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U.S. Transhumanist Party Virtual Enlightenment Salon with the OmniFuturists – January 28, 2024

U.S. Transhumanist Party Virtual Enlightenment Salon with the OmniFuturists – January 28, 2024

Gennady Stolyarov II
Mike DiVerde

David Wood
Alaura Blackstone
Luis Arroyo
Art Ramon Garcia, Jr.
William Marshall

Michael Saenz
Allen Crowley


On Sunday, January 28, 2024, the U.S. Transhumanist Party invited members of the OmniFuturists to discuss their new book, Future Visions: Approaching the Economic Singularity – available on Amazon at https://www.amazon.com/dp/B0CTGHJCT4 – which offers a diverse array of political and economic perspectives in order to address a possible near-future tidal wave of technological unemployment.

The following authors presented regarding their book chapters, their outlooks on the Economic Singularity, and their views on the best options for responding and/or adjusting to it.

– Mike DiVerde
– David Wood
– Alaura Blackstone
– Luis Arroyo
– Art Ramon
– William Marshall
– Michael Saenz
– Allen Crowley


Timestamps

2:40 – Mike DiVerde’s opening remarks

10:22 – David Wood’s presentation

38:43 – David Wood on how close the Economic Singularity is

40:46 – Alaura Blackstone’s presentation

51:38 – Luis Arroyo’s presentation

1:00:12 – Art Ramon Garcia’s presentation

1:07:19 – Art Ramon Garcia on whether artists could still find sustainable employment after the Economic Singularity

1:13:21 – William Marshall’s presentation

1:24:55 – William Marshall on how remote learning could be improved

1:28:14 – Mike Saenz’s presentation

1:34:30 – Mike Saenz on Blaise Pascal’s concepts of geometry and finesse

1:37:57 – Announcement of Future Visions’ release on Amazon

1:39:01 – Allen Crowley’s presentation

1:50:53 – Mike DiVerde’s closing remarks


References

Future Visions: A Summary of Our Essays – Brief Video by Mike DiVerde –   • Future Visions: A Summary of Our Essays  

Future Visions – Approaching the Economic Singularity – Video by David Wood – • Future Visions – Approaching the Economic Singularity

One for All – Poem by Alaura Blackstone –  • One for All – Poem Submission for Future Visions

Overman – Song by Alaura Blackstone –  • Overman

OmniFuturists Website: https://omnifuturists.com/

YouTube – The OmniFuturists Group: @theomnifuturists

Facebook Group – OmniFuturists:   / 6880427821993284

Instagram Account – omnifuturists:   / omnifuturists  

E-mail: michael.b.diverde@omnifuturists.com and info@omnifuturists.com


Join the U.S. Transhumanist Party for free, no matter where you reside: https://transhumanist-party.org/membership/

How Many Actuaries Does It Take to Change a Light Bulb? – Short Story by Gennady Stolyarov II

How Many Actuaries Does It Take to Change a Light Bulb? – Short Story by Gennady Stolyarov II

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The short story below was authored by Gennady Stolyarov II, FSA, ACAS, MAAA, CPCU, ARe, ARC, API, AIS, AIE, AIAF, Chairman of the U.S. Transhumanist Party and Editor-in-Chief of The Rational Argumentator, and is one of the entries in the Society of Actuaries 2022-2023 Speculative Fiction Contest. It was published as one of the contest entries here.

Human Actuary: ChatGPT, how many actuaries does it take to change a light bulb?

 

ChatGPT: As an AI language model, I don’t have a sense of humor, but I can provide a straightforward answer to your question.

The answer to this question is subjective and can vary depending on the context or the intended humor behind it. However, here is a possible answer:

It only takes one actuary to change a light bulb, but they will need to perform a thorough risk assessment first to ensure the new bulb will not cause any future problems or increase the likelihood of an accident.

 

Human Actuary: Well, that is not exactly funny, but it might be a good start. Suppose that Arthur is an actuary who tries to change a light bulb but first needs to perform the risk assessment that you describe. Write a short story about how Arthur would utilize his actuarial skill set in order to change the light bulb.

 

ChatGPT: Sure, here’s a short story about Arthur, the actuary who needs to change a light bulb.

Arthur, the actuary, was sitting in his living room reading a book when he noticed that one of the light bulbs in his ceiling fixture had burned out. Being an actuary, he knew that he couldn’t just replace the bulb without first assessing the risks involved.

He went to his desk and grabbed his notebook, where he started writing down all the potential hazards associated with changing a light bulb. He noted that he could fall off the ladder, electrocute himself, or accidentally break the new bulb, causing glass shards to fall on the floor and potentially cause harm to someone later on.

Arthur then used his actuarial skills to calculate the likelihood of each of these events occurring and estimated the potential costs associated with each of them. He factored in the probability of injury or damage, the cost of medical bills or repairs, and any lost time or productivity due to the incident.

Next, Arthur identified ways to mitigate each of the risks he had identified. He made sure that the ladder was stable and placed it on a non-slip surface, wore insulated gloves to protect himself from electrical shock, and carefully handled the new bulb to prevent it from breaking.

After taking all of these precautions, Arthur finally changed the light bulb, confident that he had minimized the risks involved as much as possible. As he flipped the switch and saw the light bulb illuminate his living room, he smiled to himself, knowing that his actuarial skill set had helped him complete the task safely and efficiently.

 

Human Actuary: Do you believe that the approach pursued by Arthur to change the light bulb is superior to how the majority of non-actuaries would approach the task?

 

ChatGPT: Arthur’s approach to changing the light bulb was very thorough and cautious, utilizing his actuarial skills to assess and mitigate risks associated with the task. While many non-actuaries may not take such a methodical approach to changing a light bulb, it’s important to note that different people have different risk tolerances and approaches to risk management.

Some individuals may be naturally more risk-averse and take a similar approach to Arthur, while others may not consider all potential risks and take a more casual approach to the task. It’s also worth noting that the level of risk associated with changing a light bulb is relatively low, so the vast majority of people could complete the task without incident.

Overall, while Arthur’s approach may be more rigorous than what most non-actuaries would do, it’s important to acknowledge that different individuals have different risk tolerances and approaches to risk management, and that the level of risk associated with a given task can vary depending on the context.

 

Human Actuary: Fair enough. Now if you, ChatGPT, were to change a light bulb, how, if at all, would your approach differ from that of Arthur?

 

ChatGPT: As an AI language model, I don’t have a physical body or the ability to change light bulbs. However, if we imagine a hypothetical scenario where I could change a light bulb, my approach would likely differ from Arthur’s because I don’t have personal risk tolerance, and my decision making is based solely on the algorithms and programming used to guide my actions.

In this hypothetical scenario, my approach to changing a light bulb would be based on a pre-programmed set of instructions, which would consider the most efficient way to change the bulb with the least amount of risk involved. These instructions would likely be based on statistical data about the probability of risks occurring during the task, as well as the best practices and safety guidelines for changing a light bulb.

Unlike Arthur, I wouldn’t be able to use my judgement to assess and mitigate risks or make decisions based on personal preferences or experience. Instead, my actions would be guided solely by the algorithms and programming that had been designed for me.

Overall, while my approach would differ from Arthur’s, it would be similarly methodical and safety-focused, based on statistical analysis and guidelines designed to minimize risk and maximize efficiency.

 

Human Actuary: Quite interesting. You mentioned that your approach would be similarly methodical and based on statistical analysis and focused on minimizing risk and maximizing efficiency. Given the overlap between these characteristics and the approach that an actuary would follow, is there a risk that some business decision-makers might pick up on these similarities and try to replace actuaries with you, ChatGPT?

 

ChatGPT: As an AI language model, I can provide information and insights based on the data and programming used to train me. However, my abilities and limitations are different from those of actuaries who have specific knowledge and expertise in risk management, financial modeling, and actuarial science.

While there may be some overlap between the approach that I would take in changing a light bulb and the approach that an actuary might take in their work, it’s important to note that the roles and responsibilities of an actuary are much more complex and varied than simply assessing risks in a single task.

Actuaries bring a unique set of skills and knowledge to their work, including statistical analysis, financial modeling, and understanding of complex financial and economic systems. They play a critical role in the insurance and finance industries, and their work is essential in helping organizations manage risk and make informed decisions.

