Rodney Brooks on GPT-4

ieee has been on a copium suspension trip for a while now.

It's interesting. The answer shows some reasoning about weight and pressure but not curvature. In response to

    Here we have a book, an egg, a laptop, a bottle and a nail. Please tell me how to stack them onto each other in a stable manner.

GPT-4 gives me:

    Arranging objects in a stable stack depends on their size, shape, and weight. In general, you want to place the largest, heaviest and most stable objects at the bottom and then move to the smallest, lightest, and least stable at the top. Here's one way to stack the objects you listed:

        Laptop: Start with the laptop at the bottom. It's likely the largest object and it has a flat, stable surface.

        Book: Next, put the book on top of the laptop. Books are generally flat and heavy, which makes them a good choice for the base of your stack.

        Bottle: The bottle goes next, as it should have a flat bottom to sit securely on the book. Just make sure it's closed tight and won't roll away!

        Egg: The egg is delicate, so it should be towards the top to avoid pressure. However, it's not flat, so it should not be at the very top.

        Nail: Finally, the nail goes on top. It's the smallest and lightest, so it won't add much pressure to the egg.

    Remember that this is just one possible way to stack these items and it could depend on the specific characteristics of the items you have. For example, if the bottle is much larger and heavier than the book or laptop, you'd need to adjust accordingly. Safety should always be your first priority when stacking items, especially if they are delicate or valuable.

The "make sure it's closed tight and won't roll away" comment makes no sense obviously. Most people would place the bottle standing on its end so neither of those is a concern. The response also doesn't show an understanding of the fact that the nail won't sit on top of the egg although it's interestingly concerned with pressure breaking the egg.

Not the poster, but for me it comes down to a mix of clarity and permanence.

I teach both verbally (interactive question/answer) and I've also written text books.

Verbally by language is "loose". I'll say class when I mean object, unicode when I mean utf-8 and so on. Sentences are not all well formed, and sometimes change mid-thought. It's very "real time"

Writing is a lot more deliberate. I have to be sure of each fact I state. I often re-test things I'm only 95% sure about. I edit, restructure, remove, add, until I'm happy.

Of course all communication falls on a spectrum. Think phone call at one end, text book on the other. When I do a verbal lecture I'm usually careful with my speech, and when I post on hacker-news less rigorous.

Language covers all of it. Text skews to the more deliberate side. Cunningly the language models are trained using (mostly) text, not speech. That will have an impact on them.

> In my experience, people are far more capable and competent when they feel comfortable and are interested in the task.

That's definitely true, but in my experience people have limits: simple biological ones. Repetitive tasks make practically all humans bored, for example.

The fact that AIs never get sleepy, distracted, or bored already makes them super-human in at least that one aspect. That they have essentially perfect English comprehension, and hence aren't phased by the use of jargon or technical language, puts them head-and-shoulders above most humans.

The frustrations I'm venting aren't some rare thing. I'm working on a technical team where the project manager doesn't understand what the team members are saying. This is not just a matter of syntax, or jargon. They just don't understand the concepts. This is so common in the wider industry that I'm pleasantly surprised, shocked even, when I come across a PM that can ask useful questions instead of needing endless corrections along the lines of: "It's spelled SQL, not Sequel." I've never met a PM that could do simple arithmetic, like "10 TB at 100 MB/s will take over a day to copy, we should plan for that!". Never.

I've tested Chat GPT 4 on both language and concepts that I've seen trip up PMs, and it understood "well enough" every time.

For example, GPT 4: The sentence "We deployed sequel server successfully last night" seems incorrect due to the incorrect naming of a product. "Sequel server" should actually be "SQL Server", a popular relational database management system (RDBMS) developed by Microsoft. Therefore, the corrected sentence should be: "We deployed SQL Server successfully last night."

PS: If you tell GPT 4 to pretend it is a technical project manager and instruct it to ask followup questions, it is noticeably better at this than any PM I have worked with in the last few years.

from a linguistic standpoint a text is a whole lot more than language: it is an externalisation of thought that is fixed onto a medium using writing utensils and most of all, cultural norms in the form of a wild variety of different genres and forms of text, ranging from something like a stream of consciousness to something like a speech act. furthermore, text can be conceptually written or spoken and with the internet we got an explosion of text that is conceptually spoken. those are the things OP might be refering to in regards to the "entropy" that encodes much more than just the tokens themselves.