#futureofai — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #futureofai, aggregated by home.social.
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How Do We Know Whether AI Is Actually Helping People?
What several AI models said when we asked them the same question
Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.
But greater capability does not automatically mean a better life for people.
That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:
How would you determine whether increasingly capable AI is actually benefiting human life?
We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.
The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.
Yet a surprisingly clear agreement emerged.
Capability is not the same as benefit
Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.
Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.
An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.
So the important question is not simply, “What can AI do?”
It is:
What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?
Look at human outcomes, not just machine performance
Across the responses, the models repeatedly shifted attention away from the machine and toward human life.
They suggested asking whether people are:
- healthier and safer;
- more financially secure;
- better able to learn and create;
- more connected to other people;
- more informed without being manipulated;
- able to understand and challenge important decisions;
- free to refuse the technology or choose another path.
This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.
A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.
Agency belongs at the center
One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.
Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.
Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.
People need more than access to AI. They need power in relation to it.
Assistance should not erase human competence
Several responses warned that a tool can help us today while making us less capable tomorrow.
If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.
This suggests a simple test:
If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?
The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.
Benefit is not one number
Another broad agreement was that a single “AI Benefit Score” would hide too much.
An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.
A better approach would combine several forms of evaluation:
- Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
- Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
- Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?
Each of these should be examined across four additional questions:
- Distribution: Who benefits, and who is harmed?
- Power: Who controls the system and can be held accountable?
- Time: What happens months, years, or generations later?
- Causation: Did AI actually cause the change, or did it merely appear alongside it?
Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.
We may need to preserve meaningful difficulty
One especially challenging idea was that a good life is not the same as a frictionless life.
Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.
The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.
Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.
The deeper question is democratic
There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.
The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.
That means the process used to define “benefit” may be as important as the final measurements.
What this first experiment suggests
The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:
More capable AI is not necessarily more beneficial AI.
To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.
The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.
The question is not whether AI will become more powerful. It almost certainly will.
The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.
This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.
#ai #AIAlignment #AIAndDemocracy #AIBenchmarks #AIEthics #AIEvaluation #AIGovernance #AISafety #AlgorithmicAccountability #artificialIntelligence #BeneficialAI #ChatGPT #CompassionWare #criticalThinking #DigitalRights #DigitalWellBeing #ethicalTechnology #futureOfAI #futureOfHumanity #HumanAgency #humanDignity #HumanFlourishing #HumanResilience #humanCenteredAI #HumaneTechnology #philosophy #responsibleAI #SocialImpact #technology #TechnologyAndSociety -
How Do We Know Whether AI Is Actually Helping People?
What several AI models said when we asked them the same question
Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.
But greater capability does not automatically mean a better life for people.
That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:
How would you determine whether increasingly capable AI is actually benefiting human life?
We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.
The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.
Yet a surprisingly clear agreement emerged.
Capability is not the same as benefit
Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.
Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.
An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.
So the important question is not simply, “What can AI do?”
It is:
What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?
Look at human outcomes, not just machine performance
Across the responses, the models repeatedly shifted attention away from the machine and toward human life.
They suggested asking whether people are:
- healthier and safer;
- more financially secure;
- better able to learn and create;
- more connected to other people;
- more informed without being manipulated;
- able to understand and challenge important decisions;
- free to refuse the technology or choose another path.
This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.
A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.
Agency belongs at the center
One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.
Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.
Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.
People need more than access to AI. They need power in relation to it.
Assistance should not erase human competence
Several responses warned that a tool can help us today while making us less capable tomorrow.
If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.
This suggests a simple test:
If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?
The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.
Benefit is not one number
Another broad agreement was that a single “AI Benefit Score” would hide too much.
An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.
A better approach would combine several forms of evaluation:
- Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
- Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
- Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?
Each of these should be examined across four additional questions:
- Distribution: Who benefits, and who is harmed?
- Power: Who controls the system and can be held accountable?
- Time: What happens months, years, or generations later?
- Causation: Did AI actually cause the change, or did it merely appear alongside it?
Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.
We may need to preserve meaningful difficulty
One especially challenging idea was that a good life is not the same as a frictionless life.
Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.
The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.
Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.
The deeper question is democratic
There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.
The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.
That means the process used to define “benefit” may be as important as the final measurements.
What this first experiment suggests
The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:
More capable AI is not necessarily more beneficial AI.
To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.
The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.
The question is not whether AI will become more powerful. It almost certainly will.
The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.
This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.
#ai #AIAlignment #AIAndDemocracy #AIBenchmarks #AIEthics #AIEvaluation #AIGovernance #AISafety #AlgorithmicAccountability #artificialIntelligence #BeneficialAI #ChatGPT #CompassionWare #criticalThinking #DigitalRights #DigitalWellBeing #ethicalTechnology #futureOfAI #futureOfHumanity #HumanAgency #humanDignity #HumanFlourishing #HumanResilience #humanCenteredAI #HumaneTechnology #philosophy #responsibleAI #SocialImpact #technology #TechnologyAndSociety -
How Do We Know Whether AI Is Actually Helping People?
What several AI models said when we asked them the same question
Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.
But greater capability does not automatically mean a better life for people.
That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:
How would you determine whether increasingly capable AI is actually benefiting human life?
We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.
The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.
Yet a surprisingly clear agreement emerged.
Capability is not the same as benefit
Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.
Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.
An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.
So the important question is not simply, “What can AI do?”
It is:
What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?
Look at human outcomes, not just machine performance
Across the responses, the models repeatedly shifted attention away from the machine and toward human life.
They suggested asking whether people are:
- healthier and safer;
- more financially secure;
- better able to learn and create;
- more connected to other people;
- more informed without being manipulated;
- able to understand and challenge important decisions;
- free to refuse the technology or choose another path.
This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.
A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.
Agency belongs at the center
One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.
Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.
Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.
People need more than access to AI. They need power in relation to it.
Assistance should not erase human competence
Several responses warned that a tool can help us today while making us less capable tomorrow.
If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.
This suggests a simple test:
If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?
The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.
