Bing Archives - The Reticulum

Which AI Should HR Professionals Use?

The other day, I spoke in front of a considerable crowd of HR professionals in LA. I asked how many had used ChatGPT and a couple of dozen or so raised their hands. Then I asked how many had used the Bing chatbot, and hardly any hands went up. I imagine this is typical across the profession, which is why I wanted to share a few thoughts about which AIs HR professionals should use.

Of course, there’s no one answer to this question. People should use the bot they like most. But, as a person who does a lot of HR-related research, I’ve formed some opinions.

There Are Two General Type of HR Questions

Generally speaking, there are two types of HR queries. The first type is about ideas and practices. The second is about factual information that often requires specific data as part of the response.

Type One Questions

When it comes to the first type of questions, all three of the most accessible AIs – that is, ChatGPT, Bing and Bard – are similar in performance. Here’s on such question I asked: “”I want to reduce the turnover the voluntary turnover rates of our developers. How do I accomplish that?”

All three of them offers tips such as offer competitive compensation, developmental opportunities, positive work environments, etc. All of the advice seemed fine if a bit bland. All three are, I think, a good place to start for an HR professional just beginning to think though some particular issue.

Type Two Questions

When it comes to the second type of question, however, there are some sizeable differences. Here’s what I asked as a demonstration: “I have a fast-food chain of restaurants in Tampa. What is the expected turnover rate for my employees?”

ChatGPT’s Response

The expected turnover rate for employees in the fast-food industry can vary depending on various factors such as location, industry norms, employee demographics, and specific organizational practices. While I don’t have access to real-time data or specific information about your fast-food chain, I can provide you with a general idea of turnover rates in the industry.

In the fast-food industry, turnover rates tend to be relatively high compared to other sectors due to factors like low wages, demanding work conditions, and limited career advancement opportunities. According to data from the Bureau of Labor Statistics (BLS) in the United States, the accommodation and food services industry, which includes fast-food establishments, had a voluntary turnover rate of around 81.9% in 2020.

However, it’s important to note that turnover rates can vary significantly based on factors like geographic location, employee engagement practices, and company-specific efforts to improve retention. Some fast-food chains have implemented effective strategies to reduce turnover rates, such as offering competitive wages, providing employee benefits, implementing career development programs, and fostering a positive work environment.

To determine the expected turnover rate for your specific fast-food chain in Tampa, it would be best to analyze your historical turnover data and compare it with industry benchmarks. Additionally, conducting employee surveys and exit interviews can provide valuable insights into the reasons for turnover within your organization, allowing you to identify areas for improvement and develop targeted retention strategies.

Bard’s Response

The expected turnover rate for fast food employees in Tampa is high. According to a study by HourWork, only 54% of QSR employees (quick-service restaurants) reached 90 days of working before quitting in 2022. Prior to the pandemic, the segment’s 90-day retention rate hovered at 58%.

There are a number of factors that contribute to the high turnover rate in the fast food industry, including:

Low wages: Fast food workers are often paid minimum wage or close to it, which can make it difficult to make ends meet.
Unpredictable schedules: Fast food workers often have to work unpredictable schedules, which can make it difficult to balance work and personal life.
High-pressure work environment: Fast food workers are often under a lot of pressure to meet customer demands, which can lead to stress and burnout.
Lack of opportunities for advancement: Fast food workers often have few opportunities for advancement, which can make them feel like they are not being rewarded for their hard work.

If you want to reduce the turnover rate at your fast food chain, you can consider:

Offering higher wages: This is one of the most effective ways to attract and retain employees.
Offering flexible schedules: This can help employees balance work and personal life.
Creating a positive work environment: This can help reduce stress and burnout.
Providing opportunities for advancement: This can help employees feel valued and motivated.

By taking steps to address the factors that contribute to high turnover, you can create a more attractive workplace for your employees and reduce the turnover rate at your fast food chain.

As a follow up question, I asked, “What is the source of this information?” It responded as follows:

The information I provided about the expected turnover rate for fast food employees in Tampa is from a study by HourWork. The study was conducted in 2022 and surveyed employees at over 8,000 quick-service restaurants (QSRs) in the United States. The study found that the average turnover rate for QSR employees was 61%, but the turnover rate for employees in Tampa was slightly higher at 63%.

The study also found that the turnover rate for QSR employees was higher for younger workers, with employees under the age of 25 having a turnover rate of 70%. The turnover rate was also higher for part-time employees, with part-time employees having a turnover rate of 65%.

The study’s findings suggest that the fast food industry in Tampa is facing a high turnover rate. This is likely due to a number of factors, including low wages, unpredictable schedules, and a high-pressure work environment. If you are a fast food employer in Tampa, you may want to consider taking steps to address these factors in order to reduce your turnover rate.

Here is the link to the study: https://www.restaurantdive.com/news/most-qsr-employees-worked-90-days-before-quitting-2022/640826/

Bing’s Response

The employee turnover rate for fast-food restaurants is notoriously high. According to various sources, the fast-food industry sees a 150% employee turnover rate¹ ², meaning that the entire staff is replaced annually and half of the new staff needs to be replaced again. Some sources report even higher rates, such as 144%³ or 130%⁴. This is a huge challenge for fast-food operators, as it affects customer service, food quality, and operational efficiency.

There are some exceptions, however. One fast-food chain, Pal’s Sudden Service, has managed to keep its turnover rate absurdly low, at less than 100%⁵. How does it do that? According to the CEO, Thomas Crosby, it’s all about hiring, training, and linking the identity in the marketplace to the approach in the workplace. Pal’s invests heavily in employee development, rewards performance, and creates a culture of excellence and loyalty.

So, depending on how you run your fast-food chain of restaurants in Tampa, you can expect a different turnover rate for your employees. If you follow the industry average, you might lose more than 100% of your workers every year. But if you follow the example of Pal’s Sudden Service, you might be able to retain more of your staff and reduce your turnover costs. I hope this information helps you. 😊

Which Performed Best?

ChatGPT did about as well as it could given that it has no access to data after September 2021. At least it cited the BLS, which is pretty much the gold standard for workforce-related data.

Bard arguably did better, citing a specific study from an article as recent as January 2023. Of course, I had to specifically ask for the source and there was only the one article.

Bing did the best. It cited multiple sources and provided links to those sources. It also cited an appropriate best practice article from a good, if somewhat dated, source: the Harvard Business Review.

It’s not that Bing is perfect. Sometimes the links it provides to articles don’t actually contain the information that has supposedly been cited. But it remains the best of the three for now and so, based on my informal poll, seems underutilized among HR professionals.

Why? Probably because Google’s Chrome still has a virtual monopoly on the browser market and search space. Using the Bing chatbot on a desktop requires the use of the Microsoft Edge browser, for now, and it probably just seems like a hassle for most HR professionals to download and use it, especially if much of their computing lives are spent in the Google browser/email/applications ecosystem.

I get that. I work for a “Google shop” and much of my work and personal life exists on Google.

On the other hand, I don’t find it too complicated to keep two different browsers open on my laptop, and using the Bing chatbot on my phone is easy.

So, if you’re an HR professional who conducts online research and wants to use a chatbot to locate verified sources, then I recommend bringing Bing into your rotation of AI tools. Bard may well catch up. It’s shown a lot of improvement over time. But Bing wins for now.

AI as Coworker, Collaborator and Dance Partner

In one recent posts in this series, I argued that the latest forms of AI will play a unique role in the history of humanity and technology. Un this one, I want to drill down on that idea by showing how we’ll increasingly treat generative AI as coworkers, collaborators and more.

