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The Future of AI: Navigating the Data Bottleneck
The era of AI implementation is progressing at a significant rate. Organizations are adopting advanced models, such as GPT 4 by OpenAI for advanced voice modes and SAM 2 for video segmentation. Large language models (LLMs) are developing into perfect conversationalists. However, the lack of structured data impairs their potential for proper, fact-based conversation. Smaller businesses and enterprise data consortiums, in particular, might contend with the cost challenge that makes data collaboration difficult due to data being in silos.
Structured vs Unstructured Data*
Long-standing organizations have decades of experience with AI and machine learning. However, applications of unstructured data are a relatively recent objective, primarily with generative AI and large language models. Differentiating and understanding structured and unstructured data is crucial.
Harmonizing Data Across the Ecosystem
The unification of data sources and datasets is a significant hurdle due to the considerable cost of managing complex data infrastructures. The financial aspect often impedes data consortiums from realizing their potential. An organization-wide effort towards standardizing data across different sources and creating a platform that automatically categorizes the input data would enable easy access and encourage innovation.
Data – More than Just Numbers
Data tells a story, and understanding the importance of explaining and contextualizing data is key. A well-prepared dataset serves several applications such as setting up support chatbots that predictably access data across multiple sources and add metadata for context provision. Starting with a goal in mind is essential to effectively leverage data.
Exploring New Avenues of Data Collection
Reimagining data collection involves looking beyond conventional sources. Large scale studies, leveraging knowledge from employees or repurposing machine-generated data can be a viable path towards data acquisition. Crowdsourcing data is an unconventional yet efficient way of garnering the required input.
Memory Efficiency in Large Datasets
As datasets become more extensive, strategies to improve memory efficiency when handling these datasets can be employed. These strategies can help manage the increased strain on systems caused by massive data inflow.
Data Quality - An Uphill Battle
While data collection is a significant consideration, the quality of data is also crucial. Debate over the data quality varies largely in the current scenario, especially when synthetic data comes into play. Synthetic data, once an object of skepticism, has proven to improve the quality of large language models.
Collaboration - The Way Forward
The cost and implications for quality push the next-generation AI towards high-quality, human-generated data, an area that currently only big companies can consider. This potentially puts smaller firms and community-driven initiatives at a disadvantage. Hence, fostering collaboration can help overcome data quality challenges, even in organizations with limited data science resources.
Business leaders must not fear exposing their data for more expansive applications. Collaboration is key to conquering data quality challenges. Prioritize obtaining and contextualizing data early in the implementation phase and drive innovation by making data more accessible and context-oriented. It is now more vital than ever for businesses to embrace the technology that can help extract meaningful insights from their data. Reimagining data collection and stepping up the collaboration game can potentially pave the way to a data-centric future, thus ensuring businesses stay ahead of the curve.
Topics Covered in This Episode
1. Data and Large Language Models (LLMs)
2. Practical Data Strategies and AI
3. Data Quality Issues
Podcast Transcript
Jordan Wilson [00:00:16]:
Is data the next big bottleneck when it comes to generative AI? I mean, we've heard about a lot of these things that slow AI down when it comes to companies actually using it. Right? First, it was, should we use generative AI and large language models? And then it was, how do we use it? Well, now that most companies, especially here in the US, understand that they need to be using generative AI, is data that next big bottleneck? We're gonna be talking about that today and answering those big questions and your questions. So, thank you for tuning in, and this is Everyday AI. What's going on y'all? My name is Jordan, and I'm the host of Everyday AI, and this is for you. This is your daily livestream podcast, free daily newsletter, helping us all learn and leverage generative AI to grow our companies and to grow our careers. So I'm excited today to talk about the elephant in the room and how data will be AI's next big bottleneck. Before we get started, as a reminder, if you're joining us in the podcast, thank you as always. Check out your show notes.
Jordan Wilson [00:01:19]:
You can always read our recap for today's show. We always share more insights as well as what's going on in the world of AI news. Before we get to the AI news, as a reminder, also in the newsletter, check out our thanks a million giveaway to celebrate, everyday AI hitting a 1000000 downloads. You don't want to miss that giveaway. Alright. Let's get into the AI news for today. So OpenAI has launched advanced voice mode for a select few ChatGPT plus users. So OpenAI has finally begun rolling out their advanced voice mode initially available to only a small group of ChatGPT plus users.