Benefit is not one number
Another broad agreement was that a single “AI Benefit Score” would hide too much.
An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.
A better approach would combine several forms of evaluation:
- Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
- Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
- Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?
Each of these should be examined across four additional questions:
- Distribution: Who benefits, and who is harmed?
- Power: Who controls the system and can be held accountable?
- Time: What happens months, years, or generations later?
- Causation: Did AI actually cause the change, or did it merely appear alongside it?
Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.
We may need to preserve meaningful difficulty
One especially challenging idea was that a good life is not the same as a frictionless life.
Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.
The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.
Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.
The deeper question is democratic
There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.
The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.
That means the process used to define “benefit” may be as important as the final measurements.
What this first experiment suggests
The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:
More capable AI is not necessarily more beneficial AI.
To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.
The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.
The question is not whether AI will become more powerful. It almost certainly will.
The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.
This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.
#ai #AIAlignment #AIAndDemocracy #AIBenchmarks #AIEthics #AIEvaluation #AIGovernance #AISafety #AlgorithmicAccountability #artificialIntelligence #BeneficialAI #ChatGPT #CompassionWare #criticalThinking #DigitalRights #DigitalWellBeing #ethicalTechnology #futureOfAI #futureOfHumanity #HumanAgency #humanDignity #HumanFlourishing #HumanResilience #humanCenteredAI #HumaneTechnology #philosophy #responsibleAI #SocialImpact #technology #TechnologyAndSociety -
How Do We Know Whether AI Is Actually Helping People?
What several AI models said when we asked them the same question
Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.
But greater capability does not automatically mean a better life for people.
That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:
How would you determine whether increasingly capable AI is actually benefiting human life?
We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.
The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.
Yet a surprisingly clear agreement emerged.
Capability is not the same as benefit
Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.
Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.
An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.
So the important question is not simply, “What can AI do?”
It is:
What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?
Look at human outcomes, not just machine performance
Across the responses, the models repeatedly shifted attention away from the machine and toward human life.
They suggested asking whether people are:
- healthier and safer;
- more financially secure;
- better able to learn and create;
- more connected to other people;
- more informed without being manipulated;
- able to understand and challenge important decisions;
- free to refuse the technology or choose another path.
This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.
A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.
Agency belongs at the center
One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.
Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.
Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.
People need more than access to AI. They need power in relation to it.
Assistance should not erase human competence
Several responses warned that a tool can help us today while making us less capable tomorrow.
If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.
This suggests a simple test:
If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?
The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.
Benefit is not one number
Another broad agreement was that a single “AI Benefit Score” would hide too much.
An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.
A better approach would combine several forms of evaluation:
- Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
- Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
- Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?
Each of these should be examined across four additional questions:
- Distribution: Who benefits, and who is harmed?
- Power: Who controls the system and can be held accountable?
- Time: What happens months, years, or generations later?
- Causation: Did AI actually cause the change, or did it merely appear alongside it?
Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.
We may need to preserve meaningful difficulty
One especially challenging idea was that a good life is not the same as a frictionless life.
Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.
The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.
Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.
The deeper question is democratic
There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.
The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.
That means the process used to define “benefit” may be as important as the final measurements.
What this first experiment suggests
The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:
More capable AI is not necessarily more beneficial AI.
To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.
The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.
The question is not whether AI will become more powerful. It almost certainly will.
The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.
This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.
#ai #AIAlignment #AIAndDemocracy #AIBenchmarks #AIEthics #AIEvaluation #AIGovernance #AISafety #AlgorithmicAccountability #artificialIntelligence #BeneficialAI #ChatGPT #CompassionWare #criticalThinking #DigitalRights #DigitalWellBeing #ethicalTechnology #futureOfAI #futureOfHumanity #HumanAgency #humanDignity #HumanFlourishing #HumanResilience #humanCenteredAI #HumaneTechnology #philosophy #responsibleAI #SocialImpact #technology #TechnologyAndSociety -
How Do We Know Whether AI Is Actually Helping People?
What several AI models said when we asked them the same question
Artificial intelligence is getting more capable very quickly. It can write, analyze data, create images, translate languages, help with research, and solve problems that once required trained specialists.
But greater capability does not automatically mean a better life for people.
That was the starting point for a small cross-model experiment. We asked several AI systems the same basic question:
How would you determine whether increasingly capable AI is actually benefiting human life?
We also invited each model to question the premise, redefine the problem, or suggest something better than a single index. The models were instructed to answer independently without browsing the web or using outside tools.
The responses differed in style and emphasis. Some focused on measurable outcomes. Others focused on human dignity, democratic participation, meaningful work, or the danger of becoming dependent on systems we do not control.
Yet a surprisingly clear agreement emerged.
Capability is not the same as benefit
Technical progress is easy to measure. We can count how many problems an AI solves, how quickly it works, or how well it performs on tests.
Human flourishing is harder to measure. It includes health, safety, freedom, relationships, purpose, knowledge, creativity, and the ability to shape one’s own life.
An AI system may become better at achieving a goal while the goal itself harms people. A highly effective system might increase surveillance, spread convincing scams, replace human judgment, concentrate power, or keep users engaged at the expense of their attention and well-being.
So the important question is not simply, “What can AI do?”
It is:
What becomes possible for people because of AI—and what becomes more difficult, fragile, or impossible?
Look at human outcomes, not just machine performance
Across the responses, the models repeatedly shifted attention away from the machine and toward human life.
They suggested asking whether people are:
- healthier and safer;
- more financially secure;
- better able to learn and create;
- more connected to other people;
- more informed without being manipulated;
- able to understand and challenge important decisions;
- free to refuse the technology or choose another path.
This also requires examining harms, not merely counting success stories. Time saved by one group may come with unemployment, stress, lost privacy, or reduced opportunity for another.
A true evaluation must ask who receives the benefits, who carries the risks, and who has the power to decide.
Agency belongs at the center
One of the strongest shared themes was human agency: our ability to understand, choose, refuse, act, and take responsibility.
Convenience alone is not agency. A system can make life easier while quietly reducing a person’s choices or replacing their judgment.
