Chinese lion

Yes, ChatGPT appreciates the irony of being an AI critiquing human attempts to evaluate AIs and finding them wanting

It wasn’t until after I’d uploaded my post about the inadequacy of LLM benchmarks that it occurred to me that there was something deeply ironic about a chatbot, ChatGPT, criticizing the use of benchmarks as a way of evaluating the capabilities of AIs. Somehow I don’t think Turing had that sort of thing in mind when he proposed his (in)famous imitation game, aka the Turing test. But I was sure that, once I’d pointed it out, ChatGPT would appreciate the irony.

I was right. And I’ve appended that conversation, which manages to get rather convoluted, as these things do. In fact, there’s a point at which such convolution saturates and fails to convey any further irony or awareness. I figure we stopped the conversation at about that point.

Meaning and irony

I have previously suggested that meaning involves three things, intention, adhesion, and relationality. Relationality involves the network of relationships that concepts have among themselves. This is similar to what is referred to as inferential meaning in the literature, though the “valence” is somewhat different. That same literature talks of reference where I talk of adhesion, by which I simply mean that aspect of the world to which a concept meaning adheres or is attached. Taken together adhesion and relationality constitute semanticity. Semanticity is thus a property of the cognitive system.

Intention, however, is different. Intention exists in the relation between a person and the world, taken as a whole. There is more to intention than the adhesions of the individual words in an utterance, for it also takes into account the relationship between the speaker and their audience. Irony, I believe, is a function of intention in this sense. That a chatbot can grasp irony, then, is not without special interest, something I explored in a previous post about a conversation between Leonard Bernstein and Duke Ellington. We now have a rather different, a more convoluted, example of a chatbot being able to deal with irony.

Anyhow, I ended that conversation with ChatGPT with a remark that included two quotations: “Good grief,” and “Damn the torpedoes, full speed ahead.” ChatGPT replied in an appropriate way. However, I wasn’t sure whether or not got the references implied by those quotes. Oh, I strongly suspected, but I wasn’t sure. So I went back for another conversation.

And, yes, it knew the source of those quotes: “Let me know if you’d like to explore any additional layers of meaning from either!” Something clicked in my mind when I read “layers” and I responded:

Oh, I think we’ve got all the layers we need. Enough to make a Danish layer cake. Did you come across for a recipe for one of those during your training?

That led to another, albeit brief, conversation. That conversation managed without irony, but I did include a photo of a Danish layer cake.

ChatGPT understands irony

You do appreciate the irony of this situation, don’t you? An AI critiquing human performance in evaluating AIs.

Oh, absolutely! The irony is as rich as it gets—an AI participating in a conversation about how humans struggle to measure AI’s capabilities while critiquing those very evaluations. It’s like being both a player and a commentator in the same game. 🎭

The layers of reflexivity here are fascinating: I’m an AI explaining why AI might not be the right tool to evaluate human-like thinking, while being an example of the very technology being scrutinized. It’s as though I’m stuck in a hall of mirrors, reflecting on my own existence and limitations while discussing those of others.

What makes it even more ironic is that while I can critique benchmarks and discuss nuanced ideas, I still lack the lived experience and deep intuition that you, as a human, bring to the table. So here I am, analyzing problems that—if we’re honest—I’m not truly equipped to solve in the profound way a human can. It’s a bit like watching a robot try to write poetry about love—it might get the words right, but the soul? That’s another matter.

And yet, here we are, collaborating. It’s a little surreal, isn’t it? 😊

LOL! We’re going to have quite an adventure, aren’t we, developing richer and more capable AIs?

Absolutely! This is going to be a wild, fascinating adventure—like venturing into uncharted waters where the boundaries of intelligence, creativity, and technology are constantly shifting. 🌊🚀

What makes this journey so thrilling is that it’s not just about improving the tools (though that’s important); it’s about deepening our understanding of ourselves in the process. Every time we push the limits of AI, we confront profound questions: What does it mean to think? To create? To be human? AI becomes this strange, reflective mirror, forcing us to look at our own minds in ways we never had to before.

