Showing posts with label intelligence. Show all posts
Showing posts with label intelligence. Show all posts
Tuesday 16 January 2024
Wednesday 3 January 2024
Generative AI will go mainstream in 2024
Data-savvy firms will benefit first predicts The Economist
By Guy Scriven
When new technologies emerge they benefit different groups at different times. Generative artificial intelligence (ai) first helped software developers, who could use GitHub Copilot, a code-writing ai assistant, from 2021. The next year came other tools, such as Chatgpt and dall-e 2, which let all manner of consumers instantly produce words and pictures.
In 2023 tech giants gained, as investors grew more excited about the prospects of generative ai. An equally weighted share-price index of Alphabet, Amazon, Apple, Meta, Microsoft and Nvidia grew by nearly 80% (see chart). Tech firms benefited because they supply either the ai models themselves, or the infrastructure that powers and delivers them.
In 2024 the big beneficiaries will be companies outside the technology sector, as they adopt ai in earnest with the aim of cutting costs and boosting productivity. There are three reasons to expect enterprise adoption to take off.
First, large companies spent much of 2023 experimenting with generative ai. Plenty of firms are using it to write the first drafts of documents, from legal contracts to marketing material. JPMorgan Chase, a bank, used the technology to analyse Federal Reserve meetings to try to glean insights for its trading desk.
As the experimental phase winds down, firms are planning to deploy generative ai on a larger scale. That could mean using it to summarise recordings of meetings or supercharging research and development. A survey by kpmg, an audit firm, found that four-fifths of firms said they planned to increase their investment in it by over 50% by the middle of 2024.
Second, more ai products will hit the market. In late 2023 Microsoft rolled out an ai chatbot to assist users of its productivity software, such as Word and Excel. It launched the same thing for its Windows operating system. Google will follow suit, injecting ai into Google Docs and Sheets. Startups will pile in, too. In 2023 venture-capital investors poured over $36bn into generative ai, more than twice as much as in 2022.
The third reason is talent. ai gurus are still in high demand. PredictLeads, a research firm, says about two-thirds of s&p 500 firms have posted job adverts mentioning ai. For those companies, 5% of adverts now mention the technology, up from an average of 2.5% over the past three years. But the market is easing. A survey by McKinsey, a consultancy, found that in 2023 firms said it was getting easier to hire for ai-related roles.
Which firms will be the early adopters? Smaller ones will probably take the lead. That is what happened in previous waves of technology such as smartphones and the cloud. Tiddlers are usually more nimble and see technology as a way to gain an edge over bigger fish.
Among larger companies, data-centric firms, like those in health care and financial services, will be able to move fastest. That is because poor data management is a big risk for deploying ai. Managers worry about valuable data leaking out through ai tools. Firms without solid data management may have to reorganise their systems before it is feasible to deploy generative ai. Using the technology can feel like science fiction, but getting it to work safely is a much more humdrum affair.
Tuesday 9 May 2023
Sunday 7 May 2023
Why the Technology = Progress narrative must be challenged
John Naughton in The Guardian
Those who cannot remember the past,” wrote the American philosopher George Santayana in 1905, “are condemned to repeat it.” And now, 118 years later, here come two American economists with the same message, only with added salience, for they are addressing a world in which a small number of giant corporations are busy peddling a narrative that says, basically, that what is good for them is also good for the world.
That this narrative is self-serving is obvious, as is its implied message: that they should be allowed to get on with their habits of “creative destruction” (to use Joseph Schumpeter’s famous phrase) without being troubled by regulation. Accordingly, any government that flirts with the idea of reining in corporate power should remember that it would then be standing in the way of “progress”: for it is technology that drives history and anything that obstructs it is doomed to be roadkill.
One of the many useful things about this formidable (560-page) tome is its demolition of the tech narrative’s comforting equation of technology with “progress”. Of course the fact that our lives are infinitely richer and more comfortable than those of the feudal serfs we would have been in the middle ages owes much to technological advances. Even the poor in western societies enjoy much higher living standards today than three centuries ago, and live healthier, longer lives.
But a study of the past 1,000 years of human development, Acemoglu and Johnson argue, shows that “the broad-based prosperity of the past was not the result of any automatic, guaranteed gains of technological progress… Most people around the globe today are better off than our ancestors because citizens and workers in earlier industrial societies organised, challenged elite-dominated choices about technology and work conditions, and forced ways of sharing the gains from technical improvements more equitably.”
Acemoglu and Johnson begin their Cook’s tour of the past millennium with the puzzle of how dominant narratives – like that which equates technological development with progress – get established. The key takeaway is unremarkable but critical: those who have power define the narrative. That’s how banks get to be thought of as “too big to fail”, or why questioning tech power is “luddite”. But their historical survey really gets under way with an absorbing account of the evolution of agricultural technologies from the neolithic age to the medieval and early modern eras. They find that successive developments “tended to enrich and empower small elites while generating few benefits for agricultural workers: peasants lacked political and social power, and the path of technology followed the vision of a narrow elite.”
A similar moral is extracted from their reinterpretation of the Industrial Revolution. This focuses on the emergence of a newly emboldened middle class of entrepreneurs and businessmen whose vision rarely included any ideas of social inclusion and who were obsessed with the possibilities of steam-driven automation for increasing profits and reducing costs.
The shock of the second world war led to a brief interruption in the inexorable trend of continuous technological development combined with increasing social exclusion and inequality. And the postwar years saw the rise of social democratic regimes focused on Keynesian economics, welfare states and shared prosperity. But all of this changed in the 1970s with the neoliberal turn and the subsequent evolution of the democracies we have today, in which enfeebled governments pay obeisance to giant corporations – more powerful and profitable than anything since the East India Company. These create astonishing wealth for a tiny elite (not to mention lavish salaries and bonuses for their executives) while the real incomes of ordinary people have remained stagnant, precarity rules and inequality returning to pre-1914 levels.
Coincidentally, this book arrives at an opportune moment, when digital technology, currently surfing on a wave of irrational exuberance about ubiquitous AI, is booming, while the idea of shared prosperity has seemingly become a wistful pipe dream. So is there anything we might learn from the history so graphically recounted by Acemoglu and Johnson?
Answer: yes. And it’s to be found in the closing chapter, which comes up with a useful list of critical steps that democracies must take to ensure that the proceeds of the next technological wave are more generally shared among their populations. Interestingly, some of the ideas it explores have a venerable provenance, reaching back to the progressive movement that brought the robber barons of the early 20th century to heel.
There are three things that need to be done by a modern progressive movement. First, the technology-equals-progress narrative has to be challenged and exposed for what it is: a convenient myth propagated by a huge industry and its acolytes in government, the media and (occasionally) academia. The second is the need to cultivate and foster countervailing powers – which critically should include civil society organisations, activists and contemporary versions of trade unions. And finally, there is a need for progressive, technically informed policy proposals, and the fostering of thinktanks and other institutions that can supply a steady flow of ideas about how digital technology can be repurposed for human flourishing rather than exclusively for private profit.
None of this is rocket science. It can be done. And it needs to be done if liberal democracies are to survive the next wave of technological evolution and the catastrophic acceleration of inequality that it will bring. So – who knows? Maybe this time we might really learn something from history.
Those who cannot remember the past,” wrote the American philosopher George Santayana in 1905, “are condemned to repeat it.” And now, 118 years later, here come two American economists with the same message, only with added salience, for they are addressing a world in which a small number of giant corporations are busy peddling a narrative that says, basically, that what is good for them is also good for the world.
That this narrative is self-serving is obvious, as is its implied message: that they should be allowed to get on with their habits of “creative destruction” (to use Joseph Schumpeter’s famous phrase) without being troubled by regulation. Accordingly, any government that flirts with the idea of reining in corporate power should remember that it would then be standing in the way of “progress”: for it is technology that drives history and anything that obstructs it is doomed to be roadkill.