Jordan Wilson [00:01:57]:
So this new feature, known as GPT 4 it's under the GPT 4 o kind of umbrella. It delivers hyperrealistic audio responses and is expected to be available to all Plus users by fall 2024. When first demonstrated in May, g p t four o's voice named Sky caused quite a stir due dirt due to its striking, resemblance to actress Scarlett Johansson, who subsea, who subsequently took legal action. OpenAI has since removed the Sky Voice and delayed the release to enhance safety features. Advanced voice mode allows chat gbt to talk and listen using a single multimodal model, reducing latency and improving conversation quality. This alpha version that's rolling out will not include, though, the video and screen sharing capabilities shown in the OpenAI's spring update, which will be released later. OpenAI's claims that this new, advanced voice mode can detect emotional, you you know, emotion in your voice, such as sadness or excitement, and it is wild. Right? If you've seen the demos, it is, I think, a whole next step in looking at generative AI more as an assistant than just a large language model.
Jordan Wilson [00:03:12]:
Speaking of models, Meta has released a new multimodal AI that can segment video in its SAM 2. So segment anything model 2, SAM 2 for short, has been released by Meta, extending the capabilities of the original SAM to video. So the new SAM 2 can segment any object in an image or video and track it consistently in real time across all video frames. Pretty, again, pretty wild breakthrough technology. So for that, we'll have more in the newsletter. Speaking of Meta, also, they revealed AI Studio for personalized chatbot creation. So Meta, the parent company of Facebook, Instagram, and WhatsApp, has just released a new suite or a new tool called AI Studio aimed at enabling users to create and share personalized AI chatbots. So Meta's, Meta introduced this tool that really just allows anyone.
Jordan Wilson [00:04:11]:
You can go on their platform, and we'll have the link, you know, in our newsletter. Drag and drop. You don't have to know how to code. And, also, you can use these in Instagram. So Instagram creators can use these AI characters to handle common direct message questions and story replies acting as an extension of themselves. And, obviously, this new AI studio is powered by Meta's llama 3.1, their latest and pretty pretty impressive open source model. So, pretty big news there, from Meta and ChatGPT. There's always gonna be more, you know, midjourney 6 point 1 dropped, AMD's, big stock jump.
Jordan Wilson [00:04:53]:
We'll have all that in the newsletter. Alright. So, enough about AI news. Let's go ahead and get to the big topic for today, which is how is data going to be slowing companies down and what we can ultimately, do about it. So, please help me welcome on the show today. I'm excited to have, there we go, Matt deFries, who is the founder of Nuke, Nuke.ai. Matt, thank you so much for joining the Everyday AI Show.
Matthijs de Vries [00:05:23]:
Super excited to be here. Thank you so much for for having me, and I'm really looking forward to just have, fun talking about data today.
Jordan Wilson [00:05:31]:
Alright. Yeah. Same. And and, hey, for our livestream audience, appreciate y'all tuning in, from from Tara to Rolando and Fred and Douglas and Daniel and everyone in between. If you have a question about data and AI, please get it in. Now before we dive into the topic, Matt, tell us a little bit about what you all do at Nuclei.
Matthijs de Vries [00:05:51]:
Yeah. So we are building infrastructure for everything around data. That means that, we identify a couple of problems in many different industries where, you want to innovate on data. Imagine you want to do something with AI. Even if it's generative AI, you need data for that. And that data is usually locked in silos. It's in many different data sources. You have to write a lot of adapters.
Matthijs de Vries [00:06:14]:
So we, provide the layer to have generic access to all the data without having to deploy new infrastructure, having to deploy another ETL or whatever. And, additionally, we make it very easy to add context to the data so that that data is understood at the same level across all data sources or data sets so that you can innovate very easily on top of that, create many different kind of data pipelines that can eventually lead to the creation of novel AI models or to train, existing models, including LLMs.
Jordan Wilson [00:06:48]:
And, you know, you kind of brought up a very good point there, and that's kind of the the crux of this show is is data is so important for large language models. Why do you think right. Let's just kinda skip to the end. Why has, data become such a big bottleneck for AI implementation? Because, you know, before this big generative AI wave, right, we all heard, oh, data is is the next oil or or whatever it is. So, you know, we've known about the importance of of data for for decades. So why is it still, this this big bottleneck for companies to, implement generative AI around their data?
