Everyday AI Podcast – An AI and ChatGPT Podcast - EP 203: Translation in the World of AI - Will we have a job tomorrow?

Episode Date: February 8, 2024

If you want your company to compete on a global stage, you need to be able to speak to global customers. It's something that we may overlook but there's a whole industry dedicated to translation. Olga Beregovaya, VP of AI at Smartling, joins us to discuss how AI is changing the translation space and its effects on related jobs.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode pageJoin the discussion: Ask Jordan and Olga questions on AI and translationUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:02:00 Daily AI news04:25 About Olga and Smartling06:57 Translating content globally involves technology and linguists.12:54 Large language models successfully deployed for multiple applications.14:32 LLMs and machine translation have pros and cons.19:45 Rapid changes in translation industry due to AI.22:16 Ethical considerations in translation demand heightened vigilance.23:38 Ensure ethical AI deployment through training data.29:45 AI in language used for production and vetting.Topics Covered in This Episode:1. Role of AI in Translation2. Large Language Models in Translation3. Change of Jobs in the Translation Industry4.  Ethical Deployment of AIKeywords:AI in translation, job opportunities, democratizing translation, insufficient training data, reskilling, personalized language learning, learning disabilities, vetting language accuracy, Smartling, global communication, multilingual capabilities, large language models, generative AI, machine translation, fluency, idioms, metaphors, context, subject matter expertise, fact-checking, validation, project management, data analysis, ethical AI deployment, language bias, prompt engineering, strong language skills.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. If you want your company to compete on a global stage, you need to be able to speak to global

customers and global clients. I think a lot of us don't understand how that works, but there is an industry that makes sure that we can all speak together. And that's the translation, right? So I'm very excited today to talk about how AI is going to impact that translation industry. Are there still going to be jobs there tomorrow? All right, we're going to be talking about that today and more on Everyday AI. Welcome.

My name is Jordan Wilson and I am the host of Everyday AI and this is for you. We do this literally every single day bringing you a live stream podcast and our free daily newsletter helping everybody people learn generative AI and how we can all leverage it to grow our companies and to grow our careers. So yeah, maybe your company is ready to compete on a global stage and you're worried about How can we take our language? How can we take our product and our services and better cater to a different country, to different regions?

So I'm excited to talk about that. But first, before we do, let's go ahead and go over the AI news as we do every day. And if you are listening on the podcast, thank you. Make sure to go to your everyday AI.com. Sign up for that free daily newsletter. And if you're joining us live, like we have Tara joining us from Nashville, Woozy from Kansas City, Brian from Minnesota. We got everyone join it. Jason, thanks for joining us from Florida.

Make sure to get your questions in. What questions do you have, like maybe Maybrit here, who's recently worked as a freelance translator? What questions do you have about AI in the world of translation? Make sure to get them in. All right, let's go over what's going on in the world of AI news. There's actually a lot today, y'all. So first, LinkedIn is bringing an AI chatbot to job seekers with LinkedIn premium.

So LinkedIn is rolling out its new AI powered features to help job seekers, navigate the modern job application process and gain insights into job opportunities. So the new features include an AI-powered chatbot for job listings and personalized career advice based on a user's LinkedIn feed. These new tools offer advice on how to improve your profile and stand out to potential employers. So right now, these features are just being rolled out and it's only two premium users for now. Speaking of new updates, we have new AI image editing features. features rolling out on Microsoft co-pilot. So Microsoft is updating its co-pilot platform with a new

design and enhanced image editing capabilities, bringing sustained growth to their edge browser and their Bing search engine. So the focus is on productivity and creativity rather than one competing with Google for market dominance. So so far, five billion images have been generated through the co-pilot platform since it's launched a year ago. It obviously uses the Dolly image generating powers from its partnership with open AI. All right. Last but not least, Google Bard is officially gone. Well, it's just kind of changed its name.