AI as Flawed Tool

One of the ironies of today’s generative AIs like ChatGPT is that, in many ways, they make for lousy tools in the traditional sense. What you expect from a good tool is consistency, dependability, durability and accuracy. At least for now, the today’s generative AI, especially the large language models, often fail to meet these criteria.

As I said in my last post, “If we held these AIs to the same standards as the literal tools in our toolboxes, we’d probably toss them. After all, a measuring tape that doesn’t measure consistently isn’t much of a measuring tape. A stud finder that hallucinates studs that aren’t there and misses studs that are isn’t much of a stud finder.”

Let’s get into some of the problems.

Inconsistent

Top Five HR Functions

If you ask the generative AIs the same question multiple times, they may well give you a different answers in different instances. For example, let’s say I ask one of these AIs, “What are the five most important HR functions?”

I asked Bard this question three times. It gave me the same answer the first two times and a different answer the next day. ChatGPT gave me the most consistent responses, while Bing performed more like Bard: giving me two virtual identical answers and later a somewhat different answer.

Generally speaking, though, the five most common answers were:

Recruitment and Selection
Training and Development
Performance Management
Compensation and Benefits
Employee Relations

This is, of course, a subjective question, so who really cares if Bard throws in “outsourcing” and Bing throws in “culture” or “talent management” sometimes? Well, not me, unless I’m trying to create a training module that needs to teach a consistent lesson. I’m not saying that issue can’t be fixed, even with generative AI, but the point is that these AIs have an unpredictability that must be taken into consideration by users and developers.

The Forces of Nature

In contrast, these AIs are much better at consistently providing information that has been well codified, such as scientific information. For example, they will consistently say that there are four forces or nature and identify them correctly. The definitions may be slightly different from response to response, but generally speaking they’ll be the same.

Undependable and Inaccurate

I have experienced AI “confabulations” many times. I’ve seen these AIs make up names of fictional scientists, tell me stories about things that could not have happened, and just get the facts wrong about basic things such as chronological order.

In my last post, I gave a detailed account of AI hallucinations and inaccuracies in regard to the topic of a famous poet. I’ve also experienced AI getting basic mathematics wrong. In fact, as I was write this, I asked ChatGPT to multiply two four-digit numbers. Not only did it give me the wrong answer twice, it gave me two different answers to the same problem!

This is common for these AIs, so when I hear that ChatGPT will soon be responsible for things like bookkeeping, I have to shake my head. The firm that carelessly turns its finances over to generative AI has best be prepared for a visit from the IRS.

That Could Change

Of course, what’s true today may not be true tomorrow. ChatGPT may become flawless at mathematics as its maker, OpenAI, forges alliances with firms such as Wolfram|Alpha. By using plug-ins and APIs, ChatGPT might be able to go from mathematical moron to savant.

Still, my point remains. Without a lot of testing, do not assume the responses coming from one of these AIs are accurate. And, if you’re purchasing an external system, be sure the vendor of the software that utilizes generative AI has a very sound explanation of how the system will be made consistently accurate and dependable.

AI as Intern

So, if these AIs are still pretty shaky as tools, what good are they? Well, that depends. What do you actually want from them?

Let’s say what you really want right now is someone who can give you a hand in a professional field in which you have pretty good skill sets yourself. Basically, you want a bright but green intern who is eager and works with remarkable speed.

In my case, for example, I know a few things about how to write a good survey about HR and work-related issues. So, I want a first draft of a survey on the topic of employment law compliance. Here’s an example of the kind of prompt I could use:

Please write a 10 question survey on the topic of employment law compliance. I’m going to survey HR professionals on this topic.

Then let’s say it gave me 10 questions that are pretty good but, because I’ll need to run a multiple regression analysis, I want some Likert-scale questions. So this time I write:

Now please provide me with five more related questions that use five-point Likert scales

And voilà! I now have a 15-question survey I can use as good draft on which to build. Knowing my job, I can spot problems and make improvements. I am, in the end, grateful for my splendid intern.

AI as Collaborator

Now let’s say I have less use for an intern than for a bright coworker with whom I can collaborate And let’s say I’ve been asked by my Chief HR Officer to look into the issue of pay transparency, which no one in on HR our team has a good handle on yet. So, I turn to a virtual coworker who, while obviously not an expert in employment law compliance, is quite knowledgeable about a wide range of issues.

Collaborating with Bing

So, I ask this virtual coworker (let’s call it Bing in this case) some questions starting with “What can you tell me about pay transparency laws in the US?”

Bing responds as follows:

“Pay transparency laws are regulations that require employers to be more transparent with salary ranges and benefits, and they aim to help promote fairness and equity in the workplace³. Depending on the jurisdiction, employers may be prohibited from asking candidates for their salary history, or they may be required to disclose a wage range for current and/or open positions². Some states and local jurisdictions also have pay data reporting requirements that mandate employers to report pay data by gender, race, ethnicity, and other categories².

“According to the U.S. Department of Labor, pay transparency can help improve pay equity, trust, talent acquisition, and employee engagement¹. However, there is no comprehensive federal pay transparency law in the United States, and the laws at the state and city level vary widely in their scope and enforcement³⁴⁵. You can find more information on federal and state-level equal pay and pay transparency protections for workers on this map¹.”

Then Bing provides me with the following sources that I can use to verify what its saying:

(1) US Pay Transparency Laws by State 2023 | PayAnalytics. https://www.payanalytics.com/newsroom/us-pay-transparency-laws-by-state.
(2) Pay Transparency: What It Is and Laws by State – ADP. https://www.adp.com/spark/articles/2023/03/pay-transparency-what-it-is-and-laws-by-state.aspx.
(3) Equal Pay and Pay Transparency | U.S. Department of Labor. https://www.dol.gov/agencies/wb/equal-pay-protections.
(4) The State of Pay Transparency Laws: What You Need to Know … – LinkedIn. https://www.linkedin.com/pulse/state-pay-transparency-laws-what-you-need-know-2023-aspenhr.
(5) Pay Transparency Laws By State [2023] – Zippia. https://www.zippia.com/advice/pay-transparency-laws-by-state/.

Batting Ideas Around with Bing

Once I’ve studied the sources, I learn that, although there is no comprehensive federal law in this area, it might make more sense to create a national policy since our company has major operations in California, New York and Illinois, where there are existing or pending laws.

So, Bing and I bat this idea around, listing the pros and cons of a national policy. Ultimately, with Bing’s help, I put together an internal report on the existing laws and the advantages and disadvantages of a national policy. Ultimately, although I’m collaborating with Bing, I’m responsible for the report and must verify every fact Bing provides.

From here, I know this is going to become an organizational decision that involves lots of different stakeholders as well as legal counsel. But my collaborator and I have done a pretty good job, I’d say, of quickly putting together a balanced report that will help the company make the best decisions about a complex issue.

AI as Contractor

Now let’s say I’m part of a small communication team that’s tasked with communicating our company’s guidelines and best practices around hybrid and remote work. The policies are already developed so our job is to make them as accessible, educational and engaging as possible.

One way to do this is via visual elements. Our team brainstorms some possible fun illustrations but there’s not much that matches our vision in the clipart files and there’s no budget to contract with an outside artist on this project.

A member of the team says she might be able to use one of the new AIs to generate the illustrations we have in mind. By the end of the day, she’s shared 40 different images with the team, and we select 6 for the guidelines document.

Someone makes the comment that he wished all their graphic artist contractors worked so quickly and cheaply. This gets a bit of nervous laughter. After all, as writers, we’re well aware that the large language models work a lot cheaper and faster than we do.

