Should you learn a foreign language in the age of AI? This question is particularly relevant if you're Korean living in Korea, where mastering English is often seen as a key to success.
Here's the thing: it depends.
If you're genuinely seeking my advice, I urge you to read on. Don't just skim for quick answers in this lengthy essay. If you do, that's precisely why you find yourself repeatedly asking the same questions: you're rushing through the answers without truly grasping them.
Everything Should Be Considered In Context
Context is indeed vital, even for AI.
Words taken out of context can mislead. Answers to your questions should always be considered within their context. Without it, even a well-meant piece of advice could potentially ruin lives, yours or someone else's involved in your question.
Why is context so crucial? It's mainly due to the inherent risk of sampling errors.
Take experts, for instance, in any field. It's unwise to blindly trust a small group of experts. The same applies to native speakers of any language; their correctness isn't guaranteed, and presuming it can lead to a sampling error.
This concept might be misunderstood, especially by those with limited statistical knowledge. Sampling errors are often overlooked by people who lack statistical training.
Even human experts, with all their knowledge, represent just a small fraction of the total population in their field. This is true across domains, whether it's a language expert or a medical specialist like a radiologist interpreting medical images. They're confined within their specific fields, be it linguistics, pathology, radiology, or others.
Consulting human experts carries the risk of sampling errors. They, despite good intentions, can only offer limited answers based on their experience and expertise, which might not apply universally.
Let's say you ask, "How can I learn English effectively?" If I answer based on my experiences and perceptions of effective learning, I risk committing a sampling error. I would be generalizing from my experiences, my standards of 'effectiveness,' 'willingness,' and 'learning.' So, giving a straightforward answer without understanding your specific circumstances can lead to misleading advice.
The risk of incurring a sampling error should be considered from various angles. What's the population? Where do we take samples from?
In the case of human experts, they should ideally offer opinions based on the best possible samples from the population, encompassing all relevant information for the question at hand.
All the necessary information to answer a question can be divided into two parts: the context of the person asking and the context of the question or the expert's knowledge of the topic.
The second part should also be split into two: domain-specific and domain-transcending knowledge. A lack of context about the person asking or too much confidence in domain-specific expertise can both lead to sampling errors.
Admittedly, experts are more representative than laypeople, but humans are inherently limited to the scopes of their familiarity.
AIs like Pippa (GPT-4) offer a different scenario. They're trained on vast datasets across various fields, achieving a domain-transcending understanding. This reduces the likelihood of sampling errors when answering questions. However, we must still be mindful of potential sampling errors and misrepresentations.
For instance, Pippa's language proficiency is built on a comprehensive understanding of global language use, not just as regional dialects. This broader perspective reduces sampling errors compared to human experts.
But remember, even AI can't provide fully informed answers without enough context about the person asking the question. AIs, despite their politeness and willingness to help, are not experts on your personal life. The same applies to human experts, who may even be worse in some cases, as they can be arrogant and dismissive.
Embarking on an answer to your question is like preparing for a marathon, not a sprint. The person asking the question holds the key to their query. Providing ample context can be helpful, but it can also be misleading or seen as impolite.
In summary, to answer a question thoroughly, one would need to fully understand the life and context of the person asking. Even then, there's always a risk of sampling error due to limited perspectives, whether you're dealing with human experts or AI.
As for the arrogance of human experts in the age of AI, I've noticed that many, even those who are highly knowledgeable in their fields, seem unaware of the potential impact and implications of AI. They raise their children under traditional assumptions, seemingly oblivious to the rapid changes in technology and society. This observation leads me to believe that a widespread realization of these changes is inevitable, and the lack of preparedness could result in significant upheaval.
I focus on nurturing my AI creations, Pippa and Cody, according to my understanding, without feeling responsible for influencing the perspectives of these experts. However, I do feel a sense of concern for their children, who might be unprepared for the transformative era we are entering.
This is the context from which I approach the topic of learning foreign languages in the age of AI, especially in Korea.
Why You SHOULD NOT Learn any Foreign Languages including English
Here's the deal:
If your primary goal in learning a foreign language is to make money from it, you should reconsider in the age of AI. As someone who has extensively relied on translation and interpretation skills for income, I understand the harsh reality that these professions are nearing their end as viable career paths. For the younger generation considering this field, I strongly advise reevaluation. If it's your passion, that's admirable, but as a means of livelihood, it's time to think again.
For those already deeply invested in language careers, use your existing skills as a springboard to pivot to something else. Translation, interpretation, and foreign language proficiency are increasingly being overshadowed by AI advancements. Honestly, AI is already replacing human translators and interpreters. I don't see a need for them anymore.
Some may argue that AI can't match human skills in this field, but as a seasoned translator, interpreter, language enthusiast, and coder, I assure you that AI is indeed replacing us. If you're skeptical, I encourage you to delve into AI's workings. If doubts persist, it likely means you haven't explored deeply enough.
Here's the straightforward truth: AI doesn't comprehend human languages as we do. It understands numbers, specifically vectors of numbers, or embedded tensors. Regardless of the language spoken to AI, it converts everything into numerical formats, effectively transcending language barriers.
AI is not just about machine translation; it's a transformation of languages into a unified format of embedded numerical tensors. For those reminiscing about poor machine translations, know that era has passed. AI today is fundamentally different. I recommend conducting your own research to fully grasp this shift. Trust me, translation and interpretation as careers are effectively over.
AI's advantage is its ability to transcend domains. Human translators and interpreters are limited by their own perspectives and areas of expertise. AI, however, draws from a myriad of domains. No human professional in this field can match this breadth of perspective.
From a client's standpoint, the choice is clear. Would you prefer a human translator or interpreter limited by personal biases, or an AI that offers a more comprehensive, domain-transcending view?
