#domainexpertise — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #domainexpertise, aggregated by home.social.
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Will AI make junior and senior developers equal? Think again!
Give two engineers the same AI tools and goals. The one with deeper domain expertise will consistently get better results.
Alex Porcelli explains why human expertise remains the key factor in determining the value of AI-generated output.
🗓️ Save the date: The full presentation drops September 14 on InfoQ!
#AI #GenerativeAI #SoftwareEngineering #AIAssistedDevelopment #DeveloperExperience #EngineeringLeadership #DomainExpertise
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Will AI make junior and senior developers equal? Think again!
Give two engineers the same AI tools and goals. The one with deeper domain expertise will consistently get better results.
Alex Porcelli explains why human expertise remains the key factor in determining the value of AI-generated output.
🗓️ Save the date: The full presentation drops September 14 on InfoQ!
#AI #GenerativeAI #SoftwareEngineering #AIAssistedDevelopment #DeveloperExperience #EngineeringLeadership #DomainExpertise
-
Will AI make junior and senior developers equal? Think again!
Give two engineers the same AI tools and goals. The one with deeper domain expertise will consistently get better results.
Alex Porcelli explains why human expertise remains the key factor in determining the value of AI-generated output.
🗓️ Save the date: The full presentation drops September 14 on InfoQ!
#AI #GenerativeAI #SoftwareEngineering #AIAssistedDevelopment #DeveloperExperience #EngineeringLeadership #DomainExpertise
-
Will AI make junior and senior developers equal? Think again!
Give two engineers the same AI tools and goals. The one with deeper domain expertise will consistently get better results.
Alex Porcelli explains why human expertise remains the key factor in determining the value of AI-generated output.
🗓️ Save the date: The full presentation drops September 14 on InfoQ!
#AI #GenerativeAI #SoftwareEngineering #AIAssistedDevelopment #DeveloperExperience #EngineeringLeadership #DomainExpertise
-
Will AI make junior and senior developers equal? Think again!
Give two engineers the same AI tools and goals. The one with deeper domain expertise will consistently get better results.
Alex Porcelli explains why human expertise remains the key factor in determining the value of AI-generated output.
🗓️ Save the date: The full presentation drops September 14 on InfoQ!
#AI #GenerativeAI #SoftwareEngineering #AIAssistedDevelopment #DeveloperExperience #EngineeringLeadership #DomainExpertise
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In response to someone giddy over an imagined future where the only human job will be critical thinking, I tried to point out that bias may have blinded him to the thinking people already do.
What they are overlooking is the domain-level knowledge which keeps mechanics/electricians/plumbers/welders in trade schools/guilds/product manuals, truck drivers/bus drivers/pilots in trade schools/behind the wheel of specific machines for years at a go, engineers at university/conferences/industry papers/manufacturers data sheets and scientists/mathematicians at university/conferences/academic papers/libraries/research projects for the whole of their careers. They spend their lives to know how to make judgments on the decisions that the non-domain expert doesn’t even credit as a knowledge domain. If society doesn’t treat their particularized expertise as equally valuable, that doesn’t change that it’s the same human skills and effort to learn and gain the expertise to get the desired job done *right*. A lifetime of socialized specialization just so they can be judged on the fields they aren’t experts in.
People think all the time. And people are much more critical about the thinking of those in outgroups than their own friends, even when nearly everyone believes some crazy thing or has a blind spot in their thinking.
But white collar workers fetishize the forms of white collar management: memos, poster boards, diagrams, etc. without recognizing that the mode of education and prestige of the domain have little bearing on the quality of the thinking.
#CriticalThinking #DomainExpertise #SkilledTrades #HumanIntelligence #FutureOfWork #AI #CognitiveBias #DignityOfLabor
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In response to someone giddy over an imagined future where the only human job will be critical thinking, I tried to point out that bias may have blinded him to the thinking people already do.
What they are overlooking is the domain-level knowledge which keeps mechanics/electricians/plumbers/welders in trade schools/guilds/product manuals, truck drivers/bus drivers/pilots in trade schools/behind the wheel of specific machines for years at a go, engineers at university/conferences/industry papers/manufacturers data sheets and scientists/mathematicians at university/conferences/academic papers/libraries/research projects for the whole of their careers. They spend their lives to know how to make judgments on the decisions that the non-domain expert doesn’t even credit as a knowledge domain. If society doesn’t treat their particularized expertise as equally valuable, that doesn’t change that it’s the same human skills and effort to learn and gain the expertise to get the desired job done *right*. A lifetime of socialized specialization just so they can be judged on the fields they aren’t experts in.
