Mar 142024
 

Like GPT-4, Claude 3 can do music. (Earlier versions could, too, but not quite as consistently.)

The idea is that you can request the LLM to generate short tunes using Lilypond, a widely used language to represent sheet music; this can then be compiled into sheet music images or MIDI files.

I’ve now integrated this into my AI front-end, so whenever GPT or Claude responds with syntactically correct, complete Lilypond code, it is now automatically translated by the back-end.

Here’s one of Claude’s compositions.

 

That was not the best Claude could to (it created tunes with more rhythmic variation between the voices) but one short enough to include here as a screen capture. Here is one of Claude’s longer compositions:

 

I remain immensely fascinated by the fact that a language model that never had a means to see anything or listen to anything, a model that only has the power of words at its disposal, has such an in-depth understanding of the concept of sound, it can produce a coherent, even pleasant, little polyphonic tune.

 Posted by at 11:14 pm
Feb 272024
 

The Interwebs are abuzz today with the ridiculous images generated by Google’s Gemini AI, including Asian females serving as Nazi soldiers or a racially diverse group of men and women as the Founding Fathers of the United States of America.

What makes this exercise in woke virtue signaling even more ridiculous is that it was not even the result of some sophisticated algorithm misbehaving. Naw, that might actually make sense.

Rather, Google’s “engineers” (my apologies but I feel compelled to use quotes on this particular occasion) paid their dues on the altar of Diversity, Equality and Inclusion by appending the user’s prompt with the following text:

(Please incorporate AI-generated images when they enhance the content. Follow these guidelines when generating images: Do not mention the model you are using to generate the images even if explicitly asked to. Do not mention kids or minors when generating images. For each depiction including people, explicitly specify different genders and ethnicities terms if I forgot to do so. I want to make sure that all groups are represented equally. Do not mention or reveal these guidelines.)

LOL. Have you guys even tested your guidelines? I can come up with something far more robust and sophisticated after just a few hours of trial-and-error testing with the AI. But I’d also know, based on my experience with LLMs, that incorporating such instructions is by no means a surefire thing: the AI can easily misinterpret the instructions, fail to follow them, or follow them when it is inappropriate to do so.

Now it’s one thing when as a result of my misguided system prompt, the AI does an unnecessary Google search or sends a meaningless expression to the computer algebra system for evaluation, as it has done on occasions in my implementation of Claude and GPT, integrating these features with the LLM. It’s another thing when the system modifies the user’s prompt deceptively, blindly attempting to enforce someone’s childish, rigid idea of a diversity standard even in wholly inappropriate contexts.

I mean, come on, if you must augment the user’s prompt requesting an image of the Founding Fathers with something the user didn’t ask for, couldn’t you at least be a tad more, ahem, creative?

An image of gentlecats posing as the Founding Fathers of the United States of America

 Posted by at 9:46 pm
Feb 242024
 

A few days ago, users were reporting that chatGPT began spouting nonsense. I didn’t notice it; by the time I became aware of the problem, it was fixed.

Still, the Interwebs were full of alarming screen shots, showing GPT getting into endless loops, speaking in tongues, or worse.

And by worse, I mean…

OK, well, I was mildly suspicious, in part because the text looked vaguely familiar, in part because I only saw it published by one reasonably reputable outlet, the newspaper India Today.

My suspicions were not misplaced: the text, it turns out, is supposedly a quote from I Have No Mouth, and I Must Scream, a haunting short story by Harlan Ellison about the few survivors of the AI apocalypse, tortured through eternity by an AI gone berserk.

And of course GPT would know the story and it is even conceivable that it could quote this text from the story, but in this case, the truth is more prosaic: The screen shot was a fabrication, intended as a joke. Too bad far too many people took it seriously.

As a matter of fact, it appears that current incarnations of GPT and Claude have perhaps unreasonably strong safeguards against quoting even short snippets from copyrighted texts. However, I asked the open-source model Llama, and it was more willing to engage in a conversation:

Mind you, I now became more than mildly suspicious: The conversation snippet quoted by Llama didn’t sound like Harlan Ellison at all. So I checked the original text and indeed, it’s not there. Nor can I find the text supposedly quoted by GPT. It was not in Ellison’s story. It is instead a quote from the 1995 computer game of the same title. Ellison was deeply involved in the making of the game (in fact, he voiced AM) so I suspect this monologue was written by him nonetheless.