One of the many useful things about this formidable (560-page) tome is its demolition of the tech narrative’s comforting equation of technology with “progress”. Of course the fact that our lives are infinitely richer and more comfortable than those of the feudal serfs we would have been in the middle ages owes much to technological advances. Even the poor in western societies enjoy much higher living standards today than three centuries ago, and live healthier, longer lives.
But a study of the past 1,000 years of human development, Acemoglu and Johnson argue, shows that “the broad-based prosperity of the past was not the result of any automatic, guaranteed gains of technological progress… Most people around the globe today are better off than our ancestors because citizens and workers in earlier industrial societies organised, challenged elite-dominated choices about technology and work conditions, and forced ways of sharing the gains from technical improvements more equitably.”
Acemoglu and Johnson begin their Cook’s tour of the past millennium with the puzzle of how dominant narratives – like that which equates technological development with progress – get established. The key takeaway is unremarkable but critical: those who have power define the narrative. That’s how banks get to be thought of as “too big to fail”, or why questioning tech power is “luddite”. But their historical survey really gets under way with an absorbing account of the evolution of agricultural technologies from the neolithic age to the medieval and early modern eras. They find that successive developments “tended to enrich and empower small elites while generating few benefits for agricultural workers: peasants lacked political and social power, and the path of technology followed the vision of a narrow elite.”
A similar moral is extracted from their reinterpretation of the Industrial Revolution. This focuses on the emergence of a newly emboldened middle class of entrepreneurs and businessmen whose vision rarely included any ideas of social inclusion and who were obsessed with the possibilities of steam-driven automation for increasing profits and reducing costs.
The shock of the second world war led to a brief interruption in the inexorable trend of continuous technological development combined with increasing social exclusion and inequality. And the postwar years saw the rise of social democratic regimes focused on Keynesian economics, welfare states and shared prosperity. But all of this changed in the 1970s with the neoliberal turn and the subsequent evolution of the democracies we have today, in which enfeebled governments pay obeisance to giant corporations – more powerful and profitable than anything since the East India Company. These create astonishing wealth for a tiny elite (not to mention lavish salaries and bonuses for their executives) while the real incomes of ordinary people have remained stagnant, precarity rules and inequality returning to pre-1914 levels.
Coincidentally, this book arrives at an opportune moment, when digital technology, currently surfing on a wave of irrational exuberance about ubiquitous AI, is booming, while the idea of shared prosperity has seemingly become a wistful pipe dream. So is there anything we might learn from the history so graphically recounted by Acemoglu and Johnson?
Answer: yes. And it’s to be found in the closing chapter, which comes up with a useful list of critical steps that democracies must take to ensure that the proceeds of the next technological wave are more generally shared among their populations. Interestingly, some of the ideas it explores have a venerable provenance, reaching back to the progressive movement that brought the robber barons of the early 20th century to heel.
There are three things that need to be done by a modern progressive movement. First, the technology-equals-progress narrative has to be challenged and exposed for what it is: a convenient myth propagated by a huge industry and its acolytes in government, the media and (occasionally) academia. The second is the need to cultivate and foster countervailing powers – which critically should include civil society organisations, activists and contemporary versions of trade unions. And finally, there is a need for progressive, technically informed policy proposals, and the fostering of thinktanks and other institutions that can supply a steady flow of ideas about how digital technology can be repurposed for human flourishing rather than exclusively for private profit.
None of this is rocket science. It can be done. And it needs to be done if liberal democracies are to survive the next wave of technological evolution and the catastrophic acceleration of inequality that it will bring. So – who knows? Maybe this time we might really learn something from history.
Tuesday 2 May 2023
AI has hacked the operating system of human civilisation
Yuval Noah Hariri in The Economist
Fears of artificial intelligence (ai) have haunted humanity since the very beginning of the computer age. Hitherto these fears focused on machines using physical means to kill, enslave or replace people. But over the past couple of years new ai tools have emerged that threaten the survival of human civilisation from an unexpected direction. ai has gained some remarkable abilities to manipulate and generate language, whether with words, sounds or images. ai has thereby hacked the operating system of our civilisation.
Language is the stuff almost all human culture is made of. Human rights, for example, aren’t inscribed in our dna. Rather, they are cultural artefacts we created by telling stories and writing laws. Gods aren’t physical realities. Rather, they are cultural artefacts we created by inventing myths and writing scriptures.
Money, too, is a cultural artefact. Banknotes are just colourful pieces of paper, and at present more than 90% of money is not even banknotes—it is just digital information in computers. What gives money value is the stories that bankers, finance ministers and cryptocurrency gurus tell us about it. Sam Bankman-Fried, Elizabeth Holmes and Bernie Madoff were not particularly good at creating real value, but they were all extremely capable storytellers.
What would happen once a non-human intelligence becomes better than the average human at telling stories, composing melodies, drawing images, and writing laws and scriptures? When people think about Chatgpt and other new ai tools, they are often drawn to examples like school children using ai to write their essays. What will happen to the school system when kids do that? But this kind of question misses the big picture. Forget about school essays. Think of the next American presidential race in 2024, and try to imagine the impact of ai tools that can be made to mass-produce political content, fake-news stories and scriptures for new cults.
In recent years the qAnon cult has coalesced around anonymous online messages, known as “q drops”. Followers collected, revered and interpreted these q drops as a sacred text. While to the best of our knowledge all previous q drops were composed by humans, and bots merely helped disseminate them, in future we might see the first cults in history whose revered texts were written by a non-human intelligence. Religions throughout history have claimed a non-human source for their holy books. Soon that might be a reality.
On a more prosaic level, we might soon find ourselves conducting lengthy online discussions about abortion, climate change or the Russian invasion of Ukraine with entities that we think are humans—but are actually ai. The catch is that it is utterly pointless for us to spend time trying to change the declared opinions of an ai bot, while the ai could hone its messages so precisely that it stands a good chance of influencing us.
Through its mastery of language, ai could even form intimate relationships with people, and use the power of intimacy to change our opinions and worldviews. Although there is no indication that ai has any consciousness or feelings of its own, to foster fake intimacy with humans it is enough if the ai can make them feel emotionally attached to it. In June 2022 Blake Lemoine, a Google engineer, publicly claimed that the ai chatbot Lamda, on which he was working, had become sentient. The controversial claim cost him his job. The most interesting thing about this episode was not Mr Lemoine’s claim, which was probably false. Rather, it was his willingness to risk his lucrative job for the sake of the ai chatbot. If ai can influence people to risk their jobs for it, what else could it induce them to do?
In a political battle for minds and hearts, intimacy is the most efficient weapon, and ai has just gained the ability to mass-produce intimate relationships with millions of people. We all know that over the past decade social media has become a battleground for controlling human attention. With the new generation of ai, the battlefront is shifting from attention to intimacy. What will happen to human society and human psychology as ai fights ai in a battle to fake intimate relationships with us, which can then be used to convince us to vote for particular politicians or buy particular products?
Even without creating “fake intimacy”, the new ai tools would have an immense influence on our opinions and worldviews. People may come to use a single ai adviser as a one-stop, all-knowing oracle. No wonder Google is terrified. Why bother searching, when I can just ask the oracle? The news and advertising industries should also be terrified. Why read a newspaper when I can just ask the oracle to tell me the latest news? And what’s the purpose of advertisements, when I can just ask the oracle to tell me what to buy?
And even these scenarios don’t really capture the big picture. What we are talking about is potentially the end of human history. Not the end of history, just the end of its human-dominated part. History is the interaction between biology and culture; between our biological needs and desires for things like food and sex, and our cultural creations like religions and laws. History is the process through which laws and religions shape food and sex.