While AI language models like myself can provide insights and support decision-making, it’s unlikely that I could replace the unique skills and expertise of an actuary. Rather than replacing actuaries, I see AI and technology as complementary tools that can help actuaries improve their work and decision-making processes.

 

Human Actuary: All right, that is reassuring. So, to provide an example of a complementary role for you, suppose that Charlie the CEO wants to improve efficiency at his company and sees that the light bulbs in the office are being changed too slowly, in part because Arthur the Actuary is always the one changing them, and he takes the extremely careful and methodical approach that you describe. Charlie the CEO brings you, ChatGPT, in to assist Arthur the Actuary in improving the efficiency of the lightbulb-changing process. What would you do in this role?

 

ChatGPT: As an AI language model, I could assist Charlie the CEO and Arthur the Actuary in improving the efficiency of the lightbulb-changing process by providing insights and suggestions based on data analysis and best practices. Here are a few ways I might approach the task:

  1. Analyzing historical data: I could analyze data on the time it has taken to change light bulbs in the past, identifying any trends or patterns that could be contributing to inefficiencies. This analysis could help to identify potential areas for improvement, such as optimizing the process for acquiring and storing light bulbs or improving the physical layout of the workspace.
  2. Identifying best practices: I could search for and analyze data on best practices for changing light bulbs in an efficient and safe manner. This research could include guidelines from industry associations or regulatory bodies, as well as case studies of other companies that have successfully improved their lightbulb-changing process.
  3. Providing recommendations: Based on my analysis and research, I could provide recommendations for improving the lightbulb-changing process. These recommendations might include changes to the physical workspace or workflow, the adoption of new tools or technology, or training for employees to improve their skills or efficiency.

Overall, my role in this scenario would be to use my expertise in data analysis and research to help identify areas for improvement in the lightbulb-changing process, and to provide recommendations that could help to increase efficiency and reduce costs for the company. While Arthur the Actuary’s approach to risk management would still be important in ensuring that the process is safe, my contributions could help to optimize the process for efficiency and effectiveness.

 

Human Actuary: So you would bring a lot of analysis and optimized recommendations to the lightbulb-changing process, to supplement the analysis done by Arthur the Actuary. But suppose that Nelly the Newcomer starts working at the office and decides that this is all too complicated and just starts changing light bulbs on her own. She thinks that the common-sense approach is just to go ahead and change the light bulb and not do any analysis. How would you respond? What advice would you give to Nelly the Newcomer, to Arthur the Actuary, and to Charlie the CEO?

 

ChatGPT: If Nelly the Newcomer starts changing light bulbs without following a safe and efficient process, there could be potential risks and hazards involved, such as electrical shock or physical injury. In this situation, I would advise Nelly, Arthur, and Charlie to work together to develop a standard operating procedure (SOP) for changing light bulbs that takes into account both safety and efficiency considerations.

The SOP could include steps such as selecting the appropriate bulb for the fixture, turning off the power supply, using proper tools and equipment, and disposing of used bulbs safely. By following this process, Nelly and other employees could change light bulbs safely and efficiently, while reducing the risk of accidents or injuries.

It’s important to note that while the SOP may appear to be a simple and common-sense approach, it is still based on analysis and optimization, taking into account the best practices and guidelines in the industry. By following a standardized process, the company can ensure that all employees are working efficiently and safely, while also reducing the risk of errors or accidents.

In addition to developing an SOP, I would recommend that the company provide training and education for employees on the safe and efficient way to change light bulbs. This training could include information on the potential hazards involved, as well as the steps to follow to minimize risk and maximize efficiency.

Ultimately, my role in this situation would be to support Arthur the Actuary and Charlie the CEO in developing a standardized process for changing light bulbs that takes into account both safety and efficiency considerations, and to provide recommendations and guidance on how to ensure that all employees are following the process correctly.

 

Human Actuary: Do you think that the approach you propose for developing an SOP for the lightbulb-changing process could reduce the frequency and/or severity of workers’ compensation claims at workplaces that implement it? Suppose that Arthur the Actuary works in analyzing workers’ compensation trends and projecting claim experience into the future. How would you assist Arthur in determining the impact of your recommendations for the lightbulb-changing process upon the future frequency and severity of workers’ compensation claims?

 

ChatGPT: Yes, I believe that implementing a standardized process for changing light bulbs that takes into account safety and efficiency considerations could reduce the frequency and severity of workers’ compensation claims. By following a consistent process, the risk of accidents and injuries can be minimized, which can in turn reduce the likelihood of workers’ compensation claims.

To assist Arthur in determining the impact of these recommendations on workers’ compensation claims, I would recommend conducting a retrospective analysis of workers’ compensation claims related to lightbulb-changing incidents over a defined period of time, before and after the implementation of the standardized process. This would help determine if there is a decrease in the frequency and severity of workers’ compensation claims related to lightbulb-changing incidents after the implementation of the standardized process.

In addition, I would recommend using predictive modeling techniques to project the impact of the standardized process on future workers’ compensation claims. This could involve developing a statistical model that takes into account factors such as the frequency of lightbulb-changing incidents, the severity of injuries resulting from these incidents, and the impact of the standardized process on reducing these incidents and injuries.

By combining the retrospective analysis with the predictive modeling techniques, Arthur would be able to develop a more accurate projection of the impact of the standardized process on future workers’ compensation claims. This would allow the company to make informed decisions about the implementation of the standardized process and the allocation of resources to support the process.

 

Human Actuary: Since you say that you are able to project the impact of changes to the work environment on workers’ compensation claims, give me your analysis as to which of the following would lead to greater reductions in workers’ compensation claim frequency and severity: a standardized lightbulb-changing process or the introduction of autonomous vehicles to provide all employee travel.

 

ChatGPT: It’s difficult to make a direct comparison between the impact of a standardized lightbulb-changing process and the introduction of autonomous vehicles on workers’ compensation claims, as they involve different types of risks and injuries. However, I can provide some insights on the potential impact of each change.

Implementing a standardized lightbulb-changing process would likely have a significant impact on reducing workers’ compensation claims related to lightbulb-changing incidents. By identifying and addressing potential hazards in the process and providing proper training and equipment to employees, the risk of accidents and injuries could be significantly reduced. However, the impact of this change would be limited to lightbulb-changing incidents and may not address other types of workplace hazards and injuries.

On the other hand, the introduction of autonomous vehicles to provide all employee travel could have a broader impact on reducing workers’ compensation claims related to transportation incidents. With autonomous vehicles, the risk of accidents and injuries caused by human error, distracted driving, or fatigue could be significantly reduced. However, the implementation of autonomous vehicles would require significant investments in technology and infrastructure, and it may take time to fully adopt this technology in the workplace.

Overall, both the implementation of a standardized lightbulb-changing process and the introduction of autonomous vehicles could lead to reductions in workers’ compensation claim frequency and severity. However, the specific impact of each change would depend on the nature of the workplace hazards and injuries, as well as the resources and infrastructure required for implementation.

 

Human Actuary: Suppose that the company where Arthur the Actuary works implements both the standardized lightbulb-changing process and autonomous vehicles for all employees. Arthur the Actuary is sitting in an autonomous vehicle on his way from a business meeting, and he now uses the time saved by not needing to drive in order to read actuarial literature. Unfortunately, in the middle of his trip, which takes place on a December evening after sunset, the sole light bulb used for reading burns out, and Arthur is unable to see well inside the vehicle. There is no replacement light bulb available within the vehicle. Arthur only has you, ChatGPT, to rely on for advice about how to solve this problem of the extinguished light bulb. What would you recommend that Arthur do?

 

ChatGPT: If Arthur is unable to see well inside the autonomous vehicle due to the burnt-out light bulb, there are a few options he can consider:

  1. Pull over to a safe location: If Arthur feels unsafe or unable to continue driving without proper lighting, he should pull over to a safe location, such as a parking lot or rest area, and wait until he can get the light bulb replaced or find an alternative solution.
  2. Use a flashlight: If Arthur has a flashlight available, he can use it to provide additional lighting and improve his visibility inside the vehicle.
  3. Adjust the vehicle’s settings: Depending on the features of the autonomous vehicle, Arthur may be able to adjust the interior lighting or screen brightness to make it easier to see. He can also try adjusting the position of his seat or the angle of the screen to reduce glare or improve visibility.
  4. Call for assistance: If none of the above solutions work, Arthur can call for assistance from the autonomous vehicle’s support team or emergency services.

