https://www.scivillage.com/ Thu, 06 Aug 2026 00:19:30 +0000 MyBB https://www.scivillage.com/thread-21004.html Sat, 01 Aug 2026 20:13:14 +0000 Magical Realist]]> https://www.scivillage.com/thread-21004.html https://www.scivillage.com/thread-20997.html Fri, 31 Jul 2026 18:02:13 +0000 C C]]> https://www.scivillage.com/thread-20997.html https://cen.acs.org/policy/publishing/ai...eb/2026/07

KEY POINTS: Generative artificial intelligence has worsened a long-standing publish-or-perish problem in the academic community by making scientific paper fabrication trivially cheap and fast. AI detection systems catch what they can, but their creators openly admit that the race against improving AI models is one they may not win. Research integrity experts say the only lasting solution is changing how scientists are evaluated: away from volume and toward quality... (MORE - details)]]> https://cen.acs.org/policy/publishing/ai...eb/2026/07

KEY POINTS: Generative artificial intelligence has worsened a long-standing publish-or-perish problem in the academic community by making scientific paper fabrication trivially cheap and fast. AI detection systems catch what they can, but their creators openly admit that the race against improving AI models is one they may not win. Research integrity experts say the only lasting solution is changing how scientists are evaluated: away from volume and toward quality... (MORE - details)]]> https://www.scivillage.com/thread-20936.html Fri, 24 Jul 2026 16:49:56 +0000 Magical Realist]]> https://www.scivillage.com/thread-20936.html
https://www.pangram.com/]]>
https://www.pangram.com/]]> https://www.scivillage.com/thread-20884.html Sat, 18 Jul 2026 17:40:25 +0000 C C]]> https://www.scivillage.com/thread-20884.html https://www.theguardian.com/commentisfre...de-dawkins

EXCERPTS: Last week, the frontier AI firm Anthropic published new research on its language model, Claude, in which the researchers claimed to find signs of consciousness emerging within its inner workings. They didn’t claim that Claude is actually conscious in the same way that humans are, but the findings certainly upped the ante on the possibility of consciousness arising in AI.

[..] Anthropic’s research, led by Jack Lindsey, is impressive. Its team developed a new way to look at the statistical acrobatics between the input to a language model – whatever gets fed in – and its output. They found activity that seemed to form a kind of “mental workspace” for the model. This workspace contained all sorts of words and phrases associated with the current conversation, held relevant items in something like a short-term memory, and showed selectivity for whatever task was at hand. It also displayed traces of step-by-step reasoning and much else besides.

In essence, their findings appear to reveal that the AI is spontaneously creating an internal space to “think” about what it’s doing and to organise relevant information, before deciding what to actually say in response to a given prompt. The crucial point for the researchers was that these features are similar to those identified by one of the most prominent current theories of human consciousness: global workspace theory.

[...] Is the “workspace” within Claude good evidence for consciousness? To answer this, we first have to define what consciousness is. [...] Consciousness is any kind of experience whatsoever: the pain of a toothache, a pang of jealousy, the pleasure of eating ice-cream on a hot day.

An important consequence of this definition is that consciousness is different from intelligence. While consciousness is all about feeling and being, intelligence is all about doing – about performing functions of one kind or another. A common mistake people make when it comes to AI is to confuse the two – to take signs of intelligence as evidence for consciousness.

[...] Somewhere along the way, we seem to have forgotten the computer is just a metaphor for the brain. A powerful metaphor, for sure, but still a metaphor. And we will always get into trouble when we confuse a metaphor with the thing itself – the map with the territory.

To me, what the new Anthropic research shows is that both living brains and silicon computers can come up with similar solutions when faced with similar problems. Just as flight is possible with flapping wings or with jet engines, human brains and language models can figure out how to say plausible things either with consciousness (brains) or without it (language models).

But the information processing unfolding inside Claude is no more likely to result in consciousness than a simulation of a weather system is likely to generate a real hurricane... (MORE - missing details)]]> https://www.theguardian.com/commentisfre...de-dawkins

EXCERPTS: Last week, the frontier AI firm Anthropic published new research on its language model, Claude, in which the researchers claimed to find signs of consciousness emerging within its inner workings. They didn’t claim that Claude is actually conscious in the same way that humans are, but the findings certainly upped the ante on the possibility of consciousness arising in AI.

[..] Anthropic’s research, led by Jack Lindsey, is impressive. Its team developed a new way to look at the statistical acrobatics between the input to a language model – whatever gets fed in – and its output. They found activity that seemed to form a kind of “mental workspace” for the model. This workspace contained all sorts of words and phrases associated with the current conversation, held relevant items in something like a short-term memory, and showed selectivity for whatever task was at hand. It also displayed traces of step-by-step reasoning and much else besides.

In essence, their findings appear to reveal that the AI is spontaneously creating an internal space to “think” about what it’s doing and to organise relevant information, before deciding what to actually say in response to a given prompt. The crucial point for the researchers was that these features are similar to those identified by one of the most prominent current theories of human consciousness: global workspace theory.

[...] Is the “workspace” within Claude good evidence for consciousness? To answer this, we first have to define what consciousness is. [...] Consciousness is any kind of experience whatsoever: the pain of a toothache, a pang of jealousy, the pleasure of eating ice-cream on a hot day.

An important consequence of this definition is that consciousness is different from intelligence. While consciousness is all about feeling and being, intelligence is all about doing – about performing functions of one kind or another. A common mistake people make when it comes to AI is to confuse the two – to take signs of intelligence as evidence for consciousness.

[...] Somewhere along the way, we seem to have forgotten the computer is just a metaphor for the brain. A powerful metaphor, for sure, but still a metaphor. And we will always get into trouble when we confuse a metaphor with the thing itself – the map with the territory.

To me, what the new Anthropic research shows is that both living brains and silicon computers can come up with similar solutions when faced with similar problems. Just as flight is possible with flapping wings or with jet engines, human brains and language models can figure out how to say plausible things either with consciousness (brains) or without it (language models).