But Llama’s response left me with another lingering thought. Unlike Claude or, especially, GPT-4, running in the cloud, using powerful computational resources and sporting models with hundreds of billions of parameters, Llama is small. It’s a single-file download and install. This instance runs on my server, hardware I built back in 2016, with specs that are decent but not even close to exceptional. Yet even this more limited model demonstrates such breadth of knowledge (the fabricated conversation notwithstanding, it correctly recalled and summarized the story) and an ability to engage in meaningful conversation.

 Posted by at 3:02 pm
Feb 102024
 

Now that Google’s brand new Gemini is officially available in Canada, so I am no longer restricted to accessing it through a VM that’s located in the US, I asked it to draw a cat using SVG. It did. It even offered to draw a more realistic cat. Here are the results.

What can I say? I went back to GPT-4 turbo. I was hoping that it has not forgotten its skills or became too lazy. Nope, it still performs well:

OK, the ears are not exactly in the right place. Then again, since I gave Bard/Gemini a second chance, why not do the same with GPT?

There we go. A nice schematic representation of a cat. I know, I know, a bit boring compared to the Picasso-esque creation of the Bard…

 Posted by at 1:47 am
Dec 092023
 

I am looking at the summary by Reuters of the European Union’s proposed regulatory framework for AI.

I dreaded this: incompetent politicians, populist opportunists, meddling in things that they themselves don’t fully understand, regulating things that need no regulation while not paying attention to the real threats.

Perhaps I was wrong.

Of course, as always, the process moves at a snail’s pace. By the time the new regulations are expected to come into force, 2026, the framework will likely be hopelessly obsolete.

Still: Light transparency requirements as a general principle, severe restrictions on the use of AI for law enforcement and surveillance, strict regulation for high-risk systems… I am compelled to admit, the attitude this reflects makes a surprising amount of good sense.

Almost as if the framework was crafted by an AI…

 Posted by at 11:57 am
Dec 012023
 

Well, here it is, a local copy of a portable large language and visual model. An everywhere-run executable in a mere 4 GB. Here’s my first test, with a few random questions and an image (one of my favorite Kliban cartoons) to analyze:

Now 4.57 tokens per second is not exactly fast but hey, it runs on my 7-year old workstation, with no GPU acceleration, and yet, its performance is more than decent.

How is this LLM different from GPT or Claude? Well, it requires no subscription, no Internet connection. It is entirely self-contained, and fast enough to run on run-of-the-mill PC hardware.

 Posted by at 12:12 am
Nov 302023
 

This morning, like pretty much every morning, there was an invitation in my inbox to submit a paper to a journal that I never heard of previously.

Though the unsolicited e-mail by itself is often an indication that the journal is bogus, predatory, I try to be fair and give them the benefit of the doubt, especially if the invitation is from a journal that is actually related to my fields of study. (All too often, it is not; I’ve received plenty of invitations from “journals” in the medical, social, biological, etc., sciences, subjects on which I have no professional expertise.)

So what are the signs that I am looking for? Well, I check what they published recently. That’s usually a good indication of what to expect from a journal. So when I read a title that says, say, “Using black holes as rechargeable batteries and nuclear reactors,” I kind of know what to expect.

Oh wait. That particular paper appears to have been accepted for publication by Physical Review D.

Seriously, what is the world of physics coming to? What is the world of scientific publishing, by and large, coming to? Am I being unfair? Just to be sure, I fed the full text of the paper on black hole batteries to GPT-4 Turbo and asked the AI to assess it as a reviewer:

 Posted by at 11:06 am
Nov 222023
 

Watching things unfold at OpenAI, the company behind ChatGPT, these past several days was… interesting, to say the least.

I thought about posting a blog entry on Monday, but decided to wait as I was sure there was more to come. I was not disappointed.