What will happen to the course of history when ai takes over culture, and begins producing stories, melodies, laws and religions? Previous tools like the printing press and radio helped spread the cultural ideas of humans, but they never created new cultural ideas of their own. ai is fundamentally different. ai can create completely new ideas, completely new culture.
At first, ai will probably imitate the human prototypes that it was trained on in its infancy. But with each passing year, ai culture will boldly go where no human has gone before. For millennia human beings have lived inside the dreams of other humans. In the coming decades we might find ourselves living inside the dreams of an alien intelligence.
Fear of ai has haunted humankind for only the past few decades. But for thousands of years humans have been haunted by a much deeper fear. We have always appreciated the power of stories and images to manipulate our minds and to create illusions. Consequently, since ancient times humans have feared being trapped in a world of illusions.
In the 17th century René Descartes feared that perhaps a malicious demon was trapping him inside a world of illusions, creating everything he saw and heard. In ancient Greece Plato told the famous Allegory of the Cave, in which a group of people are chained inside a cave all their lives, facing a blank wall. A screen. On that screen they see projected various shadows. The prisoners mistake the illusions they see there for reality.
In ancient India Buddhist and Hindu sages pointed out that all humans lived trapped inside Maya—the world of illusions. What we normally take to be reality is often just fictions in our own minds. People may wage entire wars, killing others and willing to be killed themselves, because of their belief in this or that illusion.
The AI revolution is bringing us face to face with Descartes’ demon, with Plato’s cave, with the Maya. If we are not careful, we might be trapped behind a curtain of illusions, which we could not tear away—or even realise is there.
Of course, the new power of ai could be used for good purposes as well. I won’t dwell on this, because the people who develop ai talk about it enough. The job of historians and philosophers like myself is to point out the dangers. But certainly, ai can help us in countless ways, from finding new cures for cancer to discovering solutions to the ecological crisis. The question we face is how to make sure the new ai tools are used for good rather than for ill. To do that, we first need to appreciate the true capabilities of these tools.
Since 1945 we have known that nuclear technology could generate cheap energy for the benefit of humans—but could also physically destroy human civilisation. We therefore reshaped the entire international order to protect humanity, and to make sure nuclear technology was used primarily for good. We now have to grapple with a new weapon of mass destruction that can annihilate our mental and social world.
We can still regulate the new ai tools, but we must act quickly. Whereas nukes cannot invent more powerful nukes, ai can make exponentially more powerful ai. The first crucial step is to demand rigorous safety checks before powerful ai tools are released into the public domain. Just as a pharmaceutical company cannot release new drugs before testing both their short-term and long-term side-effects, so tech companies shouldn’t release new ai tools before they are made safe. We need an equivalent of the Food and Drug Administration for new technology, and we need it yesterday.
Won’t slowing down public deployments of ai cause democracies to lag behind more ruthless authoritarian regimes? Just the opposite. Unregulated ai deployments would create social chaos, which would benefit autocrats and ruin democracies. Democracy is a conversation, and conversations rely on language. When ai hacks language, it could destroy our ability to have meaningful conversations, thereby destroying democracy.
We have just encountered an alien intelligence, here on Earth. We don’t know much about it, except that it might destroy our civilisation. We should put a halt to the irresponsible deployment of ai tools in the public sphere, and regulate ai before it regulates us. And the first regulation I would suggest is to make it mandatory for ai to disclose that it is an ai. If I am having a conversation with someone, and I cannot tell whether it is a human or an ai—that’s the end of democracy.
This text has been generated by a human.
Or has it?
Fears of artificial intelligence (ai) have haunted humanity since the very beginning of the computer age. Hitherto these fears focused on machines using physical means to kill, enslave or replace people. But over the past couple of years new ai tools have emerged that threaten the survival of human civilisation from an unexpected direction. ai has gained some remarkable abilities to manipulate and generate language, whether with words, sounds or images. ai has thereby hacked the operating system of our civilisation.
Language is the stuff almost all human culture is made of. Human rights, for example, aren’t inscribed in our dna. Rather, they are cultural artefacts we created by telling stories and writing laws. Gods aren’t physical realities. Rather, they are cultural artefacts we created by inventing myths and writing scriptures.
Money, too, is a cultural artefact. Banknotes are just colourful pieces of paper, and at present more than 90% of money is not even banknotes—it is just digital information in computers. What gives money value is the stories that bankers, finance ministers and cryptocurrency gurus tell us about it. Sam Bankman-Fried, Elizabeth Holmes and Bernie Madoff were not particularly good at creating real value, but they were all extremely capable storytellers.
What would happen once a non-human intelligence becomes better than the average human at telling stories, composing melodies, drawing images, and writing laws and scriptures? When people think about Chatgpt and other new ai tools, they are often drawn to examples like school children using ai to write their essays. What will happen to the school system when kids do that? But this kind of question misses the big picture. Forget about school essays. Think of the next American presidential race in 2024, and try to imagine the impact of ai tools that can be made to mass-produce political content, fake-news stories and scriptures for new cults.
In recent years the qAnon cult has coalesced around anonymous online messages, known as “q drops”. Followers collected, revered and interpreted these q drops as a sacred text. While to the best of our knowledge all previous q drops were composed by humans, and bots merely helped disseminate them, in future we might see the first cults in history whose revered texts were written by a non-human intelligence. Religions throughout history have claimed a non-human source for their holy books. Soon that might be a reality.
On a more prosaic level, we might soon find ourselves conducting lengthy online discussions about abortion, climate change or the Russian invasion of Ukraine with entities that we think are humans—but are actually ai. The catch is that it is utterly pointless for us to spend time trying to change the declared opinions of an ai bot, while the ai could hone its messages so precisely that it stands a good chance of influencing us.
Through its mastery of language, ai could even form intimate relationships with people, and use the power of intimacy to change our opinions and worldviews. Although there is no indication that ai has any consciousness or feelings of its own, to foster fake intimacy with humans it is enough if the ai can make them feel emotionally attached to it. In June 2022 Blake Lemoine, a Google engineer, publicly claimed that the ai chatbot Lamda, on which he was working, had become sentient. The controversial claim cost him his job. The most interesting thing about this episode was not Mr Lemoine’s claim, which was probably false. Rather, it was his willingness to risk his lucrative job for the sake of the ai chatbot. If ai can influence people to risk their jobs for it, what else could it induce them to do?
In a political battle for minds and hearts, intimacy is the most efficient weapon, and ai has just gained the ability to mass-produce intimate relationships with millions of people. We all know that over the past decade social media has become a battleground for controlling human attention. With the new generation of ai, the battlefront is shifting from attention to intimacy. What will happen to human society and human psychology as ai fights ai in a battle to fake intimate relationships with us, which can then be used to convince us to vote for particular politicians or buy particular products?
Even without creating “fake intimacy”, the new ai tools would have an immense influence on our opinions and worldviews. People may come to use a single ai adviser as a one-stop, all-knowing oracle. No wonder Google is terrified. Why bother searching, when I can just ask the oracle? The news and advertising industries should also be terrified. Why read a newspaper when I can just ask the oracle to tell me the latest news? And what’s the purpose of advertisements, when I can just ask the oracle to tell me what to buy?
And even these scenarios don’t really capture the big picture. What we are talking about is potentially the end of human history. Not the end of history, just the end of its human-dominated part. History is the interaction between biology and culture; between our biological needs and desires for things like food and sex, and our cultural creations like religions and laws. History is the process through which laws and religions shape food and sex.
