Maybe the definition of what "intelligence" is could be sharpened by having a look at LLMs and "traditional" computer programs and asking what exactly the difference between the two is.
Almost all the traditional criteria of intelligence - reasoning, planning, decisionmaking, memory etc - are exhibited pretty trivially by standard computer programs. Nevertheless no one would think of them as "intelligent" in the sense that humans or animals are.
On the other hand, we now have LLMs, that sent the entire tech world into a multi-year frenzy, precisely because they appear to possess that human-like intelligence.
And that is even though they perform worse than classical programs in some of the "intelligence" measures: For the first time, we have to worry that a computer program is "bad at math". They cannot reflect on past decisions and are physically unable to store long-term memories. And yet, we're much more likely to believe that an LLM is "intelligent" than a classical program.
This makes me think that our formal decisions of "intelligence" (the ones that would also qualify fungal networks, swarms, cells, societies, etc) and what we intuitively look out for, are really two different things.
>This makes me think that our formal [definitions] of "intelligence" […] and what we intuitively look out for, are really two different things.
Just two? You can name so many more terms in this concept cloud, e.g.: personhood, moral agency, consciousness, self-awareness, processing power, wit, autonomy, feeling-and-experiencing capacity, and so on… We don’t seem to agree on what’s separate from what, and yes, it would be useful.
The only proof of intelligence that humans accept are where they are utterly dominated by the more intelligent “thing.”
In my experience it comes down to speed of processing and response
Nobody views trees as intelligent because despite having extremely complex interdependencies with their ecology, including fungus and all kinds of other organisms, they don’t move quickly or appear to respond to input (even though they do, just slowly).
Meanwhile the mantis shrimp because of it speed, superhuman vision and flexibility is considered extremely intelligent.
The only thing that humans will accept as more intelligent than them is (something) that they cannot control and can dominate them.
This is why AI is “the things we haven’t done yet” - once a technology is pervasive and integrated it is “just computing.”
Because we take freely from trees. They must be stupid or something because they give things freely. It's kind of why many employers view labor as stupid. They give their labor for very little. Humans dabble in arrogance.
The article mentions intelligence a lot actually. It's also conjecture that consciousness and intelligence are unrelated. Qualia could be functional, and emerge naturally from the development of ever more sophisticated intelligence.
The difference between traditional software and LLMs is the generality of their intelligence, and there are formal definitions of general intelligence such as https://en.m.wikipedia.org/wiki/AIXI
AIXI is a definition of the optimal agent and is hence uncomputable but LLMs are approximations which are approaching AIXI.
I recommend Fridman's interview with Hutter.
intelligence is a property of the species and the property of the individual, even unintelligent individuals (except for very pronounced extremes) still have the species property of intelligence.
The species property of intelligence encompasses stupidity.
I wouldn't say that's true at all for traditional computer programs. They're doing explicitly what they are designed to do, there is no adaptation/learning.
Do polymorphic computer viruses adapt? Border Gateway Protocol? Exponential backoff? Autocomplete? And that's aside from any algorithmic search results or "social" feeds, which are nothing but that.
Any matrix multiplication can be trivially unwrapped into a bunch of for loops with basic arithmetic. In fact, that is exactly what any GPU kernel ultimately does in some sense.
What differentiates a “traditional computer program” from a single matmul? How about a bunch of matmuls? How about a bunch of matmuls with some non-linear arithmetic thrown in between them? And if we are adding in sampling from a distribution but set temperature to zero and use random seeds so that it is fully deterministic?
What you seem to be saying is that the machinary your traditional computer program executes on is capable of executing intelligence.
What I said is thst a traditional computer program (say Excel) is not intelligent in itself, as it cannot, itself, without further input from a human, adapt.
> What I said is thst a traditional computer program (say Excel) is not intelligent in itself, as it cannot, itself, without further input from a human, adapt.
Excel is as capable of adapting to new input as any AI, and the input need not come from a human specifically: the act of adapting to input is an automatic consequence of being able to perform linear regression.
In fact, this is the real value of a spreadsheet over a piece of gridded paper where the sums are done by hand.
If "adapting to input" is your test for intelligence, however, it is a test which a thermostat will pass.
>Excel is as capable of adapting to new input as any AI, and the input need not come from a human specifically: the act of adapting to input is an automatic consequence of being able to perform linear regression.
I said 'sensory input', as in, "any primary, real world data". Can I just download excel, and without feeding it machine learning algorithms, feed it a bunch of videos or text and have it learn history?
>If "adapting to input" is your test for intelligence, however, it is a test which a thermostat will pass.
Not just adapting to specific input the system is designed for, adapt it to ANY input. A thermostats function will never change on its own without modification, just like excels, or whatever other traditional program.
How do you define adaptation and learning? What about say, an autoscaler which is programmed to just track the the load for every hour over the last week, and use the average of the last 7 days at 8am to pre-emptively auto-scale? Is that learning and adapting?
Alternatively, neural networks are also just doing explicitly what they are designed to do… sure there is a larger computational graph with lots of operations, but it’s all deterministic… backprop is not really much different on a procedural level than the simple fitting algorithm that I outlined above, in as much as it is just a specific well-defined algorithm or sequence of steps designed to compute some parameters from data.
What if we define adaptation and learning as the ability to concentrate. Our single cell ancestors would have to concentrate and deliberately store the first memory. Otherwise it would have just taken in the world with its sensors but never do anything with it.
Adapting and learning means it chose to concentrate on packing the world into retrievable storage.
When do we not adapt and learn? When we ignore our inputs and do nothing with it (don’t store it, don’t retrieve it).
In the example you gave, those classical programs cannot concentrate, it’s a one and done.
> When do we not adapt and learn? When we ignore our inputs and do nothing with it (don’t store it, don’t retrieve it).