Google Bard is gone. Google Gemini is here and as is Google Gemini advanced. All right. So Google has released its new updated AI chatbot, Gemini Advanced, as part of their Google One Premium subscription package. So it is kind of comparable to Open. AI's chat GPT Plus and promises enhance capabilities for understanding and completing tasks. So access to Gemini Advanced is available through Google One's new AI premium plan,

which costs $20 per month. Right now, though, hey, speaking of translation, Google Advance is currently only available in English, but may expand to other languages in the near future. So, yeah, if you log on, probably sometimes today if you use Google Bard, you're not going to see it anymore. You're going to see Google Gemini. So that is the base model. And then the Gemini advance $20 a month, bringing you a more powerful version and using Gemini Ultra 1.0 versus the free version that just uses Gemini Pro.

All right. So many buzzwords, so many words. Speaking of words, we got to learn how to translate them all, y'all. And talk about specifically how is AI changing how we communicate on a global stage with global audiences. All right. So I'm excited for this and please help me welcome to our show. There we go. We got her.

All right. So Olga Baragavaya is the VP of AI at Smartling. Olga, thank you so much for joining the Everyday AI show. Thanks for having me. All right. And tell me a little bit what you do as the VP of AI at Smartling. I was actually listening to a description of another company that defined themselves as the trust. layer between everything that's happening in the wild west of the AI world and the actual end users.

I would think about smartling and what I do in the exact same way, right? There's so much happening in the world of AI. Large language models are called language models for a reason, right? They're built of language. So what we do, we actually help our customers navigate the waters of language and global language and help plug in AI and make sure that they harvest the benefits, right? So actually, one of the taglines for recent industry presentation was from frenzy to trust, and that's where I would probably put us.

So that's in short. I mean, the shorter answer is we provide AI powered translations. Okay. I love that. And maybe tell us a little bit for those of us who maybe don't understand how this industry works. Like what in general is translation? Because I think sometimes people think of, you know, oh, is this just Google Translate? Oh, is it just when you go to a website in your browser and it's, you know,

all of a sudden it's automatically translated into a new language? Like give us a little bit of background of how the industry in general works. Is it all just humans like yourself who are reading something in one language and making sure everything is kind of perfect in many other languages? So what is translation? I mean, first things first, there is absolutely nothing wrong with Google Translate. And there are a lot of times where Google Translate or, or Microsoft Translate or Amazon Translate or Deepel or any other engine are perfectly fit for

pros. And I'll touch on that a little bit later. But in general, there is much more to this industry. It is not just words, right? If you think about all your content, it resides in content management system, damn, somewhere else, right? It lives. Your website is also populated from somewhere. Possibly you could stitch those things together manually in one language, that being English. Now go try doing it for 105 languages. If you're going to do it. if your company is present in 105 countries, right? And that's basically what the industry is about. It is definitely, again, by virtue of we handle words,

and there are a lot of words in this world, by virtue of that we're definitely on the forefront of the digitization and the digital revolution, just for the sake of necessity. If you take your company global, your content is multiplied by 105 or as many markets where you operate. So there is technology to it. There is linguists, and I was very happy to see a fellow linguist in the audience. There are linguists, there are internationalization engineers that make sure your code is actually global from the get-go.

There are subject matter experts that make sure that things are factually relevant. So it's actually a huge production. To get to your website in Estonia, there are many people working on it. And AI to Our Rescue, right now things have become significantly easier. Yeah, and let's talk about that. So with, you know, this kind of recent boom of generative AI, large language, models now be incorporated into our everyday lives, whether we even fully realize it or not, right? How specifically is this impacting the translation industry?

The impact on translation industry can definitely cannot even be overstated or overestimated. Again, I'll go back to the concept of words and linguistics, right? Large language models started from words through now we have multi-model, model modality, language models, but in principle, they help us handle words. You mentioned something in the opening when you were talking about the news, right, that a lot of LLMs are now multilingual, right? So that alone helps us because AI can now handle translation. The question, we'll have jobs tomorrow, will be covered later, AI can handle translation. If you don't want to translate, you can actually even generate source content, right? And we see a lot of writing compilates, writing assistance. But

But equally, you can actually even generate the target content. So the whole paradigm of how we do business shifted tremendously with the advancements of AI. It's using technology in the translation industry is no news. I think the paradigm shift is before it was computer-assisted translation. Now we can actually trust AI with doing the heavy lifting and we do what's interesting and we do where the human reasoning is absolutely necessary. So I would say that's probably, that's the main change. That's the most dramatic change.

