This article does a great job summarizing many things I felt for a long time. I studied neurobiology in college, and became obsessed with ANNs in the late '80s and '90s. It was always clear to me that all ANN efforts were strongly constrained by the use of gradient descent, which has no correlate in biological systems. Even Geoffrey Hinton's forward forward algorithm uses gradients at the local level, and a strictly layered architecture.
I strongly suspect that other learning algorithms are possible. That non-layered architectures with complex node dynamics and interrelationships can provide a qualitatively different behavior than today's increasingly byzantine layered monstrosities.
The field of ANN went up a path that was first indicated by that early simple neuronal "model" (which we understand now to be so simplistic as to stretch the boundaries of the term). It has ascended very far over the decades, but perhaps the peak it aims for is on an entirety different mountain, reached via a path that diverged at the very beginning.
My biggest frustration in AI is a misalignment of incentives. I am personally interested in the science of AI, which means posing a meaningful scientific question on the nature of intelligence. My own which I adopted from researcher Pei Wang at Temple University is: how can an agent arbitrarily adapt to a new environment under insufficient knowledge and resources (I abbreviate AIKR)? Useful evaluation methodology should follow. See https://cis.temple.edu/~pwang/GTI-book/
In the pursuit of AI, the first major difference in motivation is whether you are doing this for science or engineering. Nothing wrong with engineering, but even before ChatGPT research has mostly shifted towards engineering. Of course now it’s all shallow engineering when money is on the line. Caveat: you need science to really push the boundaries of engineering, no transistors without electromagnetism.
I think there is some good connectionist research on the AIKR in the field of continual learning, although this is basically a coincidence: the scientific question was not the premise of this line of research, although I hope it becomes clearer.
Personally I think you are sort of mixing questions when noting the difference between perceptrons and real neurons. I think this confusion is understandable given the explosion of connectionism. Understanding and modeling neurons is an important scientific question applicable to biology and medicine, but it might not tell us much about intelligence. We could simulate an entire brain yet fail to understand anything about how it works at the operational level.
Yeah, I wanted to add a section clarifying this part, that simulation doesn't entail understanding (we can simulate the entire brain of a fruit fly and yet we know nothing of how its behavior emerges nor can we predict it at all).
Great essay Alberto, thanks for taking the time and energy for educating me on important aspects of how and why we got where we are with 'AI' today. I fear the 'unreasonable effectiveness' of LLMs will continue to drive us all forward in the creation on increasingly powerful 'ghosts' .. it's really up to us to not be fooled and mistake them as fellow humans or God forbid... as God.
Max Planck’s principle—“progresses one funeral at a time,” does not mean “that dead ideas are the norm and a requirement for progress.”
It means that dead *scientists* are necessary to progress.
Eminences grises are the foundation for science and simultaneously the barrier to progress because they are so bound to the models they build their careers around.
Capital doesn’t give a f___ about optimization or exploration, only exploitation. And adoption at this rate has never been seen before, so going back into the woods to find truth is not going to happen.
A+ for you today, Alberto! I will be sharing this essay with my class in Integrative Neuroscience and seminar in Biologically Plausible AI.
That's awesome!!
This article does a great job summarizing many things I felt for a long time. I studied neurobiology in college, and became obsessed with ANNs in the late '80s and '90s. It was always clear to me that all ANN efforts were strongly constrained by the use of gradient descent, which has no correlate in biological systems. Even Geoffrey Hinton's forward forward algorithm uses gradients at the local level, and a strictly layered architecture.
I strongly suspect that other learning algorithms are possible. That non-layered architectures with complex node dynamics and interrelationships can provide a qualitatively different behavior than today's increasingly byzantine layered monstrosities.
The field of ANN went up a path that was first indicated by that early simple neuronal "model" (which we understand now to be so simplistic as to stretch the boundaries of the term). It has ascended very far over the decades, but perhaps the peak it aims for is on an entirety different mountain, reached via a path that diverged at the very beginning.
"perhaps the peak it aims for is on an entirely different mountain" 100%. My entire article in one line, well done haha
My biggest frustration in AI is a misalignment of incentives. I am personally interested in the science of AI, which means posing a meaningful scientific question on the nature of intelligence. My own which I adopted from researcher Pei Wang at Temple University is: how can an agent arbitrarily adapt to a new environment under insufficient knowledge and resources (I abbreviate AIKR)? Useful evaluation methodology should follow. See https://cis.temple.edu/~pwang/GTI-book/
In the pursuit of AI, the first major difference in motivation is whether you are doing this for science or engineering. Nothing wrong with engineering, but even before ChatGPT research has mostly shifted towards engineering. Of course now it’s all shallow engineering when money is on the line. Caveat: you need science to really push the boundaries of engineering, no transistors without electromagnetism.
I think there is some good connectionist research on the AIKR in the field of continual learning, although this is basically a coincidence: the scientific question was not the premise of this line of research, although I hope it becomes clearer.
Personally I think you are sort of mixing questions when noting the difference between perceptrons and real neurons. I think this confusion is understandable given the explosion of connectionism. Understanding and modeling neurons is an important scientific question applicable to biology and medicine, but it might not tell us much about intelligence. We could simulate an entire brain yet fail to understand anything about how it works at the operational level.
Yeah, I wanted to add a section clarifying this part, that simulation doesn't entail understanding (we can simulate the entire brain of a fruit fly and yet we know nothing of how its behavior emerges nor can we predict it at all).
Great essay Alberto, thanks for taking the time and energy for educating me on important aspects of how and why we got where we are with 'AI' today. I fear the 'unreasonable effectiveness' of LLMs will continue to drive us all forward in the creation on increasingly powerful 'ghosts' .. it's really up to us to not be fooled and mistake them as fellow humans or God forbid... as God.
Keep up the great work!
Thank you Luca 🙏🏻🙏🏻
Max Planck’s principle—“progresses one funeral at a time,” does not mean “that dead ideas are the norm and a requirement for progress.”
It means that dead *scientists* are necessary to progress.
Eminences grises are the foundation for science and simultaneously the barrier to progress because they are so bound to the models they build their careers around.
Yes, I reinterpreted it haha
On another note. Great essay!
Capital doesn’t give a f___ about optimization or exploration, only exploitation. And adoption at this rate has never been seen before, so going back into the woods to find truth is not going to happen.
;)