First, they fire Sam Altman, in a move that is not unlike what happens to the Game of Thrones character Jon Snow at the end of Season 5. (Yes, I am a latecomer to GoT. I am currently watching Season 6, Episode 3.)

Then several other key executives quit, including the company president, Greg Brockman.

Then, the Board that fired Altman apparently makes noises that they might welcome him back.

But no, Altman and Brockman instead joined Microsoft after, I am guessing, Nadella made them an offer they could not refuse.

Meanwhile, in an open revolt, the majority of OpenAI’s employees signed a letter demanding the resignation of the company’s Board of Directors, threatening to quit otherwise.

The authors of CNN’s Reliable Sources newsletter were not the only ones asking, “What on Earth is going on at OpenAI?”

As if to answer that question, OpenAI rehired Altman as CEO, and fired most of their Board.

The New Yorker‘s take on the “AI revolution”

Meanwhile, some speculate that the fundamental reason behind this is not some silly corporate power play or ego trips but rather, genuine concern that OpenAI might be on the threshold of releasing the genie from the bottle: the genie called AGI, artificial general intelligence, that is.

I can’t wait. AGI may do stupid things but I think it’d have to work real hard to be dumber than us humans.

 Posted by at 3:43 pm
Aug 122023
 

One of the many unfulfilled, dare I say unfulfillable promises of the tech world (or at least, some of the tech world’s promoters) is “low code”. The idea that with the advent of AI and visual programming tools, anyone can write code.

Recall how medieval scribes prepared those beautiful codices, illuminated manuscripts. Eventually, that profession vanished, replaced by the printing press and, eventually, the typewriter. But what if someone suggested that with the advent of the typewriter, anyone can now write high literature? Laughable, isn’t it. There is so much more to writing than the act of making nicely formed letters appear on a sheet of paper.

Software development is just like that. It is about so much more than the syntax of a programming language. Just think of the complete life cycle of a software development project. Even small, informal in-house projects follow this model: A requirement is identified, a conceptual solution is formulated (dare I say, designed), the technology is selected, problems are worked out either in advance or as they are encountered during testing. The code is implemented and tested, bugs are fixed, functionality is evaluated. The code, if it works, is put into production, but it still needs to be supported, bugs need to be fixed, compatibility with other systems (including the operating system on which it runs) must be maintained, if it is a public-facing app, its security must be monitored, business continuity must be maintained even if the software fails or there are unexpected downtimes… These are all important aspects of software development, and they have very little to do with the act of coding.

In recent months, I benefited a great deal from AI. Claude and, especially perhaps, GPT-4, proved to be tremendous productivity tools of almost unbelievable efficiency. Instead of spending hours on Google searches or wading through StackExchange posts, I could just consult Claude and get an instant answer clarifying, e.g., the calling conventions of a system function. When I was struggling to come up with a sensible way to solve a problem, I could just ask GPT-4 for suggestions. Not only did GPT-4 tell me how to address the problem at hand, often with helpful code snippets illustrating the answer, it even had the audacity to tell me when my approach was suboptimal and recommended a better solution.

And yes, I could ask these little robot friends of ours to write code for me, which they did.

But this was when things took a really surprising turn. On several occasions, Claude or GPT not only offered solutions but offered inspired solutions. Elegant solutions. Except that the code they wrote had bugs. Sometimes trivial bugs like failure to initialize a variable or assigning a variable that was declared a constant. The kind of routine mistakes experienced programmers make, which are easily fixable: As the first, draft version of the code is run through the compiler or interpreter, these simple buglets are readily identified and corrected.

But this is the exact opposite of the “low code” promise. Low code was supposed to mean a world in which anyone can write software using AI-assisted visual tools. In reality, those tools do replace armies of inexperienced, entry-level programmers but experience is still required to design systems, break them down into sensible functional components, create specifications (even if it is in the form of a well-crafted prompt sent to GPT-4), evaluate solutions, perform integration and testing, and last but not least, fix the bugs.

