Tag: #FOWC

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B is for Big

Brachiosaurus head at the Am Museum of Natural History. Photo from Am Museum.

Holy cow! Or, maybe I should say Holy Brachiosaurus … or Holy Argentinasaurus…. or Holy Breviparopus….

These dudes got big!

In today’s dinosaur B-themed post, I’m going to share a little bit (and it’s already three days late and I haven’t much time, so not too much) on what, how, and why about these big-a@@ed creatures.

How Big Was Big?

Think almost half the length of a football field (American or European). The longest and largest dinosaur where much of the skeleton was discovered is either Argentinosaurus huinculensis or Patagotitan mayorum. Both of them were in a group labeled “Titanosaurs” and both were identified from bones discovered in — yep — Argentina. They ranged in length from 30 to 40 meters… about 45 yards and may have weighed around 80 tons.

As soon as you get measurements, of course, you start wondering, well, how much is that? Football fields are handy just because many people have seen them. For reference, a 757 aircraft weighs around 100 tons and is about 40 m, so visualize a living, stalking creature that looks like a giant airplane. Walking around on a football field, waving its intensely long neck around and wondering where all the veggies went. Forty meters is also the world record (officially Guinness WR) for flinging a Frisbee, so imagine throwing a Frisbee as long as a dinosaur!

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A is for Antorbital Fenestra

Albuquerque museum, photo by kajmeister.

Dinosaurs had an extra hole in their head.

As we start this journey of 26 posts all about dinosaurs, you may have noticed that A does not start with a kind of dinosaur. This is not going to be about 26 different dinosaurs, although I promise I will throw in a few. So A is not for Apatosaurus, Ankylosaurus, Albertosaurus, or even Archosaur. This A is about how dinosaurs were grouped and identified as dinosaurs.

So one thing to know, even before I say more about what the ant-orbit-a-whatchamacallit, is that these posts are going to wrestle with questions about dinosaurs, such as:

  • What made a dinosaur a dinosaur?
  • Where did the dinosaurs come from? And where did they go?
  • What did they look like?
  • How did they behave?
  • And, most of all, how do we know?

In other words, I’m going to talk about the things that dinosaurs did. Their habits. Their loves and losses.. well, maybe not that. But the dinosaur ouvre, so to speak (i.e. their “body of work.” Body get it?) Since they lived 200 million years ago, it gets a little tricky trying to guess. But you would be surprised at what those clever scientists who study bones can figure out, just from the bones.

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The Wild West of Chat/AI

Image created by the AI Art Generator. See explanation below.

Rampant fear or unquestioning enthusiasm. These seem to be the two fundamental attitudes people have about Generative AI models. Programs like Chat GPT, Bloom, and Replika are demonstrating the power, potential, and problems associated with having technology that seems to talk. Aside from getting some definitional clarity around what Chat/AI is and is not, I present here a couple of Use Cases. They may inspire in you, as they have in me, both fear and enthusiasm.

Let’s Call It What It Is: Simulated Talking

A few definitions might be in order. First of all, Artificial Intelligence (AI) is a large field, so let’s be clear that AI and Generative AI are different things. AI models are ones where you feed in data to get recommendations and predictions. We use simple models and algorithms ourselves, for instance, checking the weather by looking outside; that’s an unsophisticated algorithm that isn’t terribly predictive. In the past, computer models were similarly limited. They broke down fairly quickly if the variables got complicated or the model tried to look too far in the future. I can guess the weather in an hour, but what about next Sunday at 11 am, when I want to play pickleball? AI means that the models are big enough and full of enough data that the predictive accuracy is far beyond what computers “used” to predict. A self-driving car might be an example of AI. It’s not creating text or art, but it needs a huge influx of data and sophisticated decision-making capabilities in order to navigate a very complex environment.

Generative AI, which I’ll also call Chat/AI here, is a model that can create “new” content as part of its predictive output. You feed it tons of examples, and it creates something “new” or seems new, based on previous human-created patterns that made sense. The following example came from Prof. Louis Hyman, who will be discussed below. Suppose you asked Chat/AI to fill in the cat sat on the ____. The model might suggest floor, chair, lap, or mat. But mat might be the highest likelihood, perhaps 50%, so the generative AI picks that word.

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