Artificial Intelligence (AI)

Standing ovation for chx:

Karoly Negyesi: Well, even framing this as "AI" is misleading. The entire field is essentially based on a short paper written by John von Neumann in the 1950s. In that paper, he declared—without a single shred of proof, and yet people readily believed it—that the human brain is obviously digital. People have believed this so strongly that even today, neuroscientists struggle to describe how the brain works without using digital metaphors. But the truth is, the human brain does not work like a computer.

So, calling these statistical pattern-matching systems "artificial intelligence" is just misleading. 'Retrieve a memory', your brain doesn’t retrieve a memory. It’s not a computer. It never was. Everybody knows this. You never retrieve a memory the way a computer does. You do not store your memories as a computer does. That whole concept is just not true.  

There was a brilliant book about this a couple of years back that described how, in different eras, people compared the brain to whatever technology was available to them. Descartes compared it to a machine. Von Neumann compared it to a digital computer. None of that is true. Of course, we still don’t quite know how the brain actually works. So then we pursue something called artificial intelligence, and by that, we mean something that matches this completely misplaced and untrue metaphor of the brain.  

The whole premise of artificial intelligence is broken. It’s just not true. You are building a castle on quicksand. There’s nothing there. And beyond this, there’s just so much wrong with it. Almost blindly trusting whatever a large language model spits back at you—because, once again, I don’t think people fully understand or even partially understand what they are getting.  

So, no, I don’t think AI is progressing in the way people think it is. I mean, obviously, there’s some progress, but it is not going where people think it can go.  It’s never going to match a human brain—at least not this way.  And quite likely, not within our lifetimes. Probably not even within a few centuries. We will not have a machine that is capable of doing what the human brain is capable of. Mostly because—we still have no clue how the brain actually works. 

ChatGPT can’t draw a glass of wine full to the brim. Why? And what might it have to do with David Hume and the missing shade of blue?

Microsoft has, through a combination of canceled leases, pullbacks on Statements of Qualifications, cancellations of land parcels and deliberate expiration of Letters of Intent, effectively abandoned data center expansion equivalent to over 14% of its current capacity.

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The reason I'm writing in such blunt-force terms is that I want to make it clear that Microsoft is effectively cutting its data center expansion by over a gigawatt of capacity, if not more, and it’s impossible to reconcile these cuts with the expectation that generative AI will be a massive, transformative technological phenomenon. 

I believe the reason Microsoft is cutting back is that it does not have the appetite to provide further data center expansion for OpenAI, and it’s having doubts about the future of generative AI as a whole. If Microsoft believed there was a massive opportunity in supporting OpenAI's further growth, or that it had "massive demand" for generative AI services, there would be no reason to cancel capacity, let alone cancel such a significant amount.

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Microsoft is cancelling plans to massively expand its data center capacity right at a time when OpenAI just released its most computationally-demanding model ever. How do you reconcile those two things without concluding either that Microsoft expects GPT-4.5 to be a flop, or that it’s simply unwilling to continue bankrolling OpenAI’s continued growth, or that it’s having doubts about the future of generative AI as a whole?

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Generative AI does not have meaningful mass-market use cases, and while ChatGPT may have 400 million weekly active users, as I described last week, there doesn’t appear to be meaningful consumer adoption outside of ChatGPT, mostly because almost all AI coverage inevitably ends up marketing one company: OpenAI. Argue with me all you want about your personal experiences with ChatGPT, or how you’ve found it personally useful. That doesn’t make it a product with mass-market utility, or enterprise utility, or worth the vast sums of money being ploughed into generative AI. 

The stupid use cases for AI just keep coming:

Human capital---encompassing cognitive skills and personality traits---is critical for labor market success, yet the personality component remains difficult to measure at scale. Leveraging advances in artificial intelligence and comprehensive LinkedIn microdata, we extract the Big 5 personality traits from facial images of 96,000 MBA graduates, and demonstrate that this novel ``Photo Big 5'' predicts school rank, compensation, job seniority, industry choice, job transitions, and career advancement. Using administrative records from top-tier MBA programs, we find that the Photo Big 5 exhibits only modest correlations with cognitive measures like GPA and standardized test scores, yet offers comparable incremental predictive power for labor outcomes. Unlike traditional survey-based personality measures, the Photo Big 5 is readily accessible and potentially less susceptible to manipulation, making it suitable for wide adoption in academic research and hiring processes. However, its use in labor market screening raises ethical concerns regarding statistical discrimination and individual autonomy

From the authors of "People Who Don't Understand Magic Trick More Likely To Be Impressed By It".