AI is a co-pilot and enabler of the translation process. Sure. And you know, I'm curious, Ogo, because I sit down and like, I think about the first time that I saw or used the GPT technology, which I think for us and our team was, you know, 20-20 when it came out in some third-party products, right? Do you remember the first time that you know, you kind of saw, whether it's, you know, the GPD technology or a large language model? Like I'd love to hear, you know, as someone that works with words every day and works with, you know, making sure words make sense together. What was your first reaction, you know, kind of when you saw large language models kind of bust onto the scene?

I would say that, I mean, first things first, transform our models in general, no news, right? You just mentioned Google Bard, right? And then there are birds and there are, I mean, there are so many, well, Bard's more recent, but let's touch a couple of other transform models that have been around for a while. Even neural machine translation is based on transformer models. So in principle, that has been around for a while. What happens, what drives the breakthrough is the trillion of parameters

and tons and tons of data points that the later generation of large language models has been trained on. So my immediate reaction, I think the value, the first real large language model, like true large language model I saw. There was probably GPT2. And we're talking, what, two generations of GPT ago? My immediate reaction was, holy lord, this thing can do everything. How am I even staying employed?

But, yeah, so, I mean, the first thing was like, oh, wow, what do we do now? What do we do now when it's so capable? Like, write me Shakespeare-style poem, voila, give me a summary of such and such, So there was first this wave of fear meeting excitement, but then you start unpacking it. And as you start unpacking it, you actually realize that along with massive capabilities, massive shortcomings. You know, like I'm almost thinking that maybe your industry is ahead of so many industries, right? Just because I feel that anyone working in the translation space is always, you know,

like you just said, keeping up on, you know, transformer models. You know, I hadn't even used GPT2. I only used GPT3 when it first came out. So like, how would you say the translation industry has so far successfully used, you know, these large language models? And then maybe what are some of also the red flags that you've seen or the industry has seen by using these models? Okay.

So there is, you said initially there was the sentiment about, let's just deploy Google translator across the board. The initial sentiment, and I would imagine there are certain group of maybe executive and business leaders in our audience, the initial sentiment, like from me running balance sheet or running the P&L sheet, the initial reaction was, let's just plug it in. GPT is awesome. Let's just plug it in. It does translations. It is multilingual.

So the initial reaction was, again, it will do everything. And successfully, to a great extent, large language models have been successfully deployed for translation cases. As I said, generative AI is deployed quite a bit when it comes to digital marketing, digital content. Summarization is another very successful case, right? When you are an attorney, say you're running an e-discovery case, and that's a prominent part of our industry, clientele is actually an attorney doing a discovery, you are dealing with massive, massive volumes of emails. And they happen to be in Japanese. Do you have any time whatsoever to read through terabytes of emails? And do you equally have sufficient knowledge of Japanese? I don't.

So this is where translation and summarization combined services, multiple industries. Another application, and I want to pause a little bit on the limitations. Think about machines. translation and neural machine translation. It was built for purpose and for a single task of translating. Right. And you can also train it. You can customize to your company's tone and voice. There are tremendous capabilities around neural machine translation, which, by the way, happens to be NLP application, natural language processing application, and subsequently, equally happens to be AI. So think about neural machine translation. Now, compare it to a model that was built to perform variety of tasks, starting from math problems to, hey, write my college essay,

except my teacher friends are telling me that now they're getting identical essays, like 17 of those. So there's a bit of a question mark there. But again, if you deploy LLM that is built for multiple purposes, and then you deploy something that was built for purpose, often neural machine translation still wins. So there are quite a few. cases where you still want to default to machine translation and all the customization capabilities. Having said that, machine translation is flawed when it comes to fluency. And those of you having used, like I try to use Google Translate, I spent quite a bit of time in Mexico and Google Translate is not always my friend when it comes to understandability and

fluency of translation. LLMs are tremendously fluent. So the best successes our industry sees is combining the best of both worlds and the convergence of Gen AI and other NLP technologies is where we get best results. Okay. You let the mean translation do what it's good at, which is factual accuracy. And then you apply Gen AI on top of it and you get factual accurate fluid translation. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the All In One Creative

AI Studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the Assistant. The Assistant orchestrates multi-step workflows drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrative Premier, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible

so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at Firefly. It's explained it so well there. So, okay, we actually have two questions that are pretty much the same here, Ogo. So Raul, thank you for the question. And Tara are both just kind of asking. So how can AI accurately translate idioms in context and clarify this to the reader, right? Yeah, when I think especially in the English language, but I'm sure it holds true across the board in other languages.