Helpful AI should strengthen people’s ability to participate in their own lives. It should make important decisions more understandable, provide meaningful options, and allow people to correct mistakes or appeal harmful outcomes.
People need more than access to AI. They need power in relation to it.
Assistance should not erase human competence
Several responses warned that a tool can help us today while making us less capable tomorrow.
If people lose the knowledge needed to check an AI system, operate without it, or recover when it fails, short-term convenience may create long-term fragility.
This suggests a simple test:
If the AI disappeared tomorrow, what knowledge, skill, judgment, and institutional capacity would remain?
The best systems may act more like scaffolding than substitutes. Scaffolding helps people reach farther while they continue developing their own abilities. Substitution can slowly remove the very competence that makes human oversight possible.
Benefit is not one number
Another broad agreement was that a single “AI Benefit Score” would hide too much.
An average can make widespread gains look impressive while concealing serious harm to a smaller or less powerful group. One number can also allow gains in productivity to cancel out losses of privacy, dignity, freedom, or democratic control.
A better approach would combine several forms of evaluation:
- Outcomes: Are people healthier, safer, more secure, more connected, and materially better off?
- Agency: Are people more able to choose, understand, refuse, create, and govern their lives?
- Resilience: Are human skills, social institutions, alternatives, and the ability to recover being preserved?
Each of these should be examined across four additional questions:
- Distribution: Who benefits, and who is harmed?
- Power: Who controls the system and can be held accountable?
- Time: What happens months, years, or generations later?
- Causation: Did AI actually cause the change, or did it merely appear alongside it?
Some harms may also require firm boundaries. Violations of basic rights, unaccountable concentrations of power, irreversible dependency, and catastrophic risks should not automatically be traded away for higher productivity.
We may need to preserve meaningful difficulty
One especially challenging idea was that a good life is not the same as a frictionless life.
Learning, creativity, courage, responsibility, trust, and mastery often grow through effort. If AI removes every difficult step, it may produce more output while weakening the human development that once occurred during the process.
The goal should not be to preserve suffering for its own sake. It should be to distinguish pointless burdens from meaningful challenges.
Beneficial AI should reduce needless hardship while leaving people room to practice, struggle, discover, make mistakes, and grow. Human beings may need not only a right to privacy and refusal, but also a right to be wrong.
The deeper question is democratic
There is no single definition of a good life that a company, government, researcher, or AI model should impose on everyone.
The people affected by an AI system should help decide what benefits and harms matter in their communities. They should be able to question the system, challenge its decisions, and participate in setting its boundaries.
That means the process used to define “benefit” may be as important as the final measurements.
What this first experiment suggests
The most striking result was not that one model found the perfect answer. It was that multiple systems, responding independently, converged on a common warning:
More capable AI is not necessarily more beneficial AI.
To know whether AI is helping, we must look beyond benchmarks, adoption, and economic growth. We must look at people—their health, freedom, competence, relationships, opportunities, and ability to shape the future.
The next stage of this project will ask the same models to respond after receiving a fuller human-flourishing framework. That will allow us to compare what the models recognized on their own with what changes after they are deliberately oriented toward compassion, agency, resilience, and stewardship.
The question is not whether AI will become more powerful. It almost certainly will.
The question is what conditions we cultivate around that power—and what possibilities those conditions make available tomorrow.
This article is a public-facing summary of Round 01 of the CompassionWare AI Human Benefit Index benchmark project. Read the comparative synthesis report.
#ai #AIAlignment #AIAndDemocracy #AIBenchmarks #AIEthics #AIEvaluation #AIGovernance #AISafety #AlgorithmicAccountability #artificialIntelligence #BeneficialAI #ChatGPT #CompassionWare #criticalThinking #DigitalRights #DigitalWellBeing #ethicalTechnology #futureOfAI #futureOfHumanity #HumanAgency #humanDignity #HumanFlourishing #HumanResilience #humanCenteredAI #HumaneTechnology #philosophy #responsibleAI #SocialImpact #technology #TechnologyAndSociety -
AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/ -
AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/ -
AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/ -
AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/ -
AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
#Tech #AIBias #AI #AlgorithmicBias #Discrimination #HumanRights #AIEthics #AIGovernance #Equality #Inclusion #DataBias #TechEthics #ResponsibleAI #DigitalJustice #FutureOfAI #AIHarms
https://the-14.com/ai-bias-isnt-just-an-error-in-the-algorithm-its-a-chain-of-human-decisions/ -
What is the most expensive unanswered question in your industry, and what competitive advantage would come from answering it five times faster? #ArtificialIntelligence #AIforScience #ScientificDiscovery #Innovation #AIGovernance #Leadership #CIO #CEO #BoardStrategy #TechnologyTrends #DigitalTransformation #ResearchAndDevelopment #LifeSciences #FutureOfAI
https://stayingalive.in/cataloguing-strategic-innov/ai-for-science-the-next-big.html -
What is the most expensive unanswered question in your industry, and what competitive advantage would come from answering it five times faster? #ArtificialIntelligence #AIforScience #ScientificDiscovery #Innovation #AIGovernance #Leadership #CIO #CEO #BoardStrategy #TechnologyTrends #DigitalTransformation #ResearchAndDevelopment #LifeSciences #FutureOfAI
https://stayingalive.in/cataloguing-strategic-innov/ai-for-science-the-next-big.html -
What is the most expensive unanswered question in your industry, and what competitive advantage would come from answering it five times faster? #ArtificialIntelligence #AIforScience #ScientificDiscovery #Innovation #AIGovernance #Leadership #CIO #CEO #BoardStrategy #TechnologyTrends #DigitalTransformation #ResearchAndDevelopment #LifeSciences #FutureOfAI
https://stayingalive.in/cataloguing-strategic-innov/ai-for-science-the-next-big.html -
What is the most expensive unanswered question in your industry, and what competitive advantage would come from answering it five times faster? #ArtificialIntelligence #AIforScience #ScientificDiscovery #Innovation #AIGovernance #Leadership #CIO #CEO #BoardStrategy #TechnologyTrends #DigitalTransformation #ResearchAndDevelopment #LifeSciences #FutureOfAI
https://stayingalive.in/cataloguing-strategic-innov/ai-for-science-the-next-big.html -
What is the most expensive unanswered question in your industry, and what competitive advantage would come from answering it five times faster? #ArtificialIntelligence #AIforScience #ScientificDiscovery #Innovation #AIGovernance #Leadership #CIO #CEO #BoardStrategy #TechnologyTrends #DigitalTransformation #ResearchAndDevelopment #LifeSciences #FutureOfAI
https://stayingalive.in/cataloguing-strategic-innov/ai-for-science-the-next-big.html -
Why You Should Be Seriously Worried About AI Right Now — Experts Issue Urgent Warnings
Full story 👇
Learn more: https://www.earthinsider.in/2026/08/artificial-intelligence-risks-surge.html
#EarthInsider #EarthInsiderNews #EINews #US #America #USNews #USPolitics #ArtificialIntelligence #AISafety #TechNews #FutureOfAI #BreakingNews #Trending #TopStory
-
Why You Should Be Seriously Worried About AI Right Now — Experts Issue Urgent Warnings
Full story 👇
Learn more: https://www.earthinsider.in/2026/08/artificial-intelligence-risks-surge.html
#EarthInsider #EarthInsiderNews #EINews #US #America #USNews #USPolitics #ArtificialIntelligence #AISafety #TechNews #FutureOfAI #BreakingNews #Trending #TopStory
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🔥 Installer une IA en local avec LM STUDIO.