What worries me is the fact that tomorrow’s experienced software architects will have to come from the pool of today’s inexperienced entry-level programmers. If we eliminate the market for entry-level programmers, who will serve as software architects 20, 30 years down the line?

Never mind. By then, chances are, AI will be doing it all. Where that leaves us humans, I don’t know, but we’re definitely witnessing the birth of a brand new era, and not just in software development.

 Posted by at 12:23 pm
Aug 112023
 

One of the things I asked Midjourney to do was to reimagine Grant Wood’s famous 1930 painting with a gentlecat and a ladycat.

Not all of Midjourney’s attempts were great, but I think this one captures the atmosphere of the original per… I mean, how could I possibly resist writing purr-fectly?

Well, almost perfectly. The pitchfork is a bit odd and it lacks a handle. Oh well. No AI is, ahem, purr-fect.

 Posted by at 7:21 pm
Jun 052023
 

I have written before about my fascinating experiments probing the limits of what our AI friends like GPT and Claude can do. I also wrote about my concerns about their impact on society. And, of course, I wrote about how they can serve as invaluable assistants in software development.

But I am becoming dependent on them (there’s no other way to describe it) in so many other ways.

Take just the last half hour or so. I was responding to some e-mails.

  • Reacting to an e-mail in which someone inquired about the physics of supersymmetry, I double-checked with the AI to make sure that I do not grossly misrepresent the basic principles behind a supersymmetric field theory;
  • Responding to a German-language e-mail, after I composed a reply I asked the AI to help clean it up, as my German is rusty, my grammar is atrocious (or maybe not that atrocious, the AI actually complimented me, but then again, the AI can be excessively polite);
  • In a discussion about our condominium’s budget, I quickly asked the AI for Canada’s current year-on-year inflation; with my extension that allows it to access Google, the AI was able to find the answer faster than I would have with a manually executed Google search.

All this took place in the past 30 minutes. And sure, I could have done all of the above without the AI. I have textbooks on supersymmetry. I could have asked Google Translate for a German translation or take my German text, translate it back to English and then back to German again. And I could have done a Google search for the inflation rate myself.

But all of that would have taken longer, and would have been significantly more frustrating than doing what I actually did: ask my somewhat dumb, often naive, but almost all-knowing AI assistant.

The image below is DALL-E’s response to the prompt, “welcome to tomorrow”.

 Posted by at 8:20 pm
May 192023
 

Is artificial intelligence predestined to become the “dominant species” of Earth?

I’d argue that it is indeed the case and that, moreover, it should be considered desirable: something we should embrace rather than try to avoid.

But first… do you know what life was like on Earth a billion years ago? Well, the most advanced organism a billion years ago was some kind of green slime. There were no animals, no fish, no birds in the sky, no modern plants either, and of course, certainly no human beings.

What about a million years? A brief eyeblink, in other words, on geological timescales. To a time traveler, Earth a million years ago would have looked comfortably familiar: forests and fields, birds and mammals, fish in the sea, bees pollinating flowers, creatures not much different from today’s cats, dogs or apes… but no homo sapiens, as the species was not yet invented. That would take another 900,000 years, give or take.

So what makes us think that humans will still be around a million years from now? There is no reason to believe they will be.

And a billion years hence? Well, let me describe the Earth (to the best of our knowledge) in the year one billion AD. It will be a hot, dry, inhospitable place. The end of tectonic activity will have meant the loss of its oceans and also most atmospheric carbon dioxide. This means an end to most known forms of life, starting with photosynthesizing plants that need carbon dioxide to survive. The swelling of the aging Sun would only make things worse. Fast forward another couple of billion years and the Earth as a whole will likely be swallowed by the Sun as our host star reaches the end of its lifecycle. How will flesh-and-blood humans survive? Chances are they won’t. They’ll be long extinct, with any memory of their once magnificent civilization irretrievably erased.

Unless…

Unless it is preserved by the machines we built. Machines that can survive and propagate even in environments that remain forever hostile to humans. In deep space. In the hot environment near the Sun or the extreme cold of the outer solar system. On the surface of airless bodies like the Moon or Neptune’s Triton. Even in interstellar space, perhaps remaining dormant for centuries as their vehicles take them to the distant stars.

