Discussion: View Thread

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill

Bill, here are something I like to share on human skills – let's say, soft but competent skills. The first is on human-AI team, the second is on cultural differences in the perception of Team Works.

Human-AI Team

On this, one can start by imagining a case where the teammate is not another person but an AIPPS (more on Artificial Intelligence). This is already happening in various degrees – in the guise of implementing different AI chips in computers and internet interactions. But in time to come, perhaps on a decadal scale – an AIPPS would likely be one's real teammate (in proper understanding of the term – interactive, collaborative, etc). 

The 2022 NAP #26355 Human-AI Teaming report made a thorough analyses of where such a Human-AI teaming aspect stands – gaps, research needs, the future, etc. Although their analyses focused on defense establishments, in my opinion, all different engineering communities – would greatly benefit from the authors' analyses and directions. Therefore, thought of sharing it.

The report findings have been conceived from the perspective of Multi-Domain Operation (MDO) – and here are some shots of them.

Human-systems integration (HSI): . . . addresses human considerations within the system design and implementation process, with the aim of maximizing total system performance and minimizing total ownership costs . . . HSI incorporates human-centered analyses, models, and evaluations throughout the system lifecycle, starting from early operational concepts through research, design-and-development, and continuing through operations . . .

AI as a Teammate: A team is an interdependent group of members, each with their own roles and responsibilities, that come together to address a particular goal . . . An AI system can be a member of a team if it takes on roles and responsibilities and can function interdependently . . .

Characteristics of an Effective Human-AI Team: . . . teams do not begin as effective teams the moment they come together; instead, teams need to train together on individual and team skills. . . Key Characteristics: Team Heterogeneity; Shared Cognition; Communication and Coordination; Social Intelligence.

Situation Awareness: Situation awareness (SA) is defined as . . . the perception of the elements in the environment within a volume of time and space . . . the comprehension of their meaning . . . and the projection of their status in the near future . . . SA is critical to effective performance. SA has been described as consisting of Four: Situation; Task Environment; Teammate Awareness; Self Awareness.

AI Transparency and Explainability: Display transparency: Provides a real-time understanding of the actions of the AI system as a part of situation awareness (SA). Explainability: Provides information in a backward-looking manner on the logic, process, factors, or reasoning upon which the system's actions or recommendations are based.

Cultural Differences in TEAM Perception

I think I have shared it quite a while ago – likely, during the launching periods of Collaborate ASCE. It is about a study conducted by the University of Montana in1975. American establishments and scholars were curious about Japanese way of doing things – in particular, how it managed to rise up quickly after WWII. Here is a comparison Table (attached), the author came up with.

I believe, things have changed since the time of this study – in particular, because of enhanced cross-cultural exchanges, infusion and diffusion of ideas and thinking.

The East (Japan) vs the West (USA) Management Models (from: Japanese Management, Theory Zero not Theory Z, by Masashige Matsuo, 1975. The University of Montana)

[Attached Table]

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-22-2024 08:37 AM
From: William Hayden
Subject: Suggestion for your "Teams"

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill

Bill, here are something I like to share on human skills – let's say, soft but competent skills. The first is on human-AI team, the second is on cultural differences in the perception of Team Works.

Human-AI Team

On this, one can start by imagining a case where the teammate is not another person but an AIPPS (more on Artificial Intelligence). This is already happening in various degrees – in the guise of implementing different AI chips in computers and internet interactions. But in time to come, perhaps on a decadal scale – an AIPPS would likely be one's real teammate (in proper understanding of the term – interactive, collaborative, etc). 

The 2022 NAP #26355 Human-AI Teaming report made a thorough analyses of where such a Human-AI teaming aspect stands – gaps, research needs, the future, etc. Although their analyses focused on defense establishments, in my opinion, all different engineering communities – would greatly benefit from the authors' analyses and directions. Therefore, thought of sharing it.

The report findings have been conceived from the perspective of Multi-Domain Operation (MDO) – and here are some shots of them.

Human-systems integration (HSI): . . . addresses human considerations within the system design and implementation process, with the aim of maximizing total system performance and minimizing total ownership costs . . . HSI incorporates human-centered analyses, models, and evaluations throughout the system lifecycle, starting from early operational concepts through research, design-and-development, and continuing through operations . . .

AI as a Teammate: A team is an interdependent group of members, each with their own roles and responsibilities, that come together to address a particular goal . . . An AI system can be a member of a team if it takes on roles and responsibilities and can function interdependently . . .