Sometimes we have weird, you know, sayings or, you know, words that mean multiple things or descriptive ways to say something that it's like, oh, that's like word for word, that's not, doesn't really make a like a lot of sense. So how does, does the AI or the, you know, translation industry kind of tackle idioms? I know that at some point about like five years ago, US government was pouring a lot of money into getting machine translation. And I'll start with machine translation to understand idioms, euphemisms, and metaphors. And that was not the most successful project because you're absolutely right. The way machine translation is designed is not as much work to word,

but it's still the context window is very limited, right? The attention mechanism of machine translation only covers this much. Now the beauty of applying Gen. AI is that it does indeed read and translate your content in context, right? And the later generation, GenAI, as we know, different LLMs have different context windows, but you are still able to feed your context. You still go beyond a sentence, beyond a paragraph.

So what we see is because of the context, you can disambiguate terminology. It's accurate, right? It's not going to be the bed for, like, flower bed. It's not going to be translated as a sleeping bed. If Gen.A.I. sees that actually, hey, we're talking about gardening here. So in terms of context, that's what allows Jenny I to better capture idioms, metaphors, and in general understand more just because it's presented with more.

Just one word of more warning, don't give it too much because the attention mechanism gets confused and just translates the beginning and the end and that gets completely lost in the middle. But if you've nailed just the right amount of context you need to give it, that's when the user will benefit from understanding idioms. I hope I answer the question. No, yeah, you did. And it's funny. You talked about nailed.

Like I always think of like an idiom like hitting the nail on the head. And I'm like, yeah, how would that translate in so many other languages, you know, 10 years ago versus I'm guessing now today, because of AI, it's much better and it's much more accurate out of the box, right? Like you don't as in your industry, you don't have to spend as much time, you know, checking those because presumably the idioms, similes, metaphors translate better. Now they would, again, based on the context and also because Gen. Generally, generally, LLMs are much better at NLU, natural language understanding, than NLG. You don't have much room for hallucination. So if you provided enough context, it would actually predict that, hey, I'm dealing with an idiom here. I don't really take a hammer and hammer the poor nail on the head.

So because I'd say the context window and also ability to identify what text I'm dealing with, that I'm actually dealing with more idiomatic text as opposed to user manual to a war, machine. Nothing against user manuals, by the way. Yeah, we need them every once in a while, right? Are you ever? Okay, anyways. Yeah, let's talk about the big question here, Ogo, because we, this is the title of the episode, you know, how is this going to impact jobs? Because, you know, from someone who isn't in the industry and you see large language models in their flexibility, their power, even like what you were just saying, their ability to handle idioms, you know, similes, metaphors across multiple languages.

So what does this mean for the many, many humans who are working in this industry, now that large language models are, you know, doing this fairly well. What does it mean? I'm going to have an English as a second language moment here and ask you, can you say in English we cannot play ostrich in as we cannot stick our head in the sand and pretend that nothing is happening. Now I'm translating from my mother tongue. We would be very naive to say that jobs are not going to change in this.

the translation industry. There will be a very, very naive approach. Of course they will. AI translations, AI power translation, machine translation, what's called smoothing, which is basically post-processing output of other applications with AI. It hasn't reached human parity yet, but it's on its safe way there. It is eventually going to reach somewhat something that resembles human parity. Now, what's going to happen? We do know that models hallucinate, right? And we do know, and it's funny, there is a fun fact. Majority of US built models are built on English phenomena and local phenomena, right? So what would happen is really funny. It would have enough words in Italian to express the concept, but it's not going to

have enough anthropological knowledge to actually reflect Italian phenomena. Job one, fact, checking, and validation. You are an Italian native and you are told that, I don't know, the best Italian football team soccer team is Barcelona. Something is off here, right? So fact checking and validation are definitely jobs that are going to be around for quite some time until the models have enough data and enough world knowledge to produce accurate output across languages. One, two, there is something that's called false fluency. When the sentence is fluent, again, and a little bit on the topic of hallucination, but means absolutely nothing.