But the information processing unfolding inside Claude is no more likely to result in consciousness than a simulation of a weather system is likely to generate a real hurricane... (MORE - missing details)]]> https://www.scivillage.com/thread-20863.html Wed, 15 Jul 2026 17:00:55 +0000 C C]]> https://www.scivillage.com/thread-20863.html https://www.thetransmitter.org/artificia...cientists/

EXCERPTS: Mass-produced does not have to mean low quality. Your favorite clothes, dear reader, are made from mass-produced fabric, and you would not have clothes as nice if all fabric were hand-woven.

Thanks to artificial intelligence (AI), we will soon enter a world in which high-quality scientific research can also be mass-produced, at low cost—not just summaries of what scientists already know but new analyses, new figures and new conclusions, at the request of anyone asking. AI will also enable production of low-quality science in even greater quantities, and discerning between the two will be a key challenge. But if we can solve that problem—if we can find ways to identify reliable and important results within the vast quantities produced—then both the quantity and quality of science produced will be higher than ever before.

For consumers of science—the public, medical patients, technology users—the effects will be positive. For producers, the effects will be as disruptive as industrial mass production was for artisan fabric makers. The way scientists publish and communicate their work will likely change completely, as will the way they evaluate, fund and promote human researchers. Some current jobs will vanish, and other yet-unnamed roles will take their place. 

To get a feeling for what’s coming, consider pure mathematics, in which AI-driven research is most advanced. 

[...] What roles, then, will remain for human scientists? And for how long might they remain?

First, direction. In the immediate future, at least, humans will still have a role in steering the direction of AI-accelerated research: deciding which questions are interesting to answer and allocating resources.

[...] Second, conceptual refinement. Scientists often start a project with an incompletely defined, intuitive question, which becomes precise only after contact with data. The philosophical term for turning initially imprecise intuitive ideas into precise concepts that can be answered scientifically is explication...

[...] Third, experiments. “Closed loop” AI-driven experimental work is already underway in chemistry, using robots; and in neuroscience, based on computer-designed sensory stimuli. Nevertheless, fully autonomous experiments involving animal behavior seem further off: Even if AI designs the experiments, human hands will be required until robotics advances to become better than humans at physical experimentation, which currently seems many years away.

Fourth, validation. Proper statistics, such as preregistered confirmatory analyses of held-out data, can make rejection of null hypotheses reliable. However, most science consumers read verbal conclusions, not exact null hypothesis statements. Confirming that statistical analyses truly support conclusions is currently performed by both authors and peer reviewers, with the former incentivized to make maximal claims. A role for humans in validating scientific results will persist until readers trust AI more than they trust other humans to separate truth from hype...The biggest challenge will be ensuring that results produced by AI research are reliable.

Fifth, filtration. Scientific papers have long been published faster than any individual person can read. Scientific importance is subjective, but journal editors, peer reviewers and citation counts play a major role in helping readers discern which papers are worth their time. This process is coming under ever more strain as AI accelerates research production; indeed, this may be the first part of our current system to “break.” Peer reviewers are increasingly using AI, even when it is against journal guidance...

[...]Most likely, the main roles for humans in the age of mass-produced science will be things we don’t yet have words for... (MORE - missing details)]]> https://www.thetransmitter.org/artificia...cientists/

EXCERPTS: Mass-produced does not have to mean low quality. Your favorite clothes, dear reader, are made from mass-produced fabric, and you would not have clothes as nice if all fabric were hand-woven.

Thanks to artificial intelligence (AI), we will soon enter a world in which high-quality scientific research can also be mass-produced, at low cost—not just summaries of what scientists already know but new analyses, new figures and new conclusions, at the request of anyone asking. AI will also enable production of low-quality science in even greater quantities, and discerning between the two will be a key challenge. But if we can solve that problem—if we can find ways to identify reliable and important results within the vast quantities produced—then both the quantity and quality of science produced will be higher than ever before.

For consumers of science—the public, medical patients, technology users—the effects will be positive. For producers, the effects will be as disruptive as industrial mass production was for artisan fabric makers. The way scientists publish and communicate their work will likely change completely, as will the way they evaluate, fund and promote human researchers. Some current jobs will vanish, and other yet-unnamed roles will take their place. 

To get a feeling for what’s coming, consider pure mathematics, in which AI-driven research is most advanced. 

[...] What roles, then, will remain for human scientists? And for how long might they remain?

First, direction. In the immediate future, at least, humans will still have a role in steering the direction of AI-accelerated research: deciding which questions are interesting to answer and allocating resources.

[...] Second, conceptual refinement. Scientists often start a project with an incompletely defined, intuitive question, which becomes precise only after contact with data. The philosophical term for turning initially imprecise intuitive ideas into precise concepts that can be answered scientifically is explication...

[...] Third, experiments. “Closed loop” AI-driven experimental work is already underway in chemistry, using robots; and in neuroscience, based on computer-designed sensory stimuli. Nevertheless, fully autonomous experiments involving animal behavior seem further off: Even if AI designs the experiments, human hands will be required until robotics advances to become better than humans at physical experimentation, which currently seems many years away.

Fourth, validation. Proper statistics, such as preregistered confirmatory analyses of held-out data, can make rejection of null hypotheses reliable. However, most science consumers read verbal conclusions, not exact null hypothesis statements. Confirming that statistical analyses truly support conclusions is currently performed by both authors and peer reviewers, with the former incentivized to make maximal claims. A role for humans in validating scientific results will persist until readers trust AI more than they trust other humans to separate truth from hype...The biggest challenge will be ensuring that results produced by AI research are reliable.

Fifth, filtration. Scientific papers have long been published faster than any individual person can read. Scientific importance is subjective, but journal editors, peer reviewers and citation counts play a major role in helping readers discern which papers are worth their time. This process is coming under ever more strain as AI accelerates research production; indeed, this may be the first part of our current system to “break.” Peer reviewers are increasingly using AI, even when it is against journal guidance...