In the example I mentioned, the program clearly is taking inputs (load), storing them, querying for previous values that satisfy certain conditions (the value at a certain timestamp for each of the last 7 days), running computation (computing a mean), and operationalizing the result (pre-emptively scaling) to achieve a goal (avoiding having insufficient capacity) that affects the world (whoever interacting with the system will have different experiences because of the actions taken by the system). That seems like it satisfies your criterion to me.
C’mon now, you and I know both know your program is discontinuous. A few null or unknown inputs that you didn’t even consider will break it. You’ll have to keep going in there and adding more if/else statements. Your program couldn’t survive a 100 years without you debugging it.
Not sure I follow your usage of discontinuous: clearly the load inputs are continuous variables (albeit discretely sampled in time and magnitude, but I don’t see the significance of either of those sampling frequencies, each of which can be chosen to be effectively arbitrarily small). Also don’t understand the relevance of continuity at all since you didn’t reference it in the post I replied to.
> A few null or unknown inputs that you didn’t even consider will break it.
Null handling should be assumed to be a trivial problem here (ie just use the last 7 valid values for the time step of interest), and I’m not sure what “unknown” values would be here, can you give an example? I think it’s safe to assume that the inputs have already been normalized to some meaningful scale as an implementation detail of the load sensors. Even if the telemetry scheme changes because the instrumentation changes or the infrastructure changes, so long as the instrumentation produces values at the same scale and the actuators can still respond to the outputs, the core kernel of sense/compute/actuate can still be left unchanged.
I’m assuming that you are citing the failure to respond to null or unknown conditions as a lack of adaptation capabilities, especially as you are referencing its inability to last 100 years - though good luck finding any piece of software that can last 100 years, especially more robust machine learning algorithms that have to deal with problems like data distribution drift, hardware changes, etc etc - same goes with wetware (besides turtles?). I’m strawmanning you a little bit on the appeal to time though, since I assume your main point was to just emphasize that “it won’t deal with changing conditions/new scenarios well” which I think is somewhat fair. On the other hand, I think it is safe to say that what I described does at least respond to the world and in turn affect it in a kind of cybernetic feedback loop, which includes adapting to the shifting conditions to meet some desired state. So maybe it would be helpful if you could define more precisely what you mean by adaptation? Not trying to be snarky, just trying to genuinely push you to try to make a clear definition of what you mean by adaptation.
Also don’t understand the relevance of continuity at all since you didn’t reference it in the post I replied to.
A calculator is discontinuous graph/program because there is no point to plot when you divide by 0 (undefined). This is true for your classical program. Over time it won't know how to adapt its f(x) function to handle something as crazy as dividing by zero (it can never do it, but unless you put in an error output for that input, the program will never do so on its own).
The belief is these AI programs will be able to make that adaptation. All the changes you bring up that your program can apparently handle are still under your conceptual control (you can map in your mind if this happens, then that happens). You can prove this to yourself when you read your own statement "inputs have already been normalized" - sure, in a world you control.
I'm suggesting a world where your telemetry monitoring function can change into a Reddit function if need be, so yeah, kinda batshit. Why would it need to do that? Remember, we can't imagine why, that's the whole point.
That's how a program could possibly live a hundred years in a world that is constantly changing. Your program can only exist in a very static and predefined world.
There are some that will tell you that you are an ever-changing function. I try to stay out of that simulation :)
A structured program analyzing data as a graph, and optimizing access, is interacting with phenomenon, updating its working knowledge of the phenomenon, and can produce results that are very non-intuitive.
Likewise, any symbolic mathematical system that accumulates theorems that speed up future work as it solves current tasks, seems like a high intelligence type of activity.
Deep learning is “just” structured arithmetic.
I think different kinds of intelligence can look quite different, and they will all be “structured” or “tropic” at their implementation levels.
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Stepping away from the means, I see at least four “intelligence” dimensions:
1. Span: The span of novel situations for which it can create successful responses.
2. Efficiency: The efficiency of problem solving.
I.e. When vast lookup tables, exhaustive combination searches, and indiscriminate logging of past experience can be matched instead by more efficient Boolean logic, arithmetic, pattern recognition and logging, we consider the latter more intelligent.
3. Abstraction: The degree to which solving previous different novel situations improves the success or efficiency in solving new problems. I.e. generalizable, composable learning.
4. Social education: Ability to communicate and absorb learned information from other entities.
Plants, and I expect all surviving life forms, are very high in intelligence types 1 and 2.
Adaptive nervous systems and especially brains excel at 3.
Many animals, but most profoundly humans (whose languages for communicating are themselves actively adapted for compounding effects), excel at 4.
Today’s humans are effectively more intelligent than 10,000 year ago humans, not because of 1-3, but because of 4. Learning as a child to read/write, do arithmetic, understand zero and negative numbers, and countless other information processing activities and patterns, from others, profoundly impacts our intellectual abilities.
Deep learning, as with the human species, non-trivially spans, and continues to improve, on all 4 types of intelligence.
Almost all the traditional criteria of intelligence - reasoning, planning, decisionmaking, memory etc - are exhibited pretty trivially by standard computer programs. Nevertheless no one would think of them as "intelligent" in the sense that humans or animals are.
On the other hand, we now have LLMs, that sent the entire tech world into a multi-year frenzy, precisely because they appear to possess that human-like intelligence.
And that is even though they perform worse than classical programs in some of the "intelligence" measures: For the first time, we have to worry that a computer program is "bad at math". They cannot reflect on past decisions and are physically unable to store long-term memories. And yet, we're much more likely to believe that an LLM is "intelligent" than a classical program.
This makes me think that our formal decisions of "intelligence" (the ones that would also qualify fungal networks, swarms, cells, societies, etc) and what we intuitively look out for, are really two different things.