People with less knowledge about AI are actually more open to using the technology. We call this difference in adoption propensity the “lower literacy-higher receptivity” link.

This link shows up across different groups, settings and even countries. For instance, our analysis of data from market research company Ipsos spanning 27 countries reveals that people in nations with lower average AI literacy are more receptive towards AI adoption than those in nations with higher literacy.

Similarly, our survey of US undergraduate students finds that those with less understanding of AI are more likely to indicate using it for tasks like academic assignments. 

A comprehensive roundup of the LLM hype dumpster fire.

We observe a recent trend toward applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use would mean. We argue that it is important to reassert the central research focus of the field of information retrieval, because information access is not merely an application to be solved by the so-called ‘AI’ techniques du jour. Rather, it is a key human activity, with impacts on both individuals and society. As information scientists, we should be asking what do people and society want and need from information access systems and how do we design and build systems to meet those needs? With that goal, in this conceptual article we investigate fundamental questions concerning information access from user and societal viewpoints. We revisit foundational work related to information behavior, information seeking, information retrieval, information filtering, and information access to resurface what we know about these fundamental questions and what may be missing. We then provide our conceptual framing about how we could fill this gap, focusing on methods as well as experimental and evaluation frameworks. We consider the Web as an information ecosystem and explore the ways in which synthetic media, produced by LLMs and otherwise, endangers that ecosystem. The primary goal of this conceptual article is to shed light on what we still do not know about the potential impacts of LLM-based information access systems, how to advance our understanding of user behaviors, and where the next generations of students, scholars, and developers could fruitfully invest their energies.

Good grief.

SAN FRANCISCO (AP) — Tech behemoth OpenAI has touted its artificial intelligence-powered transcription tool Whisper as having near “human level robustness and accuracy.”

But Whisper has a major flaw: It is prone to making up chunks of text or even entire sentences, according to interviews with more than a dozen software engineers, developers and academic researchers. Those experts said some of the invented text — known in the industry as hallucinations — can include racial commentary, violent rhetoric and even imagined medical treatments.

Experts said that such fabrications are problematic because Whisper is being used in a slew of industries worldwide to translate and transcribe interviews, generate text in popular consumer technologies and create subtitles for videos.

More concerning, they said, is a rush by medical centers to utilize Whisper-based tools to transcribe patients’ consultations with doctors, despite OpenAI’ s warnings that the tool should not be used in “high-risk domains.”

The stated goal of many organizations in the field of artificial intelligence (AI) is to develop artificial general intelligence (AGI), an imagined system with more intelligence than anything we have ever seen. Without seriously questioning whether such a system can and should be built, researchers are working to create “safe AGI” that is “beneficial for all of humanity.” We argue that, unlike systems with specific applications which can be evaluated following standard engineering principles, undefined systems like “AGI” cannot be appropriately tested for safety. Why, then, is building AGI often framed as an unquestioned goal in the field of AI? In this paper, we argue that the normative framework that motivates much of this goal is rooted in the Anglo-American eugenics tradition of the twentieth century. As a result, many of the very same discriminatory attitudes that animated eugenicists in the past (e.g., racism, xenophobia, classism, ableism, and sexism) remain widespread within the movement to build AGI, resulting in systems that harm marginalized groups and centralize power, while using the language of “safety” and “benefiting humanity” to evade accountability. We conclude by urging researchers to work on defined tasks for which we can develop safety protocols, rather than attempting to build a presumably all-knowing system such as AGI.

Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.

Modern AI models are trained by feeding them "publicly-available" text from the internet, scraped from billions of websites (everything from Wikipedia to Tumblr, to Reddit), which the model then uses to discern patterns and, in turn, answer questions based on the probability of an answer being correct.

Theoretically, the more training data that these models receive, the more accurate their responses will be, or at least that's what the major AI companies would have you believe. Yet AI researcher Pablo Villalobos told the Journal that he believes that GPT-5 (OpenAI's next model) will require at least five times the training data of GPT-4. In layman's terms, these machines require tons of information to discern what the "right" answer to a prompt is, and "rightness" can only be derived from seeing lots of examples of what "right" looks like.

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In essence, the AI boom requires more high-quality data than currently exists to progress past the point we're currently at, which is one where the outputs of generative AI are deeply unreliable. The amount of data it needs is several multitudes more than currently exists at a time when algorithms are happily-promoting and encouraging AI-generated slop, and thousands of human journalists have lost their jobs, with others being forced to create generic search-engine-optimized slop. One (very) funny idea posed by the Journal's piece is that AI companies are creating their own "synthetic" data to train their models, a "computer-science version of inbreeding" that Jathan Sadowski calls Habsburg AI.