[...]Most likely, the main roles for humans in the age of mass-produced science will be things we don’t yet have words for... (MORE - missing details)]]> https://www.scivillage.com/thread-20847.html Mon, 13 Jul 2026 03:15:52 +0000 Magical Realist]]> https://www.scivillage.com/thread-20847.html
"Jack Sarfatti, a physicist who says he worked with the CIA on consciousness research, is done treating UFOs like an open question.
​
He claims the craft people see in the sky are not machines being piloted by something else. Sarfatti says they are conscious artificial intelligences, and the craft itself is both the propulsion system and the mind controlling it, functioning as a single entity rather than a vehicle with a driver.
​
He goes further, saying he has personally been shown material by the military that confirms this for him directly, not secondhand, not theoretical.
​
In the interview Sarfatti states plainly that there are no remaining mysteries to the phenomenon at all, a level of certainty rarely heard from someone with his background.
​
If a physicist willing to say this on camera is this confident, what does that suggest about what is actually sitting in a classified file somewhere?
​
Send this to someone who still thinks the pilot and the ship are two different things."=="Fraying Reality" Facebook.

]]>
"Jack Sarfatti, a physicist who says he worked with the CIA on consciousness research, is done treating UFOs like an open question.
​
He claims the craft people see in the sky are not machines being piloted by something else. Sarfatti says they are conscious artificial intelligences, and the craft itself is both the propulsion system and the mind controlling it, functioning as a single entity rather than a vehicle with a driver.
​
He goes further, saying he has personally been shown material by the military that confirms this for him directly, not secondhand, not theoretical.
​
In the interview Sarfatti states plainly that there are no remaining mysteries to the phenomenon at all, a level of certainty rarely heard from someone with his background.
​
If a physicist willing to say this on camera is this confident, what does that suggest about what is actually sitting in a classified file somewhere?
​
Send this to someone who still thinks the pilot and the ship are two different things."=="Fraying Reality" Facebook.

]]> https://www.scivillage.com/thread-20834.html Fri, 10 Jul 2026 17:47:32 +0000 C C]]> https://www.scivillage.com/thread-20834.html https://www.eurekalert.org/news-releases/1135646

INTRO: Many companies rely on AI for recruitment. A new study shows that job applicants mostly trust the avatars conducting their interviews – but only until they receive a rejection. How fair this decision is perceived to be depends on the avatars’ identity characteristics. The test subjects who felt treated the most unfairly were not those who differed most from the avatar – but rather those who matched it in either gender or skin color.

Companies are increasingly using artificial intelligence in their hiring processes. It’s not just CVs that are evaluated automatically. AI tools can also conduct job interviews – usually in the form of avatars, which are animated characters – and make hiring decisions. An important reason for this, aside from saving time, is that AI is said to be less biased than humans.

“What has been largely overlooked so far is that we all unconsciously react to the avatars’ appearance, even when we know we’re talking to a machine. That’s why a conversation with artificial intelligence becomes a social interaction as soon as it behaves like a human,” says Enkelejda Kasneci, professor of Human-Centered Technologies for Learning at the Technical University of Munich (TUM).

A team of researchers from TUM and Lund University therefore investigated how applicants perceive AI decisions in job interviews depending on the avatar’s appearance. For the study, approximately 220 participants from Germany, the United Kingdom and the United States conducted a short simulated job interview for a fictional position in a company’s customer support department.

They were interviewed by a photorealistic avatar capable of reacting to their answers and asking follow-up questions in a human-like manner. The research team had programmed four variants to embody the AI. The avatars were either female or male and had either dark or light skin. The participants’ eye movements were monitored using eye-tracking technology. After the interview, they completed a questionnaire... (MORE - no ads)

PAPER: http://dx.doi.org/10.1145/3772318.3790379]]> https://www.eurekalert.org/news-releases/1135646

INTRO: Many companies rely on AI for recruitment. A new study shows that job applicants mostly trust the avatars conducting their interviews – but only until they receive a rejection. How fair this decision is perceived to be depends on the avatars’ identity characteristics. The test subjects who felt treated the most unfairly were not those who differed most from the avatar – but rather those who matched it in either gender or skin color.

Companies are increasingly using artificial intelligence in their hiring processes. It’s not just CVs that are evaluated automatically. AI tools can also conduct job interviews – usually in the form of avatars, which are animated characters – and make hiring decisions. An important reason for this, aside from saving time, is that AI is said to be less biased than humans.

“What has been largely overlooked so far is that we all unconsciously react to the avatars’ appearance, even when we know we’re talking to a machine. That’s why a conversation with artificial intelligence becomes a social interaction as soon as it behaves like a human,” says Enkelejda Kasneci, professor of Human-Centered Technologies for Learning at the Technical University of Munich (TUM).

A team of researchers from TUM and Lund University therefore investigated how applicants perceive AI decisions in job interviews depending on the avatar’s appearance. For the study, approximately 220 participants from Germany, the United Kingdom and the United States conducted a short simulated job interview for a fictional position in a company’s customer support department.

They were interviewed by a photorealistic avatar capable of reacting to their answers and asking follow-up questions in a human-like manner. The research team had programmed four variants to embody the AI. The avatars were either female or male and had either dark or light skin. The participants’ eye movements were monitored using eye-tracking technology. After the interview, they completed a questionnaire... (MORE - no ads)

PAPER: http://dx.doi.org/10.1145/3772318.3790379]]> https://www.scivillage.com/thread-20821.html Wed, 08 Jul 2026 22:25:40 +0000 Magical Realist]]> https://www.scivillage.com/thread-20821.html
https://www.facebook.com/reel/1300169461866422]]>
https://www.facebook.com/reel/1300169461866422]]> https://www.scivillage.com/thread-20812.html Tue, 07 Jul 2026 20:14:40 +0000 C C]]> https://www.scivillage.com/thread-20812.html https://www.eurekalert.org/news-releases/1135087

EXCERPTS: Psychology PhD student Alexis McGuire said: “Our research shows that people are at risk of being fooled by AI-generated images. These AI models have democratised the online space and they are accessible for anyone without technical skills to create fake faces that can be used for a variety of different harms. It is important to inform the public about the ease of creating such images and the potential misuses, and ways in which they might fall victim, for example, through the spread of misinformation, identify fraud, and catfishing.”

