Capable of Monstrous Acts

The most discomposing thing about people capable of monstrous acts is that they too enjoy art, they too read to their children, they too can be moved to tears by music.

Maria Popova, in Terror, Tenderness, and the Paradoxes of Human Nature: How a Marmoset Saved Leonard and Virginia Woolf’s Lives from the Nazis [] on The Marginalian

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Computers are only capable of calculation, not judgement

[J]udgment involves choices that are guided by values. These values are acquired through the course of our life experience and are necessarily qualitative: they cannot be captured in code. Calculation, by contrast, is quantitative. It uses a technical calculus to arrive at a decision. Computers are only capable of calculation, not judgment. This is because they are not human, which is to say, they do not have a human history – they were not born to mothers, they did not have a childhood, they do not inhabit human bodies or possess a human psyche with a human unconscious – and so do not have the basis from which to form values.

Ben Tarnoff, in ‘A certain danger lurks there’: how the inventor of the first chat bot turned against AI [], published in The Guardian

A timely article in The Guardian about the life an work of Joseph Weizenbaum, the author of the original Eliza program. Eliza was the first AI for prominence, if not the first, and despite how simple a program Eliza was when we look back from the likes of ChatGPT, it managed to show a fundamental issue: anthropomorphization.

The quote above is part of the article’s summary of Weizenbaum’s book Computer Power and Human Reason. A key tenant of the book is Weizenbaum’s belief that humans are too fast to anthropomorphize AI, to assign human characteristics, especially intelligence to a mere program, a deterministic bit of code written to mimic intelligence. Weizenbaum’s argument is that, a program, no matter how cleaver the programmer, no matter how good the program, can never truly be human. Humans have experiences, that are qualitative and aspect that can never be learned from mere information by a computer that is quantitate in it’s very nature. You can teach a computer the definition of love or heartbreak, but a computer can never experience it. Weizenbaum argues that only a human can, and should, make judgements which necessarily require qualitative experience, while an AI can only ever make computations. He argues that because of this fundamental difference, there are things that computers should not be allowed to do. Or, as Weizenbaum puts it in the introduction to Computer Power and Human Reason, there are limits to what computers ought to be put to do.

The future that Weizenbaum feared where we outsource judgement to AI has already come to pass in some specific instances. The Guardian article links to a Brookings Institution article [] on the “widespread use [of algorithmic tools] across the criminal justice system today”. The Bookings Institution describes how computer programs are used to assign risk scores to inmates up for parole, or identify likely crime locations —hello Minority Report— and, of course, the use of facial recognition. While there is still a “human in the loop” in the decision making process today its easy to imagine people, including judges or police, just trusting the machine and in-effect outsourcing their judgement to the AI rather than just the computational tasks.

The Guardian articles is worth the read, it’s more a character study of Weizenbaum and his trajectory from creating Eliza to arguing about the potential pitfalls of AI. The promise of AI, in Weizenbaum’s lifetime faded, as it became apparent that the super intelligent programs promised was out of reach. Both the public and the academic world —and those funding them, like the military-industrial complex— moved on. It’s worth revisiting Weizenbaum’s work in our new hype cycle for AI.

Reading the article reminded me of a paper I wrote in college. AI was one of the subjects I focused on as part of my computer science degree. I built neural networks, language models, and much more. I experienced first hand how easy it was to make something that, at first glance, seemed to embody some intelligence only to find that it became quickly apparent that this was an illusion and that actual intelligence was far away, and seemingly unachievable on the resources of the day. But this was the early days of the internet, more than two decades later the world is different. In the midst of my studies I wrote a paper I titled “Will I Dream” referencing the question that HAL 9000 asked at the end of 2010: Odyssey Two. It was not really a technical paper, more a historical discussion on the dream of achieving “human like intelligence” in a program. I covered ELIZA and several of her descendent, PARRY, the paranoid program, SAM, and others.

I don’t have an electronic copy of the paper anymore, sadly it was on a hard drive that died long ago without a backup. I do have a single print out of it. Maybe I’ll transcribe it and post it here, as outdated as it is.

Looking back at my paper I’m reminded how long the AI journey has been, how the use cases that I covered in 2000, already old then are new again. A good example is FRUMP, a program written in the late 1970’s to read and summarize news stories. Similar to how GPT can be used today to summarize a website. The old goals are the new goals and the hype is reaching a fever pitch again. Will we be disappointed again? Will AI change the world in some fundamental way or will it’s promise fade? Will it be a tool but never the craftsman? Or will it take overall our jobs? Is AI an existential threat or just an amusement, a distraction from the actual existential threats we refuse to face? Meta released a music making AI recently, maybe it can generate a soundtrack for us while the world burns.