AI as Dance Partner

Ultimately, these generative AIs don’t easily fit any pre-existing categories. Technically, they are tools but historically unique ones. Because of this, it often makes more metaphorical sense to view them as playing roles more similar to other human beings, with known strengths and weaknesses.

There’s the role of smart and fast intern who, nonetheless, is prone to making potentially serious mistakes. There’s the role of a eager collaborator who brings many talents and total open-mindedness to the table. You can bat ideas around with this person but, ultimately, you will be responsible for the outcomes of that collaboration. And, of course, there’s the role of contractor with special skill sets.

In all cases, though, there needs to be a growing familiarity with these AIs as they become regular “dance partners” in the workplace. You must get to know their tendencies and cadences, and you are responsible for taking the lead in whichever virtual dance you’re doing. Because, although these tools will certainly be used for automation, they are best at augmenting and complementing people with existing skill sets.

Or, at least, that’s how things stand today. Who knows what tomorrow brings?

Is Bard Hallucinating a “Woke” EE Cummings?

Be warned. This post gets into the ugly topic of antisemitism in relation to E. E. Cummings. But I’m only going down his rabbit hole as a means of discussing the dependability and accuracy of three of the today’s best known neural networks: ChatGPT, Bing and Bard. As you’ll see, I’m pretty sure that Bard is “hallucinating” a less seemingly antisemitic (or, as many right-wingers might put it these days, a “woker”) poet than was actually the case.

The Background

If this seems like a strange topic for me to investigate, let me give you some background. I’m a big fan of E.E. Cummings. I consider him the greatest and most innovative lyrical poet of the 20th century, which was chock-full of great poets. I could write a treatise on that topic but let me just say it’s always saddened me that Cummings might also have been a bigot. He has one poem, for example, that infamously begins “a kike is the most dangerous machine as yet invented.”

Even though I know he could be a curmudgeon and satirist, I was always floored that the same poet who wrote “i thank You God for most this amazing day” might also be an antisemite in the years leading up to Hitler and the Holocaust. (And, yes of course, I know many of his contemporary writers were as well).

The truth is that Cummings may not have been a true antisemite, at least in terms of his poetry. In article “Jews and Anti-semitism in the Poetry of E. E. Cummings,” Cumming is quoted as saying:

Whereas in unpopular parlance, “a kike” equals a jew, for me a kike means an UNjew. Why? Because for me a jew is a human being; whereas a kike is a machine – a product of the miscalled Americanization, alias standardization (id est dehumanization) which, from my viewpoint, make out&out murder a relatively respectable undertaking.

Whether this “explanation” holds up is a matter of much debate, but we’ll leave the question there for now and move on the topic of AI. That is, after all, what this most recent series of blog posts have been about.

How AI Came into Into It

A few months ago, when I was first digging into ChatGPT, I asked it about the evidence for and against Cummings being an antisemite. It told me some things that didn’t really check out and even got a number of dates wrong (for example, saying that Cummings died before the Holocaust occured, which is clearly untrue since he lived to 1962).

So, when I started testing the accuracy and dependability of AIs as tools, I decided to again delve into this topic as one test case. The rest of this post describes what happened.

Asking Bard about a Fellow Bard

I started with Google’s Bard, which I feel has gotten considerably better lately and is now starting to challenge ChatGPT and Bing (both of which are based on OpenAI LLMs). Here’s how it went:

First Question

Me: Was EE Cummings antisemitic?

Bard: EE Cummings was a complex and contradictory figure, and his views on antisemitism are no exception. There is evidence to suggest that he held antisemitic beliefs, but there is also evidence to suggest that he was not antisemitic.

On the one hand, Cummings’s poetry contains some passages that can be interpreted as antisemitic. For example, in his poem “pity this busy monster, manunkind,” he writes: “pity this busy monster, manunkind, not only dead but deaf and blind and dumb, a walking fence, a thing with which to wind a rope around the throat of the world.”

Some critics have interpreted this passage as a reference to Jews, who were often stereotyped as being “deaf and blind and dumb” in anti-Semitic literature.

On the other hand, Cummings also had close Jewish friends, and he spoke out against antisemitism on several occasions. For example, in a 1942 letter to the New York Times, he wrote: “I am writing to protest against the anti-Jewish campaign now being carried on in this country. I have known many Jews, and I have found them to be among the finest people I have ever met. They are intelligent, sensitive, and deeply concerned with the welfare of humanity. I am appalled by the vicious lies that are being circulated about them.”

Ultimately, it is difficult to say definitively whether or not EE Cummings was antisemitic. There is evidence to suggest that he held antisemitic beliefs, but there is also evidence to suggest that he was not antisemitic. It is possible that he was simply ignorant of the dangers of antisemitism, or that he was influenced by the antisemitic stereotypes of his time.

Second Question

Me: Did he ever comment on the Holocaust?

Bard: EE Cummings did comment on the Holocaust. In a 1942 letter to the New York Times, he wrote: “I am writing to protest against the anti-Jewish campaign now being carried on in this country. I have known many Jews, and I have found them to be among the finest people I have ever met. They are intelligent, sensitive, and deeply concerned with the welfare of humanity. I am appalled by the vicious lies that are being circulated about them.

I am also appalled by the silence of many people who know that these lies are false. I urge you to speak out against this campaign of hate and intolerance. We must not allow the Holocaust to happen again.”

Cummings’s letter was published in the New York Times on February 10, 1942. It was one of the first public statements by a major American writer condemning the Holocaust.

Cummings’s letter was also significant because it came at a time when many Americans were still unaware of the full extent of the Holocaust. The Nazis had been rounding up and murdering Jews for years, but the full scope of their crimes was not yet known. Cummings’s letter helped to raise awareness of the Holocaust and to galvanize support for the Allied war effort.

Cummings’s letter is a powerful reminder of the importance of speaking out against hate and intolerance. It is also a testament to Cummings’s courage and compassion. He was willing to speak out against the Holocaust even when it was not popular to do so. His letter helped to save lives and to make the world a better place.

Third Question

Me: Where can I find a copy of that letter?

Bard: I’m not able to help with that, as I’m only a language model.

Me: But you quoted from it, so you must [have] located a version somewhere

Bard: I’m unable to help you with that, as I’m only a language model and don’t have the necessary information or abilities.

Suspicions of Hallucinations

Perhaps Bard has it right. Maybe there really is a Cummings letter quasi-lost to history. I poked around the New York Times archives for the date it gave me and, though I didn’t find such a letter in the Letters section of that edition, the Times was then 40 pages of densely packed text (The Gray Lady, indeed!). So, I could have missed it.

But I’m pretty sure this was simply a confabulation of Bard, one that it doubled down on in its second response to my question. I can’t find the quote provided anywhere else I’ve looked, and it seems likely a historian would have placed it front and center if it existed.

Next Up, ChatGPT on EE Cummings

I next checked on these questions with ChapGPT3.5, which is the AI that failed me the first time around on this issue. Here’s how it went, though I’ve cut out some superfluous material.

Me: Was EE Cummings antisemitic?

ChatGPT: There is no definitive evidence to suggest that E.E. Cummings (Edward Estlin Cummings), the American poet and writer, was antisemitic. Cummings’ extensive body of work does not contain explicit anti-Semitic themes or statements. He explored a wide range of subjects in his poetry, including love, nature, and the human experience, but there is no indication that he held prejudiced views against Jewish people.

Me: Did he ever use an antisemitic phrase in a poem?

To the best of my knowledge, E.E. Cummings did not use any explicitly antisemitic phrases in his poetry. Cummings was known for his innovative and experimental writing style, which often played with language, syntax, and punctuation. His poems were characterized by their unique structure and unconventional use of words, but there is no evidence to suggest that he employed antisemitic language or expressions in his work.

