An audio engineer describes transitioning into AI research through formal education: bootcamps, mathematics study, and a master's degree in artificial intelligence and machine learning, with plans for a PhD. The post reflects on how rapidly the AI field has evolved since 2019 and emphasizes the combination of domain expertise (audio) with structured AI training as a pathway into specialized research areas.
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A person with an audio engineering background has been pursuing formal education in AI and machine learning since 2022, including coding bootcamps, mathematics study, a master's degree in artificial intelligence and machine learning, and plans for a PhD.
Why it matters
The post reflects how the AI field has evolved since 2019 (when GPT-2 was a major reference point), and illustrates one career path—structured academic training combined with domain expertise—for entering AI research, particularly in specialized areas like audio and music technology.
What to watch
The author notes that many classmates and colleagues are interested in business applications, suggesting a divide in how people are approaching AI careers, though the post cuts off before elaborating on that distinction.
An audio engineer who became interested in AI and machine learning around 2019 describes their long-term goal to become an AI researcher specializing in audio and music technology. The author credits the rapid evolution of the field since 2019—when GPT-2 was still a major topic—as a source of excitement about working in technology, research, and innovation. To pursue this goal, the author returned to school, completed coding bootcamps, studied the mathematics underlying machine learning, and is currently enrolled in a master's degree program in artificial intelligence and machine learning. They are also planning to pursue a PhD after completing the master's. The author notes that many peers and classmates are interested in business applications of AI, implying that there are divergent paths within the field, though the post does not elaborate on this distinction or explore its implications further.
The post is a personal narrative about career transition into AI research, not a news report with concrete findings or announcements. It documents one individual's deliberate pathway into the field—combining domain expertise (audio engineering) with formal credentials (bootcamps, degree-level mathematics, a master's in AI/ML, and planned PhD study). The author frames this within the broader context of rapid AI field evolution since 2019, when GPT-2 represented the cutting edge; this observation suggests that staying current in AI now requires ongoing formal study, not just self-teaching. The post hints at a bifurcation in how people are approaching AI—some classmates focus on business applications while the author pursues research—though the full context is cut off and not developed.
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