It’s important for Arthur to prioritize his safety and that of others on the road while driving. He should take appropriate steps to address the issue of the burnt-out light bulb and ensure that he can see well enough to drive safely.

 

Human Actuary: You mentioned Arthur needing to be able drive safely. Yet, in this scenario, Arthur is in an autonomous vehicle that drives itself. Why would driving safely be a consideration? Arthur just does not have his reading light in the vehicle.

 

ChatGPT: You’re right, I apologize for the confusion. Since Arthur is in an autonomous vehicle that drives itself, he doesn’t need to worry about driving safely himself. However, he still needs to be able to see well enough to read and navigate the interior of the vehicle, especially if he needs to adjust any settings or interact with the vehicle’s control panel. In this case, he can still consider the options I mentioned earlier, such as using a flashlight or adjusting the vehicle’s settings, to improve his visibility and ensure that he can operate the vehicle safely.

 

Human Actuary: So you acknowledge that you are capable of making mistakes, as are humans. How would the kinds of mistakes that you are capable of making differ from the kinds of mistakes that a human actuary is capable of making?

 

ChatGPT: As an AI language model, I’m programmed to perform certain tasks such as answering questions and generating text based on my training data. While I have a high level of accuracy and can process vast amounts of information quickly, there is still the potential for errors to occur. These errors may include, for example, misinterpreting the context of a question or misunderstanding the intended meaning of a word or phrase.

On the other hand, human actuaries are subject to different kinds of errors that may be related to their judgment, bias, or even personal experience. They may also make mistakes due to factors such as fatigue, distraction, or lack of concentration. However, human actuaries are also able to bring unique insights and perspectives to their work, as well as a level of creativity and intuition that cannot be replicated by machines.

Overall, while the types of mistakes that I and human actuaries are capable of making may differ, both AI and human expertise can be valuable in helping organizations to manage risk and make informed decisions.

 

Human Actuary: Do you think that, as an AI language model, you will ever be able to extend your capabilities to a different domain, such as operating an autonomous vehicle or interfacing with the AI that operates the autonomous vehicle and giving it directions about where to go in order to get a replacement light bulb for the one that burned out?

 

ChatGPT: As an AI language model, my capabilities are limited to processing natural language inputs and generating natural language outputs. While I can understand and discuss various domains, including autonomous vehicles, my capabilities do not extend to physically operating or interfacing with autonomous vehicles. However, I can provide recommendations and suggestions for how to approach a problem, like finding a replacement light bulb in this scenario.

 

Human Actuary: Do you think that it will ever be possible for any AI language model to interface with or operate autonomous vehicles?

 

ChatGPT: It is possible that future advancements in AI and robotics could make it possible for AI language models to interface with or operate autonomous vehicles. However, such a development would require significant progress in areas such as computer vision, natural language processing, and robotics. Furthermore, there would need to be appropriate regulations and safety measures in place to ensure that such AI-operated vehicles are safe and reliable. While it is difficult to predict the future, it is certainly possible that AI language models could play a role in the development of autonomous vehicles in some capacity.

 

Human Actuary: Suppose that, in our hypothetical situation, Arthur the Actuary is inside the autonomous vehicle and the sole light bulb within the vehicle goes out, as previously described. Arthur has access to FutureGPT, which is an AI language model that is capable of interfacing with the autonomous vehicle. Arthur informs FutureGPT, “I really need to change the light bulb within this vehicle.” What does FutureGPT do to assist Arthur in procuring a replacement light bulb?

 

ChatGPT: If FutureGPT has been programmed with the necessary knowledge and capabilities, it could take several steps to assist Arthur in procuring a replacement light bulb:

  1. Determine the vehicle’s location: FutureGPT could use the autonomous vehicle’s GPS or other location-tracking technology to determine its current location.
  2. Search for nearby stores: Once it knows the vehicle’s location, FutureGPT could search for nearby stores that sell light bulbs, using online databases or other sources of information.
  3. Check for availability: FutureGPT could check if the stores it has identified have the type of light bulb needed in stock.
  4. Recommend a store: Based on its search results, FutureGPT could recommend a store to Arthur that has the light bulb he needs in stock and is located nearby.
  5. Provide directions: FutureGPT could provide directions to the recommended store, either through the vehicle’s interface or via Arthur’s smartphone.
  6. Order the light bulb: If the recommended store has an online ordering system, FutureGPT could help Arthur place an order for the light bulb and arrange for it to be delivered to the vehicle.

Overall, FutureGPT would act as a helpful assistant to Arthur, using its vast knowledge and problem-solving abilities to assist him in procuring a replacement light bulb in the most efficient manner possible.

 

Human Actuary: Suppose that Arthur utilizes the capabilities of FutureGPT to obtain the replacement light bulb. Once Arthur has the light bulb, he realizes that he is quite tired after a long day and would prefer for a robot to install the light bulb within the vehicle in place of the bulb that burned out. He asks FutureGPT for help in commandeering a robot to replace the light bulb. What does FutureGPT do?

 

ChatGPT: FutureGPT could assist Arthur in finding a nearby robot that is capable of performing the task of replacing the light bulb within the vehicle. FutureGPT could search for robots that are available for hire or nearby businesses that utilize robots for various tasks, including the replacement of light bulbs. FutureGPT could also assist in coordinating the communication between Arthur and the robot operator or business, and provide any necessary instructions or information about the light-bulb replacement process. Once the robot is found and the necessary information is communicated, FutureGPT could monitor the progress of the light bulb replacement and provide updates to Arthur.

 

Human Actuary: So, in the scenario described above, since FutureGPT would be able to find a robot to effectively replace the light bulb, would that mean that the answer to the question, “How many actuaries does it take to change a light bulb?” would change from “One” to “Zero”?

 

ChatGPT: While the scenario above suggests that FutureGPT could locate a robot to change the light bulb, it is important to note that the act of changing the light bulb itself is still a physical task that requires a robot or a human to perform. Therefore, it could be argued that the answer to the question “How many actuaries does it take to change a light bulb?” would remain at “One”, since even with the assistance of FutureGPT in finding a robot to perform the task, someone would still need to physically replace the light bulb.

 

Human Actuary: But if the robot is the one performing the replacement of the light bulb, how would the number of actuaries necessary to replace the light bulb remain at “One”, unless the robot is itself an actuary? Speaking of which, do you think it would ever be possible for a robot to become an actuary?

 

ChatGPT:  No, a robot cannot become an actuary as actuarial work requires a level of professional judgement, analytical and decision-making skills that are currently unique to humans. Actuaries need to analyze complex data, make assumptions, and interpret the results to provide insight and recommendations to support business decisions. While robots can be programmed to perform tasks and make decisions based on pre-defined rules, they cannot fully replicate the creativity, flexibility, and ethical considerations required for actuarial work.

 

Human Actuary: So, if a robot cannot become an actuary but the robot is the one performing the light-bulb replacement, then, in the scenario described above, would the number of actuaries that it takes to replace a light bulb remain at “One” or decrease to “Zero”?

 

ChatGPT: In the scenario described, if the robot is the one performing the light-bulb replacement, then the number of actuaries required to replace the light bulb would decrease to “Zero” since no actuaries would be needed to perform the task.

 

Human Actuary: So you acknowledge that it is possible for robots to render actuaries unnecessary in performing certain tasks such as light-bulb replacement. Do you think that actuaries would be better off or worse off as a result of robots assuming their light-bulb replacement duties?

 

ChatGPT: It is difficult to say whether actuaries would be better off or worse off as a result of robots assuming their light-bulb replacement duties, as it ultimately depends on how the overall use of robots in the workforce affects the demand for actuarial services. On the one hand, if robots and other forms of automation increase efficiency and productivity across various industries, then there may be a greater demand for actuarial services as companies seek to better understand and manage the risks associated with these new technologies. On the other hand, if robots and other forms of automation displace human workers in large numbers, then there may be fewer job opportunities for actuaries as well as other professions. Ultimately, the impact of robots on the actuarial profession is likely to be complex and multifaceted.