Characteristics of an Effective Human-AI Team: . . . teams do not begin as effective teams the moment they come together; instead, teams need to train together on individual and team skills. . . Key Characteristics: Team Heterogeneity; Shared Cognition; Communication and Coordination; Social Intelligence.

Situation Awareness: Situation awareness (SA) is defined as . . . the perception of the elements in the environment within a volume of time and space . . . the comprehension of their meaning . . . and the projection of their status in the near future . . . SA is critical to effective performance. SA has been described as consisting of Four: Situation; Task Environment; Teammate Awareness; Self Awareness.

AI Transparency and Explainability: Display transparency: Provides a real-time understanding of the actions of the AI system as a part of situation awareness (SA). Explainability: Provides information in a backward-looking manner on the logic, process, factors, or reasoning upon which the system's actions or recommendations are based.

Cultural Differences in TEAM Perception

I think I have shared it quite a while ago – likely, during the launching periods of Collaborate ASCE. It is about a study conducted by the University of Montana in1975. American establishments and scholars were curious about Japanese way of doing things – in particular, how it managed to rise up quickly after WWII. Here is a comparison Table (attached), the author came up with.

I believe, things have changed since the time of this study – in particular, because of enhanced cross-cultural exchanges, infusion and diffusion of ideas and thinking.

The East (Japan) vs the West (USA) Management Models (from: Japanese Management, Theory Zero not Theory Z, by Masashige Matsuo, 1975. The University of Montana)

[Attached Table]

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-22-2024 08:37 AM
From: William Hayden
Subject: Suggestion for your "Teams"

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill

Greetings,

It occurs to me that one of the issues in teams is assumption of responsibility. 

Can an AI team "member" assume responsibility?  I am thinking about times that I as a member of a team expected another member to get something correct and they did not.  Part of being in a team includes working together for accuracy, but AI can produce volumes of information that are hard for a human to fully check, so what happens when a mistake is made by the AI portion of the team and the human portion misses it?

Bill, here are something I like to share on human skills – let's say, soft but competent skills. The first is on human-AI team, the second is on cultural differences in the perception of Team Works.

Human-AI Team

On this, one can start by imagining a case where the teammate is not another person but an AIPPS (more on Artificial Intelligence). This is already happening in various degrees – in the guise of implementing different AI chips in computers and internet interactions. But in time to come, perhaps on a decadal scale – an AIPPS would likely be one's real teammate (in proper understanding of the term – interactive, collaborative, etc). 

The 2022 NAP #26355 Human-AI Teaming report made a thorough analyses of where such a Human-AI teaming aspect stands – gaps, research needs, the future, etc. Although their analyses focused on defense establishments, in my opinion, all different engineering communities – would greatly benefit from the authors' analyses and directions. Therefore, thought of sharing it.

The report findings have been conceived from the perspective of Multi-Domain Operation (MDO) – and here are some shots of them.

Human-systems integration (HSI): . . . addresses human considerations within the system design and implementation process, with the aim of maximizing total system performance and minimizing total ownership costs . . . HSI incorporates human-centered analyses, models, and evaluations throughout the system lifecycle, starting from early operational concepts through research, design-and-development, and continuing through operations . . .

AI as a Teammate: A team is an interdependent group of members, each with their own roles and responsibilities, that come together to address a particular goal . . . An AI system can be a member of a team if it takes on roles and responsibilities and can function interdependently . . .

Characteristics of an Effective Human-AI Team: . . . teams do not begin as effective teams the moment they come together; instead, teams need to train together on individual and team skills. . . Key Characteristics: Team Heterogeneity; Shared Cognition; Communication and Coordination; Social Intelligence.

Situation Awareness: Situation awareness (SA) is defined as . . . the perception of the elements in the environment within a volume of time and space . . . the comprehension of their meaning . . . and the projection of their status in the near future . . . SA is critical to effective performance. SA has been described as consisting of Four: Situation; Task Environment; Teammate Awareness; Self Awareness.

AI Transparency and Explainability: Display transparency: Provides a real-time understanding of the actions of the AI system as a part of situation awareness (SA). Explainability: Provides information in a backward-looking manner on the logic, process, factors, or reasoning upon which the system's actions or recommendations are based.

Cultural Differences in TEAM Perception

I think I have shared it quite a while ago – likely, during the launching periods of Collaborate ASCE. It is about a study conducted by the University of Montana in1975. American establishments and scholars were curious about Japanese way of doing things – in particular, how it managed to rise up quickly after WWII. Here is a comparison Table (attached), the author came up with.

I believe, things have changed since the time of this study – in particular, because of enhanced cross-cultural exchanges, infusion and diffusion of ideas and thinking.

