I of course mean we're using these LLMs for a lot of tasks that they're inappropriate for, and a clever manually coded algorithm could do better and much more efficiently.
Sure, but how long would it take to implement this algorithm, and would that be worth it for one-off cases?
Just today I asked Claude to create a jq query that looks for objects with a certain value for one field, but which lack a certain other field. I could have spent a long time trying to make sense of jq's man page, but instead I spent 30 seconds writing a short description of what I'm looking for in natural language, and the AI returned the correct jq invocation within seconds.
Writing a python script, because it can't do math or any form of more complex reasoning is not what I would call "own algorithm". It's at most application of existing ones/calling APIs.
The superset of the LLM knowledge pool is human knowledge. They can't go beyond the boundaries of their training set.
I'll not go into how humans have other processes which can alter their and collective human knowledge, but the rabbit hole starts with "emotions, opposable thumbs, language, communication and other senses".
How so? I'd imagine a robot connected to the data center embodying its mind, connected via low-latency links, would have to walk pretty far to get into trouble when it comes to interacting with the environment.
The speed of light is about three orders of magnitude faster than the speed of signal propagation in biological neurons, after all.
Recent research from NVIDIA suggests such an efficiency gain is quite possible in the physical realm as well. They trained a tiny model to control the full body of a robot via simulations.
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"We trained a 1.5M-parameter neural network to control the body of a humanoid robot. It takes a lot of subconscious processing for us humans to walk, maintain balance, and maneuver our arms and legs into desired positions. We capture this “subconsciousness” in HOVER, a single model that learns how to coordinate the motors of a humanoid robot to support locomotion and manipulation."
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"HOVER supports any humanoid that can be simulated in Isaac. Bring your own robot, and watch it come to life!"
More here: https://x.com/DrJimFan/status/1851643431803830551
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This demonstrates that with proper training, small models can perform at a high level in both cognitive and physical domains.
Hmm .. my intuition is that humans' capabilities are gained during early childhood (walking, running, speaking .. etc) ... what are examples of capabilities pretrained by evolution, and how does this work?
A more high level example, sea sickness is a evolutionary pre-learned thing, your body things it's poisoned and it automatically wants to empty your stomach.
Maybe evolution could be better thought of as neural architecture search combined with some pretraining. Evidence suggests we are prebuilt with "core knowledge" by the time we're born [1].
See: Summary of cool research gained from clever & benign experiments with babies here:
[1] Core knowledge. Elizabeth S. Spelke and Katherine D. Kinzler. https://www.harvardlds.org/wp-content/uploads/2017/01/Spelke...
Chimpanzees score pretty high on many tests of intelligence, especially short term working memory. But they can't really learn language: they lack the specialised hardware more than the general intelligence.
But there are plenty of non-learned control/movement/sensing in utero that are "pretrained".
This is a great milestone, but OpenAI will not be successful charging 10x the cost of a human to perform a task.
Obviously the drop in cost for capability in the last 2 years is big, but I'd wager it's closer to 10x than 100x.
True, but they might be successful charging 20x for 2x the skill of a human.
If it can be spun up with Terraform, I bet you they could.
Right now when I ask an LLM… I have to sit there and verify everything. It may have done some helpful reasoning for me but the whole point of me asking someone else (or something else) was to do nothing at all…
I’m not sure you can reliably fulfill the first scenario without achieving AGI. Maybe you can, but we are not at that point yet so we don’t know yet.
The difference, to me, is that humans seem to be good at canceling each other's mistakes when put in a proper environment.
Finding reliable honest humans is a problem governments have struggled with for over a hundred years. If you have cracked this problem at scale you really need to write it up! There are a lot of people who would be extremely interested in a solution here.
But if it's not enough then maybe it might come as a second-order effect (e.g. reasoning machines having to bootstrap an AGI so then you can have a Waymo taxi driver who is also a Fields medalist)
Broadly speaking you can think that the mental reduces to the physical (physicalism), that the physical reduces to the mental (idealism), both reduce to some other third thing (neutral monism) or that neither reduces to the other (dualism). There are many arguments for dualism but I’ve never heard a philosopher appeal to “magic spirits” in order to do so.
Here’s an overview: https://plato.stanford.edu/entries/dualism/
(In fact, the very idea of "computable functions" was invented to narrow down the space of "all things" to something much smaller, tighter and manageable. And now we've come full circle and apparently everything in the universe is a computable function? Well, if all you have is a hammer, I guess everything must necessarily look like a nail.)
So yeah, the o3 result is impressive but if the difference between o3 and the previous state of art is more compute to do a much longer CoT/evaluation loop, I am not so impressed. Reminder that these problems are solved by humans in seconds, ARC-AGI is supposed to be easy.
It is also entirely possible to learn a skill without prior experience. That's how it(whatever skill) was first done
This is the way I think about it.
I = E / K
where I is the intelligence of the system, E is the effectiveness of the system, and K is the prior knowledge.
For example, a math problem is given to two students, each solving the problem with the same effectiveness (both get the correct answer in the same amount of time). However, student A happens to have more prior knowledge of math than student B. In this case, the intelligence of B is greater than the intelligence of A, even though they have the same effectiveness. B was able to "figure out" the math, without using any of the "tricks" that A already knew.
Now back to your question of whether or not prior knowledge is required. As K approaches 0, intelligence approaches infinity. But when K=0, intelligence is undefined. Tada! I think that answers your question.
Most LLM benchmarks simply measure effectiveness, not intelligence. I conceptualize LLMs as a person with a photographic memory and a low IQ of 85, who was given 100 billion years to learn everything humans have ever created.
IK = E
low intelligence * vast knowledge = reasonable effectiveness
