No, AI won't take your job. At least not yet. As I've written, the best uses of artificial intelligence and machine learning (AI/ML) are to complement, not replace, human creativity. Ironically, the best large-scale language models (LLMs) are trained using copyrighted, if not necessarily legal, fruits of human creativity. Humans and robots will peacefully coexist for the foreseeable future.
Still, some industries are embracing AI more than others, as revealed in the latest 2022 AI Index report from the Stanford Institute for Human-Centered Artificial Intelligence. Nearly every industry has increased investment in AI-savvy talent over the past year, with more AI-centric job ads coming from companies in industries such as information (5.3%), professional, scientific, and technical services (4.1%), and finance and insurance (3.3%). If you're nervous about your job or simply want to take advantage of this trend, Python is the way to go.
Business leads the way
Until 2014, academia was the center of the ML world. But that is no longer the case. Large corporations have led the AI/ML push since 2014, but in 2022, academia released only 3 ML models compared to 32 released by corporations. Academia cannot keep up with the data, CPU cycles, and funding that industry brings to the table.
How much does it cost? Back in 2019, training an LLM like GPT-2 cost $50,000, while training a PaLM would cost about $8 million, and it has 360 times as many parameters as GPT-2 (which was, of course, state of the art at the time). Governments could afford to make this kind of investment, but they were primarily interested in trying to regulate LLMs (unsuccessfully), so industry has filled the void.
In doing so, companies across nearly every U.S. industry sector are growing their appetite for AI/ML talent. On average, AI/ML-related job openings are surging from 1.7% in 2021 to 1.9% in 2022. While this number may seem small, the percentage all US job ads. Approaching 2% is a big number considering AI/ML is still unproven for most companies, and as mentioned earlier, some industries have a much higher percentage of jobs requiring AI/ML expertise.
The proportion of jobs requiring AI skills has increased across most industries, especially over the past year.
Of course, jobs aren't the only measure of investment. In terms of cash, medical and healthcare lead the way with $6.1 billion in AI investments in 2022. Healthcare is followed by data management, processing and cloud ($5.9 billion) and fintech ($5.5 billion). These industries make sense when you think about how they're spending AI funds. According to the report, companies are using AI in a variety of ways, but the main areas include robotic process automation (39%), computer vision (34%), natural language text understanding (33%) and virtual agents (33%). In terms of use cases, the main one adopted in 2022 was optimizing service operations (24%). Other popular uses were creating new AI-based products (20%), customer segmentation (19%), customer service analytics (19%) and new AI-based product enhancements (19%).
what does this mean your According to another study conducted by researchers at the University of Pennsylvania and funded by OpenAI, “about 80% of U.S. workers say that at least 10% of their jobs could be affected by the introduction of LLMs, and about 19% of workers say that at least 50% of their jobs could be affected.” Who's at risk? Accountants, mathematicians, translators, creative writers, etc. Who's not at risk? People with a more manual labor focus, like cooks, mechanics, and oil and gas errand boys. (Although electric cars might be coming for the latter group.)
Of course, this news isn't bad. As we've seen in software development, AI can take some of the tedium out of certain jobs, freeing up employees (in this case, developers) to focus on higher-value work. For those looking to improve their opportunities in this AI-driven future, the Stanford report singles out one technology in particular: Python, among others.
The Holy Grail of Python and AI
Python's impact on data science is not surprising. As I wrote in 2021, “The language most likely to become dominant is [data science] “Python is the most accessible to the broadest range of people in the enterprise.” A year later, that statement remained the same. “As organizations look to more diverse groups to help with data science, Python's broad popularity makes it easier to adopt.” Python is increasingly the common language for both experts and novices working in data science.
In the Stanford University report, Python stood out not only for its relative growth compared to other desirable skills, but also for its absolute growth.
All AI-related skills are seeing a massive increase in demand, with Python leading the way.
There are several reasons why Python continues to rise to the top of data science in general, and AI/ML in particular: It reduces the complexities inherent in AI/ML by providing a large number of powerful libraries that simplify development; it has a simple, consistent, and clear human-readable syntax, making it easy to get started; it also has a broad and welcoming community that helps developers become productive faster; and it runs on almost every platform you use.
Sure, AI may make some jobs unnecessary, as machines are capable of doing things more efficiently than humans. But for those who have mastered Python, there will be plenty of opportunities to embrace the rise of the robot revolution and extend it to suit your needs (and those of your employer) using Python and other tools.