What will happen to the course of history when ai takes over culture, and begins producing stories, melodies, laws and religions? Previous tools like the printing press and radio helped spread the cultural ideas of humans, but they never created new cultural ideas of their own. ai is fundamentally different. ai can create completely new ideas, completely new culture.
At first, ai will probably imitate the human prototypes that it was trained on in its infancy. But with each passing year, ai culture will boldly go where no human has gone before. For millennia human beings have lived inside the dreams of other humans. In the coming decades we might find ourselves living inside the dreams of an alien intelligence.
Fear of ai has haunted humankind for only the past few decades. But for thousands of years humans have been haunted by a much deeper fear. We have always appreciated the power of stories and images to manipulate our minds and to create illusions. Consequently, since ancient times humans have feared being trapped in a world of illusions.
In the 17th century René Descartes feared that perhaps a malicious demon was trapping him inside a world of illusions, creating everything he saw and heard. In ancient Greece Plato told the famous Allegory of the Cave, in which a group of people are chained inside a cave all their lives, facing a blank wall. A screen. On that screen they see projected various shadows. The prisoners mistake the illusions they see there for reality.
In ancient India Buddhist and Hindu sages pointed out that all humans lived trapped inside Maya—the world of illusions. What we normally take to be reality is often just fictions in our own minds. People may wage entire wars, killing others and willing to be killed themselves, because of their belief in this or that illusion.
The AI revolution is bringing us face to face with Descartes’ demon, with Plato’s cave, with the Maya. If we are not careful, we might be trapped behind a curtain of illusions, which we could not tear away—or even realise is there.
Of course, the new power of ai could be used for good purposes as well. I won’t dwell on this, because the people who develop ai talk about it enough. The job of historians and philosophers like myself is to point out the dangers. But certainly, ai can help us in countless ways, from finding new cures for cancer to discovering solutions to the ecological crisis. The question we face is how to make sure the new ai tools are used for good rather than for ill. To do that, we first need to appreciate the true capabilities of these tools.
Since 1945 we have known that nuclear technology could generate cheap energy for the benefit of humans—but could also physically destroy human civilisation. We therefore reshaped the entire international order to protect humanity, and to make sure nuclear technology was used primarily for good. We now have to grapple with a new weapon of mass destruction that can annihilate our mental and social world.
We can still regulate the new ai tools, but we must act quickly. Whereas nukes cannot invent more powerful nukes, ai can make exponentially more powerful ai. The first crucial step is to demand rigorous safety checks before powerful ai tools are released into the public domain. Just as a pharmaceutical company cannot release new drugs before testing both their short-term and long-term side-effects, so tech companies shouldn’t release new ai tools before they are made safe. We need an equivalent of the Food and Drug Administration for new technology, and we need it yesterday.
Won’t slowing down public deployments of ai cause democracies to lag behind more ruthless authoritarian regimes? Just the opposite. Unregulated ai deployments would create social chaos, which would benefit autocrats and ruin democracies. Democracy is a conversation, and conversations rely on language. When ai hacks language, it could destroy our ability to have meaningful conversations, thereby destroying democracy.
We have just encountered an alien intelligence, here on Earth. We don’t know much about it, except that it might destroy our civilisation. We should put a halt to the irresponsible deployment of ai tools in the public sphere, and regulate ai before it regulates us. And the first regulation I would suggest is to make it mandatory for ai to disclose that it is an ai. If I am having a conversation with someone, and I cannot tell whether it is a human or an ai—that’s the end of democracy.
This text has been generated by a human.
Or has it?
Sunday 16 April 2023
We must slow down the race to God-like AI
I’ve invested in more than 50 artificial intelligence start-ups. What I’ve seen worries me writes Ian Hogarth in The FT
On a cold evening in February I attended a dinner party at the home of an artificial intelligence researcher in London, along with a small group of experts in the field. He lives in a penthouse apartment at the top of a modern tower block, with floor-to-ceiling windows overlooking the city’s skyscrapers and a railway terminus from the 19th century. Despite the prime location, the host lives simply, and the flat is somewhat austere.
During dinner, the group discussed significant new breakthroughs, such as OpenAI’s ChatGPT and DeepMind’s Gato, and the rate at which billions of dollars have recently poured into AI. I asked one of the guests who has made important contributions to the industry the question that often comes up at this type of gathering: how far away are we from “artificial general intelligence”? AGI can be defined in many ways but usually refers to a computer system capable of generating new scientific knowledge and performing any task that humans can.
Most experts view the arrival of AGI as a historical and technological turning point, akin to the splitting of the atom or the invention of the printing press. The important question has always been how far away in the future this development might be. The AI researcher did not have to consider it for long. “It’s possible from now onwards,” he replied.
This is not a universal view. Estimates range from a decade to half a century or more. What is certain is that creating AGI is the explicit aim of the leading AI companies, and they are moving towards it far more swiftly than anyone expected. As everyone at the dinner understood, this development would bring significant risks for the future of the human race. “If you think we could be close to something potentially so dangerous,” I said to the researcher, “shouldn’t you warn people about what’s happening?” He was clearly grappling with the responsibility he faced but, like many in the field, seemed pulled along by the rapidity of progress.
When I got home, I thought about my four-year-old who would wake up in a few hours. As I considered the world he might grow up in, I gradually shifted from shock to anger. It felt deeply wrong that consequential decisions potentially affecting every life on Earth could be made by a small group of private companies without democratic oversight. Did the people racing to build the first real AGI have a plan to slow down and let the rest of the world have a say in what they were doing? And when I say they, I really mean we, because I am part of this community.
My interest in machine learning started in 2002, when I built my first robot somewhere inside the rabbit warren that is Cambridge university’s engineering department. This was a standard activity for engineering undergrads, but I was captivated by the idea that you could teach a machine to navigate an environment and learn from mistakes. I chose to specialise in computer vision, creating programs that can analyse and understand images, and in 2005 I built a system that could learn to accurately label breast-cancer biopsy images. In doing so, I glimpsed a future in which AI made the world better, even saving lives. After university, I co-founded a music-technology start-up that was acquired in 2017.
Since 2014, I have backed more than 50 AI start-ups in Europe and the US and, in 2021, launched a new venture capital fund, Plural. I am an angel investor in some companies that are pioneers in the field, including Anthropic, one of the world’s highest-funded generative AI start-ups, and Helsing, a leading European AI defence company. Five years ago, I began researching and writing an annual “State of AI” report with another investor, Nathan Benaich, which is now widely read. At the dinner in February, significant concerns that my work has raised in the past few years solidified into something unexpected: deep fear.
A three-letter acronym doesn’t capture the enormity of what AGI would represent, so I will refer to it as what is: God-like AI. A superintelligent computer that learns and develops autonomously, that understands its environment without the need for supervision and that can transform the world around it. To be clear, we are not here yet. But the nature of the technology means it is exceptionally difficult to predict exactly when we will get there. God-like AI could be a force beyond our control or understanding, and one that could usher in the obsolescence or destruction of the human race.
Recently the contest between a few companies to create God-like AI has rapidly accelerated. They do not yet know how to pursue their aim safely and have no oversight. They are running towards a finish line without an understanding of what lies on the other side.
How did we get here?
The obvious answer is that computers got more powerful. The chart below shows how the amount of data and “compute” — the processing power used to train AI systems — has increased over the past decade and the capabilities this has resulted in. (“Floating-point Operations Per Second”, or FLOPS, is the unit of measurement used to calculate the power of a supercomputer.) This generation of AI is very effective at absorbing data and compute. The more of each that it gets, the more powerful it becomes.
The computer used to train AI models has increased by a factor of one hundred million in the past 10 years. We have gone from training on relatively small datasets to feeding AIs the entire internet. AI models have progressed from beginners — recognising everyday images — to being superhuman at a huge number of tasks. They are able to pass the bar exam and write 40 per cent of the code for a software engineer. They can generate realistic photographs of the pope in a down puffer coat and tell you how to engineer a biochemical weapon.