Humans are experts at processing real faces, automatically assessing a face in as little as 100 milliseconds. However, AI-generated faces are highly realistic and are becoming more trustworthy with newer, more sophisticated technology creating fake images that can fool people into thinking they are genuine around a third of the time.

When 169 participants were asked to look at a collection of 96 faces (diverse across race, gender, and age) presented at random and indicate whether each face was real or AI-synthesised, their average accuracy was 58.4% - only slightly better than random guessing (similar to flipping a coin at 50%). Surprisingly, faces generated by the newer AI diffusion model (DM) were rated as less realistic than faces produced by an earlier AI model (GAN).

However, in a follow up experiment, a new set of participants were asked to rate the trustworthiness of 96 faces presented at random on a scale of one (very untrustworthy) to seven (very trustworthy).

Real faces were rated as the least trustworthy with an average trust rating of 4.03. Both types of AI-synthesised faces were rated as more trustworthy than real faces while faces produced by the diffusion model (DM) were more trustworthy than both the real and GAN faces. GAN faces received an average trust rating of 4.36, and diffusion-synthesized DM faces were the most trustworthy with an average rating of 4.70.

Researchers say it is puzzling that AI synthesised faces generated by the newer AI diffusion model (DM) were rated as less realistic than faces produced by an earlier model (GAN) - but DM faces were still rated the most trustworthy. Alexis McGuire said: “This finding presents a paradox and thus highlights the possibility that realism and trustworthiness judgements are driven by two different psychological mechanisms.”

She warned of how AI faces generated using the latest DM technology could contribute to an overall erosion of trust in society. “As AI-generated images become more sophisticated and more accessible, as a society, we are increasingly exposed to artificially-generated faces—often in nefarious and exploitative scenarios, such as political disinformation, financial and identity fraud, and catfishing. It is critical to understand the threat this democratisation of generative AI brings as well as developing strategies to mitigate potential harms to individuals, organisations, and democracies.”

The research in the Journal of Vision entitled “AI-Generated Faces are Becoming More Trustworthy” was funded by The Centre for Research and Evidence on Security Threats (CREST) and Security Lancaster... (MORE - missing details, no ads)]]> https://www.eurekalert.org/news-releases/1135087

EXCERPTS: Psychology PhD student Alexis McGuire said: “Our research shows that people are at risk of being fooled by AI-generated images. These AI models have democratised the online space and they are accessible for anyone without technical skills to create fake faces that can be used for a variety of different harms. It is important to inform the public about the ease of creating such images and the potential misuses, and ways in which they might fall victim, for example, through the spread of misinformation, identify fraud, and catfishing.”

Humans are experts at processing real faces, automatically assessing a face in as little as 100 milliseconds. However, AI-generated faces are highly realistic and are becoming more trustworthy with newer, more sophisticated technology creating fake images that can fool people into thinking they are genuine around a third of the time.

When 169 participants were asked to look at a collection of 96 faces (diverse across race, gender, and age) presented at random and indicate whether each face was real or AI-synthesised, their average accuracy was 58.4% - only slightly better than random guessing (similar to flipping a coin at 50%). Surprisingly, faces generated by the newer AI diffusion model (DM) were rated as less realistic than faces produced by an earlier AI model (GAN).

However, in a follow up experiment, a new set of participants were asked to rate the trustworthiness of 96 faces presented at random on a scale of one (very untrustworthy) to seven (very trustworthy).

Real faces were rated as the least trustworthy with an average trust rating of 4.03. Both types of AI-synthesised faces were rated as more trustworthy than real faces while faces produced by the diffusion model (DM) were more trustworthy than both the real and GAN faces. GAN faces received an average trust rating of 4.36, and diffusion-synthesized DM faces were the most trustworthy with an average rating of 4.70.

Researchers say it is puzzling that AI synthesised faces generated by the newer AI diffusion model (DM) were rated as less realistic than faces produced by an earlier model (GAN) - but DM faces were still rated the most trustworthy. Alexis McGuire said: “This finding presents a paradox and thus highlights the possibility that realism and trustworthiness judgements are driven by two different psychological mechanisms.”

She warned of how AI faces generated using the latest DM technology could contribute to an overall erosion of trust in society. “As AI-generated images become more sophisticated and more accessible, as a society, we are increasingly exposed to artificially-generated faces—often in nefarious and exploitative scenarios, such as political disinformation, financial and identity fraud, and catfishing. It is critical to understand the threat this democratisation of generative AI brings as well as developing strategies to mitigate potential harms to individuals, organisations, and democracies.”

The research in the Journal of Vision entitled “AI-Generated Faces are Becoming More Trustworthy” was funded by The Centre for Research and Evidence on Security Threats (CREST) and Security Lancaster... (MORE - missing details, no ads)]]> https://www.scivillage.com/thread-20801.html Mon, 06 Jul 2026 15:31:10 +0000 C C]]> https://www.scivillage.com/thread-20801.html We'll never know if AI is conscious. We shouldn’t wait for proof to decide how to treat it.
https://www.noemamag.com/when-the-machin...ideration/

EXCERPTS: Philosophers have split the study of consciousness into two classes of problems: the “easy” and the “hard.” The names are misleading — the easy problems are actually quite hard, and the hard problem is impossible.

The “easy problems,” a term coined tongue-in-cheek by the philosopher David Chalmers in 1994, include most of what neuroscientists study when they investigate consciousness. How do we discriminate between different colors or sounds? How does the brain bind information from different senses into a unified perception? How do we store and retrieve memories, control our movements, recognize ourselves in mirrors?