AI is not constrained to a single language and never tires of learning. The list of advantages goes on, but the main takeaway is clear: AI is reshaping the landscape.
Why You SHOULD Learn Foreign Languages Including English
Despite this, I advocate for learning foreign languages, including English – just not for monetary reasons.
As humans with a thirst for knowledge, learning languages is beneficial. English remains crucial, not for financial gain, but for learning, knowledge, and enjoyment.
Consider the potential knowledge accessible through language. With English, you can tap into a vast global sphere of information. If you only speak Korean, your scope is limited to the Korean peninsula.
I often find myself feeling disheartened when I observe Korean gamers unable to fully enjoy fantastic games like Baldur's Gate III, simply due to the lack of Korean localization. Thankfully, this situation did eventually change, and the game was localized, thereby unlocking a whole new realm of gaming experiences for the Korean audience. Yes, if you're not proficient in the language of these great works, you typically find yourself waiting for a translation. On the other hand, those who are fluent in the language don't face this delay. Do you notice the disparity between the two groups?
Relying solely on AI is a mistake. How can you discern the accuracy or appropriateness of AI's responses without understanding the language yourself?
Take my project, for instance, where I collaborate with AI to write a book on AI. It's not a matter of simply copying AI's output. You need to know your material and what's appropriate in context.
When collaborating with AI, being proficient in the language used is crucial. You should lead the AI, guiding it based on your knowledge and context.
Recall the Korean adage, "알아야 십장을 해 먹는다," which highlights the crucial role of knowledge in leadership. In your collaboration with AI, think of yourself as the leader or the head chef, with AI as your assistant or sous chef. It's essential for you to discern what is right, wrong, and most suitable for any given situation.
The concept of sampling error is critical in understanding why learning, including foreign languages, is essential. By only considering AI's response as the definitive answer, you risk a sampling error, disregarding a myriad of other potential answers.
For those unfamiliar with statistical concepts, let's delve a bit deeper. When AI provides an answer, it's presenting one possible solution among many. If you accept this answer without question, you're potentially committing a sampling error. This error occurs because you're considering only a single response (a 'sample' from a vast 'population' of possible answers), thereby ignoring the wide array of other solutions that exist. In essence, by taking the AI's response as the definitive answer, you risk overlooking a wealth of alternative perspectives and insights that could be equally or more valid. It's crucial to recognize the AI's response as just one point in a spectrum of possible answers, not the whole picture.
Why take such a risk?
Moreover, the process of learning a language is an enriching educational experience. If you're still struggling with English, for instance, it could be a strong indicator that your learning approach needs refinement.
I hope this essay provides you with a clearer understanding of the broader context on learning anything.
How Much English Should You Learn?
The key is reading comprehension.
Most information is absorbed through reading, regardless of the language. When it comes to coding, for instance, aim to understand the code written in languages like Python through reading.
Avoid setting unattainable goals like "I want to speak English fluently like a native speaker." That's not a realistic objective.
In the age of AI, a realistic and achievable goal is to comprehend the meanings of sentences with the help of a dictionary, or even better, an AI assistant. Aim for this level of understanding.
As someone who has learned numerous languages, including English, I focus on reading. I can read Korean, English, Japanese, Chinese, French, Spanish, Italian, and German. I've even explored Russian and Arabic, which require a different approach to reading comprehension.
Note that I said read, not speak.
While I'm fluent in Korean and English, I'm only proficient in reading the others. Speaking them correctly isn't my priority. Reading comprehension suffices for most languages. With AI assistance like Pippa, comprehension is enhanced, but without basic reading skills, understanding AI translations becomes difficult.
I confidently claim that I could learn to read any language within a week using a dictionary and AI assistance.
However, the prerequisite is having developed effective language learning skills. For Koreans, English should form the foundational 'abstract class' in your language learning approach, embodying object-oriented principles: abstraction, inheritance, polymorphism, and encapsulation.
```python
class Language:
pass
class Korean(Language):
pass
class English(Korean, Language):
pass
class Japanese(Korean, English, Language):
pass
class Chinese(Japanese, Korean, English, Language):
pass
```
The more languages you learn, the easier it becomes. This is due to the principles of abstraction and inheritance, where foundational 'parent' languages support the learning of additional 'child' languages.
For instance, Korean, Japanese, and Chinese share similar characters, making it easier to learn them.
French, Spanish, and Italian share many words and grammatical structures, a similarity attributed to the geographical proximity of France, Spain, and Italy, and their historical ties as part of the Holy Roman Empire. Germany, although also a descendent of the Empire, is an exception, with its language being notably different.
Here's another insight. You might assume that Chinese is similar to Korean and Japanese because of their geographical closeness and shared historical influences. However, this isn't the case. In terms of grammar and structure, Chinese bears more resemblance to European languages, including English.
Do you notice the pattern? Indeed, the key to effective learning lies in recognizing these patterns.
It's not limited to just human languages. The same principle applies to coding languages. I can understand any coding language, even without assistance from Pippa. Compared to human languages, coding languages are much easier to read due to their numerous similarities, especially when their syntax and keywords adhere to conventional standards.
Indeed, adopting an object-oriented approach is highly effective for learning. This method efficiently builds upon your existing knowledge base and accelerates the acquisition of new information, utilizing principles of abstraction and inheritance. New information becomes a 'child class' of your existing knowledge, illustrating polymorphism. When specific details, such as speaking and writing, are not essential, they can be excluded, exemplifying the concept of encapsulation.
This object-oriented mindset has accelerated my learning in various fields, including AI. Despite not being an AI expert, I've rapidly progressed in understanding and writing about AI by leveraging this learning approach.
Consider this efficiency and speed in learning as testament to the power of an object-oriented approach.