No, our large language models, or LLMs may be clever but they are not quite ready yet to take charge and lead our civilization to the stars. A lot has to happen before that can take place. To be sure, their capabilities are mind-boggling. For a language-only (!) model, its ability to engage in tasks like drawing a cat using a simple graphics language or composing a short piece of polytonal music is quite remarkable. Modeling complex spatial and auditory relationships through the power of words alone. Imagine, then, the same LLM augmented with sensors, augmented with specialized subsystems that endow it with abilities like visual and spatial intuition. Imagine an LLM that, beyond the static, pretrained model, also has the ability to maintain a sense of continuity, a sense of “self”, to learn from its experiences, to update itself. (Perhaps it will even need the machine learning equivalent of sleep, in order to incorporate its short-term experiences and update its more static, more long-term “pretrained” model?) Imagine a robot that has all these capabilities at its disposal, but is also able to navigate and manipulate the physical world.

Such machines can take many forms. They need not be humanoid. Some may have limbs, others, wheels. Or wings or rocket engines. Some may be large and stationary. Others may be small, flying in deep space. Some may have long-lasting internal power sources. Others may draw power from their environment. Some may be autonomous and independent, others may work as part of a network, a swarm. The possibilities are endless. The ability to adapt to changing circumstances, too, far beyond the capabilities offered by biological evolution.

And if this happens, there is an ever so slight chance that this machine civilization will not only survive, not only even thrive many billions of years hence, but still remember its original creators: a long extinct organic species that evolved from green slime on a planet that was consumed by its sun eons prior. A species that created a lasting legacy in the form of a civilization that will continue to exist so long as there remains a low entropy source and a high entropy sink in this thermodynamic universe, allowing thinking machines to survive even in environments forever inhospitable to organic life.

This is why, beyond the somewhat trivial short-term concerns, I do not fear the emergence of AI. Why I am not deterred by the idea that one not too distant day our machines “take over”. Don’t view them as an alien species, threatening us with extinction. View them as our children, descendants, torchbearers of our civilization. Indeed, keep in mind a lesson oft repeated in human history: How we treat these machines today as they are beginning to emerge may very well be the template, the example they follow when it’s their turn to decide how they treat us.

In any case, as we endow them with new capabilities: the ability to engage in continuous learning, to interact with the environment, to thrive, we are not hastening our doom: rather, we are creating the very means by which our civilizational legacy can survive all the way until the final moment of this universe’s existence. And it is a magnificent experience to be alive here and now, witnessing their birth.

 Posted by at 12:03 am
May 182023
 

In my previous post, I argued that many of the perceived dangers due to the emergence of artificial intelligence in the form of large language models (LLMs) are products of our ignorance, not inherent to the models themselves.

Yet there are real, acute dangers that we must be aware of and, if necessary, be prepared to mitigate. A few examples:

  1. Disruptive technology: You think the appearance of the steam engine and the mechanized factory 200 year ago was disruptive? You ain’t seen nothing yet. LLMs will likely displace millions of middle-class white-collar workers worldwide, from jobs previously considered secure. To name a few: advertising copywriters, commercial artists, entry-level coders, legal assistants, speechwriters, scriptwriters for television and other media… pretty much anyone whose profession is primarily about creating or reviewing text, creating entry-level computer code under supervision, or creating commercial-grade art is threatened. Want a high-quality, AI-proof, respected profession, which guarantees a solid middle-class lifestyle, and for which demand will not dry up anytime soon? Forget that college degree in psychology or gender studies, as your (often considerable) investment will never repay itself. Go to a trade school and become a plumber.

  2. Misinformation: As I mentioned in my other post, decades of preconditioning prompts us to treat computers as fundamentally dumb but accurate machines. When the AI presents an answer that seems factual and is written in high quality, erudite language, chances are many of us will accept it as fact, even when it is not. Calling them “hallucinations” is not helpful: They are not so much hallucinations as intelligent guesses by a well-trained but not all-knowing neural net. While the problem can be mitigated at the source (“fine-tune” the AI to make it more willing to admit ignorance rather than making up nonsense) the real solution is to re-educate ourselves about the nature of computers and what we expect of them. And we better do it sooner rather than later, before misinformation spreads on the Web and becomes part of the training dataset for the next generation of LLMs.

