Ep 203: Translation in the World of AI – Will we have a job tomorrow?

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Discovering AI's Impact on the Translation Industry

Artificial Intelligence (AI) has become a central player in the translation industry, paving the way for a new era characterized by global communication and seamless linguistic exchanges. Key players in AI and translation have employed language models to successfully navigate global languages and bridge communication gaps. Coupled with multilingual features and a data-driven approach, generative AI and large language models have emerged as significant game-changers in the industry.

Democratizing Translation with AI

AI in translation isn't just about innovation; it's also about accessibility. By bridging language gaps, AI has democratized the translation process, compensating for insufficient training data for specific languages and making communication smoother across borders. With the help of these advanced language models, businesses now have the power to compete on a global scale and connect with clients in any part of the world.

Reskilling, Not Replacement: AI and Jobs

Nevertheless, the advent of AI in translation has brought to light concerns about job opportunities in the industry. However, industry players suggest a focus on reskilling over the fear of job replacement. With AI aiding in personalized language learning and aiding individuals with learning disabilities, human expertise in language isn't redundant but elevated, resulting in a shift towards specialized subject matter expertise, fact-checking, and validation.

Innovation and Accuracy: Vetted Outputs with AI

In the world of AI, accuracy matters, and translations are no exception. Deploying AI effectively involves using it to vet language output for precision, with humans maintaining responsibility for ensuring the ethical deployment of AI to mitigate bias and toxicity. These large language models have also proved skilled at capturing idioms, metaphors, and context, enhancing the fluency of machine translations.

Transforming Roles in the Translation Industry

As the nature of the translation industry evolves, so do its roles and responsibilities. Project managers, for instance, are morphing into data analysts to make AI-based decisions, highlighting the increasing reliance on AI and the subsequent need for data literacy. And for translators and linguists, their expertise extends beyond linguistics, playing an essential via in prompt engineering.


The impact of AI advancements on the translation industry underscores the importance of staying informed about developments in the field. By understanding and leveraging AI's abilities, businesses succeed in a globalized industry while advancing a more accessible level of communication across languages and cultures. It's not just about how AI is transforming the industry today, but also what potential it holds for translation tomorrow.

Video Insights

Topics Covered in This Episode

1. Role of corporations and Government in AI Governance
2. Organization's Governance Structure
3. Risks of AI and Generative AI
4. Practical Tips for AI Governance
5. Ethical and Global Technical Standards

Podcast Transcript

Jordan Wilson [00:00:16]:
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 gonna be jobs there tomorrow? Alright. We're gonna be talking about that today and more on everyday AI. Welcome. My name is Jordan Wilson, and I'm the host of Everyday AI. And this is for you.

Jordan Wilson [00:00:54]:
We do this literally every single day, bringing you a livestream Podcast and our free daily newsletter, helping everyday 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 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 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.

Jordan Wilson [00:01:34]:
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 joining. Jason, thanks for joining us from Florida. Make sure to get your questions in. What questions do you have? Like, maybe Mabryte here, Who's, you know, recently worked as a freelance translator. What questions do you have about AI in the world of translation? Make sure to get them in. Alright. Let's go over What's going on in the world of AI news? There's actually a lot today y'all.

Jordan Wilson [00:02:00]:
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 used, 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 to Premium users for now. Speaking of new updates, we have new AI image editing features rolling out on Microsoft Copilot. So Microsoft is updating its copilot platform with a new design and enhanced image editing capabilities, bringing the same 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.

Jordan Wilson [00:03:00]:
So so far, 5,000,000,000 images have been generated through the Copilot platform since its launch a year ago that obviously uses, the DALL E, image, generating powers from its partnership with OpenAI. Alright. 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 Advance. Alright. So Google has released its new updated AI chatbot, Gemini advanced as part of their Google One premium subscription package.

Jordan Wilson [00:03:38]:
So it is kind of comparable to OpenAI's chat g p t plus And promises enhanced 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 advanced is Currently only available in English, but may expand it to other languages in the near future. So, yeah, if you log on probably sometime today, if you use Google You're not gonna see it anymore. You're gonna see Google Gemini. So that is the base model. And then the Gemini advanced, $20 a month, bringing you a more powerful version in using Gemini Ultra 1 point o versus the free version that just uses Gemini Pro.