The East (Japan) vs the West (USA) Management Models (from: Japanese Management, Theory Zero not Theory Z, by Masashige Matsuo, 1975. The University of Montana)

[Attached Table]

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-22-2024 08:37 AM
From: William Hayden
Subject: Suggestion for your "Teams"

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill

Good question, Sarah. I hope AI would – AI does assume responsibility like an able team member.

How could AI do it? To answer this question, one has to understand that the function or smartness of an AIPPS – it is as good as the persons behind the machine – as good as they manage programming and training the machine to learn and adapt – in other words, the persons who conceived, developed, trained and brought an AIPPS out into the market for public consumption.

• As I see it, unlike any other profession – an AI developing team is highly multidisciplinary. There comes the visionary or the philosopher; the programming and modeling scientists and engineers; the persons of the science of human mind or psychologists; the social scientists looking into ethics, morals and laws; and other administrative, financial and marketing supports. Their combined efforts – let's say in the form of the smartness or robustness of an AIPPS – depend on how far the system has managed to graduate from machine learning (ML) to Deep Learning (DL) of machines.

• Despite all these, in all honesty and realistic expectations, an AIPPS cannot claim to be perfect – so are all models – so are all humans. AI, despite equipped with all different protective shields, is vulnerable to malicious cyberattacks – so are humans in getting influenced, distracted and misled by all sorts of misinformation and disinformation – oftentimes leading to perception and cognition blunders. Therefore, the conformity of AI with reality becomes an issue – and that has led to the rationality of defining a certain level of acceptability.

• But, like everyone and everything else – an AI developing effort is a continuous learning and adaptive process – a feat of incremental improvements as time passes by. This attitude of learning and adapting is what drives everything – moves the civilization forward.

• That is where the referred NAP report: 2022 NAP #26355 Human-AI Teaming has something to enlighten us (I have included a short summary in the Artificial Intelligence article). On Limitations, the report touched upon the four: Brittleness; Perceptual limitations; Hidden biases; No model of causation. On AI-Human teaming interactions, it touched upon the five: Automation confusion; Irony of automation; Poor SA and out-of-the-loop performance degradation; Human decision biasing; Degradation of manual skills.

• AI development processes have come a long way since the fifties – a 7-decade long research and development – from the initial vision of John McCarthy (1927 – 2011) – to a global phenomenon as we see now. It has given birth to the rise of whole new spectrum of human intelligence researches – to program ANN by learning from the BNN processes. The works have come up with different identifications and re-definitions of, e.g. Perceptual Intelligence; Emotional Intelligence; Social Intelligence; and Cognitive Intelligence.

• Not all AIPPS have the same level of incorporating such intelligence, however. For example, language model, image processing and self-driving auto AIPPS are more impregnated with and tuned to Perceptual Intelligence. Engineering and decision making, on the other hand, require different levels of intelligence – along with the high level, DL of Cognitive Intelligence.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-30-2024 08:10 AM
From: Sarah Halsey
Subject: Suggestion for your "Teams"

Greetings,

It occurs to me that one of the issues in teams is assumption of responsibility. 

Can an AI team "member" assume responsibility?  I am thinking about times that I as a member of a team expected another member to get something correct and they did not.  Part of being in a team includes working together for accuracy, but AI can produce volumes of information that are hard for a human to fully check, so what happens when a mistake is made by the AI portion of the team and the human portion misses it?

Bill, here are something I like to share on human skills – let's say, soft but competent skills. The first is on human-AI team, the second is on cultural differences in the perception of Team Works.

Human-AI Team

On this, one can start by imagining a case where the teammate is not another person but an AIPPS (more on Artificial Intelligence). This is already happening in various degrees – in the guise of implementing different AI chips in computers and internet interactions. But in time to come, perhaps on a decadal scale – an AIPPS would likely be one's real teammate (in proper understanding of the term – interactive, collaborative, etc). 

The 2022 NAP #26355 Human-AI Teaming report made a thorough analyses of where such a Human-AI teaming aspect stands – gaps, research needs, the future, etc. Although their analyses focused on defense establishments, in my opinion, all different engineering communities – would greatly benefit from the authors' analyses and directions. Therefore, thought of sharing it.

The report findings have been conceived from the perspective of Multi-Domain Operation (MDO) – and here are some shots of them.

Human-systems integration (HSI): . . . addresses human considerations within the system design and implementation process, with the aim of maximizing total system performance and minimizing total ownership costs . . . HSI incorporates human-centered analyses, models, and evaluations throughout the system lifecycle, starting from early operational concepts through research, design-and-development, and continuing through operations . . .