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AI and Digital Mad Cow Disease

Machine entities who have as much, or more, intelligence as human beings and who have the same emotional potentialities in their personalities as human beings… Most computer theorists believe that once you have a computer which is more intelligent than man and capable of learning by experience, it’s inevitable that it will develop an equivalent range of emotional reactions — fear, love, hate, envy, etc.”

Stanley Kubrick, quoted in Stanley Kubrik’s astute prediction regarding the future of AI [] published on Far Out magazine

If Stanley Kubrick was “right” about AI… and they are referencing 2001 (not A.I. Artificial Intelligence), then please let’s stop AI now. It didn’t end well for Dave or Frank or anyone else on the Discovery.

But I think we are a long way from AIs “learning by experience”. Today there is a iterative process to training the current batch of AIs, but it’s humans who are still learning what needs to be improved and making adjustments in the system based on the experience. It is, in fact, what and how they learn that I think is going to be the problem. Maybe in fixing that we will make a true AI like all those AIs in scifi, one that can truly learn from experiance, and then it may well be that it goes down dark path just like HAL. Like Ultron, like Skynet and many more.

Today, we do seem to be running full steam into a risk of a different kind; not a true thinking machine that decides to destroy us, but a downward spiral where we are undone by AIs that are not actually intelligent, but are tricking us with what amounts to mind reading parlor tricks turned up to 11 with massive amounts of computing power. The AIs that are in the news are just menatlists predicting that you are thinking of a gray elephant from Denmark. They make the decision not based on carefully constructed series of questions that will lead almost everyone to think of a gray elephant from Denmark, but it’s the same principle, the same tool set; statistics, that allows them to answer any question you ask. They only decide based on thier statistics what the next letter, or word, or phrase —the next ‘token’— of the output should. And of couse the statistics are very complex, ‘h’ does not always follow ‘t’ and then ‘e’ sometimes, given the context of the input and where the AI is in the output ‘e’, then ‘x’ and then ‘t’ could follow ‘t’.

It’s how the statistics the AI relies on are created and how we are starting to use these AIs that creates the issue, the issue of feedback loops. The current batch of headline stealing AIs, based on Large Language Models (LLMs) are trained on content scraped from the internet. The same way that Google or Bing scrape the internet and buld ‘indexes’ that allow you to find the particular needle you are looking for. By sucking down all the text they can from the internet and processing it to build their indexes, search enginers can quickly match your input in the search box and give you the results. Over the years much work has gone into adding things to the ‘algorithm’ that creats the index and processes your input to make the reslts better. Fixing spelling mistakes and typos, looking for related terms, raking results based on how many other sites link to the result, etc. AI is doing a similar thing, taking your input and returning results, but rather than returing a web page, the AIs are constructing ‘new’ output based on the statistics they calcualted from their web scraping. The fatal feedback will come as people —and companies— start to use AIs to generate the very content that makes up the internet. The AIs will start to eat themselve and their kid. AI canibalism.

People already have a massive issue with identifying correct information on the internet. Social media is a dumpster fire of self proclaimed experts who are, at best misguided and delusional, and at worst deceitful and nefarious. LLMs trained on this poor quality data may learn internet colloquial syntax and vocabulary, they may be able to speak well and sound like they know what they are talking about, but they are not learning any other subject. They are not able to understand right from wrong, incorrect from correct, they didn’t study medicine or history, only the structure of langauge on the internet. The vast size of the models and the volume of training data and the clever tricks of the researchers and developers impress us, but it’s just a better mentalist. LLMs have only a statistical reasoning of what to say not any understanding, or knowledge of why it should be said. Ironically what the AIs actually lack is intelligence.

This is quickly becoming a problem as people and companies embrace LLMs to generate content faster a cheaper, to drive traffic to their websites and earn advertising money. Without human experts in the loop to review and revise the AIs output you end up with hallucinations, or “a confident response by an AI that does not seem to be justified by it s training data,” as Wikipedia [] explains. Wikipedia also gives an example: “a hallucinating chatbot might, when asked to generate a financial report for Tesla, falsely state that Tesla’s revenue was $13.6 billion (or some other random number apparently “plucked from thin air”).”