Matthijs de Vries [00:07:29]:
Yeah. I think, you know, first of all, I think we already passed the point of calling data as the new oil. I think, really, data is already our oxygen because, frankly, with many of the things in our life, we can't live without data anymore. Like, we can't function as a society normally anymore without data. Everything that we do by using our phone, taking public transport, we use data. Right? So it's really the new oxygen. And I think that introduces a new problem because the data landscape is so extremely fragmented that there is not a lot of access to it. Like, it serves a purpose and that's it.
Matthijs de Vries [00:08:07]:
And that leads to a lot of siloed data. And that siloed data, that boring structured data, that's actually very necessary for the next generation of generative AI to hallucinate less. Because if you look at the generative AI landscape today, you know, it tries to come up with really proper answers based on a conversation that makes sense. Right? Based on the next word prediction or word part prediction, but without taking the really factual data into account. And having more access to structured datasets will definitely help improve on that level.
Jordan Wilson [00:08:43]:
And we talk about this a lot here, on the show, Matt, but maybe for people who are tuning in for the first time or don't know the difference, can you explain simply what's the difference between structured data and unstructured data?
Matthijs de Vries [00:08:55]:
Absolutely. So structured data is best to be compared with what you have in Excel. So if you have a boring Excel where you have the first row, you have just column names, and below that, you have data numbers, yes and no, dates. You know, it's predictable. The next row is the same as the row before that. It's structured. Unstructured data is typically, what you would find on an Internet forum, a block article. Like, it's it's text, but it's not formatted in a specific structure.
Matthijs de Vries [00:09:25]:
So you have to understand the context or you have to take the the the whole text as a text, which makes LLMs perfect conversationalist, but the lack lack of structured data make them very lacking a proper, fact based conversation. So that's the difference.
Jordan Wilson [00:09:45]:
Yeah. And and, yeah, thank you for that explanation. And I think that companies that, you know, have been using AI because, you know, AI is not new. Machine learning is not new. It's been used for many decades. But this concept of of using unstructured data, I would say, is newer, right, with generative AI and large language models. That's where they truly shined being able to work with this unstructured data. You know, with that in mind, you know, a great question to start off the bat here from, Douglas.
Jordan Wilson [00:10:11]:
So thanks for this. So he's asking, how do you recommend people who do not know where to start leveraging their data? That's great. Yeah. Companies, if if if there's maybe smaller and they haven't had a huge data team for a long time, where do they start?
Matthijs de Vries [00:10:26]:
I mean, it's I mean, you have to start with a goal in mind. Right? If you want to leverage your data, where do you want to leverage for? So let's assume that in most cases, the data will have to be used for a large language model integration. I think there are a lot of use cases for support chatbots and stuff like that. That is where companies start nowadays. And, having a predictable access to the data across multiple datasets, data sources is good start, and adding context to that data. Like, going back to an example of the spreadsheet, like the Excel spreadsheet where you have just a bunch of columns and rows, that first column name, that's your data point, the the column name. And that tells you something about that data, but not everything. And this is why we think that metadata is extremely important that further contextualizes the data.
Matthijs de Vries [00:11:22]:
In fact, we did a proof of concept with, integrating an LLM on top of our data infrastructure so that you could prompt LLM with a question, and it would see what kind of data source or dataset would it need to answer this question or a combination of different datasets that will give back that fact based answer. But it wouldn't do that properly without having that additional context on top of that data. So back to that Excel spreadsheet, you have the column names. Now imagine that you add descriptions to that column names. You can explain what kind of limitations that data has. Like, if it's if it's just a column full of numbers, what do these numbers mean? Is there a limit? Is there a range? Are there exceptions? So if you start deeply explaining that, that kind of context will lead to better answers. So it's having generic access to, the different datasets that you already have and, adding context to, to to that data.
Jordan Wilson [00:12:17]:
Yeah. That's that's a great point because all data, it does have a story. It has a meaning. It it it has a reason why it can help or hurt your business. So, I love what you said there, Matt, about, you know, essentially start explaining your data, which, you know, oddly enough is something large language models can can help with. Right? But but even before we get into that, I wanna, you know, hit reverse here and and tackle this problem even a little bit more. Right? So, you you know, I I I love what you said about data isn't oil. It is oxygen.