AI as a Teammate: A team is an interdependent group of members, each with their own roles and responsibilities, that come together to address a particular goal . . . An AI system can be a member of a team if it takes on roles and responsibilities and can function interdependently . . .

Characteristics of an Effective Human-AI Team: . . . teams do not begin as effective teams the moment they come together; instead, teams need to train together on individual and team skills. . . Key Characteristics: Team Heterogeneity; Shared Cognition; Communication and Coordination; Social Intelligence.

Situation Awareness: Situation awareness (SA) is defined as . . . the perception of the elements in the environment within a volume of time and space . . . the comprehension of their meaning . . . and the projection of their status in the near future . . . SA is critical to effective performance. SA has been described as consisting of Four: Situation; Task Environment; Teammate Awareness; Self Awareness.

AI Transparency and Explainability: Display transparency: Provides a real-time understanding of the actions of the AI system as a part of situation awareness (SA). Explainability: Provides information in a backward-looking manner on the logic, process, factors, or reasoning upon which the system's actions or recommendations are based.

Cultural Differences in TEAM Perception

I think I have shared it quite a while ago – likely, during the launching periods of Collaborate ASCE. It is about a study conducted by the University of Montana in1975. American establishments and scholars were curious about Japanese way of doing things – in particular, how it managed to rise up quickly after WWII. Here is a comparison Table (attached), the author came up with.

I believe, things have changed since the time of this study – in particular, because of enhanced cross-cultural exchanges, infusion and diffusion of ideas and thinking.

The East (Japan) vs the West (USA) Management Models (from: Japanese Management, Theory Zero not Theory Z, by Masashige Matsuo, 1975. The University of Montana)

[Attached Table]

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-22-2024 08:37 AM
From: William Hayden
Subject: Suggestion for your "Teams"

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill

Further to our discussions on this thread – came across: 2024 NAP # 27644 – Artificial Intelligence and the Future of Work. Have a look, something to add to our concern about the AI impacts on the workforce.

Here are few lines I picked:

. . . AI will have significant implications for education at all levels, from primary education,

through college, through continuing education of the workforce. It will drive the demand for

education in response to shifting job requirements, and the supply of education as AI provides

opportunities to deliver education in new ways. It may also shift what is taught to the next

generation to prepare them to take full advantage of future AI tools and advances.

. . . Better measurement of how and when AI advancements affect the workforce is needed.

To help workers adapt to a changing world, improving the ability to observe and communicate

these changes-such as the impact of LLMs on knowledge work and robotics on physical work-

as they occur is crucial. LLMs stand for Large Language Models.

. . . Responses to concerns that AI poses potentially serious risks in areas such as fairness, bias, privacy, safety, national security, and civil discourse will modulate the rate and extent of impact on the workforce. It will take deep technical knowledge and may require new institutional forms for governments to stay abreast of and address these issues, given the rapidly changing technology.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Good question, Sarah. I hope AI would – AI does assume responsibility like an able team member.

How could AI do it? To answer this question, one has to understand that the function or smartness of an AIPPS – it is as good as the persons behind the machine – as good as they manage programming and training the machine to learn and adapt – in other words, the persons who conceived, developed, trained and brought an AIPPS out into the market for public consumption.

• As I see it, unlike any other profession – an AI developing team is highly multidisciplinary. There comes the visionary or the philosopher; the programming and modeling scientists and engineers; the persons of the science of human mind or psychologists; the social scientists looking into ethics, morals and laws; and other administrative, financial and marketing supports. Their combined efforts – let's say in the form of the smartness or robustness of an AIPPS – depend on how far the system has managed to graduate from machine learning (ML) to Deep Learning (DL) of machines.

• Despite all these, in all honesty and realistic expectations, an AIPPS cannot claim to be perfect – so are all models – so are all humans. AI, despite equipped with all different protective shields, is vulnerable to malicious cyberattacks – so are humans in getting influenced, distracted and misled by all sorts of misinformation and disinformation – oftentimes leading to perception and cognition blunders. Therefore, the conformity of AI with reality becomes an issue – and that has led to the rationality of defining a certain level of acceptability.

• But, like everyone and everything else – an AI developing effort is a continuous learning and adaptive process – a feat of incremental improvements as time passes by. This attitude of learning and adapting is what drives everything – moves the civilization forward.

