My conversation with ChatGPT on impact of large language models on employment

There are many facets of life where large language models (LLMs) will make an impact, I have started with its impact on employment and skilling. What better way to find out, then to start with ChatGPT, irony is not lost on me.

In conclusion, As per ChatGPT from openai, llms will replace humans performing repetitive tasks that can be automated, that will help enable humans to perform more valuable tasks. Humans have to upgrade themselves to learn new skills that are required, just like when the steam engine came along and replaced manual labor who got upgraded to factory workers or the first cotton mills replacing weavers who got into designing. ?

These are conversations, I have had with ChatGPT, its responses are contextual, to the point but lacks in one aspect, it is not taking into account how humans are using large language models recently, there is a disclaimer at the bottom of the page that says information till 2021 is being captured. Read through the abstract below and you will find strengths and weakness of ChatGPT.

You can also try these prompts or something similar to test what ChatGPT thinks about the social impact of LLM

Text in italics is generated by an AI agent ChatGPT

how will large language models like ChatGPT help with reduction in unemployment that leads by loss of job because of use of large language models

Large language models like ChatGPT can help with reduction in unemployment in several ways:

1. Automation of repetitive tasks: ChatGPT and similar models can be trained to perform repetitive tasks, such as data entry or customer service, which can free up human workers to focus on more complex and creative tasks.
2. Improved efficiency: Language models can help businesses streamline their operations and improve efficiency, which can lead to increased productivity and growth, creating new job opportunities.
3. Development of new industries: The use of language models can also lead to the development of new industries, such as chatbot development, natural language processing, and machine learning, creating new job opportunities for those with the skills to work in these fields.
4. Assist and augment human's job: Language models can also be used to assist and augment human workers, making them more effective in their jobs. For example, chatbots powered by large language models can help customer service representatives respond to more queries in less time, increasing their productivity and effectiveness.

It's important to note that, while language models can automate some jobs, they can also create new job opportunities and improve efficiency in a way that ultimately leads to economic growth. However, it's also important to consider the potential impact of automation on employment and take steps to mitigate any negative effects.

what negative effects do you see due to automation and improved efficiency on unemployment?

Automation and improved efficiency brought by large language models can have negative effects on unemployment in several ways:

1. Job displacement: Automation can lead to job displacement, as machines and algorithms take over tasks that were previously done by humans. This can be particularly challenging for workers in industries that are highly susceptible to automation, such as manufacturing or data entry.
2. Income inequality: Automation can also contribute to income inequality, as it can lead to higher profits for businesses, but may not necessarily result in higher wages for workers.
3. Lack of job opportunities: Automation can also lead to a lack of job opportunities, particularly for workers with lower levels of education or skills.
4. Uneven distribution of effects: The effects of automation can be unevenly distributed, with some regions and industries being more affected than others. This can lead to economic disparities and social unrest.
5. Social consequences: Automation can also have social consequences, such as increased isolation and a lack of purpose for those who have lost their jobs.

It's important to note that although automation and improved efficiency can have negative effects on unemployment, it's also important to consider the potential benefits in terms of economic growth and improved productivity. Governments and businesses have a role to play in mitigating the negative effects of automation by providing support for displaced workers and investing in training and education programs to help them acquire new skills.

which new skills should workers acquire to gain employment lost due to use of large language models?

Workers who have lost their jobs due to the use of large language models may need to acquire new skills to gain employment in the following areas:

1. Data analysis and interpretation: As more and more data is being generated, there is a growing demand for workers who can analyze and interpret it. This includes skills such as data visualization, data mining, and statistical analysis.
2. Artificial intelligence and machine learning: As the use of language models and other AI technologies continue to increase, there will be a growing demand for workers with skills in these areas. This includes skills such as programming, machine learning, deep learning, and natural language processing.
3. Digital marketing and e-commerce: As more and more business is conducted online, there is a growing demand for workers with skills in digital marketing, e-commerce, and social media.
4. Cybersecurity: With the increasing use of technology in business and society, there is a growing need for workers with skills in cybersecurity, including network security, data security, and cloud security.
5. Human-computer interaction: As language models and other AI technologies continue to evolve, there will be a growing demand for workers with skills in human-computer interaction, including user experience design, user interface design, and human-centered design.
6. Technical writing and content creation: With the increased use of language models in automated writing, there will be a growing demand for workers with skills in technical writing and content creation, including writing for the web, SEO, and social media.