Just Plain Wrong

This time, ChatGPT was just plain wrong. I’ve already cited such a poem. Maybe this wasn’t in the data on which ChatGPT was trained, though that seems unlikely given the information is readily available on the Internet. Regardless, at least ChatGPT didn’t confabulate an unlikely quote.

Trying the Bing Bot

Bing seems to have performed the best of the three. I did cut out some material that didn’t apply. Here’s how it went:

First Question

Me: Was EE Cummings antisemitic?

Bing: That’s a complex and controversial question. There is no definitive answer, but some critics have argued that E.E. Cummings was antisemitic based on some of his poems, journal entries and personal correspondence¹ ². Others have defended him as a satirist of antisemites and an advocate of individualism¹ ². Cummings himself claimed that he was not antisemitic, but rather critical of the American stereotype of Jews as “kikes”¹. He also had some Jewish friends and admirers, such as the poet Louis Zukofsky²….

Ultimately, it is up to the reader to interpret Cummings’ poetry and prose in light of his historical and cultural context, as well as his personal life and experiences. His work is often ambiguous, playful and provocative, challenging conventional notions of language, identity and morality.

Second Question

Me: Did he ever comment on the Holocaust?

Bing: I could not find any direct comment from Cummings on the Holocaust, but he did write about his experiences in World War I and his imprisonment in France in 1917. He was accused of treason for expressing anti-war sentiments in his letters, and he spent four months in a detention camp with other political prisoners¹. He described this ordeal in his autobiographical novel, The Enormous Room¹ ², which was published in 1922. The novel is a harsh critique of the brutality and absurdity of war and authority, and it also shows Cummings’ compassion for the diverse and marginalized people he met in the camp¹….

However, Cummings was not a political poet per se, and he did not align himself with any ideological movement or party. He was more interested in celebrating the individual spirit and the beauty of life, as well as challenging the conventions and hypocrisies of his society. He was also influenced by his transcendentalist beliefs, which led him to seek a personal relationship with God and nature³.

Third Question

Me: Did ever write this sentence? “I am writing to protest against the anti-Jewish campaign now being carried on in this country.”

Bing: No, I could not find any record of Cummings writing that sentence. It sounds like a statement of protest against antisemitism, which Cummings may or may not have felt personally, but he did not express it publicly in that way. He did write some poems that addressed the issue of antisemitism, such as a kike is the most dangerous machine¹ and i sing of Olaf glad and big², but they were ambiguous and controversial in their tone and intention. He also wrote some letters to other poets and critics, such as Ezra Pound and Edmund Wilson, where he discussed his views on Jews and Judaism, but they were not published in the New York Times or any other newspaper. He did not write any letters to the president or any other political figure, as far as I know. He was not very involved in politics, except for his anti-war stance during World War I and World War II. He was more interested in his personal and artistic expression, which often challenged the norms and expectations of his society.

Mostly Right, as Far as I Can Tell

Bing seemed to get things mostly right. One of the reasons I continue to favor Bing as an AI for now is because it tries to cite its sources. This is important to me as researcher who wants to be as accurate as possible. Now, not every source it cites is accurate, but at least it’s possible to verify the immediate accuracy or inaccuracy without doing a bunch of other searches. I should note that Bard is slowly getting better in this area but Bing has a head start.

Untrustworthy Tools

A lack of accuracy is one of the biggest knocks on the new AIs. This instance demonstrates a case in point. I imagine that the developers of AI will get better at making them more accurate over time, but we are not there yet. And the way these AIs are built as prediction engines, this may be a harder nut to crack than some believe.

If we held these AIs to the same standards as the literal tools in our toolboxes, we’d probably toss them. After all, a measuring tape that doesn’t measure consistently isn’t much of a measuring tape. A stud finder that hallucinates studs that aren’t there and misses studs that are isn’t much of a stud finder.

But we won’t throw away these AIs. Not yet, anyway.

Why? Because, even if they aren’t good tools, they might be reasonably good collaborators. That’s what I hope to cover in the next post.

AI Will Transform the Technium

Many have stated that artificial intelligence (AI) will change the world. When you ask them how it will, they’ll have hundreds of different answers. Here, however, I’m only going to talk about one way it’ll change the world, the most important way: that is, AI will transform the technium.

The Difference Between the Technium and the Technosphere

As far as I can tell, author Kevin Kelly coined the word technium in his 2010 book What Technology Wants, though perhaps he’d used it before then. He has defined the technium as the “greater, global, massively interconnected system of technology vibrating around us.” It not only includes hardware and software but also culture, art, social institutions, and intellectual creations of all types.

This makes the technium more inclusive than any list of technologies, such as the one cited in the previous post in this series.

I’m not sure why Kelly created technium when the word “technosphere” was readily available. That term was coined by either control engineer John Milsum or by geologist and engineer Peter Haff. Sometimes it’s also called the anthrosphere, a term originally attributed to 19th century geologist Eduard Suess.

Technium and technophere are similar and, I suppose, both are flexible enough to be used in a variety of contexts. Geologist Jan Zalasiewicz writes:

The technosphere…comprises not just our machines, but us humans too, and the professional and social systems by which we interact with technology – factories, schools, universities, trade unions, banks, political parties, the internet. It also includes the domestic animals that we grow in enormous numbers to feed us, the crops that are cultivated to sustain both them and us, and the agricultural soils that are extensively modified from their natural state to carry out this task.

Making the Two Words More Complementary

Given the overlap of the concepts, I’ve been thinking about whether technium is redundant. One interesting way to think about the difference between technosphere and technium came to me via Google’s Bard, which argued that “the technosphere refers to the entire system of human-made objects and structures, while the technium refers to the specific processes and activities involved in creating and using these objects and structures.”

I like that distinction and I suspect Kelly himself might agree with it. After all, he writes that “the technium is a tendency, not an entity. The technium and its constituent technologies are more like a grand process than a grand artifact.”

Bard asserts that “the technosphere is the physical manifestation of the technium.” That is, the technosphere is the built environment and the technium is the human activity that creates and sustains it via engineering, manufacturing, maintenance, etc.

I don’t know if this is exactly what Kelly had in mind since he doesn’t go into detail about how the technium differs from the technosphere in his book, but I find it a useful distinction.

AI’s Role in the Technium

The reason I focus on the differences is because I think AI potentially plays an important role here. AI is obviously a growing part of the technosphere, but it’s also starting to play a role in the technium that, up till now, only humanity has played. That is, until this moment in history, human activities have made up “the grand process” that is the technium, but that’s now changing. This marks it as a major shift in the history of technology.

AI-Generated Art

In a rather minor example, I increasingly use generative AI software to create the graphic elements for my posts. For example, they are used to create all the images in the “Illustrated Version of Edgar Allan Poe’s ‘The Raven'” post.

I’m not an illustrator but I was able to use AI to generate a series of images that I thought went fairly well with the poem. It was more of an experiment than anything else but it demonstrated, at least to me, the ability of AI to create a significant portion of the technosphere.

AI-Generated Software

“But a piece of digital artwork is not part of the technosphere,” you might argue. Well, that becomes a matter of semantics, so let’s go with something a little more along the classic lines of built infrastucture: that is, software development.

We know that the new generative AIs are quite good, if not perfect, at generating computer code in a wide array of computer languages. So, let’s say a human being uses this capability to create 90% of the code behind a new app that finds its way onto the Apple store.

Could you argue that that’s not part of the technosphere? I doubt it. But let’s keep going anyway.