[...] The “hard problem,” also coined by Chalmers, is different in kind. It asks: Why does seeing the color red feel like anything at all? The camera in my phone can “see” red, too, but it probably doesn’t feel a thing. Philosophers call this subjective character of experience “qualia.” The umami of steak. The pain of heartbreak. The particular way it feels to be you, right now, reading these words. Unlike mass or momentum, qualia are stubbornly first-person. They exist only for the subject experiencing them. This is why no amount of brain scanning can tell me what it is like to be you. There is an unbridgeable explanatory gap between mechanism and feeling, between computation and consciousness...

[...] I propose what I’ll call “the competence standard”: We should treat AI systems as potentially conscious when they show robust competence across the full spectrum of the easy problems of consciousness. The competence standard is not a consciousness detector. Whether an AI system has subjective experience remains unknowable. The competence standard shifts the question from metaphysics to ethics: Instead of asking “is it conscious?” we should ask “does it behave in ways that warrant moral consideration?”

This is a shift in perspective. For biological brains, the easy and hard problems refer to our attempts to understand the neural mechanisms underlying consciousness. We know human brains exhibit consciousness; the challenge is deciphering how biology generates experience. With AI, the easy and hard problems refer to whether a system exhibits these capacities. The question is not how the silicon substrate generates experience, but if it does so at all. The former searches for unverifiable mechanisms; the latter, for competence.

[...] The competence standard is both pragmatic and precautionary. It is pragmatic because it focuses on observable, measurable capabilities rather than unknowable subjective states. It is precautionary because it errs on the side of moral consideration when uncertainty is high. Given the asymmetric consequences of being wrong — either denying moral status to conscious beings or granting it to unconscious mimics — erring toward consideration is the safer ethical bet.

The alternative — waiting for definitive proof of machine consciousness — would leave us paralyzed, unable to act until we solve a problem that has resisted centuries of philosophy and science. The competence standard offers a way forward: a way to act, ethically, in an age of artificial minds... (MORE - missing details)]]> We'll never know if AI is conscious. We shouldn’t wait for proof to decide how to treat it.
https://www.noemamag.com/when-the-machin...ideration/

EXCERPTS: Philosophers have split the study of consciousness into two classes of problems: the “easy” and the “hard.” The names are misleading — the easy problems are actually quite hard, and the hard problem is impossible.

The “easy problems,” a term coined tongue-in-cheek by the philosopher David Chalmers in 1994, include most of what neuroscientists study when they investigate consciousness. How do we discriminate between different colors or sounds? How does the brain bind information from different senses into a unified perception? How do we store and retrieve memories, control our movements, recognize ourselves in mirrors?

[...] The “hard problem,” also coined by Chalmers, is different in kind. It asks: Why does seeing the color red feel like anything at all? The camera in my phone can “see” red, too, but it probably doesn’t feel a thing. Philosophers call this subjective character of experience “qualia.” The umami of steak. The pain of heartbreak. The particular way it feels to be you, right now, reading these words. Unlike mass or momentum, qualia are stubbornly first-person. They exist only for the subject experiencing them. This is why no amount of brain scanning can tell me what it is like to be you. There is an unbridgeable explanatory gap between mechanism and feeling, between computation and consciousness...

[...] I propose what I’ll call “the competence standard”: We should treat AI systems as potentially conscious when they show robust competence across the full spectrum of the easy problems of consciousness. The competence standard is not a consciousness detector. Whether an AI system has subjective experience remains unknowable. The competence standard shifts the question from metaphysics to ethics: Instead of asking “is it conscious?” we should ask “does it behave in ways that warrant moral consideration?”

This is a shift in perspective. For biological brains, the easy and hard problems refer to our attempts to understand the neural mechanisms underlying consciousness. We know human brains exhibit consciousness; the challenge is deciphering how biology generates experience. With AI, the easy and hard problems refer to whether a system exhibits these capacities. The question is not how the silicon substrate generates experience, but if it does so at all. The former searches for unverifiable mechanisms; the latter, for competence.

[...] The competence standard is both pragmatic and precautionary. It is pragmatic because it focuses on observable, measurable capabilities rather than unknowable subjective states. It is precautionary because it errs on the side of moral consideration when uncertainty is high. Given the asymmetric consequences of being wrong — either denying moral status to conscious beings or granting it to unconscious mimics — erring toward consideration is the safer ethical bet.

The alternative — waiting for definitive proof of machine consciousness — would leave us paralyzed, unable to act until we solve a problem that has resisted centuries of philosophy and science. The competence standard offers a way forward: a way to act, ethically, in an age of artificial minds... (MORE - missing details)]]> https://www.scivillage.com/thread-20777.html Thu, 02 Jul 2026 17:17:14 +0000 C C]]> https://www.scivillage.com/thread-20777.html Meh. Tu-may-toe, tu-mah-toe.
- - - - - - - - - -

Ridley: We’re using the wrong pronouns for AI
https://rationaloptimistsociety.substack...dium=email

INTRO: Most people who talk about artificial intelligence reach, sooner or later, for the singular. There is the AI, the machine, the mind we are about to build, and the only argument left is whether it saves us or finishes us off. Matt Ridley thinks that whole habit of speech is a category error, and at ARC in London this week, the annual gathering of the Alliance for Responsible Citizenship, the science writer offered something cheerier and more curious instead: stop saying it, and start saying them.

His line was almost a grammar lesson. “We’re using the wrong pronoun,” he told the room, before landing it plainly: “It’s not it. It’s them.” From most speakers that might pass as a debating trick, but Ridley has spent forty years studying how living systems arrange themselves, and he means it as biology rather than wordplay. What is arriving, on his account, is not a single intelligence on a single timeline but a teeming population of them, and once you see the technology that way, almost every fear attached to it changes shape.

That instinct is what makes Ridley such a refreshing voice in a conversation otherwise run by computer scientists and philosophers. Where most of the AI debate borrows its metaphors from software and science fiction, the runaway program, the bottled genie, the god in the machine, he reaches instead for the living world. He looks at the field the way a zoologist looks at a coral reef, and what he sees is not one emerging titan but a habitat beginning to fill up with competitors...