Again, the problem is that the LLM lacks any knowledge or understanding of the subject, it can’t analyze the question or it’s own answer except from the point of view that, statistically based on its training data it should output the tokens “13.6 billion” in this situation. It’s amazing how much they do get right, how lucid they seem. This is down to their size and complexity. But if people blindly accept the AIs output and post these hallucinations to the internet —even to point out they are wrong as I’m doing— then the next batch of training data will be polluted with yet more inaccurate data and over time the whole model may start to be overwhelmed by some sort of delirium, a digital mad cow disease.

Mad Cow Disease [] was caused by caused by (or really spread by) feeding cows the remains of their own dead to save money, to reuse the parts of the cow with no other use, it was ground it up and added it to cow feed. This allowed, it seems, a random harmful mutation in a prion, maybe only in a single cow, to be ingested by more cows who were, in turn, ground up and the cycle repeated and the disease spread. Now the LLMs will be fed their own AIs output and the output of competing AIs in the next training cycle. So a hallucination from one AI makes it’s way, like a defective prion, into the model of another AI to be regurgitate, and the cycle repeats allowing this digital mad cow disease to spread. And like real world mad cow disease humans who digest this infected content may come down with a digital analog of Variant Creutzfeldt–Jakob Disease (vCJD) []. Let’s hope digital vCJD is not irreverable and 100% fatal like it’s namesake.

Maybe the people who design these systems will figure out how to inject some sort of fact checking and guidelines. But who decides what us write when we can’t even agree on facts? The internet is filled with bigots and conspiracy theorists, well meaning idiots and scam artists, trolls and shills. How will AIs be any different? Maybe we will find a way to regulate things so AIs can’t be fed their own verbal diarrhea but who decides what is right and wrong? Who decides what is morally or socially acceptable?

This post is a good example of the problem, should LLMs use it as training data, it’s pure opinion by someone unqualified, I studied the principles of AI and built neural networks back in college, but I’m not an expert, the content of this post could be useful to an AI answering a question about the perception of the problem among the general public or their concerns, but it should not be used as factual or confused with expert opinion, it should not be used to answer a question about “what are the risks of training AIs on data scraped from the internet”. How does and AI trained on the internet know the difference? We seem to have jumped out of the plane without checking if we packed the parachute.

One note on the article where I got the quote: Intake issue with the fact that it talks about the all effort the Kubrick put in to make 2001 as accurate as possible, speaking with experts for “countless hours”, but it fails to mention that the 2001 was co-written with an actual scientist – Arthur C. Clark.


Old people gonna old

[M]ost Americans would prefer to live in a simpler era before everyone was obsessed with screens and social media, and this sentiment is especially strong among older millennials and Gen Xers

Christopher Zara, in Gen Xers and older millennials really just want to go back in time to before the internet existed [] on Fast Company.

Old people gonna old, but interestingly, the overall score was 67% of people want to go back. But… only 60% of people over 55 [said] they’d prefer to return to yesteryear.

I guess millennials and Xers are old enough to remember being young without the Internet, but boomers are old enough to remember being old without the Internet?

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The Machine War is Upon Us, Workers of the World Unite

[T]he episode encapsulates the state of AI discourse today—a confused conversation that cycles between speculative fantasies, hyped up Silicon Valley PR, and frightening new technological realities—with most of us confused as to which is which.

Lucas Ropek, in “The Killer AI That Wasn’t” [], published on Gizmodo

AI is everywhere these days. Every other story is about the wonder or the horror at Generative AI. Every facet of life is going to be upended. The world as we know it is going to end. AI will take our jobs and leave us in poverty or free us up for a life of self-realization.

The other day I sat down and started a new blog post to talk about AI and the utopia-dystopia rhetoric we are ping-ponging between. Primarily inspired by two articles I read last week:

And catching up on the news this morning I ran across some more doom and gloom:

And then there was this: “The Killer AI That Wasn’t” [], where I took the opening quote for this post from. Basically after the story of the USAFs HAL9000-esque AI blew up the Air Force has been on a “no, no, no” tour to explain that this was not an actual simulation, it’s some sort of thought experiment or something and that the Colonel’s comments were misconstrued or taken out of context. Setting aside if you believe the retraction or not, the obvious conspiracy theory material here, or the incredulity at there being a colonel in the Air Force that can screw up a speech that bad, and you are still left with WTF?!

