does that lead to where we are today well one of the things that we've found in recent work with gpt3 is that a lot of people think about gpt3 in terms of generative text problems like we use this too I'll just say transparently candidly like we actually use gpt3 to write blurbs and pieces of marketing material for our newsletters and we're excited by the novelty and extreme relevance when you ask questions or set up the right prompts to get a an interesting blurb from gpt3 it often leads to like real deep questions like we ask might use chat GPT which is a recent Innovation to say write a blurb for our company that reflects the following values and or write a blog post that's when the interesting part happens right the engagement of humans with generative text it's almost like some of us walk into a meeting without a plan or an agenda and those meetings are inevitably ineffective or you spend lot of time trying to establish ground rules in terms of meeting about a meeting Amazon I think famously has meeting protocols that require people to spend the early part of a meeting reading and then React to what was in the initial narrative that was shared I think that the existence of generative text for marketing use cases leads to higher quality conversations because you already have a framework to start from that's not the example I wanted to give that just happens to be one example gosh Jasper's doing amazing things in that space it's a great company yeah certainly I can identify with the idea that like having a machine right marketing materials is great because it's one of those things where it doesn't necessarily have to be Innovative text it's just sort of saying the right thing and the right tone of voice a lot of the time one of the things that we've found pretty cool pretty exciting is that there are specific problem spaces where given that framework of like managing complex data that requires human review goes through Transformations integrates into third parties systems in that General problem space there are several things that tend to come up like classification problems so it could be that broadly that you have options associated with steak or with a pizza that don't apply to fish or specifically that you may have items in your catalog that have been misclassified so you could imagine a situation where I'm looking to buy a laptop battery replacement and I go and I look at batteries and I see double a batteries and D cell batteries and all kinds of batteries that don't seem to relate to computer batteries and then I go and I look in the different part of the hierarchy of the taxonomy and I go oh I had to start at computer before I could get to batteries that are relevant to me batteries at the top level wasn't the batteries I was looking for and that's just a navigation problem imagine what happens when things are wrongly classified so even if I knew to go to the computer space I might find double a batteries in the computer battery space and I might find my laptop replacement battery over there with something entirely wrong so just broadly I would describe those as classification problems for inventory management or catalog management so we were doing some recent testing for one of those use cases where we had a classification problem and we started with the assumption that we might have to work with a specially trained model to address this particular classification issue but we actually found that like not even a fine-tuned model and I'm sure we can talk about that a bit more if we need to but just plain old stock gpt3 was able to help us meaningfully to solve classification problems there was a specific example we looked at where gpt3 beat our human testers at identifying a particular item I believe it was like a women's makeup like distinguishing between eyeliner and mascara which I don't think I know the difference now I'm embarrassed but I need to go look it up so subject for a different podcast perhaps eyeliner versus mascara maybe my makeup expertise is is poor but yeah that was an example of a situation where when you think about the problem space that I've posed of how do we have accurately classified items in a taxonomy whether that be for an accurate catalog or for an accurate diagnostic assessment for a radiologist classification problems are a significant problem in Tech and we're finding that open AI is amazing work in this space has made the cost and barrier to entry for solving some of those problems much lower and that's just one example I don't want to brag too much on ourselves I do want to say like I think notion is an example of a company that has released some AI features in their platform that address some of the same issues that we see in that kind of inventory space as well like you might want to clean up misspellings you might want to clean up grammar and we've lived in a world for a long time where you think that you might need dedicated tools for that you know and I think the grammarly still exists right like I think there are companies that exist to Target spell check and grammar but increasingly those sorts of tools become table stakes in mature platforms because of the capabilities provided by like open ai's API for gpt3 so if it's super simple for me to say like here's an input can you clean up the grammar and it does and it's effective then every product gets better and those kind of things like for those who haven't seen what notion is doing in that space they have some just really nice little generative text thing a little grammar and spell check thing like those kind of immediately super practical things kind of run at odds with what you hear sometimes in the media which is more obsessed with robots taking over the universe or Skynet or something like the reality is that the AI Tech that's coming out every week these days is really impressive at solving simple practical problems and if you can solve small simple practical problems in a scalable process that sound that you hear is a cash register right you're saving money on every single run and if you're trying to process 10 000 menus or update a hundred thousand inventory items then the ability to take a little bit of the load off is 20 if it's eighty percent these are totally real world viable scenarios for us where we find that problems that used to require humans now can be solved to a reasonable level of accuracy not 100 to a reasonable level of accuracy with commonly available not a lot of customization required off-the-shelf models with human in the loop review well that's like a dream come true for a company like us because