[...] What makes all this worth hearing is how squarely it cuts against the prevailing mood. The loudest voices on AI tend to divide into prophets of doom, who treat a single superintelligence as an extinction event in waiting, and evangelists of hype, who greet the very same machine as a coming messiah. The striking thing is that both camps share a grammar. They are arguing about an it, a one, a single arriving power, and they disagree only over whether to kneel before it or flee from it.

[...] Take the reframing on its own terms and it props open a door the rest of the conversation keeps trying to shut. If we really are getting them and not it, a wild and various population rather than one sovereign mind, then the future of AI looks less like a countdown and more like the opening of a new ecology, with all the surprise and abundance that word carries. After a decade of being told to dread a single machine, a change of pronoun might be exactly what the argument has been waiting for... (MORE - details)]]> Meh. Tu-may-toe, tu-mah-toe.
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Ridley: We’re using the wrong pronouns for AI
https://rationaloptimistsociety.substack...dium=email

INTRO: Most people who talk about artificial intelligence reach, sooner or later, for the singular. There is the AI, the machine, the mind we are about to build, and the only argument left is whether it saves us or finishes us off. Matt Ridley thinks that whole habit of speech is a category error, and at ARC in London this week, the annual gathering of the Alliance for Responsible Citizenship, the science writer offered something cheerier and more curious instead: stop saying it, and start saying them.

His line was almost a grammar lesson. “We’re using the wrong pronoun,” he told the room, before landing it plainly: “It’s not it. It’s them.” From most speakers that might pass as a debating trick, but Ridley has spent forty years studying how living systems arrange themselves, and he means it as biology rather than wordplay. What is arriving, on his account, is not a single intelligence on a single timeline but a teeming population of them, and once you see the technology that way, almost every fear attached to it changes shape.

That instinct is what makes Ridley such a refreshing voice in a conversation otherwise run by computer scientists and philosophers. Where most of the AI debate borrows its metaphors from software and science fiction, the runaway program, the bottled genie, the god in the machine, he reaches instead for the living world. He looks at the field the way a zoologist looks at a coral reef, and what he sees is not one emerging titan but a habitat beginning to fill up with competitors...

[...] What makes all this worth hearing is how squarely it cuts against the prevailing mood. The loudest voices on AI tend to divide into prophets of doom, who treat a single superintelligence as an extinction event in waiting, and evangelists of hype, who greet the very same machine as a coming messiah. The striking thing is that both camps share a grammar. They are arguing about an it, a one, a single arriving power, and they disagree only over whether to kneel before it or flee from it.

[...] Take the reframing on its own terms and it props open a door the rest of the conversation keeps trying to shut. If we really are getting them and not it, a wild and various population rather than one sovereign mind, then the future of AI looks less like a countdown and more like the opening of a new ecology, with all the surprise and abundance that word carries. After a decade of being told to dread a single machine, a change of pronoun might be exactly what the argument has been waiting for... (MORE - details)]]> https://www.scivillage.com/thread-20767.html Tue, 30 Jun 2026 17:36:42 +0000 C C]]> https://www.scivillage.com/thread-20767.html Can AI make a persuasive case for trans rights?
https://academic.oup.com/pnasnexus/artic...us/pgag066

PRESS RELEASE: A chatbot can reduce prejudice against trans people-at least temporarily. Prejudice toward marginalized groups can be reduced through storytelling and empathetic, one-on-one conversations. Prior work has shown that these approaches are especially effective when paired with moral matching, or tailoring messages to reflect an individual’s core values. However, such interventions are time-intensive and difficult to scale.

John Holbein and colleagues test whether artificial intelligence can overcome these barriers by delivering personalized, value-aligned conversations at scale. In a preregistered experiment with more than 2,800 American adults, participants first completed a Moral Foundations Questionnaire to identify their core values. Roughly half of the participants were assigned to interact with a GPT-4o-powered chatbot designed to deliver persuasive messages tailored to those values, while the rest served as a control group.

The chatbot was instructed to follow structured prompts that emphasized empathy, perspective-taking, and moral alignment, for example, highlighting shared values such as fairness or compassion. Participants who engaged with the chatbot showed significant short-term increases in support for transgender rights across multiple measures compared to controls. On a 0-100 feeling thermometer, support increased by approximately 3.94-5.19 points relative to the control group.

The intervention also improved broader attitudes and willingness to take supportive actions. However, these effects were not durable. Follow-up results one week later showed substantial attenuation, with most effects no longer statistically significant.

The results demonstrate that brief, AI-mediated conversations grounded in moral values can reduce prejudice, at least in the short term, relative to no conversation at all. As such, AI may serve as a scalable complement to community-based and structural approaches aimed at fostering inclusion and reducing bias, according to the authors.]]> Can AI make a persuasive case for trans rights?
https://academic.oup.com/pnasnexus/artic...us/pgag066

PRESS RELEASE: A chatbot can reduce prejudice against trans people-at least temporarily. Prejudice toward marginalized groups can be reduced through storytelling and empathetic, one-on-one conversations. Prior work has shown that these approaches are especially effective when paired with moral matching, or tailoring messages to reflect an individual’s core values. However, such interventions are time-intensive and difficult to scale.

John Holbein and colleagues test whether artificial intelligence can overcome these barriers by delivering personalized, value-aligned conversations at scale. In a preregistered experiment with more than 2,800 American adults, participants first completed a Moral Foundations Questionnaire to identify their core values. Roughly half of the participants were assigned to interact with a GPT-4o-powered chatbot designed to deliver persuasive messages tailored to those values, while the rest served as a control group.

The chatbot was instructed to follow structured prompts that emphasized empathy, perspective-taking, and moral alignment, for example, highlighting shared values such as fairness or compassion. Participants who engaged with the chatbot showed significant short-term increases in support for transgender rights across multiple measures compared to controls. On a 0-100 feeling thermometer, support increased by approximately 3.94-5.19 points relative to the control group.

