Tag: Large language models

The Bumblebee’s Flight: Generative AI And Language Production

Posted on by Jochen Szangolies

by Jochen Szangolies

Bumblebee quenching its thirst with a bit of sugar water. Unlike the bumblebee’s flight, its sheer adorableness is still a scientific mystery. Author’s photo.

The humble bumblebee is a mainstay character of a certain kind of motivational tale. One widely shared version of the story goes as follows:

The mathematical relationship between the wings and the weight of the bumblebee shows that flight is impossible for it. But the bumblebee does not know this, so it flies.

It’s a good line. It has a clear, uplifting message: no matter what other people—even supposed authorities—say, you can achieve your goals. What matters is what you believe (for if the bumblebee had believed the scientific verdict, it would surely have remained safely aground). It’s also a cautionary tale against scientific hubris: all its ambitions to unravel the mysteries of the cosmos thwarted by a tiny garden insect.

But—and I’m sorry to have to be the party pooper here—the problem is that it’s just not true. There is no great mystery about the bumblebee’s flight. But the nature of the error being made here is instructive (and more interesting than the myth).

In 1934, French entomologist Antoine Magnan, together with his assistant André Sainte-Lague, performed a short calculation for his book, Le Vol des Insectes (“The Flight of Insects”), which concluded that the lift produced by the bumblebee’s comparatively small wings could not suffice to keep its stocky body airborne—it would have to be about ten times lighter to work.

So what went wrong? Did they just bungle the calculation? No—their math checks out. The problem lies in assuming a particular model of flight, suitable for airplanes, but ill fitted to the case at hand. Read more »

Thursday, March 5, 2026

Turing And The Village Verificationist: The AGI That Wasn’t

Posted on by Jochen Szangolies

by Jochen Szangolies

Alan Turing in the 1930s. Image credit: Public Domain.

“The moment someone mentions the Turing test at you, assume they know nothing.” This somewhat grandstanding declaration comes from an AI in Tom Sweterlitsch’s recent novel The Gone World. Earlier, the AI had confided that its creator—of whom it is a digital replica—had considered it a ‘failure of consciousness’, a ‘simulation’, but not the real deal. The implication here is clear: passing the Turing test may be a necessary, but not a sufficient criterion for being more than a mere ‘simulation’. So what, exactly, is it that passing this test allows us to conclude?

A recent comment in Nature with the provocative title ‘Does AI already have human-level intelligence? The evidence is clear’ argues that what it calls ‘Turing’s vision’ has been realized: current LLMs do, indeed, pass the Turing test with flying colors. This is certainly a remarkable achievement: for the first time in history, we have non-human, indeed artificial entities that we can talk to, ask things, discuss with, bat ideas back and forth with, and so on, almost exactly as if we were talking to another human—one with a large percentage of the collective knowledge of humanity at their fingertips, no less. Indeed, just this morning a brief conversation with ChatGPT helped me sort out an issue with a piece of code I use to track appointments and tasks on a wall-mounted e-paper display that’d started misbehaving. But what, exactly, should be the takeaway from this?

According to the authors of the Nature comment, it is that ‘[g]eneral intelligence can indeed emerge from simple learning rules applied at scale to patterns latent in human language’ and that hence ‘[o]ur place in the world, and our understanding of mind, will not be the same’. This, if true, would be nothing short of revolutionary: we are, right now, sharing this planet with intelligences every bit our equal, yet products of code and mathematics, rather than of evolution and biology. But while I don’t exactly share the dismissive attitude of Sweterlitsch’s AI, I do believe there is a lot of middle ground hastily excluded here. Read more »

Monday, April 3, 2023

Open Letter Season: Large Language Models and the Perils of AI

Posted on by Fabio Tollon

by Fabio Tollon and Ann-Katrien Oimann

DALL·E 2 generated image

Getting a handle on the impacts of Large Language Models (LLMs) such as GPT-4 is difficult.  These LLMs have raised a variety of ethical and regulatory concerns: problems of bias in the data set, privacy concerns for the data that is trawled in order to create and train the model in the first place, the resources used to train the models, etc. These are well-worn issues, and have been discussed at great length, both by critics of these models and by those who have been developing them.

What makes the task of figuring out the impacts of these systems even more difficult is the hype that surrounds them. It is often difficult to sort fact from fiction, and if we don’t have a good idea of what these systems can and can’t do, then it becomes almost impossible to figure out how to use them responsibly. Importantly, in order to craft proper legislation at both national and international levels we need to be clear about the future harm these systems might cause and ground these harms in the actual potential that these systems have.

In the last few days this discourse has taken an interesting turn. The Future of Life Institute (FLI) published an open letter (which has been signed by thousands of people, including eminent AI researchers) calling for a 6-month moratorium on “Giant AI Experiments”. Specifically, the letter calls for “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”. Quite the suggestion, given the rapid progress of these systems.

A few days after the FLI letter, another Open Letter was published, this time by researchers in Belgium (Nathalie A. Smuha, Mieke De Ketelaere, Mark Coeckelbergh, Pierre Dewitte and Yves Poullet). In the Belgian letter, the authors call for greater attention to the risk of emotional manipulation that chatbots, such as GPT-4, present (here they reference the tragic chatbot-incited suicide of a Belgian man). In the letter the authors outline some specific harms these systems bring about, advocate for more educational initiatives (including awareness campaigns to better inform people of the risks), a broader public debate, and urgent stronger legislative actions. Read more »