it just validates the thesis that technology is best when it's invisible humans are a key part of every meaningful bit of work and for us to put together AI That's going into spaces that we never could imagine it going in before with well-trained highly capable human agents that's magic okay so there's a lot to impact that but it seems like the central message is just that if you can make these writing tasks a little bit easier then it's going to help you scale and so the other businesses who are kind of interested in making use of gpt3 or other text generation tools where do you think is a good place to get started well I think Chad the GPT playground apparently if you look on Twitter everybody's gone crazy over in the last few weeks I even think about like how when you and I first spoke and the time when we're recording a podcast and the time that this is published there's so much innovation in this space that the story will change materially in each of those windows if this is published in a month I fear that people will be going oh I already know about chat of course but Twitter is blowing up right now with examples of people see seeing these kind of transformative interactions they can have by effectively it feels like a conversation where you maintain context instead of it being a single prompt with a single response you send a message get a message in response you send another message so I just would encourage people to Google Chat GPT and go have some fun I think that if you ask me for an example how GPT is changing the day-to-day lives of software Engineers including Engineers on my team if you asked me a few months ago I would talk about kodaks and co-pilot and GitHub and how we're now at a place where Engineers on my team are already using those tools to figure out how to do tasks that are you know machines are good at remembering minutia and people aren't always and I would rather have Engineers who are thinking about architecture and business problems and how to solve those rather than remembering the exact syntax to call a particularly idiosyncratic API that maybe doesn't look exactly the way that other apis look or that there might be some Nuance to how to invoke these other Frameworks or libraries through tools like copilot GPT powered you could a few months ago say I need to write a method that does the following or I need to write a function that does the following and have it pre-populated well the technology has moved so quickly that now you're seeing people have those sorts of conversations with chat GPT that have context and maintain it across questions so there's an example I'm going to bring up like with Elon pushing so hard on the Twitter culture and executing a bunch of layoffs and kind of being a harsh task master in that space some joking tweets popped up with one person providing an example of how they would respond if they had to produce a document for Elon Musk describing what they've worked on this week and they asked a question like chat GPT I work at Twitter and I would like to come up with 10 good ideas that might be worth exploring that would show that I'm providing value as a software engineer at this company and it responds with 10 numbered and relevant results oh cool that's amazing well let's take number nine can you go ahead and generate a document for me that describes how I would use that and include some statistics okay now generate some sample code and then after the sample code was generated you know what that sample code looks too simple can you add some enums and some extra variables and then it does that literally does that right so that kind of interaction wasn't possible at least it wasn't broadly available a month ago I know some people it's very easy to get attached to this Narrative of like fear about jobs being lost but historically you know I don't go too far on a limb here but I'll say that seems like the Industrial Revolution did some good stuff for us and the ability to connect through the internet has done some good things for us and I think that there's always a little bit of twinge of fear associated with rapid technological advancement there also is an excitement and they're really kind of the same feeling it's just how you interpret it so if you feel your heart race a little bit when you think about these kind of things coming into existence well good you're paying attention it is amazing and the implications are complex but what I personally find is that by having at our philosophical Center as a company and even me as a person that knowing that these are tools that enable humans to do amazing things that's what motivates me and excites me at the end of the day we all have Iron Man suits we're all Iron Man absolutely it just seemed like it's a really sort of transformative time and generative AI is really sort of come of age maybe in the last month but I suppose in terms of using it you've talked about a few of these use cases around like automated code we talked earlier about marketing things like that I suppose to the things on a lot of business Minds at the moment are things around productivity increases and cost savings so are there any specific use cases that you think are going to be important in those areas it's hard to say I know that the ones that I see that are particularly powerful relate to some of the examples that I already described of managing complex data sets that require human review and that's a very Broad and general way of saying something that applies to almost every company so clearly there are a lot of e-commerce use cases and clearly there are use cases that relate to supply chain in general I think that one of the things that I've found exciting in the ml space really from like my first experience with doing bioinformatics use cases I really think that there's a lot of opportunity for us to remove inefficiencies and reduce costs and health care and it's an area that's of enough interest for us as a company that we're very mindful of HIPAA and we do work with some clients that have sensitivities related to that I think think that one of the more compelling use cases that I saw recently was related to increased accuracy on classification of medical scans and I spoke to a man who's running a company who one of their primary focuses is on doing analysis of medical scans where there's an AI component and a human review component and the outcomes are fairly measurable and the classification problems are pretty complex so let me unpack that a lot of folks may still have a mental frame of classification problems being really simple like hot