People think all the time. And people are much more critical about the thinking of those in outgroups than their own friends, even when nearly everyone believes some crazy thing or has a blind spot in their thinking.
But white collar workers fetishize the forms of white collar management: memos, poster boards, diagrams, etc. without recognizing that the mode of education and prestige of the domain have little bearing on the quality of the thinking.
#CriticalThinking #DomainExpertise #SkilledTrades #HumanIntelligence #FutureOfWork #AI #CognitiveBias #DignityOfLabor
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In response to someone giddy over an imagined future where the only human job will be critical thinking, I tried to point out that bias may have blinded him to the thinking people already do.
What they are overlooking is the domain-level knowledge which keeps mechanics/electricians/plumbers/welders in trade schools/guilds/product manuals, truck drivers/bus drivers/pilots in trade schools/behind the wheel of specific machines for years at a go, engineers at university/conferences/industry papers/manufacturers data sheets and scientists/mathematicians at university/conferences/academic papers/libraries/research projects for the whole of their careers. They spend their lives to know how to make judgments on the decisions that the non-domain expert doesn’t even credit as a knowledge domain. If society doesn’t treat their particularized expertise as equally valuable, that doesn’t change that it’s the same human skills and effort to learn and gain the expertise to get the desired job done *right*. A lifetime of socialized specialization just so they can be judged on the fields they aren’t experts in.
People think all the time. And people are much more critical about the thinking of those in outgroups than their own friends, even when nearly everyone believes some crazy thing or has a blind spot in their thinking.
But white collar workers fetishize the forms of white collar management: memos, poster boards, diagrams, etc. without recognizing that the mode of education and prestige of the domain have little bearing on the quality of the thinking.
#CriticalThinking #DomainExpertise #SkilledTrades #HumanIntelligence #FutureOfWork #AI #CognitiveBias #DignityOfLabor
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In response to someone giddy over an imagined future where the only human job will be critical thinking, I tried to point out that bias may have blinded him to the thinking people already do.
What they are overlooking is the domain-level knowledge which keeps mechanics/electricians/plumbers/welders in trade schools/guilds/product manuals, truck drivers/bus drivers/pilots in trade schools/behind the wheel of specific machines for years at a go, engineers at university/conferences/industry papers/manufacturers data sheets and scientists/mathematicians at university/conferences/academic papers/libraries/research projects for the whole of their careers. They spend their lives to know how to make judgments on the decisions that the non-domain expert doesn’t even credit as a knowledge domain. If society doesn’t treat their particularized expertise as equally valuable, that doesn’t change that it’s the same human skills and effort to learn and gain the expertise to get the desired job done *right*. A lifetime of socialized specialization just so they can be judged on the fields they aren’t experts in.
People think all the time. And people are much more critical about the thinking of those in outgroups than their own friends, even when nearly everyone believes some crazy thing or has a blind spot in their thinking.
But white collar workers fetishize the forms of white collar management: memos, poster boards, diagrams, etc. without recognizing that the mode of education and prestige of the domain have little bearing on the quality of the thinking.
#CriticalThinking #DomainExpertise #SkilledTrades #HumanIntelligence #FutureOfWork #AI #CognitiveBias #DignityOfLabor
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In response to someone giddy over an imagined future where the only human job will be critical thinking, I tried to point out that bias may have blinded him to the thinking people already do.
What they are overlooking is the domain-level knowledge which keeps mechanics/electricians/plumbers/welders in trade schools/guilds/product manuals, truck drivers/bus drivers/pilots in trade schools/behind the wheel of specific machines for years at a go, engineers at university/conferences/industry papers/manufacturers data sheets and scientists/mathematicians at university/conferences/academic papers/libraries/research projects for the whole of their careers. They spend their lives to know how to make judgments on the decisions that the non-domain expert doesn’t even credit as a knowledge domain. If society doesn’t treat their particularized expertise as equally valuable, that doesn’t change that it’s the same human skills and effort to learn and gain the expertise to get the desired job done *right*. A lifetime of socialized specialization just so they can be judged on the fields they aren’t experts in.