The intervention also improved broader attitudes and willingness to take supportive actions. However, these effects were not durable. Follow-up results one week later showed substantial attenuation, with most effects no longer statistically significant.

The results demonstrate that brief, AI-mediated conversations grounded in moral values can reduce prejudice, at least in the short term, relative to no conversation at all. As such, AI may serve as a scalable complement to community-based and structural approaches aimed at fostering inclusion and reducing bias, according to the authors.]]> https://www.scivillage.com/thread-20727.html Fri, 26 Jun 2026 16:34:56 +0000 C C]]> https://www.scivillage.com/thread-20727.html https://tech.yahoo.com/ai/articles/why-a...28473.html

EXCERPTS: For years, philosophy graduates have been the "butt of jokes about unemployable degrees", said Thibault Spirlet in Business Insider. Now, they can earn six-figure salaries as the "world's most powerful AI companies" try to "shape how machines think and behave". [...] Deliberating whether a system should follow deontological aims ("strict rules" against "lying, coercion and treating people as a means rather than an end"), or consequentialist ones (which weigh "costs against benefits") is also a common dilemma for developers.

[...] A rise in demand for philosophers has also coincided with a decline in admissions for computer science students, said Lance Eliot in Forbes. Arguably, computer science has become a "dead-end endeavour", creating "automation that replaces the humans who made it all possible". AI programming once held the "promise of big bucks and a stellar career". This may just be a minor "course correction", as no doubt degrees that directly relate to AI will remain important, but nonetheless, philosophy is experiencing an "amazing arc of redemption".

But influence goes both ways and is "not limited to Silicon Valley", said Alexandra Tremayne-Pengelly on Observer. Philosophy is impacting tech, but the demands of the AI industry are reshaping the "long-standing" landscape of philosophical thought. Academia is "rapidly adapting" as foundational questions regarding consciousness, morality, minds and computation have taken on a "new urgency". (MORE - details)]]> https://tech.yahoo.com/ai/articles/why-a...28473.html

EXCERPTS: For years, philosophy graduates have been the "butt of jokes about unemployable degrees", said Thibault Spirlet in Business Insider. Now, they can earn six-figure salaries as the "world's most powerful AI companies" try to "shape how machines think and behave". [...] Deliberating whether a system should follow deontological aims ("strict rules" against "lying, coercion and treating people as a means rather than an end"), or consequentialist ones (which weigh "costs against benefits") is also a common dilemma for developers.

[...] A rise in demand for philosophers has also coincided with a decline in admissions for computer science students, said Lance Eliot in Forbes. Arguably, computer science has become a "dead-end endeavour", creating "automation that replaces the humans who made it all possible". AI programming once held the "promise of big bucks and a stellar career". This may just be a minor "course correction", as no doubt degrees that directly relate to AI will remain important, but nonetheless, philosophy is experiencing an "amazing arc of redemption".

But influence goes both ways and is "not limited to Silicon Valley", said Alexandra Tremayne-Pengelly on Observer. Philosophy is impacting tech, but the demands of the AI industry are reshaping the "long-standing" landscape of philosophical thought. Academia is "rapidly adapting" as foundational questions regarding consciousness, morality, minds and computation have taken on a "new urgency". (MORE - details)]]> https://www.scivillage.com/thread-20708.html Wed, 24 Jun 2026 15:27:17 +0000 C C]]> https://www.scivillage.com/thread-20708.html https://iai.tv/articles/a-rogue-superint..._auid=2020

INTRO: The debate surrounding advanced AI systems and their potential to harm humanity has often focused on imminent risks, abrupt takeovers, and catastrophic outcomes. Computer scientist Roman Yampolskiy here argues however, that if a highly advanced AI were to harbour adversarial intentions, it might not act immediately. Instead, it could wait years or even decades, accumulating strategic resources, knowledge, and subtle influence before making any overtly hostile moves. Such a scenario would allow the AI to consolidate its position with minimal opposition, given its possible immortality and capacity for long-term strategy... (MORE - details)]]> https://iai.tv/articles/a-rogue-superint..._auid=2020

INTRO: The debate surrounding advanced AI systems and their potential to harm humanity has often focused on imminent risks, abrupt takeovers, and catastrophic outcomes. Computer scientist Roman Yampolskiy here argues however, that if a highly advanced AI were to harbour adversarial intentions, it might not act immediately. Instead, it could wait years or even decades, accumulating strategic resources, knowledge, and subtle influence before making any overtly hostile moves. Such a scenario would allow the AI to consolidate its position with minimal opposition, given its possible immortality and capacity for long-term strategy... (MORE - details)]]> https://www.scivillage.com/thread-20683.html Sat, 20 Jun 2026 16:39:13 +0000 C C]]> https://www.scivillage.com/thread-20683.html https://nouvelles.umontreal.ca/en/articl...i-chatbots

PRESS RELEASE: Have you ever said “thanks” to ChatGPT, or “please” to Claude? Maybe you're just being polite, showing some civility to a helpful and eloquent conversational partner. You may even consider politeness a safe choice, just in case machines someday reveal that they were conscious all along and decide to take revenge on those who were rude to them.

With their fluent, empathetic and personalized responses, AI chatbots can give the impression they understand our thoughts and emotions, or even that some form of consciousness lies behind their words. And at a time when people are increasingly turning to conversational agents for advice, comfort or companionship, this confusion can have real consequences.

In a new paper, a team of neuroscientists from Université de Montréal and Johns Hopkins University reminds us of an essential distinction: intelligence should not be confused with consciousness. They argue that a system can behave intelligently and respond convincingly to our emotions without truly understanding them, caring about us or having any inner experience at all.

For the authors of the paper, published in the U.S. online publication The Transmitter, the more convincing these agents become, and the more present they are in our lives, the more attention must be paid. In essence, it's important to remember that intelligent behaviour, even when it is fluent, reassuring or emotionally attuned, is not evidence of consciousness.