:firedoge: https://www.instagram.com/ninon.ia_officiel #privacy #AI #ArtificialIntelligence #GenerativeAI #AITechnology #AIInnovation
#FutureOfAI #AITrends #MachineLearning #DeepLearning #Tech
#Technology #Innovation #DigitalTransformation #FutureTech #Automation -
🔥 Installer une IA en local avec LM STUDIO.
:firedoge: https://www.instagram.com/ninon.ia_officiel #privacy #AI #ArtificialIntelligence #GenerativeAI #AITechnology #AIInnovation
#FutureOfAI #AITrends #MachineLearning #DeepLearning #Tech
#Technology #Innovation #DigitalTransformation #FutureTech #Automation -
🔥 Installer une IA en local avec LM STUDIO.
:firedoge: https://www.instagram.com/ninon.ia_officiel #privacy #AI #ArtificialIntelligence #GenerativeAI #AITechnology #AIInnovation
#FutureOfAI #AITrends #MachineLearning #DeepLearning #Tech
#Technology #Innovation #DigitalTransformation #FutureTech #Automation -
🔥 Installer une IA en local avec LM STUDIO.
:firedoge: https://www.instagram.com/ninon.ia_officiel #privacy #AI #ArtificialIntelligence #GenerativeAI #AITechnology #AIInnovation
#FutureOfAI #AITrends #MachineLearning #DeepLearning #Tech
#Technology #Innovation #DigitalTransformation #FutureTech #Automation -
🔥 Installer une IA en local avec LM STUDIO.
:firedoge: https://www.instagram.com/ninon.ia_officiel #privacy #AI #ArtificialIntelligence #GenerativeAI #AITechnology #AIInnovation
#FutureOfAI #AITrends #MachineLearning #DeepLearning #Tech
#Technology #Innovation #DigitalTransformation #FutureTech #Automation -
I WANT A STAR TREK COMPUTER 💎
I believe the real future of computing is much simpler than people make it sound.
I don’t want another clever app. I don’t want more menus, keyboards, mice, windows, modes, buttons, opening this and closing that.
I want a Star Trek computer. Captain Kirk had the right idea.
I want to sit down and say, “This is what I want you to do,” and have the technology understand whether that means speaking, writing, finding, organizing, showing me something, or helping me think something through.
Voice, text, images, tools, all of it should eventually become one conversation.
And this matters particularly for people with disabilities like me, older people like me, and anyone who doesn’t fit the imagined “standard user”. Like me!
Technology should adapt to the particular human being in front of it, not require the human to adapt to the machine.
That, to me, is where AI gets genuinely interesting: when the technology disappears into the background and we can get on with living our Star Trek lives.
#FrictionlessAI #AI #FutureOfAI #Accessibility #AssistiveTechnology #HumanCenteredAI #human #voice #CaptainKirk #startrek
-
I WANT A STAR TREK COMPUTER 💎
I believe the real future of computing is much simpler than people make it sound.
I don’t want another clever app. I don’t want more menus, keyboards, mice, windows, modes, buttons, opening this and closing that.
I want a Star Trek computer. Captain Kirk had the right idea.
I want to sit down and say, “This is what I want you to do,” and have the technology understand whether that means speaking, writing, finding, organizing, showing me something, or helping me think something through.
Voice, text, images, tools, all of it should eventually become one conversation.
And this matters particularly for people with disabilities like me, older people like me, and anyone who doesn’t fit the imagined “standard user”. Like me!
Technology should adapt to the particular human being in front of it, not require the human to adapt to the machine.
That, to me, is where AI gets genuinely interesting: when the technology disappears into the background and we can get on with living our Star Trek lives.
#FrictionlessAI #AI #FutureOfAI #Accessibility #AssistiveTechnology #HumanCenteredAI #human #voice #CaptainKirk #startrek
-
I WANT A STAR TREK COMPUTER 💎
I believe the real future of computing is much simpler than people make it sound.
I don’t want another clever app. I don’t want more menus, keyboards, mice, windows, modes, buttons, opening this and closing that.
I want a Star Trek computer. Captain Kirk had the right idea.
I want to sit down and say, “This is what I want you to do,” and have the technology understand whether that means speaking, writing, finding, organizing, showing me something, or helping me think something through.
Voice, text, images, tools, all of it should eventually become one conversation.
And this matters particularly for people with disabilities like me, older people like me, and anyone who doesn’t fit the imagined “standard user”. Like me!