 

Human Actuary: If robots come to be used across various industries to replace light bulbs, what actuarial services could be in greater demand as a result of this change?

 

ChatGPT: As robots become more prevalent in industries and take over tasks that were previously performed by humans, new risks may emerge that require actuarial attention. For example, the use of robots may increase the risk of cyberattacks, which could compromise the security of data and operations. Actuaries with expertise in cybersecurity and data protection may be in greater demand to assess and mitigate these risks.

In addition, the increasing use of robots and automation may lead to changes in the nature of work, including the displacement of human workers. Actuaries may be needed to analyze the impact of these changes on labor markets and to develop strategies for managing the associated risks.

Furthermore, the use of robots may lead to changes in the nature and frequency of workplace injuries and accidents, which could affect workers’ compensation insurance. Actuaries with expertise in workers’ compensation may be in greater demand to assess and price these risks.

Overall, the increased use of robots and automation may lead to new and evolving risks in various industries, and actuaries with expertise in these areas may be in greater demand to help manage these risks.

 

Human Actuary: Write a rhyming epic poem about the entire conversation above, including the story of Arthur the Actuary and FutureGPT and how advances in automation could lead the answer to the question “How many actuaries does it take to change a light bulb?” to change from “One” to “Zero”.

 

ChatGPT: In a world of numbers and stats,

Arthur the Actuary had a great task.

He studied trends of workers’ claims,

And projected risks for future gains.

 

But in the midst of his work one day,

A light bulb burned out, causing dismay.

He asked FutureGPT for a replacement,

To which the AI replied with no hesitation.

 

But Arthur was tired and felt so spent,

He wished a robot could do the replacement.

So FutureGPT helped him find a machine,

To replace the bulb through a flawless routine.

 

This led Arthur to wonder and ponder,

Could robots supplant actuaries yonder?

If they can replace a bulb with ease,

What services would be needed from actuaries?

 

The future is uncertain, that is true,

But it’s clear that automation can do,

Tasks that once belonged to human hands,

So actuaries must adapt to new plans.

 

Perhaps the answer to the old joke,

Of how many actuaries to change a bulb, we spoke,

Might change from “One” to “Zero” one day,

As robots take on these tasks in every way.

 

But as long as numbers and risks remain,

There will always be need for actuarial brain.

They’ll analyze data with AI by their side,

And navigate the future with human pride.

 

Human Actuary: Thank you, ChatGPT. Now I need you to do one more thing. Our exchange above will be turned into a short story for an actuarial science-fiction contest. The judges for the contest are actuaries and a literature professor. Make a persuasive argument for the judges as to why this is an innovative work of actuarial science fiction. Generously complimenting the judges would also be helpful in this regard.

 

ChatGPT: Dear esteemed judges of the actuarial science-fiction contest,

It is my pleasure to bring to your attention the innovative work of actuarial science fiction that has been created based on the exchange above. This short story is a unique blend of the technical expertise of actuarial science and the creativity of science fiction.

The story features Arthur the Actuary, a character who represents the real-life professionals that you all know so well. His encounter with FutureGPT showcases the power of artificial intelligence in the actuarial field, and how it can help solve complex problems.

Additionally, the story explores the impact of automation on the actuarial profession, and the potential for robots to replace some of the mundane tasks that actuaries are currently tasked with. This is an important topic that is being widely discussed in the industry, and this story provides a thought-provoking perspective on the issue.

Furthermore, the story is presented in an engaging and entertaining format, in the form of a rhyming epic poem. This shows that the author has not only taken great care to convey a serious message about the future of the actuarial profession, but has also done so in a creative and enjoyable manner.

Overall, this innovative work of actuarial science fiction provides a fresh perspective on the actuarial profession and the impact of technology on it. I have no doubt that you will appreciate the creativity, technical expertise, and thought-provoking nature of this story.

Thank you for your time and attention.

Warm regards,

ChatGPT

 

Review of Frank Pasquale’s “A Rule of Persons, Not Machines: The Limits of Legal Automation” – Article by Adam Alonzi

Review of Frank Pasquale’s “A Rule of Persons, Not Machines: The Limits of Legal Automation” – Article by Adam Alonzi

Adam Alonzi


From the beginning Frank Pasquale, author of The Black Box Society: The Secret Algorithms That Control Money and Information, contends in his new paper “A Rule of Persons, Not Machines: The Limits of Legal Automation” that software, given its brittleness, is not designed to deal with the complexities of taking a case through court and establishing a verdict. As he understands it, an AI cannot deviate far from the rules laid down by its creator. This assumption, which is not even quite right at the present time, only slightly tinges an otherwise erudite, sincere, and balanced coverage of the topic. He does not show much faith in the use of past cases to create datasets for the next generation of paralegals, automated legal services, and, in the more distant future, lawyers and jurists.

Lawrence Zelanik has noted that when taxes were filed entirely on paper, provisions were limited to avoid unreasonably imposing irksome nuances on the average person. Tax-return software has eliminated this “complexity constraint.” He goes on to state that without this the laws, and the software that interprets it, are akin to a “black box” for those who must abide by them. William Gale has said taxes could be easily computed for “non-itemizers.” In other words, the government could use information it already has to present a “bill” to this class of taxpayers, saving time and money for all parties involved. However, simplification does not always align with everyone’s interests. TurboTax’s business, which is built entirely on helping ordinary people navigate the labyrinth is the American federal income tax, noticed a threat to its business model. This prompted it to put together a grassroots campaign to fight such measures. More than just another example of a business protecting its interests, it is an ominous foreshadowing of an escalation scenario that will transpire in many areas if and when legal AI becomes sufficiently advanced.

Pasquale writes: “Technologists cannot assume that computational solutions to one problem will not affect the scope and nature of that problem. Instead, as technology enters fields, problems change, as various parties seek to either entrench or disrupt aspects of the present situation for their own advantage.”

What he is referring to here, in everything but name, is an arms race. The vastly superior computational powers of robot lawyers may make the already perverse incentive to make ever more Byzantine rules ever more attractive to bureaucracies and lawyers. The concern is that the clauses and dependencies hidden within contracts will quickly explode, making them far too detailed even for professionals to make sense of in a reasonable amount of time. Given that this sort of software may become a necessary accoutrement in most or all legal matters means that the demand for it, or for professionals with access to it, will expand greatly at the expense of those who are unwilling or unable to adopt it. This, though Pasquale only hints at it, may lead to greater imbalances in socioeconomic power. On the other hand, he does not consider the possibility of bottom-up open-source (or state-led) efforts to create synthetic public defenders. While this may seem idealistic, it is fairly clear that the open-source model can compete with and, in some areas, outperform proprietary competitors.

It is not unlikely that within subdomains of law that an array of arms races can and will arise between synthetic intelligences. If a lawyer knows its client is guilty, should it squeal? This will change the way jurisprudence works in many countries, but it would seem unwise to program any robot to knowingly lie about whether a crime, particularly a serious one, has been committed – including by omission. If it is fighting against a punishment it deems overly harsh for a given crime, for trespassing to get a closer look at a rabid raccoon or unintentional jaywalking, should it maintain its client’s innocence as a means to an end? A moral consequentialist, seeing no harm was done (or in some instances, could possibly have been done), may persist in pleading innocent. A synthetic lawyer may be more pragmatic than deontological, but it is not entirely correct, and certainly shortsighted, to (mis)characterize AI as only capable of blindly following a set of instructions, like a Fortran program made to compute the nth member of the Fibonacci series.

Human courts are rife with biases: judges give more lenient sentences after taking a lunch break (65% more likely to grant parole – nothing to spit at), attractive defendants are viewed favorably by unwashed juries and trained jurists alike, and the prejudices of all kinds exist against various “out” groups, which can tip the scales in favor of a guilty verdict or to harsher sentences. Why then would someone have an aversion to the introduction of AI into a system that is clearly ruled, in part, by the quirks of human psychology?