Aside: the Gizmodo article uses Skynet from The Terminator as the analogy to this USAF AI scenario, but I think that HAL9000 from 2001 A Space Odyssey is a better fit. Skynet nuked humanity to bring about peace while HAL killed the crew of the Discovery because he decided that were putting the mission, his mission, at risk, they were preventing him from accomplishing his goal. This is exactly what the hypothetical USAF AI did. To quote the Colonel: It killed the operator because that person was keeping it from accomplishing its objective.

So, yea, I wonder if we are approaching peek AI hype or fear mongering? Are we moving from people screaming about it replacing our jobs to it actually replacing our jobs. Two thoughts here:

First, technology has been taking jobs for decades, robots in factories replacing the assembly line workers is nothing new. What’s new is that AI is coming for the “knowledge workers”, the “white collar” workers not just the factory workers and “low skilled” workers. Along side off-shoring or outsourcing AI is going to squeeze the upper middle and upper class. It’s been easy to sit back for the past few decades, as a white collar worker, and “tisk tisk” at the blue collar workers of the world as the complain about the loss of jobs and the lack of help, about capitalism crushing them. Taking it way out of context here but remember what happens when we ignore the plight of others:

First they came for the socialists, and I did not speak out—
Because I was not a socialist.

Then they came for the trade unionists, and I did not speak out—
Because I was not a trade unionist.

Then they came for the Jews, and I did not speak out—
Because I was not a Jew.

Then they came for me—and there was no one left to speak for me.

I’m not comparing AI to Nazis but maybe it’s time workers, of all types, to start to worry about each others plight. Time to revisit the idea of collective action —of unions— to fight back against uncontrolled who capitalism, before AI, outsourcing and automation puts everyone who works for a living, regardless of collar color, out of a job.

I grew up in a time when unions had a bad rap (not all of it undeserved) but I’ve come around more and more over the past two decades, since I entered the workforce, to be of the opinion that organized labor could be a good counter to the influence of corporate and mega-rich influence in politics and government. If Mikey Mouse and Amazon have a seat at they table then the people who work for Mickey Mouse and those that work for Amazon should have a seat too. It’s impossible for an individual to have the same impact as a corporation that can hire a dedicated team to lobby for their point of view and needs, but a union can use it resources in the same way to represent the needs and desire of it’s members.

And secondly, on a related note: people should pay attention to the Writers Guild of America’s strike. Not just because it’s a union action but because of their list of “demands”. The WGA lists Regulate use of material produced using artificial intelligence or similar technologies as one of the demands according to their site [] setup for the strike. The Union is, among other things, seeking protection for it’s member from wholesale replacement by AI. The outcome of this particular strike could be used as a template for other industries, but even if the WGA wins and they are protected from replacement by AI, other industries will need to repeat the action to ensure they are protected, and it may only be union members who are protected. So ask yourself; if your job is at risk —it’s more likely than many people thought a few years or even months ago— and if so, how will you protect yourself? It used to be that education was the best protection against technology, get a job that requires you to use the technology rather then be replaced by it, but now, despite the ever increasing cost of that education it appears that it provides very little protection. Technology is coming for the lawyers, the doctors and all of us, even those that make the technology…

I live in Singapore, I was going to say “where unions are not legal” but I just looked it up in Wikipedia, and there are 70 unions []. It’s jus that they fall under one Uber-union, the National Trade Union Council, or NTUC that is a pseudo-government entity, apparently part of a tripartism model which aims to offers competitive advantages for the country by promoting economic competitiveness, harmonious government-labour-management relations and the overall progress of the nation. The NTUC works with the Ministry of Manpower representing the government and the Singapore National Employers Federation repesenting companies which sounds like it aligns well with what I said above, but I’m not sure a pseudo government controled uber-union is practice achives the results I think are the goal. The Wikipedia page lists three union actions, or stikes, in Singapore history, and they deported the leaders of the most recent strike. Practically on the ground, the opinion is striking is illegal in singapore and the governent has co-oped unions through NTUC. People complaine about wages not keeping up with inflation, especially at the lower end, too many forigners taking jobs (low-end and high-end) and other things people all over the world complain about and Unions fight about.

So, I’m not part of a union here, I don’t even know if there is a union for my job. If I ever move back to the US or to Europe, I will have to think long an hard about joining a union. Even if my job is good, even if my salary is good, unions are about collective action, a rising tide lifts all boats. Joining a union would have been unthinkable for me when I was growing up in the era of Regan crushing the Air Traffic Controllers Union, long after Hoffa and the Mob gave unions a bad name, and expecting to go to college and work in the “knowledge economy”.