Jordan Wilson [00:12:51]:
Right? Businesses need data in order to breathe and survive. But why are companies not able to, you know, kind of grab their data out of silos. Right? Because, yeah, companies will you know, I we we consult with companies all the time. They're like, oh, here's our Salesforce data or, you know, here's data from this, source, but they just live there. So why is that a problem, data being in silos, and how can companies start to, kind of fix that issue?
Matthijs de Vries [00:13:20]:
Yeah. Actually, so companies can already collaborate and innovate on data. There are a lot of bigger companies, they already leverage the data, and smaller companies do too. I don't think that there are a lot of tech technical limitations. I think the biggest limitation is cost. Like, it's very expensive to do something with data, very expensive to collaborate with others, especially enterprises that have really complicated data infrastructure in very big data ecosystems, with a lot of rules and exception and standards and frameworks, and it's very hard to be interoperable with these standards as a smaller business. So this where cost so even for the bigger enterprises, like, I've talked to a lot of enterprises about, data consortiums where you have a group of enterprises or even also with small, medium business, even cities, governments, they're inside of this data consortium, then want to work together on a use case or platform creation on top of all the collective data that they put in. The biggest problem why most of these data consortiums fail is funding.
Matthijs de Vries [00:14:26]:
They stop being funded at some point because it's getting too expensive. And why is that? Because they have to bring in one of the biggest consultancy firms like Capgemini or Cognizant inside of this data consortium to build all the adapters and, build other expensive data pipelines. So most of this money is going into the hours that are written by the consultancy firms. And we are speaking about 100 of 1,000,000 before something tangible is, coming from the ground. So we need something that is really low effort and very low barrier for both enterprises, the small medium businesses to start leveraging the data without having big investment. That also allows trying new things without investing a lot of things, without investing a lot of money so that you can get started and test out things early.
Jordan Wilson [00:15:15]:
How should you know, let's say someone here works at a, you know, medium sized company, you know, here in the US. So, you know, they're they're not working with a Capgemini, and and maybe, you know, they have a decent hold on their data, but it is still a little bit siloed. What questions should they be asking themselves, or what steps should they be taking specifically when it comes to better preparing, and and better organizing their data for large language models?
Matthijs de Vries [00:15:46]:
Yeah. So I I suggest to find a way to, first, have the same type of data across all the data sources and data sets. So what I mean is that if you have CSV files there, you have a MongoDB there, and you have a MySQL DB there, You all have to access that data in a different way. So first, you know, try to get a layer, and this is what we from from Nuclei, specialize in focus on, is have this layer where, all this data is onboarded very easily, and you can just have one SQL query to access, you know, a CSV file, join it with data from a MongoDB without discriminating, what that context of the data engine is. And that way, you have, you know, that first if that first step is done and it doesn't cost you a lot, you're already halfway for the innovation because you have you can tap into all the data and start playing around with it. A good data scientist is is crucial to have a board of.
Jordan Wilson [00:17:39]:
You know, a a good question here from from Tara. This one's a little specific, Matt, but, I I think it begs a good question. So she's asking what strategies can be employed to improve memory efficiency when working with datasets containing millions of rows? Yeah. That's a great question, especially when these datasets become so big.
Jordan Wilson [00:18:17]:
What steps can companies be taking, if any, to to to kind of counteract this issue?
Matthijs de Vries [00:18:23]:
So to be honest, that's not really my specialty. So I don't know if I would be answering this question properly. But there is a lot of free, free or very cheap technology out there, that can work with this data very efficiently. I don't think that, datasets containing millions of rows should be of any limitation anymore.
Jordan Wilson [00:18:46]:
That's a good point. Alright. Let's let's get to something a little bit more of your specialty which, you know, I wanna get a little bit more into the data that makes up models themselves. Right? You know, if you follow large language models or or read our newsletter, you know, you saw that there's been this kind of divide recently about the quality of the data in the models themselves. Right? Because when companies are trying to really implement, you know, generative AI across different, sectors of their organization, you You know, they're not only trying to bring, you know, maybe fine tune or bring in, their own data with RAG, but, ultimately, they are relying heavily on the actual dataset of these large language models. But we've heard recently with synthetic data is that, you you know, we've thought it was bad, but, you know, Meta and others have come out and said, no. You know, when you use synthetic data, it's actually can help your models improve better. What's what's your thoughts on this, Matt, and and what should business owners and decision makers be aware of, when it comes to synthetic data in large language models?