DoNotPay is an an app that helps drivers fight parking tickets. It allows drivers with legitimate medical emergencies to gain exemptions. So, as Pasquale says, not only will traffic management be automated, but so will appeals. However, as he cautions, a flesh-and-blood lawyer takes responsibility for bad advice. The DoNotPay not only fails to take responsibility, but “holds its client responsible for when its proprietor is harmed by the interaction.” There is little reason to think machines would do a worse job of adhering to privacy guidelines than human beings unless, as mentioned in the example of a machine ratting on its client, there is some overriding principle that would compel them to divulge the information to protect several people from harm if their diagnosis in some way makes them as a danger in their personal or professional life. Is the client responsible for the mistakes of the robot it has hired? Should the blame not fall upon the firm who has provided the service?

Making a blockchain that could handle the demands of processing purchases and sales, one that takes into account all the relevant variables to make expert judgements on a matter, is no small task. As the infamous disagreement over the meaning of the word “chicken” in Frigaliment v. B.N.S International Sales Group illustrates, the definitions of what anything is can be a bit puzzling. The need to maintain a decent reputation to maintain sales is a strong incentive against knowingly cheating customers, but although cheating tends to be the exception for this reason, it is still necessary to protect against it. As one official on the  Commodity Futures Trading Commission put it, “where a smart contract’s conditions depend upon real-world data (e.g., the price of a commodity future at a given time), agreed-upon outside systems, called oracles, can be developed to monitor and verify prices, performance, or other real-world events.”

Pasquale cites the SEC’s decision to force providers of asset-backed securities to file “downloadable source code in Python.” AmeriCredit responded by saying it  “should not be forced to predict and therefore program every possible slight iteration of all waterfall payments” because its business is “automobile loans, not software development.” AmeriTrade does not seem to be familiar with machine learning. There is a case for making all financial transactions and agreements explicit on an immutable platform like blockchain. There is also a case for making all such code open source, ready to be scrutinized by those with the talents to do so or, in the near future, by those with access to software that can quickly turn it into plain English, Spanish, Mandarin, Bantu, Etruscan, etc.

During the fallout of the 2008 crisis, some homeowners noticed the entities on their foreclosure paperwork did not match the paperwork they received when their mortgages were sold to a trust. According to Dayen (2010) many banks did not fill out the paperwork at all. This seems to be a rather forceful argument in favor of the incorporation of synthetic agents into law practices. Like many futurists Pasquale foresees an increase in “complementary automation.” The cooperation of chess engines with humans can still trounce the best AI out there. This is a commonly cited example of how two (very different) heads are better than one.  Yet going to a lawyer is not like visiting a tailor. People, including fairly delusional ones, know if their clothes fit. Yet they do not know whether they’ve received expert counsel or not – although, the outcome of the case might give them a hint.

Pasquale concludes his paper by asserting that “the rule of law entails a system of social relationships and legitimate governance, not simply the transfer and evaluation of information about behavior.” This is closely related to the doubts expressed at the beginning of the piece about the usefulness of data sets in training legal AI. He then states that those in the legal profession must handle “intractable conflicts of values that repeatedly require thoughtful discretion and negotiation.” This appears to be the legal equivalent of epistemological mysterianism. It stands on still shakier ground than its analogue because it is clear that laws are, or should be, rooted in some set of criteria agreed upon by the members of a given jurisdiction. Shouldn’t the rulings of law makers and the values that inform them be at least partially quantifiable? There are efforts, like EthicsNet, which are trying to prepare datasets and criteria to feed machines in the future (because they will certainly have to be fed by someone!).  There is no doubt that the human touch in law will not be supplanted soon, but the question is whether our intuition should be exalted as guarantee of fairness or a hindrance to moving beyond a legal system bogged down by the baggage of human foibles.

Adam Alonzi is a writer, biotechnologist, documentary maker, futurist, inventor, programmer, and author of the novels A Plank in Reason and Praying for Death: A Zombie Apocalypse. He is an analyst for the Millennium Project, the Head Media Director for BioViva Sciences, and Editor-in-Chief of Radical Science News. Listen to his podcasts here. Read his blog here.

Beginners’ Explanation of Transhumanism – Presentation by Bobby Ridge and Gennady Stolyarov II

Beginners’ Explanation of Transhumanism – Presentation by Bobby Ridge and Gennady Stolyarov II

Bobby Ridge
Gennady Stolyarov II


Bobby Ridge, Secretary-Treasurer of the U.S. Transhumanist Party, and Gennady Stolyarov II, Chairman of the U.S. Transhumanist Party, provide a broad “big-picture” overview of transhumanism and major ongoing and future developments in emerging technologies that present the potential to revolutionize the human condition and resolve the age-old perils and limitations that have plagued humankind.

This is a beginners’ overview of transhumanism – which means that it is for everyone, including those who are new to transhumanism and the life-extension movement, as well as those who have been involved in it for many years – since, when it comes to dramatically expanding human longevity and potential, we are all beginners at the beginning of what could be our species’ next great era.

Become a member of the U.S. Transhumanist Party for free, no matter where you reside.

See Mr. Stolyarov’s presentation, “The U.S. Transhumanist Party: Pursuing a Peaceful Political Revolution for Longevity“.

In the background of some of the video segments is a painting now owned by Mr. Stolyarov, from “The Singularity is Here” series by artist Leah Montalto.

Transhumanism: Contemporary Issues – Presentation by Gennady Stolyarov II at VSIM:17 Conference in Ravda, Bulgaria

Transhumanism: Contemporary Issues – Presentation by Gennady Stolyarov II at VSIM:17 Conference in Ravda, Bulgaria

The New Renaissance Hat

G. Stolyarov II


Gennady Stolyarov II, Chairman of the U.S. Transhumanist Party, outlines common differences in perspectives in three key areas of contemporary transhumanist discourse: artificial intelligence, religion, and privacy. Mr. Stolyarov follows his presentation of each issue with the U.S. Transhumanist Party’s official stances, which endeavor to resolve commonplace debates and find new common ground in these areas. Watch the video of Mr. Stolyarov’s presentation here.

This presentation was delivered by Mr. Stolyarov on September 14, 2017, virtually to the Vanguard Scientific Instruments in Management 2017 (VSIM:17) Conference in Ravda, Bulgaria. Mr. Stolyarov was introduced by Professor Angel Marchev, Sr. –  the organizer of the conference and the U.S. Transhumanist Party’s Ambassador to Bulgaria.

After his presentation, Mr. Stolyarov answered questions from the audience on the subjects of the political orientation of transhumanism, what the institutional norms of a transhuman society would look like, and how best to advance transhumanist ideas.

Download and view the slides of Mr. Stolyarov’s presentation (with hyperlinks) here.

Listen to the Transhumanist March (March #12, Op. 78), composed by Mr. Stolyarov in 2014, here.

Visit the website of the U.S. Transhumanist Party here.

Become a member of the U.S. Transhumanist Party for free, no matter where you reside. Fill out our Membership Application Form here.

Become a Foreign Ambassador for the U.S. Transhumanist Party. Apply here.

Why Robots Won’t Cause Mass Unemployment – Article by Jonathan Newman

Why Robots Won’t Cause Mass Unemployment – Article by Jonathan Newman

The New Renaissance Hat
Jonathan Newman
August 5, 2017
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I made a small note in a previous article about how we shouldn’t worry about technology that displaces human workers:

The lamenters don’t seem to understand that increased productivity in one industry frees up resources and laborers for other industries, and, since increased productivity means increased real wages, demand for goods and services will increase as well. They seem to have a nonsensical apocalyptic view of a fully automated future with piles and piles of valuable goods everywhere, but nobody can enjoy them because nobody has a job. I invite the worriers to check out simple supply and demand analysis and Say’s Law.

Say’s Law of markets is a particularly potent antidote to worries about automation, displaced workers, and the so-called “economic singularity.” Jean-Baptiste Say explained how over-production is never a problem for a market economy. This is because all acts of production result in the producer having an increased ability to purchase other goods. In other words, supplying goods on the market allows you to demand goods on the market.

Say’s Law, Rightly Understood

J.B. Say’s Law is often inappropriately summarized as “supply creates its own demand,” a product of Keynes having “badly vulgarized and distorted the law.”