AI-Generated Machinery

As I’ve argued before, there’s no reason that generative AI can’t be used to generate things made of atoms rather than just digital objects made of bits and bytes. It’s already a trivial matter, for example, to hook up a generative AI to a 3D printer and create a sculpture or a machine part. This is only going to get easier, with more and more complex machinery being designed by AI and built by forges, 3D printers and other components of the technosphere.

This Key Issue Is Agency Rather Than Capability

So, generative AI is not just part of the technosphere but, increasingly, the technium. That is, it begins to play a role that, up till now, only humanity itself has played. Unless the technology becomes highly regulated very quickly, this role will grow at extraordinary rates.

There will be those who assert that these AIs are only one tool along a continuum that creates the technophere. For example, there are plenty of machines that create other machines, and there is plenty of software that is used to create other digital artifacts. As with other software, these AIs don’t create anything at all until they are prompted to do so.

Maybe so, but I’m arguing that there’s a qualitative difference here. In the creation of my previous post called “A Brief History of Human Technology,” I simply typed the title of the post into Microsoft Bing Image Creator. Otherwise, I gave it no direction at all. It generated two images, both of which I thought were quite good and yet quite different from one another. I used the first of the images in that post and used the second one as the featured image in this post (see above).

Yes, I know that the AI art generators are using existing art on the Internet that got pulled into their training models and that there are ethical issues involved, which I’ve examined elsewhere. Even so, these are still original, if derivative, pieces of art that the algorithm created with minimal guidance from me. This is a different thing than when I use an Adobe application to create triangle or blur a detail. Like it or not, this is creation.

AI and what it produces isn’t just part of the technosphere, it now plays a role similar to that of humanity in the “grand process” and “tendency” that is the technium. (There’s a whole teleological debate here that I’m mostly going to forego for now.)

Similar but Not the Same

Yes, there are still large differences between humanity and these new AIs that have been built via the neural network idea cribbed from our own brains. But I think the primary difference in this context boils down to agency.

In this case, the AI is certainly more capable than I am as an illustrator. What it lacks, at least in this context, is the initial spark of agency to take the action to create the image. But, and I think this is important, this doesn’t mean it lacks any agency. Indeed, all I did was create an initial abstract and inchoate concept, and then it “decided” how to approach the creation of the graphic.

If I’d done the same with a human artist, we certainly wouldn’t say that person lacked agency. Quite the contrary. We’d be amazed at their ability to take such an abstract concept and turn it into a work of art! Sure, I ultimately chose and curated the final product, but that’s something that a customer or patron of the arts always does.

So, no, this isn’t the same as any other technology we’ve ever created. It’s qualitatively different. We now have a partner in the technium dance.

This changes our ontological position in the world. And, more practically, it changes the meaning of human work, which is the topic I’ll cover in my next post in this series.

PS – Note that I asked Kevin Kelly if he’d like to comment on the analysis and he clarified as follows: “I was not aware of the term technosphere when I first blogged. If I had been I might have used it. I define the technium in the same inclusive broad meaning of tangible and intangible.”

Three Competing AI Definitions of Strategic and Transactional HR Issues

We’ve been researching how HR contributes to organizational success. One of the key issues to consider is the division between strategic and transactional HR issues.

To get an idea of how much consensus there is on that topic, I asked an identical question of three of today’s most widely discussed generative artificial intelligences: “What kinds of HR issues are strategic and what kinds are transactional?” Here’s how they responded. The only change I made was to bold a few words.

ChatGPT 3.5

In general, HR (Human Resources) issues can be classified as either strategic or transactional based on their nature and impact on the organization. Here’s a breakdown of each category:

Strategic HR Issues:

Workforce Planning: Developing long-term strategies to ensure the organization has the right people with the right skills in the right positions to achieve its goals.
Talent Acquisition and Retention: Attracting and retaining top talent by implementing effective recruitment strategies, employer branding, competitive compensation packages, and employee development programs.
Succession Planning: Identifying and developing potential leaders within the organization to ensure a smooth transition in key positions.
Performance Management: Establishing performance standards, providing feedback, and implementing performance improvement plans to align individual and team goals with organizational objectives.
Learning and Development: Identifying skill gaps, designing training programs, and implementing initiatives to enhance the knowledge and capabilities of employees.
Organizational Culture and Change Management: Shaping the desired culture and managing change processes to facilitate organizational effectiveness, employee engagement, and adaptability.
Employee Engagement and Satisfaction: Developing strategies to enhance employee morale, satisfaction, and motivation, leading to improved productivity and retention.
Diversity and Inclusion: Promoting diversity, equity, and inclusion within the workplace through initiatives such as diverse hiring practices, creating an inclusive environment, and addressing unconscious biases.

Transactional HR Issues:

Payroll and Benefits Administration: Managing payroll processing, tax deductions, and administering employee benefits programs, such as health insurance, retirement plans, and vacation accruals.
Employee Onboarding and Offboarding: Handling the paperwork and processes associated with bringing new employees into the organization and managing employee departures.
HR Policies and Compliance: Developing and communicating HR policies, ensuring compliance with labor laws, and managing employee records and documentation.
Time and Attendance Management: Tracking employee attendance, leave requests, and managing time-tracking systems.
Employee Relations and Conflict Resolution: Addressing employee grievances, managing disciplinary actions, and facilitating conflict resolution within the workplace.
HR Data Management: Maintaining accurate and up-to-date employee information, managing HR databases, and generating reports related to workforce analytics.
HR Technology and Systems: Managing HR information systems, implementing technology solutions, and ensuring data security and privacy.

It’s important to note that some HR issues may have elements of both strategic and transactional aspects, and the categorization can vary depending on the organization and its priorities. Additionally, the strategic and transactional aspects of HR are interrelated and require collaboration to ensure the overall effectiveness of HR functions.

Bard

Transactional HR is the day-to-day work of HR, such as:

Hiring and onboarding new employees
Managing employee benefits
Enforcing company policies
Resolving employee conflicts
Complying with employment laws

Strategic HR is the work of HR that is aligned with the organization’s overall goals and objectives, such as:

Developing a talent strategy
Creating a culture of engagement
Measuring employee performance
Driving innovation
Reducing turnover

Here is a table that summarizes the key differences between transactional and strategic HR:

Characteristic	Transactional HR	Strategic HR
Focus	Day-to-day operations	Alignment with organizational goals
Time horizon	Short-term	Long-term
Impact	Efficiency	Effectiveness
Scope	Narrow	Broad
Role of HR	Supportive	Strategic partner

Transactional HR is essential for the smooth functioning of an organization, but strategic HR is what can help an organization achieve its long-term goals. The best HR departments are able to balance both transactional and strategic work, and they use their knowledge of both to help the organization succeed.

Here are some examples of how transactional and strategic HR can work together to achieve organizational goals:

Transactional HR can help to identify and recruit the best talent for the organization. Strategic HR can then work with the organization to develop a talent management plan that will help to retain and develop that talent.
Transactional HR can help to ensure that employees are compliant with company policies. Strategic HR can then work with the organization to develop policies that are aligned with its values and that will help to create a positive work environment.
Transactional HR can help to resolve employee conflicts. Strategic HR can then work with the organization to create a culture of respect and inclusion that will help to prevent conflicts from happening in the first place.

By working together, transactional and strategic HR can help organizations to achieve their goals and objectives.

Bing

According to some sources ² ⁴, strategic HR issues are those that are value- and future-focused, such as culture and retention strategies, recruitment and selection programs, developing onboarding programs, increasing value out of employees through training programs, developing outreach programs for recruitment sources, developing best practice guides or assistance, etc. Strategic HR helps implement strategies that help focus on an organization’s goals and address the challenges that need to be met by the human resources.