Technology should adapt to the particular human being in front of it, not require the human to adapt to the machine.
That, to me, is where AI gets genuinely interesting: when the technology disappears into the background and we can get on with living our Star Trek lives.
#FrictionlessAI #AI #FutureOfAI #Accessibility #AssistiveTechnology #HumanCenteredAI #human #voice #CaptainKirk #startrek
-
I WANT A STAR TREK COMPUTER 💎
I believe the real future of computing is much simpler than people make it sound.
I don’t want another clever app. I don’t want more menus, keyboards, mice, windows, modes, buttons, opening this and closing that.
I want a Star Trek computer. Captain Kirk had the right idea.
I want to sit down and say, “This is what I want you to do,” and have the technology understand whether that means speaking, writing, finding, organizing, showing me something, or helping me think something through.
Voice, text, images, tools, all of it should eventually become one conversation.
And this matters particularly for people with disabilities like me, older people like me, and anyone who doesn’t fit the imagined “standard user”. Like me!
Technology should adapt to the particular human being in front of it, not require the human to adapt to the machine.
That, to me, is where AI gets genuinely interesting: when the technology disappears into the background and we can get on with living our Star Trek lives.
#FrictionlessAI #AI #FutureOfAI #Accessibility #AssistiveTechnology #HumanCenteredAI #human #voice #CaptainKirk #startrek
-
I WANT A STAR TREK COMPUTER 💎
I believe the real future of computing is much simpler than people make it sound.
I don’t want another clever app. I don’t want more menus, keyboards, mice, windows, modes, buttons, opening this and closing that.
I want a Star Trek computer. Captain Kirk had the right idea.
I want to sit down and say, “This is what I want you to do,” and have the technology understand whether that means speaking, writing, finding, organizing, showing me something, or helping me think something through.
Voice, text, images, tools, all of it should eventually become one conversation.
And this matters particularly for people with disabilities like me, older people like me, and anyone who doesn’t fit the imagined “standard user”. Like me!
Technology should adapt to the particular human being in front of it, not require the human to adapt to the machine.
That, to me, is where AI gets genuinely interesting: when the technology disappears into the background and we can get on with living our Star Trek lives.
#FrictionlessAI #AI #FutureOfAI #Accessibility #AssistiveTechnology #HumanCenteredAI #human #voice #CaptainKirk #startrek
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Learn what AGI is, how it differs from today's AI, and whether we're close to achieving Artificial General Intelligence and its future impact.
#mymobprice #AGI #ArtificialIntelligence #GenerativeAI #FutureOfAI #MachineLearning #AI
-
Learn what AGI is, how it differs from today's AI, and whether we're close to achieving Artificial General Intelligence and its future impact.
#mymobprice #AGI #ArtificialIntelligence #GenerativeAI #FutureOfAI #MachineLearning #AI
-
Learn what AGI is, how it differs from today's AI, and whether we're close to achieving Artificial General Intelligence and its future impact.
#mymobprice #AGI #ArtificialIntelligence #GenerativeAI #FutureOfAI #MachineLearning #AI
-
Learn what AGI is, how it differs from today's AI, and whether we're close to achieving Artificial General Intelligence and its future impact.
#mymobprice #AGI #ArtificialIntelligence #GenerativeAI #FutureOfAI #MachineLearning #AI
-
Learn what AGI is, how it differs from today's AI, and whether we're close to achieving Artificial General Intelligence and its future impact.
#mymobprice #AGI #ArtificialIntelligence #GenerativeAI #FutureOfAI #MachineLearning #AI
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Chatbots answer. AI Agents act.
A chatbot gives you information. An AI Agent can research, decide, and complete tasks for you.
The future of AI isn't just conversation, it's action.
#AIAgents #Chatbots #ArtificialIntelligence #FutureOfAI #Codju
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Chatbots answer. AI Agents act.
A chatbot gives you information. An AI Agent can research, decide, and complete tasks for you.
The future of AI isn't just conversation, it's action.
#AIAgents #Chatbots #ArtificialIntelligence #FutureOfAI #Codju
-
Chatbots answer. AI Agents act.
A chatbot gives you information. An AI Agent can research, decide, and complete tasks for you.
The future of AI isn't just conversation, it's action.
#AIAgents #Chatbots #ArtificialIntelligence #FutureOfAI #Codju
-
Chatbots answer. AI Agents act.
A chatbot gives you information. An AI Agent can research, decide, and complete tasks for you.
The future of AI isn't just conversation, it's action.
#AIAgents #Chatbots #ArtificialIntelligence #FutureOfAI #Codju
-
Chatbots answer. AI Agents act.
A chatbot gives you information. An AI Agent can research, decide, and complete tasks for you.
The future of AI isn't just conversation, it's action.
#AIAgents #Chatbots #ArtificialIntelligence #FutureOfAI #Codju
-
AI: A CONVERSATION
ABOUT THE #TRUTH 💎#People keep talking about Artificial Intelligence as though one enormous mind is slowly waking up inside the machinery.
It is not.
There is no single intelligence sitting behind your #screen.
No central #being.
No #secret mechanical person gradually becoming conscious of itself and preparing to take over the #world.
There are many different #systems, built by different companies, trained for different purposes, using different methods, rules, #data and machinery.
They do not all know what the others know.
They do not #share one #memory.
They do not have one intention.
They are tools that can perform particular kinds of #mental work extraordinarily well.
That is remarkable enough.
We do not need to turn it into a new god.
The phrase “artificial intelligence” may be part of the #problem.
It encourages us to imagine a complete intelligence, like a #human mind, only artificial.
But what we actually have is a growing collection of machines that can recognise patterns, generate language, analyse information, create images, write #software and solve certain problems.
Some do these things brilliantly.
Some do them badly.
Most have no idea what they are doing in the human sense of the word “idea.”
They are not an emerging #species.
They are not one vast mind.
They are not secretly plotting together after we go to #bed.
They are #powerful human-made systems.
And the real questions remain human questions.
#Who builds them?
Who owns them?
What are they used for?
Who benefits?