dog or not hot dog the Silicon Valley example or color identification or if you've ever done one of those captions where it's like pick the horse that's smiling or whatever those kind of classification problems seem fairly simple and trivial and it's great like we're getting to a place where you're providing training data to make sure that those things are more accurate over time by in engaging with the captcha but I think that the possibilities associated with more accurate medical testing are lower costs and saved lives and that's something that I think it's useful for me to remind myself that cost Savings in certain spaces like it's not just to increase wealth it's actually to enable opportunity and to extend lives so when I heard this example and I walked through it in great detail with the CEO of this company that they were seeing no offense to Radiologists out there but they were able to demonstrably produce more accurate results from analyzing scans through a combination of complex classification with hundreds of potential outcomes and then human review with humans and I know we're supposed to be talking about technology here but the humans piece is just amazing to me that if you think about the work that the work associated with human in the loop processes those types of people they're a different breed they're very smart very retrainable very capable people whose job is to pay a lot of attention very detail-oriented they're kind of like my brethren in the engineering space they're a different breed they think differently they're able to focus on a skill set that still surpasses even the greatest capabilities of AI models which is they're the ultimate Arbiter of whether this scan means this or this scan means that and what I find really fascinating about that is that the psychographic profile the way that those people operate is more important than their existing training so the real heroes in this particular kind of scanning use case it's not the ml scientists or the engineers who built the model although God bless them you know they did something really important but the people who are performing that final review are intelligent High judgment high impact individuals who are saving lives and they're able to do it at a level of oversight and quality that's hard for a radiologist to do because that radiologist has a lot of jobs he's having to deal with the insurance company or bedside manner or Praxis like running the scans whereas this person who sits at a machine looks at the data looks at it very carefully those people who are close to my heart and we have thousands of them in our company we call them agents I think sometimes people confuse when we say agent they think they mean a machine technology but we have thousands of Agents all around the world who log into our platform invisible's platform every day and they solve complex problems and they're very retrainable malleable intelligent people they're The Secret Sauce the Technologies we just kind of take commoditized Tech off the shelf and orchestrate it in the right way you got to have the right people to make sure that we're making the right decisions that's really interesting and exists of healthcare is really is a sort of life or death decision my intuition would have been that the radiologist or the doctor or because they're the kind of the most senior person is going to be like the real expert in interpreting these medical images or test results but actually having someone who's really just dedicated to that one task turns out even if they haven't got like years and years of University experience and sort of Medical Training it's like just doing that one task is going to make them more efficient at that job so one thing that seems to have cropped up a few times here is the idea that you do the machine side first so you've got AI first and then it's a human reviewing what the machine's done is it always that way round or do you ever have human first then AI as the review step it's more often for us in our business that it's human first but let me walk through that a little bit one of the key value props of our platform and our business from the start I think this is just reflects the entrepreneurial ethos we've always been very excited to tell our clients we can deliver results within the first 48 hours and we know we have really bright people I mean we have a platform that enables those people to maximize their talents so it's a very common pattern for us in alignment with our business model to show early impact so typically what happens is that a company comes to us with a problem and they say this is how we're solving it right now this is the frame in which we're looking at this and that problem probably being solved by a combination of overworked employees of a particular company and some third-party system they bought maybe and what we do is we first start with the idea that like we're going to take your process if it's well documented we can encode it into our system very quickly like in an afternoon and at that point what we've done is we've taken what warts and all what your approach is to solving a problem and we're putting it into a system that enables it to scale so if you can imagine like a magical shrinking Ray or an exploding Ray like shine our Ray on on your process and make it scale up to run at orders of magnitude larger but obviously that's not the real path to efficiency what we want to do is apply our expertise so our more typical model is to start with we'll just kind of like air lift your process out of human beings encoded into our platform execute it with our agents and then find Opportunities to optimize so by having a process canvas in front of you where you can literally see every block of work that's being performed the time that it takes to handle each of those requests the cost associated with it it enables us to make very rational decisions about what's in the best interest of our clients there's a key factor and like I told you I'm enthusiastic about code I'm enthusiastic about design but I'm also enthusiastic about business models and I think that one of our secret weapons and invisible is clearly that we charge based on results we charge based on outcomes so a company that whose job it is to bring in an army of people and who's paid by hours like person hours is only going to be motivated to increase that number it's the contractor problem if I have someone come out to my house and I'm paying them you know 20 an hour to fix my deck they're not going to finish early they're going to take every minute it's just in their