It's important to note that as technology continues to evolve, the skills that are in demand may change as well. Therefore, it's important for workers to continuously update their skills and be open to learning new technologies and techniques.

how about repetitive jobs lost in manufacturing and data entry

For workers who have lost their jobs in manufacturing and data entry due to the use of large language models and automation, some skills they may want to acquire to gain employment include:

1. Robotics and automation: With the increasing use of automation in manufacturing, there is a growing demand for workers with skills in robotics and automation, including programming, troubleshooting, and maintenance.
2. Industrial data analytics: As more and more data is being generated in manufacturing, there is a growing demand for workers who can analyze and interpret it. This includes skills such as data visualization, data mining, and statistical analysis.
3. Maintenance and repair: As automation and large language models become more prevalent in manufacturing, there will be a growing need for workers with skills in maintenance and repair, including electrical, mechanical, and pneumatic systems.
4. Quality control: With the increased use of automation in manufacturing, there will be a growing need for workers with skills in quality control, including statistical process control, metrology, and inspection.
5. Technical writing and documentation: Automation and large language models are becoming more prevalent in manufacturing, so there will be a growing need for workers with skills in technical writing and documentation, including creating and maintaining technical manuals, procedures, and work instructions.

For data entry jobs,

1. Data analysis and interpretation: As more and more data is being generated, there is a growing demand for workers who can analyze and interpret it. This includes skills such as data visualization, data mining, and statistical analysis.
2. Database management: With the increasing use of automation in data entry, there will be a growing demand for workers with skills in database management, including SQL, data warehousing, and data governance.
3. Business intelligence: With the increasing use of automation in data entry, there will be a growing demand for workers with skills in business intelligence, including data visualization, data warehousing, and reporting.
4. Cybersecurity: With the increasing use of technology in business and society, there is a growing need for workers with skills in cybersecurity, including network security, data security, and cloud security.
5. Programming: As automation becomes more prevalent in data entry, there will be a growing need for workers with skills in programming, including Python, R, and SQL.

It's important to note that as technology continues to evolve, the skills that are in demand may change as well. Therefore, it's important for workers to continuously update their skills and be open to learning new technologies and techniques.

large language models will be able to do most of the tasks mentioned above then why do you need humans?

While large language models like ChatGPT are capable of performing many tasks, they still require human input and oversight in order to function effectively. The use of language models is not meant to completely replace humans, but rather to assist and augment their abilities. Some of the reasons why humans are still needed even with the use of large language models are:

1. Creativity and Innovation: Humans are capable of creativity and innovation, something that language models lack. They can come up with new ideas and approaches to problems that language models may not be able to replicate.
2. Human touch and empathy: Many tasks, especially in areas such as customer service and healthcare, require a human touch and empathy. Language models can assist in providing information and completing routine tasks, but they are not able to fully replace the emotional intelligence and understanding that a human can provide.
3. Monitoring and maintenance: Language models require monitoring, maintenance and updates, which can only be done by humans.
4. Ethical and moral considerations: Many tasks require the ability to make ethical and moral decisions, which language models currently lack the capability to do.
5. Complex decision making: Language models can assist in decision making, but they are not yet able to fully replace the human ability to reason and make complex decisions.
6. Human oversight is needed to ensure the accuracy and fairness of the model's output, as language models are only as good as the data they are trained on.

Large language models, when used in conjunction with human workers, can increase efficiency, improve productivity, and create new job opportunities. While some jobs may be automated, the use of language models can also lead to the development of new industries and the creation of new job opportunities for those with the right skills.