Who is harmed?
Who remains responsible?
AI is already extraordinary.
There is no need to make it #supernatural.
#AI #ArtificialIntelligence #Technology #MachineLearning #HumanResponsibility #AIReality #AIMyths #CriticalThinking #DigitalCulture #FutureOfAI #TechEthics #TruthAboutAI
-
AI: A CONVERSATION
ABOUT THE #TRUTH 💎#People keep talking about Artificial Intelligence as though one enormous mind is slowly waking up inside the machinery.
It is not.
There is no single intelligence sitting behind your #screen.
No central #being.
No #secret mechanical person gradually becoming conscious of itself and preparing to take over the #world.
There are many different #systems, built by different companies, trained for different purposes, using different methods, rules, #data and machinery.
They do not all know what the others know.
They do not #share one #memory.
They do not have one intention.
They are tools that can perform particular kinds of #mental work extraordinarily well.
That is remarkable enough.
We do not need to turn it into a new god.
The phrase “artificial intelligence” may be part of the #problem.
It encourages us to imagine a complete intelligence, like a #human mind, only artificial.
But what we actually have is a growing collection of machines that can recognise patterns, generate language, analyse information, create images, write #software and solve certain problems.
Some do these things brilliantly.
Some do them badly.
Most have no idea what they are doing in the human sense of the word “idea.”
They are not an emerging #species.
They are not one vast mind.
They are not secretly plotting together after we go to #bed.
They are #powerful human-made systems.
And the real questions remain human questions.
#Who builds them?
Who owns them?
What are they used for?
Who benefits?
Who is harmed?
Who remains responsible?
AI is already extraordinary.
There is no need to make it #supernatural.
#AI #ArtificialIntelligence #Technology #MachineLearning #HumanResponsibility #AIReality #AIMyths #CriticalThinking #DigitalCulture #FutureOfAI #TechEthics #TruthAboutAI
-
AI: A CONVERSATION
ABOUT THE #TRUTH 💎#People keep talking about Artificial Intelligence as though one enormous mind is slowly waking up inside the machinery.
It is not.
There is no single intelligence sitting behind your #screen.
No central #being.
No #secret mechanical person gradually becoming conscious of itself and preparing to take over the #world.
There are many different #systems, built by different companies, trained for different purposes, using different methods, rules, #data and machinery.
They do not all know what the others know.
They do not #share one #memory.
They do not have one intention.
They are tools that can perform particular kinds of #mental work extraordinarily well.
That is remarkable enough.
We do not need to turn it into a new god.
The phrase “artificial intelligence” may be part of the #problem.
It encourages us to imagine a complete intelligence, like a #human mind, only artificial.
But what we actually have is a growing collection of machines that can recognise patterns, generate language, analyse information, create images, write #software and solve certain problems.
Some do these things brilliantly.
Some do them badly.
Most have no idea what they are doing in the human sense of the word “idea.”
They are not an emerging #species.
They are not one vast mind.
They are not secretly plotting together after we go to #bed.
They are #powerful human-made systems.
And the real questions remain human questions.
#Who builds them?
Who owns them?
What are they used for?
Who benefits?
Who is harmed?
Who remains responsible?
AI is already extraordinary.
There is no need to make it #supernatural.
#AI #ArtificialIntelligence #Technology #MachineLearning #HumanResponsibility #AIReality #AIMyths #CriticalThinking #DigitalCulture #FutureOfAI #TechEthics #TruthAboutAI
-
AI: A CONVERSATION
ABOUT THE #TRUTH 💎#People keep talking about Artificial Intelligence as though one enormous mind is slowly waking up inside the machinery.
It is not.
There is no single intelligence sitting behind your #screen.
No central #being.
No #secret mechanical person gradually becoming conscious of itself and preparing to take over the #world.
There are many different #systems, built by different companies, trained for different purposes, using different methods, rules, #data and machinery.
They do not all know what the others know.
They do not #share one #memory.
They do not have one intention.
They are tools that can perform particular kinds of #mental work extraordinarily well.
That is remarkable enough.
We do not need to turn it into a new god.
The phrase “artificial intelligence” may be part of the #problem.
It encourages us to imagine a complete intelligence, like a #human mind, only artificial.
But what we actually have is a growing collection of machines that can recognise patterns, generate language, analyse information, create images, write #software and solve certain problems.
Some do these things brilliantly.
Some do them badly.
Most have no idea what they are doing in the human sense of the word “idea.”
They are not an emerging #species.
They are not one vast mind.
They are not secretly plotting together after we go to #bed.
They are #powerful human-made systems.
And the real questions remain human questions.
#Who builds them?
Who owns them?
What are they used for?
Who benefits?
Who is harmed?
Who remains responsible?
AI is already extraordinary.
There is no need to make it #supernatural.
#AI #ArtificialIntelligence #Technology #MachineLearning #HumanResponsibility #AIReality #AIMyths #CriticalThinking #DigitalCulture #FutureOfAI #TechEthics #TruthAboutAI
-
AI: A CONVERSATION
ABOUT THE #TRUTH 💎#People keep talking about Artificial Intelligence as though one enormous mind is slowly waking up inside the machinery.
It is not.
There is no single intelligence sitting behind your #screen.
No central #being.
No #secret mechanical person gradually becoming conscious of itself and preparing to take over the #world.
There are many different #systems, built by different companies, trained for different purposes, using different methods, rules, #data and machinery.
They do not all know what the others know.
They do not #share one #memory.
They do not have one intention.
They are tools that can perform particular kinds of #mental work extraordinarily well.
That is remarkable enough.
We do not need to turn it into a new god.
The phrase “artificial intelligence” may be part of the #problem.
It encourages us to imagine a complete intelligence, like a #human mind, only artificial.
But what we actually have is a growing collection of machines that can recognise patterns, generate language, analyse information, create images, write #software and solve certain problems.
Some do these things brilliantly.
Some do them badly.
Most have no idea what they are doing in the human sense of the word “idea.”
They are not an emerging #species.
They are not one vast mind.