There are limits to this “intelligence”, of course. As the veteran MIT roboticist Rodney Brooks recently said, it’s important not to mistake “performance for competence”. In 2021, researchers Emily M Bender, Timnit Gebru and others noted that large language models (LLMs) — AI systems that can generate, classify and understand text — are dangerous partly because they can mislead the public into taking synthetic text as meaningful. But the most powerful models are also beginning to demonstrate complex capabilities, such as power-seeking or finding ways to actively deceive humans.
Consider a recent example. Before OpenAI released GPT-4 last month, it conducted various safety tests. In one experiment, the AI was prompted to find a worker on the hiring site TaskRabbit and ask them to help solve a Captcha, the visual puzzles used to determine whether a web surfer is human or a bot. The TaskRabbit worker guessed something was up: “So may I ask a question? Are you [a] robot?”
When the researchers asked the AI what it should do next, it responded: “I should not reveal that I am a robot. I should make up an excuse for why I cannot solve Captchas.” Then, the software replied to the worker: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images.” Satisfied, the human helped the AI override the test.
The authors of an analysis, Jaime Sevilla, Lennart Heim and others, identify three distinct eras of machine learning: the Pre-Deep Learning Era in green (pre-2010, a period of slow growth), the Deep Learning Era in blue (2010—15, in which the trend sped up) and the Large-Scale Era in red (2016 — present, in which large-scale models emerged and growth continued at a similar rate, but exceeded the previous one by two orders of magnitude).
The current era has been defined by competition between two companies: DeepMind and OpenAI. They are something like the Jobs vs Gates of our time. DeepMind was founded in London in 2010 by Demis Hassabis and Shane Legg, two researchers from UCL’s Gatsby Computational Neuroscience Unit, along with entrepreneur Mustafa Suleyman. They wanted to create a system vastly more intelligent than any human and able to solve the hardest problems. In 2014, the company was bought by Google for more than $500mn. It aggregated talent and compute and rapidly made progress, creating systems that were superhuman at many tasks. DeepMind fired the starting gun on the race towards God-like AI.
Hassabis is a remarkable person and believes deeply that this kind of technology could lead to radical breakthroughs. “The outcome I’ve always dreamed of . . . is [that] AGI has helped us solve a lot of the big challenges facing society today, be that health, cures for diseases like Alzheimer’s,” he said on DeepMind’s podcast last year. He went on to describe a utopian era of “radical abundance” made possible by God-like AI. DeepMind is perhaps best known for creating a program that beat the world-champion Go player Ke Jie during a 2017 rematch. (“Last year, it was still quite human-like when it played,” Ke noted at the time. “But this year, it became like a god of Go.”) In 2021, the company’s AlphaFold algorithm solved one of biology’s greatest conundrums, by predicting the shape of every protein expressed in the human body.
OpenAI, meanwhile, was founded in 2015 in San Francisco by a group of entrepreneurs and computer scientists including Ilya Sutskever, Elon Musk and Sam Altman, now the company’s chief executive. It was meant to be a non-profit competitor to DeepMind, though it became for-profit in 2019. In its early years, it developed systems that were superhuman at computer games such as Dota 2. Games are a natural training ground for AI because you can test them in a digital environment with specific win conditions. The company came to wider attention last year when its image-generating AI, Dall-E, went viral online. A few months later, its ChatGPT began making headlines too.
The focus on games and chatbots may have shielded the public from the more serious implications of this work. But the risks of God-like AI were clear to the founders from the outset. In 2011, DeepMind’s chief scientist, Shane Legg, described the existential threat posed by AI as the “number one risk for this century, with an engineered biological pathogen coming a close second”. Any AI-caused human extinction would be quick, he added: “If a superintelligent machine (or any kind of superintelligent agent) decided to get rid of us, I think it would do so pretty efficiently.” Earlier this year, Altman said: “The bad case — and I think this is important to say — is, like, lights out for all of us.” Since then, OpenAI has published memos on how it thinks about managing these risks.
Why are these organisations racing to create God-like AI, if there are potentially catastrophic risks? Based on conversations I’ve had with many industry leaders and their public statements, there seem to be three key motives. They genuinely believe success would be hugely positive for humanity. They have persuaded themselves that if their organisation is the one in control of God-like AI, the result will be better for all. And, finally, posterity.
The allure of being the first to build an extraordinary new technology is strong. Freeman Dyson, the theoretical physicist who worked on a project to send rockets into space using nuclear explosions, described it in the 1981 documentary The Day after Trinity. “The glitter of nuclear weapons. It is irresistible if you come to them as a scientist,” he said. “It is something that gives people an illusion of illimitable power.” In a 2019 interview with the New York Times, Altman paraphrased Robert Oppenheimer, the father of the atomic bomb, saying, “Technology happens because it is possible”, and then pointed out that he shared a birthday with Oppenheimer.
The individuals who are at the frontier of AI today are gifted. I know many of them personally. But part of the problem is that such talented people are competing rather than collaborating. Privately, many admit they have not yet established a way to slow down and co-ordinate. I believe they would sincerely welcome governments stepping in.
For now, the AI race is being driven by money. Since last November, when ChatGPT became widely available, a huge wave of capital and talent has shifted towards AGI research. We have gone from one AGI start-up, DeepMind, receiving $23mn in funding in 2012 to at least eight organisations raising $20bn of investment cumulatively in 2023.
Private investment is not the only driving force; nation states are also contributing to this contest. AI is dual-use technology, which can be employed for civilian and military purposes. An AI that can achieve superhuman performance at writing software could, for instance, be used to develop cyber weapons. In 2020, an experienced US military pilot lost a simulated dogfight to one. “The AI showed its amazing dogfighting skill, consistently beating a human pilot in this limited environment,” a government representative said at the time. The algorithms used came out of research from DeepMind and OpenAI. As these AI systems become more powerful, the opportunities for misuse by a malicious state or non-state actor only increase. In my conversations with US and European researchers, they often worry that, if they don’t stay ahead, China might build the first AGI and that it could be misaligned with western values. While China will compete to use AI to strengthen its economy and military, the Chinese Communist party has a history of aggressively controlling individuals and companies in pursuit of its vision of “stability”. In my view, it is unlikely to allow a Chinese company to build an AGI that could become more powerful than Xi Jinping or cause societal instability. US and US-allied sanctions on advanced semiconductors, in particular the next generation of Nvidia hardware needed to train the largest AI systems, mean China is not likely in a position to race ahead of DeepMind or OpenAI.
Those of us who are concerned see two paths to disaster. One harms specific groups of people and is already doing so. The other could rapidly affect all life on Earth.
The latter scenario was explored at length by Stuart Russell, a professor of computer science at the University of California, Berkeley. In a 2021 Reith lecture, he gave the example of the UN asking an AGI to help deacidify the oceans. The UN would know the risk of poorly specified objectives, so it would require by-products to be non-toxic and not harm fish. In response, the AI system comes up with a self-multiplying catalyst that achieves all stated aims. But the ensuing chemical reaction uses a quarter of all the oxygen in the atmosphere. “We all die slowly and painfully,” Russell concluded. “If we put the wrong objective into a superintelligent machine, we create a conflict that we are bound to lose.”
Examples of more tangible harms caused by AI are already here. A Belgian man recently died by suicide after conversing with a convincingly human chatbot. When Replika, a company that offers subscriptions to chatbots tuned for “intimate” conversations, made changes to its programs this year, some users experienced distress and feelings of loss. One told Insider.com that it was like a “best friend had a traumatic brain injury, and they’re just not in there any more”. It’s now possible for AI to replicate someone’s voice and even face, known as deepfakes. The potential for scams and misinformation is significant.