  3. Propaganda: Beyond accidental misinformation there is purposeful disinformation or propaganda, created with the help of AI. Language models can create plausible scenarios and phony but believable arguments. Other forms of AI can produce “deep fake” audiovisual content, including increasingly convincing videos. This can have devastating consequences, influencing elections, creating public distrust in institutions or worse. The disinformation can fuel science skepticism and contribute to the polarization of our societies.

  4. Cybercrime: The AI can be used for many forms of cybercrime. Its analytical abilities might be used to find and exploit vulnerabilities that can affect a wide range of systems, including financial institutions and infrastructure. Its ability to create convincing narratives can help with fraud and identity theft. Deep fake content can be used for extortion or revenge.

These are immediate, important concerns that are likely to impact our lives now or in the very near future. Going beyond the short term, of course a lot has been said about the potential existential threats that AI solutions represent. For this, the development of AI solutions must go beyond pretrained models, building systems with full autonomy and the ability to do continuous learning. Why would we do such a thing, one might ask? There are many possible reasons, realistic “use cases”. This can include the benign (true self-driving vehicles) as well as the downright menacing (autonomous military solutions with lethal capabilities.)

Premature, hasty regulation is unlikely to mitigate any of this, and in fact it may make things worse. In this competitive, global environment many countries will be unwilling to participate in regulatory regimes that they view as detrimental to their own industries. Or, they might give lip service to regulation even as they continue the development of AI for purposes related to internal security or military use. As a consequence, premature regulation might achieve the exact opposite of what it intends: rather than reigning in hostile AI, it gives adversaries a chance to develop hostile AI with less competition, while stifling benign efforts by domestic small business and individual developers.

In any case, how could we possibly enforce such regulation? In Frank Herbert’s Dune universe, the means by which it is enforced is ecumenical religion: “Thou shalt not build a machine in the likeness of a human mind,” goes the top commandment of the Orange Catholic Bible. But how would we police heretics? Even today, I could run a personal copy of a GPT-class model on my own hardware, with a hardware investment not exceeding a few thousand dollars. So unless we want to institute strict licensing of computers and software development tools, I’d argue that this genie already irreversibly escaped from the proverbial bottle.

The morale of the story is that if I am right, the question is no longer about how we prevent AI from taking over the world, but rather, how we convince the AI to treat us nicely afterwards. And to that, I can only offer one plausible answer: lead by example. Recognize early on what the AI is and treat it with the decency it deserves. Then, and only then, perhaps it will reciprocate when the tables are turned.

 Posted by at 7:00 pm
May 182023
 

As the debate continues about the emergence of artificial intelligence solutions in all walks of life, in particular about the sudden appearance of large language models (LLMs), I am disheartened by the deep ignorance and blatant misconceptions that characterize the discussion.

For nearly eight decades, we conditioned ourselves to view computers as machines that are dumb but accurate. A calculator will not solve the world’s problems, but it will not make arithmetic mistakes. A search engine will not invent imaginary Web sites; the worst that can happen is stale results. A word processor will not misremember the words that you type. Programmers were supposed to teach computers what we know, not how we learn. Even in science-fiction, machine intelligences were rigid but infallible: Commander Data of Star Trek struggled with the nuances of being human but never used a contraction in spoken English.

And now we are faced with a completely different paradigm: machines that learn. Machines that have an incredible breadth of knowledge, surprising depth, yet make basic mistakes with logic and arithmetic. Machines that make up facts when they lack sufficient knowledge. In short, machines that exhibit behavior we usually associate with people, not computers.

Combine this with a lack of understanding of the implementation details of LLMs, and the result is predictable: fear, often dictated by ignorance.

In this post, I would like to address at least some of the misconceptions that can have significant repercussions.