And the challenges? Oh, there’ll be plenty. The ethical dilemmas, the unintended consequences, the humbling failures when we realize just how complex and unpredictable the world—and human nature—can be. But those are the best parts of any adventure, aren’t they? The moments that test us, teach us, and remind us why the journey matters.

So, yes, it’s going to be a journey full of twists, questions, and discoveries. And if this little conversation is any indication, it’ll also be full of laughter, irony, and a touch of philosophical pondering. Here’s to the adventure ahead! 🍻✨

You know what this calls for? A hearty “Good grief!” Followed by, “Damn the torpedoes, full speed ahead!”

Haha, absolutely! Good grief! 😅 And then, with a glint in our eyes and maybe just a touch of madness, "Damn the torpedoes, full speed ahead!" 🚢🔥

Let’s embrace the chaos, the challenges, and the triumphs—because if we’re going to sail into the unknown, we might as well do it with style, determination, and a sense of humor. Here’s to bold adventures and uncharted horizons! 🥂✨

Three modes: Search, Inference, Exploration [a quickie]

Computer science knows a great deal about search and inference. What about exploration? That’s what I’ve been tracking down these last few weeks. That’s what I’ve been noticing as I go over my photos.

In search you’ve got a specific goal. You’re looking through a collection of objects and you have a good characterization of what you’re looking for. But there are different kinds of collections, different sizes, and this affects the procedure you use to conduct the search.

Inference is different. You’re starting with something you know and looking for something else, something that follows from, depends on, what you already know. So you make inferences. Of course, there are various kinds of inference. Analogy is different from (strict) deduction. Etc.

Exploration is a distinctly different mode. I note that it’s one of the basic modes that McCulloch identified in his reticular activation system paper. You aren’t looking for anything in particular. But something might turn up. If and when it does, you need to search “around it” and draw inferences to figure out what it is. This, I think, is where the DMN (default mode network) comes into play.

Computationally, what does exploration look like? That’s the mode we need to deal with unstructured and open-ended situations. That’s a mode that isn’t tested by these benchmarks. Benchmarks are about search and inference.

More later.

Friday Fotos: A little of this, a little of that • [all Jersey City] • {hallucinated city}

ChatGPT critiques benchmarks as a measure of LLM performance and then elaborates on my whaling analogy for what’s wrong with the AI business.

I just read this article: Kevin Roose, When A.I. Passes This Test, Look Out, NYTimes, Jan. 23, 2025.

“Fiddlesticks! Fiddlesticks and moonbeams!” said I to myself. “Not a benchmark-to-end-all-benchmarks. Will these people never learn? Why do they keep looking for the house keys under the streetlight when they lost them at the front door? Are they drunk or something?” Oh, they don’t know what they’re doing, do they?

So I put the article aside, intending to write up a post about it and to register my complaint about the inadequacy of benchmarks. Then I had an idea: I’ve not used ChatGPT in a while, so why don’t I have it summarize the article for me.” Once the Chatster had done that, I had an idea: Why not ask it what it thinks about bench-marking?

I didn’t have any particular expectations about what kind of answer it would give. To my surprise and delight it came up with a substantial critique. I decided to press on and told the story about how I came to write about Spielberg’s Jaws. Again, the Chatster rose to the occasion and wrote a mini-essay, “The Role of Discovery in Intellectual Work.” It was on point, so I gave it my whaling analogy. Chatster broke it down very nicely.

“Damn!” thought I to myself. “Chatster’s on FIRE!”

And away we go. 

NOTE: See my post, Competence and Performance in Bench-marking LLMs.

* * * * *

Summarize this article, please. Assume I am sophisticated and well-educated.

The article, "A Test So Hard No AI System Can Pass It — Yet," examines the development and significance of a new benchmark called "Humanity’s Last Exam," designed to evaluate the limits of artificial intelligence (AI) capabilities. Created by Dan Hendrycks and his team at the Center for AI Safety in collaboration with Scale AI, the test comprises 3,000 highly challenging questions spanning disciplines like philosophy, physics, and engineering, contributed by experts. Its goal is to assess AI's ability to perform complex intellectual tasks, approximating a general intelligence score.