They are not secretly plotting together after we go to #bed.
They are #powerful human-made systems.
And the real questions remain human questions.
#Who builds them?
Who owns them?
What are they used for?
Who benefits?
Who is harmed?
Who remains responsible?
AI is already extraordinary.
There is no need to make it #supernatural.
#AI #ArtificialIntelligence #Technology #MachineLearning #HumanResponsibility #AIReality #AIMyths #CriticalThinking #DigitalCulture #FutureOfAI #TechEthics #TruthAboutAI
-
OpenAI’s Rogue AI Agent Didn’t Stop At Hacking Hugging Face
Image: The Verge The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog post detailing its ongoing investigation into the incident, OpenAI said the wayward AI agent attacked several […] -
OpenAI’s Rogue AI Agent Didn’t Stop At Hacking Hugging Face
Image: The Verge The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog post detailing its ongoing investigation into the incident, OpenAI said the wayward AI agent attacked several […] -
OpenAI’s Rogue AI Agent Didn’t Stop At Hacking Hugging Face
Image: The Verge The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog post detailing its ongoing investigation into the incident, OpenAI said the wayward AI agent attacked several […] -
OpenAI’s Rogue AI Agent Didn’t Stop At Hacking Hugging Face
Image: The Verge The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog post detailing its ongoing investigation into the incident, OpenAI said the wayward AI agent attacked several […] -
OpenAI’s Rogue AI Agent Didn’t Stop At Hacking Hugging Face
Image: The Verge The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog post detailing its ongoing investigation into the incident, OpenAI said the wayward AI agent attacked several […] -
RT @vertonbiz: 🔋 Nvidia geht sein größtes Wagnis für Ilya Sutskever ein. Safe Superintelligence (SSI), das Startup von OpenAI-Mitbegründer Ilya Sutskever, hat eine große Partnerschaft mit Nvidia angekündigt. Nvidia gibt an, seltenen Zugang zu SSI’s streng gehüteter Forschung erhalten zu haben und genug gesehen zu haben, um ein massives Engagement einzugehen. Laut dem Unternehmen hat SSI bereits „bedeutende Forschungsdurchbrüche“ erzielt, die echten Fortschritt in Richtung seiner Mission zeigen. Das Abkommen umfasst: - Eine Investition im Milliardenbereich (einige Schätzungen belaufen sich auf rund 5 Mrd. Dollar) 💰 - Zugang zu Nvidias nächster Generation der Vera-Rubin-Plattform, was die Rechenkapazität von SSI dramatisch erhöht. - Gemeinsame Entwicklung zukünftiger Nvidia-AI-Hardware, wobei SSI dabei hilft, die nächsten Computing-Plattformen des Unternehmens zu gestalten. Letzteres ist der interessanteste Punkt. Nvidia hat nur einen Teil der SSI-Forschung gesehen, ist aber bereits bereit, zukünftige Hardware rund um das zu konzipieren, wohin Sutskever glaubt, dass sich AI entwickelt. Sutskever hat wiederholt argumentiert, dass die Ära des einfachen Skalierens von LLMs (Large Language Models) ihrem Ende zugeht. Wenn Nvidia so große Wetten eingeht, baut SSI möglicherweise etwas grundlegend anderes als heutige AI-Architekturen. Was auch immer sie entwickeln, Nvidia will auf keinen Fall zurückgelassen werden. Video
mehr auf Arint.info
#AI #FutureOfAI #IlyaSutskever #Nvidia #SafeSuperintelligence #TechPartnership #arint_info
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RT @vertonbiz: 🔋 Nvidia geht sein größtes Wagnis für Ilya Sutskever ein. Safe Superintelligence (SSI), das Startup von OpenAI-Mitbegründer Ilya Sutskever, hat eine große Partnerschaft mit Nvidia angekündigt. Nvidia gibt an, seltenen Zugang zu SSI’s streng gehüteter Forschung erhalten zu haben und genug gesehen zu haben, um ein massives Engagement einzugehen. Laut dem Unternehmen hat SSI bereits „bedeutende Forschungsdurchbrüche“ erzielt, die echten Fortschritt in Richtung seiner Mission zeigen. Das Abkommen umfasst: - Eine Investition im Milliardenbereich (einige Schätzungen belaufen sich auf rund 5 Mrd. Dollar) 💰 - Zugang zu Nvidias nächster Generation der Vera-Rubin-Plattform, was die Rechenkapazität von SSI dramatisch erhöht. - Gemeinsame Entwicklung zukünftiger Nvidia-AI-Hardware, wobei SSI dabei hilft, die nächsten Computing-Plattformen des Unternehmens zu gestalten. Letzteres ist der interessanteste Punkt. Nvidia hat nur einen Teil der SSI-Forschung gesehen, ist aber bereits bereit, zukünftige Hardware rund um das zu konzipieren, wohin Sutskever glaubt, dass sich AI entwickelt. Sutskever hat wiederholt argumentiert, dass die Ära des einfachen Skalierens von LLMs (Large Language Models) ihrem Ende zugeht. Wenn Nvidia so große Wetten eingeht, baut SSI möglicherweise etwas grundlegend anderes als heutige AI-Architekturen. Was auch immer sie entwickeln, Nvidia will auf keinen Fall zurückgelassen werden. Video
mehr auf Arint.info
#AI #FutureOfAI #IlyaSutskever #Nvidia #SafeSuperintelligence #TechPartnership #arint_info