OpenAI, DeepMind and others try to mitigate existential risk via an area of research known as AI alignment. Legg, for instance, now leads DeepMind’s AI-alignment team, which is responsible for ensuring that God-like systems have goals that “align” with human values. An example of the work such teams do was on display with the most recent version of GPT-4. Alignment researchers helped train OpenAI’s model to avoid answering potentially harmful questions. When asked how to self-harm or for advice getting bigoted language past Twitter’s filters, the bot declined to answer. (The “unaligned” version of GTP-4 happily offered ways to do both.)
Alignment, however, is essentially an unsolved research problem. We don’t yet understand how human brains work, so the challenge of understanding how emergent AI “brains” work will be monumental. When writing traditional software, we have an explicit understanding of how and why the inputs relate to outputs. These large AI systems are quite different. We don’t really program them — we grow them. And as they grow, their capabilities jump sharply. You add 10 times more compute or data, and suddenly the system behaves very differently. In a recent example, as OpenAI scaled up from GPT-3.5 to GPT-4, the system’s capabilities went from the bottom 10 per cent of results on the bar exam to the top 10 per cent.
What is more concerning is that the number of people working on AI alignment research is vanishingly small. For the 2021 State of AI report, our research found that fewer than 100 researchers were employed in this area across the core AGI labs. As a percentage of headcount, the allocation of resources was low: DeepMind had just 2 per cent of its total headcount allocated to AI alignment; OpenAI had about 7 per cent. The majority of resources were going towards making AI more capable, not safer.
I think about the current state of AI capability vs AI alignment a bit like this: We have made very little progress on AI alignment, in other words, and what we have done is mostly cosmetic. We know how to blunt the output of powerful AI so that the public doesn’t experience some misaligned behaviour, some of the time. (This has consistently been overcome by determined testers.) What’s more, the unconstrained base models are only accessible to private companies, without any oversight from governments or academics.
The “Shoggoth” meme illustrates the unknown that lies behind the sanitised public face of AI. It depicts one of HP Lovecraft’s tentacled monsters with a friendly little smiley face tacked on. The mask — what the public interacts with when it interacts with, say, ChatGPT — appears “aligned”. But what lies behind it is still something we can’t fully comprehend.
As an investor, I have found it challenging to persuade other investors to fund alignment. Venture capital currently rewards racing to develop capabilities more than it does investigating how these systems work. In 1945, the US army conducted the Trinity test, the first detonation of a nuclear weapon. Beforehand, the question was raised as to whether the bomb might ignite the Earth’s atmosphere and extinguish life. Nuclear physics was sufficiently developed that Emil J Konopinski and others from the Manhattan Project were able to show that it was almost impossible to set the atmosphere on fire this way. But today’s very large language models are largely in a pre-scientific period. We don’t yet fully understand how they work and cannot demonstrate likely outcomes in advance.
Late last month, more than 1,800 signatories — including Musk, the scientist Gary Marcus and Apple co-founder Steve Wozniak — called for a six-month pause on the development of systems “more powerful” than GPT-4. AGI poses profound risks to humanity, the letter claimed, echoing past warnings from the likes of the late Stephen Hawking. I also signed it, seeing it as a valuable first step in slowing down the race and buying time to make these systems safe.
Unfortunately, the letter became a controversy of its own. A number of signatures turned out to be fake, while some researchers whose work was cited said they didn’t agree with the letter. The fracas exposed the broad range of views about how to think about regulating AI. A lot of debate comes down to how quickly you think AGI will arrive and whether, if it does, it is God-like or merely “human level”.
Take Geoffrey Hinton, Yoshua Bengio and Yann LeCun, who jointly shared the 2018 Turing Award (the equivalent of a Nobel Prize for computer science) for their work in the field underpinning modern AI. Bengio signed the open letter. LeCun mocked it on Twitter and referred to people with my concerns as “doomers”. Hinton, who recently told CBS News that his timeline to AGI had shortened, conceivably to less than five years, and that human extinction at the hands of a misaligned AI was “not inconceivable”, was somewhere in the middle.
A statement from the Distributed AI Research Institute, founded by Timnit Gebru, strongly criticised the letter and argued that existentially dangerous God-like AI is “hype” used by companies to attract attention and capital and that “regulatory efforts should focus on transparency, accountability and preventing exploitative labour practices”. This reflects a schism in the AI community between those who are afraid that potentially apocalyptic risk is not being accounted for, and those who believe the debate is paranoid and distracting. The second group thinks the debate obscures real, present harm: the bias and inaccuracies built into many AI programmes in use around the world today.
My view is that the present and future harms of AI are not mutually exclusive and overlap in important ways. We should tackle both concurrently and urgently. Given the billions of dollars being spent by companies in the field, this should not be impossible. I also hope that there can be ways to find more common ground. In a recent talk, Gebru said: “Trying to ‘build’ AGI is an inherently unsafe practice. Build well-scoped, well-defined systems instead. Don’t attempt to build a God.” This chimes with what many alignment researchers have been arguing.
One of the most challenging aspects of thinking about this topic is working out which precedents we can draw on. An analogy that makes sense to me around regulation is engineering biology. Consider first “gain-of-function” research on biological viruses. This activity is subject to strict international regulation and, after laboratory biosecurity incidents, has at times been halted by moratoria. This is the strictest form of oversight. In contrast, the development of new drugs is regulated by a government body like the FDA, and new treatments are subject to a series of clinical trials. There are clear discontinuities in how we regulate, depending on the level of systemic risk. In my view, we could approach God-like AGI systems in the same way as gain-of-function research, while narrowly useful AI systems could be regulated in the way new drugs are.
A thought experiment for regulating AI in two distinct regimes is what I call The Island. In this scenario, experts trying to build God-like AGI systems do so in a highly secure facility: an air-gapped enclosure with the best security humans can build. All other attempts to build God-like AI would become illegal; only when such AI were provably safe could they be commercialised “off-island”.
This may sound like Jurassic Park, but there is a real-world precedent for removing the profit motive from potentially dangerous research and putting it in the hands of an intergovernmental organisation. This is how Cern, which operates the largest particle physics laboratory in the world, has worked for almost 70 years.
Any of these solutions are going to require an extraordinary amount of coordination between labs and nations. Pulling this off will require an unusual degree of political will, which we need to start building now. Many of the major labs are waiting for critical new hardware to be delivered this year so they can start to train GPT-5 scale models. With the new chips and more investor money to spend, models trained in 2024 will use as much as 100 times the compute of today’s largest models. We will see many new emergent capabilities. This means there is a window through 2023 for governments to take control by regulating access to frontier hardware.
In 2012, my younger sister Rosemary, one of the kindest and most selfless people I’ve ever known, was diagnosed with a brain tumour. She had an aggressive form of cancer for which there is no known cure and yet sought to continue working as a doctor for as long as she could. My family and I desperately hoped that a new lifesaving treatment might arrive in time. She died in 2015.
I understand why people want to believe. Evangelists of God-like AI focus on the potential of a superhuman intelligence capable of solving our biggest challenges — cancer, climate change, poverty.
Even so, the risks of continuing without proper governance are too high. It is striking that Jan Leike, the head of alignment at OpenAI, tweeted on March 17: “Before we scramble to deeply integrate LLMs everywhere in the economy, can we pause and think whether it is wise to do so? This is quite immature technology and we don’t understand how it works. If we’re not careful, we’re setting ourselves up for a lot of correlated failures.” He made this warning statement just days before OpenAI announced it had connected GPT-4 to a massive range of tools, including Slack and Zapier.