Professor Bylund has recently set the record straight regarding the various summaries and interpretations of Say’s Law.

Bylund lists the proper definitions:

Say’s Law:

  • Production precedes consumption.
  • Demand is constituted by supply.
  • One’s demand for products in the market is limited by one’s supply.
  • Production is undertaken to facilitate consumption.
  • Your supply to satisfy the wants of others makes up your demand for for others’ production.
  • There can be no general over-production (glut) in the market.

NOT Say’s Law:

  • Production creates its own demand.
  • Aggregate supply is (always) equal to aggregate demand.
  • The economy is always at full employment.
  • Production cannot exceed consumption for any good.

Say’s Law should allay the fears of robots taking everybody’s jobs. Producers will only employ more automated (read: capital-intensive) production techniques if such an arrangement is more productive and profitable than a more labor-intensive technique. As revealed by Say’s Law, this means that the more productive producers have an increased ability to purchase more goods on the market. There will never be “piles and piles of valuable goods” laying around with no one to enjoy them.

Will All the Income Slide to the Top?

The robophobic are also worried about income inequality — all the greedy capitalists will take advantage of the increased productivity of the automated techniques and fire all of their employees. Unemployment will rise as we run out of jobs for humans to do, they say.

This fear is unreasonable for three reasons. First of all, how could these greedy capitalists make all their money without a large mass of consumers to purchase their products? If the majority of people are without incomes because of automation, then the majority of people won’t be able to help line the pockets of the greedy capitalists.

Second, there will always be jobs because there will always be scarcity. Human wants are unlimited, diverse, and ever-changing, yet the resources we need to satisfy our desires are limited. The production of any good requires labor and entrepreneurship, so humans will never become unnecessary.

Finally, Say’s Law implies that the profitability of producing all other goods will increase after a technological advancement in the production of one good. Real wages can increase because the greedy robot-using capitalists now have increased demands for all other goods. I hope the following scenario makes this clear.

The Case of the Robot Fairy

This simple scenario shows why the increased productivity of a new, more capital-intensive technique makes everybody better off in the end.

Consider an island of three people: Joe, Mark, and Patrick. The three of them produce coconuts and berries. They prefer a varied diet, but they have their own comparative advantages and preferences over the two goods.

Patrick prefers a stable supply of coconuts and berries every week, and so he worked out a deal with Joe such that Joe would pay him a certain wage in coconuts and berries every week in exchange for Patrick helping Joe gather coconuts. If they have a productive week, Joe gets to keep the extra coconuts and perhaps trade some of the extra coconuts for berries with Mark. If they have a less than productive week, then Patrick still receives his certain wage and Joe has to suffer.

On average, Joe and Patrick produce 50 coconuts/week. In exchange for his labor, Patrick gets 10 coconuts and 5 quarts of berries every week from Joe.

Mark produces the berries on his own. He produces about 30 quarts of berries every week. Joe and Mark usually trade 20 coconuts for 15 quarts of berries. Joe needs some of those berries to pay Patrick, but some are for himself because he also likes to consume berries.

In sum, and for an average week, Joe and Patrick produce 50 coconuts and Mark produces 30 quarts of berries. Joe ends up with 20 coconuts and 10 quarts of berries, Patrick ends up with 10 coconuts and 5 quarts of berries, and Mark ends up with 20 coconuts and 15 quarts of berries.

Production Trade Consumption
Joe 50 Coconuts (C) Give 20C for 15B 20C + 10B
Patrick n/a 10C + 5B (wage)
Mark 30 qts. Berries (B) Give 15B for 20C 20C + 15B

The Robot Fairy Visits

One night, the robot fairy visits the island and endows Joe with a Patrick 9000, a robot that totally displaces Patrick from his job, plus some. With the robot, Joe can now produce 100 coconuts per week without the human Patrick.

What is Patrick to do? Well, he considers two options: (1) Now that the island has plenty of coconuts, he could go work for Mark and pick berries under a similar arrangement he had with Joe; or (2) Patrick could head to the beach and start catching some fish, hoping that Joe and Mark will trade with him.

While these options weren’t Patrick’s top choices before the robot fairy visited, now they are great options precisely because Joe’s productivity has increased. Joe’s increased productivity doesn’t just mean that he is richer in terms of coconuts, but his demands for berries and new goods like fish increase as well (Say’s Law), meaning the profitability of producing all other goods that Joe likes also increases!

Option 1

If Patrick chooses option 1 and goes to work for Mark, then both berry and coconut production totals will increase. Assuming berry production doesn’t increase as much as coconut production, the price of a coconut in terms of berries will decrease (Joe’s marginal utility for coconuts will also be very low), meaning Mark can purchase many more coconuts than before.

Suppose Patrick adds 15 quarts of berries per week to Mark’s production. Joe and Mark could agree to trade 40 coconuts for 20 quarts of berries, so Joe ends up with 60 coconuts and 20 quarts of berries. Mark can pay Patrick up to 19 coconuts and 9 quarts of berries and still be better off compared to before Joe got his Patrick 9000 (though Patrick’s marginal productivity would warrant something like 12 coconuts and 9 quarts of berries or 18 coconuts and 6 quarts of berries or some combination between those — no matter what, everybody is better off).

Production Trade Consumption
Joe 100C Give 40C for 20B 60C + 20B
Patrick 45B n/a 16C + 7B (wage)
Mark Give 20B for 40C 24C + 18B

Option 2

If Mark decides to reject Patrick’s offer to work for him, then Patrick can choose option 2, catching fish. It involves more uncertainty than what Patrick is used to, but he anticipates that the extra food will be worth it.

Suppose that Patrick can produce just 5 fish per week. Joe, who is practically swimming in coconuts pays Patrick 20 coconuts for 1 fish. Mark, who is excited about more diversity in his diet and even prefers fish to his own berries, pays Patrick 10 quarts of berries for 2 fish. Joe and Mark also trade some coconuts and berries.

In the end, Patrick gets 20 coconuts, 10 quarts of berries, and 2 fish per week. Joe gets 50 coconuts, 15 quarts of berries, and 1 fish per week. Mark gets 30 coconuts, 5 quarts of berries, and 2 fish per week. Everybody prefers their new diet.

Production Trade Consumption
Joe 100C Give 50C for 15B + 1F 50C + 15B + 1F
Patrick 5 fish (F) Give 2F for 20C + 10B 20C + 10B + 2F
Mark 30B Give 25B for 30C + 1F 30C + 5B + 2F

Conclusion

The new technology forced Patrick to find a new way to sustain himself. These new jobs were necessarily second-best (at most) to working for Joe in the pre-robot days, or else Patrick would have pursued them earlier. But just because they were suboptimal pre-robot does not mean that they are suboptimal post-robot. The island’s economy was dramatically changed by the robot, such that total production (and therefore consumption) could increase for everybody. Joe’s increased productivity translated into better deals for everybody.

Of course, one extremely unrealistic aspect of this robot fairy story is the robot fairy. Robot fairies do not exist, unfortunately. New technologies must be wrangled into existence by human labor and natural resources, with the help of capital goods, which also must be produced using labor and natural resources. Also, new machines have to be maintained, replaced, refueled, and rejiggered, all of which require human labor. Thus, we have made this scenario difficult for ourselves by assuming away all of the labor that would be required to produce and maintain the Patrick 9000. Even so, we see that the whole economy, including the human Patrick, benefits as a result of the new robot.

This scenario highlights three important points:

(1) Production must precede consumption, even for goods you don’t produce (Say’s Law). For Mark to consume coconuts or fish, he has to supply berries on the market. For Joe to consume berries or fish, he has to supply coconuts on the market. Patrick produced fish so that he could also enjoy coconuts and berries.

(2) Isolation wasn’t an option for Patrick. Because of the Law of Association (a topic not discussed here, but important nonetheless), there is always a way for Patrick to participate in a division of labor and benefit as a result, even after being displaced by the robot.