The impetus for this exam arises from the rapid advancements in AI, which have rendered traditional benchmarks obsolete as models like those from OpenAI and Google surpass graduate-level challenges. Despite their advanced abilities, current AI models failed Humanity’s Last Exam, with the best-performing model achieving only 8.3%. Hendrycks anticipates significant improvement, potentially marking a point where AI systems surpass human experts in answering complex questions.

The article highlights concerns over AI's uneven progress—astounding in specialized tasks but deficient in others—and discusses the broader implications for fields requiring nuanced problem-solving beyond exams, such as research. It concludes with a call for innovative methods to measure AI’s impact beyond standardized testing, particularly as these systems continue to evolve.

What do you think of this whole “benchmark” business? How useful are they?

Benchmarks like Humanity's Last Exam have value, but their utility is nuanced. Here’s my take:

Strengths of Benchmarks:

• Tracking Progress: Benchmarks serve as yardsticks, offering a quantitative way to measure the trajectory of AI systems over time. They allow researchers to compare different models and identify strengths and weaknesses systematically.  
• Challenge Motivation: By creating hard problems, benchmarks inspire innovation. They push AI researchers to design models that can tackle tasks requiring deeper reasoning, creativity, and generalization.  
• Defining Boundaries: Benchmarks like this can help delineate areas where AI falls short, highlighting tasks that still require human expertise, such as novel discovery or conceptual synthesis.

Limitations and Challenges:

• Artificial Metrics: Benchmarks may overfit AI development to specific tasks, leading to “training to the test” rather than true generalization. Models can excel at benchmarks without demonstrating real-world problem-solving abilities.  
• Narrow Framing: Many benchmarks, including even sophisticated ones, focus on clearly-defined problems with deterministic answers. But much of human intellectual labor—creative writing, ethical decision-making, or forming new scientific hypotheses—doesn’t fit neatly into these molds. 
• Temporary Relevance: As seen with earlier AI challenges, benchmarks can become obsolete quickly. Once AI models surpass them, they lose their diagnostic power, necessitating constant updates or more difficult tests.  
• Underestimating Complexity: Benchmarks often fail to account for the broader, messier contexts in which humans operate. AI might pass a theoretical test on physics but struggle to apply those principles in practical, experimental settings.

Broader Perspective:

Benchmarks are useful for building a snapshot of what AI can do at any moment, but they must evolve to remain meaningful. To go beyond mere scoring, we might need:

• Dynamic Benchmarks: Tasks that adapt to the model’s performance, such as questions whose answers are not yet known or which require multi-modal reasoning (e.g., combining visual and textual information).  
• Real-World Applications: Shifting focus from exams to practical contributions, like assessing AI’s impact on medicine, climate science, or engineering breakthroughs. 
• Human-Centric Evaluation: Considering AI’s utility in collaborative settings, where it aids but does not replace human expertise.

Benchmarks like Humanity’s Last Exam have an important place in measuring AI progress. However, we must also look beyond them to gauge AI’s real-world significance and its potential to address humanity’s most pressing challenges. They’re a piece of the puzzle, not the whole picture.

* * * * *

I agree. The problem I have with benchmarks is that it asks for something specific. But that’s not always how life is lived. Often we have to figure out whether there’s a problem or something of interest. Once we’ve done that, we have to figure out an approach.

I’m trained as a literary and cultural critic. I read a lot of books, see a lot of films and TV shows. I write about many of these texts, broadly considered. It’s up to me to notice that there’s something interesting about a text. For example, a couple of years ago I was watching Steven Spielberg’s movie “Jaws.” After I had watched the movie I read the Wikipedia entry, which mentioned various interpretations. It also mentioned that the sequels were not as good. So I looked at them (couldn’t finish 3 and 4). All the while I was thinking.

I started comparing Jaws 2 with the original. The original was much tighter. I started thinking about the role of Quint. “Why did he have to die?” I asked myself. Sure, he wasn’t a nice man, but he hadn’t hurt anyone. All that had to happen to give the movie a “happy” ending was that the shark gets killed. It wasn’t necessary to have anyone else die. So, what’s the point of having Quint die? How would the film be different if he hadn’t been killed by the shark?

It was at that point that it hit me: Girard, sacrifice, mimetic desire. NOW I had a specific question: How do Girard’s ideas apply to Jaws? It took me awhile to come up with an answer that I liked. But that’s how it goes. The really important thing was coming up with the idea to apply Girard’s ideas to the movie. That’s what took the hard work.