Matthijs de Vries [00:19:50]:
So actually, in fact, I've always been a believer in the value of synthetic data, but I also have to honestly admit that it kind of shocked me when I read last week that the quality of the LLM actually improved by bringing in more synthetic data than without. And it was actually for me, it was counterintuitive. I I I would not, you know, guess this. So for me, you know, it's the question immediately came, like, this is largely about unstructured data. And I'm very curious because we actually work together with a partner that converts, existing structured data into synthetic structured datasets in order to scramble the data so that you cannot, you know, get personal identifiable information out there, and the statistical qualities remain the same in that synthetic data. So that was always the goal. That was a very clear use case for synthetic data. But now I'm very curious, and we haven't we haven't got there with testing it yet.
Matthijs de Vries [00:20:51]:
But now I'm very curious if that structured synthetic data, bringing more of that in into AI model training, if that will also yield the same results.
Jordan Wilson [00:21:01]:
Yeah. Same. Right? I've I've I've always had this this thought in my head, not just with synthetic data. And, you know, if you are new here, that's just essentially, you know, artificially generated data that mimics real world data that is then used in models. Right? So you can make models a little bit, you know, technically cheaper, bring down inference costs, etcetera. But I've always said it's also gonna be a problem. Right? If so much of the, the the data and the content in these large language models is ultimately just kind of regurgitated. Right? Studies say that more than 90% of new information posted online by 2026 is going to be coming from large language models.
Jordan Wilson [00:21:43]:
Even on on that end. Right? Just the the the data quality of what goes in outside of, you know, synthetic data, Matt. Is is, is there problems there? Is there problems with, you know, hey. Yeah. All these large language models scrape the Internet. And is the Internet just getting a little worse because people are overreliant on large language models?
Matthijs de Vries [00:22:03]:
So my my expectation would be that because that's also a kind of a, type of synthetic data, all this generative AI created data. Right? So having more and more of this data might lead in the short term to better models at first, but we will hit the hit the ceiling for sure. And, by then, more high quality data, and I think that's also immediately the elephant in the room that we wanna discuss. Like, the bottleneck of next generation AI is gonna be around data because it's gonna be more difficult to obtain high quality and, true data that has been generated by human. Right? And it's gonna be, and and I'm also gonna be very philosophical, in in the moment. Because we when when we have all of this structured sorry, unstructured data out there that's human written, it's it starts to become more valuable. And a problem that we've seen so far is that the the data that has been scraped hasn't been paid for. So you saw New York Times suing OpenAI for using the data, without paying for that.
Matthijs de Vries [00:23:13]:
So it's gonna be increasingly difficult for, creating budget next generation budget generative AI models, that that are gonna be, able to go beyond that ceiling that, that I just introduced. So it's gonna be a problem that, I mean, I find it a problem that only the big companies will be able to buy their way way out of there. So you will have the large, organization like Meta, like OpenAI, that will have the funding in order to, pay for that data. But the smaller ones, the open initiatives, community driven initiatives, will hit that ceiling and will be very hard for them to pass. And now, for a little bit of philosophy, you could argue and this is about synthetic data again. You could argue that everything humans is in one way or another already out there. But it just had because a lot of innovation is just bringing existing things together, and to come up with something new. So in that sense, it makes sense to say, like, it it just it doesn't matter if there is too many LLM generated data or too many synthetic data out there.
Matthijs de Vries [00:24:33]:
It's just how you're gonna mix that together in order to find new creative ideas, new innovations. So this is a little bit of of philosophy for me.
Jordan Wilson [00:24:43]:
Hey. Let's let's go down this this this philosophy road. I like that, you know. Everything that you can come up with as humans has already been out there. You know, that's a good point. But I think maybe, you know, especially if we're talking about smaller smaller companies, smaller to medium sized companies. When they look at data, right, they think data science, they think business intelligence. And, you know, oftentimes, you know, smaller companies don't have big resources to devote to that piece.
Jordan Wilson [00:25:12]:
Right? When they think of data, they think of big data, and they say, oh, you know, that's not necessarily for us. So maybe however we use large language models is is going to not be as impactful as those companies that have their own, you know, first party data. How can even smaller or medium sized companies, still kind of, you know, break down this elephant in the room and and and break through this bottleneck and still create valuable, you know, first party or first company data? How can they do that if, you know, going back to your, philosophy question there is, you know, essentially saying, hey. All this data that, you know, that big companies have can kind of already exist in a in in a way, shape, or form.
