Automation tools are increasingly being used across all types of industries. Although innovative technologies have improved business operations, many are concerned about potential negative ramifications in job losses and career obsolescence.
PwC has predicted that up to 38% of U.S. jobs could eventually be lost due to automation over the next decade.
People know that manufacturing positions have been compromised due to automation, but few are aware of the potential impact in white-collar positions.
Computer programmers now are in high demand and command large salaries, but could this change one day?
A technology firm recently developed a natural language processing model capable of self-generating code with only a simple set of instructions. These innovations have excited industry leaders, but have led some to question whether the technology could impact future job opportunities.
The Future of Coding Automation
OpenAI, a global AI research laboratory, recently formulated the world's largest natural language processing model. Known as GPT-3, the tool can code automatically with only brief instructions. The technology has shown promising outcomes when tested under experimental conditions.
Sharif Shaheem, founder of debuild, tweeted how the model can operate through the use of simple coding phrases. The NLP model can generate JSX code with the help of a brief, precise command.
Although Shaheem trained the model using simple phrases, the results show promise moving forward. Many skeptics now wonder if AI will be able to handle more complex tasks in the years ahead.
Project Limitations
The GPT-3 model is only useful in limited circumstances. Although it may be helpful for social media and other smaller projects, it's unable to handle other coding endeavors' complexities. The code generator is intriguing in nature but lacks practical applications in the real world.
AI may be able to perform more complex functions someday, but it's unlikely we'll be able to rely upon it for writing code fully. The CEO of OpenAI has openly admitted, "The GPT-3 hype is way too much. It's impressive but it still has serious weaknesses."
Like many early technologies, the GPT-3 code generator may be promising in theory but proves limited under most circumstances. The algorithmic capabilities of the model show efficient coding may be years or decades away.
Using AI to Complement Human Capabilities
The imperfections and limitations of GPT-3 make for more theoretical rather than practical applications. The GPT-3 model is considered an AI breakthrough technology, but it's unlikely to send coders to the unemployment line.
Human analysts should focus on using AI to complement rather than replace current technological capabilities. By minimizing tedious and monotonous tasks associated with data science, AI can vastly improve workplace efficiencies and outcomes.
Effective analysis relies upon using the appropriate datasets and numerical inputs. The problem of managing massive datasets can be an overwhelming task for data scientists and analysts. Professionals may spend up to 80% of their workdays refining and filtering through massive amounts of data. AI can be used to expedite the data mining process while maximizing human resources.
Bitvore uses AI to manage unstructured information before converting it into AI-ready data. Data scientists and analysts can prioritize mission-critical objectives instead of wasting valuable working hours on repetitive tasks. Bitvore's unique technology effectively generates clean, normalized, business-centric data that can be used immediately by decision-making teams.
For additional information on how Bitvore can improve your business operations, be sure to review our white paper for further information: Using AI-Processed News Datasets to Perform Predictive Analytics.