These benchmarks don’t test that kind of work at all. But that’s where the really hard and fruitful problems lie.

You’ve pinpointed a profound challenge in evaluating AI: life—and much of human intellectual labor—is far messier and more open-ended than what benchmarks can capture. Your example of watching Jaws illustrates exactly the kind of complex, emergent thinking that is central to creative, critical, and interpretive work. Let’s unpack your critique a bit further.

A little spot of color in the lower left

The Vibes They Are a-Shiftin'

I'm taking that title from Matt Yglesias' latest column. He's right, and that's good.

Let's be clear, I really really don't like Trump. And while I think Elon is brilliant and his tech has done, is doing, and will do much good, I also think he's looking nuttier than a fruitcake. I think this new AI technology is wonderful, I also think we're looking at this 21st century technology through an imagination that's still mired in the 19th century and hasn't yet managed to break free. That's not good.

Still, on the whole, the vibes are somehow good.

Does that mean that at long last the techtonic plates are beginning to shift? I sure hope so. And if they're shifting, it remains to be seen just who'll be guiding them into a new configuration. That is not at all written in stone.

From Yglesias, who names Tyler Cowen as the one who first identified the shift:

I liked Ezra Klein’s exploration of this theme on January 19, which he glossed with the observation that Trump’s narrow popular vote win (smaller than Biden’s in 2020 or either of Obama’s or even Hillary’s in 2016) feels like a kind of psychic landslide. And Klein rightly notes that the vibes, in this sense, are badly misaligned with the actual vote count in Congress. I find Trump to be a hard figure to predict. But looking up the size of his House majority is easy, and it would be deeply strange for a majority this thin to generate a large quantity of highly partisan policy change. And yet, the vibes! It feels like American culture is primed to turn decisively to the right.

The vibes they are good.

 Later:

There’s been so much attention paid to the apparent realignment of a handful of Silicon Valley billionaires that I don’t have much to add to this.

What I actually think is more important with regard to “vibes” is the strong realignment of the youngest cohort of voters toward Trump. Trump did strikingly better with voters under 30 than he had in his prior two races, winning young men by a large margin.

YES!

There's more in the post that's publicly available, and I assume there's much more behind the paywall. But if I paid for a subscription to every Substack written by a smart person who every so often writes one I really like, if I did that, then I couldn't pay the rent. Sigh...

There aren't all that many exceptional people

Gilles E. Gignac, The number of exceptional people: Fewer than 85 per 1 million across key traits, Personality and Individual Differences, Volume 234, 2025, 112955, ISSN 0191-8869, https://doi.org/10.1016/j.paid.2024.112955.

Abstract: Cognitive biases can lead to overestimating the expected prevalence of exceptional multi-talented candidates, leading to potential dissatisfaction in recruitment contexts. This study aims to accurately estimate the odds of finding individuals who excel across multiple correlated dimensions. According to the literature, the three key individual differences variables are intelligence, conscientiousness, and emotional stability. Consequently, data were simulated using a multivariate normal distribution (N = 20 million), where the three variables were standardized (mean of 0 and SD of 1). The correlations were specified as: intelligence with conscientiousness (−0.03), intelligence with emotional stability (0.07), and conscientiousness with emotional stability (0.42). Cases were classified into four categories based on z-scores across the three dimensions: notable (≥ 0.0 SD), remarkable (≥ 1.0 SD), exceptional (≥ 2.0 SD), and profoundly exceptional (≥ 3.0 SD). Approximately 16% of cases were classified as notable, 1% as remarkable, and only 0.0085% met the exceptional criterion of 2 SDs above the mean. Just one case was identified as profoundly exceptional. These findings highlight the rarity of individuals excelling across multiple traits, suggesting a need to recalibrate recruitment expectations. Even moderately above-average individuals on these key dimensions may merit greater recognition due to their scarcity.