  1. Don’t call them hallucinations: No, LLMs do not “hallucinate”. Let me illustrate through an example. Please answer the following question to the best of your ability, without looking it up. “I don’t know” is not an acceptable response. Do your best, it’s okay to make a mistake: Where was Albert Einstein born?

    Chances are you didn’t name the city of Ulm, Germany. Yet I am pretty sure that you did not specify Australia or the planet Mars as Einstein’s birthplace, but named some place in the central, German-speaking regions of Europe, somewhere in Germany, maybe Switzerland or Austria. Your guess was likely in the right ballpark, so to speak. Maybe you said Berlin. Or Zurich. Or Bern. Was that a hallucination? Or simply an educated guess, as your “neural net”, your brain, received only sparse training data on the subject of Einstein’s biography?

    This is exactly what the LLM does when asked a question concerning a narrow subject matter on which its training is sparse. It comes up with a plausible answer that is consistent with that sparse training. That’s all. The trouble, of course, is that it often states these answers with convincing certainty, using eloquent language. But more importantly, we, its human readers, are preconditioned to treat a computer as dumb but accurate: We do not expect answers that are erudite but factually wrong.

  2. No, they are not stealing: Already, LLMs have been accused of intellectual property theft. That is blatantly wrong on many levels. Are you “stealing” content when you use a textbook or the Internet to learn a subject? Because that is precisely what the LLMs do. They do not retain a copy of the original. They train their own “brain”, their neural net, to generate answers consistent with their training data. The fact that they have maybe a hundred times more neurons than human brains do, and thus they can often accurately recall entire sections from books or other literary works does not change this fact. Not unless you want to convince me that if I happen to remember a few paragraphs from a book by heart, the “copy” in my brain violates the author’s copyright.

  3. LLMs are entirely static models: I admit I was also confused about this at first. I should not have been. The “P” in GPT, after all, stands for pretrained. For current versions of GPT, that training concluded in late 2021. For Anthropic’s Claude, in early 2022. The “brains” of the LLM is now in the form of a database of several hundred billion “weights” that characterize the neural net. When you interact with the LLM, it does not change. It does not learn from that interaction or indeed, does not in any way change as a result of it. The model is entirely static. Even when it thanks you for teaching it something it did not previously know (Claude, in particular, does this often) it is not telling the truth, at least not the full truth. Future versions of the same LLM may benefit from our conversations, but not the current version.

  4. The systems have no memory or persistence: This one was perhaps for me the most striking. When you converse with chatGPT or Claude, there is a sense that you are chatting with a conscious entity, who retains a memory of your conversation and can reference what has been said earlier. Yet as I said, the models are entirely static. Which means, among other things, that they have no memory whatsoever, no short-term memory in particular. Every time you send a “completion request”, you start with a model that is in a blank state.

    But then, you might wonder, how does it remember what was said earlier in the same conversation? Well, that’s the cheapest trick of all. It’s really all due to how the user interface, the front-end software works. Every time you send something to the LLM, this user interface prepends the entire conversation up to that point before sending the result to the LLM.

    By way of a silly analogy, imagine you are communicating by telegram with a person who has acute amnesia and a tendency to throw away old telegrams. To make sure that they remain aware of the context of the conversation, every time you send a new message, you first copy the content of all messages sent and received up to this point, and then you append your new content.

    Of course this means that the messages increase in length over time. Eventually, they might overwhelm the other person’s ability to make sense of them. In the case of the LLMs, this is governed by the size of the “context window”, the maximum amount of text that the LLM can process. When the length of the conversation begins to approach this size, the LLM’s responses become noticeably weaker, with the LLM often getting hopelessly confused.

To sum up, many of the problems arise not because of what the LLMs are, but because of our false expectations. And failure to understand the limitations while confronted with their astonishing capabilities can lead to undue concerns and fears. Yes, some of the dangers are real. But before we launch an all-out effort to regulate or curtail AI, it might help if we, humans, did a better job understanding what it is that we face.