self-interest but for us because we are charging based on an agreed upon rate for the output we are always incentivized to optimize an incentive to optimize usually comes in the form of a kind of a second wave of automation after we've encoded your process so another thing that's implicit there is that we aren't a satin for Gat company we're a relationship company and the term that we use sometimes is work sharing so when we take on work for a client you don't think of it as Outsourcing some task to some room full of people what we're doing is we're sharing the work we're going to mature it over time we're going to enable it to scale to levels that you're having trouble achieving on your own and while we're doing that we're going to find optimizations that are going to reduce the cost because that's good for us and we have a principle of deflationary pricing which is that you know some people call that volume pricing but for us conceptually what we're doing is we're not just offering a discount on good faith or reducing our margins what we're doing is if you're invested in us then we're invested in you and we will find optimizations that reduce the cost for ourselves and for our clients so how does that relate to kind of the bigger picture of automation first there are certainly situations where we go to a client we understand that their pain point is they have a largely automated process that needs help they will airlift that out too we can take that and run a largely automated process from day one as well but typically the folks who come to us are having problems because they're Innovative companies they're growing rapidly and they've hit a wall with the approach that they're taking which usually is a wall that involves a decision of what tech should be engaged so take your process We'll add the right time okay we've talked a lot about different use cases around the business one area that we've sort of quickly not touched on is how these Technologies affect data teams so how does this obviously like teams are usually familiar with AI but how they make use of like other AI tools or this combination of AI plus humans together the most impactful way that our data teams can engage with the company and driving its success with our own company are in understanding what the Right Moves are to drive the right level of efficiency and quality so we want to make our clients happy and we want a lower cost so when we evaluate tools and we evaluate a lot of tools I've been sharing with you we're like kids and candy stores when it comes to all the different technological advancements whether they be in AI or with openai specifically or with other companies who are doing great work out there the key thing for us is to figure out how do you onboard internalize the right approaches in a way that moves us out of science experiment and into business outcomes that are favorable for the company so as an example like how do you decide to use gpt3 how do you decide to use any particular technology especially new technology where it's there's some element of risk you're sticking your neck out and you've got a client who's dependent on you making the right decisions so we try to be data driven in all of our decisions I think that's everyone's aspiration the details of how you achieve that are sometimes complicated so we've established a model a common process practice for how we do things that typically revolves around using notebooks or Google collab notebooks that allow us to answer business questions with literal technical Integrations embedded so as an example because openai and gpt3 are API accessible based on general principles from the start right the idea is that there's a simple engagement that we can embed in a live notebook and we can work through business problems and do tests and trials together and the same way we would decide to prioritize a specific feature in our platform based based on previous data for how we've experienced pain points so if it's like okay we find that we're spending too much time in this stage because of X Y and Z because of poor training our hypothesis is we add training to this number of people we see the outputs and we measure them it's the it's the same thing here and the key though is to kind of pull AI ml work out of the lab and into the factory floor you have to have a clear way to productionalize that I'm still hearing sometimes these scare numbers of like 50 percent of trained models never go into production and generally it to me is not a different problem than has ever existed before in the technology space it used to be there were the same scary numbers about all IT projects and large corporations oh half of them fail and they're all late in reality what we need is we need to have an agreed-upon set of measurable factors that make a difference to the business that we're all aligned on like I mean let's increase our gross margin let's increase our Revenue when you look at those specific problems and you are factual in your approach and you think about what the literal impact will be and you can measure it and observe it in the form of processes and you can a B test it against human results and you have a lot higher confidence in deploying those so for me the question is how do we make smart decisions with technology and the answer as it relates to AI is you try to do the same things you do elsewhere which is like make sure that there's a business case understand the cost understand the impact and if the cost benefit analysis is on your side you go for it you just make the right decision that seems a pretty sensible approach is like do the same as what you do elsewhere see if this is going to actually benefit you try it and if it doesn't work I guess move on or do something different you'd be surprised that how many people don't think of it that long AI is really cool I need to sprinkle some magic pixie dust on my platform let me go use this just because it would be amazing if that worked I wish all right so I know making predictions is a bit of a mugs game but gpt3 we've sort of established it's pretty much a game changer Essay with chat GPT now there are sort of rumors that gpt4 is coming out sometime in 2023 so we're sort of at a tipping point of having very useful generative AI so can you talk about like what you think your predictions are for the effects of this on businesses this sort of ever increasing power of generative AI well I'm going to make some boring predictions not because I'm scared to stick my neck out but because I think that there are some things that are pretty straightforward I do