People think all the time. And people are much more critical about the thinking of those in outgroups than their own friends, even when nearly everyone believes some crazy thing or has a blind spot in their thinking.
But white collar workers fetishize the forms of white collar management: memos, poster boards, diagrams, etc. without recognizing that the mode of education and prestige of the domain have little bearing on the quality of the thinking.
#CriticalThinking #DomainExpertise #SkilledTrades #HumanIntelligence #FutureOfWork #AI #CognitiveBias #DignityOfLabor
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So true:
“LLMs Reward Expertise”, Sean Goedecke (https://www.seangoedecke.com/llms-reward-expertise/).
On HN: https://news.ycombinator.com/item?id=49161518
On Lobsters: https://lobste.rs/s/d1jeag/llms_reward_expertise
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So true:
“LLMs Reward Expertise”, Sean Goedecke (https://www.seangoedecke.com/llms-reward-expertise/).
On HN: https://news.ycombinator.com/item?id=49161518
On Lobsters: https://lobste.rs/s/d1jeag/llms_reward_expertise
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So true:
“LLMs Reward Expertise”, Sean Goedecke (https://www.seangoedecke.com/llms-reward-expertise/).
On HN: https://news.ycombinator.com/item?id=49161518
On Lobsters: https://lobste.rs/s/d1jeag/llms_reward_expertise
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So true:
“LLMs Reward Expertise”, Sean Goedecke (https://www.seangoedecke.com/llms-reward-expertise/).
On HN: https://news.ycombinator.com/item?id=49161518
On Lobsters: https://lobste.rs/s/d1jeag/llms_reward_expertise
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So true:
“LLMs Reward Expertise”, Sean Goedecke (https://www.seangoedecke.com/llms-reward-expertise/).
On HN: https://news.ycombinator.com/item?id=49161518
On Lobsters: https://lobste.rs/s/d1jeag/llms_reward_expertise
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Domain expertise has always been the real moat
https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
#HackerNews #domainexpertise #moat #businessstrategy #knowledge #economy
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Domain expertise has always been the real moat
https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
#HackerNews #domainexpertise #moat #businessstrategy #knowledge #economy
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Domain expertise has always been the real moat
https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
#HackerNews #domainexpertise #moat #businessstrategy #knowledge #economy
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Domain expertise has always been the real moat
https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
#HackerNews #domainexpertise #moat #businessstrategy #knowledge #economy
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Domain expertise has always been the real moat
https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
#HackerNews #domainexpertise #moat #businessstrategy #knowledge #economy
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#Softwareengineering dominates the #AIagent market, accounting for nearly half of all tool calls: However, other sectors like healthcare, legal, and finance are largely untapped, presenting a significant opportunity for 300 new #verticalAI unicorns. The key to success lies in building #domainexpertise into #AIagents and navigating the specific workflows and regulatory constraints of each industry. https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open?eicker.news?AIagents.at #AIagent #AI #ML #NLP #LLM #GenAI
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#Softwareengineering dominates the #AIagent market, accounting for nearly half of all tool calls: However, other sectors like healthcare, legal, and finance are largely untapped, presenting a significant opportunity for 300 new #verticalAI unicorns. The key to success lies in building #domainexpertise into #AIagents and navigating the specific workflows and regulatory constraints of each industry. https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open?eicker.news?AIagents.at #AIagent #AI #ML #NLP #LLM #GenAI
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#Softwareengineering dominates the #AIagent market, accounting for nearly half of all tool calls: However, other sectors like healthcare, legal, and finance are largely untapped, presenting a significant opportunity for 300 new #verticalAI unicorns. The key to success lies in building #domainexpertise into #AIagents and navigating the specific workflows and regulatory constraints of each industry. https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open?eicker.news?AIagents.at #AIagent #AI #ML #NLP #LLM #GenAI
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#Softwareengineering dominates the #AIagent market, accounting for nearly half of all tool calls: However, other sectors like healthcare, legal, and finance are largely untapped, presenting a significant opportunity for 300 new #verticalAI unicorns. The key to success lies in building #domainexpertise into #AIagents and navigating the specific workflows and regulatory constraints of each industry. https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open?eicker.news?AIagents.at #AIagent #AI #ML #NLP #LLM #GenAI
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#Softwareengineering dominates the #AIagent market, accounting for nearly half of all tool calls: However, other sectors like healthcare, legal, and finance are largely untapped, presenting a significant opportunity for 300 new #verticalAI unicorns. The key to success lies in building #domainexpertise into #AIagents and navigating the specific workflows and regulatory constraints of each industry. https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open?eicker.news?AIagents.at #AIagent #AI #ML #NLP #LLM #GenAI