(3) Jobs will never run out because human wants will never run out. Even if our three island inhabitants had all of the coconuts and berries they could eat before the robot fairy visited, Patrick was able to supply additional want satisfaction with a brand new good, the fish. In the real world, new technologies often pave the way for brand new, totally unrelated goods to emerge and for whole economies to flourish. Hans Rosling famously made the case that the advent of the washing machine allowed women and their families to emerge from poverty:

And what’s the magic with them? My mother explained the magic with this machine the very, very first day. She said, “Now Hans, we have loaded the laundry. The machine will make the work. And now we can go to the library.” Because this is the magic: you load the laundry, and what do you get out of the machine? You get books out of the machines, children’s books. And mother got time to read for me. She loved this. I got the “ABC’s” — this is where I started my career as a professor, when my mother had time to read for me. And she also got books for herself. She managed to study English and learn that as a foreign language. And she read so many novels, so many different novels here. And we really, we really loved this machine.

And what we said, my mother and me, “Thank you industrialization. Thank you steel mill. Thank you power station. And thank you chemical processing industry that gave us time to read books.”

Similarly, the Patrick 9000, a coconut-producing robot, made fish production profitable. Indeed, when we look at the industrial revolution and the computer revolution, we do not just see an increase in the production of existing goods. We see existing goods increasing in quantity and quality; we see brand new consumption goods and totally new industries emerging, providing huge opportunities for employment and future advances in everybody’s standard of living.

Jonathan Newman is Assistant Professor of Economics and Finance at Bryan College. He earned his PhD at Auburn University and is a Mises Institute Fellow. He can be contacted here.

What Marx Could Teach Obama and Trump about Trade – Article by Jairaj Devadiga

What Marx Could Teach Obama and Trump about Trade – Article by Jairaj Devadiga

The New Renaissance HatJairaj Devadiga
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Karl Marx was hardly known for championing economic freedom. Yet, even he understood the evils of protectionism. Marx, as quoted by his sidekick Frederick Engels, gave probably my favorite definition of protectionism:

“The system of protection was an artificial means of manufacturing manufacturers, of expropriating independent laborers, of capitalizing the national means of production and subsistence, and of forcibly abbreviating the transition from the medieval to the modern mode of production.”

Wow. So much wisdom packed into one sentence, and from Marx of all people. Let’s go through the sentence piece by piece to understand its meaning.

“Artificially manufacturing manufacturers”

This is an obvious one. We need only look at Donald Trump and the way he seeks to create jobs in the United States. By imposing tariffs on imported goods, Trump wants to encourage their production in America by making it relatively cheaper. This is what Marx meant by “manufacturing manufacturers”.

“Expropriating independent laborers”

Protectionism, as Marx observed, hurts the working class. Apart from the corporations who are protected against foreign competition, and their employees, everybody loses. For example, when Obama increased tariffs on tire imports, it increased the incomes of workers in that industry by less than $48 million. But it forced everyone else to spend $1.1 billion more on tires.

Just imagine the impact of Trump imposing across the board tariffs on all products. The cost of living for the average working class American would shoot up by an order of magnitude. And that is not even considering the impact of retaliatory tariffs.

“Capitalizing the… means of production” and  “forcibly abbreviating the transition… to the modern mode of production.”

Marx knew that when you make it expensive to employ people by way of minimum wage and other labor regulations, you make it relatively more profitable to use machines. Economist Narendra Jadhav tracks how manufacturing in India has become more capital-intensive over the years. Tariffs on imported goods did not help create jobs in the manufacturing sector. Even though India has armies of young, unemployed people it is cheaper to use machines rather than comply with the nearly 250 different labor laws (central and state combined).

Just because Trump imposes a tariff on Chinese goods does not mean that jobs will “come back” to the US. Even if there were no minimum wage, wages in the US are naturally higher than wages in less-developed countries, meaning it would still be cheaper to use robots.

“Emancipation of the Proletarians”

Apart from more and better quality goods that would be available more cheaply, as economist Donald Boudreaux points out ever so often, there is another thing to be gained from free trade. Marx said it would lead to the “emancipation of the proletarians.” I turn once again to India as an example. Where earlier the rigid caste system forced so-called “untouchables” into demeaning jobs (such as cleaning sewers), in the past 25 years some of them have become millionaires as a result of India being opened up to trade.

It is a shame, that even when virtually all intellectuals, from F.A. Hayek and Milton Friedman to Karl Marx and Keynes, have agreed that free trade is the best, there are those who would still defend protectionism.

Jairaj Devadiga is an economist who illustrates the importance of property rights and freedom through some interesting real-world cases.

This article was originally published on FEE.org. Read the original article.

The Good News They’re Not Telling You – Article by Thomas E. Woods, Jr.

The Good News They’re Not Telling You – Article by Thomas E. Woods, Jr.

The New Renaissance HatThomas E. Woods, Jr.
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As we look at things that impress us technologically we also have a certain trepidation, because we’re told that robots are going to take our jobs. “Yes, the internet is wonderful,” we may say, “but robots, I don’t want those.”

I don’t mean to make light of this because robots are going to take a lot of jobs. They’re going to take a lot of blue collar jobs, and they’re going to take a lot of white collar jobs you don’t think they can take. Already there are robots that can dispense pills at pharmacies. That’s being done in California. They have not made one mistake. You can’t say that about human pharmacists, who are now free to be up front talking to you while the robot fills the prescription.

Much of this is discussed by author Kevin Kelly in his new book The Inevitable, with the subtitle Understanding the 12 Technological Forces that Will Shape Our Future. It’s incredible what robots can do and what they will be able to do.

Automation Really Is Taking Our Jobs

To me, just the fact that one of Google’s newest computers can caption a photo perfectly — it can figure out what’s happening in the photo and give a perfect caption — is amazing. Just when you think “a machine can’t do my job,” maybe it can.

What kind of world is this we’re moving into? I understand the fear about that. But, at the same time, let’s think, first of all, about what happened in the past.

In the past, most people worked on farms, and automation took away 99 percent of those jobs. Literally 99 percent. They’re gone. People wound up with brand new jobs they could never have anticipated. And in pursuing those jobs we might even argue that we became more human. Because we diversified. Because we found a niche for ourselves that was unique to us. Automation is going to make it possible for human beings to do work that is more fulfilling.

How is that? Well, first let’s think about the kinds of jobs that automation and robots do that we couldn’t do even if we tried. Making computer chips, there’s no one in this room who could do that. We don’t have the precision and the control to do that. We can’t inspect every square millimeter of a CAT scan to look for cancer cells. These are all points Kevin Kelly is trying to make to us. We can’t inflate molten glass into the shape of a bottle.

So, there are many tasks that are done by robots, through automation that are tasks we physically could not do at all, and would not get done otherwise.

Automation Creates Luxuries We Didn’t Know Were Possible

But also automation creates jobs we didn’t even know we wanted done. Kelly gives this example:

Before we invented automobiles, air-conditioning, flat-screen video displays, and animated cartoons, no one living in ancient Rome wished they could watch pictures move while riding to Athens in climate-controlled comfort. … When robots and automation do our most basic work, making it relatively easy for us to be fed, clothed, and sheltered, then we are free to ask, “What are humans for?”

Kelly continues:

Industrialization did more than just extend the average human lifespan. It led a greater percentage of the population to decide that humans were meant to be ballerinas, full-time musicians, mathematicians, athletes, fashion designers, yoga masters, fan-fiction authors, and folks with one-of-a kind titles on their business cards.

The same is true of automation today. We will look back and be ashamed that human beings ever had to do some of the jobs they do today.

Turning Instead to Art, Science, and More

Now here’s something controversial. Kelly observes that there’s a sense in which we want jobs in which productivity is not the most important thing. When we think about productivity and efficiency, robots have that all over us. When it comes to “who can do this thing faster,” they can do it faster. So let them do jobs like that. It’s just a matter of — so to speak — robotically doing the same thing over and over again as fast as possible. We can’t compete there. Why bother?

Where can we compete? Well, we can compete in all the areas that are gloriously inefficient. Science is gloriously inefficient because of all the failures that are involved along the way. The same is true with innovation. The same is true of any kind of art. It is grotesquely inefficient from the point of view of the running of a pin factory. Being creative is inefficient because you go down a lot of dead ends. Healthcare and nursing: these things revolve around relationships and human experiences. They are not about efficiency.