believe that there have been some people who've said that the internet hasn't made a massive difference on actual like worked hours or productivity even and what I generally tend to believe is that whatever the field the hype bubble pops and the concept that like overnight we're going to have autonomous 18-wheelers all of our interstate highways and that every truck driver in the country will be unemployed like those kind of predictions don't tend to come true and sadly like the productivity enhancements sometimes don't come true so I'll my modest prediction is simply that there will be widespread adoption of AI Technologies and it will become normalized so what that means is that people will find practical ways to improve their margins by 10 percent here and there and that they'll find that it's not so scary after all and they'll forget why we were talking so excitedly about it in the first place my other prediction which is much more idealistic and hopeful and it's probably going to be wrong is that at some point in the near future what the advancement of Technologies in this space is going to start to actually effect in the real world implementation of people's jobs is that their lives will be better and that they will be doing less grunt work and be doing more high value work and that they'll be happier in their jobs and they'll spend more time with their families and they'll feel more of a sense of peace and well-being I realize that's wildly idealistic and optimistic to say that I have to say that's not what the technology revolution has brought and some of us are ready for it you know we believe that it's about time for the fact that technology exists to help us to solve problems and do the work that we used to do or spend a bunch of time doing it's about time to take a few of those hours back for ourselves absolutely shorter working week and a happy life is all you could wish for really I truly believe it super that is a little bit General though so I'm not quite letting you off the hook so just is there anything you think that like doesn't quite work yet but it will do in the near future are there any things that are sort of that you see are just around the corner well actually I think the generative images and texts are somewhat at the level of Technology demo and one of the things that I find exciting and interesting is I'll just take a few examples that are happening around me right now so we have a PR person Andrew works on our team who's using gpt3a to generate text and what he's doing by doing that is modeling a sort of behavior that will change the future career path of other people in that role and they're going to see their identity and their career development in a different way and we have a designer on the team who's not afraid of Dolly who's not talking about how it's not real art or being defensive and scared but seeing it as a way to make it easier for him to iterate through ideas and have a scratch pad and I think that right now those sorts of solutions are part of a creative brainstorming workflow but I do think that they will become more part of a production workflow so right now we in our internal tools there's some places where there's imagery and artwork and in the past those were generated through a designer sitting down talking with the team maybe coming up maybe pulling some stock photos you know the standard like group of people around a conference table all pointing at something and what where we are now is that we're using generative Tech like dolly or stable diffusion to create images that then we react to and adjust and touch up and I do think that we'll get to a place where there is an understanding of I don't really like the term prompt engineering but an understanding of the way that we interact with these powerful models to get the outcomes that we want I know that sounds super vague and you're not gonna let me off the book so I'll be real specific one of the things that I think is really exciting about generating images from text is developing text style guides that produce predictable result sets so as an example for us we have started to iterate on a standard way of how we communicate in terms of constructing prompts in order to deliver outputs that are in alignment with our style I saw an artistic example of that in New York last week I saw an art opening where there was a piece of work three artworks three images that were created from different sections of a poem by William Blake and I found that tremendously inspiring to think about how poetry in William Blake's words creates these visual images that felt very blank and when I think about what we can do in the generative image space as designers it's more about honing your communication and your language rather than just honing the way that you move a mouse or a paintbrush and I think that what we will have I hope this is a concrete enough prediction is that we will create more Andrews and Noah's and folks like we have that I'm seeing grow on our team that there will be a whole class of people who have internalized the use of these tools and become experts in them they will be virtuosos at generative art generative design generative text and they will be able to do way more than old people like me can do because they won't be stuck with the old programming that does sound pretty amazing and it would be great to have like enough people like widespread adoption of these tools that there are enough people that can use them well but I really like that point you made about having a style guide for prompts in order to be able to have reproducible like images and now I'm thinking well maybe you just need to get chat gbt to generation style guide and then feed that into stable diffusion it could be fun all right so just to finish up do you have any final advice for people wanting to adopt generative AI I think don't be scared start playing the excitement of new technology is something to not to be feared like come with a beginner's mind what are the cool things we can do with this come with a playful mind and think about all the possibilities I think it's very easy to get caught up in prevailing narratives or discussions of AGI or like there's not an artificial intelligence that you're communicating with it's you with a cool tool and have fun and play with it and don't be scared of it because it's probably going to help your business it's probably going to help your life all right brilliant thank you very much and thank you for your time Scott thanks great to be here you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next time\n"