And models are still able to produce, I don't know, whatever. I'm going to the store, the crocodile went to the ravea, zero relationship between the two parts. So absolutely post-editing, human validation, fact-checking. Translators will probably gravitate more and more towards specialized subject matter expertise. So that would be one for you. Okay. Project managers, AI-based predictions, do I send it to transatl?

do I push it straight to publishing, project managers in our industry are becoming more and more of data analysts, validating AI-based decisions. So here is two for you, from business, from project management, like hands-on moving files around to actual business analysis. You know, Olga, I'm curious because I'm guessing that there's, you know, certain companies in the translation space that are doing this the right and proper way. and, you know, taking ethics into consideration. And then there's maybe those that aren't. Is there actually a more, like an increased responsibility on the humans in this process to be even more vigilant, right? Because maybe now, you know, your company is able to do 50% more or double, right?

But maybe things might slip through, you know, a little faster or a little more often. So maybe is the role of humans in this space maybe much more important? than normal because it's much more likely that, you know, errors could in theory slip through. Absolutely. And hence, I mean, first of all, you're spot on. The topic of ethical AI is extremely prominent and extremely visible in our industry, exactly because it's so human-driven that you absolutely want to deploy your AI responsibly, making sure that you do have humans in the loop to mitigate potential risks. There is a term that I love, which is toxic. again, it's funny because I'm supposed to be a proponent of AI, but I also know of all the

shortcomings and the role for the human. So you want to mitigate bias, you want to mitigate toxicity. What do you do? You actually become a part of the process and you help curate the training data and validate the algorithms of large language models, making sure the bias and potential toxicity is mitigated. So ethical AI deployment is definitely a huge topic for us. It was a recent case when I don't know if it was GPT or machine translation was used to make decisions, immigration decisions on the border based on the input, based on translation. And, hey, there could be better applications than actually make decisions about human lives based on what a model can produce.

So, I mean, there are things. And it was very, very, it was very, very prominent case that obviously raised awareness of deployed when it's fit for purpose, when no. nobody's life or nobody's livelihood is at risk. Yeah, yeah. I mean, yeah, in that case, the stakes are extremely high, you know, to have accurate translation. Yeah.

I know we're running out of time, so I want to mention one other thing where translators and linguists are becoming extremely, extremely important. And that's prompt engineering. The model is like a taxi driver, right? I mean, the model will take you only where you tell the model to take you. you and it's all about prompting. So there are only this many data scientists in the world, but there are a lot of linguists in the world. So a linguist can actually help design the right prompt that would produce the right output for their target language. Formality-wise, gender

wise, tone and voice-wise, right? So there is a huge role and I see a lot of linguists developing prompt engineering skills because it's an absolute necessity. And again, I don't speak, I don't know, Bahasa Indonesia. I cannot get by without a local expert helping build the prompt and validating that it's producing a relevant output for their language. So it's not that the jobs are going anywhere. It's tons of jobs surfacing and emerging. That's, okay, that's such a great point.

I was actually having a conversation at a tech event last night. You know, this gentleman asked me, hey, my daughter's in college. Here's what I think she should be focusing on. said, what do you think are the most important skills in AI? You know, they said, is it prompt engineering? Is it this and then that? And I said, you need to have strong language skills, right? Because if you have strong language skills, strong writing skills, it can take you very far in this world.

So, all right, let's, I think we have a couple of questions. Maybe we can go rapid fire. We can see if we can get some quick question and answer here, Olga. So let's give it a try. So Woozy asking, any thoughts about some of these new stories you hear about them translating lost languages or some of the groups that are working with understanding language and animals. What are your thoughts on that one? I know a lot of my friends started teaching their dogs how to speak. I'm not quite there. And I don't nurse high hopes about my specific dog.