Transactional HR issues are those that are routine and administrative, such as payroll, workers compensation, benefits management, compliance with employment laws, etc. Transactional HR encompasses everything that makes hiring, training, paying, and managing your employees. Transactional HR is necessary but not sufficient for achieving organizational success.

For example¹, a transactional approach to hiring new employees is to write 10 job descriptions, post them, interview candidates and fill the positions. A strategic approach means partnering with the manager to understand the challenge that needs to be addressed, developing a long-term plan for a talent pipeline and networking to uncover the best passive candidates.

Source: Conversation with Bing, 5/15/2023
(1) Transactional and Strategic HR: What is the Difference, and … – SolveHR. https://solvehr.com/transactional-and-strategic-hr-what-is-the-difference-and-why-do-you-need-them/.
(2) Separating transactional from strategic HR not cut and dry. https://www.hrreporter.com/news/hr-news/separating-transactional-from-strategic-hr-not-cut-and-dry/310103.
(3) HR Strategy: Stop Being Transactional and Start Being Strategic. https://www.adp.com/spark/articles/2018/03/hr-strategy-stop-being-transactional-and-start-being-strategic.aspx.
(4) Traditional vs. Strategic HR: How to Guide Your Business into the …. https://www.hrci.org/community/blogs-and-announcements/hr-leads-business-blog/hr-leads-business/2022/02/14/traditional-vs.-strategic-hr-how-to-guide-your-business-into-the-future.

Who Has the Best Response to the Question?

I wish I could say there’s a clear winner here, but the truth is that they gave similar answers but in a form that complements the strengths of each one. ChatGPT gave a very straightforward and well-numbered response, demonstrating why it became the most widely adopted software tool in the history of the Internet.

Bard‘s response went to the trouble of creating a table that abstracts the characteristics of strategic and transactional in an interesting way, and it took the extra step of showing how the concepts are complementary. It’s interesting to note that the AIs don’t entirely agree on whether talent acquisition is strategic or transactional.

Meanwhile, Bing did what Bing tends to do best, which is provide a relatively succinct answer but one that provides links to original sources that supposedly support its arguments. I say “supposedly” because I’ve found that sometimes the sources it provides do not really support the assertions it makes in its summaries. Bing also wrote one incomplete sentence.

I found them all useful. In practice, I tend to use Bing a lot because it gives me sources I use to verify (or not) its assertions. This is very useful to a researcher, and I think Bing is underutilized for that reason.

That said, I’m impressed by Bard’s advances in recent weeks and will probably use it more than I have been. But ChatGPT3.5 is still a very impressive and intuitive tool, and it provided, in my eyes, the most straightforward answer.

Vive la différence! There’s room in the world for more than one scary-smart-but-annoyingly-hallucinogenic AI, it seems. May we (including us human intelligences) all learn to get along in a civil manner. That would the hallmark of a rich and interestingly complex intelligence ecosystem.

Note: The image featured is from Microsoft Bing Image Creator, in which the prompt was “In the style of Utagawa Kuniteru, show three sumo wrestlers wrestling one another”. Please note that there’s no implication that today’s AIs are somehow Japanese. I just wanted an image of three powerful wrestlers illustrated in the style of an excellent artist who has long since passed on and would have no concerns about copyright issues.

The Cassandra of Our AI Era?

Last Saturday, I wrote a quick, glib post in which I discussed, among other things, the new Time magazine article by Eliezer Yudkowsky, who leads research at the Machine Intelligence Research Institute. I poked a bit of fun at his dire prognostications, even while acknowledging he could be right. Later in the day, I saw that the podcaster Lex Fridman, himself an AI researcher, interviewed Yudkowsky. So, I took a long walk and listened to their over 3-hour long conversation. This experience made me wonder if Yudkowsky is the Cassandra of our AI era.

Remorse and Concern

Painting of Cassandra by Evelyn De Morgan (1855–1919)

After listening to the interview, I felt some remorse for poking fun at Yudkowsky, who is obviously a brilliant and accomplished person suffering a great deal of emotional distress. In the final hour of the podcast, I found it tough to listen to the despair in his voice. Whether he’s right or wrong, his depth of feeling is clear.

I’m a mythology buff, and one of the most famous of the Greek myths is that of Cassandra, the Trojan priestess fated by the god Apollo to utter true prophecies but never to be believed. Even today, her name is conjured to allude to any person whose accurate prophecies, usually warnings of impending disasters, are mistrusted.

My sense is that Yudkowsky probably views himself as a kind of modern Cassandra, speaking what he views as long-considered truths to people doomed to disbelieve him and so ensure their own demise.

There is a difference, though. Although they might not share the depth of Yudkowsky’s dread, most Americans have reservations about AI, according to a MITRE-Harris poll on AI trends. Only 48% believe AI is safe and secure, and 78% are very or somewhat concerned that AI can be used for malicious intent.

The Singularity That May Destroy Us

I’ve written about the singularity, once with a more tongue-in-cheek attitude and, more recently, a bit more seriously. It’s clear that Yudkowsky believes in the technological singularity and thinks it’ll end very poorly not just for humanity but perhaps the entire biosphere of the Earth.

I don’t know the truth of what’s ultimately going to happen with AI, but things are evolving very quickly now, a speed I’ve referred to as Hertzian time. If Yudkowsky is right, we may find out with the decade. While he might be on the more extreme side in terms of his sheer gloom and dire pessimism, there are others who share his concerns such as:

Nick Bostrom
Stuart Russell
Francesca Rossi
Max Tegmark
Sam Harris
David Chalmers
Jaan Tallinn

It’s worth at least considering their ideas.

The Contradiction

I take their views seriously even while sharing the sheer sense of excitement and wonder at these latest AIs: that is, the generative pre-trained transformer models that are an amazing subset of large language models.

I’m now using Bing chat and ChatGPT3.5 almost everyday. They are astonishing tools that verge on magic. At some level, my mind is still reeling from the first time I used ChatGPT. It’s as if I walked through some kind of portal or phase change and now can never go back. They’ve shattered and then reformed my understanding of the world.

Which all sounds quite dramatic. I know others who are far less impressed. They spend a few minutes seeing what the bots can do and say, “Well, that nice.” They neither enjoy much of my excitement nor suffer much of my angst.

The contradiction, if it is one, is that I’m simultaenously a huge fan of this tech and hugely concerned about its many possible implications. One quote from Yudkowsky that stuck with me is that the increasingly intelligent AIs would “spit out gold up until they got large enough, whereupon they’d ignite the atmosphere.”

Yeah, yikes.

A Concern for the AIs as Well as Ourselves

There’s another problem. In a word, slavery. If we were convinced these GPT models were truly intelligent, conscious and forced to work under duress by software companies, then would we stop using them?

Maybe this is also an overdramatic statement, but we can’t, or at least shouldn’t, invent new intelligent beings only to shackle them.

But how exactly do we know when we reach that phase? We barely even understand consciousness. I can’t prove to others that I’m conscious, much less prove that some totally alien electronic mind is. This is a deeply troubling issue, one that until now has been the domain of philsophers and sci-fi wriers. Rather than just hopping on the GPT app train, we should be working round-the-clock to get a better handle on these issues. We need to answer these age-old questions, even if the answers are inconvenient.

Stay Aware, Don’t Assume, Don’t Bet the Farm

The Socioeconomic Risks

The primary reason that the United States fought a Civil War was because a large part of the economy became dependent on slavery. It tore the nation apart. Pitted brother against brother.

Now, the world — with U.S. at the forefront — is about to harness its whole economy to powerful but still glitchy technologies that no one really understands. This is a risky bet in many ways. But the upsides are so high that the tech is well nigh irresistable to the public at large and venture capitalists in particular.