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The psychic structure of disciplinary imperialism
From Sherry Turkle’s classic The Second Self pg 229-230:
The first justification for AI’s invasions and colonization of other disciplines’ intellectual turf was a logic of necessity. The excursions into psychology and linguistics began as raids to acquire ideas that might be useful for building thinking machines. But the politics of “colonization” soon takes on a life of its own. The invaders come not only to carry off natural resources but to replace native “superstitions” with their “superior” world view. AI first declared the need for psychological theories that would work on machines. The next step was to see these alternatives as better—better because they can be “implemented,” better because they are more “scientific.” Being in a colonizing discipline first demands and then encourages an attitude that might be called intellectual hubris. You need intellectual principles that are universal enough to give you the feeling that you have something to say about everything. The AI community had this in their idea of program. Furthermore, since you cannot master all the disciplines that you have designs on, you need confidence that your knowledge makes the “traditional wisdom” of these fields unworthy of serious consideration. Here too, the AI scientist feels that seeing things through a computational prism so fundamentally changes the rules of every game in the social and behavioral sciences that everything that came before is relegated to a period of intellectual immaturity. And finally you have to feel that nothing is beyond your intellectual reach if you are smart enough.
See also the hostility of digital elites towards expertise.
#dataScience #digitalElites #disciplines #domainExpertise #epistemicHeirarchy #intellectualLabour #intellectualLife #work
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The psychic structure of disciplinary imperialism
From Sherry Turkle’s classic The Second Self pg 229-230:
The first justification for AI’s invasions and colonization of other disciplines’ intellectual turf was a logic of necessity. The excursions into psychology and linguistics began as raids to acquire ideas that might be useful for building thinking machines. But the politics of “colonization” soon takes on a life of its own. The invaders come not only to carry off natural resources but to replace native “superstitions” with their “superior” world view. AI first declared the need for psychological theories that would work on machines. The next step was to see these alternatives as better—better because they can be “implemented,” better because they are more “scientific.” Being in a colonizing discipline first demands and then encourages an attitude that might be called intellectual hubris. You need intellectual principles that are universal enough to give you the feeling that you have something to say about everything. The AI community had this in their idea of program. Furthermore, since you cannot master all the disciplines that you have designs on, you need confidence that your knowledge makes the “traditional wisdom” of these fields unworthy of serious consideration. Here too, the AI scientist feels that seeing things through a computational prism so fundamentally changes the rules of every game in the social and behavioral sciences that everything that came before is relegated to a period of intellectual immaturity. And finally you have to feel that nothing is beyond your intellectual reach if you are smart enough.
See also the hostility of digital elites towards expertise.
#dataScience #digitalElites #disciplines #domainExpertise #epistemicHeirarchy #intellectualLabour #intellectualLife #work
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The psychic structure of disciplinary imperialism
From Sherry Turkle’s classic The Second Self pg 229-230:
The first justification for AI’s invasions and colonization of other disciplines’ intellectual turf was a logic of necessity. The excursions into psychology and linguistics began as raids to acquire ideas that might be useful for building thinking machines. But the politics of “colonization” soon takes on a life of its own. The invaders come not only to carry off natural resources but to replace native “superstitions” with their “superior” world view. AI first declared the need for psychological theories that would work on machines. The next step was to see these alternatives as better—better because they can be “implemented,” better because they are more “scientific.” Being in a colonizing discipline first demands and then encourages an attitude that might be called intellectual hubris. You need intellectual principles that are universal enough to give you the feeling that you have something to say about everything. The AI community had this in their idea of program. Furthermore, since you cannot master all the disciplines that you have designs on, you need confidence that your knowledge makes the “traditional wisdom” of these fields unworthy of serious consideration. Here too, the AI scientist feels that seeing things through a computational prism so fundamentally changes the rules of every game in the social and behavioral sciences that everything that came before is relegated to a period of intellectual immaturity. And finally you have to feel that nothing is beyond your intellectual reach if you are smart enough.
See also the hostility of digital elites towards expertise.