But let's, okay, animals aside, long-tail languages, spot on. Actually, Gen. AI helps us take language in general take translation and democratize translation and make it AI translation go hand in hand with translation goes hand in hand with accessibility so with advancements in large language models first of all you don't need to pivot through a particular language you can go between two languages even if you don't have sufficient amount of training data for a particular language you can still compensate. There is a huge field of generating synthetic data. Like, for instance, there is a known language and well-resourced language in a language group. You can actually draw parallels between an existing and covered language and language that's less covered, and you can actually develop a

corpus, a synthetic corpus for long-tail languages. That's fascinating. It's fascinating. Now, again, training animals and understanding animal language, I might not quite be the experts in the field. I might want, although there are a lot of, I know that there is a lot of work being done in that direction. I'm not quite there. I still focus on human translation. All right. All right. We'll go two more here quick.

So Frank asking how much of this job replacement talking about versus acquiring new skills in retooling the work that will be done? Also, will AI help teach others how to do translation? Absolutely. So I actually have a lot of conversations with professors from the Middlebury Institute. if I'm pronouncing it right, what used to be a Monterey Institute. So the risk-killing, it's not as much job replacement. I would probably go with risk-killing. The risk-killing can happen, learn at work, right?

Or there are a lot of classes, for instance, offered in translation schools, localization schools that actually teach you that, I don't know, Python basics, prompt engineering syntax. So there is quite a bit that's already being done for risk-killing. Again, I'll be very careful about replacement. I'll really talk about grace-killing. And it's very much true. If you look, for instance, what's happening in language learning application,

there is way more AI plugged into how you learn the language. It's much more personalized. Like, again, I spend a lot of time in Mexico, so I spend a lot of time in Duolingo, and I'm watching it evolve more and more and plugging more. Like, OK, Olga, you failed here. Let's go back. Let's revisit that.

So AI-powered education applications are fantastic. My own personal passion is how can AI help kids with learning disabilities actually acquire professional skills, translation or not, right? It can suggest options. It can act as a co-pilot. So that's one thing that I'm personally very passionate about. But absolutely. It's, yeah. Love that.

Love that. All right. Here, we'll do our last audience question. So, Lana, asking as a consumer, how do you validate the output, assuming you don't know the language? to which most the model translates to. That's a fantastic question. How can you do that as a consumer? I know how you can do it as a professional,

but let me think how you would do it as a consumer. So let's take professional first. As a professional, like as a language professional, AI is equally used for producing language, but also for vetting language, vetting the output, for accuracy, for like how good or bad your translation or your generated content is. And I would imagine that, and I know that some of the GPs in the GPT market do exactly this,

and I'm pretty sure there are widgets that can do exactly this. Large language models can beautifully produce content, but they also can self-heel and self-judge. For instance, you take GPT 3.5, you validate it with four. So I would say I know that it's happening in professional translation, and I'm pretty confident there is something out there that can help you help you edit. Let AI judge itself and make decisions on how good a bet it is. That's great. It's great advice.

So, all right, Ogo, we've covered a lot here. We've talked about different ways that, you know, this is used in our daily lives, how important, you know, translation is to businesses, you know, trying to expand into new markets and also some of the pros and the cons. But, you know, as we wrap up here, what is maybe your one most important piece of advice? specifically when it comes to AI impacting, you know, translation and just the jobs and careers of people in there. What's your biggest piece or your best takeaway advice for everyone? Somehow managed to wake up in the morning and listen to podcasts and glance through AI news

and know what's the latest, making sure that if you are in school, you're learning what's relevant. So that would be the first advice. Absolutely, stay appraised of what's happening in the world of AI. And another important skill that's extremely demanded now, use your best judgment to deploy AI where it's fit for purpose and don't just deploy it across the board. And actually, AI analysts and advisors that are appraised of the capabilities and shortcomings are in huge demand now. If you can pass that judgment, you will always have the most fantastic job in the world of multilingual AI. I love that. So, so important, such great advice. Olga, thank you so much for joining the Everyday AI show and sharing your insights with us.

We appreciate it. Thank you so much for having me. And hey, as a reminder, y'all, there was a lot there. So much good information. You know what I'm going to do as a human? Once we get off this call, I'm going to go listen to this again and write more information. So all of this great knowledge that Olga just shared with us, you can read about it, see how you can apply it to grow your career, to grow your company. So if you haven't already, go to Your EverydayAI.com.

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