Jordan Wilson [00:04:21]:
Alright. So many buzzwords. So many words. Speaking of words, we gotta learn how to translate them all. You know? Like and and and talk about specifically how is AI Changing how we communicate on a global stage with global audiences. Alright. So, I'm excited for this and let please help me welcome To our show. There we go.

Jordan Wilson [00:04:43]:
We got her. Alright. So Olga Baragavayat is the VP of AI at and Smartling, Olga, thank you so much for joining the Everyday AI Show.

Olga Beregovaya [00:04:52]:
Thanks for having me.

Jordan Wilson [00:04:54]:
Alright. And tell me a little bit what you do as the VP of AI at Smartling.

Olga Beregovaya [00:05:01]:
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 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 have built up 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 that's in short.

Olga Beregovaya [00:05:46]:
I mean, the shorter answer is we provide AI powered translations.

Jordan Wilson [00:05:50]:
Okay. I love that. And and and maybe tell us, a a a little bit for those of us who maybe don't understand how this industry works. Like, what in in general is, you know, translation? You know? Because I think sometimes people think of, you know, oh, is this just Google Translate? Oh, is it just when you, you know, go to a 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 of how the industry in general works. Is it all just humans like like yourself who are Reading something in 1 language and making sure everything is kind of perfect in many other languages.

Olga Beregovaya [00:06:28]:
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 Microsoft Translate or Amazon Translate or DeepL or any other engine are perfectly fit for purpose, 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.

Olga Beregovaya [00:06:57]:
Possibly, you could stitch those things together manually in one language, that being English. Now go try doing it for 105 languages if your 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's linguists, and I was very happy to see fellow linguists in the audience.

Olga Beregovaya [00:07:35]:
There are linguists. There are, internationalization engineers that make sure your code It's 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.

Jordan Wilson [00:07:59]:
Yep. And and and let's talk about that. So with, you know, this kind of recent boom of generative AI, large language models now being incorporated into our everyday lives, Whether we even fully realize it or not. Right? How specifically is is this impacting the translation industry?

Olga Beregovaya [00:08:17]:
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. True. Now we have multimodal Model modality, language models, but in principle, they help us handle worlds. You mentioned something in the opening, when When you 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, will we have jobs tomorrow, will be covered later, AI can handle translation.

Olga Beregovaya [00:08:56]:
If you don't want to translate, you can actually even generate source content. Right? And we see a lot of writing copilots, writing assistance. 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 or the human reasoning is absolutely necessary. So I would say that's Probably, that's that's the main change.

Olga Beregovaya [00:09:39]:
That's the most dramatic change. AI is a copilot and enabler of the translation process.

Jordan Wilson [00:09:44]:
Sure. And, you know, I'm curious, Olga, because I I sit down and, like, I I 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, 2020 when it came out in some third party products. Right?

Olga Beregovaya [00:10:01]:

Jordan Wilson [00:10:01]:
you remember, like, the first time that, you know, you kind of saw whether it's, you know, the GPT 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?

Olga Beregovaya [00:10:22]:
I would say that, I mean, first things first, transformer models in general, no news. Mhmm. Right? You just mentioned Google bard. Right? And then there are birds and there are I mean, there are so many well, birds is more recent, but let's let's touch. Couple other transform models that've 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.

Olga Beregovaya [00:10:54]:
So my immediate reaction, I think The first real large language model, like, true large language model I saw, that was probably GPT 2. And we're talking, what, 2 generations of GBT 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, well, what do we do now? What do we do now when it's so capable? Like, write me Shakespeare style poem, Give me a summary of such and such, Voila. 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 come massive shortcomings.

Jordan Wilson [00:11:42]:
You know what? Like, I'm almost thinking that maybe your industry is ahead of 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 I hadn't even used GPT 2. I only used GPT 3 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?

Olga Beregovaya [00:12:17]:
Okay. So, there is you said initially there was this sentiment about, let's just deploy Google, translator across the board, the initial sentiment and, I I would imagine there are certain, Balance sheet or running the, p and l sheet. The re 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.

Olga Beregovaya [00:12:54]:
And successfully, to a great extent, large language models have been successfully deployed For translation tech, 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 ediscovery case, and that's a Prominent part of our industry clientele is actually attorney doing e 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 machine translation and neural machine translation.