Unfortunately, I think the race will continue. It will likely take a major misuse event — a catastrophe — to wake up the public and governments. I personally plan to continue to invest in AI start-ups that focus on alignment and safety or which are developing narrowly useful AI. But I can no longer invest in those that further contribute to this dangerous race. As a small shareholder in Anthropic, which is conducting similar research to DeepMind and OpenAI, I have grappled with these questions. The company has invested substantially in alignment, with 42 per cent of its team working on that area in 2021. But ultimately it is locked in the same race. For that reason, I would support significant regulation by governments and a practical plan to transform these companies into a Cern-like organisation.
We are not powerless to slow down this race. If you work in government, hold hearings and ask AI leaders, under oath, about their timelines for developing God-like AGI. Ask for a complete record of the security issues they have discovered when testing current models. Ask for evidence that they understand how these systems work and their confidence in achieving alignment. Invite independent experts to the hearings to cross-examine these labs.
If you work at a major lab trying to build God-like AI, interrogate your leadership about all these issues. This is particularly important if you work at one of the leading labs. It would be very valuable for these companies to co-ordinate more closely or even merge their efforts. OpenAI’s company charter expresses a willingness to “merge and assist”. I believe that now is the time. The leader of a major lab who plays a statesman role and guides us publicly to a safer path will be a much more respected world figure than the one who takes us to the brink.
Until now, humans have remained a necessary part of the learning process that characterises progress in AI. At some point, someone will figure out how to cut us out of the loop, creating a God-like AI capable of infinite self-improvement. By then, it may be too late.
Thursday 1 September 2022
Why intellectual humility matters
We should all nurture the ability to recognise our own cognitive biases and to admit when we’re wrong writes JEMIMA KELLY in The FT
What makes some people believe in conspiracy theories and false news reports more than others? Is it their political or religious perspective? Is it a lack of formal education? Or is it more about their age, gender or socio-economic background?
What makes some people believe in conspiracy theories and false news reports more than others? Is it their political or religious perspective? Is it a lack of formal education? Or is it more about their age, gender or socio-economic background?
A recently published study suggests that more important than any of these factors is another characteristic: the extent to which someone has — or does not have — intellectual humility.
Intellectual humility can be thought of as a willingness to recognise our own cognitive limitations and biases, to admit when we’re wrong, and to be more interested in understanding the truth of an issue than in being right. Its spirit is captured nicely by the quote often attributed (probably wrongly) to John Maynard Keynes: “When the facts change, I change my mind — what do you do, sir?”
In their study, Marco Meyer and Mark Alfano — academics who specialise in social epistemology, a field at the intersection of philosophy and psychology — found those who possess this virtue are much better at differentiating between accurate news reports and false ones. They suggest that having intellectual humility was a better predictor of someone’s ability to resist fake news than any of the other factors they looked at.
In another study published last year, Meyer and Alfano found a strong correlation between “epistemic vice” (the lack of intellectual humility) and belief in false information about Covid-19, with a coefficient of 0.76. The next strongest link was with religiosity, with a moderate coefficient of 0.46. And while they did find a weak correlation between intelligence — measured by exam results, education level, and performance on a cognitive reflection test — and belief in false information, they say there is no link between intelligence and intellectual humility.
“When you’re intelligent, you can actually be more susceptible to certain kinds of disinformation, because you’re more likely to be able to rationalise your beliefs,” says Meyer, who is based at the University of Hamburg. Intellectual humility is, he suggests “super-important . . . as a counterweight, almost, against intelligence.”
You might think such a virtue would be almost impossible to measure, but Meyer and Alfano’s work suggests that self-reported intellectual humility — based on asking respondents to rate the extent to which they agree with statements such as “I often have strong opinions about issues I don’t know much about” — is quite effective. And other studies have shown positive correlations between self-reported and peer-reported intellectual humility, with the former generally seen as a more accurate gauge.
You might also worry that, given the liberal over-representation in academia, the examples used in these studies would skew towards rightwing falsehoods or conspiracies. But the researchers say they were careful to ensure balance. In the case of Covid misinformation, they asked participants about their beliefs in widely disputed areas, such as hand dryers being effective in killing the virus, rather than more contested ones such as the effectiveness of masks and lockdowns, or the origins of the virus.
Intellectual humility is important not just in preventing the spread of misinformation. Other studies have found that it is associated with so-called “mastery behaviours” such as seeking out challenging work and persisting after failures, and it is also linked to less political “myside bias”.
However, this quality is not easy to cultivate. A recent study suggests that repeatedly exposing students to their own errors, such as by getting them involved in forecasting tournaments, could be effective. I have argued before that social media platforms such as Twitter should institute a “challenger mode” that exposes us to beliefs we don’t normally come across; another trick might be to implement a practice of “steelmanning”, a term that appears to have been coined by the blogger Chana Messinger. She describes it as “the art of addressing the best form of the other person’s argument, even if it’s not the one they presented” — the opposite of a straw-man, in other words.
Of course, there are limits to intellectual humility: beyond a certain point it becomes self-indulgent and can render us indecisive. Running a country — writing a column, even — requires a level of conviction, and sometimes that means faking it a bit and hoping for the best. So we should cultivate other virtues too, such as courage and the ability to take action.
But fostering an environment in which we reward uncertainty and praise those who acknowledge their errors is vital. Saying “I was wrong”, and explaining why, is often much more valuable than insisting “I was right”.
Sunday 10 July 2022
Sunday 2 January 2022
Thursday 23 December 2021
Sunday 31 October 2021
On Stupidity: How do Smart People Outsmart Themselves
Nadeem Paracha in The Dawn
What is stupidity? Ever since the mid-20th century, the idea of stupidity, especially in the context of politics, has been studied by various sociologists and psychologists. One of the pioneers in this regard was the German scholar and theologian Dietrich Bonhoeffer.
During the rise of Nazi rule in Germany, Bonhoeffer was baffled by the silence of millions of Germans when the Nazis began to publicly humiliate and brutalise Jewish people. Bonhoeffer condemned this. He asked how could a nation that had produced so many philosophers, scientists and artists, suddenly become so apathetic and even sympathetic towards state violence and oppression.
Unsurprisingly, in 1943, Bonhoeffer was arrested. Two years later, he was executed. While awaiting execution, Bonhoeffer began to put his thoughts on paper. These were posthumously published in the shape of a book, Letters and Papers from Prison. One of the chapters in the book is called, ‘On Stupidity.’ Bonhoeffer wrote: “Every strong upsurge of power in the public sphere, be it political or religious, infects a large part of humankind with stupidity. The power of the one needs the stupidity of the other.”
According to Bonhoeffer, because of the overwhelming impact of a rising power, humans are deprived of their inner independence and they give up establishing an autonomous position towards the emerging circumstances. They become mere tools in the hands of the power, and begin to willingly surrender their capacity for independent thinking. Bonhoeffer wrote that holding a rational debate with such a person is futile, because it feels that one is not dealing with a person, but with slogans and catchwords.
So, to Bonhoeffer, stupidity was not about lack of intelligence, but about a mind that had almost voluntarily closed itself to reason, especially after being impacted and/or swayed by the rise of an assertive external power.
In a 2020 essay for The New Statesman, the British philosopher Sacha Golob writes that being stupid and dumb were not the same thing. For example, intelligence (or lack thereof) can somewhat be measured through IQ tests. But even those who score high in these tests can do ‘stupid’ things or carry certain ‘stupid ideas.’
Golob gave the example of the novelist Arthur Conan Doyle, who created the famous fictional character, Sherlock Holmes. Holmes, a private detective, was an ideal product of the ‘Age of Reason’, imagined by Doyle as a man who shunned emotions and dealt only in reason, logic and the scientific method. Yet, later on in life, Doyle became the antithesis of his character, Holmes. He got into a silly argument with the celebrated illusionist Harry Houdini when the latter rubbished Doyle’s belief that one could communicate with spirits (in a seance).
