“開新創來” ; Opening a new path to create a better future!
Mi-Seon Park
YoungLimWon Soft-Lab
‘A conversation with a program?’
‘What’s different from the existing chatbots? Programs surely have limits.’
‘No way, this is possible?!’
In December 2022, the conversational AI, ChatGPT, was introduced.
Users, who initially threw questions at ChatGPT out of curiosity, were astonished by its smooth responses.
Of course, ChatGPT didn’t always provide the right answer to every question, but it wasn’t disappointing either.
ChatGPT continually learns and grows through conversation, striving for perfection each day.
From gathering information, creating an index, drawing pictures, to even composing music, ChatGPT can do it all.
If we imagine the timeline of human history, the advent of ChatGPT might have made a significant impact.
In 2022, after the game between the Go AI program AlphaGo and the professional player Lee Sedol, the term “AI” began to appear everywhere in the news and media.
Working in the IT industry, I was intrigued but not actively researching AI.
What pushed me was an AI training program offered to all employees.
Within a decade after AlphaGo, I was amazed at the rapid and significant advancements in AI technology, which further piqued my interest.
I attended the second AI training and was fortunate to challenge the AI & BigData MBA course with company support.
The ultimate goal of the SSM Graduate School MBA program could be said to be training Data Scientists.
A ‘Data Scientist’, using AI technology, analyzes vast amounts of big data to provide organizations with new strategies (or insights). I believe it’s an essential profession in this age of ‘information deluge’.
Data scientists are not developers but need to understand programming languages and logic, so naturally, they learn the ‘Python’ language.
There are also courses in basic statistics, specific AI algorithms, current issues like BI, natural language processing, and more.
SSM’s classes occur outside work hours or on weekends.
Balancing compressed lectures and assignment submissions with work was always a rush.
On average, one course ended every two weeks, requiring a report submission on what was studied.
While not too difficult, assignments were a burden regardless of study effort.
Amidst this hectic schedule, doubts like ‘Am I doing this right?’ and ‘Can I analyze real data on my own?’ often arose.
A professor once said it’s natural not to understand everything from the beginning, which comforted my concerns.
Courses overlapped content, making repetition helpful. I began to understand the professor’s emphasis on completing the entire course.
Starting graduate school out of pure curiosity wasn’t always smooth, but I have no regrets.
I sometimes chuckled at myself, replaying recorded lectures and clumsily following coding exercises.
Spending hours on trivial errors was frustrating, but I felt joy when obtaining desired data outcomes.
Time has flown since entering graduate school, now in my second semester, with a thesis due next.
Reflecting on the past six months, how much have I grown?
Can I call myself an AI & BigData expert?
Only one thing is certain; ‘It’s just the beginning’.
As mentioned, AI will deeply integrate into our lives.
Some may call it the ‘retaliation of AI’, but few would argue its inevitability.
The key will be effectively utilizing AI, and those providing insights will be highlighted.
When first proposed graduate school, I hesitated as it was unfamiliar territory.
But, bolstered by the company’s ‘aggressive’ welfare policy of fully supporting tuition if one’s willing, I took the plunge.
Such opportunities aren’t common for employees.
I’m grateful to my company for offering this chance and am determined to contribute to its growth.
In conclusion, if someone seeks advice on graduate programs, I’d say:
Start right now.”