Matthijs de Vries [00:25:56]:
Exactly. Exactly. And I think I think the crucial, key component here is collaboration. Right? I don't think, because I really believe in, that ceiling and that the smaller initiatives will hit that ceiling much quicker than, the companies like Meta and OpenAI and stuff like that. But through collaboration, it will be easier to break through that ceiling. And, also from nuclear, we chose collaboration over competition. We have around 40, 45 partners that we collaborate with, each, you know, playing in in unique role in, in what we try to to to solve, and what we what we, what we offer.
Jordan Wilson [00:26:37]:
You know, I have a hot take, Matt. And, you know, normally, I don't do this, but, you you know, we're talking data and we're talking about how important it is for, you know, companies and and for large language models. One of my hot takes is this, is that we're gonna start looking for data in places that we generally wouldn't look for, especially when it comes for our company or, you know, large language models. That could be, I think, large scale university studies. It could be, I think, talking to employees, right, and and capturing their knowledge and turning that into, unique first party or first company data. Is that a crazy thought to say that that could be a a not a future, but or or or the future of of data when it comes to large language models and generative AI. But is that a a path forward that's worth exploring, just bringing in this more human, human level data?
Matthijs de Vries [00:27:33]:
Actually, I would argue that this is a hot take, but it's just simply common sense, man. I mean, look. I think crowdsourcing data, which is, kind of, you know, really broadly describes what, what you describe. When you ask something from your employee, it's like kind of crowdsourcing data. I think crowdsourcing data is gonna be much more important in, in the in the short term future, and it's gonna be a much bigger thing, whether it's about polling or stuff like that. And when it comes to trying to find data out there that's not looking into place that you normally would, I think there are some cool examples out there where, you know, once I talked to an organization, they had a factory where they were baking bread, and, there was data generated by the machines. And that data was used in order to monitor the machines and see when they had to have maintenance. And then some, software company was hired that was, really specialized in innovation, typically innovation on data.
Matthijs de Vries [00:28:38]:
And they said, let's look at that data and see if we can find patterns that would indicate, a better quality of, bread that is being baked. So then they used that data for the purpose of improving the quality of the food instead of the initial purpose, which is always served and that the data was, like, thrown away. And that's just really cool. I mean, that's how you look at data where you normally wouldn't look, and I think that's kind of multiple purpose data. It's out there everywhere already, and we will start scrambling more and more to get access to that data. And the funny thing is is that it's not necessarily super valuable data. It's not like you will have to you won't have to sell it for big bucks. So access to it should hopefully be very easy.
Jordan Wilson [00:29:25]:
So, Matt, we've covered a lot in today's episode. We've talked about how data is not oil. It's oxygen. How companies should be giving a story to their data, pros and cons of synthetic data, and even, you know, this concept of crowdsourcing data. So, you know, as we wrap up, today's show, how what's kind of your one biggest takeaway for business leaders, decision makers out there, in order for data to not become a bottleneck in their AI strategy?
Matthijs de Vries [00:29:59]:
Again, like, don't be scared, to put your data out there, and focus on getting this data as early on as possible in a generic way and immediately put in context to that. If if you're a small business, you're just starting to, work with data, start doing this already, like, today. Even if you don't have an AI or data strategy yet, like, start doing this. And collaboration collaboration is key.
Jordan Wilson [00:30:28]:
I love it. So so many great insights in there. And, yeah, like, I love this. Data is not oil. It is oxygen. So if you want your business to grow, you gotta know your data, and you also have to keep tuning in to great episodes like this. So, thank you so much, Matt, for joining the Everyday AI Show. We appreciate your time.
Matthijs de Vries [00:30:47]:
The pleasure is all mine. It was super cool, man. Thank you so much. And everyone who's watching right now, have an amazing rest of the day. Alright.
Jordan Wilson [00:30:54]:
And, hey, as a reminder, a lot of great information in there. We're gonna be recapping it all in our newsletter. So make sure you go to your everydayai.com. Sign up for that free daily newsletter. And, also, while you're there, make sure if you haven't already joined our thanks a million giveaway. Appreciate y'all tuning in. So we'll see you back tomorrow and every day for more everyday AI.
Jordan Wilson [00:31:15]:
Thanks, y'all.