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This general comment of "It can't handle things outside it's training database" is very common and also entirely wrong IMO. Take the following question which I
by Closi 3y ago
This general comment of "It can't handle things outside it's training database" is very common and also entirely wrong IMO.
Take the following question which I just wrote and I'm fairly certain hasn't been asked before:
> How many giraffes would it take to fly a plane in an imaginary world, assuming that the plane was designed to be flown by a giraffe. Assume that giraffe brains can be attached together to create a human-like intelligence assuming enough giraffes are connected, and assume that there are 500 passengers that will need to be served food (e.g. as per airline staff). Assume that the full airport is also staffed by giraffes.
> Use logic and reasoning to estimate the number of giraffes that would be required, and detail specific practical considerations for the design of the aircraft.
That's not been asked before - I just made it up. GPT-4 gives as good as an answer to this as I can come up with:
> This is an interesting thought experiment. It's important to note that it's highly speculative and relies on numerous assumptions about the cognitive capabilities of giraffes, their physical abilities, and the mechanics of designing and operating an aircraft specifically for them. However, assuming we are in this hypothetical scenario, let's break down some of the elements:
> Flying the Plane: Normally, a commercial aircraft is operated by two pilots. The main brain functions involved in flying a plane include spatial awareness, understanding complex systems, decision making, problem-solving, and communication. The human brain has approximately 86 billion neurons. In contrast, a giraffe's brain has approximately 1.2 billion neurons. Therefore, to achieve the complexity and cognitive capacity of one human pilot, approximately 72 giraffes would be needed (86 divided by 1.2). Since there are typically two pilots on a plane, this gives us a rough estimate of 144 giraffes.
> Cabin Crew: Flight attendants serve multiple purposes including ensuring the safety of passengers and providing service such as meals. The average ratio of flight attendants to passengers on long-haul flights is around 1:50. Therefore, for 500 passengers, we would require about 10 human-equivalent flight attendants. Assuming the same ratio of giraffe brains to human brains, this would mean we need about 720 giraffes (72 giraffes equivalent to 1 human multiplied by 10).
> Airport Staff: This could include a wide range of roles such as baggage handlers, check-in staff, security personnel, air traffic controllers, etc. It's difficult to estimate the exact number as it can vary widely depending on the size and busyness of the airport. However, as an approximation, let's say a small airport requires a staff of 100 human equivalents. This would mean approximately 7200 giraffes.
> Design of the Aircraft and Airport: The design of the aircraft and airport would need to be significantly altered to accommodate giraffes, considering their size, physical abilities, and requirements. The size of the cockpit and cabin would need to increase substantially to allow for multiple giraffes. The controls would also need to be adapted to be usable by giraffes, likely involving some sort of body harness or direct neural interface given giraffes lack opposable thumbs or similar gripping ability. The height of the plane and airport infrastructure would also need to be increased due to the giraffes' tall stature. These changes would certainly increase the weight and fuel requirements of the plane.
> Adding the numbers up, we get a rough total of about 8064 giraffes to run this operation (144 for pilots, 720 for cabin crew, and 7200 for airport staff). Please note, however, that these are extremely rough estimates and in reality, the number could be significantly different based on the many factors and variables involved in such a hypothetical situation.
> In this scenario, the number of giraffes needed is estimated based on an equivalence of the number of neurons in giraffe brains to those in human brains. However, it's critical to remember that this is a simplified way of comparing cognitive abilities. In reality, intelligence and cognitive function are not solely determined by the number of neurons, and they certainly don't scale linearly. The organization and connections within the brain, the type of neurons, and the role of other brain cells are also critical. This serves as an imaginative exploration rather than a scientifically or practically accurate prediction.
I mean, it's not perfect but it's clearly formulating something there.
- flimsypremise 3y agoI'm going to bet that if you do some searching, you can turn up some text on the internet where someone was asking "how many of x animal would it take to y human task", and answered the question using a neuron comparison. It's not inventing this approach from scratch (because it can't). Every word in that answer is there because statistically, based on the text in its database, it was the most likely word to occur in that context. Hell look at the way it chose to describe flight attendants, you can see it grabbed that directly from some snippet somewhere. I think a lot of the misconceptions about these LLMs (which I actually think are pretty cool and useful) stem from not really understanding what they are doing and how they arrive at their answers. This is pretty well illustrated by the fact that ChatpGTP only does well on tests where the content is well-represented in its training dataset, and terrible on tests on otherwise. But you have to keep in mind that the training dataset is enormous, and likely includes every random forum or reddit post ever made, so you shouldn't be surprised that it can formulate an answer like this.
- Closi 3y ago> I think a lot of the misconceptions about these LLMs (which I actually think are pretty cool and useful) stem from not really understanding what they are doing and how they arrive at their answers. I think the misconception is actually the opposite - focussing too much on the method rather than the output. Bread is not just yeasty-wheat - if you mix bread and wheat together and apply some heat you get something a little surprising. I think the same is true for LLM's - in the task of training a next-word guesser there are emergent capabilities which end up going beyond what people expect when looking at the method. Yes, it is designed to statistically guess the next word, but in order to do that the LLM has (surprisingly) gained internal representations of things like 'what is a giraffe'. In order to do the task of 'what is the next word' most effectively, it has had to understand/learn about about the world. Can you provide an example where you think GPT-4 performs badly that proves your point? (i.e. something not in the training corpus that I could probably answer but a leading LLM could not)
- svieira 3y agoJesting Pilot's question is one, for a start. "Quid est veritas" can only be answered by one who sees.