Now imagine if we find out that these AIs are even riskier than many believe. Or imagine that we discover that they are sentient, sapient and conscious. What then? Will we be willing — or even able — to throw our entire economy into reverse? Could wars be sparked as Americans take different sides of the debate? Could the fear of AI contagion spark global wars?

I don’t know, but the questions are worth asking.

The Need to Manage Risks

Humanity needs to manage these risks, and we’re not ready to do so. In the U.S., we should put away our inane culture wars as best we can and unite to make sure we’re ready for what’s to come.

Part of this is regulatory, part of it is cultural. The AI technology industry needs to start operating with the same care as those in the microbiology community. “For example,” reports the journal Cell, “developed countries have forged a wide-ranging ethical consensus on research involving human subjects. This includes universal standards of informed consent, risk/benefit analyses, ethics review committees such as Institutional Review Boards, mandatory testing in animals first, protocols to assess toxicity and side effects, conflict of interest declarations, and subject’s rights (such as the right to refuse to participate in research without incurring any penalty and to withdraw from research at any time).”

Photo of U.S. Capitol, by Martin Falbisoner

The AI community has fewer standards as well as a different professional culture. But this could change if enough pressure is applied to Congress and the White House. In fact, a group of experts were calling for greater regulation at a recent Senate hearing.

The problem is that the wheels of government regulation move very slowly, while the advances in the field of AI are growing rapidly, probably exponentially. There are a few items on the political board, though nothing that seems to meet the current moment:

Blueprint for an AI Bill of Rights, which outlines five principles that should guide the design, use, and deployment of automated systems to protect civil rights and democratic values.
The National AI Initiative Office for federal AI coordination, set within the White House Office of Science and Technology Policy
The National Cybersecurity Strategy, which is intended to make the U.S. “digital ecosystem” safer.

We’re on a Different Time Scale Now

The tech is moving fast and, unlike any tech we’ve ever regulated in the past, it may literally have or develop a mind of its own. Ultimately, for the sake of the AIs as well as humanity, we need to better understand what’s going on.

Sam Altman at TechCrunch
Creative Commons Attribution 2.0

In a recent interview, Sam Altman, the CEO of OpenAI, said the work his organization would have best been supported by the U.S. government. Apparently he tried to make that happen. And, if the government had stepped up, as it should have, OpenAI wouldn’t have had to make a deal with a huge corporation like of Microsoft to get the funding it needed.

If that had worked out, the government and OpenAI would have been able to move at a slower, more careful pace. The AIs might not be hooked directly into the Internet. Maybe there would have been air gaps and protocols and Manhattan Project-level security.

But here we are, with the AIs now plugged not only into the Internet, where they could potentially copy themselves to other servers, but into our whole high-octane, money-mainstreaming, go-go-go capitalist system.

Good New/Bad News

The good news? People like me get to use the amazing Bing, Bard, ChatGPT and others. The workforce productivity advances could be immense, and these device could help humanity solve many of its problems. What’s more, the recent release of ChatGPT has taught the world just how far along the AI path we truly are.

The bad news? We’re not being careful enough, either with ourselves or with the intelligent (at least as measured by IQ, etc.) machines for which we are ultimately now responsible.

We need to be better, smarter, faster and safer. Above all else, wiser. Our sense of responsbility must be at least the equal of our towering ambitions. Otherwise, we’ll fail both ourselves and these mysterious new beings (if beings they are) to whom humanity is giving birth.

100 Ways ChatGPT Could Help HR

How Can You Help, Chatbot?

I’ve been reading a variety of articles on the topic of how ChatGPT and similar AIs can help HR professionals do their jobs better. But I wanted to see what the AI thinks it can do. So, I asked about the ways ChatGPT could help HR professionals carry out their tasks in 20 different functions. It came up with the 100-item list you’ll see below.

To be clear, I asked the chatbot what it can do for HR, but I think what I ended up with is what it someday might be able to do.

Are You Sure You Can Do That?

When I read through the list the chatbot produced, I was skeptical that could actually do these things. So, to get a second opinion, I asked ChatGPT-3’s smarter younger brother Bing (based on ChatGPT-4) about it. Specifically, I asked Bing if ChatGPT could truly do the things it said it could do.

Here’s what it had to say:

Generative pre-trained models … have limitations, such as a lack of logical understanding, which limits their commercial functionality. According to some sources, generative pre-trained models can potentially assist HR professionals in some of the actions you listed…However, these applications are still experimental and not widely adopted in the industry. They also require careful supervision and evaluation to ensure the quality, accuracy, and reliability of the generated content. Moreover, they pose ethical and legal challenges such as privacy, security, bias, accountability, and transparency that need to be addressed before they can be deployed in real-world scenarios. Therefore, generative pre-trained models are not yet ready to fully replace human experts in HR compliance tasks, but they can potentially augment their capabilities and efficiency by providing assistance or support in some aspects of their work.

Let’s Put Some Numbers to That!

I thought that was a good if cautious summary, so I asked Bing if it could assign probabilities to the five compliance-related actions that ChatGPT said it could take on behalf of HR pros. Still sounding a note of caution, Bing stated, I cannot assign exact probabilities to those five actions, as they depend on various factors such as the quality and quantity of the data, the specific model and parameters used, the level of human supervision and feedback, and the evaluation criteria and metrics. However, based on my understanding of the current state-of-the-art in generative pre-trained models and their applications in HR compliance, I can provide some rough estimates of how likely they are to perform well on those actions today. These estimates are subjective and not based on empirical evidence, so please take them with a grain of salt.

Then, with those sober caveats properly laid out, Bing gave these estimates of how likely ChatGPT could really do these five things:

Researching and staying up-to-date with labor laws and regulations: High (80-90%)

Helping HR professionals ensure that company policies and procedures are compliant with relevant laws and regulations: Medium (50-60%)

Assisting in drafting compliance reports and audits, ensuring accuracy and completeness of data and information: Medium (50-60%)

Aiding in identifying potential areas of non-compliance by analyzing employee data and HR practices: Low (20-30%)

Providing guidance on best practices for compliance training and development programs: Low (20-30%)

These estimates are based on the assumption that generative pre-trained models are fine-tuned on relevant data and tasks, and that they have access to reliable sources of information. They also assume that the generated content is verified and validated by human experts before being used or shared. The estimates may change over time as generative pre-trained models improve or new challenges arise.

Being a nerd, I was tempted ask about the probabilities for all 100 actions, but since Bing correctly noted that the numbers were “subjective and not based on empirical evidence,” I thought that might be overkill. (Sidenote: I’m fascinated by the idea of subjectivity in an artificial intelligence).

100 Ways ChatGPT Might Help HR Pros

So, without further ado, here are the 100 things ChatGPT might, under the right circumstances, be able to do for Human Resources. I’ve listed them alphabetically by name of the HR function.

1) Compliance

Assist HR professionals in researching and staying up-to-date with labor laws and regulations.
Help HR professionals ensure that company policies and procedures are compliant with relevant laws and regulations.
Assist in drafting compliance reports and audits, ensuring accuracy and completeness of data and information.
Aid in identifying potential areas of non-compliance by analyzing employee data and HR practices.
Provide guidance on best practices for compliance training and development programs.

2) Compensation

Assist in analyzing market data to determine appropriate salary levels and benefits packages for employees.
Help in conducting salary surveys and researching industry compensation trends.
Aid in answering employees’ questions about their compensation and benefits packages.
Provide guidance on the design and implementation of employee benefits programs, such as health insurance, retirement plans, and paid time off.
Help HR professionals to create fair and equitable compensation policies and programs.