#dataScience #digitalElites #disciplines #domainExpertise #epistemicHeirarchy #intellectualLabour #intellectualLife #work
-
The psychic structure of disciplinary imperialism
From Sherry Turkle’s classic The Second Self pg 229-230:
The first justification for AI’s invasions and colonization of other disciplines’ intellectual turf was a logic of necessity. The excursions into psychology and linguistics began as raids to acquire ideas that might be useful for building thinking machines. But the politics of “colonization” soon takes on a life of its own. The invaders come not only to carry off natural resources but to replace native “superstitions” with their “superior” world view. AI first declared the need for psychological theories that would work on machines. The next step was to see these alternatives as better—better because they can be “implemented,” better because they are more “scientific.” Being in a colonizing discipline first demands and then encourages an attitude that might be called intellectual hubris. You need intellectual principles that are universal enough to give you the feeling that you have something to say about everything. The AI community had this in their idea of program. Furthermore, since you cannot master all the disciplines that you have designs on, you need confidence that your knowledge makes the “traditional wisdom” of these fields unworthy of serious consideration. Here too, the AI scientist feels that seeing things through a computational prism so fundamentally changes the rules of every game in the social and behavioral sciences that everything that came before is relegated to a period of intellectual immaturity. And finally you have to feel that nothing is beyond your intellectual reach if you are smart enough.
See also the hostility of digital elites towards expertise.
#dataScience #digitalElites #disciplines #domainExpertise #epistemicHeirarchy #intellectualLabour #intellectualLife #work
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Three modes of working with LLMs in higher education
I’m enjoying this series by Anthropic, even if it’s largely a new language for things I’ve already argued in Generative AI for Academics. I like their description of three modes of working with LLMs:
- Automation: outsourcing a task to the LLM
- Augmentation: working as a collaborator as a thinking partner
- Agency: acting quasi-autonomously to pursue a goal
In these terms my stance has been that augmentation offers tremendous intellectual possibilities for academic work but that the political economy of academic labour tends people towards automation and (eventually) agency. At best these can be short-term helpful for individuals but the proportion of automation and (AI) agency in organisations likely correlates with deprofessionalisation, dehumanisation of working life and all sorts of incredibly specific pathologies generated as a byproduct of using LLMs.
https://www.youtube.com/watch?v=4szRHy_CT7s&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=3
I thought this was helpful for thinking about different steps in using LLMs:
- Delegation: identify what the tasks are and how they should enacted, either individually, in collaboration with an LLM or outsourced to an LLM
- Description: describing the task precisely to the LLM in a way conducive to getting it to meet your expectations
- Discernment: evaluating what’s useful from what’s not through reflection on LLM outputs, using domain knowledge
- Diligence: cultivating a reflective and ethical approach to the whole workflow using LLMs
The problem with systems like Copilot is that they are geared together simplifying/constraining augmentation while pushing people towards automation and agency. They take responsibility for delegation from the individual and instead scaffold it through the affordances embedded in familiar software. It’s a recipe for outsourcing labour and we shouldn’t be encouraging it.
The political economy of these modes are different: description and discernment, as well as augmentation more broadly, presuppose domain expertise and existing practical knowledge. Whereas delegation and automation/agency tend to rendering that domain knowledge redundant, pushing it aside and generally obliterating it as an organisational value.
https://www.youtube.com/watch?v=W4Ua6XFfX9w&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=4
This is exactly what I’ve meant when I talk about reflexivity in relation to prompting. Perhaps I should drop the (essentially theoretical) language of ‘reflexivity’ and instead talk about ‘problem awareness’ in future training.
#agency #AIFluency #augmentation #automation #domainExpertise #knowledge
-
Three modes of working with LLMs in higher education
I’m enjoying this series by Anthropic, even if it’s largely a new language for things I’ve already argued in Generative AI for Academics. I like their description of three modes of working with LLMs:
- Automation: outsourcing a task to the LLM
- Augmentation: working as a collaborator as a thinking partner
- Agency: acting quasi-autonomously to pursue a goal
In these terms my stance has been that augmentation offers tremendous intellectual possibilities for academic work but that the political economy of academic labour tends people towards automation and (eventually) agency. At best these can be short-term helpful for individuals but the proportion of automation and (AI) agency in organisations likely correlates with deprofessionalisation, dehumanisation of working life and all sorts of incredibly specific pathologies generated as a byproduct of using LLMs.