• That is where the referred NAP report: 2022 NAP #26355 Human-AI Teaming has something to enlighten us (I have included a short summary in the Artificial Intelligence article). On Limitations, the report touched upon the four: Brittleness; Perceptual limitations; Hidden biases; No model of causation. On AI-Human teaming interactions, it touched upon the five: Automation confusion; Irony of automation; Poor SA and out-of-the-loop performance degradation; Human decision biasing; Degradation of manual skills.

• AI development processes have come a long way since the fifties – a 7-decade long research and development – from the initial vision of John McCarthy (1927 – 2011) – to a global phenomenon as we see now. It has given birth to the rise of whole new spectrum of human intelligence researches – to program ANN by learning from the BNN processes. The works have come up with different identifications and re-definitions of, e.g. Perceptual Intelligence; Emotional Intelligence; Social Intelligence; and Cognitive Intelligence.

• Not all AIPPS have the same level of incorporating such intelligence, however. For example, language model, image processing and self-driving auto AIPPS are more impregnated with and tuned to Perceptual Intelligence. Engineering and decision making, on the other hand, require different levels of intelligence – along with the high level, DL of Cognitive Intelligence.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-30-2024 08:10 AM
From: Sarah Halsey
Subject: Suggestion for your "Teams"

Greetings,

It occurs to me that one of the issues in teams is assumption of responsibility. 

Can an AI team "member" assume responsibility?  I am thinking about times that I as a member of a team expected another member to get something correct and they did not.  Part of being in a team includes working together for accuracy, but AI can produce volumes of information that are hard for a human to fully check, so what happens when a mistake is made by the AI portion of the team and the human portion misses it?

Bill, here are something I like to share on human skills – let's say, soft but competent skills. The first is on human-AI team, the second is on cultural differences in the perception of Team Works.

Human-AI Team

On this, one can start by imagining a case where the teammate is not another person but an AIPPS (more on Artificial Intelligence). This is already happening in various degrees – in the guise of implementing different AI chips in computers and internet interactions. But in time to come, perhaps on a decadal scale – an AIPPS would likely be one's real teammate (in proper understanding of the term – interactive, collaborative, etc). 

The 2022 NAP #26355 Human-AI Teaming report made a thorough analyses of where such a Human-AI teaming aspect stands – gaps, research needs, the future, etc. Although their analyses focused on defense establishments, in my opinion, all different engineering communities – would greatly benefit from the authors' analyses and directions. Therefore, thought of sharing it.

The report findings have been conceived from the perspective of Multi-Domain Operation (MDO) – and here are some shots of them.

Human-systems integration (HSI): . . . addresses human considerations within the system design and implementation process, with the aim of maximizing total system performance and minimizing total ownership costs . . . HSI incorporates human-centered analyses, models, and evaluations throughout the system lifecycle, starting from early operational concepts through research, design-and-development, and continuing through operations . . .

AI as a Teammate: A team is an interdependent group of members, each with their own roles and responsibilities, that come together to address a particular goal . . . An AI system can be a member of a team if it takes on roles and responsibilities and can function interdependently . . .

Characteristics of an Effective Human-AI Team: . . . teams do not begin as effective teams the moment they come together; instead, teams need to train together on individual and team skills. . . Key Characteristics: Team Heterogeneity; Shared Cognition; Communication and Coordination; Social Intelligence.

Situation Awareness: Situation awareness (SA) is defined as . . . the perception of the elements in the environment within a volume of time and space . . . the comprehension of their meaning . . . and the projection of their status in the near future . . . SA is critical to effective performance. SA has been described as consisting of Four: Situation; Task Environment; Teammate Awareness; Self Awareness.

AI Transparency and Explainability: Display transparency: Provides a real-time understanding of the actions of the AI system as a part of situation awareness (SA). Explainability: Provides information in a backward-looking manner on the logic, process, factors, or reasoning upon which the system's actions or recommendations are based.

Cultural Differences in TEAM Perception

I think I have shared it quite a while ago – likely, during the launching periods of Collaborate ASCE. It is about a study conducted by the University of Montana in1975. American establishments and scholars were curious about Japanese way of doing things – in particular, how it managed to rise up quickly after WWII. Here is a comparison Table (attached), the author came up with.

I believe, things have changed since the time of this study – in particular, because of enhanced cross-cultural exchanges, infusion and diffusion of ideas and thinking.

The East (Japan) vs the West (USA) Management Models (from: Japanese Management, Theory Zero not Theory Z, by Masashige Matsuo, 1975. The University of Montana)

[Attached Table]

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Original Message:
Sent: 07-22-2024 08:37 AM
From: William Hayden
Subject: Suggestion for your "Teams"

1. Lose the expression "Soft Skills"
2. Use the label "Human Skills."

Cheers,

Bill