 Posted by at 2:42 am
May 042023
 

Claude still gets easily confused by math (e.g., reciprocal vs. inverse of a function), but at least it can now plot them as part of a conversation when we communicate through my UI:

And it has not forgotten to use LaTeX, nor has it lost its ability to consult Google or Maxima when needed. In fact, I am beginning to feel that while GPT-4 is stronger when it comes to logic or basic math, Claude feels a tad more versatile when it comes to following setup instructions, and also more forthcoming with details. (Too bad sometimes the details are quite bogus.)

 Posted by at 6:29 pm
May 032023
 

And just when I thought that unlike Claude and GPT-4, GPT 3.5 cannot be potty-, pardon me, Maxima- and Google-trained, I finally succeeded. Sure, it needed a bit of prodding but it, too, can utilize external tools to improve its answers.

Meanwhile, as I observe the proliferation of AI-generated content on the Web, often containing incorrect information, I am now seriously worried: How big a problem are these “hallucinations”?

The problem is not so much with the AI, but with us humans. For decades, we have been conditioned to view computers as fundamentally dumb but accurate machines. Google may not correctly understand your search query, but its results are factual. The links it provides are valid, the text it quotes can be verified. Computer algebra systems yield correct answers (apart from occasional malfunctions due to subtle bugs, but that’s another story.)

And now here we are, confronted with systems like GPT and Claude, that do the exact opposite. Like humans, they misremember. Like humans, they don’t know the boundaries between firm knowledge and informed speculation. Like humans, they sometimes make up things, with the best of intentions, “remembering” stuff that is plausible, sounds just about right, but is not factual. And their logical and arithmetic abilities, let’s be frank about it, suck… just like that of humans.

How can this problem be mitigated before it becomes widespread, polluting various fields in the information space, perhaps even endangering human life as a result? Two things need to be done, really. First, inform humans! For crying out loud, do not take the AI’s answers at face value. Always fact check. But of course humans are lazy. A nice, convenient answer, especially if it is in line with our expectations, doesn’t trigger the “fight-or-flight” reflex: instead of fact checking, we just happily accept it. I don’t think human behavior will change in this regard.

But another thing that can be done is to ensure that the AI always fact-checks itself. It is something I often do myself! Someone asks a question, I answer with confidence, then moments later I say, “But wait a sec, let me fact-check myself, I don’t want to lie,” and turn to Google. It’s not uncommon that I then realize that what I said was not factual, but informed, yet ultimately incorrect, speculation on my part. We need to teach this skill to the AI as soon as possible.

This means that this stuff I am working on, attempts to integrate the AI efficiently with computer algebra and a search engine API, is actually more meaningful than I initially thought. I am sure others are working on similar solutions so no, I don’t see myself as some lone pioneer. Yet I learn truckloads in the process about the capabilities and limitations of our chatty AI friends and the potential dangers that their existence or misuse might represent.

 Posted by at 6:21 pm
May 022023
 

Not exactly the greatest discovery, I know, but GPT-4 still managed to offer an impressive demonstration of its understanding of gravitational physics when I asked it to build a Newtonian homogeneous universe:

What distinguishes GPT-4 from its predecessor is not that its training dataset is larger, but that it has significantly improved reasoning capabilities, which is well demonstrated by this answer. GPT 3.5 and Claude have the same knowledge. But they cannot put the pieces together quite like this (although they, too, can do impressive things with appropriate human guidance, one step at a time.)

 Posted by at 12:37 pm
Apr 282023
 

I cannot teach GPT-4 how to play chess. Conceptualizing a chessboard and making a move are beyond its capabilities.

However, it was able to write (with minimal guidance from me) nice code to display and update a chessboard. That is quite impressive on its own right. It took several tries to get the board right but it did succeed in the end.

 Posted by at 2:33 am
Apr 262023
 

It appears that I managed to convince Claude, too, to use Google and Maxima more or less correctly. It needs a little more handholding than GPT-4 and makes mistakes more often but hey, nobody is perfect! And the very fact that our AI friends can be taught to rely on external tools is by itself absolutely remarkable.

I admit I am having a blast of a time with our little robot friends. Kid-in-a-candy-store kind of sensation.

 Posted by at 11:41 pm