Olga Beregovaya [00:13:51]:
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 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.

Olga Beregovaya [00:14:32]:
So there is there is a bit of a question mark there. But, again, if you, 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 tried to use Google Translate. I I spent quite a bit of time in Mexico, and Google Translate is not always my friend when it comes to and Understandability and fluency of translation. LLMs are tremendously fluent. So the best successes our industry is combining the best of both worlds and the convergence of Gen AI and other NLP technologies is where we get best results.

Olga Beregovaya [00:15:30]:
Okay. Translation do what it's good at, which is factual accuracy. And then you apply Gen AI on top of it, and you get factually accurate and

Jordan Wilson [00:15:40]:
Fluid translation. Explained it so well there. So okay. We actually have 2 questions that are Pretty much the same here, Olga. 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's it Holds true across the board in other languages. Sometimes we have weird, you you know, sayings or, you know, words that mean multiple things or descriptive, ways to Say something that is like, oh, that's like word for word that's not doesn't really make a, like, a lot of sense.

Jordan Wilson [00:16:16]:
So how, does does the AI or the, you know, Translation, industry kind of tackle idioms.

Olga Beregovaya [00:16:25]:
I know that at some point about, like, 5 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. That was not the most successful project because you're absolutely right. The way machine translation is designed is not as much word 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, GNAIs, 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.

Olga Beregovaya [00:17:15]:
So what we see is because of the context, You can disambiguate terminology. It's accurate. Right? It's not gonna be the bed for, like, flower bed. It's not gonna be translated as a sleeping bed. If Gen AI sees that actually, hey. We're talking about gardening here. So in terms of context, that's what allows Gen AI to better capture idioms, Metaphors and, in general, understand more just because it's presented with more. Just one word of more worrying.

Olga Beregovaya [00:17:43]:
Don't give it too much because then attention mechanism gets confused and just translates the beginning and the end and that complete 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 hope I answered the

Jordan Wilson [00:18:00]:
No. Yeah. You did. And it's it's funny. You talked about nailed. Like, I 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 it's much better and it's much more accurate out of The box.

Jordan Wilson [00:18:17]:
Right? Like, you don't as in your in, in your industry, you don't have to spend as much time, you know, checking those because presumably the idioms, and Similes, metaphors, translate better.

Olga Beregovaya [00:18:29]:
Now they would. Again, based on the context and also because Gen AI, generally generate generally, LLMs are much better at NLU, natural language understanding, that NLG, you don't have much room for hallucination. So if you're provided enough context, It would actually predict that, hey. I'm dealing with an idiom here. I don't really take a hammer and nail hammer the poor nail on the head. So because I 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 user manual to a washing machine. Nothing against nothing against user manuals, by the way.

Jordan Wilson [00:19:06]:
Yeah. We need them every once in a while. Right?

Olga Beregovaya [00:19:09]:
You have one ever. Okay. Anyways, it's

Jordan Wilson [00:19:11]:
Yeah. Let's let's talk about the big question here, Olga, because we we this is the 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 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?

Olga Beregovaya [00:19:45]:
I'm gonna have an e English as a second language model, 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 the translation industry. That'll be a very, very naive approach. Of course, they will. AI translations, AI powered 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 some somewhat something that resembles human parity.

Olga Beregovaya [00:20:27]:
Now what's gonna 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 what would happen it's really funny. It would have enough words in Italian to express the context but concept, But it's not gonna have enough anthropological knowledge to actually reflect Italian phenomena. Job 1, fact checking and validation. You are an Italian native, and you are told that I don't know.

Olga Beregovaya [00:21:03]:
The best Italian football team, soccer team is Barcelona. Something is off here. Right. So fact checking and validation are definitely jobs that are gonna be around for quite some time until the models have enough data And enough world knowledge to produce accurate output across languages, 1. 2, there is something that's called false fluency. When the sentence is fluent, again, a little bit on the topic of hallucination, but means absolutely nothing. And models are still able to produce, I don't know. Whatever.

Olga Beregovaya [00:21:34]:
I'm going to the store. The crocodile went to the Riva. 0 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.

Jordan Wilson [00:21:53]:

Olga Beregovaya [00:21:54]:
Project managers, AI based predictions, do I send it to Translation. Do I push it straight straight to publishing? Project managers in our industry are becoming more and more of data analysts validating AI based decisions. So here is 2 for you. From business, from project management, like hands on Moving files around to actual business analysis.