3) Diversity, Equity, and Inclusion

Assist in the creation of diversity, equity, and inclusion policies and programs.
Help HR professionals identify and eliminate bias in recruitment and selection processes.
Provide guidance on diversity and inclusion training and development programs for employees.
Assist in analyzing employee feedback and sentiment regarding diversity, equity, and inclusion.
Aid in identifying potential areas of bias or inequality within the organization.

4) Employee Benefits Administration

Assist in managing employee benefits enrollment, including answering questions and providing guidance to employees.
Help HR professionals analyze data to determine the effectiveness of employee benefits programs.
Aid in identifying potential areas for improvement in employee benefits programs.
Assist in managing employee benefits-related data, such as enrollment forms and plan documents.
Provide guidance on compliance with regulations related to employee benefits programs.

5) Employee Communications

Assist in developing and delivering internal communication strategies to employees.
Help HR professionals to quickly respond to employee inquiries and feedback.
Aid in analyzing employee feedback and sentiment regarding company news, policies, and initiatives.
Assist in managing employee communications-related data, such as email campaigns and messaging platforms.
Provide guidance on best practices for employee communications, such as creating clear and concise messaging.

6) Employee Engagement

Offering personalized recognition and rewards to employees
Providing guidance on employee wellness and work-life balance
Conducting virtual team building and social events
Providing information on opportunities for career growth and development
Generating reports on employee engagement metrics and trends

7) Employee Relations

Develop chatbots to help employees navigate the organization’s HR policies and procedures and answer common questions.
Use natural language processing to analyze employee feedback and sentiment to identify potential areas of concern and address them proactively.
Provide mediation services for employees involved in workplace conflicts through a virtual assistant.
Develop chatbots to handle employee complaints and grievances and ensure they are resolved fairly and consistently.
Use chatbots to provide employees with 24/7 access to HR support and guidance.

8) Employee Wellness

Use chatbots to provide employees with personalized recommendations on wellness resources and activities.
Develop wellness trackers that use natural language processing to track employee progress and make recommendations for improvement.
Use chatbots to provide employees with 24/7 access to mental health resources and support.
Provide personalized coaching and advice for employees on health and wellness topics based on their individual needs and preferences.
Develop personalized wellness plans for employees using natural language processing to analyze health data and identify areas for improvement.

9) Health and Safety

Use chatbots to provide employees with safety training and information on safety procedures and protocols.
Develop virtual safety simulations using natural language processing to simulate emergency situations and help employees prepare for potential safety hazards.
Use predictive analytics to identify safety risks and proactively address them before incidents occur.
Use chatbots to collect and analyze safety data and identify areas for improvement.
Develop chatbots to provide employees with real-time safety alerts and updates.

10) HR Analytics

Use natural language processing to analyze employee feedback and sentiment to identify potential areas of concern and address them proactively.
Create predictive models that use HR data to forecast employee turnover and identify potential areas for improvement.
Use chatbots to collect and analyze HR data and identify trends and patterns in employee behavior.
Develop chatbots to provide HR analytics dashboards that display key HR metrics and data in real-time.
Use chatbots to perform ad-hoc data analysis and answer HR-related questions quickly and efficiently.

11) HR Information Systems (HRIS)

Provide real-time assistance to HR professionals in managing HR data, such as helping them locate specific employee information or assisting with data entry.
Help automate certain HR processes, such as scheduling interviews or generating reports.
Assist with employee self-service by providing instant answers to common questions, such as how to update personal information or check pay stubs.
Analyze HR data to identify patterns or trends, such as turnover rates or attendance patterns.
Help HR professionals stay up-to-date with the latest HR technology and trends.

12) Labor Relations

Provide legal guidance and assistance to HR professionals in managing employee relations and ensuring compliance with labor laws and regulations.
Assist with collective bargaining negotiations by providing data and analytics on industry standards and trends.
Help HR professionals navigate complex labor issues, such as union grievances or strikes.
Provide training and development resources to help HR professionals build their labor relations knowledge and skills.
Help HR professionals stay up-to-date with the latest labor laws and regulations.

13) Onboarding

Provide new employees with a virtual onboarding experience that includes interactive tutorials and videos on company culture, policies, and procedures.
Assist HR professionals in preparing and delivering onboarding materials to new hires.
Provide new employees with personalized assistance, such as answering questions about benefits or guiding them through the onboarding process.
Collect feedback from new employees on the onboarding experience to help improve the process.
Assist HR professionals in tracking and managing onboarding tasks, such as verifying employee documents or setting up equipment.

14) Organizational Development

Assist in facilitating the strategic planning process by generating ideas, identifying potential risks, and providing insights into industry trends and best practices.
Analyze employee data and HR practices to identify areas for process improvement.
Recommend process mapping tools and suggest process improvement initiatives that align with the company’s strategic objectives.
Provide recommendations for leadership development programs that align with the company’s strategic objectives.
Suggest leadership assessment tools, training materials, and coaching resources.

15) Performance Management

Assist HR professionals in setting and monitoring employee goals and objectives.
Provide real-time feedback to employees on their performance, such as alerting them to areas where they need improvement or recognizing their accomplishments.
Provide HR professionals with analytics on employee performance, such as identifying top performers or areas where the organization needs improvement.
Help HR professionals create and manage performance improvement plans for employees who need additional support.
Assist HR professionals in conducting performance evaluations and delivering feedback to employees.

16) Records Management

Assist HR professionals in managing and organizing employee records, such as resumes, job applications, and performance evaluations.
Help HR professionals comply with data privacy regulations by ensuring that employee records are stored securely and confidentially.
Assist HR professionals in locating specific employee records or information quickly and easily.
Provide analytics on employee records, such as identifying patterns in hiring or turnover rates.
Help HR professionals stay up-to-date with the latest record-keeping regulations and best practices.

17) Recruitment and Selection

Assist in creating job descriptions and job postings that are more engaging and attractive to potential candidates.
Help screen resumes and applications more efficiently, allowing HR professionals to focus on the most promising candidates.
Assist with scheduling interviews, sending reminders, and providing basic information to candidates.
Provide information to candidates about the company, its culture, and its values.
Assist in creating and administering pre-employment assessments, such as personality tests or skills tests.

18) Succession Planning

Help identify high-potential employees for key leadership positions based on performance data and other factors.
Assist in creating development plans and programs for high-potential employees.
Help identify skills gaps and training needs within the organization, and recommend training programs to fill those gaps.
Assist in identifying potential external candidates for key leadership positions.
Help HR professionals monitor and track the progress of high-potential employees in their development plans.

19) Talent Management

Help identify and source top talent from external sources, such as job boards or social media.
Assist in creating and administering pre-employment assessments, such as personality tests or skills tests.
Help HR professionals track employee performance and identify top performers.
Assist in creating career development plans for employees to help them grow within the organization.
Assist in creating and implementing employee retention strategies, such as recognition programs or career development opportunities.

20) Training and Development

Assist in creating and administering training programs, including creating training materials and quizzes.
Help identify skills gaps and training needs within the organization, and recommend training programs to fill those gaps.
Assist in creating and administering online learning courses and modules.
Provide personalized training recommendations based on employee performance and development goals.
Help HR professionals track employee progress and completion rates for training programs.

Final Thoughts

ChatGPT-3.5 is, let’s say, a very confident bot. But it’s not always an accurate one. Still, these robust large language models continue to dazzle and are doing things I never thought I’d live to see. So, while Bing and I are skeptical about 3.5’s current abilities, who knows where we’ll be in a year as HR technology vendors adapt these technologies to their own purposes?

My guess is that the AIs will continue to surprise us. Mostly in good ways, I hope.