https://www.youtube.com/watch?v=4szRHy_CT7s&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=3
I thought this was helpful for thinking about different steps in using LLMs:
- Delegation: identify what the tasks are and how they should enacted, either individually, in collaboration with an LLM or outsourced to an LLM
- Description: describing the task precisely to the LLM in a way conducive to getting it to meet your expectations
- Discernment: evaluating what’s useful from what’s not through reflection on LLM outputs, using domain knowledge
- Diligence: cultivating a reflective and ethical approach to the whole workflow using LLMs
The problem with systems like Copilot is that they are geared together simplifying/constraining augmentation while pushing people towards automation and agency. They take responsibility for delegation from the individual and instead scaffold it through the affordances embedded in familiar software. It’s a recipe for outsourcing labour and we shouldn’t be encouraging it.
The political economy of these modes are different: description and discernment, as well as augmentation more broadly, presuppose domain expertise and existing practical knowledge. Whereas delegation and automation/agency tend to rendering that domain knowledge redundant, pushing it aside and generally obliterating it as an organisational value.
https://www.youtube.com/watch?v=W4Ua6XFfX9w&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=4
This is exactly what I’ve meant when I talk about reflexivity in relation to prompting. Perhaps I should drop the (essentially theoretical) language of ‘reflexivity’ and instead talk about ‘problem awareness’ in future training.
#agency #AIFluency #augmentation #automation #domainExpertise #knowledge
-
Three modes of working with LLMs in higher education
I’m enjoying this series by Anthropic, even if it’s largely a new language for things I’ve already argued in Generative AI for Academics. I like their description of three modes of working with LLMs:
- Automation: outsourcing a task to the LLM
- Augmentation: working as a collaborator as a thinking partner
- Agency: acting quasi-autonomously to pursue a goal
In these terms my stance has been that augmentation offers tremendous intellectual possibilities for academic work but that the political economy of academic labour tends people towards automation and (eventually) agency. At best these can be short-term helpful for individuals but the proportion of automation and (AI) agency in organisations likely correlates with deprofessionalisation, dehumanisation of working life and all sorts of incredibly specific pathologies generated as a byproduct of using LLMs.
https://www.youtube.com/watch?v=4szRHy_CT7s&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=3
I thought this was helpful for thinking about different steps in using LLMs:
- Delegation: identify what the tasks are and how they should enacted, either individually, in collaboration with an LLM or outsourced to an LLM
- Description: describing the task precisely to the LLM in a way conducive to getting it to meet your expectations
- Discernment: evaluating what’s useful from what’s not through reflection on LLM outputs, using domain knowledge
- Diligence: cultivating a reflective and ethical approach to the whole workflow using LLMs
The problem with systems like Copilot is that they are geared together simplifying/constraining augmentation while pushing people towards automation and agency. They take responsibility for delegation from the individual and instead scaffold it through the affordances embedded in familiar software. It’s a recipe for outsourcing labour and we shouldn’t be encouraging it.
The political economy of these modes are different: description and discernment, as well as augmentation more broadly, presuppose domain expertise and existing practical knowledge. Whereas delegation and automation/agency tend to rendering that domain knowledge redundant, pushing it aside and generally obliterating it as an organisational value.
https://www.youtube.com/watch?v=W4Ua6XFfX9w&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=4
This is exactly what I’ve meant when I talk about reflexivity in relation to prompting. Perhaps I should drop the (essentially theoretical) language of ‘reflexivity’ and instead talk about ‘problem awareness’ in future training.
#agency #AIFluency #augmentation #automation #domainExpertise #knowledge
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#GCCs zoom in on #Chennai
The #GlobalCapabilityCentre ecosystem is characterised by #technologytalent availability and #domainexpertise.
https://www.msn.com/en-in/news/other/gccs-zoom-in-on-chennai/ar-AA1t1GqO?ocid=msedgntp&pc=U531&cvid=b4e6b601532d4f2993210c0fbcfd50d0&ei=12 -
#GCCs zoom in on #Chennai
The #GlobalCapabilityCentre ecosystem is characterised by #technologytalent availability and #domainexpertise.
https://www.msn.com/en-in/news/other/gccs-zoom-in-on-chennai/ar-AA1t1GqO?ocid=msedgntp&pc=U531&cvid=b4e6b601532d4f2993210c0fbcfd50d0&ei=12