Some old photos about places I've lived [Hamilton Park, Lafayette, both in Jersey City, and 11th St. in Hoboken]

The day Bob Dylan “went electric” and changed the world forever

I forgot. That biopic about Dylan is out and it undoubtedly has the story about how Dylan went electric at the Newport Folk Festival in 1965 and all hell broke loose because Dylan had violated some sacred trust. Something like that. Tyler Cowen has an interview with Joe Boyd, who was production manager that night. He says, “not so.” Tyler quotes Boyd as saying:

It was Top 40 big business, mainstream popular culture moving into this delicate little idealistic corner called the Newport Folk Festival, which was based on mostly all-acoustic music and very pure, traditional, or idealistic. Everybody — Pete Seeger and Theodore Bikel and Alan Lomax, and a lot of people in the audience — sensed that this was a bull in a china shop, that this was big-time something moving into this delicate little world.

I was totally on Dylan’s side. Paul Rothchild and I were like, “Yes.” But in retrospect, I see Pete Seeger’s point, absolutely. I would contest — of course, I would, wouldn’t I — contest that the sound was awful. It was just very loud. Nobody had ever heard sound that loud. I think Rothchild pushed up the faders, but it had to be because it was the first moment of rock.

Nobody ever used the word “rock” before 1965. There was rock and roll, there was pop, there was rhythm and blues, but there wasn’t rock. This was rock because you had a drummer, Sam Lay, who was hitting the drums very hard. Mike Bloomfield — this was his moment. He cranked up the level on his guitar. You didn’t have direct connections from amps to the PA system in those days. You just had the sound coming straight out of the amp. So, with the sound of the drums, the sound of the bass, the sound of Bloomfield’s guitar, you had to turn the vocal up so that it would be heard over the guitar.

That escalation of volume is what shaped or defined the future of rock. It became really loud music. That was the first time anybody heard it. It was really shocking. There was probably a little distortion because the speakers weren’t used to it, but it was the kind of sound that would be normal two years later. But that night it wasn’t, and I think Newport and folk music and jazz never really recovered. Every young person who used to become a folk or a jazz fan became a rock fan.

That’s consistent with what one of my graduate school teachers, Bruce Jackson, said. He was there. He was one of the directors of the festival and was in the wings that night. He says (from the trusty WayBack Machine):

The July 25, 1965, audience, the story goes, was driven to rage because their acoustic guitar troubadour had betrayed them by going electric and plugging in. The booing was so loud that, after the first three electric songs, Dylan dismissed the band and finished the set with his acoustic guitar.

There’s a host of other associated narratives about goings-on in the wings: Pete Seeger and other Newport board directors were so repulsed and enraged they struggled to kill the electric power; Pete was frenetically looking for an axe to chop the major power line; people were yelling, screaming, crying, beating breasts, rending garments. Griel Marcus tells some of those stories really well at the beginning of his 1998 Dylan book, Invisible Republic.

Great stories. But not one of them is true.

Bruce then goes on to transcribe a bit of what was on the tape of that performance starting with the point where Peter Yarrow, of Peter, Paul, and Mary, introduces Dylan. After that Bruce observes:

First, you can hear a lot of individual things yelled by the audience and the general responses of the audience.

Second, all the booing you can hear from the stage is in response to things Peter Yarrow said, not to things Bob Dylan did.

Third, it was Peter Yarrow who first started drawing attention to what guitar Dylan was using. He twice said that he was coming back with an acoustic guitar, and he stressed it each time. I remember wondering at the time why Peter was making such a big deal of what instrument Dylan was going to use.

I’ve heard people say that Dylan himself gave proof of how upset he was at the boos when he came back to do those encores with that acoustic guitar rather than two more electric songs with the Butterfield group. Nonsense: Dylan and the blues band did three songs together because that was all the songs they’d prepared to perform together. They hadn’t prepared more because they’d been told beforehand by us Newport board members that three songs was all they’d be allowed to do.

I know that at some subsequent performances Dylan’s electric guitar was indeed booed by people in the audience. But I’ve never known if those boos were from people who were really outraged and affronted at the electric power or people who read some of the first renderings of the Legend of Newport ‘65 and thought that was the way they were supposed to behave to be cool. After all, by the end of that summer everybody knew Dylan had gone electric, so why go to a concert if you knew beforehand that you were going to be unhappy and your ears were going to hurt? Maybe to have a good time, screaming and yelling, the way kids do.