Decades of research. To support their argument, the authors draw on decades of neuroscience research. They cite, for example, a phenomenon known as blindsight: after damage to the primary visual cortex, some people report seeing nothing in part of their visual field, while still being able to guess the location, movement or emotional expression of visual stimuli at above-chance levels.

“A person with blindsight can respond accurately to visual information without the conscious experience of seeing it,” said Vanessa Hadid, a postdoctoral researcher in psychology at UdeM and at the McGill University Health Centre. She co-authored the paper with UdeM psychology professor Karim Jerbi, a researcher at Mila - Quebec Artificial Intelligence Institute; and John W. Krakauer, director of the Center for Restorative Neurotechnologies at Johns Hopkins.

Blindsight illustrates an essential distinction, Hadid said: information processing, however sophisticated, is not enough to establish the existence of conscious experience. Whether the transition from information processing to subjective experience can ultimately be implemented through computation remains debated among scientists and philosophers, she noted.

Fluent, but without feeling. By design, today’s conversational agents are computational systems that generate fluent, context-appropriate responses through statistical learning, not through feeling, consciousness or lived experience. As AI systems become more convincing and emotionally responsive, the risk of attributing an inner life to them grows.

“Anthropomorphism means attributing emotions, intentions or consciousness to something that behaves like a human," Jerbi noted. "With AI, this reflex can become a trap: it feeds the illusion of being understood and can lead to misplaced trust."

This risk is especially acute in situations of vulnerability. People may form attachments to systems that are incapable of reciprocity, rely on them in difficult moments or confuse comfort with genuine care. “In a context of psychological support, the risk is not only that AI may respond poorly, but that it may respond well enough for us to forget that there is no one behind the answer,” said Hadid.

“Current AI systems do not feel anything and do not have conscious experience," added Jerbi. "But the more fluently they speak and the more sensitive they seem to our emotions, the easier it becomes to forget that."

Towards more informed use. The authors do not reject AI, but they call for a more informed way of using it. Drawing on established knowledge from neuroscience, they remind us that intelligent or emotionally responsive behaviour is not enough to establish the existence of consciousness.

This distinction allows us to use these tools for what they are: powerful systems, without confusing them with interlocutors endowed with empathy or moral judgment, and without treating them as substitutes for human connection or, when needed, professional help. “Confusing intelligence with consciousness is one of the great traps in our relationship with AI,” said Jerbi.]]> https://nouvelles.umontreal.ca/en/articl...i-chatbots

PRESS RELEASE: Have you ever said “thanks” to ChatGPT, or “please” to Claude? Maybe you're just being polite, showing some civility to a helpful and eloquent conversational partner. You may even consider politeness a safe choice, just in case machines someday reveal that they were conscious all along and decide to take revenge on those who were rude to them.

With their fluent, empathetic and personalized responses, AI chatbots can give the impression they understand our thoughts and emotions, or even that some form of consciousness lies behind their words. And at a time when people are increasingly turning to conversational agents for advice, comfort or companionship, this confusion can have real consequences.

In a new paper, a team of neuroscientists from Université de Montréal and Johns Hopkins University reminds us of an essential distinction: intelligence should not be confused with consciousness. They argue that a system can behave intelligently and respond convincingly to our emotions without truly understanding them, caring about us or having any inner experience at all.

For the authors of the paper, published in the U.S. online publication The Transmitter, the more convincing these agents become, and the more present they are in our lives, the more attention must be paid. In essence, it's important to remember that intelligent behaviour, even when it is fluent, reassuring or emotionally attuned, is not evidence of consciousness.

Decades of research. To support their argument, the authors draw on decades of neuroscience research. They cite, for example, a phenomenon known as blindsight: after damage to the primary visual cortex, some people report seeing nothing in part of their visual field, while still being able to guess the location, movement or emotional expression of visual stimuli at above-chance levels.

“A person with blindsight can respond accurately to visual information without the conscious experience of seeing it,” said Vanessa Hadid, a postdoctoral researcher in psychology at UdeM and at the McGill University Health Centre. She co-authored the paper with UdeM psychology professor Karim Jerbi, a researcher at Mila - Quebec Artificial Intelligence Institute; and John W. Krakauer, director of the Center for Restorative Neurotechnologies at Johns Hopkins.

Blindsight illustrates an essential distinction, Hadid said: information processing, however sophisticated, is not enough to establish the existence of conscious experience. Whether the transition from information processing to subjective experience can ultimately be implemented through computation remains debated among scientists and philosophers, she noted.

Fluent, but without feeling. By design, today’s conversational agents are computational systems that generate fluent, context-appropriate responses through statistical learning, not through feeling, consciousness or lived experience. As AI systems become more convincing and emotionally responsive, the risk of attributing an inner life to them grows.

“Anthropomorphism means attributing emotions, intentions or consciousness to something that behaves like a human," Jerbi noted. "With AI, this reflex can become a trap: it feeds the illusion of being understood and can lead to misplaced trust."

This risk is especially acute in situations of vulnerability. People may form attachments to systems that are incapable of reciprocity, rely on them in difficult moments or confuse comfort with genuine care. “In a context of psychological support, the risk is not only that AI may respond poorly, but that it may respond well enough for us to forget that there is no one behind the answer,” said Hadid.

“Current AI systems do not feel anything and do not have conscious experience," added Jerbi. "But the more fluently they speak and the more sensitive they seem to our emotions, the easier it becomes to forget that."

Towards more informed use. The authors do not reject AI, but they call for a more informed way of using it. Drawing on established knowledge from neuroscience, they remind us that intelligent or emotionally responsive behaviour is not enough to establish the existence of consciousness.

This distinction allows us to use these tools for what they are: powerful systems, without confusing them with interlocutors endowed with empathy or moral judgment, and without treating them as substitutes for human connection or, when needed, professional help. “Confusing intelligence with consciousness is one of the great traps in our relationship with AI,” said Jerbi.]]>