The question is, how could a man who had created a super-rationalist character such as Sherlock Holmes, begin to believe in seances? In fact, Doyle also began to believe in the existence of fairies. Every time someone would successfully debunk Doyle’s beliefs, Doyle would go to great lengths to provide a counter-argument, but one which was even more absurd.
Golob writes this is what stupidity is. And it can even be found in supposedly very intelligent people too. According to the American psychologist Ray Hyman, “Conan Doyle used his smartness to outsmart himself.” This can also answer why one sometimes comes across highly educated and informed men and women unabashedly spouting conspiracy theories that have either been convincingly debunked, or cannot be proven outside the domain of wishful thinking. By continuing to insist on the validity of such theories, one is simply using his/her smartness to outsmart oneself.
What about the leaders whose rise to power, according to Bonhoeffer, triggers stupidity across a large body of people? Take the example of today’s prominent populists, whose supporters are often referred to as being stupid. But as mentioned earlier, these leaders too are explained in a similar manner.
The truth is, dumbness, if it means a substantial lack of intelligence, is not what explains prominent political leaders. Had they been dumb, they would never be at the top of the heap. But as we have already established, stupidity and dumbness are two very different things; leaders can be stupid.
In this context, Golob explains stupidity as “the lack of conceptual resources.” By this he means that some leaders lack the right conceptual tools for the job. He writes that this can lead to a ‘conceptual failure’, where a leader is unable to fully grasp the concept of (political, economic or social) reality that he/she is operating in. They may excel in what they understand, but enter the domain of stupidity when they don’t. However, it is quite clear by now that today’s populist leaders may have had the intelligence to propel themselves to power, but they really do not have the conceptual tools to remain there.
Take PM Imran Khan. As an opposition leader, he understood well the concept of fiery, emotional rhetoric that can become a venting vessel for many. However, this tool becomes impotent in the conceptual context of actually being in power. Khan lacks the conceptual tools to understand the many economic and political quagmires the country has slid into. The more he fails in this, the more he falls back on concepts that he actually understands: i.e. fiery rhetoric (but one that does not sound very convincing anymore), and issues of morality.
He understands the latter well because, when he was a dashing ‘playboy’ in his pre-political days, he was often attacked for being immoral. He understood what the concept of morality is in Pakistani society. He now uses this as a tool to distract his thinning support from his obvious lack of understanding of what is actually happening around him in terms of the country’s drastic economic meltdown.
So, politically and economically, as things crumble around him, he stubbornly continues to “address issues of social immorality” because, by now, this is the only concept he can grasp. This is another case of political stupidity and conceptual failure, or of smartness outsmarting itself.
What is stupidity? Ever since the mid-20th century, the idea of stupidity, especially in the context of politics, has been studied by various sociologists and psychologists. One of the pioneers in this regard was the German scholar and theologian Dietrich Bonhoeffer.
During the rise of Nazi rule in Germany, Bonhoeffer was baffled by the silence of millions of Germans when the Nazis began to publicly humiliate and brutalise Jewish people. Bonhoeffer condemned this. He asked how could a nation that had produced so many philosophers, scientists and artists, suddenly become so apathetic and even sympathetic towards state violence and oppression.
Unsurprisingly, in 1943, Bonhoeffer was arrested. Two years later, he was executed. While awaiting execution, Bonhoeffer began to put his thoughts on paper. These were posthumously published in the shape of a book, Letters and Papers from Prison. One of the chapters in the book is called, ‘On Stupidity.’ Bonhoeffer wrote: “Every strong upsurge of power in the public sphere, be it political or religious, infects a large part of humankind with stupidity. The power of the one needs the stupidity of the other.”
According to Bonhoeffer, because of the overwhelming impact of a rising power, humans are deprived of their inner independence and they give up establishing an autonomous position towards the emerging circumstances. They become mere tools in the hands of the power, and begin to willingly surrender their capacity for independent thinking. Bonhoeffer wrote that holding a rational debate with such a person is futile, because it feels that one is not dealing with a person, but with slogans and catchwords.
So, to Bonhoeffer, stupidity was not about lack of intelligence, but about a mind that had almost voluntarily closed itself to reason, especially after being impacted and/or swayed by the rise of an assertive external power.
In a 2020 essay for The New Statesman, the British philosopher Sacha Golob writes that being stupid and dumb were not the same thing. For example, intelligence (or lack thereof) can somewhat be measured through IQ tests. But even those who score high in these tests can do ‘stupid’ things or carry certain ‘stupid ideas.’
Golob gave the example of the novelist Arthur Conan Doyle, who created the famous fictional character, Sherlock Holmes. Holmes, a private detective, was an ideal product of the ‘Age of Reason’, imagined by Doyle as a man who shunned emotions and dealt only in reason, logic and the scientific method. Yet, later on in life, Doyle became the antithesis of his character, Holmes. He got into a silly argument with the celebrated illusionist Harry Houdini when the latter rubbished Doyle’s belief that one could communicate with spirits (in a seance).
The question is, how could a man who had created a super-rationalist character such as Sherlock Holmes, begin to believe in seances? In fact, Doyle also began to believe in the existence of fairies. Every time someone would successfully debunk Doyle’s beliefs, Doyle would go to great lengths to provide a counter-argument, but one which was even more absurd.
Golob writes this is what stupidity is. And it can even be found in supposedly very intelligent people too. According to the American psychologist Ray Hyman, “Conan Doyle used his smartness to outsmart himself.” This can also answer why one sometimes comes across highly educated and informed men and women unabashedly spouting conspiracy theories that have either been convincingly debunked, or cannot be proven outside the domain of wishful thinking. By continuing to insist on the validity of such theories, one is simply using his/her smartness to outsmart oneself.
What about the leaders whose rise to power, according to Bonhoeffer, triggers stupidity across a large body of people? Take the example of today’s prominent populists, whose supporters are often referred to as being stupid. But as mentioned earlier, these leaders too are explained in a similar manner.
The truth is, dumbness, if it means a substantial lack of intelligence, is not what explains prominent political leaders. Had they been dumb, they would never be at the top of the heap. But as we have already established, stupidity and dumbness are two very different things; leaders can be stupid.
In this context, Golob explains stupidity as “the lack of conceptual resources.” By this he means that some leaders lack the right conceptual tools for the job. He writes that this can lead to a ‘conceptual failure’, where a leader is unable to fully grasp the concept of (political, economic or social) reality that he/she is operating in. They may excel in what they understand, but enter the domain of stupidity when they don’t. However, it is quite clear by now that today’s populist leaders may have had the intelligence to propel themselves to power, but they really do not have the conceptual tools to remain there.
Take PM Imran Khan. As an opposition leader, he understood well the concept of fiery, emotional rhetoric that can become a venting vessel for many. However, this tool becomes impotent in the conceptual context of actually being in power. Khan lacks the conceptual tools to understand the many economic and political quagmires the country has slid into. The more he fails in this, the more he falls back on concepts that he actually understands: i.e. fiery rhetoric (but one that does not sound very convincing anymore), and issues of morality.
He understands the latter well because, when he was a dashing ‘playboy’ in his pre-political days, he was often attacked for being immoral. He understood what the concept of morality is in Pakistani society. He now uses this as a tool to distract his thinning support from his obvious lack of understanding of what is actually happening around him in terms of the country’s drastic economic meltdown.
So, politically and economically, as things crumble around him, he stubbornly continues to “address issues of social immorality” because, by now, this is the only concept he can grasp. This is another case of political stupidity and conceptual failure, or of smartness outsmarting itself.
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