Jordan Wilson [00:22:16]:
You you know, I'm, Olga, I'm I'm 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 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? And but Maybe things might slip through, you you you know, a little a little faster or a little more often. So maybe is the role of of humans in this face may be much more important, than normal because it's much more likely that, you know, errors could in theory slip through.

Olga Beregovaya [00:23:03]:
Absolutely. Enhance 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 also know of all the shortcomings and the role for the human. So you want to mitigate bias. You want to mitigate toxicity.

Olga Beregovaya [00:23:38]:
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. I was 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 It was very, very it was very, very prominent case that obviously raised awareness of deployed when it's fit for purpose Mhmm. When nobody's life or nobody's livelihood is at risk.

Jordan Wilson [00:24:30]:
Yeah. Yeah. I mean yeah. In in in that case, the stakes are and high. You don't have to

Olga Beregovaya [00:24:35]:
crazy high. Right? Pure translation. Admit it. 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, 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.

Olga Beregovaya [00:25:06]:
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.

Jordan Wilson [00:25:44]:
That's, okay, that's such a great point. I was actually having a conversation at a tech event last night. You know, this 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 the most important skills in AI? You know? Is it they said, is it prompt engineering? Is it this and that? And I said, 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 alright.

Jordan Wilson [00:26:11]:
Let's I I think we have a couple questions. Maybe we can go rapid fire. We can see if we can get some quick, question and answer here, Olga. So, let's 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?

Olga Beregovaya [00:26:32]:
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. Anyways 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 risk, 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 2 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.

Olga Beregovaya [00:27:25]:
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 It's really 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'm still focused on human translation.

Jordan Wilson [00:27:53]:
Alright. Alright. We'll go we'll go 2 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?

Olga Beregovaya [00:28:07]:
Absolutely. So I actually had a lot of conversations with professors from the Middlebury Institute, If I'm pronouncing it right, what used to be monitoring institute. So the reskilling, it's not as much job replacement. I would probably go with reskilling. The reskilling 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 the, I don't know, Python basics, prompt engineering, syntax. So there is quite a bit that's already being done for reskilling.

Olga Beregovaya [00:28:38]:
And, again, I'll be very careful about replacement. I'll really talk about reskilling. I think 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, I'm 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, okay. Olga, you failed here. Let's go back.

Olga Beregovaya [00:29:04]:
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 copilot. So that's one thing that I'm personally very passionate about. But, absolutely, it's you Love that.

Jordan Wilson [00:29:29]:
Love that. Alright. Here, we'll do our last audience questions. So 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 how can you do that as a consumer?

Olga Beregovaya [00:29:45]:
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 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 how good or bad your translation or your generated content is. And I would imagine that and I know that some of the GPTs and 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 heal and self judge. For instance, you take GPT 3.5. You validate it with 4. So I would say I know that it's happening in professional translation, and I'm pretty confident there is something something out there that can help you help you vet it.

Olga Beregovaya [00:30:35]:
Let AI judge itself and make decisions on how good or bad it is.

Jordan Wilson [00:30:39]:
That's that's Great. That's great advice. So alright. Olga, we've covered a lot here. We we 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 as we wrap up here, What is maybe your your one most important piece of advice, specifically when it comes to AI Impacting, you know, translation and just the jobs and careers of of people in there. What's your your biggest piece or your best takeaway advice for for everyone?

Olga Beregovaya [00:31:16]:
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 and Cool. 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 advisers that are appraised of the capabilities and shortcomings are in huge demand now. If you can pass that judgment, you'll always have the most fantastic job at the world of multilingual AI.

Jordan Wilson [00:31:55]:
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.

Olga Beregovaya [00:32:05]:
Thank you so much for having me.

Jordan Wilson [00:32:06]:
And, hey, as a reminder, y'all, there was a lot there. So much good information. You know what I'm gonna do as a human? Once we get off this call, I'm gonna go listen to this again and write more more information. So 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 everyday AI.com. Sign up for that free daily newsletter written by humans. Thank you for joining us, and we'll see you back tomorrow and every day for more everyday AI. Thanks y'all.

Olga Beregovaya [00:32:36]:
Thank you.

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