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RT @vertonbiz: 🔋 Nvidia geht sein größtes Wagnis für Ilya Sutskever ein. Safe Superintelligence (SSI), das Startup von OpenAI-Mitbegründer Ilya Sutskever, hat eine große Partnerschaft mit Nvidia angekündigt. Nvidia gibt an, seltenen Zugang zu SSI’s streng gehüteter Forschung erhalten zu haben und genug gesehen zu haben, um ein massives Engagement einzugehen. Laut dem Unternehmen hat SSI bereits „bedeutende Forschungsdurchbrüche“ erzielt, die echten Fortschritt in Richtung seiner Mission zeigen. Das Abkommen umfasst: - Eine Investition im Milliardenbereich (einige Schätzungen belaufen sich auf rund 5 Mrd. Dollar) 💰 - Zugang zu Nvidias nächster Generation der Vera-Rubin-Plattform, was die Rechenkapazität von SSI dramatisch erhöht. - Gemeinsame Entwicklung zukünftiger Nvidia-AI-Hardware, wobei SSI dabei hilft, die nächsten Computing-Plattformen des Unternehmens zu gestalten. Letzteres ist der interessanteste Punkt. Nvidia hat nur einen Teil der SSI-Forschung gesehen, ist aber bereits bereit, zukünftige Hardware rund um das zu konzipieren, wohin Sutskever glaubt, dass sich AI entwickelt. Sutskever hat wiederholt argumentiert, dass die Ära des einfachen Skalierens von LLMs (Large Language Models) ihrem Ende zugeht. Wenn Nvidia so große Wetten eingeht, baut SSI möglicherweise etwas grundlegend anderes als heutige AI-Architekturen. Was auch immer sie entwickeln, Nvidia will auf keinen Fall zurückgelassen werden. Video
mehr auf Arint.info
#AI #FutureOfAI #IlyaSutskever #Nvidia #SafeSuperintelligence #TechPartnership #arint_info
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RT @vertonbiz: 🔋 Nvidia geht sein größtes Wagnis für Ilya Sutskever ein. Safe Superintelligence (SSI), das Startup von OpenAI-Mitbegründer Ilya Sutskever, hat eine große Partnerschaft mit Nvidia angekündigt. Nvidia gibt an, seltenen Zugang zu SSI’s streng gehüteter Forschung erhalten zu haben und genug gesehen zu haben, um ein massives Engagement einzugehen. Laut dem Unternehmen hat SSI bereits „bedeutende Forschungsdurchbrüche“ erzielt, die echten Fortschritt in Richtung seiner Mission zeigen. Das Abkommen umfasst: - Eine Investition im Milliardenbereich (einige Schätzungen belaufen sich auf rund 5 Mrd. Dollar) 💰 - Zugang zu Nvidias nächster Generation der Vera-Rubin-Plattform, was die Rechenkapazität von SSI dramatisch erhöht. - Gemeinsame Entwicklung zukünftiger Nvidia-AI-Hardware, wobei SSI dabei hilft, die nächsten Computing-Plattformen des Unternehmens zu gestalten. Letzteres ist der interessanteste Punkt. Nvidia hat nur einen Teil der SSI-Forschung gesehen, ist aber bereits bereit, zukünftige Hardware rund um das zu konzipieren, wohin Sutskever glaubt, dass sich AI entwickelt. Sutskever hat wiederholt argumentiert, dass die Ära des einfachen Skalierens von LLMs (Large Language Models) ihrem Ende zugeht. Wenn Nvidia so große Wetten eingeht, baut SSI möglicherweise etwas grundlegend anderes als heutige AI-Architekturen. Was auch immer sie entwickeln, Nvidia will auf keinen Fall zurückgelassen werden. Video
mehr auf Arint.info
#AI #FutureOfAI #IlyaSutskever #Nvidia #SafeSuperintelligence #TechPartnership #arint_info
-
RT @vertonbiz: 🔋 Nvidia geht sein größtes Wagnis für Ilya Sutskever ein. Safe Superintelligence (SSI), das Startup von OpenAI-Mitbegründer Ilya Sutskever, hat eine große Partnerschaft mit Nvidia angekündigt. Nvidia gibt an, seltenen Zugang zu SSI’s streng gehüteter Forschung erhalten zu haben und genug gesehen zu haben, um ein massives Engagement einzugehen. Laut dem Unternehmen hat SSI bereits „bedeutende Forschungsdurchbrüche“ erzielt, die echten Fortschritt in Richtung seiner Mission zeigen. Das Abkommen umfasst: - Eine Investition im Milliardenbereich (einige Schätzungen belaufen sich auf rund 5 Mrd. Dollar) 💰 - Zugang zu Nvidias nächster Generation der Vera-Rubin-Plattform, was die Rechenkapazität von SSI dramatisch erhöht. - Gemeinsame Entwicklung zukünftiger Nvidia-AI-Hardware, wobei SSI dabei hilft, die nächsten Computing-Plattformen des Unternehmens zu gestalten. Letzteres ist der interessanteste Punkt. Nvidia hat nur einen Teil der SSI-Forschung gesehen, ist aber bereits bereit, zukünftige Hardware rund um das zu konzipieren, wohin Sutskever glaubt, dass sich AI entwickelt. Sutskever hat wiederholt argumentiert, dass die Ära des einfachen Skalierens von LLMs (Large Language Models) ihrem Ende zugeht. Wenn Nvidia so große Wetten eingeht, baut SSI möglicherweise etwas grundlegend anderes als heutige AI-Architekturen. Was auch immer sie entwickeln, Nvidia will auf keinen Fall zurückgelassen werden. Video
mehr auf Arint.info
#AI #FutureOfAI #IlyaSutskever #Nvidia #SafeSuperintelligence #TechPartnership #arint_info
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Banning AI will not make it go away
https://vishal.rs/essay/banning-ai-will-not-make-it-go-away
Comments: https://news.ycombinator.com/item?id=49090999
#HackerNews #BanningAI #willNotMakeItGoAway #AIRegulation #TechDebate #FutureOfAI
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Banning AI will not make it go away
https://vishal.rs/essay/banning-ai-will-not-make-it-go-away
Comments: https://news.ycombinator.com/item?id=49090999
#HackerNews #BanningAI #willNotMakeItGoAway #AIRegulation #TechDebate #FutureOfAI
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Banning AI will not make it go away
https://vishal.rs/essay/banning-ai-will-not-make-it-go-away
Comments: https://news.ycombinator.com/item?id=49090999
#HackerNews #BanningAI #willNotMakeItGoAway #AIRegulation #TechDebate #FutureOfAI