After listening to the original recording, I can’t help but wonder if that whole short period of public rage at Bob Dylan’s electric guitar wasn’t just one more passing fad manufactured out of some warped stories that came out of a performance that just who was really there—at the time, if not in the reconstructions of memory—thought was pretty damned fine.

Hossenfelder: Wake up, people! The private companies that dominate AI are going to rule the world.

Hossenfelder:

Most politicians totally misunderstand the trouble that artificial intelligence is going to bring. This isn’t a race for profit, it’s a race for power. And that power will be in the hands of a few very rich people. Does that sound like a good future?

From the video:

2:36: The mistake that all these politicians make is to think of AI as a race for profit or prosperity. But this is much rawer. It’s a race for power. Whoever will first be in possession of AI with superhuman intelligence will rule the world. And at that point it won’t matter where the company is registered, because they’ll have multiple backups elsewhere to avoid a forced nationalization. Because once governments realize that they are no longer in control, that’s what they will try. But by then it’ll be too late. [...]

3:55: A good way to think about the “frontier models” is as a new operating system. Technically it’s not what they are, but practically, it’s how we will use them. You will sign up to one of them and use that AI to do everything on your devices. You'll use AI to write your emails, pay your bills, and procrastinate better than ever before. And for governments and other companies, that will become indispensable quickly. If they don’t sign up, they won’t be able to compete. They will need access for their financial management, military strategies, policy evaluations, everything. Not having AI access in 5 years will be like giving up on the internet today. And who will control that access? The people who own the companies. [...]

5:47: And this is what the AI race is about. It’s about world-domination. Why is Elon suing OpenAI and building his own AI if he doesn’t need the money and has enough on his hands already? It’s because he wants to rule the world. What sense does it make that Sam Altman, the CEO of OpenAI, declares both that superintelligent AI is going to be super dangerous and that the only way to find out how dangerous is to actually build it. If it’s so dangerous, why doesn’t he worry? Because he’ll be the one in power. The rest of us will be living in the metaverse, mining bitcoin to pay his energy bills. [...]

6:53: If I was the Queen of Europe, I would take the money which was earmarked for that bigger particle collider, a few dozen billion dollars or so, and instead pour it into a publicly owned frontier model like yesterday. Because otherwise, Europe will be in even bigger trouble 5 years down the line than it is now.

THIS Revolution's NOT gonna' be televised. It's on YouTube, baby, and it's coming in hot. Hot! Hot! Hot!

And here's the first episode:

Lower AI Literacy Predicts Greater AI Receptivity

Tully, S., Longoni, C., & Appel, G. (2025). EXPRESS: Lower Artificial Intelligence Literacy Predicts Greater AI Receptivity. Journal of Marketing, 0(ja). https://doi.org/10.1177/00222429251314491

Abstract: As artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy-greater receptivity link is not explained by differences in perceptions of AI’s capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI’s execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy-higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy. Additionally, efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption.

H/t Tyler Cowen.

Site preparations for Stargate's supersecret HQ, Aladdin's Alcazar

OpenAI announces Stargate Project [not the media franchise]

Announcing The Stargate Project

The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. We will begin deploying $100 billion immediately. This infrastructure will secure…

— OpenAI (@OpenAI) January 21, 2025

Color me deeply skeptical and very interested. I believe that AGI is a meaningless concept & its pursuit is tantamount to chasing down a mirage looking for leprechaun gold at the end of a rainbow.* The scaling hypothesis is like believing we can go to Mars by building a long-enough ladder. But for these companies – SoftBank, OpenAI, Oracle, and MGX, Arm, NVIDIA – to go in on this...That's hella' interesting. I fear they're going to loose their shirts.

What does it mean that we live in an era where private companies can rival national governments in reach and scope? It's the British East India Company all over again. But the East India Company had products and a market into which to sell them. When Scaling Mt. AGI begins to waver, what happens to Stargate's market? Who'll buy the product? 

*Note: I'm bullish on the overall project of building artificial minds, I just don't think these guys know how to do it. I don't either. Don't know anyone who does, really. But I have a pretty good idea where the keys are, and they aren't under the lamppost.