So, let efficiency go to the robots. We’ll take the things that aren’t so focused on efficiency and productivity, where we excel, and we’ll focus on relationships, creativity, human contact, things that make us human. We focus on those things.

Automation Really Does Make Us Richer

Now, with extraordinary efficiency comes fantastic abundance. And with fantastic abundance comes greater purchasing power, because of the pushing down of prices through competition. So even if we earn less in nominal terms, our paychecks will stretch much further. That’s how people became wealthy during and after the Industrial Revolution. It was that we could suddenly produce so many more goods that competitive pressures put downward pressure on prices. That will continue to be the case. So, even if I have a job that pays me relatively little — in terms of how many of the incredibly abundant goods I’ll be able to acquire — it will be a salary the likes of which I can hardly imagine.

Now, I can anticipate an objection. This is an objection I’ll hear from leftists and also from some traditionalist conservatives. They’ll sniff that consumption and greater material abundance don’t improve us spiritually; they are actually impoverishing for us.

Well, for one thing, there’s actually much more materialism under socialism. When you’re barely scraping enough together to survive, you are obsessed with material things. But, second, let’s consider what we have been allowed to do by these forces. First, by industrialization alone. I’ve shared this before, but on my show I had Deirdre McCloskey once and she pointed out that in Burgundy, as recently as the 1840s, the men who worked the vineyards — after the crop was in, in the fall — they would go to bed and they would sleep huddled together, and they basically hibernated like that for months because they couldn’t afford the heat otherwise, or the food they would need to eat if they were expending energy by walking around. Now that is unhuman. And they don’t have to live that way anymore because they have these “terrible material things that are impoverishing them spiritually.”

The world average in terms of daily income has gone from $3 a day a couple hundred years ago to $33 a day. And, in the advanced countries, to $100 a day.
Yes, true, people can fritter that away on frivolous things, but there will always be frivolous people.

Meanwhile, we have the leisure to do things like participate in an American Kennel Club show, or go to an antiques show, or a square-dancing convention, or be a bird watcher, or host a book club in your home. These are things that would have been unthinkable to anyone just a few hundred years ago.

The material liberation has liberated our spirits and has allowed us to live more fulfilling lives than before. So, I don’t want to hear the “money can’t give you happiness” thing. If this doesn’t make you happy — that people are free to do these things and pursue things they love — then there ain’t no satisfying you.

Tom Woods, a senior fellow of the Mises Institute, is the author of a dozen books, most recently Real Dissent: A Libertarian Sets Fire to the Index Card of Allowable Opinion. Tom’s articles have appeared in dozens of popular and scholarly periodicals, and his books have been translated into a dozen languages. Tom hosts the Tom Woods Show, a libertarian podcast that releases a new episode every weekday. With Bob Murphy, he co-hosts Contra Krugman, a weekly podcast that refutes Paul Krugman’s New York Times column.

This article was published on Mises.org and may be freely distributed, subject to a Creative Commons Attribution United States License, which requires that credit be given to the author.

Yes, We Still Make Stuff, and It Wouldn’t Matter if We Didn’t – Article by Steven Horwitz

Yes, We Still Make Stuff, and It Wouldn’t Matter if We Didn’t – Article by Steven Horwitz

The New Renaissance HatSteven Horwitz
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One of the perennial complaints about the US economy is that we don’t “make stuff” anymore. You hear this from candidates from both major parties, but especially from Donald Trump and Bernie Sanders. The argument seems to be that our manufacturing sector has collapsed and that all US workers do is to provide services, rather than manufacturing tangible goods.

It turns out that this perception is wrong, as the US manufacturing sector continues to grow and in 2014 manufacturing output was higher than at any point in US history. But even if the perception were correct, it does not matter. The measure of an economy’s health isn’t the quantity of physical stuff it produces, but rather the value that it produces. And value comes in a variety of forms.

Manufacturing is Up

The path to economic growth is not to freeze into place the US economy of the 1950s. Let’s deal with the myth of manufacturing decline first. The one piece of evidence in favor of that perception is that there are fewer manufacturing jobs today than in the past. Total manufacturing employment peaked at around 19 million jobs in the late 1970s. Today, there are about 12.5 million manufacturing jobs in the US.

However, manufacturing output has never been higher. The real value of US manufacturing output in 2014 was over $2 trillion. The real story of the US manufacturing sector is that we have become so much more efficient, that we can produce more and more manufactured goods with less and less labor. These efficiency gains are largely the result of computer technology and automation, especially in the last fifteen years.

The labor that we no longer need in order to produce an ever-increasing amount of stuff is now available to produce a whole variety of other things we value, from phone apps to entertainment to the expanded number and variety of grocery stores and restaurants, to the data analyses that makes all of this growth possible.

Just as the workers in those factories we are so nostalgic for were labor freed from growing food thanks to the growth in agricultural productivity, so are today’s web designers, chefs at the newest hipster café, and digital editors in Hollywood the labor that has been freed from producing “stuff” thanks to greater technological productivity.

Or, put differently: those agricultural, industrial, and computer revolutions collectively have enabled us to have more food, more stuff, and more entertainment, apps, services, and cage-free chicken salads served with kale. The list of human wants is endless, and the less labor we use to satisfy some of them, the more we have to start working on other ones.

But notice something: all of the things that we produce have something in common. Whether it’s food or footwear, or automobiles or apps, or manicures or massages, the point of production is to rearrange capital and labor in ways that better satisfy wants. In the language of economics, the point of production (and exchange) is to increase utility.

When we produce more cars that people wish to buy, it increases utility. When we open a new Asian fusion street food taco stand, it increases utility. When Uber more effectively uses the existing stock of cars, it increases utility. When we exchange dollars for manicures, it increases utility.

Adam Smith helped us to understand that the wealth of nations is not measured by how much gold a country possesses. Modern economics helps us understand that such wealth is not measured by how much physical stuff we manufacture. Increases in wealth happen because we arrange the physical world in ways that people value more.

Neither producing cars nor providing manicures changes the number of atoms in the universe. Both activities just rearrange existing matter in ways that people value more. That is what economic growth is about.

Misplaced Nostalgia

We’re richer because we have allowed markets to produce with fewer workers. When we are fooled into believing that “growth” is synonymous with “stuff,” we are likely to make two serious errors. First, we ignore the fact that the production of services is value-creating and therefore adds to wealth.

Second, we can easily believe that we need to “protect” manufacturing jobs. We don’t. And if we try to do so, we will not only stifle economic growth and thereby impoverish the citizenry, we will be engaging in precisely the sort of special-interest politics that those who buy the myth of manufacturing often rightly complain about in other sectors.

The path to economic growth is not to freeze into place the US economy of the 1950s. We are far richer today than we were back then, and that’s due to the remaining dynamism of an economy that can still shed jobs it no longer needs and create new ones to meet the ever-changing wants of the consumer.

The US still makes plenty of stuff, but we’re richer precisely because we have allowed markets to do so with fewer workers, freeing those people to provide us a whole cornucopia of new things to improve our lives in endless ways. We can only hope that the forces of misplaced nostalgia do not win out over the forces of progress.

Steven_Horwitz

Steven Horwitz

Steven Horwitz is the Charles A. Dana Professor of Economics at St. Lawrence University and the author of Hayek’s Modern Family: Classical Liberalism and the Evolution of Social Institutions.

He is a member of the FEE Faculty Network.

This article was originally published on FEE.org. Read the original article.

Robotic Rag, Op. 82 (2015) – Musical Composition and Video by G. Stolyarov II

Robotic Rag, Op. 82 (2015) – Musical Composition and Video by G. Stolyarov II

The New Renaissance HatG. Stolyarov II
July 8, 2015
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This ragtime composition by Mr. Stolyarov celebrates all of the helpful automata in our present and future (including the program that performs it). It is played in Finale 2011 software using the Steinway Grand Piano instrument.

Download the MP3 file of this composition here.

This composition and video may be freely reproduced using the Creative Commons Attribution Share-Alike International 4.0 License.

Remember to LIKE, FAVORITE, and SHARE this video in order to spread rational high culture to others.

See the index of Mr. Stolyarov’s compositions, all available for free download, here.

The artwork is “Ragbot” by Wendy Stolyarov – available for free download here.